Methods for characterizing the onset, duration, and offset of the biological effect of a therapeutic or beneficial product

Abstract

The present invention provides an innovative analytical strategy specifically designed to overcome the inherent limitations of traditional ADME (absorption, distribution, metabolism, excretion) approaches in the pharmacokinetic and pharmacodynamic characterization of natural matrix-based therapeutic or beneficial products, such as products comprising or consisting of multi-component plant extracts. The present invention provides a fundamentally different approach that is not based on the molecular profile of an administered therapeutic or beneficial product. This novel approach relies on a methodology that captures the integrated functional response of an organism to the administration of a therapeutic or beneficial product, i.e., characterizes the pharmacokinetic and pharmacodynamic behavior of a therapeutic or beneficial product through a functional target response, independent of its chemical composition. The approach is applicable to conventional drugs and beneficial products, but is particularly suited to multi-component products, such as those containing natural matrices.

Description

[Technical Field]

[0001] The present invention relates to an improved method for functionally characterizing the onset, duration, and resolution of the biological effect of a pharmaceutical product after administration to a subject. The method characterizes therapeutic or beneficial products and is particularly suited to natural matrix-based products, i.e., compositions of matter consisting entirely of natural substances and having a physiological mode of action.

[0002] Introduction The present invention relates to products in which natural matrices are appropriately processed and assembled through specific processes and methods to create a final product for therapeutic or beneficial purposes, to restore or assist an organism in restoring a healthy physiological state. All stages of the manufacturing process for such products are under the protection of the One Health principle (which recognizes the interconnectedness of human health, animal health, and environmental health), and therefore, the use of artificial forces or substances is not permitted.

[0003] Indeed, the present invention is particularly concerned with products that contain or consist of one or more natural matrices, which must maintain their natural intelligence, i.e., the imprint of the biological domain to which each component of the product belongs, thereby maintaining a network that is able to interconnect and recognize itself with other networks, whether natural or artificial, i.e., the original natural network that acquires a degree of artificiality through its interaction with artificial components. This interconnection is considered to be fundamental for rebalancing any disturbance in the network of events active in each interacting biological system. All the matrices identified present biophysical specifications that themselves represent the invention.

[0004] Each network of each natural matrix contained in the product that contributes to the formation of the final matrix network of the product of the present invention can be defined as a UVCB substance (i.e., a substance of unknown or variable composition, a complex reaction product, or a biological material) according to the REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) definition, since it is a processed product according to its self-assembly properties and therefore cannot be determined or verified based on small molecule chemistry protocols.

[0005] Each network is characterized by established connections within the matrix in the final product and within the physiological effects exerted by the product on the recipient organism. Product validation can be performed and confirmed using probabilistic models based on associations between the preservation of physiological activity profiles and descriptors of the matrix itself generated using multiple biophysical analytical systems such as spectroscopy (NIR and other techniques), mass spectrometry, and paper or X-ray crystallography (fractal measurements).

[0006] Natural matrices, such as those that form the basis of the products of interest of the present invention, contain multiple components that interact at both the structural and functional levels to produce a dynamic and global biological response. Some components may modulate the bioavailability of others, while synergistic or sequential actions on multiple molecular pathways result in pharmacodynamic effects that cannot be related to the plasma concentration of a single active ingredient.

[0007] Furthermore, it is frequently observed that biological effects persist beyond the detectability of any individual component in the bloodstream, reflecting true phenomena inherent in the complex network of actions induced by the natural matrix.

[0008] Therefore, traditional pharmacokinetic parameters such as Cmax, Tmax, AUC, and systemic clearance are not sufficiently representative or appropriate to characterize these types of products. Instead, alternative analytical strategies that focus on organism-level physiological responses and inter-network communications are needed to validate the efficacy of the present invention.

[0009] This paradigm shift is fully consistent with the aims of the biophysiological health model proposed herein, which emphasizes rebalancing the psychoneuroendocrine-immune system through endogenous activation mechanisms rather than relying on isolated exogenous substances. Traditional molecular-chemical definitions of the individual substances contained in a substance, while useful, cannot be used to validate this type of product because they do not represent its overall efficacy and quality.

[0010] The choice of matrices intended for administration must be verified according to the updated and specific current taxonomic criteria of the animal, plant and mineral kingdoms. When used in combination with natural physical phenomena, it may be necessary to verify the relationship between action and efficacy, taking into account acoustic effects (musical or other forms) and effects in the wave-particle field, including those of a quantum nature.

[0011] At the current state of the art, it is not always possible to outline a fully explained mechanism of action, but it is possible to examine the actions and reactions at the interconnections of the respective networks, which have already been examined at the biophysical level.

[0012] The present invention aims to select and provide new entities or products and systems capable of rebalancing, activating or limiting physiological functions in specific metabolic states of living organisms that are constantly in a state of continuous transformation.

[0013] The preparations thus conceived are capable of rebalancing the psychoneuroendocrine immune system, which is considered as a single system that regulates and controls all other systems.

[0014] The present invention contributes to a new cutting edge technology that goes beyond alchemy in the medical field, whose origins can be traced back to the early 16th century, and returns products and methods to the conceptual One Health goal already mentioned. The present invention proposes a new declension of artificial technologies and natural self-assembly of substances, recognizing existing rules or finding new ones to reliably construct verifiable entities, based primarily on the concept of verifying their effects and activity on other organisms. Here, living organisms are continuously transforming organisms whose physiological state must be evaluated within defined intervals, a concept now encompassed in personalized medicine. The present invention fits into the concept of science understood as a body of knowledge whose theoretical modes of action can be demonstrably verified.

[0015] The inventive activities disclosed herein are not covered by the current state of the art, therefore the entire product cycle from the end user to the relevant societal context needs to be considered under the One Health concept.

[0016] The operational paradigm within which the present invention is built is referred to herein as "Bios Physiological Health."

[0017] This paradigm aims to introduce an innovative approach to the medical technology field for the treatment and self-management of health using natural matrices alone or in combination, rebalancing the normal physiological state of various biological entities, such as humans, through the endogenous physiological effects induced by the products. The challenge is to identify, select, and assemble natural entities with newly emerging properties that can be verified by the physiological mode of action of the final product and other methods that have evolved in recent decades.

[0018] The basis of this invention is the integration of both technical and humanistic interpretations of the context. While some of the properties of each matrix component of the product may already be known, the newly discovered properties of the novel composition are unexpected.

[0019] Related is the role of determining the genetic and epigenetic aspects that determine the networks representing the natural matrix and their representation at the level of their specific isotope abundances.

[0020] To realize the biophysiological health paradigm, it is necessary to preserve as much as possible the integrity of the entire genetic inheritance of the native programming inserted into the natural intelligence of each created entity, at least as far as is known on a global scale, at each processing phase, from the selection of regenerative materials to agricultural and industrial stages and even to methods of use. It will be essential to validate matrices derived from epigenetic entities similar to reference matrices recognized as reference standards for the specific emerging characteristics of the metabolism of other organisms, such as humans. For example, one of the factors negatively influencing epigenetic differentiation is represented by different soil conditions, along with circadian, lunar, and annual variations. To preserve the properties of natural systems, the only ones that can claim physiological interconnection with the whole of creation, it is impossible to use substances derived from alchemical methods such as distillation or other synthetic or hemisynthetic processes, or products derived from genetically modified or genetically altered organisms. A new interpretation of the mystery of natural programming responsible for the evolution of organic and inorganic life is needed. The consolidation of scientific evolution in recent decades makes it possible to relocate in medicine an understanding of the origins of progress based on reductionist determinism, based on the development of alchemical methods beginning in the early 16th century, which, together with Paracelsus, marked the beginning of the current evolutionary process known as the Anthropocene.

[0021] The term Anthropocene refers to the current stage of human evolution and can be traced back to different eras. In the context of the present invention, the only significant date is 1492, which marks the end of the Humanistic / Neoplatonic period of the Early Renaissance. This period was politically represented by Cosimo the Elder and Lorenzo de' Medici, and by artists and scientists such as Piero della Francesca, Luca Pacioli, Leonardo, and Dürer. Alchemical research, which in the 16th century was seen as a possibility for human mastery over nature, has evolved to this day under the aegis of artificial intelligence, as opposed to natural intelligence, inspired by the biblical idea that "man will have dominion over all creation," with the goal of improving God's creation.

[0022] The year 1492 is symbolic, marking the deaths of Lorenzo de Medici and Piero della Francesca, the discovery of America by Columbus, the departure of humanity from its Neoplatonic path in the 15th century toward Judeo-Catholic thought, the application of the alchemical practices of Paracelsus to medicine, and the transition to the Renaissance style of the 1500s, leading to the complete and irreversible sixth mass extinction to the present day.

[0023] This invention has demonstrated the feasibility of the resulting industrial discoveries in the medical field, but is in principle adaptable to any field of production and is intended to address a shift in evolutionary paradigm. While often speaking of the protection of biodiversity, in reality it never addresses the real, sinfully obscure problem of billions of tons of exogenous, non-biodegradable, man-made substances being released into the Earth system, which are sure to irreversibly pollute the source of life. Meanwhile, a "carpe diem" (live for the moment) attitude prevails over the survival instincts of the species.

[0024] This invention is presented primarily in the context of a patent, with the hope of opening a new field of research exploring and sharing natural intelligence, rather than artificial intelligence. Artificial intelligence has little to do to stop or slow the sixth mass extinction, or to lay the foundation for alternative advances to the current one. Inventor Valentino Mercati, along with collaborator Jacopo Lucci, has chosen the path of studying nature itself, which may be useful for living systems. Over 40 years, he has developed knowledge in agricultural and industrial production systems, and has filed numerous patent applications following this operational strategy. Previously filed patents related to the methods of this invention are essentially based on the interpretation of instruments and diagnostics based on chemistry-related principles regarding the relationship between physiological effects and newly emerged properties of natural matrices and the innate defenses of each individual organism to which they are interconnected.

[0025] The analyses inspired by the approach disclosed herein were unthinkable just a few decades ago due to the technical inability to read the genetic and epigenetic information written within the cells of any living organism, as well as the role of atomic isotope differentiation in molecular self-assembly and the interconnection of any single unit / individual with the "universe." The conceptual difficulty of moving from the reassuringly controlled parameters of molecular artificiality (at least partially purified and linked by powerful thermodynamic forces that allow for strong bonds, such as covalent bonds, that act to a reduced extent on the molecules of other organisms) to natural matrices that are by definition mysterious and still considered therapeutically unreliable today is enormous.

[0026] If, after five centuries of alchemical reduction, a new interpretation of the invention is required for a new medical condition, this interpretation must unite the most distant concepts and processes in a single field of application. This, as already mentioned, is due to an ideological legacy that questions the human condition: was the human species created by an original vital intelligence, like all other species, for the purpose of life itself, insofar as it can be assumed to govern creation, or was it experimentally endowed with capacities different from other organisms, already suitably inserted into creation, in order to constitute a new ecological niche in the service of the universe?

