Method for determining when a natural therapeutic or beneficial product is exerting its therapeutic or beneficial effect via a physiological mechanism of action

Abstract

To provide a method for determining when a natural therapeutic or beneficial product will exert its therapeutic or beneficial effect via a physiological mechanism of action.SOLUTION: The present invention relates to a new method that allows the evolution of medical technologies from the use of chemically or biologically defined artificial substances to self-assembling natural products obtained from natural raw materials by industrial processes that preserve their endogenous properties, thereby preserving their ability to interact with the networks of the living world (including humans), which natural products cannot be defined with classical qualitative and quantitative composition schemes. Thus, the present invention provides a new method for determining when a therapeutic or beneficial product exerts its therapeutic or beneficial effect via a physiological mechanism of action. This method provides the tools necessary for the skilled person to assess the mechanism of action of a therapeutic or beneficial product, which has become a relevant feature to assess with new developments in regulatory regimes for medical devices and nutraceuticals, for which there is no method available in the art.SELECTED DRAWING: None

Description

[Technical Field]

[0001] The present invention relates to a new method that enables the evolution of medical technology from the use of chemically or biologically defined artificial substances to self-assembling natural products obtained from natural sources by industrial processes that preserve their intrinsic properties, thereby preserving their ability to interact with the network of the living world (including humans), which cannot be defined by classical qualitative-quantitative compositional schemes. Thus, the present invention provides a new method for determining when a therapeutic or beneficial product exerts its therapeutic or beneficial effect through a physiological mechanism of action. This method provides the skilled artisan with the necessary tool to evaluate the mechanism of action of a therapeutic or beneficial product, which, with new developments in regulatory regimes for medical devices and dietary supplements, has become a relevant feature for evaluation, for which no method is available in the art. [Background technology]

[0002] The scientific evolution of medicine over the past few centuries: In the evolution our species has undertaken over the past five centuries, dating back to the beginning of the so-called Anthropocene with the introduction of biochemical practices by Paracelsus that brought artificiality to medicine in the production of therapeutic products, we now recognize an increasingly pronounced rupture between reductionist (artificial) technological evolution and the different paths of biological entities in hyper-connected living systems, both organic entities including our species and inorganic entities that follow the directions of innate intelligence and program design.

[0003] Recent scientific evolution has applied new conceptual parameters that must find application areas aimed at limiting the iatrogenic effects of pharmaceutical APIs both in humans and in the environment.

[0004] It is known that synthetic APIs can enter ecosystems through various pathways, primarily through the discharge of pharmaceutical waste from manufacturing facilities and the improper disposal of unused or expired drugs. This can lead to the bioaccumulation of artificial and poorly biodegradable substances in aquatic and terrestrial organisms, with potential adverse effects on the food chain and threats to biodiversity.

[0005] Several studies have highlighted adverse effects on aquatic organisms, such as changes in behavior, reproduction, and even mortality after exposure to synthetic APIs (Boxall, AB et al (2012). Pharmaceuticals and personal care products in the environment: what are the big questions?. Environmental health perspectives, 120(9), 1221-1229; Fick, J., & Lindberg, RH (2015). Tysklind, M. and Larsson, DGJ (2015). Predicted critical environmental concentrations for 500 pharmaceuticals. Regulatory Toxicology and Pharmacology, 73(1), 607-616.).

[0006] It is well known that many synthetic APIs are designed to be biologically active and particularly stable, potentially hindering their natural degradation processes. As a result, these molecules can persist in the environment for long periods of time and accumulate in soil and water. This reduced biodegradability raises concerns about long-term environmental impacts and the potential for bioaccumulation in living organisms [Kasprzyk-Hordern, B., et al. (2008). The removal of pharmaceuticals, personal care products, endocrine disruptors, and illicit drugs during wastewater treatment and its impact on the quality of receiving waters. Water research, 43(2), 363-380; Verlicchi, P., et al. (2012). Occurrence of pharmaceutical compounds in urban wastewater: Removal, mass load, and environmental risk after a secondary treatment—A review. Science of the total environment, 429, 123-155].

[0007] Furthermore, there is growing concern about the potential impact of synthetic APIs on the immune system of humans and animals. Some pharmaceutical products have been found to directly or indirectly interfere with immune function, resulting in altered immune responses or increased susceptibility to infection. This can have significant consequences for both individual health and population-level immunity [Vos T. et al. (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1545-1602; Calabrese, EJ, & Baldwin, LA (2003). Toxicology rethinks its central belief. Nature, 421(6924), 691-692].

[0008] In conclusion, while synthetic APIs have undoubtedly contributed to advances in healthcare, it is now becoming clear that their environmental and health impacts must be carefully considered. Efforts to develop greener pharmaceuticals, improve waste management, and monitor environmental pollution are important steps toward mitigating these concerns.

[0009] Furthermore, synthetic molecules (intended as molecules obtained by man-made manufacturing through chemical synthesis laboratory / industrial processes) are designed to provide a desired interaction with a particular given target molecule, and the design does not take into account all the interactions the molecule has in its natural matrix, and with the entire receptor network of the environment and the organism in which they are used.

[0010] Natural matrix and matrix effects Natural matrices, such as plant matrices, are complex systems characterized by many molecular components belonging to different phytochemical classes that already interact with each other within the plant to determine the plant's biology. This interaction continues during processing, and different processing techniques affect the post-processing interactions of the components. These compounds can interact at both functional and structural levels. The supramolecular aggregates that result in both structural and functional networks, as well as their chemical-physical and structural properties, are dynamic interactions that can be modulated by environmental conditions. As can be expected, these interactions affect the reactivity of individual components, resulting in properties typical of the separate entities represented by the matrix, which differ from the sum of the properties of its single molecular components, through so-called "matrix effects." Such properties are defined as "emergent properties." This phenomenon has been particularly described and attributed to organisms that possess the driving force to self-assemble and self-assemble into supramolecular complex entities [Jean-Marie Lehn, Toward complex matter: Supramolecular chemistry and self-organization. PNAS, 2002, 99(8)4763-4768]. https: / / doi.org / 10.1073 / pnas.072065599 This inherent complexity leads to the fact that individual molecules in natural matrices cannot be considered contained in isolated and fixed packages, since there is a continuous series of mutual, non-covalent and dynamic interactions between them. Such interactions are intramolecular and intermolecular, occurring both between molecules of the same type and between molecules belonging to different chemical classes.

[0011] This introduces the need to consider that the ability of natural matrices to exert therapeutic effects on the human body depends not only on the qualitative-quantitative composition of the matrix, which tends to be variable in itself, but also on the existence of such interactions between the same and different molecules, including small molecules as well as more complex ones such as proteins, polysaccharides, lipids, RNA, etc.

[0012] It is known that knowledge of the identity and quantity of each and every molecule in a natural matrix is ​​not sufficient to predict the dynamic and kinetic properties and therapeutic efficacy of the matrix itself. When a selected single molecule, such as an API, is considered, the opposite occurs, whereby the structure-activity relationship (SAR), and thereby the pharmacodynamic and pharmacokinetic properties, are intrinsically related to the chemical identity of the active ingredient and the pharmacodynamic inertia of the excipients. The network established between all components of the matrix, resulting in a "matrix effect," makes it impossible to identify a single marker as representative of the network or to define the activity exerted by a natural matrix-based therapeutic product based on a selected API, since no single component alone can convey all of the properties inherent to the matrix, since no single component reflects the interaction between the matrix and the target organism.

[0013] Matrix effects impart specific, intrinsic properties to the matrix itself or to a mixture of matrices, resulting in new, distinct matrices, called emergent properties, that cannot be replicated by any of the components taken alone. This perfectly reflects the impossibility of accurately studying such properties using deterministic chemical methods commonly used in classical pharmacological chemistry, which, as noted above, are only adequately suited to single active ingredients and excipients in a pharmaceutical environment. Appropriate tools for properly studying such properties must be found within the framework of systems theory and the concept of "network-through-network" interactions described herein.

[0014] Rather, the dynamic and kinetic behavior of the matrix is ​​the result of a dynamic network of interactions taking place within the matrix, which indicates: - the presence of multiple components, ·Inability to reconvert the properties of the matrix into the sum of the properties of a single material · The impossibility of describing the interaction between the matrix and the receptor organism according to the key-lock paradigm (model) that is the basis of SAR.

[0015] Interconnections between organisms and the epigenetic environment: endogenous and exogenous inputs Natural biosynthetic molecules, produced in natural, non-artificial environments, possess essentially all essential features, exist in epigenetically determined contexts, and exert their functions, the description of which is inaccessible when traditional deterministic chemical approaches are used.

[0016] Products obtained from natural sources have been used for thousands of years to prevent and cure human diseases. In this context, many studies have been limited to characterizing their chemical composition and single-molecule activity at the single-molecule level, while the spontaneous assembly, interaction, and supramolecular organization of all components in the natural product have not been fully investigated and therefore not understood. With the development of modern chemistry, a reductionist approach focused on the isolation of single molecules from natural products and the subsequent artificial synthesis of molecules of therapeutic interest has shifted to the goal of developing selected active principles that act on a given target according to the key-lock paradigm.

[0017] This has led to the conviction that research in the life sciences should be directed towards substances that can be chemically verified using data such as the amounts of individual substances at the molecular level, and that use linear kinetics to generate highly potent and effective artificial products. This direction is also now clearly having detrimental effects on biodiversity and the innate immune system.

[0018] As mentioned above, the results of scientific evolution over the past few decades have led to the realization that the iatrogenic effects of synthetic APIs on humans and the environment must somehow be limited. From this, a general trend has emerged toward science being more in tune with life itself, aiming to protect and preserve the innate and inherent balance of intra- and inter-species interconnections in the animal, plant, and mineral kingdoms. Indeed, the therapeutic field has intervened for centuries in the development of synthetic pharmaceutical products, due to a lack of technology that allows for the standardization of naturally assembled sources of substances conforming to the requirements of therapeutic purposes, without recognizing the effects of such products on all resulting species and environments, in contrast to nature. This result is also encapsulated in the term "Anthropocene," which defines the current era and includes the modification of individual processes and functions of the metabolic framework of all living species. In the present era, the approaching breakpoint of coexistence between endogenous Earth systems that have existed for billions of years and all processes, methods, and artificial substances produced by humans that are not compatible / assimilated with life in the long term is evident.

[0019] During the past decades, but especially after the COVID-19 pandemic, the dominant social emotion has been anxiety, which is also due to the emergence of dramatic problems such as an increasing number of orphan, oncological and chronic degenerative diseases, as well as antibiotic resistance, and an increasingly pronounced need for childbirth support.

[0020] Among the sectors that raise the most alarm due to the irreversibility of the associated pollution caused is the pharmaceutical sector, due to the strong effect and non-degradability of each synthetic molecule, which is internalized by the animal or plant organisms treated with the synthetic API and ultimately released into the environment.

[0021] It is therefore important to note that the main threats to our species, such as climate change, the rapid decline of biodiversity, and other negative implications that define our era as the "Anthropocene", are strongly linked to the billions of tons of artificial, non-biodegradable substances that are introduced into the environment every year. A non-limiting role in this framework, due to their efficacy, is represented by drugs and their metabolites excreted by the treated organisms.

[0022] The historical concept of patents being granted for the public benefit, particularly in matters of health and safety, has its roots dating back centuries. The underlying principle is that while a patent provides an inventor with a temporary monopoly on his or her invention, its purpose is to serve the greater good of society.

[0023] In modern times, this historical concept is reflected in the various legal provisions and policies governing patents, which emphasize the understanding that inventors are entitled to recognition and protection for their contributions, but that society as a whole should ultimately benefit from these innovations, especially in areas critical to public health and safety.

[0024] In other words, the humanitarian basis of the patent system lies in its goal of striking a balance between promoting innovation and ensuring that the benefits of that innovation are shared for the betterment of society as a whole. In particular, the patent system should ensure the sharing of knowledge and the promotion of progress in the areas mentioned above.

[0025] In particular, the patent system can play an important role in addressing humanitarian and global challenges, for example by encouraging the development of sustainable medicines, environmentally sustainable technologies, and solutions to pressing problems such as clean energy and water scarcity.

[0026] From reduction / illumination to treat symptoms to rebalancing conditions and processes by reducing artificial variation: From the beginning of the 16th century until today, especially in the field of medical and beneficial products, it has only been possible to standardize artificial substances produced by chemically definable chemical processes or purified / isolated substances and therefore validate them as active principles suitable for treatment.

[0027] This pathway, which is highly reductionist, has proven to be of great value, allowing us to eradicate many diseases over the past five centuries, but is now facing its limitations, which stem from the exogenous nature of chemicals and life processes.

[0028] With regard to the development of new sustainable medicines or beneficial compositions (the latter intended as compositions exerting a complementary effect in maintaining homeostasis), it is now also recognized that artificial (especially chemically synthesized) therapeutic products have harmful effects on biodiversity and the innate immune system.

[0029] Furthermore, it must be noted that natural molecules (see definition below) in natural matrices, although chemically similar to their synthetic counterparts when isolated, are likely to have a different fingerprint with respect to their synthetic analogues due to completely different synthetic routes in terms of primary metabolites, reactants, reaction temperatures, energy sources, catalysts, etc., potentially affecting their physicochemical behavior and reactivity and therefore their biological activity.

[0030] According to the traditional paradigm, from the standpoint of chemical structure, the identity of a molecule is embedded in its atomic composition and its geometric arrangement.

[0031] As an example, estragole (1-allyl-4-methoxybenzene), which is prominently identified in essential oils such as those derived essentially from Ocimum basilicum and Artemisia dracunculus, is known in the art for its potential aromatic and medicinal uses. The molecular makeup and associated energy state of estragole, depending on its origin, have been the subject of intense scientific scrutiny. While the traditional perspective assumes uniform molecular attributes, closer scrutiny suggests subtle differences.

[0032] Given this premise, estragole, whether obtained from plant sources by distillation or synthesized in the laboratory, should ideally be identical in its inherent physicochemical attributes.

[0033] However, distinguishing between production pathways is paramount. In natural plant matrices, estragole biosynthesis is orchestrated by a series of enzymatic reactions that begin with primary metabolites and end with this specific secondary metabolite. It is known in the art that each of these enzymatic transformations operates within a distinct energy landscape, potentially conferring a unique energy state to the molecule, at temperatures and pressures compatible with the estragole-producing organism.

[0034] Conversely, laboratory synthesis of estragole relies on chemical reactions engineered with different precursors and conditions (e.g., temperature and pressure) that are incompatible with plant life. The energy dynamics of such synthetic pathways, governed by the intrinsic thermodynamics and kinetics of the reactions, are likely to deviate from plant-mediated enzymatic pathways.

[0035] Furthermore, it is clear that the isotopic abundances resulting from two different routes (natural and synthetic) are unlikely to be the same. It is known that even slight variations in isotopic abundance have a tangible effect on the vibrational frequency, bond strength, and consequently the energy state of the molecule itself [Bigeleisen, J. (1996). Nuclear spin conversion in polyatomic molecules. Journal of Chemical Physics, 105(18), 8121-8129]. Given the possible isotopic differences between plant sources and synthetic reagents, the resulting estragole molecules are likely to have differential energy imprints and biological activities. In light of the above, although chemically similar, molecules from natural and synthetic sources may reasonably have distinct energy fingerprints that potentially affect their physicochemical properties, reactivity, and therefore their biological activities. Indeed, differences in the activity of synthetic estragole and that of natural estragole (basil extract) embedded in a natural matrix have been reported in the art (Suzanne MF et al., "Basil extract inhibits the sulfotransferase-mediated formation of DNA adducts of the procarcinogen 1'-hydroxyestragole by rat and human liver S9 homogenates and in HepG2 human hepatoma cells," Food and Chemical Toxicology, 2008, 46(6)2296-2302, https: / / doi.org / 10.1016 / j.fct.2008.03.010.).

[0036] Mechanism of action Therapeutic / beneficial products may exert their activity by modifying one or more specific (defined) pathological or altered activities, or by modifying the entire pathological process or condition (or altered physiological state in the case of beneficial products).

[0037] The first activity is exerted by therapeutic or beneficial products based on the structure-activity pharmacological relationship (SAR), which is the most relevant relationship in classical pharmacological activity between an active pharmaceutical ingredient (API) and the receptor targeted by the API, and this is thought to be at the level of a single molecule. On the other hand, products containing or consisting of natural matrices (the network established between all components of the matrix, resulting in a "matrix effect," makes it impossible to identify a single marker as representing the network, since a single component cannot independently convey all the properties inherent to the matrix, and a single component does not reflect the interaction between the matrix and the target organism) can exert their therapeutic effect by modifying the entire pathological process or condition (or altered physiological state) rather than a limited number of biological functions, thanks to the network of network interactions characterized by their activity. However, currently, there is no simple method available for evaluating whether a therapeutic or beneficial product provides an overall modification of a pathological / altered state.

[0038] "Mechanism of action" is defined by the FDA as "the means by which a product achieves its intended therapeutic effect or action," and "intended effect or action" includes any effect or action intended to achieve a claimed medical / beneficial purpose. Because pharmacological mechanisms of action are specific to pharmaceutical products, it appears necessary to interpret these definitions in line with the requirements and relative guidelines of Directive 2001 / 83 on Medicinal Products for Human Use, as well as in line with the scientific literature on the subject.

[0039] It is therefore possible to identify the following characteristic features of the pharmacological mechanism of action: a) There must be at least one well-identified active ingredient. b) there must be at least one well-identified cellular target; c) The action of each active ingredient is closely related to its three-dimensional chemical structure, and small changes in the molecule appear to result in large changes in pharmacological properties. A clear relationship between ligand structure and activity (SAR) is discernible. d) Each active principle must interact with its target and induce a well-identified and scientifically proven interaction that is consistent with the expected structure-activity relationship (SAR). e) The reproducibility of activity is virtually reproducible due to the reproducibility of the structure, especially at the specific molecular level.

[0040] The coherence between the claimed effect of its interaction with the biological system and the knowledge of the relationship between the structure and activity of the ligand is a crucial aspect of a pharmacological mechanism of action because of the above-mentioned distinctive features of the mechanism of action.

[0041] The mode of action of natural matrices, given their complexity (resulting in the aforementioned matrix effects), clearly cannot meet the above requirements. While the application of quantum biology can provide a possible interpretation of natural matrices and biosynthesized molecules, it is currently not possible to interpret the complexity of natural matrices or their interactions with recipient organisms based on the schemes and tools commonly used to evaluate the interactions between synthetic or isolated molecules and recipient organisms.

[0042] Indeed, natural matrix / receptor organism interactions, which are network-over-network interactions, can only be investigated with instruments that can circumvent structure-and-activity (SAR) logic and detect their "network mechanisms of action."

[0043] Matrix modes of action can also include mechanical effects (e.g., barrier effects), but also biological activity (matrix networks acting on receptor biological networks) governed by material and non-material properties (e.g., the logic behind messages delivered by nucleic acid sequences). Therefore, their interaction with the body can only be approached through the probabilistic paradigms of systems theory and cannot currently be verified. As mentioned above, knowledge of the identity and quantity of every molecule in a natural matrix is ​​not sufficient to predict dynamic and kinetic properties. When selected single molecules, such as APIs, are considered, the opposite occurs: SAR and subsequent pharmacodynamic and pharmacokinetic properties are intrinsically related to the chemical identity of the active ingredient and the pharmacodynamic inertia of excipients. For this reason, previously developed deterministic canonical concepts of pharmacodynamics and pharmacokinetics only make sense when referring to single molecules (active principles) or their representatives (functional markers), but are insufficient when referring to natural self-assembling matrices. To accurately follow the wonders of artificial intelligence, it seems necessary to recognize the existence of natural self-determining intelligence, resulting in self-assembling entities with distinct properties that should be approached by the construction of novel state-of-the-art technologies inspired by the tools of systems theory rather than determinism.

[0044] Therefore, there is a need to provide suitable methods to recognize and validate network-over-network mechanisms of action typical of natural matrices.

[0045] As an example, the existing EU Regulation 2017 / 745 on medical devices (MDs) requires that medicinal mechanisms of action be distinguished from non-medicinal mechanisms of action.

[0046] Physiological mechanism of action: EU Regulation 2017 / 745 on medical devices (MDs) (Regulation) [REGULATION (EU) 2017 / 745 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL OF 5 April 2017 ON MEDICAL DEVICES, amending Directive 2001 / 83 / EC, Regulation (EC) No 178 / 2002 and Regulation (EC) No 1223 / 2009 and Council Directives 90 / 385 / EEC and 93 / 42 / EEC], which was officially published in Europe on 5 May 2017, introduced completely new controls over all aspects of the life cycle of MDs.

[0047] The term medical device according to the Regulation includes products that achieve a therapeutic effect but do not have a pharmacological, immunological, or metabolic (Ph.IM) mechanism of action (MOA) according to the Regulation. A Ph.IM MOA is a mechanism of action characterized by a key-lock model in which a selected API acts on its target receptor according to the SAR regulations. Notably, the Regulation also indicates that a product that modifies a pathological or physiological condition or process via a non-Ph.IM mechanism of action is an MD.

[0048] It should be noted that MD Regulation 2017 / 745 appears to refer to devices that are capable of modifying a physiological or pathological condition, and therefore appears to extend the definition of a medical device to include products that can interact with the human body to modify that condition. The modification of a pathological or physiological condition (e.g., an altered physiological state) over time results in the modification of a pathological or physiological process.

[0049] Therefore, there is a need to provide methods for assessing whether a therapeutic or beneficial product interacts with the human body in a way that alters a state, rather than simply altering function, and whether this alteration of state is due to a physiological mechanism of action.

[0050] In summary, at the regulatory level, it has become very important to establish whether a therapeutic product modifies one or a few functions or a global condition and the mechanism of action by which the therapeutic effect is achieved.

[0051] It is noted herein that without a marketing authorization, a therapeutic product cannot be manufactured and placed on the market.

[0052] Indeed, regulations regarding medical devices vary by country or region, but in many places, medical devices must go through a regulatory approval process before they can be sold or marketed, which typically involves demonstrating that the device is safe and effective for its intended use.

[0053] To ensure legal market access and patient safety, it is essential that manufacturers understand and comply with the specific regulatory requirements of the regions in which they intend to sell their medical devices.

[0054] The current development of the regulations (as described above) opens up new scenarios for manufacturers that cannot currently be explored and utilized due to the lack of appropriate tools to assess and demonstrate compliance with regulatory requirements.

[0055] The present invention provides a method for assessing when a therapeutic or beneficial product comprising one or more natural matrices exerts its therapeutic or beneficial effect via a physiological mechanism of action, i.e., a non-Ph.I.M. mechanism of action.

[0056] These methods solve a significant practical problem that manufacturers encounter when preparing the dossier for the marketing authorization of a therapeutic or beneficial product, in order to define whether the product satisfies the regulatory requirements set out, for example, by the various existing medical device regulations, and in the affirmative, to assist in achieving this. Summary of the Invention

[0057] As is known in the art, in pharmacology, when two formulations or two preparations of the same drug are claimed to be bioequivalent, it is assumed that they provide the same therapeutic effect or are therapeutically equivalent. Identical amounts of the same active ingredient Two drugs are considered pharmaceutical equivalents if they both provide the same therapeutic moiety, but not necessarily in the same amount or dosage form, or as the same salt or ester. Two drug products are said to be bioequivalents if they are pharmaceutical equivalents (i.e., similar dosage forms, possibly produced by different manufacturers) or pharmaceutical substitutes (i.e., different dosage forms) and their rate and extent of absorption, when administered at the same molar dose under similar conditions in well-designed studies, do not show significant differences in the availability of the active ingredient or active moiety in the pharmaceutical equivalent or pharmaceutical substitute to the site of action.

[0058] As explained in detail above, active ingredients in pharmaceuticals are known to act via pharmacological mechanisms of action.

[0059] Conversely, to act via a physiological mechanism of action, a product must be 100% natural. Products containing or consisting of natural materials, e.g., natural matrices, are entities that at least partially maintain the self-generating properties of their starting materials, which belong to the biological domain, and exhibit unique properties (inter-network interactions) represented by a network of material and non-material relationships that interact with the network of relationships of the treated subject, thereby reproducing physiologically similar features and interactions with complexity.

[0060] Therefore, according to this specification, a product comprising or consisting of one or more natural matrices is a product that is 100% natural, which means that the product does not contain any additional artificial substances, i.e. chemically synthesized substances made by man through laboratory processes.

[0061] Furthermore, according to the present specification, a product comprising one or more natural matrices also does not contain added isolated molecules, such as excipients or active ingredients, even if of natural origin.

[0062] The present authors have surprisingly found that when analyzing therapeutic products that contain or consist of one or more natural matrices, in contrast to API-based therapeutic products, they appear to act in a manner that does not involve a one-to-one interaction with specific components of the human body (in contrast to the pharmacological key-lock paradigm). Indeed, the authors have found that different batches of the tested products were not uniform in qualitative-quantitative composition at the molecular level, but exerted a uniform and conserved overall therapeutic or beneficial effect despite their variable composition.

[0063] In particular, as disclosed in patent applications PCT / IB2024 / 050280; US18 / 410,096; UK 2400422.8; JP 2024-003433; CA 3,225,879; AU 2024200219 (all of which are incorporated herein by reference), the authors of the present invention have been able to verify the therapeutic efficacy of different batches of a product comprising or consisting of a natural matrix with respect to a reference batch whose therapeutic effect has been confirmed at least in preclinical experiments, even though the tested batches do not have the same qualitative and quantitative composition as the reference batch.

[0064] Indeed, although each batch was prepared according to a standardized procedure in order to obtain a high degree of uniformity between different batches, as would be expected for a product containing or consisting of a natural matrix, a detailed qualitative-quantitative analysis of all tested batches (see Figure 5) clearly showed the discarding of batches that were in fact therapeutically active, as well as qualitative-quantitative differences between related batches that made it impossible to assume the presence of the API using conventional validation methods used for synthetic or isolated drugs.

[0065] It is noted herein that natural matrices, by their very nature, are variable in composition even when obtained from the same type of raw material; by way of example, those skilled in the art are very well aware that a natural matrix obtained from an individual of a plant species will never be identical to another natural matrix obtained from a different individual of the same plant species, even within the same field of plants, due to the genetic and epigenetic variability of each organism.

[0066] The present inventors analyzed the qualitative and quantitative chemical composition of different batches of products containing one or more natural matrices, as well as their therapeutic or beneficial effects. Figure 5, for example, shows the differences in the qualitative and quantitative composition of different batches of Product A, also referred to herein as Arte-GX, containing natural matrices (see also Examples). Despite the differences in the qualitative and quantitative composition of all analyzed batches, the authors surprisingly found that in all tested products containing one or more natural matrices, the different molecular entities within each matrix appear to functionally and structurally interact with each other in a redundant manner, providing the same therapeutic or beneficial (homeostatic-adjuvant) effect, despite the differences in qualitative and quantitative molecular composition.

[0067] Indeed, the products exhibited functional restorative powers (therapeutic or beneficial) despite the variability of their qualitative and quantitative molecular composition.

[0068] In other words, the authors surprisingly found that for all products tested, different batches of the same product showed consistent regulation (in terms of trend and magnitude) of all tested biological activities related to the desired therapeutic or beneficial effect, despite the qualitative and quantitative compositional differences between the batches, also defined herein as "functional resilience." The observed maintenance of biological activity may be attributed to the fact that, as noted above, the emergent properties of natural matrices result from a matrix network acting as a whole with distinctive properties and cannot be attributed to each single molecule as if it were isolated; the therapeutic effect is due to a non-pharmacological mechanism of action that differs from classical therapeutic products based on a structure-activity pharmacological relationship (SAR), which is the most relevant relationship in classical pharmacological activity considered at the level of a single molecule, between an active pharmaceutical ingredient (API) and the receptor targeted by the API.

[0069] This is consistent with the possibility that the products analyzed by the inventors may exert their therapeutic or beneficial effects by acting on an overall pathophysiological or altered physiological state.