[0027] This invention does not provide an answer to this dilemma. Humanity needs to return to the Neoplatonic thought of the early Lunensis, and the experimental duality of the human species must free itself from the spirit of domination in order to share its unique capacities within the universe with all of creation. Humanity needs to reconsider Leonardo da Vinci's warning that "Man can only produce his own offspring" and reflect on the melancholy of sages such as Piero della Francesca, Luca Pacioli, and Durer regarding the inability to understand and express the beauty of creation and decipher its mystery.

[0028] The time has come to acquire new research centers in molecular and cell biology, and it is essential to emphasize bioinformatics and new physical sciences. The inventors can now lay the foundations for research strategies and socio-economic applications in new therapeutic fields, especially in the field of complex and / or chronic degeneration, where the restoration of metabolic balance in organisms disturbed naturally or artificially will become an integral part of the already existing future.

[0029] This invention represents a new vision of medical technology that reconsiders scientific evolution from a perspective different from reductionist determinism. This alternative progress will not have to rely so much on artificial intelligence and technological advances, contrary to universal or global rules, but on the development of laws that regulate our universe and life itself. Even from a systemic perspective, driven by modern techniques of systems biology, the shift from the artificial treatment of isolated symptoms to a holistic approach that embraces the whole embodies the foundation of current progress.

[0030] In this context, the present invention addresses the urgent need to provide a validation, safety, and functional assessment of an organism's overall biological response to the administration of a therapeutic or beneficial product based solely on natural matrices that operate via mechanisms that respect the inherent complexity of biological systems.

[0031] Rather than relying on traditional metrics of pharmacokinetics designed for simple molecules, the present invention validates its efficacy through the preservation and modulation of physiological network activity across the psychoneuroendocrine and immune systems.

[0032] The biophysiological health paradigm proposed herein establishes a new scientific canon that measures the success of health interventions not by isolated chemical concentrations, but by the restored ability of an organism to maintain coherence within its internal networks and re-establish a dynamic balance with its environment.

[0033] Thus, the present invention focuses on a deeper understanding of natural intelligence and its synergistic interactions with living organisms, providing a natural alternative to current models and opening new research frontiers aimed at contributing to the survival and regeneration of life forms in times of severe ecological and biological disruption.

[0034] Background of the Invention In pharmaceutical sciences, characterization of the pharmacokinetic profile of a compound traditionally follows the ADME paradigm (absorption, distribution, metabolism, and excretion). This analytical framework has aided in the development and regulatory approval of numerous pharmacological agents, as it allows for the definition of important parameters such as half-life, peak plasma concentration (Cmax), time to peak concentration (Tmax), area under the curve (AUC), and total body clearance.

[0035] Such metrics, derived from direct quantification of active components in plasma or tissues over time, form the backbone of most pharmacokinetic and pharmacodynamic models in modern drug development. However, this approach is inherently tailored to chemically homogeneous substances, i.e., small molecules with a single, well-defined principle of action whose biological activity correlates closely with measurable systemic exposure. In these situations, the underlying assumption is that therapeutic effect is closely linked in time and quantity to the detectable presence of the drug or its metabolites in the bloodstream and target tissues.

[0036] This assumption becomes problematic when applied to multicomponent plant extracts or plant natural matrices, which are inherently structurally and functionally heterogeneous. Such extracts typically contain tens to hundreds of different chemical components. These compounds may act synergistically, antagonistically, or sequentially across multiple biological targets and pathways. These interactions can occur at the structural level, where different molecules modulate each other's absorption, bioavailability, or metabolic fate, potentially through complexation, micelle formation, or competition for transporters, and at the functional level, where combined effects result from the activation or inhibition of multiple cellular or molecular pathways, creating a pharmacodynamic footprint that cannot be attributed to any single component.

[0037] Furthermore, the pharmacokinetic behavior of such extracts is inherently difficult to elucidate. Some components are rapidly metabolized and eliminated, others accumulate in tissues, and still others may exert their biological effects indirectly by modulating endogenous signaling cascades or by generating active metabolites. Therefore, defining the "effective" concentration of a plant natural matrix based on the plasma levels of a few arbitrarily selected marker compounds is overly simplistic and ignores the holistic and interactive nature of the product. Furthermore, plant matrices act on the entire pathological condition, rather than on isolated molecular targets, and contribute to restoring the dynamic physiological balance of the organism through endogenous regulatory mechanisms, rather than through purely pharmacological inhibition or stimulation. From this perspective, following the ADME parameters of individual compounds within natural matrices, such as plant natural matrices, is inherently insufficient. The absorption, distribution, metabolism, and excretion of isolated molecules are inherently insufficient because they do not represent the overall systemic effects and physiological rebalance caused by the natural matrix as a whole.

[0038] The implications of this conceptual and methodological constraint are concrete. When traditional ADME strategies are applied to complex botanical products, they often fail to adequately explain the observed biological activity or its persistence over time. In some cases, pharmacodynamic effects are detected even when traces of the putative active ingredient cannot be measured in plasma or tissues. Such findings are frequently dismissed as "abnormal" or due to analytical shortcomings, but may reflect purely biological phenomena inherent in the multi-layered action of complex natural products.

[0039] Importantly, this disconnect between detectable pharmacokinetic exposure and observed biological effect is not unique to natural matrix-based therapeutic or beneficial products. Even for conventional drugs whose pharmacokinetic behavior can be accurately characterized, there have been cases where prolonged or sustained effects have been observed despite the absence of detectable drug in the systemic circulation. These phenomena challenge a simple correlation between drug presence and pharmacological effect and highlight the existence of mechanisms that can maintain biological activity well beyond drug clearance.

[0040] Some conventional drugs exhibit pharmacodynamic effects that persist beyond their measurable presence in plasma, clearly demonstrating that traditional ADME assessments provide only a partial understanding of their biological actions. A well-known example is the postantibiotic effect (PAE), in which bacterial growth remains inhibited even after antibiotic concentrations fall below detectable levels. This phenomenon, which has been reported for several classes of antimicrobial agents, including aminoglycosides and fluoroquinolones, has been attributed to the persistence of sublethal bacterial damage and the involvement of the host immune system, and has practical implications for dose-setting strategies that allow for extended intervals without compromising efficacy (Craig WA. Pharmacokinetic / pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin Infect Dis. 1998 Jan;26(1):1-10; quiz 11-2. doi: 10.1086 / 516284.).

[0041] Duration of effect is also observed with drugs that irreversibly bind to their biological targets. For example, aspirin permanently acetylates platelet cyclooxygenase-1 (COX-1), resulting in inhibition of thromboxane A2 synthesis for the life of the platelet, even though aspirin itself is cleared from the bloodstream within hours. This long-lasting antiplatelet effect is central to its cardioprotective use and is the basis for clinical recommendations such as discontinuing aspirin therapy one week before surgery (Vane JR et al. The mechanism of action of aspirin. Thromb Res. 2003 Jun 15;110(5-6):255-8. doi: 10.1016 / s0049-3848(03)00379-7). Similarly, proton pump inhibitors (PPIs) such as omeprazole irreversibly block the gastric proton pump and, despite a plasma half-life of approximately 1 hour, produce acid suppression lasting longer than 24 hours, an effect that is widely recognized in clinical practice (Sachs G., et al. Review article: the clinical pharmacology of proton pump inhibitors. Aliment Pharmacol Ther. 2006 Jun;23 Suppl 2:2-8. doi: 10.1111 / j.1365-2036.2006.02943.x.).

[0042] Another class of drugs with well-documented prolonged pharmacodynamic effects are corticosteroids. Acting through the regulation of gene transcription, corticosteroids such as methylprednisolone induce or suppress proteins that continue to affect biological processes long after plasma drug levels have declined. Animal studies have demonstrated that changes in gene expression can persist for up to 72 hours after drug clearance, supporting clinical observations of prolonged anti-inflammatory or immunosuppressive effects even after corticosteroid withdrawal (Barnes PJ. How corticosteroids control inflammation: Quintiles Prize Lecture 2005. Br J Pharmacol. 2006 Jun;148(3):245-54. doi: 10.1038 / sj.bjp.0706736).

[0043] Antidepressants further demonstrate a disconnect between plasma pharmacokinetics and clinical efficacy. These agents, such as SSRIs and SNRIs, demonstrate a delayed onset of therapeutic action and can exert effects that persist even after discontinuation. Neuroadaptive mechanisms, such as receptor modulation and epigenetic modifications affecting genes such as BDNF, contribute to the persistence of these effects, further strengthening the notion that the therapeutic benefit of antidepressants is due to persistent changes in neuronal function rather than the immediate presence of the drug (Nestler EJ. Epigenetic mechanisms of depression. JAMA Psychiatry. 2014 Apr;71(4):454-6. doi: 10.1001 / jamapsychiatry.2013.4291).

[0044] Finally, monoclonal antibodies and related immunotherapies provide striking examples of biological effects that persist far beyond drug clearance. The anti-CD20 monoclonal antibody rituximab depletes B cells for 6–12 months after administration, even though the antibody itself is eliminated within a few weeks. Similarly, immune checkpoint inhibitors can induce durable antitumor immune responses that persist even after treatment has ended by establishing immunological memory (Sharma P., et al. The future of immune checkpoint therapy. Science. 2015 Apr 3;348(6230):56–61. doi: 10.1126 / science.aaa8172).

[0045] Furthermore, there are several examples of well-known persistent pharmacodynamic effects in conventional drugs, demonstrating that traditional ADME provides only partial information about the drug's sustained effects in the organism. These examples, across diverse pharmacological classes, identify critical points, and the temporal relationship between plasma drug levels and biological activity is often complex and nonlinear. While extremely useful, traditional ADME profiling only captures a portion of the actual drug effect in vivo. Therefore, understanding the duration of pharmacodynamic action is crucial, especially when evaluating complex therapeutic approaches that rely on systemic reprogramming and physiological rebalance rather than transient chemical presence.

[0046] All these examples identify the “limits” of strictly correlating a substance’s duration of action with its plasma concentration or chemical half-life, and demonstrate the need for alternative analytical strategies that can capture organism-level biological responses to natural matrix-based products.

[0047] Summary of the Invention The present invention provides an innovative analytical strategy specifically designed to overcome the inherent limitations of traditional ADME (absorption, distribution, metabolism, excretion) approaches in the pharmacokinetic and pharmacodynamic characterization of natural matrix-based therapeutic or beneficial products, such as products comprising or consisting of multi-component plant extracts.

[0048] The present invention provides a fundamentally different approach that is not based on the molecular profile of the administered therapeutic or beneficial product (i.e., an approach that does not involve the standardization of any respective "markers"). The approach proposed herein relies on a methodology that captures the integrated functional response of an organism to the administration of a therapeutic or beneficial product, i.e., characterizes the pharmacokinetic and pharmacodynamic behavior of a therapeutic or beneficial product via a functional biological response, regardless of its chemical composition. This approach is applicable to conventional drugs and beneficial products, and is particularly suitable for multi-component products, such as products that include natural matrices, e.g., plant natural matrices, such as those derived from plant extracts, parts, fractions, etc.

[0049] Functional assessment of an organism's overall biological response to the administration of a natural matrix-based therapeutic or beneficial product can be achieved through systematic, organ-specific, and time-lapse transcriptome analysis, which can capture comprehensive gene expression profiles associated with both transient and sustained pharmacodynamic effects. This improvement over standard transcriptome analysis provides a direct, quantitative, and integrated measure of the onset, persistence, and elimination (in terms of transcriptome fluctuations in gene expression) of the biological activity induced by the product under study, regardless of whether its parent chemical components are detectable in plasma or other tissues. Furthermore, this improved method provides information on the time trends in the magnitude of the transcriptome fluctuations underlying the biological activity.

[0050] Importantly, the method is applicable to any therapeutic or beneficial product, in particular natural matrix-based products, i.e., products that comprise or consist of one or more natural matrices, including but not limited to plant extracts and / or parts, animal extracts and / or parts, natural sources of minerals such as eggshells, fossils, fungal extracts and / or parts, etc., where traditional ADME does not capture relevant information.

[0051] The analytical method of the present invention includes the following. 1. Collecting biological samples from target organ samples (liver, kidney, brain, etc.) and blood at multiple predetermined time points after administration obtained from healthy animal models and sham control animals (i.e., animals undergoing the same experimental intervention of the treatment group, e.g., same handling, same route and frequency of administration, same sampling time points, etc., minus the active therapeutic ingredient, e.g., animals administered a placebo or vehicle via the same route and timing of administration of the product of interest) under standardized experimental conditions receiving controlled administration of a natural matrix-based therapeutic or beneficial product or sham treatment; 2. Performing whole transcriptome analysis of collected tissues by array- or sequencing-based platforms to assess gene expression changes over time for each tissue at each time point. This transcriptome analysis thus detects changes in gene expression in key organs, providing a "biological signature" of the product's effect. 3. Differential expression analysis (DEA) to detect statistically significant changes in individual gene expression levels, and optionally, Molecular variability (MDP) to quantify the overall deviation of the transcriptome from a reference physiological state Conduct bioinformatic and statistical processing of transcriptome data to identify patterns of variation, using tools such as: 4. Detect and quantify gene expression changes associated with product exposure by comparing transcriptome profiles from product-treated animals with appropriate control groups, such as sham control animals and, optionally, untreated baseline animals.

[0052] This identifies both transient effects (immediate response) and sustained effects (long-term biological imprinting) induced by the product under test when compared to a control that does not require tracking of chemical markers. [Brief explanation of the drawings]

[0053] [Figure 1] Molecular variability in liver using all genes in the dataset: Molecular variability (MDP) is shown as a variability score for liver samples obtained from product-treated and sham-treated mice compared to samples from untreated control mice. MDP is a single value per sample that reflects the overall transcriptome variability compared to the untreated control group. All available genes were included in the analysis. Overall variability is represented by a box plot, with the white box representing the untreated control group at time point 0, the gray box representing the sham control group, and the checkerboard box representing the product-treated group. Comparing the product-treated group with the sham control group, there is a clear increase in transcriptome variability in the product-treated samples at approximately 2 hours post-administration, peaking at 6 hours and ending with essentially no difference at 48 hours post-administration. [Figure 2]Liver molecular variability using only ADME-related genes: Molecular variability (MDP), expressed as a variability score, is shown for liver samples from both EpigenAU / 11-treated and sham-treated mice compared to untreated controls. MDP is a single value per sample that reflects overall transcriptome variability compared to untreated controls. Only ADME-related genes available with current technology were included in the analysis (Dong Gui Hu et al. The Expression Profiles of ADME Genes in Human Cancers and Their Associations with Clinical Outcomes Cancers 2020, 12(11), 3369). Overall variability is represented by a box plot, with the white box representing the untreated control group at time point 0, the gray box representing the sham control group, and the grid-patterned box representing the product-treated group. Comparing the product-treated and sham-treated groups, there is a significant increase in transcriptome variability in the product-treated samples at approximately 6 hours post-dose, further distinguishing the product-treated and sham-treated mice. This fluctuation gradually decreased and essentially ceased to be different 48 hours after administration. [Figure 3]Molecular Degree of Perturbation (MDP) in the Kidney Using All Genes in the Dataset: Molecular Degree of Perturbation (MDP) is presented as a variability score for kidney samples from both product-treated and sham-treated mice compared to untreated control mice. MDP is a single value per sample that reflects the overall transcriptome variability compared to the untreated control group. The analysis included all genes available in the Clariom™ S Pico Assay for mice (Applied Biosystems, ThermoFisher Scientific). Overall variability is represented by a box plot, with the white box representing the untreated control group at time point 0, the gray box representing the sham-treated group, and the grid-patterned box representing the product-treated group. Comparing the product-treated group with the sham-treated group, there was a significant increase in transcriptome variability in the product-treated samples at approximately 2 hours post-administration, which gradually decreased and ended with essentially no difference at 48 hours post-administration. [Figure 4]Intra-kidney molecular variability using only ADME-related genes: Molecular variability (MDP) of kidney samples from both product- and sham-treated mice compared to untreated controls. MDP is a single value per sample that reflects overall transcriptome variability compared to untreated controls. Only ADME-related genes available with state-of-the-art technology were included in the analysis (Schlosser, P., Li, Y., Sekula, P. et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet 52, 167-176 (2020)). Overall variability is represented by box plots, with white boxes representing the untreated control group at time point 0, gray boxes representing the sham-treated group, and checkerboard boxes representing the product-treated group. Comparing the product-treated and sham-controlled groups, a significant increase in transcriptome variation in the product-treated samples was observed approximately 6 hours after administration, further distinguishing the product-treated and sham-controlled mice. This variation gradually decreased and was essentially unchanged by 48 hours after administration. [Figure 5] Molecular variability in blood using all genes in the dataset: molecular variability (MDP) of blood samples from both product-treated and sham-treated mice compared to untreated control mice. MDP is a single value per sample that reflects the overall transcriptome variability compared to the untreated control group. The analysis included all genes available in the Clariom™ S Pico Assay for mice (Applied Biosystems, ThermoFisher Scientific). Overall variability is represented by a box plot, with the white box representing the untreated control group at time point 0, the gray box representing the sham control group, and the checkerboard box representing the product-treated group. There is no clear difference in variability between the product-treated and sham-treated groups, except at the 18-hour time point, where samples from the sham control group showed higher transcriptome variability. [Figure 6]Molecular variability in the brain using all genes in the dataset: molecular variability (MDP) of brain samples from both product- and sham-treated mice compared to untreated control mice. MDP is a single value per sample that reflects the overall transcriptome variability compared to the untreated control group. The analysis included all genes available in the Clariom™ S Pico Assay for mice (Applied Biosystems, ThermoFisher Scientific), as defined above. Overall variability is represented by box plots, with white boxes representing the untreated control group at time point 0, gray boxes representing the sham control group, and checkerboard boxes representing the product-treated group. When comparing the product-treated group with the sham control group, there is an increase in variability 2 hours after administration, which is also observed in samples from other organs, such as the liver and kidney. These intergroup differences, which appear to decrease at 48 hours, can also be seen at 24 hours. [Figure 7] Line plot of the number of differentially expressed genes in the liver: the number of genes whose expression in the product-treated group is statistically different from that in the sham control group (adjusted p-value < 0.05). The solid gray line represents the total number of up-regulated genes at each time point (higher expression in the treated group compared to the sham control group), and the black line represents the total number of down-regulated genes. Meanwhile, the dashed line represents the number of differentially expressed genes that are ADME-related according to the same color code (gray up-regulated and black down-regulated). Indeed, there is a peak of DEGs at 6 hours post-administration, and very few DEGs at 48 hours (no evidence of significant changes in ADME-related genes). [Figure 8]Line plot of the number of differentially expressed genes in the kidney: the number of genes whose expression in the product-treated group is statistically different from that in the sham control group (adjusted p-value < 0.05). The solid gray line represents the total number of up-regulated genes at each time point (higher expression in the treated group compared to the sham control group), and the black line represents the total number of down-regulated genes. Meanwhile, the dashed line represents the number of differentially expressed genes that are ADME-related according to the same color code (gray up-regulated and black down-regulated). There is a peak of DEGs at 2 hours post-administration, and very few DEGs at 48 hours (no evidence of significant changes in ADME-related genes). [Figure 9] Line plot of the number of differentially expressed genes in blood: the number of genes whose expression in the product-treated group was statistically different from that in the sham control group (adjusted p-value <0.05). The gray line represents the total number of up-regulated genes at each time point (higher expression in the treated group compared to the sham control group), while the black line represents the total number of down-regulated genes. There was a peak in DEGs at 0 hours post-dose, which may reflect an immediate effect on the blood transcriptome. A second peak in the number of DEGs occurred at 18 hours post-dose, which is also influenced by the higher variability in the sham control samples observed in the molecular variability analysis. [Figure 10] Line plot of the number of differentially expressed genes in the brain: the number of genes whose expression in the product-treated group is statistically different from that in the sham control group (adjusted p-value < 0.05). The gray line represents the total number of up-regulated genes at each time point (higher expression in the treated group compared to the sham control group), and the black line represents the total number of down-regulated genes. There is a peak in DEGs 2 hours after administration, which follows the same pattern as observed in other organs such as the kidney and liver. The variation decreases sharply with a small number of DEGs at 6 hours after administration, with no significant variation at 48 hours. [Figure 11]Line plot of the number of differentially expressed genes from the physiological solution-treated sample versus the untreated sample at the 0-hour time point: the number of genes whose expression in the sham control group was statistically different from that in the untreated control group (adjusted p-value <0.05). The gray line represents the total number of up-regulated genes at each time point (expression in the sham control group was higher than that in the untreated control group), and the black line represents the total number of down-regulated genes. Blood shows similar peaks of variation at 0 and 18 hours. The brain showed a small number of down-regulated genes at 18 hours. In the kidney, some DEG variation occurred at 6 hours, peaked at 12 hours, and then decreased. In the liver, several genes were statistically varied between 6 and 18 hours after treatment. This figure demonstrates that sham treatment itself induces variation, therefore, transcriptome comparisons between the product-treated and sham control groups should be performed appropriately, and that circadian cycles also affect transcriptome variation, therefore, the same time points should be compared between the product-treated and sham-treated groups. [Figure 12] Figure 1 shows the area under the effect curve (AUEC). The figure shows the sum of all different biological samples tested (i.e., liver, kidney, brain, blood) (see Figures 1-10 and examples). The transcriptome variation induced by the product under test (Epigen11 / AU) over time (hours). Dots represent the sum of measurements at each time point. The x-axis represents time (hours) and the y-axis represents the total variation (total number of DEGs). The gray shading represents the AUEC.