[0070] Materials such as natural materials appear to be particularly compliant with the definition of MD (medical device) in the EU Directive. An example of a natural material is a natural matrix (e.g., a plant matrix). This is characterized by a large number of components that interact within the matrix in a manner similar to that of the organism of origin. This is possible when the manufacturing process does not isolate single component molecules through a process that is for this reason considered artificial, thus producing MPs (medical products) / MDMS (medical devices made of materials) of natural origin. The matrix is ​​therefore characterized by an interactive network of components. The interactions affect the reactivity of the components, resulting in the so-called "matrix effect" or "expression properties." Matrix effects indicate that the structural and functional properties of a matrix cannot be attributed based on the properties of individual isolated components when studied in isolation (Yong et al., 2022 "Supramolecular assemblies based on natural small molecules: Union would be effective", Materials Today Bio. 15. doi.org / 10.1016 / j.mtbio.2022.100327; Lehn, 2002 Toward complex matter: Supramolecular chemistry and self-organization. PNAS 99, 4763-4768 doi:10.1073 / pnas.072065599). Typically, matrices possess self-assembly and self-organization properties, resulting in supramolecular structures and functional interactions that can respond to different environmental conditions (Lehn, 2002). This phenomenon has been specifically attributed to living organisms.The networked interactions within the matrix are related to the networked interactions between physiological functions of the human body when maintaining a physiological state or re-establishing a physiological state from a pathological state (Stear, 1973 Systems Theory Aspects of Physiological Systems. IFAC Proceedings Volumes. Volume 6, Issue 4, pp. 496-500. ISSN 1474-6670. doi.org / 10.1016 / S1474-6670(17)68074-1; Bartsch et al., 2015 Network Physiology: "How Organ Systems Dynamically Interact" PLoS One 10,;10(11):e0142143. doi:10.1371 / journal.pone.0142143; Ivanov, 2021 "The New Field of Network Physiology: Building the Human Physiolome. Frontiers in Network Phys.1,doi:10.3389 / fnetp.2021.711778).

[0071] The MDR appears to specifically define these products as devices by specifying that they "...modify a physiological or pathological process or condition." Compared to Directive 93 / 42, which limited devices to modifying pathological processes, the obligation to modify a "condition" in the human body appears to encourage the evolution of state-of-the-art technology. This is also, by definition, an alternative to MPs, which modify a single biological function. Thus, a "network-through" interaction between natural materials and the human body appears to be a hallmark of medical devices. This interaction between natural materials and the human body is fundamentally different from the "pinpoint" interaction between a substance and its receptor(s) (the pharmacological, immunological, or metabolic (PhIM) mechanisms of MPs) and the mechanical / chemical / physical mechanisms of MMDs. Network mechanisms involve, in each specific situation, the physiological processes underlying the condition in question in a coordinated, cyclical, and nonlinear manner.

[0072] Natural materials are fundamentally different from "matter," including substances of natural origin. Because they are not represented by their individual components, they require dedicated models. Therefore, to describe natural materials, it is necessary to extend reductionist approaches and use innovations from the last century. Conceptually, this refers to systems theory. From an experimental perspective, preclinical evidence includes systems biology approaches such as omics science (e.g., transcriptomics) and bioinformatics evaluation.

[0073] These allow a proper assessment of the matrix (the network of action) and the human body (the network of recipients), and allow the interaction between the two to be considered as a "network on a network" interaction. The mechanisms with the cooperative redundancy and resilience that characterize physiological functions in each specific situation correspond to "physiological mechanisms of action" and can be characterized by a network paradigm that is distinct from the targeted and non-targeted models that describe PhIMs and mechanical / chemical / physical mechanisms, respectively.

[0074] In this application, the authors demonstrate a method for verifying whether a therapeutic or beneficial effect exerted via a product comprising one or more natural matrices (or consisting of one or more natural matrices) is exerted via modification of a pathological or altered physiological state, and via modification of one or a few pathological or physiological functions other than as a classical drug, and provides a method not previously available in the art for verifying that a therapeutic or beneficial effect modifies a pathological state (as opposed to one or a few pathological functions) or an altered physiological state (as opposed to one or a few altered physiological functions).

[0075] For the reasons explained above, if a pathophysiological or altered physiological condition is altered by a product comprising one or more natural matrices and the product exhibits functional (therapeutic or beneficial) restorative properties, the mechanism of action of the product is determined to be a physiological mechanism of action.

[0076] Regarding the mechanism of action of artificial APIs, the possibility of demonstrating different mechanisms of action in which metabolic networks, such as natural matrices, can interact with those of recipient organisms allows the evolution of new therapeutic protocols that are more adapted to the integrity of the organism, thereby allowing the replacement of artificial products and processes for their preparation with physiological products and processes as much as possible. To move in this direction, i.e., towards a holistic view of natural complex systems, such as natural matrices, it is necessary to understand the self-assembly of natural products and to provide appropriate methods to evaluate whether a therapeutic or beneficial product exerts its desired activity by modifying a physiopathological or altered pathological condition rather than one or a few functions (i.e., via network interactions), and whether the mechanism of action of such a product is physiological rather than pharmacological (indicating SAR).

[0077] A predictive and evolutionary change in the field of medicine would hopefully be to move from the classical pharmacological-chemical indications, which currently tend to focus on single symptoms, organs, active principles, reactions, etc., to a holistic view in which each actor is considered as a complex network, from forming a therapeutic product to the recipient, with expected gains based not on specific points, but on the complexity of their interactions with the body that should result in improved treatment of complex diseases such as syndromes.

[0078] Therefore, the object of the present invention is to 1. A method for assessing whether a product for treating a pathological condition or for assisting in maintaining homeostasis in an altered physiological state exerts its therapeutic or beneficial effect via a physiological mechanism of action, comprising: selecting a therapeutic or beneficial product comprising one or more natural matrices and providing different batches of said product; performing at least one cell-based assay for each of the different batches, wherein a readout of the cell-based assay indicates a degree of modulation of one or more biological activities underlying a desired therapeutic or beneficial effect; From the results read out, the following: Whether the product batches exert their therapeutic or beneficial effect by regulating the network of biological activities underlying the pathological or altered physiological condition; and determining whether a sugar mouthwash product exhibits therapeutic or beneficial functional restoration between different batches, wherein the functional restoration is intended to maintain the therapeutic or beneficial properties of different batches of a given product comprising one or more natural matrices, and such maintenance is not affected by qualitative and quantitative compositional differences between the batches of the product; If the product regulates the network of biological activities underlying the pathological or altered physiological state, and if the product exhibits therapeutic or beneficial functional restoration between different batches, it is demonstrated that the product exerts its therapeutic or beneficial effect via a physiological mechanism of action.

[0079] Batch Legend: Figures 2 to 5 Product A (Arte-GX) in lyophilized form (see detailed composition of the product in Example 1): Batch 20B1955 or L20B1955 Batch 20B0596 or L20B0596 Batch 20I1297 or L20I1297 Batch 21E1640 or L21E1640 Batch 20J1770 is also L20J1770 [Brief explanation of the drawings]

[0080] [Figure 1] Illustrative characteristics of osteoarthritis, including trends in characteristics that indicate improvement in the pathological state (Column 1), the biological activities associated with defining the pathological state (Column 2), and their degree of modulation in the pathological state (Column 3). The degree of modulation of each of these biological activities that indicates a healthy physiological state (Column 4). Dark grey: degree of upregulation. Light grey: degree of downregulation. [Figure 2] Modulation of selected biological activities in a chondrocyte-based assay in osteoarthritis: Column 1: Features, including the trend of features representing an improvement in the pathological condition; Column 2: Biological activity; Column 3: Trend of the degree of modulation of the network of biological activities in the pathological condition; Column 4: Desired modulation of the network of biological activities consistent with a healthy physiological condition; Column 5: Cell-based assay without therapeutic treatment (control group) representing the network of biological activities modulation in the pathological condition; Column 6: Modulation induced by the tested product. The cell-based assay shows that the product samples modulate the network of selected activities according to a trend consistent with the healthy physiological condition profile. The numbers reported in each box represent values ​​(in terms of z-scores) that quantify the degree of modulation of each observed biological function, calculated according to the example (parameter used: transcriptomics). [Figure 3]Modulation of selected biological activities in a chondrocyte-based assay for osteoarthritis: Column 1: Features, including trends in characteristics representing improvement of the pathological condition; Column 2: Network of biological activities; Column 3: Trends in the degree of modulation of the network of biological activities in the pathological condition; Column 4: Desired modulation of the network of biological activities consistent with a healthy physiological condition; Column 5: Cell-based assay without therapeutic treatment (control group) representing modulation of the network of biological activities in the pathological condition; Column 6: Modulation induced by the tested product; Column 7: Modulation induced by a reference drug (triamcinolone acetonide) used in osteoarthritis treatment. The cell-based assay shows that the product samples modulate all of the networks of selected activities according to trends consistent with a healthy physiological condition profile, indicating modulation of the entire pathological condition. Meanwhile, the reference drug fails to effectively modulate all of the biological activities required to define a healthy physiological condition (and therefore does not modulate the degree of pathological condition). The numbers reported in each box represent values ​​quantifying the degree of modulation (in terms of z-scores) calculated according to the example, which represents the degree of modulation of each biological activity observed. [Figure 4]Modulation of selected bioactivity networks in chondrocyte-based assays for osteoarthritis: Column 1: Features including significant trends indicating improvement of the pathological condition; Column 2: Network of bioactivity; Column 3: Trends in the degree of modulation of the network of bioactivity in the pathological condition; Column 4: Desired modulation of the network of bioactivity consistent with a healthy physiological condition; Column 5: Cell-based assay without therapeutic treatment (control group) representing the degree of modulation of the network of bioactivity in the pathological condition; Columns 6-10: Degree of modulation induced by the different batches of product tested; Column 11: Modulation induced by the reference drug (triamcinolone acetonide). The cell-based assays show that all tested product batches modulate all selected networks of activity according to modulation trends (modulation of status and functional recovery) consistent with a healthy physiological condition profile. In contrast, the reference drug fails to effectively modulate all bioactivity necessary to define a healthy physiological condition (does not act on the network). The numbers reported in each box represent values ​​quantifying the degree of regulation (in terms of z-scores) calculated according to examples representing the modulation of each biological activity of the observed network. [Figure 5] Targeted metabolomics of five batches of the main chemical class of product A. This figure clearly shows that all tested batches differ from each other in terms of qualitative-quantitative composition. Comparison with testing of selected biological activities of the same batches in cell-based assays (Figures 3 and 4) shows that when a product contains or consists of one or more natural matrices, qualitative-quantitative analysis of different batches of a product intended for use in the treatment of pathologies does not allow for accurate estimation of its activity profile. [Figure 6a] Illustrative characteristics of mild cognitive impairment (column 1), a network of selected biological activities consistent with defining the pathological state (column 2), and their degree of modulation in the pathological state (column 3). The trend of the degree of modulation of each of the biological activities is consistent with the healthy physiological state (column 4). Dark grey: degree of upregulation. Light grey: degree of downregulation. [Figure 6b]Illustrative characteristics of mild cognitive impairment (column 1), a network of selected biological activities consistent with defining the pathological state (column 2), and their degree of modulation in the pathological state (column 3). The trend of the degree of modulation of each of the biological activities is consistent with the healthy physiological state (column 4). Dark grey: degree of upregulation. Light grey: degree of downregulation. [Figure 7a] Modulation of selected biological activity networks in a neuroblastoma cell-based assay in mild cognitive impairment: column 1, characteristics; column 2, biological activity; column 3, trend in the degree of modulation of biological activity in pathological conditions; column 4, desired modulation of biological activity in healthy physiological conditions; column 5, untreated cells representing modulation of the biological activity network in pathophysiological conditions; column 6, modulation induced by the tested product (Product B). The cell-based assay shows that product samples modulate selected activity networks according to a trend consistent with the healthy physiological condition profile. The numbers reported in each box represent values ​​quantifying the degree of modulation (z-score) calculated according to the example, which represents the degree of modulation of each observed biological activity. [Figure 7b] Modulation of selected biological activity networks in a neuroblastoma cell-based assay in mild cognitive impairment: column 1, characteristics; column 2, biological activity; column 3, trend in the degree of modulation of biological activity in pathological conditions; column 4, desired modulation of biological activity in healthy physiological conditions; column 5, untreated cells representing modulation of the biological activity network in pathophysiological conditions; column 6, modulation induced by the tested product (Product B). The cell-based assay shows that product samples modulate selected activity networks according to a trend consistent with the healthy physiological condition profile. The numbers reported in each box represent values ​​quantifying the degree of modulation (z-score) calculated according to the example, which represents the degree of modulation of each observed biological activity. [Figure 8a]Modulation of selected bioactivity networks in a neuroblastoma cell-based assay for mild cognitive impairment: Column 1: Characteristics; Column 2: Bioactivity Network; Column 3: Trends in the degree of modulation of the bioactivity network in pathological conditions; Column 4: Trends in the degree of modulation of the bioactivity network consistent with a healthy physiological state; Column 5: Untreated cells representing modulation of the bioactivity network in pathophysiological conditions; Column 6: Modulation induced by the test product (Product B); Column 7: Modulation induced by the reference drug (donepezil). The cell-based assay demonstrates that product samples modulate all selected bioactivity networks according to a modulation trend consistent with the healthy physiological state profile. Meanwhile, the reference drug fails to effectively modulate the entire bioactivity network required to define a healthy physiological state. The numbers reported in each box represent values ​​quantifying the degree of modulation (z-scores) calculated according to the example, which represent the degree of modulation of each observed bioactivity. [Figure 8b] Modulation of selected bioactivity networks in a neuroblastoma cell-based assay for mild cognitive impairment: Column 1: Characteristics; Column 2: Bioactivity Network; Column 3: Trends in the degree of modulation of the bioactivity network in pathological conditions; Column 4: Trends in the degree of modulation of the bioactivity network consistent with a healthy physiological state; Column 5: Untreated cells representing modulation of the bioactivity network in pathophysiological conditions; Column 6: Modulation induced by the test product (Product B); Column 7: Modulation induced by the reference drug (donepezil). The cell-based assay demonstrates that product samples modulate all selected bioactivity networks according to a modulation trend consistent with the healthy physiological state profile. Meanwhile, the reference drug fails to effectively modulate the entire bioactivity network required to define a healthy physiological state. The numbers reported in each box represent values ​​quantifying the degree of modulation (z-scores) calculated according to the example, which represent the degree of modulation of each observed bioactivity. [Figure 9a]Illustrative characteristics of osteoporosis (column 1), the network of biological activities consistent with defining the pathological state (column 2), and its degree of regulation in the pathological state (column 3). Trends in the degree of regulation of the network of biological activities consistent with the healthy physiological state (column 4). Dark grey: degree of upregulation. Light grey: degree of downregulation. [Figure 9b] Illustrative characteristics of osteoporosis (column 1), the network of biological activities consistent with defining the pathological state (column 2), and its degree of regulation in the pathological state (column 3). Trends in the degree of regulation of the network of biological activities consistent with the healthy physiological state (column 4). Dark grey: degree of upregulation. Light grey: degree of downregulation. [Figure 10a] Regulation of a network of selected bioactivities in a human adipose-derived mesenchymal stem cell line (hADMSC), capable of differentiating into osteoblasts and mineralizing extracellular matrix (ECM) in osteoporosis. Cell-based assays show: Column 1: Characteristics; Column 2: Network of bioactivities; Column 3: Trends in the degree of regulation of the network of bioactivities in pathological conditions; Column 4: Trends in the degree of regulation of the network of bioactivities consistent with healthy physiological conditions; Column 5: Appropriately induced cells representing the network of regulation of bioactivities in unregulated conditions; Column 6: Regulation induced by the tested product (Product C). The cell-based assays show that product samples regulate the entire network of selected activities according to a trend consistent with the healthy physiological condition profile. The numbers reported in each box represent values ​​quantifying the degree of regulation (z-scores) calculated according to the example, which represent the degree of regulation of each observed bioactivity. [Figure 10b]Regulation of a network of selected bioactivities in a human adipose-derived mesenchymal stem cell line (hADMSC), capable of differentiating into osteoblasts and mineralizing extracellular matrix (ECM) in osteoporosis. Cell-based assays show: Column 1: Characteristics; Column 2: Network of bioactivities; Column 3: Trends in the degree of regulation of the network of bioactivities in pathological conditions; Column 4: Trends in the degree of regulation of the network of bioactivities consistent with healthy physiological conditions; Column 5: Appropriately induced cells representing the network of regulation of bioactivities in unregulated conditions; Column 6: Regulation induced by the tested product (Product C). The cell-based assays show that product samples regulate the entire network of selected activities according to a trend consistent with the healthy physiological condition profile. The numbers reported in each box represent values ​​quantifying the degree of regulation (z-scores) calculated according to the example, which represent the degree of regulation of each observed bioactivity. [Figure 11a] Modulation of a network of selected bioactivities in a human adipose-derived mesenchymal stem cell line (hADMSC), capable of differentiating into osteoblasts and mineralizing extracellular matrix (ECM), in osteoporosis. This cell-based assay shows: Column 1: Characteristics; Column 2: Network of bioactivities; Column 3: Trends in the degree of modulation of the network of bioactivities in pathological conditions; Column 4: Trends in the degree of modulation of the network of bioactivities consistent with a healthy physiological state; Column 5: Appropriately induced cells representing modulation of the network of bioactivities in unregulated conditions; Column 6: Modulation induced by the tested product (Product C); Column 7: Modulation induced by the reference drug (DIBASE). The cell-based assay demonstrates that the product sample modulates all selected activities of the network with a trend in degree of modulation consistent with a healthy physiological state profile. Meanwhile, the reference drug fails to effectively modulate all of the entire network of bioactivities required to define a healthy physiological state. The numbers reported in each box represent values ​​quantifying the degree of modulation (z-scores) calculated according to the example, which represent the degree of modulation of each biological activity observed. [Figure 11b]Modulation of a network of selected bioactivities in a human adipose-derived mesenchymal stem cell line (hADMSC), capable of differentiating into osteoblasts and mineralizing extracellular matrix (ECM), in osteoporosis. This cell-based assay shows: Column 1: Characteristics; Column 2: Network of bioactivities; Column 3: Trends in the degree of modulation of the network of bioactivities in pathological conditions; Column 4: Trends in the degree of modulation of the network of bioactivities consistent with a healthy physiological state; Column 5: Appropriately induced cells representing modulation of the network of bioactivities in unregulated conditions; Column 6: Modulation induced by the tested product (Product C); Column 7: Modulation induced by the reference drug (DIBASE). The cell-based assay demonstrates that the product sample modulates all selected activities of the network with a trend in degree of modulation consistent with a healthy physiological state profile. Meanwhile, the reference drug fails to effectively modulate all of the entire network of bioactivities required to define a healthy physiological state. The numbers reported in each box represent values ​​quantifying the degree of modulation (z-scores) calculated according to the example, which represent the degree of modulation of each biological activity observed. [Figure 12] Modulation of a network of selected biological activities in an assay based on a squamous cell carcinoma cell line: Column 1: Salient features of a pathological state; Column 2: Network of biological activities (represented by canonical pathways); Column 3: Predicted modulation of the network of biological activities consistent with a pathological state; Column 4: Trend in the degree of modulation of the network of biological activities consistent with a healthy physiological state; Column 5: Cell-based assay modulation induced by a reference drug represented by cisplatin: cisplatin-diamminedichloroplatinum(II) (CDDP); and Column 6: Modulation induced by Product D. The cell-based assay shows that Product D modulates more of the desired activity, according to a trend consistent with a healthy physiological state, than treatment with CDDP. The number reported in each box represents the Z-score calculated by the method of the present invention, which represents the degree of modulation of each observed biological activity. [Figure 13a] DLS detection of supramolecular structures in sweet fennel extract, the figure shows the mean particle size distribution (Figure 13a). [Figure 13b]DLS detection of supramolecular structures in sweet fennel extract, the figure shows the average correlation function, Fig. 13b (obtained from three measurements). [Figure 13c] DLS detection of supramolecular structures in sweet fennel extract, the average results of Z-average, PdI and peak results obtained from three replicate measurements are reported in Figure 13c, where Z-average is the intensity-weighted mean diameter and PdI is the polydispersity index. [Figure 14] H NMR spectra of extracts of sweet fennel (A; lot 3250), bitter fennel (B; lot 3246), and an estragole reference standard (C). Peaks associated with the aromatic protons of estragole are highlighted with asterisks. [Figure 15a] Plot of log(I / I) against G2. Black dots indicate estragole reference standards, i.e., estragole isolate (a), bitter fennel (b; lot 3246), and sweet fennel (c; lot 3250) extracts. Red dots indicate calibration standards. [Figure 15b] Plot of log(I / I) against G2. Black dots indicate estragole reference standards, i.e., estragole isolate (a), bitter fennel (b; lot 3246), and sweet fennel (c; lot 3250) extracts. Red dots indicate calibration standards. [Figure 15c] Plot of log(I / I) against G2. Black dots indicate estragole reference standards, i.e., estragole isolate (a), bitter fennel (b; lot 3246), and sweet fennel (c; lot 3250) extracts. Red dots indicate calibration standards. [Figure 16] Theoretical van der Waals radius of estragole (RνdW) and its hydrodynamic radius (RH) obtained by extending estragole with a solvent shell. [Figure 17] Graph of aerobic biodegradation of product A during biodegradation testing according to method OECD 310:2014 (Example 7). [Figure 18]Graph of the aerobic biodegradation of the reference substance sodium benzoate during biodegradation testing according to method OECD 310:2014 (Example 7). [Figure 19a] Network analysis of osteoarthritis (19 Panel A) and treatment with a reference drug (19 Panel B) versus treatment with Product A (19 Panel C). Gray boxes represent both the fundamental nodes characterizing the pathophysiological or altered physiological state and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiplication of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that Product A affects the body systemically and modulates more desired activities than treatment with the reference drug, in line with trends associated with a healthy physiological state. [Figure 19b] Network analysis of osteoarthritis (19 Panel A) and treatment with a reference drug (19 Panel B) versus treatment with Product A (19 Panel C). Gray boxes represent both the fundamental nodes characterizing the pathophysiological or altered physiological state and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiplication of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that Product A affects the body systemically and modulates more desired activities than treatment with the reference drug, in line with trends associated with a healthy physiological state. [Figure 19c]Network analysis of osteoarthritis (19 Panel A) and treatment with a reference drug (19 Panel B) versus treatment with Product A (19 Panel C). Gray boxes represent both the fundamental nodes characterizing the pathophysiological or altered physiological state and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiplication of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that Product A affects the body systemically and modulates more desired activities than treatment with the reference drug, in line with trends associated with a healthy physiological state. [Figure 20a] Network analysis of mild cognitive impairment (20 Panel A) and treatment with a reference drug (20 Panel B) versus treatment with Product B (20 Panel C). Gray boxes represent both the fundamental nodes characterizing pathophysiological or altered physiological states and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation levels) or white (downregulation levels) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that Product B affects the body systemically and modulates more desired activities than treatment with the reference drug, following the modulation trends associated with healthy physiological states. [Figure 20b]Network analysis of mild cognitive impairment (20 Panel A) and treatment with a reference drug (20 Panel B) versus treatment with Product B (20 Panel C). Gray boxes represent both the fundamental nodes characterizing pathophysiological or altered physiological states and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation levels) or white (downregulation levels) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that Product B affects the body systemically and modulates more desired activities than treatment with the reference drug, following the modulation trends associated with healthy physiological states. [Figure 20c] Network analysis of mild cognitive impairment (20 Panel A) and treatment with a reference drug (20 Panel B) versus treatment with Product B (20 Panel C). Gray boxes represent both the fundamental nodes characterizing pathophysiological or altered physiological states and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation levels) or white (downregulation levels) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that Product B affects the body systemically and modulates more desired activities than treatment with the reference drug, following the modulation trends associated with healthy physiological states. [Figure 21a]Network analysis of osteoporosis (21 Panel A) and treatment with a reference drug (21 Panel B) versus treatment with product C (21 Panel C). Gray boxes represent both the fundamental nodes characterizing pathophysiological or altered physiological states and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that product C affects the body systemically and modulates more desired activities than treatment with the reference drug, following the modulation trends associated with healthy physiological states. [Figure 21b] Network analysis of osteoporosis (21 Panel A) and treatment with a reference drug (21 Panel B) versus treatment with product C (21 Panel C). Gray boxes represent both the fundamental nodes characterizing pathophysiological or altered physiological states and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that product C affects the body systemically and modulates more desired activities than treatment with the reference drug, following the modulation trends associated with healthy physiological states. [Figure 21c]Network analysis of osteoporosis (21 Panel A) and treatment with a reference drug (21 Panel B) versus treatment with product C (21 Panel C). Gray boxes represent both the fundamental nodes characterizing pathophysiological or altered physiological states and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that product C affects the body systemically and modulates more desired activities than treatment with the reference drug, following the modulation trends associated with healthy physiological states. [Figure 22a] Network analysis of squamous cell carcinoma (22 Panel A) and treatment with a reference drug (22 Panel B) versus treatment with product D (22 Panel C). Gray boxes represent both the basic nodes characterizing the pathophysiological or altered physiological state and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that product D affects the body systemically and modulates more desired activities than treatment with the reference drug, in line with trends associated with a healthy physiological state. [Figure 22b]Network analysis of squamous cell carcinoma (22 Panel A) and treatment with a reference drug (22 Panel B) versus treatment with product D (22 Panel C). Gray boxes represent both the basic nodes characterizing the pathophysiological or altered physiological state and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that product D affects the body systemically and modulates more desired activities than treatment with the reference drug, in line with trends associated with a healthy physiological state. [Figure 22c] Network analysis of squamous cell carcinoma (22 Panel A) and treatment with a reference drug (22 Panel B) versus treatment with product D (22 Panel C). Gray boxes represent both the basic nodes characterizing the pathophysiological or altered physiological state and the specific sites where the pathology interconnects with the body. Arrows next to the nodes indicate the specific modulation level for each described condition, with strength indicated by a multiple of the arrow itself. Gray boxes connect to a network of biological activities whose modulation levels have been experimentally verified. These biological activities are represented by black (upregulation level) or white (downregulation level) circles, whose amplitudes are directly proportional to the magnitude of their experimentally verified modulation levels. The network analysis shows that product D affects the body systemically and modulates more desired activities than treatment with the reference drug, in line with trends associated with a healthy physiological state. [Figure 23a]Modulation of selected biological activity networks in a clinical trial for mild cognitive impairment: Column 1: Characteristics; Column 2: Network of biological activities; Column 3: Trends in the degree of modulation of the network of biological activities in pathological conditions; Column 4: Trends in the degree of modulation of the network of biological activities consistent with a healthy physiological state; Column 5: Mean modulation values ​​of V1 samples representing the network of biological activities at time point 0 of the clinical trial, before administration of the product set as a baseline with a value of 0; Column 6: Modulation induced by the tested product (Product B) 6 months after treatment vs. the mean modulation values ​​of V2 samples vs. V1; Columns 7-10: Selected parameters. The results of the clinical trial show that the product samples modulate all selected activity networks according to a trend in degree of modulation consistent with a healthy physiological state profile. The numbers reported in each box represent values ​​quantifying the mean degree of modulation (z-score) calculated according to the example showing the mean degree of modulation of each biological activity in group V2 relative to group V1. [Figure 23b] Modulation of selected biological activity networks in a clinical trial for mild cognitive impairment: Column 1: Characteristics; Column 2: Network of biological activities; Column 3: Trends in the degree of modulation of the network of biological activities in pathological conditions; Column 4: Trends in the degree of modulation of the network of biological activities consistent with a healthy physiological state; Column 5: Mean modulation values ​​of V1 samples representing the network of biological activities at time point 0 of the clinical trial, before administration of the product set as a baseline with a value of 0; Column 6: Modulation induced by the tested product (Product B) 6 months after treatment vs. the mean modulation values ​​of V2 samples vs. V1; Columns 7-10: Selected parameters. The results of the clinical trial show that the product samples modulate all selected activity networks according to a trend in degree of modulation consistent with a healthy physiological state profile. The numbers reported in each box represent values ​​quantifying the mean degree of modulation (z-score) calculated according to the example showing the mean degree of modulation of each biological activity in group V2 relative to group V1. [Figure 23c]Modulation of selected biological activity networks in a clinical trial for mild cognitive impairment: Column 1: Characteristics; Column 2: Network of biological activities; Column 3: Trends in the degree of modulation of the network of biological activities in pathological conditions; Column 4: Trends in the degree of modulation of the network of biological activities consistent with a healthy physiological state; Column 5: Mean modulation values ​​of V1 samples representing the network of biological activities at time point 0 of the clinical trial, before administration of the product set as a baseline with a value of 0; Column 6: Modulation induced by the tested product (Product B) 6 months after treatment vs. the mean modulation values ​​of V2 samples vs. V1; Columns 7-10: Selected parameters. The results of the clinical trial show that the product samples modulate all selected activity networks according to a trend in degree of modulation consistent with a healthy physiological state profile. The numbers reported in each box represent values ​​quantifying the mean degree of modulation (z-score) calculated according to the example showing the mean degree of modulation of each biological activity in group V2 relative to group V1. DETAILED DESCRIPTION OF THE INVENTION

[0081] Glossary In this application, "natural matrix" refers to a material consisting of a network represented by a wide range of components / constituents obtained (e.g., extracted) directly from a member of the natural world or its naturally occurring parts (i.e., from natural raw materials) without significant processing or synthetic alteration. "Without significant processing or synthetic alteration" connotes that no denaturing process is used to obtain the matrix from the raw material. In other words, the natural raw material source is processed only by manual, mechanical, or gravitational means, for example, by dissolving in water or other naturally occurring solvents such as water, water-alcohol solutions, etc.; by flotation; by extraction with water or other natural solvents; by steam distillation; or by heating only to remove water or any other naturally occurring solvent; or by extraction from air by any means, provided that the "natural matrix" excludes the member of the natural world or its naturally occurring parts themselves. In particular, according to the present invention, the natural matrix is ​​a 100% natural and biodegradable material consisting of natural components that have not been modified by the process for producing the matrix from the starting materials, without the intentional addition of synthetic products along the entire process. Herein, 100% biodegradability is considered "readily biodegradable" according to the OECD biodegradability test. These characteristics ensure the maintenance of the matrix effect imparted to the matrix by the structural interactions (material interactions) of its components and the presence of functional interactions (non-material interactions) that become apparent upon exposure of a biological system to the natural matrix. In other words, natural matrices or natural matrix mixtures are materials obtained from entities that naturally self-assemble and are processed to preserve their natural biophysical properties that determine their physiological interactions with other organisms, such as human organisms. Their emergent properties can be expressed by contributing to the rebalancing of metabolic processes or states of the recipient organism and / or some organs or tissues, along with physiological effects activated in each specific situation. According to the present invention, natural matrices can be derived from materials obtained from any source within the kingdoms of life, namely, Monera, Protesta, Fungi, Plantae, and Animalia.Thus, the term encompasses plant natural matrices, animal natural matrices, fungal natural matrices, protist (archaeal or bacterial) natural matrices, and monera natural matrices. Natural matrices can also include natural inorganic materials, such as minerals, obtained from natural sources. Synonyms for natural matrix or one or more natural matrices herein are "composite natural system" or "natural material," as defined below.