[0054] term Unless otherwise defined herein, scientific and technical terms used in connection with the present invention shall have the meanings commonly understood by those skilled in the art. Further, unless otherwise required by context, words referred to in the singular shall include the plural and words referred to in the plural shall include the singular.

[0055] At any point in this specification or claims, the words "comprising" or "comprise(s)" may be replaced with "consisting of" or "consist(s) of."

[0056] In this application, "natural matrix" refers to a material consisting of a network represented by a wide range of components / ingredients obtained (e.g., extracted) directly from a member of the natural world or its naturally occurring site (i.e., from a natural source) 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 source. In other words, the natural source is processed only by manual, mechanical, or gravitational means, such as by dissolving in water or other naturally occurring solvents such as water or water-alcohol solutions, by flotation, extraction with water or other naturally occurring solvents, steam distillation, heating only to remove water or any other naturally occurring solvent, or extracted from air by any means, provided that the "natural matrix" excludes the member of the natural world "per se," i.e., is unprocessed. According to the present invention, the natural matrix is ​​a 100% biodegradable natural material consisting of natural components that have not been modified by the process for producing the matrix from the starting material, without the intentional addition of synthetic products along the entire process. Herein, 100% biodegradability is considered "easily biodegradable" according to the OECD biodegradability test. These characteristics ensure the maintenance of the matrix effect conferred by its constituent structural interactions (material interactions) and the presence of functional interactions (insubstantial interactions) that become apparent upon exposure of a biological system to the natural matrix. In other words, natural matrices or mixtures of natural matrices are materials obtained from entities that self-assemble in nature and are processed to preserve their natural biophysical characteristics that determine their physiological interactions with other living organisms, such as human organisms. These emerging properties may manifest 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 in the kingdoms of life, namely, Monera, Protista, 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 raw materials. Synonyms for natural matrix or one or more natural matrices herein are "composite natural system" or "natural material," as defined below.

[0057] Examples of naturally occurring parts of an organism may be represented by, for example, plant roots, leaves, bark, fruits, flowers, or sections, organs, tissues thereof.

[0058] In any part of the description the general term natural matrix can be substituted 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 material and / or extracts, extracts from animal tissues or organs, fungi and / or fungal extracts, or mixtures thereof, wherein the method of extraction does not include a denaturing step (e.g., temperature or use of a denaturing solvent).

[0059] Plants are synonymous with herbs.

[0060] The term "natural" matrix emphasizes that it retains the integrity and complexity of the component / constituent network, similar to the original natural source, due to the absence of any denaturing treatment to obtain it. Thus, natural matrices do not encompass naturally occurring compositions enriched in specific molecules that have been artificially synthesized or isolated from natural sources. Furthermore, natural matrices can be obtained solely by processes that do not involve extensive processing or chemical modification, isolation, purification, or molecular extraction.

[0061] Due to the supramolecular self-assembly of the components / constituents 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 receptors (also organized as a network) in the recipient organism. Thus, the interaction between the natural matrix and the recipient organism does not occur as a result of a one-to-one point interaction, as with a typical active pharmaceutical ingredient (API), but rather as a result of a network interaction between an "interactor" network (i.e., the matrix) and a "receiver" network (i.e., the organism to which the matrix is ​​administered).

[0062] The term natural matrix may also be substituted with the terms "composite natural system" or "natural material" in any part of the specification and claims.

[0063] "Emergent properties" as used herein and in the art is a term that defines properties of natural matrices or materials, i.e., properties that are not merely the sum of the properties of each isolated component / component of the matrix / material, but are manifested by both functional and structural interactions between all components / components of the matrix / material, which are also the result of supramolecular self-assembly of the components / components within the matrix / material itself.

[0064] Thus, "emergent properties" refer to the technical effects, e.g., therapeutic or homeostatic-adjuvant properties (i.e., beneficial effects), that the interactions and relationships between components / components of a natural matrix have on a recipient biological system. Emergent properties are properties that are not immediately apparent or even predictable based solely on the individual characteristics 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 recipient biological system network in dynamic and complex ways. Emergent properties have been widely discussed in the art in various scientific and systems-oriented fields, such as physics, chemistry, biology, and complex systems theory.

[0065] Therefore, the emerging properties cannot be predicted a priori based on the qualitative and quantitative information of each component of the composition or matrix, and therefore cannot be attributed to one or more specific APIs. Thus, although a multi-drug composition may exhibit unpredictable synergistic effects, the properties of the composition remain attributable to the specific APIs and their amounts.

[0066] In the case of emergent properties characteristic of the native matrix, the discovered emergent properties cannot be attributed to a specific API and are maintained in different batches of a particular matrix or in a particular mixture of matrices despite their qualitative and quantitative composition varying from batch to batch (see below on functional resilience).

[0067] "Synthetic" herein has its conventionally accepted meaning in chemistry. 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 through artificial synthesis, i.e., laboratory chemical reactions that typically react simple chemicals to create more complex chemicals, often through processes that use pathways, temperature conditions, pressure conditions, energy sources, and / or catalysts different from those used by living organisms. Examples: Synthetic substances or materials include plastics, pharmaceuticals, and many industrial chemicals. For example, nylon is a synthetic polymer made by chemical synthesis, and aspirin is a synthetic drug produced by specific chemical reactions.

[0068] As used herein, "functional resilience" (also referred to as "redundancy") is intended as the therapeutic or beneficial (homeostatic adjuvant) persistence of a therapeutic or beneficial product comprising or consisting of one or more natural matrices, and refers to the maintenance of the therapeutic or beneficial properties of different batches of a particular product comprising or consisting of one or more natural matrices, despite the (unavoidable) batch-to-batch qualitative and quantitative compositional differences inherently present in a product comprising or consisting of one or more natural matrices. As is 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 and quantitative composition. This commonly occurs at the molecular level, which is typical of individual variations between organisms of the same species.

[0069] A "healthy physiological state" refers to a state in which the state of an organism's body, organ, device, system, or bodily region and its associated internal processes are functioning within optimal and normal parameters for that individual, i.e., a state in which homeostasis is oriented. A healthy physiological state, in the context of one or more biological activities known to contribute to the hallmarks of a given disease or pathological condition or altered physiological state, refers to a state in which 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. Given known alterations in one or more biological activities consistent with a pathological or pre-pathological state, a healthy physiological state can be considered to be represented by the opposite alterations in each of the activities. This term takes into account hallmarks of a particular disease, which are distinctive functions or characteristics typically observed in individuals affected by that disease. These hallmarks may include specific cellular behaviors, molecular pathways, canonical pathways, or physiological responses that play a key role in the development or progression of the disease.

[0070] 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 hallmark of that disease or pathological condition are modulated in a direction consistent with a non-pathological / unaltered state, in other words, in a direction opposite to the pathological / altered state.

[0071] Therefore, a healthy physiological state according to the present invention also indicates a direction of regulation of one or more biological activities that are known hallmarks of a pathological state in homeostasis, i.e., before the manifestation of a pathological state, in other words, a homeostatic direction of regulation of one or more biological activities that are attributed to a specific system, region, apparatus or organ of a healthy organism.

[0072] "Altered physiological state" and "altered 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.

[0073] 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.

[0074] Homeostasis (alterations): Homeostasis refers to the body's ability to maintain a stable internal environment despite external or pathological 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. When these regulatory mechanisms fail to maintain balance and deviations from the body's normal set points occur, alterations (disturbances) in homeostasis occur. These deviations can be temporary or chronic and may involve compensatory mechanisms to restore balance.

[0075] In summary, changes in physiological state indicate observable changes in the body's normal functioning, while changes in homeostasis refer to a fundamental disruption of the body's regulatory mechanisms that maintain internal stability.

[0076] Alterations in homeostasis underlie changes in physiological states as a disruption of homeostatic mechanisms can lead to physiological imbalances and the development of disease or dysfunction. Products that aid in the maintenance of homeostasis are those that assist the body in restoring stability to its internal environment when altered.

[0077] As used herein, the term "disease hallmark" or pathological or medical condition has the meaning conventionally used in the art. Disease hallmarks are known to be indicators that can indicate the progression or control of a particular disease or pathological or pre-pathological condition, and together they typically represent a general pathological condition associated with a particular pathology. These hallmarks (also called "key indicators") are typically a set of characteristics or patterns that physicians monitor over time to track the onset, progression, or regression of a particular disease. In essence, disease hallmarks are defining features or characteristics whose alterations indicate a given pre-medical or medical condition and aid in its identification, diagnosis, monitoring, and understanding. As an example, for neurodegenerative diseases (NDDs), at least eight hallmarks of NDDs are known in the art: (pathological protein) aggregation, synapse and neuronal network (dysfunction), (abnormal) proteostasis, cytoskeleton (abnormal), energy homeostasis (alterations), DNA and RNA (deficiencies), inflammation (increase), and neuronal cell death (increase). As an example, in cancer research, cancer hallmarks are a set of characteristic features commonly found in cancer cells. These hallmarks include (sustaining) proliferative signaling, (evading) growth suppressors, (resisting) cell death, (enabling) replicative immortalization, (inducing) angiogenesis, and (activating) invasion and metastasis. Disease hallmarks, parameters (e.g., biomarkers) associated with the hallmarks, one or more biological activities associated with the hallmarks, etc., provide a framework for studying a disease or pathological or medical condition using an integrated / holistic approach.

[0078] Hallmarks of an altered physiological state typically include observable changes in various aspects of bodily function, which may be manifested through symptoms, signs, or test findings.

[0079] Altered physiological states typically reflect a disruption of the body's homeostatic mechanisms, resulting in deviations from normal physiological parameters. These imbalances may include alterations in thermoregulation, fluid and electrolyte balance, acid-base balance, glucose metabolism, or other regulatory processes.

[0080] Overall, the hallmarks of altered physiological status provide important clues for healthcare providers to identify underlying causes, determine severity, and guide appropriate interventions to restore normal function and promote recovery.

[0081] Native natural intelligence represents the inherent capacity of natural matrices to store and transmit the biological and physicochemical information necessary to interact and integrate with other biological networks, using a logic that is already known and therefore endogenous to the organism that receives them. This intelligence is an expression of natural autopoiesis, i.e., the ability to self-organize and adapt to environmental stimuli without artificial intervention, transmitting messages according to a punctate logic and via intermediaries that are unknown to the organism that receives them and therefore exogenous to it.