[0082] Examples of naturally occurring parts of an organism may be represented by, for example, roots, leaves, bark, fruits, flowers, plants or sections thereof, organs, tissues.

[0083] In any part of the description the general term natural matrix can be replaced by: Plant natural matrices or natural matrices obtained from plants, Animal natural matrices or natural matrices obtained from animals or animal products such as eggs or milk, Fungal natural matrices or natural matrices obtained from fungi, Protist natural matrices or natural matrices obtained from protists, Monera natural matrix or natural matrix obtained from Monera, or plant materials and / or extracts, extracts from animal tissues or organs, fungi and / or fungal extracts, or mixtures thereof, where the extraction method does not involve a denaturing step (e.g., temperature or the use of denaturing solvents).

[0084] Plants are synonymous with herbs.

[0085] The term "natural" matrix emphasizes that it retains the integrity and complexity of the component / component network as in the original natural source due to the absence of denaturing treatments to obtain it. Thus, natural matrix does not encompass naturally occurring compositions enriched in specific molecules that have been artificially synthesized or isolated from natural sources. Furthermore, natural matrices can only be obtained by processes that do not involve extensive processing or chemical modification, isolation, purification, or molecular extraction.

[0086] Due to the supramolecular self-assembly of the components / ingredients of the natural matrix and the existence of functional interactions between them, the entire matrix behaves as a complex network that does not interact with a single target molecule, but with a network of recipients (also organized as a network) in the recipient organism. Thus, the interaction of the natural matrix recipient is not the result of point-to-point interactions, as with a typical pharmaceutical API, but rather the result of interactions between an "interactor" network (i.e., the matrix) and a "recipient" network (i.e., the organism to which the matrix is ​​administered).

[0087] The term natural matrix may also be substituted with complex natural system in any part of the specification and claims.

[0088] Anywhere in the description and claims, the term natural matrix can be interpreted as a "natural product" per se; rather, a natural matrix is ​​a product obtained from a natural organism and processed therefrom (e.g., extracted) by techniques that do not substantially alter the biological structure and the associated supramolecular and functional interconnections between the components within said matrix, i.e., by techniques that do not use denaturing techniques and do not involve additional isolated or synthetic molecules or classes of molecules.

[0089] Emergent properties, as used herein and in the art, are defined as properties of natural matrices or natural materials, i.e., properties that are not simply represented by the sum of the properties of each isolated component / ingredient of the matrix / material, but are instead represented by both functional and structural interactions between all components / ingredients of the matrix / material, which are also the result of supramolecular self-assembly of the components / ingredients within the matrix / material itself.

[0090] Thus, "emergent properties" refer to the technical effects, such as therapeutic or homeostatic-supportive properties (i.e., beneficial effects), that the components / interactions and relationships of a natural matrix have on a recipient biological system. By definition, emergent properties are properties that are not immediately apparent or even predictable based solely on the individual properties of each component / component of the matrix. Instead, they "emerge" when all components / components of the matrix network interact with each other and with the biological system receiving network in dynamic and complex ways. Emergent properties have been widely discussed in the art in various scientific and systems-oriented fields, including physics, chemistry, biology, and complex systems theory.

[0091] Thus, emergent properties are properties that cannot be predicted a priori by qualitative-quantitative knowledge of each component of a given composition or matrix, and therefore cannot be attributed to one or more specific APIs. Thus, while a multi-drug composition may exhibit unexpected synergistic effects, the properties of the composition are still attributable to the specific APIs and amounts thereof contained therein.

[0092] In the case of newly emerged properties characteristic of a native matrix, the observed emergent properties cannot be replicated for a particular API and are maintained in different batches of a given matrix or a given mixture of matrices, despite the different qualitative-quantitative composition of said batches (functional resilience see below).

[0093] Important points about emergent properties include: System Complexity: Emergent properties are associated with systems that exhibit a particular level of complexity. In simple systems, interactions between components / ingredients are limited and properties are more easily deducible from the properties of the individual components. However, in complex systems, interactions between components and their supramolecular organization can give rise to novel and unexpected features.

[0094] Nonlinearity: Developmental characteristics often result from nonlinear interactions, where the relationship between cause and effect is not proportional.

[0095] Holism: The concept of emergent properties emphasizes a holistic perspective, recognizing that the whole system is greater than the sum of its parts.

[0096] A product comprising or consisting of one or more natural matrices according to the present invention (alias "product comprising or consisting of one or more complex natural system(s)") is a product comprising or consisting of one or more natural matrices, also defined herein as "natural material". In particular, according to the present specification, a product comprising or consisting of one or more natural matrices is a product that is 100% natural, which means that the product does not contain any chemically synthesized substances, i.e. substances made by man.

[0097] Furthermore, according to the present specification, a product comprising one or more natural matrices, even if of natural origin, does not contain added isolated molecules, such as excipients or active ingredients. A product consisting of one or more natural matrices according to the present invention is a product consisting of a defined mixture of natural matrices, such as n natural matrices, where n is a specifically defined integer between 1 and 100, preferably between 1 and 50, preferably between 1 and 20, 1 and 10, 1 and 7, 1 and 6, 1 and 5.

[0098] In any part of the description and claims, "a product comprising or consisting of one or more natural matrices" may be replaced with "a product comprising or consisting of one or more plant matrices" or "a product comprising or consisting of complex natural system(s)", "natural material or material of natural origin".

[0099] Furthermore, the term "product comprising or consisting of one or more natural matrices" according to the present specification may be an intermediate or final formulation for the intended use (e.g., a resuspended dry product), or, particularly when the formulation for the intended use is in liquid form, the term may define a dried or lyophilized form or a concentrated form thereof to which water is added by the user or physician to prepare the formulation for administration.

[0100] Nowhere in the specification and claims can a "product containing or consisting of one or more natural matrices" be construed as a product of nature per se. When a product contains or consists of a mixture of natural matrices, the mixture is a mixture of selected natural matrices produced by humans, and the mixture cannot be found as such in any of the natural products from which each matrix contained therein is derived. Thus, when a product contains or consists of multiple natural matrices, the natural matrices have been combined by humans, giving the resulting product new expressed properties.

[0101] In this specification and claims, the term natural material is synonymous with "a product consisting of one natural matrix or a mixture of natural matrices (more natural matrices)."

[0102] According to the present specification, the expression "one or more biological activities associated with a pathological condition" refers to one or more biological activities, in particular a network of biological activities associated with a deviation / deviation from homeostasis, which may result in the onset, progression, or worsening of a pathological condition. Thus, the expression "alteration of one or more biological activities (or alteration of a network of biological activities) associated with a pathological condition" refers to the degree of regulation or change in the normal (healthy) physiological state of one or more such biological activities (or processes) in an organism (preferably a human), which is directly related to an alteration / disturbance in homeostasis and may ultimately result in a pathological condition or disease, or is observable in a pathological condition or disease. In other words, it describes a specific adjustment or deviation of one or more biological activities from a healthy physiological state that occurs as a result of, or coincides with, the onset, progression, or worsening of a pathological condition or disease.

[0103] By way of example, in the case of diabetes, one or more biological activities related to glucose metabolism, insulin production and control are directly related to the pathological state of diabetes and are therefore altered in a manner that is associated with the pathological condition or pathology of diabetes according to the present invention.

[0104] Synthesis here has its conventionally accepted meaning in chemistry.

[0105] Traditionally, in chemistry, the term "synthetic" refers to the origin or source of a material or substance. Synthetic substances or materials are produced by humans by artificial synthesis, i.e., by laboratory chemical reactions that react simpler chemicals to produce more complex chemicals, usually through processes that often use different pathways, temperature conditions, pressure conditions, energy sources, and / or catalysts than those used by living organisms.

[0106] Examples: Synthetic substances or materials include plastics, pharmaceuticals, and many industrial chemicals. For example, nylon is a synthetic polymer made through chemical synthesis, and aspirin is a synthetic drug made through a specific chemical reaction.

[0107] Functional resilience according to the present invention is intended as the therapeutic or beneficial (homeostatic adjuvant) resilience of a therapeutic or beneficial product comprising or consisting of one or more natural matrices, and this term describes the maintenance of the therapeutic or beneficial properties of different batches of a given product comprising (or consisting of) one or more natural matrices, despite the qualitative and quantitative compositional differences between the batches, which composition is always present (unique) in a product comprising or consisting of one or more natural matrices. As known to those skilled in the art, each time different batches of starting material are used, the resulting natural matrix will have a unique qualitative-quantitative composition at the molecular level, typical of the individual variations among organisms of the same species.

[0108] As used herein, the term "in vitro cell-based assay" has the meaning conventionally used in the art, and in particular refers to a cell-based analytical procedure for evaluating cellular behavior and response to injury or stimuli in the context of a disease or pathological or pre-pathological condition or altered homeostasis. This type of assay is often designed to study the biological response of cells in a controlled environment within a Petri dish or well plate. According to the present invention, a suitable in vitro cell-based assay is one whose readout relates to the degree of modulation of one or more biological activities or networks of biological activities associated with one or more characteristics of a given pathological or pre-pathological condition or altered homeostatic condition (altered physiological state).

[0109] The cells may be derived from human, animal, plant, or general cell lines that mimic a particular tissue or organ, or may be general cells that are known models for mimicking a disease.

[0110] In the context of a disease or pathological / pre-pathological condition, or a situation in which homeostasis is altered, cell-based assays are specifically designed to simulate or mimic conditions associated with the disease or pathological / pre-pathological condition, or a situation in which homeostasis is altered. In particular, cell-based assays can be used to evaluate the therapeutic, adjunctive, and / or beneficial effects of different compounds or products in vitro. This can include exposing cells to factors known to be associated with the disease or pathological condition, exposing cells to potential therapeutic or adjunctive products that help restore physiological conditions, or using cells that have been or have been genetically modified to have disease- or pathological-specific traits.

[0111] The selected cells may be treated to induce a pathological, pre-pathological or altered homeostatic state, or may be cells already displaying the desired altered phenotype.

[0112] A healthy physiological state is the state of an organism's body, organ, device, system or body area and its internal processes when they are functioning optimally within the normal parameters of that individual, i.e. A state in which homeostasis tends to be maintained A healthy physiological state in the context of one or more biological activities known to contribute to the characteristics of a given disease or pathological condition or altered physiological state refers to a state in which the one or more biological activities are operating optimally and within normal (healthy) parameters. This state is characterized by the absence of significant abnormal cellular or molecular processes associated with the particular disease under consideration. When trends in alterations of one or more biological activities consistent with a pathological or pre-pathological condition are known, a healthy physiological state can be considered to be represented by the opposite trends in alterations for each of the activities.

[0113] The term considers the hallmarks of a particular disease, which are distinctive features or characteristics typically observed in individuals affected with that disease. These hallmarks may include particular cellular behaviors, molecular pathways, canonical pathways, or physiological responses that play a key role in the development or progression of the disease.

[0114] In summary, a healthy physiological state in the context of a particular disease or pathological / altered condition is one in which one or more biological activities associated with a known characteristic of that disease or condition are modulated in a direction consistent with the non-pathological / unaltered state, or in other words, in a direction opposite to the pathological / altered state.

[0115] Therefore, a healthy physiological state according to the present invention also refers to the direction of modulation of one or more biological activities that are known characteristics of a pathological condition in the maintenance of homeostasis, i.e., the degree of modulation of one or more biological activities that are attributable to a particular system, region, organ or tissue in a healthy subject prior to the onset of the pathological condition.

[0116] Altered physiological state and alteration in homeostasis are closely related concepts that describe deviations from normal function and balance of the body's internal environment. While they overlap, there are some distinctions between the two terms.

[0117] Altered physiological state: This term encompasses a wide range of changes in the normal function of the body, including disruptions to organ systems, biochemical processes, and cellular function. Altered physiological states can result from a variety of factors, such as disease, injury, medications, environmental factors, and psychological stress. Examples include fever, inflammation, hormonal imbalance, and organ dysfunction.

[0118] Altered homeostasis: Homeostasis refers to the body's ability to maintain a stable internal environment despite external changes. This stability is achieved by regulatory mechanisms that control variables such as body temperature, blood pressure, pH balance, and blood glucose levels within narrow ranges. Altered homeostasis occurs when these regulatory mechanisms fail to maintain balance, leading to deviations from the body's normal set points. These deviations can be temporary or chronic and may involve compensatory mechanisms to restore balance.

[0119] In summary, an altered physiological state describes an observable change in the body's normal functioning, while altered homeostasis refers to a fundamental disruption of the body's regulatory mechanisms that maintain internal stability.

[0120] Alterations in homeostasis underlie altered physiological states, as disruption of homeostatic mechanisms can lead to physiological imbalances and the development of disease or dysfunction.

[0121] The term "disease or pathological condition or medical condition" as used herein has the meaning conventionally used in the art. Disease characteristics are known to be indicators that can mark the progression or control of a given disease or pathological condition or pre-pathological condition, and together they typically represent the general pathological state associated with a given pathology. These characteristics (also called "key indicators") are typically a set of features or patterns that physicians monitor over time to track the onset, progression, or regression of a particular disease. In summary, hallmarks of a disease are defining features or characteristics whose alterations indicate a given pre-medical or medical condition and aid in its identification, diagnosis, monitoring, and understanding. For example, for neurodegenerative diseases (NDDs), at least eight of the following characteristics of NDDs are known in the art: (pathological protein) aggregation, synapse and neuronal network (dysfunction), (abnormal) proteostasis, cytoskeleton (abnormal), (altered) energy homeostasis, DNA and RNA (deficiency), inflammation (increase), and neuronal cell death (increase). In cancer research, a hallmark of cancer is a set of characteristic features commonly found in cancer cells, including (sustained) proliferative signaling, (evasion of) growth inhibitors, (resistance to) cell death, (enabling) replicative immortalization, (induced) angiogenesis, and (activated) invasion and metastasis.

[0122] A disease feature, a parameter (e.g., a biomarker) associated with the feature, one or more biological activities associated with the feature, etc., is a framework for studying a disease or pathological or medical condition using an integrated / holistic approach.

[0123] Hallmarks of an altered physiological state typically include observable changes in various aspects of bodily function, which may be manifested through symptoms, signs, or laboratory findings.

[0124] Altered physiological conditions typically reflect a disruption of the body's homeostatic mechanisms, resulting in deviations from normal physiological parameters. These imbalances may include changes in temperature regulation, fluid and electrolyte balance, acid-base balance, glucose metabolism, or other regulatory processes.

[0125] Overall, the hallmarks of altered physiological states provide valuable clues for healthcare providers to identify underlying causes, assess severity, and guide appropriate interventions to restore normal function and promote recovery.

[0126] "Therapeutically effective": According to this specification, a therapeutically effective product is one that, when administered to a subject suffering from a pathological condition, reduces the severity of the condition in the subject (i.e., the severity is at least partially reduced or alleviated), and / or provides some relief, relief or reduction of at least one clinical symptom, and / or provides a delay in the progression of the condition, or restores (fully or partially) a healthy physiological state in the area affected by the condition.

[0127] Having a beneficial effect according to this specification includes products that, upon administration to a healthy subject or a healthy but not homeostatic subject, or to an in vitro cellular assay representative of a substantially healthy state, provide in vitro or in vivo evidence of restoration or assistance in maintaining homeostasis upon administration in the cellular assay or in the recipient's system / area / organ / organ of interest.

[0128] The terms "prevent," "preventing," and "prevention of" (and grammatical variations thereof) refer to a reduction and / or delay in the onset and / or progression of disease, disorder, and / or clinical symptom(s) and / or a reduction in the severity of the onset and / or progression of disease, disorder, and / or clinical symptom(s) in a subject compared to what would occur in the absence of the methods of the present invention. Prevention can be complete, e.g., the total absence of disease, disorder, and / or clinical symptom(s). Prevention can also be partial, such that the occurrence and / or severity of the onset and / or progression of disease, disorder, and / or clinical symptom(s) in a subject is less than what would occur in the absence of a composition according to the present invention.

[0129] Having a "beneficial / healthy / health-beneficial" effect according to this specification includes products that, upon administration to a healthy subject or a non-homeostatic healthy subject, or to an in vitro cellular assay representative of a substantially healthy state, provide in vitro or in vivo evidence of restoration or assistance in maintaining homeostasis upon administration in the cellular assay or in the recipient's system / area / organ / tissue of interest.

[0130] According to this specification, the term homeostasis has its meaning conventionally accepted in the art and thus refers to the physiological process by which an organism maintains a stable internal environment despite external changes. This stability is important for the proper functioning of cells, tissues, and organs. The purpose of homeostasis is to ensure that the internal conditions of an organism remain within an optimal range for survival and proper physiological function (a healthy physiological state). Homeostasis is achieved by an organism by regulating one or more biological activities, preferably a network of biological activities, and processes aimed at maintaining a healthy physiological state.

[0131] A product that aids in the maintenance of homeostasis is a product that regulates one or more biological activities, preferably a network of biological activities, in the direction of a healthy physiological state and is therefore suitable for healthy individuals and can be used by healthy individuals to support the mechanisms of homeostasis that contribute to a healthy physiological state and to aid in the regulation of the homeostasis of one or more biological activities, preferably a network of biological activities, that are associated with the characteristics of a given pathology.

[0132] A medical device (also MD) according to this specification is a product as defined above according to the definition in Article 2(1) points 1 to 3 of EU Regulation (EU) 2017 / 745, which is necessarily used for therapeutic purposes, and therefore "medical device" means any... [omitted]... material intended by the manufacturer to be used in humans, alone or in combination, for one or more of the following specific medical purposes: - treatment or alleviation of disease, - Treatment, mitigation, or compensation for injury or disability; -Alteration of physiological or pathological processes or conditions, ...omission... and does not achieve its primary intended action in or on the human body by pharmacological, immunological, or metabolic means, although its function may be assisted by such means.

[0133] "Performance" of a medical device means the ability of a medical device, as defined herein, to achieve its intended purpose as stated by the manufacturer.

[0134] "Clinical performance" of a medical device means the ability of a medical device, as defined herein, to achieve its intended purpose as claimed by the manufacturer, resulting from any direct or indirect medical effect resulting from its technical or functional properties, including diagnostic properties, thereby providing a clinical benefit to the patient when used as intended by the manufacturer.

[0135] "Clinical benefit" of a medical device means the positive impact of the device, as defined herein, on the health of an individual or on patient management or public health, expressed in terms of meaningful, measurable, patient-relevant clinical outcome(s) (including diagnosis-related outcome(s)).

[0136] Additionally, if a composition (e.g., a product or other device with therapeutic properties that includes one or more natural matrices) has a medical purpose, such as the diagnosis, treatment, mitigation, or prevention of disease, or is intended to affect the structure or function of the body, and meets the criteria outlined in the definition, it may be classified by the U.S. FDA as a medical device.

[0137] Pursuant to section 201(h)(1) of the Food, Drug, and Cosmetic Act, a device is: Any instrument, apparatus, device, machine, contrivance, implant, in-vitro reagent, or other similar or related article, including any component parts or accessories thereof; (A) recognized in the official National Pharmacopoeia or the United States Pharmacopoeia, or any supplement thereto; (B) intended for use in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease in humans or other animals; or (C) is intended to affect the structure or any function of the human or other animal body, does not achieve its primary intended purpose by chemical action in or on the human or other animal body, and does not depend on being metabolized for the achievement of its primary intended purpose. The term "device" does not include software functionality excluded pursuant to section 520(o).

[0138] The classification of medical devices within a risk class is typically based on factors such as the intended use, instructions for use, and the risks associated with the device.

[0139] A pathophysiological or physiopathological condition refers to an abnormal physiological state or process in the body that is typically associated with disease or dysfunction. It is also defined as a "pathological condition" in this specification and claims. It includes the study of functional changes that occur as a result of disease or injury and how these changes manifest at the cellular, tissue, organ, and systemic levels. Pathophysiology encompasses understanding both the underlying mechanisms of disease and the body's response to these disruptions in order to diagnose, treat, and manage various health conditions. Understanding pathophysiological conditions is important in medicine for both research and clinical use.

[0140] Pathophysiology involves the study of how various factors, such as genetic abnormalities, environmental influences, or disease processes, disrupt the normal physiological functions of the body, resulting in the development of ill health or disease. Understanding physiopathological conditions is essential for diagnosing, treating, and managing a wide range of medical conditions across different medical specialties.

[0141] Different batches or lots of a product herein, such as the state of the art, refer to distinct groups of items produced or manufactured at different times or under different conditions, but still belonging to the same product line. In different batches, the starting materials may be from the same stock or different stocks. Each batch or lot typically receives a unique identifier that distinguishes it from other batches. These identifiers aid in quality control, inventory control, and traceability throughout the production process and supply chain.

[0142] A batch of product containing or consisting of one or more natural matrices is expected to vary in its qualitative and quantitative chemical composition due to factors such as variations in raw materials, etc. Furthermore, a batch may vary as any other product batch due to manufacturing conditions or equipment used.

[0143] The present invention provides methods for determining whether a therapeutic or beneficial product, comprising or consisting of one or more natural matrices, exerts its therapeutic or beneficial effect via a physiological mechanism of action.

[0144] In order to act through a physiological mechanism of action, a product must be 100% natural. Thus, according to this specification, a product that comprises or consists of one or more natural matrices is a 100% natural product, which means that the product does not contain any additional artificial substances, i.e. chemically synthesized substances made by man through laboratory processes.

[0145] Furthermore, according to the present specification, a product comprising one or more natural matrices also does not contain added isolated molecules, such as excipients or active ingredients, even if of natural origin.

[0146] The present specification discloses in the examples experimental data for Products A, B, C and D (see examples of product compositions) for the treatment of osteoarthritis, mild cognitive impairment, osteoporosis and cancer, respectively (Product A, also referred to herein as Arte GX, is also disclosed in WO 2018 / 138678).

[0147] All tested products are intended for use in treating or aiding in the treatment of a pathological condition or altered state, in which the matrix is ​​derived from a properly processed natural material without any denaturing steps and formulated to obtain a final natural material (i.e., product) that, upon administration, is capable of modifying the pathological or altered physiological state and promoting the restoration of a healthy physiological state. In accordance with the definition of natural matrix provided in the glossary, all tested products were prepared by a process that does not denature the components within the natural matrix therein, and the matrix consists of 100% natural and biodegradable components only. A detailed description of the tested products is provided in the Examples section.

[0148] In the tested products, the hundreds of components derived from the crude raw plant parts are not deprived of the ability to establish multiple combinations of molecular and supramolecular interactions among themselves and with the target tissue, characteristic of materials of natural biological origin, and thus maintain their emergent therapeutic properties.

[0149] In particular, the products tested by the applicant are prepared according to a eubiotic protocol selected and standardized by the applicant through over 40 years of experience, starting from soil preparation to final product production, making the final product as homogeneous as possible and thus increasing efficiency in terms of the yield of effective batches. The eubiotic protocol developed by the applicant has been interpreted as standardizing each step leading to the desired final product as much as possible, providing a 100% natural final product, as opposed to synthetic or partially synthetic products (i.e., products that do not contain a single component obtained by artificial chemical synthesis) in which each component is produced under eubiotic conditions. By comparing, or even integrating, the reductionist approach described above with that of systems theory and quantum biology, it is possible to develop a fully symbiotic standardized production process involving the integration of interdisciplinary technologies and research in several different fields, from agriculture to omics and mathematical sciences.

[0150] As described above, the inventors have surprisingly found that products containing one or more natural matrices maintain their therapeutic or beneficial effects despite different qualitative and quantitative compositions (due to their natural origin), indicating functional resilience, in which the apparent properties of the matrix are maintained. That is, in contrast to the traditional paradigm, qualitative and quantitative variations in matrix composition do not affect the final matrix effect; matrices maintain at least some of the autogenous properties of the system from which they are derived. The inventors have also found that products of this type exert their therapeutic or beneficial effects by modifying a state (pathological, pre-pathological, or altered homeostatic status) rather than a single or small number of functions, due to inter-network interactions between the matrix and the treated subject. Notably, the functional resilience observed by the inventors is evident in the differential regulation of different genes (due to the qualitative and quantitative variability of the natural matrices in the analyzed products), which nevertheless result in the same regulatory effect on the tested activity (see Examples).

[0151] Together, these characteristics are indicative of a physiological mechanism of action, as opposed to the well-known key-lock pharmacological mechanism of action. By virtue of identifying the key characteristics of a physiological mechanism of action and providing a method for verifying the presence or absence of said characteristics, the present invention provides, for the first time, a method for determining whether a (therapeutic or beneficial) product acts via a physiological mechanism of action.

[0152] This is important to enable manufacturers to assess and demonstrate compliance with new requirements set out in the Medical Device Regulation, which is not currently available.

[0153] Physiological activity is an activity that can be exerted by natural and biodegradable materials, such as natural matrices, which consist only of natural components that were not degraded during the matrix production process and therefore have matrix effects provided by both structural (material) interactions between the components of the matrix and functional interactions involving non-materials that become apparent only when the matrix interacts with a biological system. Such interactions do not appear to be attributable to previous experimental models based on the existence of direct and unequivocal relationships at the molecular level between the structure and activity of selected components. Natural materials, including or consisting of natural matrices, are entities that at least partially maintain the self-generating properties of their starting materials, which belong to the biological domain, and exhibit unique properties (inter-network interactions) represented by a network of material and non-material relationships that interact with the network of relationships of the treated subject, thereby reproducing physiologically similar characteristics and interactions with similar complexity.

[0154] Provided herein are means for verifying the presence of such properties and therefore for assessing whether a (therapeutic or beneficial) product exerts its therapeutic or beneficial effect via a physiological mechanism of action.