[0082] The expression physiological interconnection, defined as "endogenous" physiological interconnection, describes the ability of a natural matrix to harmoniously and functionally interact with a recipient biological system, stimulating an internal response and restoring a balanced physiological state, based on the fact that both the natural matrix and the recipient biological system belong to the biological domain (endogenous). This interaction is based on natural dynamics without artificial intervention, represents a reciprocal dialogue between the matrix and the organism, and promotes self-regulation and physiological recovery.

[0083] Self-assembled entities in nature define complex systems composed of multiple components that spontaneously organize into functional structures through chemical-physical interactions that occur in natural environments and conditions. These systems, found in living organisms or natural matrices, exhibit expressive properties that result from their dynamic interactions and cannot be artificially replicated.

[0084] When reference is made to a subject in need of beneficial or therapeutic treatment, the description relates to a human suffering from or at risk of developing a pathological condition.

[0085] ADME, as used herein in its commonly used technical sense, is an acronym that stands for absorption, distribution, metabolism, and excretion, and describes the fundamental pharmacokinetic processes that determine the fate of a drug or other substance in the body after administration. This encompasses a substance's entry into the bloodstream (absorption), its diffusion throughout body fluids and tissues (distribution), its chemical transformation by enzymatic systems, primarily in the liver (metabolism), and its elimination from the body, typically via the renal or biliary pathways (excretion). ADME studies are important in pharmaceutical development to understand a substance's bioavailability, pharmacokinetic profile, efficacy, toxicity, and appropriate administration strategies.

[0086] Product A or EpigenAU / 11 according to the present invention is a natural matrix-based therapeutic product as defined in the Examples section. Traditional ADME pharmacokinetic parameter definitions:

[0087] In the context of **ADME** (absorption, distribution, metabolism and excretion), the following pharmacokinetic parameters are commonly used to describe how a drug behaves in the body:

[0088] Cmax (maximum concentration) definition: The highest concentration of a drug in the bloodstream after administration. Cmax represents the peak exposure of a drug and is often related to the strength of its pharmacological effect or potential toxicity.

[0089] Definition of Tmax (time to maximum concentration): The time it takes to reach Cmax after drug administration. Tmax reflects the rate of absorption; a shorter Tmax means that the drug is absorbed more rapidly.

[0090] AUC (area under the curve) definition: The area under the plasma concentration-time curve, which represents total drug exposure over time, used to determine the extent of absorption and bioavailability. The higher the AUC, the more drug is entering the systemic circulation.

[0091] MRT (Mean Residence Time): Reflects the average duration a molecule remains in a system.

[0092] When preceded by the letter f, the above acronyms refer to equivalent information regarding functionality, i.e., transcriptome variations induced by the tested product.

[0093] Clearance (CL) definition: The volume of plasma from which a drug is completely removed per unit time (e.g., mL / min or L / hr), reflecting the efficiency of drug elimination by metabolism and excretion. It is important for calculating maintenance doses.

[0094] According to the present invention, the respective ADME equivalent parameters Vmax / transcriptome peak, functional Tmax, AUEC (area under the effect curve), functional half-life (or mean residence time), and elimination time provide similar information that can be used for the same purpose. In this specification and claims, sham control animals follow the official definition in the scientific literature: animals that undergo a control intervention that mimics all aspects of the experimental procedure, such as anesthesia, surgical exposure, and handling, except for the key therapeutic or experimental product / component being tested. The sham group serves as a control to isolate the specific effects of an experimental treatment from general treatment effects. In summary, a sham procedure in animal testing is a control method designed to replicate all aspects of an experimental intervention, excluding the active ingredient / product, in order to accurately determine the specific effects of the intervention.

[0095] These definitions are intended as a basis for rephrasing or clarifying the claims or descriptions.

[0096] As mentioned above, the present invention relates to an improved transcriptome-based method for functionally characterizing the onset, duration, and offset of a biological effect of a pharmaceutical product after administration to a subject. The method characterizes therapeutic or beneficial products and is particularly suited to natural matrix-based products, i.e., compositions of matter consisting entirely of natural substances that exert a therapeutic or beneficial effect and have a physiological mode of action.

[0097] The traditional ADME (absorption, distribution, metabolism, excretion) framework, based on reductionism and a molecule-centric strategy, assumes that the biological effects of pharmacological agents can be predicted, monitored, and understood by isolating one or more chemical components, quantifying them in biological matrices (plasma, tissues), and correlating their concentration-time profiles with pharmacodynamic effects. Techniques such as HPLC, GC-MS, and LC-MS / MS are typically used to define standard pharmacokinetic parameters, such as half-life, Cmax, Tmax, AUC, bioavailability, clearance, and volume of distribution. While this approach is highly effective for chemically homogeneous drugs containing a single active ingredient, as discussed above, it becomes fundamentally inappropriate, if not misleading, when applied to natural matrix-based products, such as multi-component plant extracts or products containing them.

[0098] Natural matrices, such as plant extracts, are inherently variable and complex mixtures of structurally diverse secondary metabolites (e.g., polyphenols, flavonoids, terpenoids, alkaloids, glycosides, etc.) with widely differing physicochemical properties. Therefore, as is well known in the art, a single chemical "fingerprint" cannot adequately represent the entire extract or its biological effects. Many trace or labile constituents may lack validated analytical standards, making their accurate quantification impossible, and the presence of matrix effects (interference from other components of the extract) frequently complicates or prevents accurate measurement, even when sophisticated analytical platforms are used.

[0099] Furthermore, conventional analytical techniques typically focus on a preselected subset of "marker" compounds chosen for their chemical abundance or ease of detection, rather than their biological relevance, which introduces inherent biases that can lead to incomplete or distorted interpretations of the extract's true pharmacokinetic and pharmacodynamic profiles.

[0100] A fundamental limitation of molecular approaches (e.g., ADME) is their inability to capture the functional and expression characteristics of natural matrix-based products. As is already known, natural matrices, such as plant extracts, do not act as the simple sum of their individual components but exert their biological effects through multi-layer interactions, including synergistic interactions (where multiple molecules enhance each other's bioavailability, stability, or pharmacodynamic impact), antagonistic interactions (where certain components modulate or limit the activity of others, contributing to a balanced pharmacological profile), and sequential and complementary mechanisms (where different compounds act on distinct but interconnected molecular targets or pathways).

[0101] These multi-layer interactions usually result in pharmacodynamic outcomes that cannot be predicted based on the concentration of any single component or detectable metabolite. Furthermore, some bioactive effects may be mediated by secondary metabolites generated in vivo via metabolic transformation, microbiota activity, or conjugation processes, further complicating any attempt at direct analytical correlation. Another significant limitation of conventional approaches is the temporal dissociation between the pharmacokinetics of detectable components and the duration of biological effects. Often, the therapeutic or physiological impact of natural matrix-based products persists far beyond the time frame during which the parent compounds or their known metabolites are detectable in plasma or tissues. This can be attributed to tissue accumulation of specific compounds or their derivatives with delayed release or activity. Epigenetic modifications, transcriptional reprogramming, or other long-term biological adaptations are caused by short-term exposure and modulation of endogenous regulatory systems (e.g., immune, endocrine, or metabolic pathways) that remain altered even after the compound is metabolized and eliminated.

[0102] Under such circumstances, traditional ADME metrics cannot provide reliable estimates of the extract's biological persistence, therapeutic efficacy, or safety profile.

[0103] The analytical and conceptual challenges described above are not hypothetical but are routinely encountered in the pharmacokinetic characterization of many natural matrices. Typical examples include plant-based natural matrices containing both rapidly eliminated and persistent components, such as flavonoids and terpenes, which often have significantly different half-lives and result in complex, asynchronous concentration-time profiles. For example, several studies have shown that closely related natural compounds within the same plant matrix, despite their structural similarity, can exhibit significantly different pharmacokinetic properties, particularly their elimination half-lives. Furthermore, naturally occurring metabolites can often be metabolized to endogenous metabolites, further reinforcing the idea that tracking a single molecule does not adequately represent the pharmacokinetic behavior of the entire plant matrix. Notable examples include flavonoids (e.g., anthocyanins vs. flavonols). Polyphenol-rich extracts, anthocyanins, and catechins (e.g., in berries or green tea) typically exhibit T1 / 2s of 2–3 hours, whereas structurally similar flavonols, such as quercetin or myricetin, can persist for 10–20 hours (Hollman, PCH (2004). Absorption, Bioavailability, and Metabolism of Flavonoids. Pharmaceutical Biology, 42(sup1), 74–83. https: / / doi.org / 10.3109 / 13880200490893492); triterpenoid saponins (Saikosaponin A vs. D). Although saikosaponins A and D differ only slightly in glycosidic position, comparative pharmacokinetic studies have shown that saikosaponins A and D have elimination half-lives of approximately 4.5 hours and 8.2 hours, respectively, in rats, with the D form consistently exhibiting a longer duration of action (Xu L., et al. Analysis of saikosaponins in rat plasma by anionic adducts-based liquid chromatography tandem mass spectrometry method. Biomed Chromatogr. 2012 Jul;26(7):808-15. doi: 10.1002 / bmc.1734); and phthalides (Z-ligustilide vs. Levistilide A). The half-lives of Z-ligustilide and its closely related analog, Levistilide A, are significantly different.That is, approximately 14 hours for Z-ligustilide and approximately 24 hours for revistilide A (Liu, X et al. Comparative Pharmacokinetics Research of 13 Bioactive Components of Jieyu Pills in Control and Attention Deficit Hyperactivity Disorder Model Rats Based on UPLC-Orbitrap Fusion MS. Molecules. 2024 Mar 10;29(6):1230. doi: 10.3390 / molecules29061230).

[0104] Furthermore, some plant-based natural matrices undergo metabolic transformation by liver enzymes or gut microbiota to generate bioactive metabolites in vivo, producing compounds with greater stability, greater bioactivity, or different pharmacological properties compared to the parent components, resulting in biological effects that persist long after the measurable compounds have cleared from plasma or tissues. Furthermore, there is an objective lack of purified reference standards, and indeed, some minor constituents of natural matrices with potential biological relevance are either commercially unavailable or analytically intractable, precluding their quantification and pharmacokinetic profiling.

[0105] In such situations, the absence of measurable plasma levels of one or more "marker" compounds does not necessarily indicate the absence of a pharmacodynamic effect. Under such conditions, applying traditional ADME parameters to natural matrices will provide a misleading and certainly incomplete picture of the pharmacodynamic picture. Practical examples support these challenges. Plant extracts often contain compounds with radically different half-lives, producing asynchronous concentration profiles.

[0106] Finally, variations in plant sources, environmental factors, harvesting methods, and processing conditions result in substantial batch-to-batch differences in chemical composition, thus making ADME analysis inappropriate for natural matrix-based therapeutic or beneficial products from the outset. Therefore, the idea of ​​fixing molecular fingerprints to define pharmacokinetics or predict clinical outcome in batches of this type of product is fundamentally flawed.

[0107] Surprisingly, as is directly apparent from Figure 11, the inventors discovered that in the experimental design of the present method, samples from untreated animals are not suitable as controls for analyzing transcriptome variation because sham treatment alters the transcriptome. Therefore, the transcriptome data from samples from treated animals should and must be compared with the transcriptome data of corresponding sham control animals. Indeed, the figure shows that, compared to untreated animals, sham control animals exhibit transcriptome changes after sham administration that are not present in untreated controls, and that transcriptome changes that are also influenced by the circadian cycle of the animals' lives are also observed with respect to untreated controls at t0. To perform reliable and informative differential expression analysis, differentially expressed genes (DEGs) induced by the experimental design itself cannot be excluded in treated products compared to untreated products. In addition, comparisons between treated products and sham-treated groups must be performed at more than one time point to define the onset and disappearance of variation. Multiple time points allow for providing further relevant information such as precise clearance times, duration of fluctuations and ADME equivalent parameters as shown in the table below.

[0108] Furthermore, the inventors have also found that for those organs for which ADME-related genes have been disclosed in the art (e.g., liver and kidney), transcriptome analysis of DEG ADME-related genes is not sufficient to demonstrate the desired functional characteristics of the onset, duration, and offset of the biological effect of the analyzed therapeutic or beneficial product (preferably a natural matrix-based product) provided by the methods of the present invention. Indeed, as is evident from Figures 7 and 8, an analysis focusing only on ADME-related genes provides very useful information, such as information regarding the secretion time when kidney ADME-related genes are analyzed, but fails to provide a line plot implying the associated up- or down-regulation of genes induced by administration of the therapeutic or beneficial product under test.

[0109] The detailed description and examples in this application show that expression analysis of ADME-related genes (where available in the art) can provide additional relevant information.

[0110] The object of the present invention is to 1. A method for functionally characterizing the onset, duration and offset of the biological effect of a therapeutic or beneficial product, preferably comprising one or more natural matrices, after administration to a living organism, said method comprising: (a) Extracting RNA from biological samples from different tissues obtained at multiple time points after administering a natural matrix-based product to an organism, i.e., treated samples, and extracting RNA from corresponding biological samples from sham control organisms (e.g., with the same administration and handling protocol as the vehicle / placebo), i.e., sham control samples, obtained at the same time points; (b) performing transcriptome analysis, including gene expression profiling, on RNA extracted from treated and sham control samples; and identifying transcriptome variations based on differentially expressed genes (DEGs) for treated samples at each time point compared to the corresponding sham control samples; (c) defining the onset, duration and offset profile of the biological activity induced by administration of the product; where: The time point of expression corresponds to the time point at which significant transcriptomic variation was first observed in at least one of the treated samples relative to the corresponding sham sample; The time point of disappearance corresponds to the time point at which significant transcriptomic variation is last observed in treated samples compared to corresponding sham samples; The duration corresponds to the time interval from the time point of onset to the time point of disappearance.

[0111] Furthermore, the method of the present invention makes it possible to provide the ADME equivalent functional parameters shown in the table below. [Table 1]

[0112] The specific Pmax of a product for a particular organ defines the maximum number of DEGs observed in treated samples obtained from a particular organ, and the calculation of Pmax can be extended to the whole-body level.

[0113] The product fTmax for a particular organ is the time point of the highest significant transcriptome variation peak observed in the treated samples from that particular organ, and the global product fTmax corresponds to the time point at which the maximum total number of significant transcriptome variations was observed among all treated samples from all organs. The product AUEC is the numerical integral of the DEG or MDP area over time.

[0114] The pharmacokinetic concept of half-life, which indicates the time required for a concentration to halve, has no direct equivalent in transcriptome responses. This is because transcriptome responses typically do not decay linearly (in Figure 12, it can be seen that an initial peak is followed by a second, less intense peak). Therefore, a more appropriate analogy is the "functional mean residence time (fMRT)," which reflects the average duration that a transcriptome fluctuation remains in the system. In the art, MRT is calculated by the AUMC / AUC ratio according to the formula provided in the detailed description, and the disappearance time is the first time point at which there is no further significant transcriptome fluctuation (end of fluctuation). According to the present invention, fMRT is calculated as the AUMC / AUEC ratio.

[0115] The organ-specific clearance time fTres corresponds to the time point at which the basal transcriptome returns to normal after administration of a therapeutic or beneficial product (the end of transcriptional fluctuation is defined as a transcriptome fluctuation of 5% Pmax or less for all tissue samples except blood, and the end of transcriptome fluctuation is defined as a transcriptome fluctuation of 12% Pmax or less, preferably 5% Pmax or less). As can be expected, transcriptome fluctuations in blood samples are more volatile because blood flow is more susceptible to fluctuations due to blood glucose peaks immediately after meals, day-night changes, etc. Considering a systemic perspective, fTres is the first time point at which all tested organs reach a transcriptome fluctuation of 5% Pmax or less for excluded blood, organ-specifically, and the end of transcriptome fluctuation is defined as a transcriptome fluctuation of 12% Pmax or less, preferably 5% Pmax or less.

[0116] The references to organ-specific or whole organ in the above tables mean that, depending on the information desired, the ADME-like functional parameters of the present invention can be calculated for samples obtained from a particular treated organ of interest, or from a whole-body perspective ("whole organ"), using global DEG information obtained from all samples tested, i.e., from the overall data for each organ sample. Details regarding the calculation of each of the above parameters are provided in the detailed description and examples.

[0117] As can be seen from the figure, the method of the present invention provides time-specific line plots showing the onset, duration, and offset time frames of the biological effect (represented by measurable transcriptome variations) induced by administration of the tested product. The organ-specific data can then be compared and refined to provide information at the organism level. In other words, the method of the present invention provides a biological persistence and clearance profile of a product based on organism-level functional transcriptome responses, without relying on direct quantitation of the product's individual chemical components. The organism-level profile provided by the method of the present invention is obtained by concomitant analysis of transcriptome variations in a biorepresentative selection of tissues. Furthermore, analysis of transcriptome variation data obtained from either biorepresentative samples or selected organ tissue samples according to the present invention can provide ADME-equivalent functional parameters summarized in the table above.

[0118] The methods of the present invention advantageously allow developers and users to understand the actual duration of activity and presence of a therapeutic or beneficial product from the moment of administration to a subject (even when the original compound is, in principle, no longer measurable in plasma, data that cannot be obtained by classical ADME evaluation), and to obtain a reliable indication of any potential persistent or delayed toxic effects. In other words, the methods of the present invention allow providing a reliable measure of the effect of a therapeutic or beneficial product when administered to a subject, even when classical ADME evaluation is not technically and economically feasible.

[0119] In fact, it should be noted that ADME evaluation of products where the active compound cannot be determined, such as products based on natural matrices, requires accurate determination of each and every compound in the product, and due to the fact that the "active compound" cannot be determined, ADME evaluation is required for each and every compound. As with natural matrices such as plant extracts, there are, on average, hundreds or even thousands of compounds in the matrix, making the cost of full ADME impractical. In fact, market analysis estimates that preclinical pharmacology studies, including ADME / PK profiling, account for approximately 10% of the total preclinical development budget for new drugs. Given that the average total preclinical investment is approximately $6.2 million, this means that the average ADME / PK cost per candidate compound is approximately $600,000.

[0120] In practice, a complete DMPK preclinical package (including method development, plasma stability, protein binding, and multispecies PK testing) typically costs between $150,000 and $235,000. A single in vivo ADME study, such as a radiolabeled mass balance experiment in one animal model, is typically priced in the low tens of thousands per species range. These figures apply to both the United States and Europe, where pharmaceutical companies often outsource to established CROs for regulatory compliance and improved data quality. Therefore, ADME assessment is clearly not a technically or economically feasible tool for such products. Furthermore, as already mentioned in the Background Art section, some drugs have been reported to exhibit relevant effects even after excretion. Therefore, ADME lacks essential information for these types of drugs, i.e., it does not provide developers and users with information about the latency of therapeutic or beneficial products.

[0121] Conversely, the method of the present invention provides relevant information, namely the latency of the product, which exceeds the excretion time of the analyzed product, and is advantageously suitable for all therapeutic products, including those based on natural matrices; it provides a functional and global measure of the biological response to the product under test, independent of the quantification of chemical markers (and therefore independent of the batch-to-batch variations typical of these products); it allows the identification of both transient and persistent biological effects (persistent effects are generally indicated by the persistence of transcriptomic variations in one or more organ samples when the transcriptomic variations induced by the product have disappeared in most other organ samples), and molecular variations related to safety, providing an objective characterization of the pharmacokinetic and pharmacodynamic profiles of natural matrix-based products.

[0122] In summary, the present invention offers significant advantages over traditional ADME for a number of reasons. It allows for monitoring the actual response of an organism, rather than tracking individual molecules. The methods of the present invention also allow for the identification of temporal evidence and sustained effects of useful compound-host interactions, even when individual components of a natural matrix-based therapeutic or beneficial product cannot be measured in plasma. Thus, gene expression remains altered beyond the expected duration of action, allowing for rapid observation of such changes without any "chemical signature" of the compound.

[0123] The method of the present invention also provides an improved safety assessment method: the slow return to baseline indicates robustness (reproducibility) between batches, with transient effects rather than evidence of constant molecular level variation. This is because by detecting the "functional" effects of the extract in vivo, transcriptomics reduces the impact of chemical variation between different lots.

[0124] Furthermore, the present invention enables more powerful and useful risk management, as assessing the actual biological (transcriptomic) response provides more reliable parameters for assessing safety and efficacy, especially in the presence of lot-to-lot variability.

[0125] The present invention provides a method for functionally characterizing the onset, duration, and offset of the biological effect of a therapeutic or beneficial product after administration to a subject, the method comprising: (a) extracting RNA from biological samples of different tissues obtained from living organisms at multiple time points after administration of the natural matrix-based product (i.e., treatment samples), and from corresponding biological samples (i.e., sham control samples) obtained at the same time points from sham control organisms (i.e., administered an appropriate placebo or vehicle according to the same route of administration, amount, etc., as used for the tested product); (b) performing transcriptome analysis, including gene expression profiling, on RNA extracted from treated and sham control samples; and identifying transcriptome variations based on differentially expressed genes (DEGs) for treated samples at each time point compared to the corresponding sham control samples; (c) defining the onset, duration, persistence, and temporal disappearance of transcriptomic variations for each of the different tissues based on each transcriptomic variation identified in b) for samples of the same tissue; The method provides an onset profile, duration profile, and offset profile of the biological effect of a product without relying on direct quantitation of the individual chemical components of the product.