[0155] Therefore, the present invention provides 1. A method for assessing whether a product for treating a pathological condition or assisting in maintaining homeostasis in an altered physiological state exerts its therapeutic or beneficial effect via a physiological mechanism of action, comprising: selecting a therapeutic or beneficial product comprising one or more natural matrices and providing different batches of said product; performing at least one cell-based assay for each of the different batches, wherein a readout of the cell-based assay indicates a degree of modulation of one or more biological activities underlying a desired therapeutic or beneficial effect; From the results read out, the following: Whether the product batches exert their therapeutic or beneficial effect by regulating the network of biological activities underlying the pathological or altered physiological condition; and determining whether the product exhibits therapeutic or beneficial functional restoration between different batches, where the functional restoration is intended to maintain the therapeutic or beneficial properties of different batches of a given product comprising one or more natural matrices, and such maintenance is not affected by qualitative and quantitative compositional differences between batches of the product; If the product regulates the network of biological activities underlying the pathological or altered physiological state, and if the product exhibits therapeutic or beneficial functional restoration between different batches, it is demonstrated that the product exerts its therapeutic or beneficial effect via a physiological mechanism of action.

[0156] In the present specification and claims, functional resilience contemplates the maintenance of therapeutic or beneficial properties of different batches of a given product comprising one or more natural matrices, despite the qualitative and quantitative compositional differences between the batches of said product. As explained above, in a product comprising one or more natural matrices, the qualitative and quantitative compositional differences between batches are inherent features of the natural matrices themselves, and therefore, a product comprising or consisting of one or more natural matrices will necessarily have different batch-to-batch compositions, since it is not possible to obtain two identical (in terms of qualitative-quantitative composition) natural matrices starting from the same type of raw materials.

[0157] According to the present invention, the essential feature of a product to exert a therapeutic or beneficial effect through a physiological mechanism of action is the presence of one or more natural matrices in the product and the assessment of the maintenance of at least two features typical of the living world, namely the preservation of the functional resilience of the product, in which the permutation of the molecular composition still mediates the same biological activity, and the physiological situation (pathological or altered but not yet pathological) alteration. The temporal maintenance of an altered physiological state represents an alteration of a physiological process.

[0158] As already mentioned, in the present specification and claims, a batch of product refers to the amount of product produced at one time under given conditions. For products containing natural matrices, different batches are produced using different sets of raw materials, so different batches will necessarily differ from each other in their qualitative and quantitative composition. It is implicit that the method of the present invention is carried out on product batches whose therapeutic or beneficial effects have been verified by the manufacturer and which are therefore prepared and manufactured from the same type of starting materials according to the same manufacturing protocol and conditions.

[0159] Thus, the selected therapeutic or beneficial products are products comprising or consisting of one or more natural matrices for use in treating a pathological condition or for assisting in the maintenance of homeostasis when an altered physiological state exists in a healthy subject. Thus, a therapeutic product, when administered to a patient suffering from a given pathological condition, reduces the severity of the condition in the subject (i.e., at least partially improves or ameliorates the severity) and / or provides some relief, alleviation, or reduction of at least one clinical symptom of the condition and / or slows the progression of the condition, while a beneficial product, when administered to a healthy subject with an altered physiological state, assists in the restoration of correct homeostatic responses and helps the organism to restore a normal (unaltered) physiological state.

[0160] Subjects that can be treated in accordance with the present invention are animals, including humans (and thus the products are for human or veterinary use), or even plants.

[0161] Non-limiting examples of natural matrices according to the present invention are represented by one or more of the following: chopped or crushed plant parts, plant extracts, processed plant parts, fractions of plant extracts such as fractions obtained by filtration on semipermeable membranes (microfiltration, ultrafiltration, nanofiltration) or by treatment with adsorption resins, microorganisms, honey, propolis, silk, wax, plant resins, plant gums, plant exudates, plant oils, plant essential oils, animal tissue lysates, plant or animal fluids.

[0162] Preferably, the microorganism is an inactivated microorganism, such as a Tyndall treated organism.

[0163] In the most preferred embodiment, the product provided (selected) by the method of the present invention is a product consisting of 100% natural ingredients, intended as ingredients not obtained by humans by chemical synthesis reactions, and therefore, when the product contains one or more natural matrices, it may also contain minerals, any other organic or inorganic material generally found in nature. Preferably, the products that are the subject of the methods and processes of the present invention are products that are obtained or obtainable according to standardized protocols, more preferably through eubiotically standardized protocols.

[0164] According to the present invention, the product can be a dietary supplement, a novel food, a medical device, a pharmaceutical, or a cosmetic.

[0165] Although different batches of a product containing one or more natural matrices are, by definition, batches whose qualitative and quantitative composition necessarily varies as discussed above, according to one embodiment, qualitative and / or quantitative analysis of each batch can be performed to demonstrate the existing qualitative and / or quantitative differences in the molecular composition of each batch. This can be achieved by conventional techniques, non-limiting examples of which include chromatography, spectrophotometry, atomic absorption spectroscopy (AAS), atomic emission spectroscopy (AES), inductively coupled plasma (ICP) techniques, chromatography coupled with a detector, etc., or combinations thereof. The analysis can focus on a limited number of selected classes of substances (e.g., Figure 5) or on all components of the product (see examples).

[0166] According to the present invention, an in vitro cell-based assay is an assay whose readout is related to a pathological condition or altered physiological state treated by the product under test. In vitro cell-based assays are well-known experimental techniques involving the use of isolated cells to study biological processes or to test the effects of drugs, chemicals, or other substances. In the present invention, the in vitro cell-based assays used are designed to simulate conditions related to pathological or pathophysiological conditions or conditions related to altered physiological states. According to the present invention, cell culture models such as monolayer cultures or three-dimensional (3D) systems can be used. Where appropriate, disease-specific cell lines can be used, depending on the disease of interest (e.g., cancer), and proliferation assays can also be used.

[0167] For example, assays designed to mimic a disease / pathology may be used by using diseased cells or by inducing a disease phenotype in cells that have been treated with a specific compound, thereby inducing control of the pathophysiological state of the pathology in question, i.e., a "disease model assay" or "disease-in-a-dish" model. Alternatively, the assay may use cells that exhibit control of the pathophysiological state of the pathology of interest without additional treatment during the assay. If the product is a beneficial product, i.e., a product that aids in the maintenance of homeostasis through an altered physiological state (not pathological), the control for the unregulated state may be represented by untreated cells, or the control for the abnormally regulated state may be represented by appropriately treated, differentiated, or induced cells. Cell-based assays are assays already known in the art and may be optionally adapted or still interpreted based on knowledge in the art with respect to the pathology or alteration of interest. Preferably, validated, well-known cell-based assays are used. Depending on the pathology or abnormal physiological condition of interest, the skilled person will select the most appropriate cell-based assay from the state of the art and, optionally, further adapt it without using the techniques of the present invention.

[0168] A cell line or primary cell relevant to the disease being modeled is selected according to well-established protocols. For example, when studying osteoarthritis (OA), a suitable and recognized cell-based assay uses chondrocytes, a disease model for OA when treated with IL1B. For further example, when studying neurodegenerative diseases, a neuronal cell line such as SH-SY5Y may be selected, or primary cells that can induce the desired phenotype upon "insult" with a given compound can be used. Cell-based assays preferably include appropriate control groups, such as untreated cells, vehicle-treated cells, and cells treated with a compound known not to affect the disease phenotype. These controls help distinguish the specific effects of test compounds. Non-limiting examples of various cell-based assays suitable for the methods of the present invention are provided in the Examples section.

[0169] According to the present invention, the readout of the cell-based assay provides qualitative and quantitative data regarding the degree of modulation of selected parameters and one or more associated biological activities related to one or more characteristics of a pathological condition or altered physiological state of interest.

[0170] Depending on the pathology of interest, the characteristic may be one or more, and preferably more characteristics are selected; for example, in the case of cancer, those skilled in the art will be well aware that a more relevant characteristic is the proliferation of cancer cells; therefore, the cell-based assay selected in this case is an assay verifying the viability of neoplastic cells or tumor masses upon administration of the product of interest. Furthermore, according to the present invention, although it is preferable to use a single cell-based assay, two or more cell-based assays can also be used; in a non-limiting example, additional cell-based assays can be performed if different cell-based assays are considered more appropriate for analyzing different characteristics.

[0171] Those skilled in the art will be able to select more appropriate parameters depending on the selected characteristics of the pathology of interest.

[0172] Such parameters may be, for example, gene expression patterns, ROS, oxidative stress, cell viability, etc.

[0173] Non-limiting examples of pathologies of interest include mild cognitive impairment (MCI), osteoporosis (OP) (including postmenopausal or perimenopausal osteoporosis (PMO)), osteoarthritis (OA), and cancer.

[0174] Cancers of interest include, but are not limited to, head and neck cancer, melanoma, breast cancer, bladder cancer, and osteosarcoma.

[0175] The characteristics of most pathologies are well known in the art. Those skilled in the art can easily search scientific literature for the characteristics of the pathology of interest and the one or more biological activities underlying the characteristics. It is clear that those skilled in the art will select the one or more biological activities underlying a given characteristic in consideration of the specific pathology of interest.

[0176] By way of example, one skilled in the art can use different prior art sources to research the state of the art of the pathophysiology of a disease of interest.

[0177] Those skilled in the art who wish to define the characteristics of a disease of interest can search for the desired information in the scientific literature. See, e.g., Robbins & Cotran Pathologic Basis of Disease (Robbins Pathology) 10th Edition; https: / / calgaryguide.ucalgary.ca / ;Biomedical literature from PubMed Central( https: / / pubmed.ncbi.nlm.nih.gov / ) Non-limiting examples of sources that can be used in

[0178] For each of these characteristics, several underlying biological activities are also known in the art, and the skilled artisan can select these activities from those disclosed in the art. If more biological activities are attributed in the art to a given characteristic of a given pathology in the method of the present invention, preferably at least two, at least three or more of these activities are selected.

[0179] If one desires to use specific software available to facilitate certain steps of the methods of the present invention, such as finding one or more biological activities underlying a disease signature, one skilled in the art may wish to adapt the searched features to better match their definitions to the software being used. As an example, if the software being used is Qiagen IPA (IPA version 94302991 Qiagen), the features can be redefined and the IPA can be explored, if necessary, using information found in the aforementioned resources. When IPA is used to rapidly identify one or more biological activities underlying a disease signature, the following procedure can be followed: Features can be written one by one in the "Diseases and Functions" query box, and then the search will begin.

[0180] The resulting recurrence table allows one skilled in the art to filter diseases / functions arising from a body of evidence. By way of example, the source of the relationships can be the Ingenuity Knowledge Base, which includes curation from journal articles, OMIM, JAX, and ClinicalTrials.gov.

[0181] Thus, the tool can associate with each one or more biological activities a predetermined number of genes whose regulation contributes to the degree of regulation.

[0182] Examples of known features associated with pathologies are shown in Figures 1 to 12.

[0183] For beneficial products, distinctive features can be selected that are indicative of pathologies that may result from the change.

[0184] By way of example only, hallmarks of OA known in the art include proliferation (development and function of the skeletal and muscular systems, e.g., joint dysfunction and joint space); inflammation; and anatomical damage. By way of further example, suitable one or more biological activities underlying the above-mentioned characteristics of OA can include: Growth: one or more biological activities of interest are the development and function of the skeletal and muscular systems, e.g., joint dysfunction and joint cavities, joint dysfunction, joint cavities and cartilage formation; Inflammation: one or more biological activities of interest are inflammatory diseases, inflammation and nociception, inflammation of the joints; Protection from anatomical damage: One or more biological activities of interest are damage and abnormalities of the organism, such as inflammation and swelling of the joints, difficulty in moving the joints, etc.: osteoarthritis.

[0185] Possible state-of-the-art keywords for defining said activity are shown in Figures 1 to 4.

[0186] Further, by way of example, known characteristics of MCI may include cognition (impaired), activation and survival (neuron, decreased), myelination and branching (decreased), inflammation (increased), and skeletal and muscular system function (decreased).

[0187] Cognitive: The one or more biological activities of interest are cognition and learning; Activation and viability: The one or more biological activities of interest are neuronal development, differentiation, and proliferation of neuronal cells; Myelination and Branching: The one or more biological activities of interest are neuronal branching, neuronal sprouting; Inflammation: The biological function of interest is chronic inflammatory disorders; Skeletal and muscular system function: One or more biological activities of interest are muscle cell proliferation and muscle necrosis.

[0188] Possible state-of-the-art keywords that define the activity are listed in 6-8.

[0189] Further by way of example, known characteristics of OPs, including PMOs, may include mineralization, inflammation (increased), adipose tissue functionality (increased), bone remodeling, osteopenia, osteoblast differentiation (decreased).

[0190] Mineralization: The biological activity of interest is bone mineral density; Inflammation: The one or more biological activities of interest is inflammation of adipose tissue and connective tissue; Adipose tissue function: one or more biological activities of interest are glucose tolerance and adipocyte mass; Bone remodeling: The one or more biological activities of interest are bone remodeling and resorption; Osteopenia: The biological activity of interest is osteopenia itself; Osteoblast Differentiation: One or more biological activities of interest are mesenchymal stem cell differentiation, osteoblast formation and quantity, and bone cell quantity.

[0191] Keywords that may be related to the activity are shown in FIGS.

[0192] For each of these characteristics, one or more underlying biological activities are known in the art.

[0193] Tumors: A typical tumor is characterized by the proliferation of tumor cells, but each known tumor has specific characteristics depending on the affected organ, system, or tissue, and an example of the characteristics of a head and neck tumor (hypopharyngeal squamous cell carcinoma) is shown in Figure 12.

[0194] For each of the one or more biological activities, the degree of modulation causally related to a pathological condition is also known in the art. Therefore, the opposite degree of modulation can be considered the degree of modulation associated with a desired therapeutic effect. As a result, a panel can be designed that exhibits a desired modulation pattern for each of the activities, and the panel represents the trend of the degree of modulation for that activity under healthy physiological conditions (see Figures 1-12). The degree of modulation herein refers to either up-regulation or down-regulation of a given activity. Up-regulation refers to alteration of the activity or expression of a particular gene, protein, cell or molecular pathway, or cellular component that results in an enhancement of the given biological activity. Down-regulation is the opposite of up-regulation. It involves alteration of the activity or expression of a gene, protein, cell or molecular pathway, or cellular component that results in a decrease of the given biological activity.

[0195] Non-limiting examples of the degree of modulation of one or more biological activities causally associated with a pathology (OA, MCI, OP (including PMO and head and neck tumors)) are shown in Figures 1-12.

[0196] Conversely, a healthy physiological state corresponding to a pathology of interest can be designed as a degree of counter-regulation of one or more selected biological activities. By way of example, but not limitation, the degrees of regulation resulting in a healthy physiological state for OA, MCI, OP (particularly PMO), and tumors are also shown in Figures 1 to 12.

[0197] If a modulation pattern resulting in a healthy physiological state is observed for each or most of the biological activities causally related to a given disease characteristic upon treatment with a given product, the product can be identified as having the desired therapeutic effect. Thus, a product that induces a degree of modulation of all selected biological activities causally related to a given pathological characteristic toward a healthy physiological state can be identified as having the desired therapeutic effect on the overall pathological condition. If only some of the biological activities causally related to the disease characteristic meet the physiological criteria defined above, the product will only have a partial therapeutic effect that does not meet the requirement for a degree of modulation of the condition.

[0198] When gene expression is selected as a marker, the genes underlying the modification of each biological activity causally related to each selected characteristic of the pathology and the expression pattern of each of said genes can be retrieved by a person skilled in the art from the state of the art, a task that can be facilitated by using ad hoc bioinformatics tools. The same Qiagen IPA mentioned above is suitable for the rapid culling of said information from scientific literature, since it is an aggregator of scientific literature that allows for the retrieval of information on genes / proteins and the construction of networks that predict the behavior of biological systems according to the gene expression state.

[0199] The pathophysiological features available in the state of the art for a given pathological condition are used to interrogate the IPA via the "IPA Bioprofiler" tool and can also be used as keywords to add additional keywords related to the feature.

[0200] The use of the "IPA Bioprofiler" allows a person skilled in the art to identify expressed genes that are causally related to one or more identified biological activities and the specific molecular pathways that support them. Information about the measured gene expression data induced by each batch (e.g., fold change cutoffs of ≦−2 and ≧+2 and p-value ≦0.05 according to the manufacturer's instructions) was then superimposed on the resulting network to define the degree of regulation of affected genes and related biological functions.

[0201] Once the most relevant genes and their expression patterns underlying the detectable alterations in the pathological condition of interest for each of the selected one or more biological activities have been identified, for each of the genes, the expression pattern opposite to the identified expression pattern is established as the expression pattern indicative of a healthy physiological state for the one or more biological activities.

[0202] As mentioned above, the degree of regulation of biological activity can be either down-regulated or up-regulated depending on the associated gene regulation. Based on the literature, the resulting predicted impact on the associated biological function can be determined by the "IPA Molecular Activity Predictor" tool (MAP) and displayed in a heatmap visualization using a color code that can be easily converted to a numerical value by the user.

[0203] When performing a cell-based assay according to the methods of the present invention, - identifying one or more parameters whose modulation analysis in a cell-based assay allows a qualitative assessment of the degree of modulation of one or more biological activities underlying the therapeutic or beneficial effect of the tested product, and determining a modulation pattern representative of a desired healthy physiological state; - for each different batch analyzed, performing an in vitro cell-based assay by treating different cell populations of the assay with different batches of product, providing at least one control cell population, and detecting a modulation pattern of each identified parameter in each cell population; - determining the degree of qualitative and quantitative modulation of each of said parameters in said control cell group and each treated cell group and calculating a respective modification value (a value that quantifies and qualifies the degree of modulation) for each of said one or more biological activities; - comparing the values ​​for each of the one or more biological activities calculated for each treated cell group.

[0204] In particular, the modulation analysis in cell-based assays identifies one or more parameters that allow for a qualitative-quantitative assessment of the degree of modulation of one or more biological activities underlying the therapeutic or beneficial effect of the tested product, and determines a modulation pattern that is indicative of a desired healthy physiological state. -providing a list of characteristics representative of pathological conditions related to imbalances that may result from the pathology of interest or from non-pathologically altered physiological conditions of interest (depending on whether the product under test is a therapeutic product or a beneficial product that helps maintain homeostasis); - for each of said characteristics, identifying the alteration of one or more biological activities underlying said pathology or non-pathologically altered physiological state, determining the degree of modulation thereof indicative of a pathophysiological state associated with said pathology or alteration or said non-pathologically altered physiological state (respectively), and evaluating the opposite modulation of each of said activities as a pattern of modulation indicative of a healthy physiological state; - identifying, for each of said one or more biological activities, one or more markers and their regulation patterns that underlie detectable alterations in said pathological state or said non-pathologically altered state, and defining, for each of said parameters, the regulation pattern opposite to said identified one as the regulation pattern consistent with said healthy physiological state.

[0205] From the results obtained in the cell-based assay, it is possible to determine the qualitative-quantitative modulation of each of the previously identified parameters induced by each product batch sample relative to the control group, and thus obtain values ​​that represent the qualitative (which activity is modulated and in what direction, i.e., positive for up-modulation or negative for down-modulation) and quantitative (how much each activity is modulated relative to the control group) degree of modulation induced by each batch for each of the one or more biological activities, the magnitude of the value representing the distance (in units of %, fold, etc.) relative to the control group. The degree of modulation of each activity can be expressed as a numerical value, such as a statistical value.

[0206] Comparison of the calculated values ​​for each group and for each one or more biological activities makes it possible to verify whether the product regulates a pathological or altered physiological state and whether the product exhibits functional resilience, i.e., whether the degree of regulation induced by each batch of product is substantially qualitatively and quantitatively equivalent, thereby resulting in the same therapeutic or beneficial effect, despite possible differential regulation of each marker due to different qualitative and quantitative differences between the batches.

[0207] Depending on the parameters selected, the degree of modulation of biological activity can be defined and characterized by those skilled in the art by various quantitative values ​​commonly used depending on the specific circumstances and type of biological activity being studied. Some common values ​​used to define the degree of regulation include: fold change, i.e., the ratio of the value of biological activity under a specific condition or treatment to its value under control or reference conditions; for example, a fold change of 1 indicates no change, while a value greater than 1 indicates upregulation and a value less than -1 indicates downregulation; log fold change, i.e., the logarithm of the fold change (usually base 2 or base 10); percentage change, i.e., the percentage difference between the value of biological activity under a specific condition and its value under control or reference conditions; z-score, which represents the deviation of the observed value of biological activity from its mean value, normalized by the standard deviation (a positive z-score indicates an increase in activity, and a negative z-score indicates a decrease); effect size, i.e., a measure of the magnitude of the difference between two groups, which is often standardized to facilitate comparisons between studies or datasets (Cohen's d is a common effect size measure calculated as the difference in means divided by the standard deviation); for dynamic biological activities such as signaling pathways or physiological responses, AUC can be used to quantify the overall activity or response over a period of time.

[0208] These values ​​can be used individually or in combination to provide a comprehensive characterization of the degree of modulation of biological activity under different conditions or treatments. The choice of which value(s) a skilled artisan decides to use will depend on the particular research question, the nature of the biological activity, and the available data.

[0209] In the context of biological activity, z-scores can be used to represent the degree of modulation or change in specific biological activity relative to its typical or baseline behavior or pathological state, which is often used in fields such as systems biology where researchers analyze high-dimensional data sets to understand complex biological processes.

[0210] In this case, the z-score represents how much a particular biological activity deviates from its expected or average behavior within a given situation, often in response to some stimulus, treatment, or situation.

[0211] For example, in gene expression analysis, z-scores can be calculated to assess the degree to which a gene's expression level changes in response to a therapeutic treatment compared to its expression in a control condition, with a positive z-score indicating upregulation and a negative z-score indicating downregulation.

[0212] Mathematically, a suitable formula for calculating a z-score of the degree of biological activity modulation may involve comparing the observed value under a particular condition to the mean and standard deviation of that value across multiple conditions or replicates: z=x-μ / σ

[0213] where x represents the observed value of biological activity (e.g., gene expression level), μ is the mean value of activity across all conditions, and σ is the standard deviation of activity across all conditions.

[0214] This z-score approach allows researchers to identify and prioritize biological activities that change significantly under specific experimental conditions, providing insight into the underlying mechanisms of biological systems.

[0215] The transcriptome data results provided in the examples below show that when gene expression parameters are used, different batches of tested product, although regulating different sets of genes, provide the same regulation of one or more biological activities of interest and therefore the same therapeutic effect.

[0216] From the calculated values ​​representing the degree of qualitative and quantitative modulation induced by each batch for each of one or more biological activities, it is possible, in accordance with the teachings provided herein, to assess whether the entire condition, rather than one or a few activities, is regulated by the product of interest and whether the different batches of product analyzed maintain (therapeutic or beneficial) functional resilience.

[0217] Assessment of altered status and functional recovery. The ability of a product under investigation to modify a pathophysiological or altered physiological state (e.g., by assisting the homeostatic response of an organism) is an important feature for establishing the physiological mechanism of action of a therapeutic or beneficial product. Indeed, products that act in network interactions are products that are expected to modify a state rather than a single function when administered to an organism.

[0218] In other words, this characteristic is likely to be met by a therapeutic or beneficial product that includes or consists of one or more natural matrices, given the network-network (product-recipient) interactions exerted by the natural matrices. However, this capability must be determined, and currently, no procedures are available for this determination.

[0219] In addition, products that exert a physiological mechanism of action are also expected to be 100% natural and exhibit the flexibility and self-regulation mechanisms observable in vivo, where different intracellular and intercellular messages and different regulation of gene pathways can provide the same result despite different messages being triggered within the cell. In the case of therapeutic or beneficial products, this corresponds to the functional resilience of different batches of the product (with qualitative-quantitative variability in chemical composition).

[0220] This specification provides a manner for determining whether a therapeutic or beneficial product exerts its therapeutic or beneficial effect by modifying a pathological or altered physiological (yet non-pathological) condition, and whether the product exhibits functional resilience (i.e., maintenance of the therapeutic or beneficial properties of different batches of a given product comprising one or more natural matrices despite qualitative and quantitative compositional differences between the batches).

[0221] According to the present invention, this can be verified by performing the cell-based assays described herein and analyzing and interpreting the data obtained therefrom.

[0222] Depending on the cell-based assay selected, analysis of the data may vary slightly and aims to verify that the degree of modulation of all of the selected biological activities underlying the pathology or altered physiological state is in the direction of a healthy physiological state (i.e., as opposed to the degree of modulation of the activity underlying the pathological or altered physiological state).

[0223] In the embodiments described below, it is understood that cell-based assays may be performed in parallel on one or more batches of product, and that in addition to a control cell population, several test populations (or duplicate or triplicate populations) of cells may be generated equal to the number of different batches tested (i.e., cell populations treated with one batch of product).

[0224] Assessment of (therapeutic or beneficial) functional recovery. As indicated in the glossary and above specification, for purposes of this specification and claims, functional resilience is the maintenance of measurable therapeutic or beneficial efficacy in different batches of product despite variability in their qualitative and quantitative molecular composition.

[0225] It is clear that batches are intended as batches that are identical in terms of the manufacturing process and the type and amount of each ingredient (since the main ingredients of the selected product are natural matrices, this means that each matrix in the product is manufactured from the same type of starting material in the same procedure, e.g., with a given type of extract from the same plant part of the same plant species), and therefore variability in their qualitative and quantitative molecular composition cannot be attributed to different manufacturing procedures or different ingredients, but can only result from the inherent differences between natural matrices obtained in the same procedure from different organisms of the same species. The functional resilience of a product can be verified if different batches of the same product containing one or more natural matrices, regardless of their qualitative and quantitative composition, maintain in a measurable and verifiable way their ultimate regulatory activity underlying their therapeutic or beneficial properties.

[0226] In one embodiment of the present invention, the in vitro cell-based assay can be an assay in which control of the pathophysiological state of the pathology of interest can be induced in cells, e.g., an assay in which the cells are subjected to an appropriate treatment, thereby inducing control of the pathophysiological state in the cells, where one or more groups of induced cells can each be treated with one or more different batches of the product of interest.

[0227] According to one embodiment of the present invention, the method includes, prior to performing one or more cell-based assays, (1) providing a list of features indicative of the pathological condition; (2) for each of the features, identifying alterations in one or more biological activities underlying the pathological condition, thereby identifying a network of biological activities whose degree of modulation is consistent with the pathological condition; and (3) identifying one or more parameters whose degree of modulation is consistent with the degree of modulation of the one or more biological activities underlying the therapeutic effect of the tested product, and determining a trend in the degree of modulation in the network, with respect to an up- or down-regulation of the one or more biological activities consistent with the pathological condition or a healthy state.

[0228] In other words, the method comprises: (1) Providing a list of characteristics representative of a pathological condition associated with the pathology that can be attributed to the pathology or the non-pathological altered physiological state, i.e., providing a list of characteristics representative of the disease or pathophysiological condition treated by the product of interest, or providing a list of characteristics that can be attributed to the non-pathological altered physiological state the maintenance of homeostasis of which is assisted by the beneficial product of interest; (2) for each of the characteristics, identifying the alteration of one or more biological activities underlying the pathology, determining the degree of modulation thereof that represents the pathophysiological state associated with the pathology, and evaluating the opposite degree of modulation of each of the activities as a pattern of modulation that represents a healthy physiological state; (3) identifying one or more markers and their regulation patterns underlying the detectable alteration in the pathological state for each of the one or more biological activities, and determining, for each of the parameters, the opposite regulation pattern to that identified as the regulation pattern consistent with the healthy physiological state.

[0229] When a therapeutic product is being tested, according to one embodiment, the method of the present invention comprises: (a) the following cell populations: (a1) at least one control group and at least two test groups of cells having a disease phenotype relevant to the intended use of the therapeutic product; or (a2) at least one population of cells having a healthy physiological phenotype, and at least one control group and at least two test groups of said cells having a healthy physiological phenotype in which a disease phenotype associated with the intended use of the therapeutic product has been induced; performing the at least one in vitro cell-based assay on treating each of the test populations of cells with one of the different batches of therapeutic product; (b) determining the degree or pattern of modulation of each of said parameters for each of said cell populations of step (a) and calculating a respective modulation value for each of said one or more biological activities; (c) comparing the adjusted values, At least 50% of the one or more biological activities for each characteristic are modulated by each product batch, and the trend in the degree of modulation in the network is consistent with a healthy state, and the modulation value determined in (b) for each of the at least 50% one or more biological activities for the test group of cells in (a1) differs from that of the control group of cells in (a1) by at least 0.15, respectively; or At least 50% of the one or more biological activities for each characteristic are modulated by each product batch, the trend of the degree of modulation in the network is consistent with a healthy state, and the modulation value determined in (b) for each of the at least 50% one or more biological activities for the test group of cells in (a2) is different from that of the control group of cells in (a2) by at least 15%, respectively. and comparing the adjusted value to indicate that the therapeutic product exerts its therapeutic effect via a physiological mechanism; The functional resilience of the product is demonstrated by a modulation value for each of one or more biological activities of each test group of cells that differs from the mean of such values ​​by less than 20%.

[0230] This means that the modulation value of a given biological activity of the test group is respectively compared to the modulation value of the same biological activity of the control group, and therefore the difference between the modulation value of a given activity in the treated group of cells and the modulation value of the same activity in the group of cells representing the control baseline is at least 0.15 or at least 15%.

[0231] In other words, the method also provides a method for evaluating whether a therapeutic product is effective in treating a pathological condition via a physiological mechanism of action, comprising: - providing different batches of a therapeutic product, the product comprising one or more natural matrices; - providing a list of features representative of said pathological condition, identifying for each of said features a set of parameters allowing the assessment of the network of biological activities whose modulation underlies the therapeutic effect of the tested product, and determining the trend of modulation in the network in diseased and healthy states, in terms of up- or down-regulation of activity; (a) the following cell populations: (1) at least one control group and at least two test groups of cells having the disease phenotype targeted by the therapeutic product; or (2) performing at least one in vitro cell-based assay on at least one population of cells having a healthy physiological phenotype, and at least one control group and at least two test groups of the cells having the healthy physiological phenotype in which a disease phenotype targeted by the therapeutic product is induced; treating each of the test populations of cells with one of the different batches of therapeutic product; (b) determining the modulation or pattern of modulation of each of said parameters for each of said cell populations and calculating a respective modulation value for each of said biological activities; (c) comparing the modulation values ​​for each biological activity in each cell population of step (b), At least 50% of the biological activity for each characteristic is modulated by each product batch using the healthy state modulation trend determined in (b) and the modulation value determined in (b), and each of the at least 50% one or more biological activities for the test group of cells in (a)(1) differs from that of the control group in (a)(1) by at least 0.15, respectively; or A therapeutic product is shown to exert its therapeutic activity through a physiological mechanism of action if at least 50% of the biological activity is modulated by each product batch for the test group of cells in (a)(2), with the trend in the degree of modulation of the healthy state determined in (b) and each modulation value of one or more biological activities of at least 50% determined in (b) differing from that of the control group in (a)(2) by at least 15% each; and and comparing the modulation values ​​for each of the biological activities of each test group of cells, where the functional resilience of the product is demonstrated by the modulation value differing from the average of the values ​​by less than 20%.

[0232] The phrase "at least 50% of the one or more biological activities for each characteristic" means that the network's modulation trend is consistent with a healthy state, and for a single biological activity for a given characteristic, 100%, i.e., the single activity, must be modulated by the tested product to meet the above requirement, and the modulation trend is consistent with a healthy state. The phrase "modulation value of each of at least 50% of the one or more biological activities determined in (b)" refers to the modulation value determined in (b) of at least 50% of the biological activities that meet the requirement of being modulated according to a modulation trend consistent with a healthy state. This applies mutatis mutandis to all embodiments disclosed herein.

[0233] When beneficial products are being tested (i.e., products that have a beneficial effect on an altered, but not yet pathological, physiological state by aiding in the maintenance of homeostasis), according to one embodiment, the method of the present invention comprises: (a) the following cell populations: conducting the at least one in vitro cell-based assay on at least one control group of cells having a healthy phenotype or at least one control group of cells in which the dysregulated phenotype targeted by the beneficial product is suitably induced, and at least two test groups of cells taken from the control group; treating each of the test groups of cells with one of the different batches of beneficial product; (b) determining the degree or pattern of modulation of each of said parameters for each of said groups of cells of step (a) and calculating a respective modulation value for each of said one or more biological activities; (c) comparing the adjusted values, If at least 50% of the one or more biological activities for each characteristic are modulated by each product batch such that the trend in the degree of network modulation is consistent with a healthy state, and the modulation value calculated in (b) for each of the at least 50% one or more biological activities for the test group of cells differs from that of the control group of cells by at least 0.15, it indicates that the beneficial product exerts an effect in supporting the maintenance of homeostasis via a physiological mechanism of action; and and comparing the modulation values, where the functional resilience of the product is demonstrated by the modulation value for each of one or more biological activities of each test group of cells differing from the average of the values ​​by less than 20%.

[0234] This means that the modulation value of a given biological activity of the test group is respectively compared to the modulation value of the same biological activity of the control group, and therefore the difference between the modulation value of a given activity in the treated group of cells and the modulation value of the same activity in the group of cells representing the control baseline is at least 0.15 or at least 15%.

[0235] In other words, the method is a method for assessing whether a beneficial product exerts an effect in assisting in maintaining homeostasis by regulating an altered physiological condition, comprising: - providing different batches of a beneficial product that supports homeostasis, the product comprising one or more natural matrices; - providing a list of features representative of pathological conditions that may result from said altered physiological state, identifying for each of said features a set of parameters whose modulation allows the assessment of the network of biological activities underlying the therapeutic effect of the tested product, and determining the trend of modulation in terms of up- or down-regulation of said activities in said network in diseased and healthy states; (a) the following cell groups: (1) performing at least one in vitro cell-based assay on at least one control group of cells having a healthy phenotype or at least one control group of cells in which the dysregulated phenotype targeted by the beneficial product has been suitably induced and at least two test groups of cells taken from the control group; treating each of the test groups of cells with one of the different batches of the beneficial product; (b) determining the degree or pattern of modulation of each of said parameters for each of said cell populations of step (a) and calculating a respective modulation value for each of said biological activities; (c) comparing the modulation level for each biological function in each cell population of step (a), A useful product comparing the modulation values ​​of at least 50% of the biological activity for each characteristic with the trend of modulation of the healthy state determined in (b) and each modulation value of at least 50% determined in (b) indicates that the cells exert an effect in supporting the maintenance of homeostasis via a physiological mechanism when each of the one or more biological activities for the test group of cells in (a)(1) differs from that of the control group in (a)(1) by at least 0.15; The functional resilience of the product may be defined as a method in which the modulation value for each of the biological activities of each test group of cells differs from the average of the values ​​by less than 20%.

[0236] In a preferred embodiment, the control group is considered the reference-modulated baseline, with a qualitative-quantitative modulation value of 0 for each of one or more biological activities.

[0237] As previously described, the method of the present invention includes: (1) providing a list of salient features representative of the pathological condition of interest (i.e., a pathological condition that is treated by the analyzed product or that can result from an altered physiological state in which the analyzed beneficial product exerts an activity that helps maintain its homeostasis); (2) for each of the features, identifying alterations in one or more biological activities underlying the pathological condition, thereby identifying a network of biological activities whose degree of modulation corresponds to the pathological condition; and (3) identifying one or more parameters whose degree of modulation corresponds to the degree of modulation of the one or more biological activities underlying the therapeutic effect of the tested product, and determining a trend in the degree of modulation in the network, with respect to up- or down-regulation of the one or more biological activities that corresponds to the pathological condition or the healthy state.

[0238] All of the above embodiments allow for determining whether a therapeutic or beneficial product exerts its therapeutic or beneficial effect by modifying a condition or a limited number of activities, or even a single function, underlying the pathology that is the abnormal physiological condition being treated by the product, and whether the therapeutic or beneficial product, as selected, maintains functional resilience as defined herein.

[0239] Modification of state is a feature not available with a single API pharmaceutical product and therefore excludes the classical pharmaceutical mechanism of action, however, modification of state can in principle be achieved with pharmaceutical products containing a cocktail of APIs.

[0240] A physiological mechanism of action requires that a therapeutic or beneficial product regulates a condition in a manner that involves the overall cellular response with the network through network interactions, rather than the point of network interaction based on the API mechanism of action (whether a single API or a cocktail thereof).

[0241] This refers to the ability of a product to act in a functional restorative capacity (either therapeutic or beneficial) in addition to regulating a state, i.e., to provide the same batch-to-batch therapeutic / beneficial effect despite qualitative-quantitative compositional differences between batches, in other words, to regulate various selected parameters in a variable manner and yet provide a preserved functional result.

[0242] Thus, the present invention also provides an embodiment for demonstrating the functional resilience of a therapeutic or beneficial product.

[0243] As already explained in the glossary and above, (therapeutic or beneficial) functional resilience is the ability of a given product to modulate one or more biological activities underlying a pathological or altered physiological state, despite variations in the qualitative and quantitative composition of different batches of the same product, and thus elicit different signals within the cell, despite the possibility of reaching the same end result, i.e., bioequivalence, intended as the same end result. As already mentioned above, it is known that pharmaceutical (API-based) products with different qualitative and quantitative compositions are not considered bioequivalent.

[0244] The physiological mechanism of action refers to the overall interaction with the cells of the treated subject, not the interaction with a specific cellular molecular target, but is a mode exerted by an organism as a result of network-network interactions. Physiological systems in the body often exhibit functional redundancy to maintain homeostasis and adapt to changes or disruptions, and redundancy is a well-known physiological mechanism in life to ensure a given goal is reached (e.g., the organism's response in producing various proteins, activating various pathways, etc.). When a therapeutic product interacts with these systems, it may engage multiple pathways or mechanisms, including redundant pathways or mechanisms, to achieve its desired effect. This redundancy, which results in functional resilience, contributes to the product's physiological mechanism of action.

[0245] Therefore, functional resilience in a therapeutic / beneficial product (defined as the ability of a therapeutic or beneficial product to maintain its intended functionality and effectiveness despite variability in its qualitative and quantitative composition from batch to batch) is an essential feature of a physiological mechanism of action.

[0246] According to one embodiment of the present invention, the selected parameters may be genes whose expression patterns highlight detectable modifications in the pathology of interest, in this embodiment, the genes exemplified above (3): For each of the one or more biological activities, the genes and their expression patterns underlying the detectable alterations in the pathological condition are identified, and an expression pattern opposite to the expression pattern identified as indicative of the healthy physiological state is established for each of the genes.

[0247] If the parameters and their regulation patterns correspond to genes and their expression patterns underlying detectable alterations in a pathological state for each of the selected one or more biological activities, transcriptome analysis can be performed on an appropriate in vitro cell-based assay representative of the pathological state of interest to identify genes and their expression patterns underlying detectable alterations in a pathological state for each of the selected one or more biological activities. If the disease phenotype is caused by administration of a particular drug to cultured cells, the alteration representative of the pathological state is the altered cells versus the untreated cells before alteration, and the alteration induced by the product batch is the altered cells versus the altered cells + product batch.

[0248] Transcriptome analysis can be performed using any suitable technique known in the art, including next-generation sequencing and gene expression microarrays, and can assess the transcriptome expression profile in basal cells as opposed to cells treated to mimic a disease state to identify significantly differentially expressed genes and their expression patterns. According to the methods of the present invention, the expression patterns of significantly differentially expressed genes in cells representing a disease phenotype versus cells prior to the insult that induces the disease phenotype are assessed, with the opposite expression pattern being considered to represent a healthy physiological state.

[0249] If gene expression is the selected marker, for each of the one or more biological activities identified in (2), the genes and their expression patterns underlying the detectable alterations in the pathological condition for each of the one or more biological activities can be identified from the state of the art using appropriate tools.

[0250] By way of example, one skilled in the art can derive this information using any suitable approach, including the use of software specifically designed for this scope, such as Ingenuity Pathway Analysis (IPA version 94302991 Qiagen).

[0251] Interpretation of high-throughput gene expression data is greatly facilitated by taking into account prior biological knowledge. This can be done using statistical gene set enrichment methods, in which differentially expressed genes are intersected with gene sets associated with one or more specific biological activities or pathways (Abatangelo, L. et al. (2009) Comparative study of gene set enrichment methods. BMC Bioinform., 10, 275).Another recent approach involves the application of causal networks to integrate previously observed causal relationships reported in the literature (Chindelevitch, L. et al. (2012a) Causal reasoning on biological networks: interpreting transcriptional changes. Bioinformatics, 28, 1114-1121; Felciano, RM et al. (2013) Predictive systems biology approach to broad-spectrum, host-directed drug target discovery in infectious diseases. Pac. Symp. Biocomput., 2013, 17-28; Kumar, R. et al. (2010) Causal reasoning identifies mechanisms of sensitivity for a novel AKT kinase inhibitor, GSK690693. BMC Genom., 11, 419; Martin, F. et al. (2012) Assessment of network perturbation amplitudes by applying high-throughput data to causal networks. BMC Syst. Biol., 6, 54; Pollard, J. Jr. et al. (2005) A computational model to define the molecular causes of type 2 diabetes mellitus. Diabetes Technol. Ther., 7, 323-336). While still relying on statistics, this is more powerful than gene set enrichment because it leverages knowledge of the direction of effect rather than mere association.

[0252] In a preferred embodiment, one skilled in the art can follow the protocols described in the publication by Kramer et al., Bioinformatics vol 30 no 4 2014, pages 523-530, "Causal analysis approaches in Ingenuity Pathway Analysis provides and discusses a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base" or the manufacturer's instructions (IPA version 94302991 Qiagen). The methods and algorithms disclosed in this paper allow one skilled in the art to predict one or more biological activities and downstream effects on diseases.

[0253] In this paper, the authors describe the causal analysis approach implemented in Ingenuity Pathway Analysis (IPA), with a particular focus on the details of the underlying algorithms and their application to several real-world use cases. In particular, the identification of trends in the degree of regulation of selected biological activities in pathological and healthy states can be easily performed by those skilled in the art by using Ingenuity Pathway Analysis (IPA version 94302991 Qiagen), a well-known pathway analysis application among the life science research community that has been cited in tens of thousands of papers and enables understanding of the causal relationships between diseases, genes, and networks of upstream regulators.

[0254] Once the desired modulation of one or more biological activities has been defined (i.e., those representative of a healthy physiological state) and the desired regulation of relevant markers (e.g., ROS scavenging activity, genes, etc.) has been identified for the pathological condition of interest, analysis of the modulation of the selected markers in the same in vitro cell-based assay representative of the pathological condition (e.g., transcriptome analysis in the case of gene expression) is performed upon treatment of cells with a sample of the therapeutic product under test. The qualitative and quantitative modulation of one or more markers (e.g., expression of genes of interest) induced by the treatment to generate a physiopathological condition control group versus untreated cells, and by the product sample under test versus the pathophysiological condition, is determined, and a value quantifying the modulation for each of the selected one or more biological activities is calculated and subsequently used for comparison in the process.

[0255] A value quantifying the degree of modulation is calculated to assess the direction and magnitude of the degree of modulation for each one or more biological activities exerted by a given sample of product. In the case of transcriptomics, the aforementioned values ​​for z-scores can be easily obtained by those skilled in the art using Qiagen's Core Analysis, IPA version 94302991, following the manufacturer's instructions.

[0256] If the core analysis does not yield sufficient relevant information, an alternative approach can be adopted. An example of an alternative method is provided below (overlay analysis).

[0257] In a non-limiting embodiment of the present invention, the identification of alterations in one or more biological activities underlying the pathological condition for each selected marker (thereby identifying a network of biological activities whose modulation corresponds to the pathological condition of interest), and the identification of one or more parameters whose modulation corresponds to the modulation of the one or more biological activities underlying the therapeutic effect of the tested product, and the determination of the trend in modulation in the network, with respect to up- or down-regulation of the one or more biological activities corresponding to the pathological or healthy condition, can be performed using IPA version 94302991 Qiagen as follows: State-of-the-art techniques for the pathophysiology of diseases and definition of pathophysiological features of diseases for investigating IPA The pathophysiological state of the art of the disease of interest is investigated using various specific sources: -Robbins&Cotran Pathologic Basis of Disease(Robbins Pathology) 10th Edition -https: / / calgaryguide.ucalgary.ca / - Biomedical literature from PubMed Central (https: / / pubmed.ncbi.nlm.nih.gov / )

[0258] The information found in the aforementioned resources is used to identify features to examine for IPA using the following procedure. · Hallmarks are written one by one in the “Disease and Function” query box and the search is initiated. The resulting recurrence table allows filtering of diseases / functions arising from a wealth of evidence. The relevant sources are the Ingenuity Knowledge Base, which includes curation from journal articles, OMIM, JAX, and ClinicalTrials.gov. In silico models are limited to genes and mRNAs.

[0259] Thus, the tool may associate with each one or more biological activities a predetermined number of genes whose degree of regulation affects the degree of regulation of the one or more biological activities themselves.

[0260] The following applies to any step of the methods of the invention of any embodiment described herein, in which an in vitro cell-based assay is performed by treating cells having a disease phenotype with a given compound, be it a gold standard, a different batch of a product of interest, a reference drug, etc.

[0261] Transcriptome raw data analysis Whole transcriptome expression profiles are assessed in control and model cell disease-representing in vitro cell models. The Human Clariom™ S Pico Assay HT (Applied Biosystems, ThermoFisher Scientific) can be used on a GeneTitan MC Instrument (Applied Biosystems, ThermoFisher Scientific) according to the manufacturer's instructions. CEL intensity files can be generated using the Affymetrix GeneChip Command Console Software (AGCC, ThermoFisher Scientific). Data analysis can be performed using the Transcriptome Analysis Console Software (TAC, ThermoFisher Scientific), which provides quality control analysis, normalization, and summarization based on the Signal Space Transformation-Robust Multichip Analysis (SST-RMA) analysis algorithm, and provides a list of differentially expressed genes (Limma Bioconductor package). This step allows the user to obtain a list of differentially expressed genes (DEGs) identified based on expression fold change relative to a relevant control experimental condition, in this case one that recapitulates the pathological condition in vitro.

[0262] The resulting transcriptional modification profile is then subjected to functional pathway enrichment analysis. One commercially available tool that can be used is Ingenuity Pathway Analysis (IPA version 94302991, Qiagen) [Kramer et al. (2014)]. Using IPA, users can estimate how and to what extent modulation of gene expression in a cellular system (cell-based assay) affects one or more biological activities associated with a pathology of interest.

[0263] IPA pre-analysis filtering of transcriptional profiles (contextual data analysis) In preparation for subsequent analysis, the transcriptome profile undergoes a filtering process to identify relevant genes and their corresponding measurements. This filtering aims to select only genes that are significantly perturbed, as indicated by their fold change relative to the pathological condition. Typically (e.g., according to the manufacturer's instructions), the fold change threshold is set to encompass values ​​of ≦−2 and ≧+2, with statistical significance indicated by a p-value of ≦0.05. However, those skilled in the art have the flexibility to adjust the cutoff based on their expert knowledge, taking into account the successful implementation of negative controls (samples representative of a pathological condition) or reference standards known to be able to fully or partially counteract a pathological condition.

[0264] Possible methodological approaches for extracting biological significance from a list of alterations in gene expression profiles via IPA can be of two types: “core analysis” and “overlay analysis of in silico models of pathophysiological conditions” (abbreviated as overlay analysis).

[0265] Therefore, at this stage of treatment, two different alternative options can be pursued.

[0266] Core Analysis The list of differentially expressed genes (DEGs) after the reference batch or any other product batch / reference drug or other administration, and the corresponding data measurements (fold change relative to the pathological state) identified in different experimental conditions, are uploaded to the application. Available identifiers are mapped to corresponding entities in the QIAGEN knowledge base.

[0267] By initiating the "core analysis," significantly perturbed DEGs, called network-eligible molecules, are overlaid onto a global molecular network developed from information contained in the QIAGEN knowledge base. A network of network-eligible molecules is then algorithmically generated based on their connectivity.

[0268] The core analysis provides a comprehensive list of approximately the top 500 bioactivities (or more) derived from the generated network. Bioactivity-gene associations are always supported by corresponding annotations from peer-reviewed scientific publications that demonstrate the direction and magnitude of the calculated bioactivity's regulation through the automatic association of a value (z-score) that quantifies the degree of regulation [Kramer et al. (2014)]. Essentially, this value represents a statistical metric that assesses the similarity between the observed pattern of differentially expressed genes (DEGs) and the expected pattern based on existing literature for a given annotation.

[0269] It is the responsibility of the skilled operator to carefully select one or more biological activities that are relevant to the particular pathology under investigation, with the selection of one or more biological activities being structured based on the identified characteristics of the pathology of interest.

[0270] An associated value quantifying the degree of modulation (eg, a z-score) is then used to indicate the direction and magnitude of modulation for one or more biological activities.

[0271] Overlay Analysis If the core analysis does not yield sufficient relevant information, an alternative approach called "overlay analysis" can be adopted. This analysis focuses on one or more biological activities identified by an "in silico model of the pathophysiological state." The selection of one or more biological activities is structured based on the identified features of the pathology of interest.

[0272] An "overlay analysis" is constructed by establishing a relationship between the pattern of differentially expressed genes and one or more selected biological activities (always supported by annotations corresponding to peer-reviewed scientific publications demonstrating the direction and magnitude of the degree of modulation of one or more biological activities) using the following procedure: □Import a set of one or more biological activities selected from an in silico model of a pathophysiological state into a new sheet called "my pathway". □ Using the "Build Tool" and "Grow Tool", identify differentially expressed genes (DEGs) that belong to the transcriptome profile under investigation and are associated with the regulation of one or more biological activities selected in the previous step. □ The degree of regulation of the identified DEGs is represented using green (indicating down-regulation) and red (indicating up-regulation). To determine the predicted calculated impact of such experimentally observed modulation of gene expression on one or more biological activities, the "Overlay" and "Molecular Activity Predictor" tools (MAP) are used. The "Predict" function is run within the MAP tool to calculate the predicted resulting modulation of one or more biological activities. Color coding is established accordingly. -Orange: Increased activity - Blue: decreased activity -White: unattainable / unpredictable

[0273] Because "overlay analysis" provides results in terms of color indicating the direction of modification and color signal intensity proportional to the magnitude of the desired degree of modulation rather than directly calculating a value that quantifies the degree of modulation for each of one or more biological activities, it is necessary to convert the intensity of the modulation signal (as displayed graphically in the "my pathway" tab) into a numerical value. This is achieved by converting the color intensity obtained for each of one or more biological activities into a value that quantifies the degree of modulation.

[0274] The biological pathways generated by QIAGEN's Ingenuity Pathway Analysis software can be converted into numerical data by using a specific algorithm (https: / / www.xrite.com / it-it / blog / lab-color-space, posted by Tim Mouw in October 2018) that can convert RGB color intensity models into LAB models. This conversion is performed within the pipeline pilot "component" using a procedure written using R software based on specific features of the colorspace package (https: / / cran.r-project.org / web / packages / colorspace / index.html, for more details see Zeileis et al. 2020 Journal of Statistical Software, doi:10.18637 / jss.v096.i01).

[0275] Values ​​quantifying the degree of modulation allow for objective comparison of the effects of different treatments.

[0276] The method finally includes comparing the values ​​calculated for each group in (c) quantifying the degree of modulation of each of one or more biological activities, and if the above conditions are met, it is indicated that the product exerts its beneficial or therapeutic effect via a physiological mechanism of action (mechanism of action).

[0277] Step (c) requires that at least 50% of the one or more biological activities analyzed are modulated by each product batch and that the trend in the degree of modulation in the network is consistent with a previously determined healthy state.

[0278] "Having a tendency toward modulation of a healthy state" can also be read as "following the modulation pattern representative of the healthy physiological state," meaning that the modulation of the analyzed activity has the same direction, i.e., an upward or downward modulation of the modulation of the same activity in the determined healthy physiological state relative to that determined for a control group of cells.

[0279] This means that each feature must be modulated in the direction of the desired therapeutic effect for at least 50% of the activities selected for that feature. Thus, if one or two activities are selected for a single feature, 100% of the determined degree of modulation must be consistent with the desired therapeutic effect (i.e., a modulation pattern representative of a healthy physiological state).

[0280] In embodiments where the product being analyzed is a therapeutic product, when the cell population used in the assay corresponds to (a)(2) above, step (c) requires that at least 50% of the one or more biological activities are modulated by each product batch, the trend in the degree of modulation of the network is consistent with the determined healthy state, and each of the modulation values ​​determined in (b) for the cell population treated with the product of (a)(2) for at least 50% of the one or more biological activities differs by at least 15% from that of the control group of (a2). In other words, the degree of modulation is considered significant if it differs by at least 15% from a value quantifying the degree of modulation calculated for control cells and must be in the same direction as the desired therapeutic effect, so that if the modulation pattern representing the healthy physiological state for a given activity is up-regulation, the degree of modulation induced by a product for that activity must be a value quantifying a degree of modulation that is at least 15% higher than the degree of modulation calculated for the same activity in a pathophysiological state, and if the modulation pattern representing the healthy physiological state for a given activity is down-regulation, the degree of modulation induced by a product for that activity must be a value quantifying a degree of modulation that is at least 15% lower than the degree of modulation calculated for the same activity in a pathophysiological state.

[0281] In embodiments in which the product being analyzed is a therapeutic product, if the cell population used in the assay corresponds to (a)(1) above, step (c) requires that at least 50% of the one or more biological activities are modulated by each product batch, the trend in the degree of modulation of the network is consistent with the determined healthy state, and each of the modulation values ​​previously determined in (b) for the cell population treated with the product of (a)(1) for at least 50% of the one or more biological activities differs by at least 0.15 from that of the control group of (a1). In this case, the qualitative-quantitative modulation determined in (b) is only that induced by the product sample with respect to the control of the pathophysiological condition, and therefore the control is considered to be the reference modulation baseline (e.g., a value of 0) for each of the one or more biological activities. When these conditions are met, the product is determined to exert its therapeutic effect by regulating the pathophysiological condition associated with the pathology.

[0282] In embodiments in which the analyzed product is a beneficial product, step (c) requires that at least 50% of the one or more biological activities are modulated by each product batch, the network modulation trend is consistent with the determined healthy state, and each of the modulation values ​​previously determined in (b) for the cell population treated with the product of (a)(1) for at least 50% of the one or more biological activities differs by at least 0.15 from that of the control group of (a1). For a beneficial product, it is intended that the modulation trend of a selected activity in a healthy state be opposite to the modulation trend of the same activity in a pathological state that may result from the altered physiological state of interest. For example, non-pathological changes in osteocalcin levels can affect adipose tissue and pancreatic metabolism, leading to metabolic syndrome, and pre-hypertension can lead to vascular damage and related pathologies.

[0283] Those skilled in the art know that beneficial products, i.e., products that are suitable for healthy individuals and have a homeostatic effect / activity, are typically used by healthy individuals to support the homeostatic response to altered physiological conditions that are not yet pathological, but which may otherwise lead to the development of pathology. These products are not considered therapeutic products in that they do not prevent or treat disease, but they assist the organism in maintaining homeostasis, i.e., maintaining a state of balance in the body systems necessary for the body to function properly.

[0284] Thus, the above method determines the degree to which a product that aids in maintaining homeostasis (prior to the onset of a pathological condition) should exhibit modulation of healthy physiological conditions in a cell-based assay.

[0285] In step (a), a suitable cell-based assay is one in which the cells are healthy and a trend in modulation is observed upon treatment with a sample of the product of interest, or alternatively, an altered state can be induced in the cells and a trend in modulation is observed upon treatment with a sample of the product of interest.

[0286] Also in this case, the healthy or induced cells are considered as a reference baseline having a value (preferably a value of 0) that quantifies the degree of modulation for each of the one or more biological activities.

[0287] In all the above embodiments, the user has the possibility to verify that any therapeutic product beneficial to health acts by modulating the state rather than a single or few activities, as each characteristic is fully or partially modulated in the same direction of a healthy physiological state.

[0288] This is the first time that a method is provided that makes it possible to define whether a product meets the requirements for conditioning according to, for example, DM EU Directive 2017 / 745.

[0289] Preferably, the one or more biological activities identified in the methods of the present invention are at least two, and preferably at least three.

[0290] As already mentioned and explained in detail above, according to one embodiment of the present invention, said parameters and their regulation patterns correspond to genes and their expression patterns underlying detectable alterations in said pathological condition for each of said one or more biological activities. Transcriptome analysis is a particularly suitable tool for assessing gene expression.

[0291] Non-limiting examples of products comprising or consisting of natural matrices are cut or crushed plant parts, plant extracts, fractions of such extracts, such as fractions obtained by filtration through semi-permeable membranes (microfiltration, ultrafiltration, nanofiltration) or by treatment with adsorption resins, or products comprising or consisting of one or more of the following: microorganisms, honey, propolis, silk, wax, plant resins, plant gums, plant exudates, plant oils, plant essential oils, animal tissue lysates, plant or animal fluids, or mixtures thereof.