[0126] In other words, rather than directly measuring specific chemical components of the product, the method of the present invention makes it possible to functionally characterize (i.e., understand the temporal dynamics of) the biological effect of natural matrix-based products (complex multi-component) of therapeutic or beneficial products after administration to a living subject by analyzing RNA expression profiles (transcriptomics) from tissue samples over time, comparing samples from treated and sham control groups, and drawing conclusions about the onset, duration, and offset of the effect based on transcriptomic changes.

[0127] Thus, the controls of the present invention are derived from sham-administered organisms sampled at the same time as the product-administered organisms. The organisms are animals, preferably mammals commonly used in preclinical trials, excluding humans.

[0128] It is clear that the sham and product treated animals and the untreated control animals are of the same species with the same genotype.

[0129] According to the method of the present invention, the appropriate control group for observing transcriptome perturbations induced by product administration is a sham-treated control, not an untreated control. The inventors have found that transcriptome perturbations caused by circadian fluctuations and / or treatment-related stress (e.g., injection) exhibit confounding effects when comparing treated and untreated groups (see Figure 11). Therefore, analysis of transcriptome perturbations should be performed on product-treated and sham-treated samples of the same tissue collected at the same time point to provide more reliable and "background-free" data.

[0130] According to the method of the present invention, the biological sample is a sample from different tissue(s) selected from among those most representative of the response of the whole organism to the administration of the product, and in one non-limiting embodiment of the present invention, the tissue can be selected from one or more of blood, liver, kidney, brain, hypothalamus, lung, heart, spleen, testis, stomach, intestine, gallbladder, pancreas, various glands, ovary, muscle tissue (for example, the injection site when the administration is by intramuscular injection). In a preferred embodiment, the tissue includes at least blood, liver, kidney, brain, lung and heart tissue.

[0131] Depending on the putative therapeutic or beneficial effect of the product being tested and the class of patients for which the product is intended, one skilled in the art can easily select the most appropriate representative tissue for carrying out the method of the present invention. The product tested in the examples and reported in the figures is an anti-cancer product (also defined herein as EpigenAU / 11, or Product A). When referring to the Clariom™ S Pico Assay, Mouse (Applied Biosystems, ThermoFisher Scientific) and its gene sets, the gene sets are those annotated by the manufacturer on July 30, 2018.

[0132] It is therefore clear that the method of the present invention provides relevant information regarding the biological effects of the analyzed product, which can encompass local effects in tissues from multiple organs and / or systems of the treated subject, thus providing a much more comprehensive "organism-level" profile of the product's biological effect over time.

[0133] Preferably, the sample is a tissue that has been treated with an RNA stabilizing reagent prior to storage to preserve RNA integrity. Any suitable commercially available product can be used, such as RNAlater® by Thermo Fisher Scientific, NA / RNA Shield™ (Zymo Research), Allprotect Tissue Reagent (QIAGEN), or RNAprotect® Tissue Reagent (QIAGEN), according to the manufacturer's instructions.

[0134] According to the present invention, the multiple time points are multiple post-administration time points for tracking the effect / impact over time, and include TO representing the time point immediately after administration of the product under test, and at least one, preferably at least two, and even more preferably at least three or more different Tn, where n represents the time after administration of the product and is 1 or more, preferably 2 or more, and one of the Tn is equal to or greater than the time point of elimination.

[0135] Preferably, according to the present invention, the Tn time points include T2 and T48. By way of non-limiting example, the Tn time points include at least three of T2, T6, T18, T24 and T48.

[0136] If the time of disappearance, i.e., the time at which significant transcriptomic variation is no longer detectable, is 48 hours after TO, then Tn will include time points where n is greater than 48, such as 72.

[0137] As described above, according to the present invention, samples are collected from two groups of animals: treated animals, i.e., animals treated with the product under test, and sham control animals, i.e., animals treated identically to the treatment group, but the administered product is, for example, a placebo, selected depending on the route of administration and formulation of the product under test. For each group of animals, tissue samples are collected from each organ or tissue at the same time points, and transcriptome comparisons are made between the product-treated samples and the corresponding sham control samples at each time point.

[0138] If T48 is found not to be sufficient to eliminate the transcriptome variations induced by administration of the tested product, the method of the invention can be repeated at additional time points where n is >48.

[0139] When transcriptome changes have disappeared in most tested organs or tissues, persistence of changes in one organ would, in principle, indicate a persistence of the tested product's effect in that organ or even the occurrence of tested product-induced damage in that organ.

[0140] Thus, in contrast to ADME, the method of the present invention not only provides a global framework for the effects of a therapeutic or beneficial product, but also makes it possible to identify potential adverse effects of the tested product in specific organs or tissues.

[0141] RNA can be extracted from the sample by any commonly known method or using any suitable commercially available kit.

[0142] Transcriptome profiling can be performed according to procedures commonly used in the art, such as RNA sequencing or hybridization to pre-designed gene expression microarrays. For example, cDNA can be obtained from extracted RNA (total RNA can be extracted using any suitable commercially available kit) and sequenced, or cDNA can be labeled and hybridized on commercially available or pre-prepared microarrays using pre-designed probes, or profiling can be performed by directly hybridizing target RNA to fluorescent barcodes, such as NanoString nCounter, or by qPCR-based arrays (e.g., TaqMan, Biomark Fluidigm), etc.

[0143] In one aspect of the present invention, transcriptome data are processed to ensure accuracy and comparability between samples. Data processing typically involves normalization to correct for technical variations and background noise. Preferably, a normalization method such as signal space transformation-robust multiarray averaging (SST-RMA) is used (https: / / assets.thermofisher.com / TFS-Assets / GSD / Reference-Materials / white-paper-microarray%20-normalization-using-sst-with-probe-guanine-cytosine-count-correction.pdf). However, depending on the platform and experimental setup, alternative normalization techniques known in the art, such as quantile normalization or cyclic lobe normalization, can also be used. A suitable tool for such processing is, for example, the Transcriptomics Analysis Console software (ThermoFisher Scientific), which applies SST-RMA normalization. Alternatively, one skilled in the art can utilize Affymetrix Power Tools (APT), the R / Bioconductor environment (e.g., the "oligo" or "affine" packages), a custom preprocessing pipeline that replicates SST adjustment followed by RMA normalization, or other equivalent software that can perform signal space transformation correction in combination with robust multi-array averaging.

[0144] After normalization, differential expression (DE) analysis is performed to identify genes that exhibit statistically significant changes in expression between groups. In a preferred embodiment, DE analysis is performed using a linear model, such as that implemented in Bioconductor's Limma package. Other suitable methods, including but not limited to DESeq2 or edgeR, may alternatively be utilized. The analysis involves using a linear model specifically designed for microarrays to estimate whether differences in gene expression between treatment groups (e.g., treated versus sham or untreated controls) are statistically significant (Phipson, B, Lee, S, Majewski, IJ, Alexander, WS, and Smyth, GK (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics 10(2), 946-963).

[0145] Statistical adjustments are also applied to control the false discovery rate (FDR), the risk of false positives due to multiple testing. Preferably, the well-known Benjamini-Hochberg (BH) procedure is utilized (Benjamini, Yoav & Hochberg, Yosef. (1995). Controlling the False Discovery Rate - A Practical And Powerful Approach To Multiple Testing. J. Royal Statist. Soc., Series B. 57. 289-300. 10.2307 / 2346101), but other correction methods, such as Bonferroni correction, may also be applied. Genes are considered to be differentially expressed when their adjusted p-value is below a predetermined threshold, typically 0.05, indicating strong evidence of true differential expression.

[0146] According to a non-limiting example of the present invention, as described above, when testing the significance of many genes (thousands of hypotheses at once), it is important to control for multiple tests to avoid false positives. This can be done, for example, using the Limma package "Linear Models of Microarray Data," a widely used biostatistical software package that uses BH to control the FDR (false discovery rate) (3), which is the expected proportion of false positives among genes declared "significant." The procedure begins by collecting all p-values ​​from individual tests and sorting them in ascending order. Each p-value is assigned a rank (i) based on its position in the sorted list, with the smallest p-value assigned a rank of 1, the second smallest a rank of 2, and so on, up to the total number of tests (m). The FDR threshold is set to 0.05, and for each ranked p-value, the corresponding threshold is calculated by multiplying the rank by the FDR threshold and dividing by the total number of tests.

[0147] According to the present invention, genes are considered differentially expressed (DEGs) if their adjusted p-value is less than 0.05. Lower values ​​correspond to a lower expected rate of false discoveries among genes declared significant, indicating stronger evidence of true differential expression.

number

[0148] Furthermore, molecular variability (MDP) analysis can be used to determine the overall transcriptome variation at sample level. MDP analysis is based on calculating the Z-score of each gene, which reflects the deviation from the median expression value of reference (preferably untreated control sample). For similar analysis, alternative statistical measures such as t-score or fold change threshold can also be considered. Only genes with significant deviation, i.e., with absolute value of Z-score ≥ 2, are used to calculate the variation score of each sample. The variation score represents the average degree of significant gene expression deviation, thereby quantifying the degree of transcriptome disorder.

[0149] Therefore, based on the time-course DEG profiles, this method makes it possible to determine the expression (when transcriptomic variations first appear after treatment), duration / persistence (how long the variations persist) and disappearance (return to baseline, end of variations) of each tissue at each time point for each product tested.

[0150] The method is based on profiling the effects of the whole product, so no specific chemical markers are required and therefore it is independent of its chemical composition.

[0151] As is evident from the examples, including the working examples, the method of the present invention provides consistent results for each tissue analyzed.The 48-hour time frame for product clearance observed in the examples is related to the specific product tested, and it is clear to those skilled in the art that the time for the disappearance of transcriptome variation may vary from product to product.If different tissue samples show different disappearance times for the transcriptome variation induced by the administration of the tested product, when determining the total body disappearance time, the disappearance time corresponds to the initial period in which no further significant variation is observed when faced with the corresponding treated product and sham control samples for all tissues examined.

[0152] Additionally, the method can further include MDP analysis. DEG analysis identifies specific genes with significant expression changes and indicates the overall evolution of transcriptome variation over time. MDP analysis provides a global measure of transcriptome shifts (referred to as "variation score" in Figures 1-6). This dual approach allows for comprehensive characterization of biological responses to treatments.

[0153] In an example embodiment, transcriptome data obtained from treated biological samples, as defined above, can be compared to a sham control sample alone or a sham control sample and a baseline untreated sample. DE analysis is performed by comparing product-treated samples (tissues / organs and specific time points as disclosed above) at each time point with their corresponding sham-treated samples and applying SST-RMA normalization, Limma-based modeling, and FDR correction.