[0292] According to one embodiment of the present invention, the product to be inspected may be a medical device as defined in Article 2(1) paragraphs 1-3 of EU Directive 2017 / 745, or a medical device as defined in FDA Section 201(h)(1) of the US Food, Drug, and Cosmetic Act, or a drug or dietary supplement, specialty food, etc.

[0293] In one embodiment, different batches of a product comprising or consisting of one or more natural matrices, i.e. batches produced according to the same protocol, including the protocol for obtaining each natural matrix, are necessarily variable in their qualitative and quantitative composition due to their biological origin (and also subject to epigenetic regulation in genetically identical individuals), but the method of the invention may further comprise a preliminary step in which a qualitative and / or quantitative analysis of the composition of different batches of the same product is carried out to select batches whose composition is found to be qualitatively and / or quantitatively different.

[0294] Furthermore, the naturalness of products that contain or consist of natural matrices (i.e., the maintenance of properties typical of the original natural material from which such matrices are derived) can also be verified.

[0295] As already mentioned, it is essential that the matrix be obtained via a non-denaturing process so that the components of the matrix are not artificially modified, but if desired, the presence of additional indicators of the retention of characteristics present in the original source material can be verified.

[0296] Thus, the methods of the present invention may further comprise analyzing one or more batches of therapeutic or beneficial product.

[0297] In particular, therapeutic or beneficial products can be analyzed for the presence of supramolecular structures, miRNAs and / or isotope abundances.

[0298] In particular, isotopic abundance analysis involves comparing the abundance of one or more isotopes of C, H, O, N atoms in a product and in the raw materials from which one or more natural matrices contained therein are derived.

[0299] Additionally, the method can include testing the therapeutic or beneficial product for biodegradability using an OECD biodegradation test. A non-limiting example of a suitable test is the OECD 310:2014 biodegradation test.

[0300] The above characteristics indicate the naturalness of the intended product, which is the maintenance of structures and properties that exist in living organisms and can therefore interact with the treated subject in a manner that respects the natural inter-network communication that exists in nature. Non-limiting examples of supramolecular structures include the results of interactions between various components of the matrix in the product, which are formed as cellular vesicles, exosomes, and superaggregates that can be examined for their physicochemical and / or biophysical properties. This phenomenon has been particularly described and attributed to living organisms, which have the driving force to self-organize and self-assemble to form supramolecular complex entities (Lehn JM. Toward complex matter: Supramolecular chemistry and self-organization. PNAS. 2002; 99(8)4763-4768. https: / / doi.org / 10.1073 / pnas.072065599.). This inherent complexity leads to the fact that individual molecules in natural matrices cannot be considered as contained in isolated and fixed packages, since non-covalent and dynamic interactions occur continuously between them, making the study of their evolution over time virtually impossible to determine and verify at present. Such interactions are intramolecular and intermolecular, occurring both between molecules of the same type and between molecules belonging to different chemical classes.

[0301] The presence of exosomes can be confirmed in a product using any commonly available technique. Non-limiting examples of such techniques include electron microscopy (EM), such as transmission electron microscopy (TEM), which allows direct visualization of exosomes by imaging their morphology and size; dynamic light scattering (DLS), which measures the size distribution of particles in solution by analyzing fluctuations in light scattering caused by Brownian motion and can provide information about the size distribution of exosomes in a sample; nanoparticle tracking analysis (NTA), which uses light scattering and Brownian motion analysis to track the movement of individual nanoparticles in a liquid medium; flow cytometry, which can be used to analyze individual exosomes labeled with fluorescent markers and allows quantitative analysis of exosome populations based on size, surface markers, and other characteristics; fluorescence-activated cell sorting (FACS) can also be used to sort exosomes based on specific markers; western blotting can be used to detect specific proteins present in exosomes; exosome lysates can be separated by gel electrophoresis, transferred to a membrane, and probed with antibodies against exosome markers; and enzyme-linked immunosorbent assays (ELISA), which can quantify specific proteins or nucleic acids in exosome samples. These include immobilizing exosome-specific antibodies on a solid surface, capturing exosomes from a sample, and detecting them with enzyme-conjugated antibodies; surface plasmon resonance (SPR), which measures changes in refractive index at the interface between the sensor surface and the sample solution and can be used to study interactions between exosomes and ligands immobilized on the sensor surface; polymerase chain reaction (PCR), which can detect and quantitate exosomal nucleic acids, including mRNA, miRNA, and DNA, using specific primers targeting these nucleic acids for amplification and detection; and mass spectrometry, which can analyze the proteomic and lipidomic profiles of exosomes. These techniques can be used individually or in combination to characterize and detect exosomes in various biological samples.The choice of technique will depend on the specific research or diagnostic goal, as well as the desired characteristics of the exosome sample. According to the present invention, simply detecting the presence of exosomes is sufficient. If desired, the exosomes can be further characterized.

[0302] The presence of superaggregates can be determined by selecting one or more target molecules known to be consistently present in a given natural matrix (e.g., estragole in fennel extract) and comparing the molecules in question within the matrix itself and in isolated or synthetic form.

[0303] The isolated or synthesized molecules are not in the form of superaggregates, although in natural matrices the molecules are expected to be in this form.

[0304] The molecules can be quantified in the matrix using standard techniques.

[0305] Several features can then be investigated, for example, the volatility of the target molecule in the matrix and in purified or synthetic form can be investigated and compared. 1H-NMR spectra of the target molecule in the matrix and in purified or synthetic form can be investigated and compared. Detailed examples of these techniques are provided in the exemplary section.

[0306] By comparing synthetic estragole with natural matrices derived from sweet and bitter fennel, as shown in the examples, the inventors found that the natural matrices exhibit the presence of estragole within a supramolecular structure, and that the estragole in the matrices behaves in a similar manner to that of synthetic estragole (thereby maintaining the broad functional resilience conserved from sweet to bitter fennel), the latter being toxic to cells when administered in the same amounts as those embedded in the matrices.

[0307] Non-limiting examples of suitable techniques for detecting the presence of supramolecular structures in the form of superaggregates in the product under test include: dynamic light scattering (DLS), which measures the fluctuations in the intensity of scattered light caused by the Brownian motion of particles in solution and can provide information about the size distribution of particles, including the presence of larger aggregates; static light scattering (SLS), which measures the intensity of scattered light at a fixed angle and can provide information about the molecular weight and size of particles in solution; analytical ultracentrifugation (AUC), which involves spinning a sample at high speed in a centrifuge and measuring the sedimentation velocity or equilibrium sedimentation, can provide information about the size, shape and molecular weight of particles in solution, including superaggregates; transmission electron microscopy (TEM), which involves imaging a sample with a high-energy electron beam and can provide high-resolution images of individual superaggregates, allowing direct visualization of their morphology and size; and transmission electron microscopy (TEM), which uses a sharp probe to scan the surface of a sample and can provide high-resolution images of individual superaggregates immobilized on the surface, allowing for direct visualization of their structure and size. small-angle X-ray scattering (SAXS), which measures the scattering of X-rays by particles in solution and can provide information about the size, shape, and internal structure of superaggregates on the nanometer scale; fluorescence correlation spectroscopy (FCS), which measures fluctuations in the fluorescence intensity of fluorescently labeled molecules diffusing through a small observation volume and can provide information about the size and dynamics of superaggregates in solution; fluorescence resonance energy transfer (FRET), which involves measuring the energy transfer between fluorescently labeled molecules and can be used to study the proximity and interactions between molecules within superaggregates; size exclusion chromatography (SEC), which can be used to separate particles in solution based on size and detect and quantitate the presence of superaggregates by comparing the elution profile of a sample with that of molecular weight standards; and nanoparticle tracking analysis (NTA), which uses a laser beam to measure the Brownian motion of nanoparticles in solution and can provide information about the size distribution and concentration of superaggregates in solution.

[0308] As known to those skilled in the art, these techniques can be used individually or in combination to detect and comprehensively characterize molecular superaggregates in biological samples or other complex systems.

[0309] It should be noted that for products that undergo ultrafiltration, exosomes can be removed, whereas molecular superaggregates can remain or reassemble in the final ultrafiltered product.

[0310] Another indicator of the "naturalness" of a product containing a natural matrix may be the presence of miRNAs. Again, ultrafiltration can remove miRNAs, but their presence can still be confirmed in the product prior to this step.

[0311] MiRNAs can be detected, optionally quantified, and / or characterized according to any method known in the art, including, but not limited to, reverse transcription quantitative polymerase chain reaction (RT-qPCR); Northern blotting; next-generation sequencing (NGS), which can be used for miRNA profiling and discovery as demonstrated herein and involves high-throughput sequencing of small RNA libraries followed by bioinformatic analysis to identify and quantify miRNAs present in a sample; microarray analysis; lateral flow assays, which typically use gold nanoparticles conjugated to miRNA-specific probes that generate a visible signal upon binding to the target miRNA; enzyme-linked immunosorbent assays (ELISA); digital PCR; or even biosensor-based approaches, such as surface plasmon resonance (SPR) or electrochemical sensors, which can be used for label-free detection of miRNAs in products and provide rapid and sensitive detection.

[0312] A further indication of a product's "naturalness" can be its C-14 activity. A C-14 activity of 100% or greater (percent modern carbon; pMC), obtained by measuring the ratio of radiocarbon in the material to the National Institute of Standards and Technology (NIST) modern reference standard (SRM 4990C), can be considered a product made from pure biocarbon with no evidence of synthetic origin. A further indication of the lack of alteration in the product manufacturing process can be the maintenance of the isotopic abundance of one or more of the atoms C, H, O, N, and S in the product batch under test relative to one or more of the raw materials from which the natural matrix contained in that batch was derived.

[0313] Of course, this is not always feasible as it requires the availability of starting materials for the manufacture of a particular product batch.

[0314] In either case, the distribution of isotopes C14 with values ​​of pMC100 or greater is a strong indicator of the naturalness of the product, and the maintenance of isotopic abundances of additional isotopes (e.g., one or more of N, O, H, S), and the absence of isotopes not present in the starting material, are strong indicators of the good quality of the product, i.e., the fact that the process to obtain the natural matrix therein did not result in alteration of the original natural material.

[0315] Isotopic abundance analysis is a method for describing a substance in terms of atoms. The isotopic distribution characterizing the starting material can be affected by phenomena of different nature, which can result in significant variations in the final product [ISPRA, Quaderni-Laboratorio 2 / 2018. ISBN 978-88-448-0873-0]. The isotopic composition of a sample is equal to the ratio of the abundance of heavy to light isotopic forms (e.g., the relationship 13C / 12C) and is expressed as deviations per thousand parts from an internationally identified standard reference material. A positive value of δ indicates that the heavy isotope is enriched in the sample compared to the standard, while a negative value indicates that the heavy isotope is depleted in the sample.

[0316] Significant differences in the isotopic abundance of a sample compared to a known good quality sample can explain different intra- and intermolecular interactions between the phytochemical classes that make up the matrix, regardless of the quantitative profile of the individual species and different chemical reaction kinetics. In the first case, this phenomenon is defined as the geometric isotope effect (GIE) and is specifically due to hydrogen bonds. In fact, the length of the hydrogen bond with oxygen is different from that between deuterium and oxygen. This can involve different structural rearrangements, both intra- and intermolecular.

[0317] Isotopic abundance also alters the kinetics of a reaction, known as the kinetic isotope effect (KIE), which can be either first- or second-order, depending on whether the isotope alters the reaction to make it faster or slower than the process of interest. It is therefore clear that the KIE establishes a link between a given isotopic abundance of a material and its ability to interact in a reproducible manner with biological systems. Therefore, isotopic abundance analysis is considered a possible tool for monitoring the suitability of products from a physicochemical and potentially biological perspective.

[0318] Generally, comparing isotope abundances in two samples involves determining the relative proportions of isotopes of a particular element in each sample. Various companies offer this analysis as a service available to those skilled in the art. Furthermore, techniques for calculating isotope abundances, for example by mass spectrometry, are part of common general knowledge and are therefore available to those skilled in the art. In any case, a general approach for comparing isotope abundances in two samples is synthesized below.

[0319] Isotope ratio mass spectrometry (IRMS): IRMS is a powerful technique used to measure the relative abundance of isotopes in a sample. It separates ions based on their mass-to-charge ratio (m / z) and quantifies the abundance of different isotopes. By comparing the isotope ratios between two samples, differences in isotopic composition can be assessed. Therefore, isotope ratios can be calculated for each sample tested and are typically expressed as the ratio of the abundance of one isotope to another. For example, carbon analysis typically compares the ratio of 13C to 12C. Furthermore, 14C activity assessment can define a system as 100% natural. This is because 14C is an unstable isotope (half-life 5730 years), and petroleum derivatives have very low abundances of this unstable carbon isotope, but it easily accumulates in biological materials.

[0320] Therefore, the isotopic abundances of the product and raw material are compared by percent deviation, and if the value is ≦15%, the natural matrix is ​​considered to have maintained the isotopic abundance of the original natural material.

[0321] The investigation of physiological interactions poses challenges not only at the preclinical level but also at the clinical level. In particular, the investigation of a patient's condition can be achieved by collecting a combination of more classical endpoints (e.g., glycemia), including bioimpedance biometry, tongue imaging, pulse signals, respiratory rate and depth, and odor analysis, as well as measurements related to building a comprehensive view of the body's condition that can monitor and represent higher-level (rather holistic) information, such as that obtained from -omic (hence, implying, for example, transcriptomics, metabolomics, genomics, and microbiomics) profiling of readily available samples such as hair, whole blood, urine, saliva, sweat, tears, and other readily available bodily fluids. Thus, the methods of the present invention can be validated at the clinical level by performing biological assays on samples from a cohort of patients treated with a therapeutic or beneficial product that is evaluated to act via a physiological mechanism of action using the cell-based methods of the present invention. The analysis of biological and clinical data collected in patients allows achieving a depiction of the patient's condition that can highlight the specificity of the multifaceted interactions with physiologically acting therapeutic solutions, and can extend the degree of innovation of the research and development paradigm centered on the concept of physiological mechanisms of action to the generation of clinical evidence.

[0322] The present authors conducted a clinical trial for Product B, a "Randomized Controlled Trial to Evaluate the Efficacy of Dietary Supplements in Subjects with Mild Cognitive Impairment," under government identification number NCT03581929. In addition to data on the achievement of endpoints related to cognitive and physical function in patients enrolled in the study, which are traditionally derived from the geriatric field, the authors were able to analyze a set of biological data ("multi-omics" parameters) (transcriptomics, metabolomics, and microbiomics) by subjecting biological assay samples collected from a patient group to the same characteristics and biological activities identified for the evaluation of Product B's physiological mechanism of action using in vitro cell-based assays according to the present invention. It was found that the data obtained from the clinical trial were consistent with the data obtained from the in vitro cell-based assays, i.e., the tested product regulated a network of biological activities in the clinical trial, as would be expected for a product with a physiological mechanism of action. The data were obtained from biological assays performed on samples collected from different patient groups in the trial at different time points.

[0323] At the start of V1 of the study, samples from patients who have not yet been treated and Vn, a sample from a patient at a specific time point, e.g., V2, V3, etc. (for Product B, V2 corresponds to 6 months from time point V1, and V3 corresponds to 12 months from time point V1)

[0324] The data obtained confirmed that Product B also acts in clinical trials by regulating the network of biological activities underlying the desired beneficial effects. In the case of Product B, the study was a 12-month, single-center, randomized controlled study (including a 6-month double-blind vs. placebo and a 6-month open-label period). After eligibility assessment, 50 subjects were enrolled in the study (V1). The purpose of this study was to evaluate whether treatment with Product B could improve cognitive performance in subjects with MCI.

[0325] Thus, the present invention also provides a method for validating the physiological mechanism of action of a product in a clinical setting for treating a pathological condition or for assisting in maintaining homeostasis in an altered physiological state, comprising: 1) subjecting at least two groups of (i) biological samples from patients treated with a therapeutic or beneficial product that exerts its therapeutic or beneficial effect via a physiological mechanism of action that is assessed by the cell-based assay method of the present invention, wherein the groups of samples are collected at different time points V1 and Vn (n is an integer greater than 1), and at least two groups of (ii) biological samples from patients treated with a therapeutic or beneficial comparator product, and / or at least two groups of (iii) biological samples from placebo-treated patients, wherein the groups of samples are collected at the same time points as group (i), to one or more biological assays, wherein a readout from the one or more biological assays is indicative of a degree of modulation of one or more biological activities underlying the desired therapeutic or beneficial effect; and determining from the readouts: determining whether the product exerts its therapeutic or beneficial effect by regulating a network of biological activities underlying an altered physiological state associated with a pathological condition or condition, and determining from the resulting readout that if the product is shown to regulate a network of biological activities underlying a pathophysiological or altered physiological state, the product is clinically confirmed to exert its therapeutic or beneficial effect via a physiological mechanism of action.

[0326] Clinical trials as well as further verify and confirm therapeutic / beneficial effects already observed in the preclinical phase.

[0327] According to the present invention, the clinical validation method is carried out on products whose physiological mechanism of action has already been evaluated by the in vitro methods disclosed above.

[0328] The samples used in this method are biological samples collected from patients during clinical trials at different time points. V1 represents the sample collected at the beginning of the trial, i.e., before treatment begins, and Vn represents the sample collected at time point n, where n is a progressive integer 1, 2, 3, etc., depending on the number of time points at which the samples are collected.

[0329] As one skilled in the art will know, the appropriate time points for collection will vary depending on the pathological or altered physiological condition targeted by the product. Typically, samples are collected during a clinical trial at time points corresponding to testing visits and controls within the trial itself.

[0330] The collected sample is a biological sample such as a biological fluid and / or tissue. Suitable samples may be exhaled breath, urine, saliva, blood, plasma, serum, tears, stool or other organic fluids, and the tissue may be a mucosal sample, or a biopsy if previewed by the trial, or hair, nail clippings, skin, etc.

[0331] According to the present invention, a method for validating the physiological mechanism of action of a therapeutic or beneficial product comprising one or more natural matrices preferably further comprises: (1) providing a list of features indicative of the pathological condition or the pathological condition that may result from the altered physiological condition; (2) for each of the features, identifying alterations in one or more biological activities underlying the pathological condition, thereby identifying a network of biological activities whose modulation corresponds to the pathological condition; and (3) identifying one or more parameters whose modulation corresponds to the modulation of the one or more biological activities underlying the therapeutic effect of the tested product, and determining a trend in the network for modulation of the one or more biological activities, either up- or down-regulation, that corresponds to the pathological condition or the healthy state.

[0332] In a preferred embodiment of the present invention, the one or more biological assays of the clinical validation are directed to the same list of relevant biological activity features and networks provided and identified in the in vitro methods of the present invention, and the one or more parameters whose modulation corresponds to the degree of modulation of the biological activity are not necessarily the same as those identified for the biological samples collected during the clinical trial and the in vitro cell-based assay, which may allow one skilled in the art to measure different parameters associated with the same biological activity of interest (e.g., circulating cytokines for inflammation, etc.); therefore, the method also includes determining a trend in the degree of modulation, with respect to up- or down-regulation of the one or more biological activities in the network, corresponding to the pathological or healthy state.

[0333] According to an embodiment, the clinical validation method of the present invention comprises: (a) performing at least one biological assay on each of said biological samples; (b) determining the degree or pattern of modulation of each of said parameters and calculating a respective modulation value for each of said one or more biological activities for each of said samples; (c) comparing said adjusted values ​​for each of said one or more biological activities, thereby providing an average adjusted value for each of said activities for each of said groups of samples, or thereby providing an adjusted value for each of said activities for each patient in said each group of samples. The product is If at least 50% of the one or more average biological activities or patient biological activities for each feature are modulated in Vn, the trend in the degree of network modulation is consistent with a healthy state, and each of the modulation values ​​in Vn determined in (b) of the at least 50% of the one or more average biological activities or patient biological activities differs from that of V1 by at least 0.15, it is indicated that the therapeutic or beneficial effect is exerted via a physiological mechanism of action.

[0334] Thus, according to the present invention, the adjusted values ​​Vn vs. V1 can be compared for each patient, or the average adjusted values ​​for each group can be calculated, and the mean Vn vs. mean V1 values ​​can be calculated.

[0335] Progressive Vn adjustment values ​​(either per patient or average) can also be compared with corresponding previous Vn adjustment values ​​(i.e., values ​​for group V3 can be compared with values ​​for V2 and values ​​for V1, etc.).

[0336] As described above, the modulation values ​​calculated for samples from different groups can be compared for each biological activity either by patient (patient x sample from V2 vs. patient x sample from V1, etc.) or by group mean modulation value (mean modulation value from V2 vs. mean modulation value from V1).

[0337] The example shows the results of validation of the physiological mechanism of action of product B in a clinical setting (see Figure 23).

[0338] Anywhere in the specification and claims, the word "comprising" may be replaced with "consisting of."

[0339] Anywhere in the specification and claims, the term "value quantifying the degree of accommodation" may be replaced with "z-score."

[0340] Examples are provided below that teach how to practice the invention, but are not intended to limit the invention.

[0341] example 1. Composition of the tested products Product A (also Arte GX) (Figures 1 to 5) Centella asiatica dried leaves 90% w / w Echinacea purpurea dried flowers 10% w / w Co-extracted in water. Five different batches were used (manufactured according to the same protocol but using different stocks of starting material and / or separate production processes for matrix and product). Batch 20B1955 Batch 20I1279 □Batch 20J1770 Batch 20B0596 □Batch 21E1640 Product B (Figures 7 to 8 and 23) Melissa officinalis leaf dry extract 2.5% w / w Royal jelly lyophilized 2.5% w / w Blueberry dry extract 0.29% w / w Concentrated blueberry juice 5% w / w Cynara scolymus L. leaf dry extract 0.05% w / w Curcuma longa L. root dry extract 0.16% w / w Medicago sativa seed dry extract 1.6% w / w Panax ginseng root dry extract 1.6% w / w Honeycomb dry extract 1.6% w / w Apple juice concentrate 44.35% w / w Clarified lemon juice 0.5% w / w Honey 30% w / w Deionized water 6.45% w / w Malpighia emarginata juice 1% w / w Sambucus nigrum juice 2% w / w Product C (Figures 10-11) Coral calcium powder 32% w / w Eggshell calcium powder 30.2% w / w Coral calcium citrate powder 13% w / w Agaricus bisporus powder 4.65% w / w Dry extract of Equisetum arvense flowering tip 2% w / w Malpighia punicifolia 1.50% w / w carried by inulin dry extract Cetraria islandica powder 2% w / w Agave sisalana leaf powder 12% w / w Acacia senegal powder 2.15% w / w Product D (Figure 11) 36.05% (by weight) of freeze-dried co-extract 1 63.06% (by weight) of freeze-dried co-extract 2 0.89% (by weight) freeze-dried extract of Agave sisalana leaves.

[0342] Co-extract 1 Laurus nobilis leaves 25% w / w Withania somnifera root 25% w / w Filipendula vulgaris leaves and flowers 25% w / w Brassica oleracea L.botrytis cymosa seeds 25% w / w Co-extraction in water

[0343] Co-extract 2 Cynara scolymus L. leaves 14.30% w / w Curcuma longa L. root 42.85% w / w Tanacetum parthenium L. flowers 42.85% w / w Co-extraction in water

[0344] 2. Examples of in vitro cell-based assays and feature definitions 2.1 Cell-based assays representative of osteoarthritis An in vitro cell model [1-3] capable of reproducing the features of osteoarthritis was established by exposing primary human chondrocytes (HC, Cell Application INC 402K-05) to IL1B [5ng / ml] for 6 hours, followed by 24 hours to five different batches of "Arte-GX" [1.4mg / ml]: Batch 20B1955 Batch 20I1279 □ Batch 20J1770 Batch 20B0596 □Batch 21E1640 Fresh IL1B [5 ng / ml] was also added to the medium every time one of the batch solutions was added.

[0345] 2.1.1. Time schedule for chondrocyte experiments The time schedule used in the experimental setup is as follows:

number

[0346] 2.1.2. Gene Expression Analysis At the end of the indicated treatment period, cells were washed with 100 μl of PBS, lysed, and collected in RLT buffer (Qiagen, 1053393) supplemented with β-mercaptoethanol (Sigma, M3148) and DX reagent (Qiagen, 19088) for gene expression analysis experiments. Total RNA was extracted from cell lysates using the QIAsymphony RNA Kit (Qiagen) with a QIAsymphony SP instrument (Qiagen).

[0347] RNA quality and quantity were determined by A230, A260, A280, and A320 measurements on a Varioskan™ LUX multimode microplate reader (Thermo Scientific™). RNA integrity was confirmed using the 2100 expert_Eukaryote Total RNA Nano Kit (Agilent). Whole-transcriptome expression profiles were assessed using the Human Clariom™ S Pico Assay HT (Applied Biosystems, ThermoFisher Scientific) on a GeneTitan MC Instrument (Applied Biosystems, ThermoFisher Scientific) according to the manufacturer's instructions. Briefly, 6 ng of total RNA was used to generate cDNA, and the fragmented and labeled cDNA was then hybridized to a Human Clariom S 96 array plate at 45°C for 17 hours. Arrays were washed, stained, and then scanned using a GeneTitan MC Instrument (Applied Biosystems, ThermoFisher Scientific), and CEL intensity files were generated by Affymetrix GeneChip Command Console software (AGCC, ThermoFisher Scientific).

[0348] 2.1.3. Transcriptome data analysis Data analysis was performed using Transcriptome Analysis Console software (TAC, ThermoFisher Scientific), which provides quality control analysis, normalization, and summarization based on the Signal Space Transformation-Robust Multichip Analysis (SST-RMA) analysis algorithm, and a list of differentially expressed genes (Limma Bioconductor package, p-value ≤ 0.05).

[0349] 2.1.4. Bioinformatic modeling of experimentally observed transcriptome data For each study batch, Ingenuity Pathways Analysis (IPA) (QIAGEN\Inc., https: / / www .qiagenbioinformatics.com / products / ingenuitypathway-analysis) was used to assess the degree of modulation of gene expression associated with the desired effect.

[0350] IPA is a scientific reference aggregator that allows searching for information about genes / proteins and building networks that predict the behavior of biological systems according to the expression state of those genes.

[0351] The pathophysiological features of the "osteoarthritic condition" according to the state of the art are reviewed with particular attention to the following areas involved: [Table 1]

[0352] This knowledge was used to investigate IPA via the "IPA Bioprofiler" tool using the keywords osteoarthritis, arthropathy, cartilage formation, cartilage breakdown, cartilage damage, connective tissue disorders, joint inflammation, immune cell trafficking and oxidative stress.

[0353] The use of the "IPA Bioprofiler" enabled the identification of clusters of expressed genes that are causally related to one or more identified biological activities and the specific molecular pathways that support them. Information on the measured gene expression data (fold change cutoffs ≦−2 and ≧+2 and p-value ≦0.05) induced by each batch was then superimposed on the resulting network to define the degree of regulation of affected genes and related biological functions.

[0354] The degree of regulation of expressed genes was shown in different intensities of blue (indicating down-regulation) or red (indicating up-regulation). Based on the literature, the resulting predicted calculated impact on the relevant biological function was determined by the "IPA Molecular Activity Predictor" tool (MAP) and presented in a heatmap visualization.

[0355] The color and intensity were converted into a numerical value.

[0356] 2.1.5.Results The results of these studies led to a comparative study of the performance and mechanism of action of five different batches of Arte GX. Analysis revealed that while all batches were able to produce reproducible biological effects, it was also possible to identify batch-specific variations in the induced transcriptional patterns. Apparently, the induction of slightly different transcriptional patterns still resulted in the same desired regulation of one or more biological activities. This is due to functional resilience caused by redundancy in the interactions between the product's components and the body, which allows different batches to induce the same effect despite their different qualitative and quantitative compositions due to the multifocal mechanism of action (Figures 3 and 4).

[0357] Therefore, the analyzed batches are considered to have equivalent biological output since induction and repression patterns are conserved.

[0358] The different transcriptional patterns and relative biological effects of different batches are intended as a characteristic of the inherent variability present in preparations composed of biological materials. From the results summarized in Figure 4, it is clear that the observed transcriptional patterns of different batches induce highly reproducible biological (functional resilience) effects, resulting in a general alteration of the pathological process and an equivalent overall pathological state for all batches reported in Figure 4.

[0359] 3. Protocol All protocols used are summarized in the table below. [Table 2-1] [Table 2-2] [Table 2-3] [Table 2-4] [Table 2-5]

[0360] 4. Isotopic abundance Isotopic abundance analysis was performed on batches 20B0596 and 20B1955, which were prepared from different batches of starting material relative to batches 20I1279, 20J1770, and 21E1640.

[0361] Samples were sent to Chelab (Tentamus Company) laboratory and tested for stable isotopes as follows: -δ18O: Method IRMS, UNIT ‰ V-SMOW. -δ13C: Method QMA-M-01, EA-IRMS, UNIT ‰ V-PDB. C14-activity was also tested: -14C activity: ISO-16620-2 method; 2015 (AMS), unit % modern carbon (pMC).

[0362] The results were as follows:

[0363] Delta ratio of major isotopes in co-extracted Centella echinacea (Product A). [Table 3]

[0364] (The values ​​in the table include the percentage error due to the formal method used.) The measured C14 activity of the Pmc samples corresponds to that of material from purely biobased carbon. There is no evidence of synthetic sources in the analyzed material. The δ18O and δ13C values ​​overlap between batches indicating that they are not affected by processing and are only influenced by biological variations in the starting material, thus identifying a high reproducibility of the production process according to the conservation of this parameter.

[0365] Two batches of co-extract (20J1770 and 21E1640) were subjected to isotopic abundance studies during different steps of the manufacturing process.

[0366] The results are reported in the table below showing the δ ratios of major isotopes in co-extracted Centella echinacea crude plant material plant parts. [Table 4]

[0367] Evaluation of the isotopic abundance of the material along the production process shows that the production process does not change the abundance ratios, thus demonstrating the fact that this process preserves the natural biophysical properties of the starting material.

[0368] Analysis of the batches under study showed substantial similarity of values ​​and maintenance of ratios during the manufacturing process.

[0369] 5. miRNA Detection 5.1 Evaluation of intermediates in the production of product A (before ultrafiltration) Biophysical characterization of biological plant material includes evaluation of biological material in its production intermediate state.

[0370] The intermediate product of Product A, i.e., "RIC199EL0, extract_BLEND CENT_ECH EL, batch R20I4716," corresponding to the aqueous co-extract of Centella asiatica and Echinacea in the proportions shown in Example 1, was analyzed before ultrafiltration. The presence of RNA was assessed both quantitatively and qualitatively. RNA was extracted using a plant matrix-specific kit (Rneasy PowerPlant kit) after homogenization using a QIAshredder column before proceeding with the kit extraction protocol. Size distribution of the resulting RNA was performed using a Bioanalyzer 2100 equipped with an RNA 6000 Nano, RNA 6000 Pico, and a small RNA kit.

[0371] A size distribution of total RNA ranging from 4 to 150 nt was detected.

[0372] To quantitatively assess the total RNA extracted from the samples, nucleic acid digestion was then performed using a New England Biolabs Nucleoside Digestion Mix Kit. RNA concentrations were expressed as total nucleosides by UHPLC-qToF analysis. The following table reports the RNA expressed as total nucleosides (obtained by Method N in the table above). [Table 5]

[0373] These observations, in addition to providing a method for examining the biological origins of the matrix, identify a further degree of both structural and functional complexity of the matrix itself.

[0374] 6. Detection of supramolecular structures in natural matrices 6.1 Supramolecular aggregates and transfer to the gas phase Estragole in natural matrices obtained from fennel showed a reduced tendency to migrate into the vapor phase compared to the volatility of pure standard solutions of estragole, and was investigated by HS-GC-QqQ in alcohol extracts of bitter fennel seeds.

[0375] Results were generated in two different ways, as described below: - Method 1, a screening method optimized for the complete quantification of the analyte, is carried out at high temperature and is based on the use of experimental conditions that force the passage of molecules from the tested solution into the gas phase when salt is added (90 °C, NaCl added).

[0376] Method 2 is designed to highlight the possibility of different rates of passage of the analyte into the gas phase when the analyte originates from different test solutions. This method is based on the use of a lower extraction temperature (30°C) and the absence of NaCl.

[0377] As for purified synthetic estragole, quantification of the isolated molecule estragole using two different methods (methods 1 and 2) shows comparable results. [Table 6]

[0378] The measured concentrations of pure estragole actually matched the high-level calibration curve in both conditions, thus suggesting that the gas-phase transition of isolated synthetic estragole is not influenced by variables unrelated to the method conditions that favor the occurrence of such a phenomenon. Regarding the naturally occurring estragole embedded in bitter fennel extract, the results obtained by applying the two different methods (Method 1 and Method 2) are not equivalent, but show a 49.4% deviation (Method 2) from the theoretical result (which was instead reproduced by Method 1) (Table 3). [Table 7]

[0379] Estragole embedded in a Fennel matrix has a reduced tendency to migrate into the vapor phase of the pure standard solution when the less harsh conditions of Method 2 are applied.

[0380] Such a decrease, and the conditions under which it is observed, suggest that estragole embedded in fennel extract may be complexed with other less volatile components of the extract, thus impairing its tendency to migrate into the gas phase.

[0381] Thus, the data reported here support the existence of a matrix effect in fennel extract, as they show that the physicochemical behavior of estragole is indeed different when the molecule is isolated (synthetic purified standard) or in the context of fennel extract.

[0382] 6.2 Diffusion NMR experiments on supramolecular aggregates, natural matrices from bitter and sweet fennel containing estragole (bitter and sweet fennel extracts). In a first experiment, the presence of estragole in bitter fennel extract (Lot 3246) and sweet fennel extract (Blend, Lot 3250) was analyzed using H NMR spectra with triple signal suppression (FIG. 14) and compared with the H NMR spectrum of a reference standard in an extraction solvent mixture (300 ppm estragole in 50:50 water / bioethanol). Inspection of the spectra reveals two specific peaks in the reference standard (doublet, measured shifts 6.48 ppm and 6.71 ppm; FIG. 14) that are characteristic of the aromatic protons of estragole and are present in both the bitter fennel extract (Lot 3246) and the sweet fennel extract (Blend, Lot 3250). The results confirmed the presence of estragole in both extracts.

[0383] Diffusion NMR experiments were performed, recording ten one-dimensional spectra by varying the strength of the pulsed field gradient (G) along the z-axis. For each sample, the logarithmic ratio of the resonance intensity (I) at a given intensity of the pulsed field gradient (G) to the initial resonance intensity (I), recorded in the absence of G, was plotted against G as reported in Figure 15(a, b, c). A similar experimental procedure was also applied to a sample of ethanol as a calibration standard. The parameters of the linear regression model are reported in the table below. Inspection of the table below shows nearly identical parameters for the calibration reference sample, suggesting no change in solution viscosity and high reliability and reproducibility of the three in dependent experiments compared to the reference standard and samples 3250 and 3246. [Table 8]

[0384] The slope of the resulting line (coefficient a) is directly proportional to the translational self-diffusion coefficient (D). Calibration using a substance (ethanol) with a known translational self-diffusion coefficient (D) or van der Waals radius (RvdW(ethanol) = 2,130 Å) allows the proportionality constant between D or RvdW and the slope (coefficient a) according to Equation 2. Note that since the actual radius of the diffusing particle (estragole) is extended by the solvent shell (RH = RvdW + ΔRshell), it is the hydrodynamic radius (RH) and not the van der Waals radius that is determined from Equation 2. Therefore, the hydrodynamic radius (RH) and hydrodynamic volume (VH) of estragole were determined for each sample studied. These values, reported in the table below, indicate the aggregation state of estragole in the three samples. [Table 9]

[0385] As shown in Figure 16, the shape of estragole in the global minimum conformation is a non-spherical particle, but the experimentally determined hydrodynamic radius (R H ) to the theoretical van der Waals radius of estragole (R vdW) suggests a good approximation of NMR diffusion studies to yield qualitatively valid results. Furthermore, the hydrodynamic volume (V H =192Å 3 ) and the volume of a theoretical sphere-like particle defined by the van der Waals radius of estragole (V vdW =177Å 3 The similarity of the values ​​between the reference standard (estragole 300 ppm; V H =192Å 3 ) compared with the hydrodynamic volume of estragole in bitter fennel extract (sample 3246, estragole 300 ppm; V H =580Å 3 The hydrodynamic volume of estragole in the extract of sweet fennel (sample 3250, estragole 30 ppm; V H =1050Å 3 ), the hydrodynamic volume of estragole is much larger, suggesting the existence of distinct aggregate states for bitter fennel in size, shape and content, which appears to be independent of or inversely proportional to the concentration of estragole, which here is 10 times lower than in sample 3246.

[0386] This study shows that bitter fennel (sample 3246) and sweet fennel (sample 3250) extracts have different aggregation states containing estragole that appear to be independent or inversely proportional to their concentration (sample 3246, estragole 300 ppm; sample 3250, estragole 30 ppm). Estragole is expressed as a function of the hydrodynamic volume (V) of estragole in the reference standard. H =192Å 3 ) and the theoretical sphere-like particle (V) defined by the van der Waals radius of estragole vdW =177 Å 3Although bitter fennel and sweet fennel extracts are unable to self-aggregate as indicated by the similarity in the values ​​between the volumes of α, β, and β, additional undefined contents in the bitter and sweet fennel extracts promote distinct aggregation states that may be related to the different chemical, physical, biophysical and biological properties of the two extracts as supramolecular complex entities.

[0387] 6.3 Detection of supramolecular structures in natural matrices DLS Description: The method of dynamic light scattering (DLS) is the most common measurement technique for particle size analysis in the nanometer range. DLS measures the hydrodynamic size of particles by the mechanism of light scattering from a laser passing through a solution, analyzing the modulation of the intensity of the scattered light as a function of time. The Brownian motion of particles correlates with their hydrodynamic diameter. Smaller particles diffuse faster than larger ones, and DLS instruments generate a correlation function mathematically related to particle size and its time-dependent light scattering ability.

[0388] DLS has been used to measure the particle size of dispersed colloidal samples, study the stability of formulations, and detect the presence of aggregation or flocculation. This method is also ideally suited to analyzing the size distribution of already isolated exosomes and microvesicles.

[0389] Sample preparation: Lyophilized extract of sweet fennel (FIN0SE#AQEC batch 23G0989) was dispersed in 0.22 μm filtered demineralized water at a concentration of 4 mg / ml.

[0390] After dispersion, the sample was vortexed for 2 minutes to ensure complete dispersion.

[0391] Samples were analyzed under three different conditions: -Unfiltered -After filtering through a 0.45 μm nylon syringe filter -After filtering through a 0.1 μm nylon syringe filter A 0.1 μm filtration was performed on the 0.45 μm filtered sample.

[0392] Before each filtration step, the sample dispersion was vortexed for 30 seconds. The filtered dispersion was allowed to stand at room temperature for approximately 15 minutes and then gently stirred manually before analysis.

[0393] result: Correlogram evaluation provides data quality information about DLS results. The graph shows the progression of the correlation function (y-axis) over time (x-axis), which should have a sigmoidal shape. The intercept value on the y-axis relates to the signal-to-noise ratio, i.e., how much of the scattering signal from the sample reaches the detector and is successfully separated from the background noise. The closer the intercept is to 1, the better the signal-to-noise ratio.

[0394] The location of the inflection point in the correlogram (on the x-axis, time) is related to particle size; the longer the decay time of the correlation, the larger the particle size. The slope of this portion of the curve is related to the polydispersity index (PdI), and therefore the dispersion of the sample size population; the steeper the decay, the less dispersion in particle sizes and the lower the PdI value. Finally, the cross section of the curve following the inflection point is related to the presence of large particles. In general, the absence of large particles and aggregates is evidenced by the curve tending to zero in this region.

[0395] Figure 13 reports the average particle size distribution (Figure 13a) and the average correlation function (Figure 13b) obtained from three measurements. The average results of Z-average, PdI and peak results obtained from three replicate measurements are reported in Figure 12c. Z-average is the intensity-weighted average diameter and PdI is the polydispersity index.

[0396] The aqueous dispersion resulted in the formation of large particles above 1 μm. The noisy right side of the correlogram of the unfiltered aqueous dispersion suggests the presence of larger, uncharacterized particles. Filtration produced a clear shift in the correlogram and size distribution. Both filtered aqueous dispersion samples provided a bimodal distribution with small particles / structures below approximately 100 nm. The data quality of the filtered samples is good.

[0397] 6.4 Detection of exosomes in intermediates of product A (before ultrafiltration) Ultracentrifugation sample preparation The starting sample from which the ultracentrifugation was prepared was weighed and resuspended in a constant volume of VIB or vesicle isolation buffer (20 mM MES; 2 mM CaCl2; 100 mM NaCl, pH 6.0), maintaining a ratio of 5 mL of buffer per 500 mg of sample. The sample was incubated at room temperature with agitation for 20-24 hours to facilitate solubilization. After incubation, several centrifugations, all at 4°C and increasing speed, were performed to isolate particles ranging in size from 30 to 500 nm. Ultracentrifugation was performed using a T-1250 rotor (Thermo Fisher Scientific, 11718-5) and a Thermo Scientific™ Sorvall™ WX+ ultracentrifuge (Thermo Fisher Scientific™ 75000080, No. 15342177). The samples were centrifuged at 700 × g for 20 minutes, and the supernatant was filtered through a 0.45 μm filter while discarding the pellet. The supernatant was then transferred to an ultracentrifuge tube and centrifuged at 40,000 × g for 70 minutes. The pellet was resuspended in VIB while discarding the supernatant and centrifuged again at 40,000 × g for 70 minutes. The pellet was finally resuspended in 600 μL of 25 mM trehalose in PBS and stored at 4 °C for use within 24 hours or at -30 °C for long-term storage.

[0398] Extracellular vesicle staining and flow cytometry analysis Extracellular vesicles from samples were stained with CellMask™ Green Plasma Membrane Stain (ThermoFisher Scientific #C37608) according to the manufacturer's instructions and quantified using an Attune NxT flow cytometer. Briefly, 27 μL of exosomes were added to 3 μL of CellMask™ Green Plasma Membrane Stain (10x) per sample for 30 minutes at 37°C. Then, 170 μL of PBS (0.22 μm double-filtered) was added to each sample and read. To eliminate any nonspecific events in the flow cytometry analysis, PBS stained with CellMask™ Green Plasma Membrane Stain was used as a negative control, and a fluorescent exosome standard (Novus Biologicals #NBP3-11691) was used as a positive control. Additionally, each sample was analyzed without staining to rule out autofluorescence.

[0399] Analysis of microvesicle size and concentration by NanoSight The Malvern NanoSight NS300 uses the technique of Nanoparticle Tracking Analysis (NTA). This proprietary technique utilizes the properties of both light scattering and Brownian motion to obtain size distribution and concentration measurements of particles in liquid suspension. A laser beam passes through the sample chamber, and suspended particles in the path of this beam scatter light in a manner that allows them to be easily visualized by a 20x microscope equipped with a camera. The same protocol was followed for a sample of Product C.

[0400] result Prior to ultrafiltration, a sample corresponding to a production intermediate of Product A, e.g., "RIC199EL0, extract_BLEND CENT_ECH EL, batch R20I4716," i.e., an aqueous co-extract of Centella asiatica and Echinacea in the proportions shown in Example 1, was analyzed using flow cytometry as follows: the PBS value marked by CellMask™ Green Plasma Membrane Stain was considered a blank sample and was therefore excluded from the region of interest. Fluorescent Exosome Standards identified many elements in the same positive area.

[0401] The samples were then analyzed taking into account the number of elements present in the same region. The number of extracellular vesicles identified in the "RIC199EL0, extract_BLEND CENT_ECH EL, batch R20I4716" sample was 5.43 × 10 5 The number of extracellular vesicles identified in the Product C sample was 2.7 × 10 6 There were 100 pieces.

[0402] 7.Biodegradability test according to OECD 310:2014 Biodegradation is the process by which organic materials are broken down by microorganisms into their simplest naturally occurring components (e.g., CO2, H2O, and NH3) that can be incorporated into natural biogeochemical cycles.

[0403] Evaluating the biodegradability of chemicals is one of the major challenges in environmental risk assessment. Biodegradation tests are designed to evaluate chemicals as the sole carbon source for the survival of microfauna under batch conditions. Ready biodegradation tests (RBTs) are the basis of an integrated testing strategy for the biodegradation of pure substances. These are a series of tests (numbers 301A-301F and 310) proposed by the Organization for Economic Cooperation and Development (OECD). Microorganisms and test substances are typically incubated in a buffered pH 7 medium (called "mineral medium") containing N, P, and trace elements. Biodegradation kinetics are monitored for at least 28 days by assessing metabolic parameters such as oxygen consumption, carbon dioxide production, or dissolved organic carbon consumption. RBTs measure ultimate or complete biodegradation; a chemical can be classified as readily biodegradable if it passes one of the RBTs.

[0404] The term primary biodegradation refers to the structural modification of a substance caused by biological events, resulting in the loss of certain properties of that substance, which can be calculated from complementary chemical analyses of the parent compound performed at the beginning and end of the test (OECD 301, 310).

[0405] For the test item "Product A Batch 20B1955", the analysis of inorganic carbon for the assessment of aerobic biodegradability in aqueous media was carried out according to the screening method described in OECD 310:2014.

[0406] For this purpose, the amount of inorganic carbon evolved was measured and reported relative to a blank.

[0407] The test was carried out using a test article with a fixed concentration of organic carbon. The samples were kept at a temperature of 20 + 1°C for the entire duration of the test (28 days).

[0408] At the beginning and end of the study, three 50 mL aliquots of blank and solution containing the test items were sent to the sponsor as requested by the sponsor.

[0409] Based on the results obtained (interpreted according to OECD 310:2014), the tested batches were considered to be readily biodegradable under aerobic conditions.

[0410] 7.1 Assay Run To perform the assay, the following preparations were made using deionized water, with any unused portion removed at the end of the test.

[0411] Test sample Elemental analysis of the test items was determined by REDOX SnC using a CHN analyzer.

[0412] The sample was used with a measured initial concentration of 22.79 mg / L total organic carbon (TOC).

[0413] reference material A starting nominal concentration of 24.07 mg / L of total organic carbon (TOC) was used, considering sodium benzoate as the reference substance using its molecular formula.

[0414] blank Water containing culture medium supplemented with muddy water inoculum was used as a blank.

[0415] Preparation of assay samples + reference materials The test sample and the reference material are combined to have a final concentration given by the sum of the total organic carbon (TOC) of the two materials (see attached sample N.2 for results).

[0416] Non-biological tests To examine the potential for abiotic degradation, the test items were combined with 50 mg / L HgClz.

[0417] Assay conditions The inoculum was added to all bottles (see bottle preparation), which were then closed and incubated in the dark for 28 days with stirring at 20 + 1°C. Data recording is carried out continuously by a validated information monitoring system (Labguard Evisense).

[0418] At least 1 h before each measurement of inorganic carbon (TIC), the reaction in one bottle of blank, one of the reference substance and one of the test item was stopped by adding 6 mL of sodium hydroxide (1 M).

[0419] Three bottles were analyzed for inorganic carbon (IC) at each checkpoint, except at the end of the study, where five bottles were analyzed.

[0420] 7.2 Results Biodegradation calculations are performed for each sampling time of the reference material, test sample and blank.

[0421] Total biodegradation is calculated using the following formula: Dt = (ICt - ICb / TOCi) × 100 During the ceremony, Dt = total biodegradability calculated with reference to the blank value (expressed as a percentage). ICt = concentration (mg) of inorganic carbon produced by biodegradation of the test item. ICb = concentration of inorganic carbon produced by biodegradation in the blank (mg). TOCi = concentration of organic carbon (mg) of test item added at the start.

[0422] Biodegradation results are expressed as the percentage ratio between the measured inorganic carbon value and the initially added organic carbon value, where the inorganic carbon value is the highest value recorded during the 28-day experimental period.

[0423] Interpretation of results A substance is considered to be readily biodegradable if its biodegradability level is at least 60% within the 10-day window under test.

[0424] On day 28, if the biodegradability in the bottle contains both the test sample and a reference material called the biodegradability in the bottle, and the result for the reference material is less than 25%, it can be assumed that the test material does not inhibit the activity of the inoculum.

[0425] If there is a significant increase (>10%) in the inorganic carbon content of the abiotic bottle over the test period, it can be concluded that abiotic degradation of the test substance has occurred, and this must be taken into account in calculating the biodegradation rate of the test item.

[0426] Efficacy criteria A test is considered valid if: - The average degradation rate of the reference substance is more than 60% after 14 days of incubation. - The average amount of total inorganic carbon (TIC) present in the blank at the end of the test is <3 mgC / L.

[0427] result Meet the validity criteria of the study.

[0428] The mean amount of TIC present in the blank controls at the end of the study is <3 mg C / L.

[0429] Non-biological degradation did not occur as the amount of TIC during testing in the bottles was less than 10% (see table below). [Table 10]

[0430] The biodegradation rates of the test items under study are detailed in Annex N.2 and summarized in the table below.

[0431] The trend of inorganic carbon and associated biodegradation rates in bottles containing both test samples and reference substances (see table below - column % of biodegradable reference substance + test item) confirms the absence of an inhibitory effect of the test sample on the inoculum at the concentration applied in the test (5%). [Table 11]

[0432] The degradation trends of the test items and reference materials are reported in Figures 17 and 18.

[0433] Based on the results obtained (interpreted according to OECD 310:2014), test item product A was considered to be readily biodegradable under aerobic conditions.

[0434] 8. Detailed analysis of product A by targeted metabolomics In order to understand whether the final matrix constituting Product A is characterized by matrix effects, a series of analyses were performed on the batches reported above to characterize the product in different ways. A targeted metabolomic analysis, which allows the identification of most of such molecular components, was performed on different batches of the product, together with other analyses reported herein (see Figure 5).

[0435] As mentioned above, the product consists of two plant matrices assembled to produce a final new plant matrix. Several analytical techniques are used to identify and quantify the compounds belonging to the main classes present in plants. Metabolomic analysis does not allow for understanding the dynamic changes within the matrix's components, but it does provide a "picture" of its composition at the moment the analysis is performed.

[0436] In the following analysis, individual components (plant metabolites) are specifically studied, hence the term "targeted metabolomics." This analysis allows capturing a frame on qualitative data by determining the chemical compounds present in the material, and quantitative data by defining the concentration of each compound in the material.

[0437] For product A, a qualitative and quantitative characterization of as many primary and secondary metabolites as possible was performed using an "omic" approach, targeted metabolomics analysis, based on the use of multiple analytical methodologies.

[0438] The analytical methods used for the chemical characterization of each batch are described below. Based on the chemical nature of the classes of compounds present, the most appropriate analytical techniques were adopted. Analysis by chromatographic methods combined with different detection techniques (e.g., GC and LC, each combined with an appropriate detector) allowed the identification and quantification of organic compounds, if necessary. Inductively coupled plasma analysis using a single quadrupole mass spectrometer (ICP-MS) or an optical emission spectrometer (ICP-OES) allowed the levels of elements present to be established, while anions were determined by ion chromatography and a conductivity detector. Other gravimetric methods were used to determine the classes of substances that could not be quantified by chromatographic methods.

[0439] The table below summarizes all the methods used. [Table 12]

[0440] The results summarized in the table below show considerable compositional variability for each batch, highlighting the impossibility of reproducing the properties of a matrix as the sum of its single components. The studies performed and reported herein (see cell-based assay results), together with the following data, demonstrate that the biological effects induced by a product cannot be reproduced by the sum of the effects induced by single molecular components, but are the result of interconnections and interactions between components: matrix effects. This leads to the impossibility of formally defining a structure-activity relationship (SAR) according to the principles standardly applied to APIs. [Table 13-1] [Table 13-2] [Table 13-3] [Table 13-4] [Table 13-5] [Table 13-6]

[0441] The results show that there was some significant variation in the amount of individual chemical classes in the five batches of co-extract. These variations, if considered as criteria, would lead to a priori predictions that these batches would have different therapeutic effects. The analysis reported here demonstrates that, although biological activity was maintained across all the different batches evaluated, none of the identified single molecular components adhered to the criteria set for a single API, thus demonstrating that the matrix cannot be considered a collection of APIs.

[0442] As mentioned above, the same therapeutic effect is preserved in all batches (functional recovery).

[0443] The product is capable of eliciting the same response in a biological system that is relevant to its intended use, through a physiological mechanism of action.

[0444] This also highlights the fact that there are both structural and functional redundant mechanisms of functional resilience typical of living organisms (reaching the same result despite individual differences between individuals of the same species) that are maintained in products containing or consisting of natural matrices.

[0445] Compliance with functional resilience requirements was assessed in the same way for all products tested (A, B, C and D).

[0446] 9. Network Analysis A network analysis of the pathologies treated by products A–D was performed, and the data obtained demonstrate how the tested natural matrix-based products can affect the body on a systemic scale.

[0447] Specifically, it relates to: Product A - In pathological conditions (Figure 19 Panel A): Body adipose tissue has been shown to be associated with the development and progression of knee OA. Total body adipose tissue is significantly and negatively associated with the presence and progression of knee osteoarthritis. Interfering with the knee osteoarthritis process increases mobility and results in a reduction in total body fat (which contributes to biomechanical damage and the release of pro-inflammatory factors that contribute to knee osteoarthritis). [Chang J, Liao Z, Lu M, Meng T, Han W, Ding C. Systemic and local adipose tissue in knee osteoarthritis. Osteoarthritis Cartilage. 2018] - Situation when treated with drug reference (Figure 19 Panel B): The beneficial effect is only partial, since only the inflammatory and pain-relieving components of the pathology are affected. - Condition when treated with product A (Figure 19 Panel C): The beneficial effects are the result of the coordination of anti-inflammatory and stimulatory effects of cell proliferation, all to counteract tissue damage and pain.

[0448] Product B - Pathological condition (Figure 20 Panel A): Mild cognitive impairment (MCI) is defined as a clinical condition characterized by mild cognitive impairment with little impact on functional status. It has been widely demonstrated that increased levels of inflammatory mediators and the number of activated glial cells are considered important risk factors for impaired neurogenesis, synaptic plasticity, and cognitive deficits. Increased susceptibility to the long-term effects of inflammation may contribute to the decline in cognitive activity and motor performance observed in both aging and neurodegenerative diseases, such as MCI (Di Benedetto S, Muller L, Wenger E, Duzel S, Pawelec G. Contribution of neuroinflammation and immunity to brain aging and the mitigating effects of physical and cognitive interventions. Neuroscience & Biobehavioral Reviews. 2017;75:114-128. Doi:10.1016 / j.neubiorev.2017.01.044.). - Situation when treated with drug reference (Figure 20 Panel B): Drug reference, a well-known acetylcholinesterase (AChE) inhibitor, exerts neuroprotective effects, improves synaptic plasticity, and improves CNS functionality, but does not produce systemic effects. - Conditions when treated with product B (Figure 20 Panel C): Product B acts at the systemic level, improving neuroprotective effects, restoring synaptic plasticity, mediating improvements in cognitive activity and physical function, and can also reduce chronic inflammation.

[0449] Product C - In pathological conditions (Figure 21 Panel A): Dysfunctioning and inflamed adipose tissue leads to an imbalance in bone homeostasis, adversely affecting the competition of mesenchymal stem cell reserves to induce osteoblast, osteoclast, or adipocyte differentiation, shifting this phenomenon toward osteoclast differentiation in an unphysiological manner. This leads to a decrease in the number of mature osteoblasts, which are unable to ensure proper mineralization of the cellular matrix with a loss of bone activity. In situations of dysregulated lipid metabolism and adipose tissue inflammation, a dysfunctional loop is established between adipose tissue and bone, leading to the accumulation of adipocytes and osteoclasts, disfavoring osteogenic components and exacerbating bone fragility. Bone is also an organ with endocrine activity and can therefore influence events in other tissues at a systemic level, for example through the secretion of osteocalcin (OCN), which stimulates insulin secretion by the pancreas, insulin sensitivity in peripheral organs such as muscle, and regulation of overall energy expenditure (Fukumoto S, Martin TJ. Bone as an endocrine organ. Trends Endocrinol Metab. 2009 Jul;20(5):230-6. Doi:10.1016 / j.tem.2009.02.001. Epub 2009 Jun 21. PMID:19546009). - The situation when treated with the drug reference (Figure 21 Panel B): The drug reference can only reduce the amount of fat cells. - Product C (Figure 21 Panel C): Treatment with Product C is able to recreate all the elements necessary to restore correct bone turnover by interacting with the mesenchymal stem pool in bone and adipose tissue, which is beneficial for the formation of solid functional bone structures, and at the systemic level, restores the balance of metabolic dysregulation and reduces inflammation.

[0450] Product D -Pathological context (Figure 22 Panel A): Systemic inflammation is closely related to the clinical manifestations of tumors and indicates their presence and progression. Cytokines, inflammatory proteins, and immune cells are readily detectable in the systemic circulation [Dolan RD, Lim J, McSorley ST, Horgan PG, McMillan DC. The role of the systemic inflammatory response in predicting outcomes in patients with operable cancer: Systematic review and meta-analysis. Sci Rep. 2017; Dolan RD, McMillan DC. The prevalence of cancer-associated systemic inflammation: Implications of prognostic studies using the Glasgow Prognostic Score. Crit Rev Oncol Hematol. 2020; Roxburgh CS, McMillan DC. Cancer and systemic inflammation: treat the tumor and treat the host. Br J Cancer. 2014]. - The situation when treated with drug reference (Figure 22 Panel B): Drug reference cannot counteract systemic inflammation. - Conditions when treated with product D (Figure 22 panel C): Product D is able to modulate certain biological activities that can beneficially influence systemic inflammation.

[0451] The graphs were designed using Gephi (Bastian M., Heymann S., Jacomy M. (2009) Gephi: an open source software for exploring and manipulating networks. International AAAI Conference on Weblogs and Social Media), an open-source network visualization platform. The layout Yifan Hu chose for the "pathology" network, preserving the graph conformation, was then applied to construct the "natural product" and "reference drug" graphs.

[0452] The direction of regulation is represented by blackening nodes for upregulation and empty nodes for downregulation. Gray nodes do not indicate a specific direction of regulation. Node size represents a previously scaled Z-score based on all values ​​used in a particular panel. In Gephi, node size was controlled by using the "ranking" parameter, which has a size range of 15 to 60.

[0453] The "HUBs" of the network represent central biological processes of interest for a particular pathology. They are depicted in gray and labeled with capital letters, and their predicted degree of regulation is indicated by arrows going up or down according to the literature (for the pathology network) or based on the degree of regulation of the disease and organism function / biological parameter (for the "natural product" and "reference drug" networks) (Figure 19).

[0454] 10. Examples of the Method of the Invention The method of the present invention was carried out on a product for the treatment of osteoarthritis.

[0455] The product selected was Product A, which contains a co-extract of Centella asiatica leaves and Echinacea purpurea flowers as a natural matrix, as described in Example 1.

[0456] Five different validated batches of the product were selected (see Example 1).

[0457] Modulations that resulted in a degree of modulation of one or more biological activities underlying osteoarthritis were identified as exemplified in Example 2 above, and the in vitro cellular assays described in Example 1 and the accompanying tables were performed.

[0458] The qualitative-quantitative adjustment values ​​for each one or more biological activities were calculated as described above for z-scores and the values ​​were compared as reported in Figures 1-4.

[0459] 2 to 4 show that the condition (c) of the method of the present invention is satisfied, that is, - at least 50% of one or more biological activities identified for each characteristic is modulated by each batch according to a trend in the degree of modulation representative of a healthy state; The calculated qualitative-quantitative modulation value for each of said at least 50% of one or more biological activities of a) above differs by at least 15% from the respective calculated qualitative-quantitative modulation value for the control of said pathological condition.

[0460] Indeed, Figures 2-4 show that all of the one or more biological activities identified for each characteristic are modulated according to a trend in degree of modulation consistent with the healthy physiological state (healthy state) depicted in Figure 1, and the same figures show that the calculated qualitative-quantitative modulation values ​​for each of the one or more biological activities differ by at least 15% from the respective modulation values ​​calculated for the control of a pathophysiological state.

[0461] The same can be observed for the other three test products (B, C, and D in the related diagram).

[0462] Therefore, product A is situation3 also shows, in line with what is known in the state of the art, that triamcinolone, a drug commonly used to treat osteoarthritis, is unable to regulate the condition or does not meet the above requirements, since the identified activity for one of the selected characteristics is not regulated according to the trend of the degree of regulation consistent with a healthy state. Indeed, for biological functions related to the formation of cartilage tissue, the analyzed reference drug shows a pathological deterioration of regulation, rather than the desired upregulation to reach a healthy physiological state.

[0463] Product A therefore contains a natural matrix and regulates the condition.

[0464] Furthermore, as is evident from Figure 4, different batches of product show functional resilience, i.e., despite their different qualitative-quantitative chemical composition (see Figure 5 and Example 8), the qualitative-quantitative adjustment value calculated for each batch for a given activity of each group differs by up to 20% from the average of said values ​​calculated for all groups.

[0465] For example, the mean adjustment value for one or more biological activities "cartilage tissue formation" is 0.15, with each adjustment value for that activity for each batch being 0.15, and the mean adjustment value for one or more biological activities "joint inflammation" is -0.29, with measurements ranging from -0.24 to -0.33, a difference from the mean of approximately ±8%, and the difference for the other two activities investigated being even narrower.

[0466] Thus, despite the apparent qualitative-quantitative variations in the components of each batch, the overall therapeutic results provided by each batch are substantially superimposable, i.e., demonstrating functional resilience.

[0467] As mentioned above, the parameters selected for product A were genes, whose expression was analyzed via conventional transcriptome analysis.

[0468] A summary of the relevant genes involved for one or more selected biological activities shows that for each activity the set of regulated genes differs from batch to batch, but nevertheless the final degree of regulation for each analysed activity is very similar, with calculated regulation values ​​varying between different batches by a maximum of ±8% relative to the average regulation value obtained for that activity.

[0469] This result can only be explained by the preservation of the physiological mechanism of action in the therapeutic product.

[0470] The gene symbols used in the table below are the official gene symbols used in the NIH genbank. [Table 14-1] [Table 14-2]

[0471] The data summarized in the table above, in which only the regulated genes underlying a particular biological activity are shown for each batch, shows that although each batch regulates a different set of genes (positive upregulation, negative downregulation), the resulting regulation of one or more of the biological activities under test is superimposable between batches.

[0472] Thus, while the products have a qualitative-quantitative composition across batches, with each batch regulating one or more biological activities of interest by modulating a different set of genes (some common, some not), the degree of regulation of the tested biological activity or activities is nonetheless maintained across batches in terms of direction and magnitude. Thus, the data demonstrate the functional resilience of the batches, indicating that this resilience results in "redundancy" in gene regulation, whereby matrix networks interact with biological target networks through different pathways, and genes trigger the same final response from the biological system. This mode of operation results from the two entities essentially "speaking the same language," i.e., a physiological language, since both entities belong to the domain of all living things, in other words, nature.

[0473] Example 11: Clinical validation of physiological mechanisms of action In addition to data on the achievement of endpoints related to cognition and physical functioning of patients enrolled in the study, classically derived from the geriatric field, in the "Randomized Controlled Trial to Assess the Efficacy of Dietary Supplements in Controls with Mild Cognitive Impairment" study, government identification number of the clinical trial: NCT03581929, it was possible to analyze a set of biological data ("multi-omics" parameters, such as transcriptomics, metabolomics and microbiomics) by subjecting samples collected from corresponding patient groups to biological assays to determine whether the product exerts its therapeutic or beneficial effect by regulating the network of biological activities underlying the desired therapeutic or beneficial effect. The study was a 12-month, single-center, randomized controlled trial (including a 6-month double-blind vs. placebo and a 6-month open-label trial). After eligibility assessment, 50 subjects were enrolled in the study (V1). The purpose of this study was to evaluate whether treatment with Product B could improve cognitive performance in subjects with MCI.

[0474] Main purpose: 1) To evaluate the effect of Product B on the overall cognitive performance of MCI patients (V2) after 6 months compared to placebo, as measured by the Free and Cued Selective Reminding Test (FCSRT) and the Addenbrooke's Cognitive Examination-Revised (ACE-R) test. 2) To evaluate the effect of Product B on global cognitive performance of the entire study population in an open-label extension phase (V3) lasting an additional 6 months, as measured by the FCSRT and ACE-R tests.

[0475] Secondary Objectives: 1) Evaluate potential variations in the following key parameters: Cognitive abilities measured with additional tests that examined multiple cognitive performances with assessment as the primary objective ·Body function Modification of biochemical parameters of inflammation -Alteration of parameters related to oxidative stress conditions · Microbiome changes After 6 (V2) and 12 (V3) months of treatment, compared to baseline (V1) and compared to placebo. 2) Evaluate the safety and tolerability of Product B through analysis of adverse events (AEs) and serious adverse events (SAEs).

[0476] Clinical trials conducted on patients to verify the therapeutic / beneficial effects of the tested products involving cognitive abilities include the ACE-R (Pigliautile et al., 2015), FCSRT (Frasson et al., 2011), Mini-Mental State Examination (MMSE) (Magni et al., 1996), Digit Span forward and backward (Monaco et al., 2013), Trail Making Test (TMT) (Amodio et al., 2002), Babcock Story Recall Test (BSRT) (Spinnler and Tognoni, 1987), Rey Auditory Verbal Learning Test (AVLT) (Carlesimo et al., 1995; 1996), Raven Test (Carlesimo et al., 1995; 1996), Token Test (Spinnler and The tests were Verbal Fluency Test (VFT letter: Carlesimo et al., 1995; 1996. VFT category: Spinnler and Tognoni, 1987), Picture Drawing (Spinnler and Tognoni, 1987), and Verbal Judgment (Spinnler and Tognoni, 1987). Physical function assessment was performed via Bioelectrical Impedance Analysis (BIA), Handgrip, Timed Up and Go test (TUG), Short Physical Performance Battery (SPPB), Frailty Index, Activities of Daily Living (ADL), and Instrumental Activities of Daily Living (IADL). The overall results of these tests confirmed the desired therapeutic / beneficial effects of the product.

[0477] Additionally, a series of biological assays were performed on samples collected by the patient at different time points during treatment to determine whether the product exerts its therapeutic or beneficial effect by regulating the network of biological activities underlying the pathophysiological or altered physiological state associated with the condition.

[0478] In particular, omics analyses were performed on fecal samples using NGS sequencing to determine the composition of the fecal microbiota and predict bacterial metabolites produced in the gut and absorbed systemically. Additionally, gene expression analysis by RNA-seq, metabolomics analysis by GC-MS (gas chromatography and mass spectrometry), determination of levels of a panel of circulating cytokines, levels of oxidative stress, and an extensive battery of markers typical of clinical biochemistry were performed on peripheral blood samples.

[0479] Materials and Methods Microbiome analysis Each patient provided three fecal samples during the study (Visits 1, 2, and 3) using a provided sample collection kit according to the instructions provided by the clinical center. Collected samples were stored at room temperature, and a small portion was transferred to a 2 ml tube and stored at -80°C. Samples were sent on dry ice to an external laboratory (Edmund Mach Foundation, San Michele all'Adige-Trento) responsible for the analysis of the gut microbiota [bacterial deoxyribonucleic acid (DNA) analysis]. Samples were processed for DNA extraction, PCR, library preparation, and sequencing. A mean concentration of 6600 ± 6100 ng of DNA was isolated from 100 mg of feces.

[0480] One hundred thirty fecal samples and eight control samples (five DNA extraction buffer and three PCR-negative controls) were sent for sequencing. Sequencing generated 47,549,492 total reads, 42,828,220 filter-passing reads (i.e., total number of clusters passing the filter), and 74% identified reads (PF) (i.e., percentage of filter-passing clusters assigned to an index), corresponding to 15,841,323.4 forward and reverse identified reads, with a CV (i.e., coefficient of variation of counts across all indexes) of 0.27 and 121,822 (9 ± 24,605, 26) raw reads per sample (mean ± SD, excluding the control, which generated 1,237 (3 ± 836, 02) reads per sample, mean ± SD). After merging forward and reverse reads and denoising and chimera removal, we obtained 10,844,273 sequences per sample, with 83414.4 ± 18174.1 (mean ± SD) being the average number of sequences per sample (excluding controls), corresponding to a total of 36,035 features. Sequences had an average length of 434.31 ± 11.63 bp.

[0481] Taxa summary analysis of relative abundance (%) and absolute abundance in phyla was performed.

[0482] Further microbiome analysis was performed to identify which bacteria were enriched in patients after treatment and which metabolic activities were restored.

[0483] Two different analytical approaches were used: once the gene sequences of the variable region V3-V4 of the 16S gene were obtained, QIIME2 software was used for the taxonomic identification of bacteria present in the fecal samples, and PICRUSt2 software was used to simulate the metagenomic content, i.e., the metabolic pathways expressed in the identified bacteria whose abundance was enriched or depleted in response to the treatment.

[0484] Clinical Laboratory Tests: Biochemical and urinalysis as well as ECG changes from baseline were evaluated. Safety laboratory tests were performed at the clinical site (Servizio di Patologia Clinica ed Ematologia-Azienda Ospedaliera di Perugia), and ECG was also performed locally. Blood and urinalysis were evaluated at baseline, after 6 months, and after 12 months of regular consumption of the study product. Analyses included glucose, nitrogen, gamma-GT, total cholesterol, potassium, calcium, vitamin B12, folate, and CRP. Results are expressed as ratios between the mean adjusted values ​​of the groups (see table below).

[0485] Plasma inflammatory cytokines and plasma markers of oxidative stress: Samples were collected at each visit and stored at -80°C before being analyzed in the laboratory of the Department of Gerontology and Geriatrics at the University of Perugia.

[0486] Plasma inflammatory cytokines: To assess the inflammatory response, different chemokines and cytokines were assessed in the plasma of all subjects using a multiplex method. This method allows for the simultaneous measurement of EGF, GM-CSF, IFNα2, IFNγ, IL-10, IL-13, IL-17, IL-1RA, IL-1β, IL-2, IL-4, IL-5, IL-6, TNFα, and VEGF. Results are expressed as a ratio of the group mean control values ​​(see table below).

[0487] Plasma markers of oxidative stress: Superoxide dismutase (SOD) concentration or enzyme activity was assessed in plasma. Results are expressed as ratios between the mean control values ​​of the groups.

[0488] Transcriptome analysis Samples were collected at each visit. Samples were stored at -20°C for 24 hours and then at -80°C. RNA profiles from patient whole blood were assessed using the PAXgene™ Blood RNA System (PreAnalytiX, QIAGEN), which includes PAXgene™ Blood RNA Tubes, vacutainers for RNA collection and stabilization, and the QIAsymphony PAXgene™ Blood RNA Kit for automated RNA purification on a QIAsymphony SP instrument. Gene expression profiles were assessed using RNA-Seq data obtained using Illumina NextSeq, sequenced in paired-end mode. Samples were mapped to the reference genome using the bioinformatics tool STAR (version 2.7.5c) with standard parameters for paired reads. The reference track was the Homo sapiens HG38 assembly obtained from GenCode (release 35 (GRCh38.p13)). Quantification of expressed transcripts for each sequenced sample was performed using the featureCount algorithm. The Bioconductor package DESeq2 was used to normalize the data using the median ratios and perform differential expression analysis. Quality control checks such as Euclidean distance (heatmap distance) and principal component analysis (PCA) were performed between all samples for each condition considered.

[0489] Metabolomic analysis: Samples were collected at each visit, centrifuged at 3000 g for 5 minutes to separate the pulse within 2 hours of collection, and stored at -80°C.

[0490] The metabolome extraction, purification, and derivatization process was performed using the MetaboPrep kit (Theoreo, Montecorvino Pugliano, SA) according to the manufacturer's guidelines.

[0491] A 2-μL aliquot of the derivatized solution was introduced into a GC-MS system consisting of a GC-2010 Plus gas chromatograph coupled to a 2010 Plus single-quadrupole mass spectrometer, both manufactured by Shimadzu Corp., Kyoto, Japan. Chromatographic separation was achieved using a CP-Sil 8 CB fused silica capillary column (Agilent, J&W) with a 1.00-μm film thickness, 30 meter length, and 0.25-mm outer diameter, with helium serving as the carrier gas.

[0492] The initial temperature was established at 100 °C and maintained for 1 min, then gradually increased to 320 °C at a rate of 6 °C / min and held at that final temperature for an additional 2.33 min. The gas flow was regulated to maintain a constant linear velocity of 39 cm / s, with the split flow configured at a 1:5 ratio. The mass spectrometer was operated in full scan mode with electron impact ionization at 70 electron volts (eV) and scanned within the m / z (mass-to-charge ratio) range of 35-600 at a rate of 3333 atomic mass units per second (amu / s). A solvent cutoff time of 6.0 min was applied. The total duration of the entire gas chromatography (GC) program was 40 min. To facilitate analysis, samples were grouped, with each group typically containing 10 (or fewer) samples. Each group was monitored with three controls: a run without injection, an injection of a mixture of standards, and a duplicate analysis of a randomly selected sample from that group (Figure 3). The linear retention index was also determined at the beginning and end of the analytical run and for each of the 50 samples analyzed. To increase the accuracy of structural annotation, this determination was performed using a mixture of alkanes with an even number of carbon atoms, as proposed by Kovats et al. For the blank injection, 2 μL of hexane was used. In contrast, the standard solution contained a mixture of 16 diverse molecules, including organic acids, sugars, amino acids, sterols, fatty acids, and vitamins. The second injection contained a randomly selected sample from the same group. Each sample (including replicates) was analyzed in triplicate. Each analytical group was considered valid if the blank did not produce a peak, if the ratio of the area under the standard peak (normalized to the area of ​​the internal standard) remained within 90–110% of the expected value, and if the variance of the ratio of the areas of 100 major peaks (normalized to the area of ​​the internal standard) of replicate injections was less than 15% from the original sample. Given the lack of baseline resolution in the resulting chromatograms, a spectral deconvolution process was applied. This procedure involved utilizing identified primary fragments in the mass spectrum associated with each metabolite if they met certain criteria, which were: area >10,000; gradient >100 / min; width >1 sec. Single ion monitoring (SIM) chromatograms were used for quantification of metabolite areas.As part of this process, 2-isopropylmalic acid was used as an internal standard and the SIM value was set to 147.

[0493] A targeted quantitative metabolomics approach was applied to analyze samples using a reversed-phase LC-MS / MS custom assay based on an Orbitrap Exploris™ 120 mass spectrometer (Thermo Fischer, USA).

[0494] This method combines analyte derivatization and extraction with selective mass spectrometry detection using accurate mass extraction signals and a data-dependent approach. Isotopically labeled internal standards and other internal standards are used for metabolite quantification. The custom assay includes a 96-deep-well plate with a filter plate attached with sealing tape, along with the reagents and solvents used to prepare the plate assay. The first 14 wells are used for one blank, 30 samples, seven standards, and three quality control samples. For all metabolites except organic acids, samples are thawed on ice, vortexed, and centrifuged at 13,000 x g. The samples are loaded onto the center of the filter on the upper 96-well plate and dried in a nitrogen stream. Phenyl isothiocyanate is then added for derivatization. After incubation, the filter spots are dried again using an evaporator. Metabolite extraction is then achieved by adding 300 μL of extraction solvent. The extract is then transferred to the lower 96-deep-well plate by centrifugation and subsequent dilution with MS running solvent.

[0495] For organic acid analysis, 150 μL of ice-cold methanol and 10 μL of isotope-labeled internal standard mixture were added to the sample for overnight protein precipitation. This was then centrifuged at 13,000 × g for 20 minutes. 50 μL of the supernatant was loaded into the center of a 96-deep-well plate, followed by the addition of 3-nitrophenylhydrazine (NPH) reagent. After a 2-hour incubation, BHT ...

Claims

1. 1. A method for assessing whether an adjuvating product for treating a pathological condition or assisting in maintaining homeostasis in an altered physiological state exerts a therapeutic or beneficial effect via a physiological mechanism of action, comprising: selecting a therapeutic or beneficial product comprising one or more natural matrices and providing different batches of said product; performing at least one cell-based assay on each of the different batches; wherein the readouts for the cell-based assays represent modulation of one or more biological activities underlying a desired therapeutic or beneficial effect. and determining from the retrieved results: - whether the product batch exerts its therapeutic or beneficial effect by regulating the network of biological activities underlying the pathological or altered physiological condition, and - whether the product exhibits therapeutic or beneficial functional resilience between different batches; provided that said functional resilience is intended to mean the maintenance of therapeutic or beneficial properties in different batches of a given product containing one or more natural matrices, and such maintenance is not affected by qualitative and quantitative compositional differences between batches of said products; The product is then shown to exert its therapeutic or beneficial effect via a physiological mechanism of action if it regulates the network of biological activity underlying the pathological or altered physiological state and if it exhibits therapeutic or beneficial functional restoration between different batches.

2. 2. The method of claim 1, further comprising: (1) providing a list of features representative of a pathological condition; (2) for each of the features, identifying alterations in one or more biological activities underlying the pathological condition, thereby identifying a network of biological activities whose degree of regulation corresponds to the pathological condition; and (3) identifying one or more parameters whose degree of regulation corresponds to the degree of regulation of the one or more biological activities underlying the therapeutic effect of the tested product, and determining a trend in the degree of regulation in the network with respect to an up- or down-regulation of the one or more biological activities that corresponds to the pathological condition or a healthy state.

3. The product is a therapeutic product, (a) a group of cells: (a1) at least one control group and at least two test groups of cells having a disease phenotype relevant to the intended use of the therapeutic product; or (a2) at least one population of cells having a healthy physiological phenotype, and at least one control group and at least two test groups of said cells having a healthy physiological phenotype in which said disease phenotype associated with the intended use of said therapeutic product has been induced; performing said at least one in vitro cell-based assay on treating each of said test populations of cells with one of said different batches of therapeutic product; (b) determining the degree or pattern of modulation of each of the parameters for each of the cell populations of step (a) and calculating the respective modulation value for each of the one or more biological activities; (c) comparing the adjustment values, at least 50% of the one or more biological activities for each characteristic are modulated by each product batch, the trend of the degree of modulation in the network is consistent with a healthy state, and the modulation value determined in (b) for each of the at least 50% one or more biological activities for the test group of cells in (a1) each differs from that of the control group of cells in (a1) by at least 0.15; or at least 50% of the one or more biological activities for each characteristic are modulated by each product batch, the trend of the degree of modulation of the network is consistent with a healthy state, and the modulation value determined in (b) for each of the at least 50% one or more biological activities for the test group of cells in (a2) is each at least 15% different from that of the control group of cells in (a2); comparing said adjusted values, which indicates that said therapeutic product exerts its therapeutic effect via a physiological mechanism; It is a product containing 3. The method of claim 2, wherein the functional resilience of the product is demonstrated by the adjusted value for each of one or more biological activities of each test group of cells differing from the average of the values ​​by less than 20%.

4. 2. The method of claim 1, further comprising: (1) providing a list of features representative of pathological conditions that may result from an altered physiological state; (2) for each of the features, identifying alterations in one or more biological activities underlying the disease or pathophysiological condition, thereby identifying a network of biological activities whose degree of regulation corresponds to the pathological condition; and (3) identifying one or more parameters whose degree of regulation corresponds to the degree of regulation of the one or more biological activities underlying the beneficial effect of the tested product, and determining a trend in the degree of regulation in the network with respect to an up- or down-regulation of the activity that corresponds to the pathological condition or a healthy condition.

5. The product is a useful product, (a) a group of cells: at least one control group of cells having a healthy phenotype or at least one control group of cells in which the dysregulated phenotype targeted by said beneficial product has been suitably induced, and at least two test groups of cells taken from said control group; performing said at least one in vitro cell-based assay on treating each of said test populations of cells with one of said different batches of beneficial product; (b) determining the degree or pattern of modulation of each of the parameters for each of the cell populations of step (a) and calculating a respective modulation value for each of the one or more biological activities; (c) comparing the adjustment values, said beneficial product comprising: at least 50% of the one or more biological activities for each characteristic are modulated by each product batch, the trend of the degree of modulation in the network is consistent with a healthy state, and the modulation value determined in (b) for each of the at least 50% one or more biological activities for the test group of cells differs from that of the control group of cells by at least 0.15, respectively; comparing said adjusted values, which indicates that said therapeutic product exerts its effect in supporting the maintenance of homeostasis via a physiological mechanism of action; It is a product containing 5. The method of claim 4, wherein the functional resilience of the product is demonstrated by the adjusted value for each of one or more biological activities of each test group of cells differing from the average of the values ​​by less than 20%.

6. 6. The method of any one of claims 1 to 5, wherein the therapeutic or beneficial product comprises or consists of one or more of the following: cut or crushed plant parts, plant extracts, fractions of said extracts, microorganisms, honey, propolis, silk, wax, plant resins, plant gums, plant exudates, plant oils, plant essential oils, animal tissue lysates, or plant or animal fluids.

7. 7. The method of any one of claims 1 to 6, wherein the product is a medical device as defined in Article 2(1) paragraphs 1-3 of EU Directive 2017 / 745, or a medical device as defined in FDA Section 201(h)(1) of the U.S. Food, Drug, and Cosmetic Act, or a drug or dietary supplement.

8. The method according to any one of claims 2 to 7, wherein the parameter is a gene expression pattern.

9. The method of claim 8, wherein gene expression is assessed by transcriptome analysis.

10. 10. The method of any one of claims 2 to 9, wherein the cell-based assay is designed to simulate a situation associated with the pathological condition or to simulate a situation associated with the altered physiological state.

11. The method of any one of claims 1 to 10, further comprising analyzing the one or more batches of therapeutic or beneficial product.

12. 12. The method of claim 11, wherein the therapeutic or beneficial product is analyzed for the presence of supramolecular structures and / or for the presence of miRNA and / or for its isotopic abundance.

13. 13. The method of claim 12, wherein when isotopic abundance is analyzed, the isotopic abundance of one or more of C, H, O, N atoms is analyzed in the product and in the raw materials from which the one or more natural matrices contained therein are prepared.

14. 14. The method of any one of claims 1 to 13, further comprising testing the therapeutic or beneficial product for ready biodegradability using the OECD Biodegradation Test.

15. 1. A method for validating the physiological mechanism of action of a product in a clinical setting to treat a pathological condition or to assist in maintaining homeostasis in an altered physiological state, comprising: 1) (i) subjecting (ii) at least two groups of biological samples from patients treated with a therapeutic or beneficial product that exerts its therapeutic or beneficial effect via a physiological mechanism of action as assessed by the method of claims 1 to 14, said groups of samples being collected at different time points V1 and Vn, where n is an integer greater than 1, and (iii) at least two groups of biological samples from patients treated with a therapeutic or beneficial comparator product and / or (iii) at least two groups of biological samples from patients treated with a placebo, said groups of samples being collected at the same time points as group (i), to one or more biological assays, the readouts of which are indicative of the degree of modulation of one or more biological activities underlying said desired therapeutic or beneficial effect; and determining from the readouts: determining whether said product exerts its therapeutic or beneficial effect by regulating said network of biological activities underlying the altered physiological state associated with said pathological condition or condition; Determining from the readout that if the product is shown to regulate the network of biological activities underlying the pathophysiological or altered physiological state, the product is clinically confirmed to exert its therapeutic or beneficial effect via a physiological mechanism of action.

16. 16. The method of claim 15, further comprising: (1) providing a list of features representative of the pathological condition or the pathological condition that may result from the altered physiological condition; (2) for each of the features, identifying alterations in one or more biological activities underlying the pathological condition, thereby identifying a network of biological activities whose degree of regulation corresponds to the pathological condition; and (3) identifying one or more parameters whose degree of regulation corresponds to the degree of regulation of the one or more biological activities underlying the therapeutic effect of the tested product, and determining a trend in the degree of regulation in the network, with respect to an up- or down-regulation of the one or more biological activities that corresponds to the pathological condition or the healthy state.

17. 17. The method of claim 16, wherein the characteristics and biological activities correspond to the characteristics and biological activities selected for the same product in the method of claim 2 or 4.

18. (a) performing at least one biological assay on each of said biological samples; (b) determining the degree or pattern of modulation of each of said parameters and calculating a respective modulation value for each of said one or more biological activities for each of said samples; (c) comparing said modulated values ​​for each of said one or more biological activities, thereby providing an average modulated value for each of said activities for each of said groups of samples, or thereby providing said modulated value for each of said activities for each patient in each group of samples. The product is 17. The method of claim 16, wherein at least 50% of the one or more average biological activities or patient biological activities for each feature are modulated in V2, the trend of the degree of modulation of the network is consistent with a healthy state, and each of the modulation values ​​in V2 determined in (b) of the at least 50% of the one or more average biological activities or patient biological activities, respectively, differs from that of V1 by at least 0.15, indicating that the therapeutic or beneficial effect is exerted via a physiological mechanism of action.

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