[0154] Furthermore, the normalized data can be used to calculate molecular variability (MDP), which is expressed as a "variability score" of samples from both product-treated and sham-treated groups relative to an additional untreated control group. This analysis provides a global picture of transcriptome changes at a specific sample level. This method does not focus on individual genes, but rather on determining how "different" the sample overall is from a typical healthy control (untreated control sample) used as a baseline. In this specification, claims, and drawings, the terms "healthy sample" and "untreated control sample" are synonymous.

[0155] MDP is derived from the concept of Molecular Distance to Health, which quantifies sample heterogeneity by assessing the degree to which the expression profile of a particular sample deviates from that of a healthy reference. Those skilled in the art can use commonly available software such as the MDP package to calculate the variation score for each sample j by:

[0156] 1. Calculate the Z-score for each gene: a measure of how different the expression of the gene in each sample is from the typical value in healthy controls (expression value of each gene i minus the median value of the same gene in the control samples (Ci) divided by the variability of this gene in the control samples (Vi)).

[0157] 2. Only genes with significant deviations (absolute Z-score value ≧2) are computed.

[0158] 3. The variability score for a sample is the average of these significant deviations and represents how "variable" its overall gene expression is compared to the untreated reference.

[0159] MDP formula:

number

number

[0160] Each gene j is included in the analysis if and only if the absolute value of its Z-score is ≧2.

[0161] Therefore, the variation score (MDPi) for each sample i is calculated by averaging the Z-scores of the significant genes in each sample i (absolute Z-score value ≥ 2) and then dividing by the total number of genes in the input data. The Z-score is calculated using the difference in expression levels of sample i from the mean of the genes in the reference group divided by the corresponding standard deviation. Essentially, this represents the number of standard deviations (Prada-Medina, CA, Fukutani, KF, Pavan Kumar, N. et al. Systems Immunology of Diabetes-Tuberculosis Comorbidity Reveals Signatures of Disease Complications. Sci Rep 7, 1999 (2017). https: / / doi.org / 10.1038 / s41598-017-01767-4).

[0162] As evidenced by the following examples and figures, both DEG analysis and MDP analysis revealed that for the tested products, transcriptome variations peaked shortly after product administration and subsided within 48 hours, indicating a trend toward recovery over time. Furthermore, certain preferred embodiments focus specifically on analyzing known ADME-related gene sets in liver and kidney tissues. Differentially expressed ADME genes are identified in Figures 2 and 4, and MDP analysis computes a variation score using only ADME-related genes, providing a focused assessment of metabolic and excretory function.

[0163] Furthermore, as mentioned above, the method provides various ADME-like functional parameters such as: [Table 2]

[0164] Thus, according to one aspect, the method disclosed herein comprises: a. providing a transcriptome peak Pmax value of the product for one or more organs that represents the maximum magnitude of transcriptome variation induced by administration of the product in the organ; b. providing functional Tmax, fTmax, values ​​of the product for one or more organs representing the time point at which the maximum peak of transcriptome variation induced by administration of the product in the organ is observed; and c. providing a systemic effect area under the curve representing the area under the curve of the overall effect / variation induced by product treatment over time. d. Providing a whole body functional mean residence time (fMRT) defining the mean effect / variation time induced by product treatment, where fMRT=AUMC / AUEC, where AUMC is the area under the first moment curve and AUEC is the area under the effect curve defined by c.

[0165] General formula for MRT MRT=AUMC / AUC

[0166] According to the present invention, the fMRT formula is:

number

[0167] According to the present invention and the state of the art, the integral can be calculated using trapezoidal estimation, and therefore the above formula can be extended to:

number

[0168] The mean residence time is generally defined as the ratio AUMC / AUC.

[0169] In the present invention, AUC corresponds to the area under the functional equivalent effect curve (AUEC).

[0170] The trapezoidal rule can be used to estimate the total area under each curve by dividing it into trapezoids, which is an accurate method for calculating integrals (Bolton, W. (2000). Mathematics for Engineering (2nd ed.). Routledge. https: / / doi.org / 10.4324 / 9780080939292).

[0171] Specifically, AUMC is calculated as the area under the curve where the x-axis represents time and the y-axis represents time multiplied by the effect / variability. In contrast, AUEC is the area under the curve where the x-axis is also time and the y-axis represents only the effect / variability.

[0172] In classical pharmacokinetics, the MRT represents the mean time a molecule remains in the body. As already mentioned, the method of the present invention does not rely on parameters related to the molecule or its concentration, and therefore the parameters provided herein are calculated on the mean effect / variation time of the analyzed treatment.

[0173] The therapeutic or beneficial product can be any therapeutic or beneficial product, and in preferred embodiments, the product consists of or comprises a natural matrix, i.e., a matrix obtained from a natural source, such as an extract from plant or animal tissue, a mineral matrix produced by a plant or animal (e.g., coral skeleton, eggshell, etc.), a fraction of an extract, etc. In particular, extracts that do not alter the original constituents of the natural source used, such as aqueous extracts, are preferred.

[0174] Non-limiting examples of plant matrices include plant-derived matrices, such as medicinal plant or plant part extracts, e.g., aloe vera gel, turmeric root extract, ginseng root extract, licorice root extract, moringa leaf powder, echinacea purpurea extract, neem oil, arnica montana extract, and grape seed extract; plant essential oils, e.g., tea tree, lavender, eucalyptus, frankincense, and other essential oils obtained from medicinal plants; plant fibers and scaffolds; myrrh, frankincense resin, acacia gum, and mastic gum. resins and gums such as thyme, thyme extract, thyme gum; fungal matrices such as mushroom mycelium, reishi extract, cordyceps extract, and chaga extract; marine derivatives such as fish collagen, krill oil, whole sea cucumber extract, brown algae extract, algae extract or parts thereof, nacre (mother of pearl), pearl) powder, animal-derived materials such as bovine or porcine collagen, placenta extract, lanolin from sheep's wool, keratin from feathers or hair, gelatin from animal bones, egg yolk or eggshell membrane extract, and colostrum; milk-derived bioactive substances such as whole casein or whey protein concentrates and colostrum fractions; insect-derived substances such as bee venom, propolis, royal jelly, silk fibroin from silkworm cocoons, chitosan from insect or crustacean shells, microbial products such as bacterial cellulose from Acetobacter, probiotics in whole-cell form, fermented plant extracts, and exopolysaccharide-rich microbial matrices, natural clay and mineral matrices enriched with bioactive plant or microbial components, such as montmorillonite clay loaded with herbal extracts.

[0175] For the purposes of this invention, these materials are typically used in their complex multi-component form.

[0176] A specific, non-limiting example of a natural matrix-based therapeutic product is the EpigenAU / 11 product disclosed in the Examples. As is evident from the specification and examples, the methods of the present invention offer the following important advantages over conventional ADME-based strategies:

[0177] This is suitable for natural matrix-based products as it is independent of chemical markers.

[0178] It effectively bridges the gap between the measurable presence of individual molecular components and the persistence of pharmacological or biological effects, thereby providing a functional and holistic measure of biological response independent of the quantification of chemical markers.

[0179] Indeed, capturing dynamic changes in gene expression over time allows for the detection and characterization of functionally relevant biological responses even when the original matrix components are no longer detectable in plasma or tissues. Furthermore, by analyzing the entire protein-coding transcriptome, rather than just ADME-related genes, the methods of the present invention ensure that all relevant biological signals are captured, whether arising from adaptive, beneficial, toxicological, or indirect or systemic mechanisms of action.

[0180] In this way, precise, time-resolved mapping of the onset, peak, and offset phases of a product's pharmacodynamic activity is provided; indeed, analysis of gene expression patterns according to the present invention provides a dynamic temporal profile of biological activity, allowing the detection of pharmacodynamic effects even when the original chemical moieties are no longer detectable in plasma or tissues. This method provides a detailed, time-resolved map of a product's activity in multiple organs, highlighting the sequential occurrence of metabolism (liver), excretion (kidney), and systemic responses.

[0181] Temporal resolution allows for clear differentiation between early and late biological effects, supporting a comprehensive understanding of the pharmacokinetic and pharmacodynamic profile of the tested product.

[0182] In this way, both transient and persistent biological effects can be detected, and indeed any long-lasting molecular changes induced by the tested product can be easily identified, and furthermore, persistent pharmacodynamic or delayed toxicity signals can be objectively determined.

[0183] It also allows for the identification of safety-related molecular perturbations, as the gradual regression of transcriptomic signatures to baseline values ​​over time is a robust and reliable indicator of biological recovery and the absence of chronic molecular perturbations.

[0184] Because it focuses on the functional biological outcome of the extract rather than its chemical composition, this method reduces the variability associated with differences in product composition between product batches.

[0185] As discussed and illustrated above, and shown in the examples, the methods of the present invention provide ADME-like parameters and are therefore applicable to enhanced risk management and regulatory requirements. Analysis of genome-wide gene expression profiles provides biologically relevant parameters for assessing the safety, efficacy, and consistency of multi-component botanical products, thereby improving the robustness of preclinical and regulatory documentation.

[0186] Finally, the present invention represents a significant methodological advance in the pharmacokinetic and pharmacodynamic characterization of multi-component natural products.

[0187] By shifting the analytical focus from static chemical quantification to dynamic biological response profiling, the proposed approach offers a more realistic, comprehensive, and biologically relevant alternative to traditional ADME methodologies and is directly applicable to preclinical development, safety assessment, efficacy profiling, regulatory submission preparation, and product standardization strategies for plant- and natural matrix-based therapeutics.

[0188] This method allows for unprecedented functional profiling of natural matrices, overcoming the inherent limitations of traditional ADME approaches, yet provides ADME-like information and is therefore applicable to multiple therapeutic and regulatory contexts.

[0189] The overall advantage is that it becomes possible to objectively characterize the pharmacokinetic and pharmacodynamic profiles of products that contain or are composed of natural plant-derived matrices.

[0190] In any part of this specification and claims, the word "comprising" may be replaced with the word "consisting of."

[0191] Wherever in this specification an internet address or URL is given, this relates to information retrieved from the internet address available on the filing date of this application, i.e., the most recent version of the content at the internet address on the filing date of this application.

[0192] Wherever in this specification or claims reference is made to commercially available products, the symbols TM (trademark) or C (copyright) are considered implicit and may be added to each of the products from time to time.

[0193] The following examples illustrate the foregoing description, but are not limiting thereof, and such examples should in no way be construed as limiting the scope of the foregoing description and any claims that follow. Furthermore, the following examples report all work performed on the products of the present invention and support all claimed subject matter.

[0194] Example 1. Composition of the product to be tested Product A herein is also EpigenAU / 11 for the treatment of cancer. 36.05% by weight of freeze-dried component 1 63.06% by weight of freeze-dried component 2 0.89 wt% of lyophilized component 3, totaling 100% w / w of Product A

[0195] Ingredient 1 Laurus nobilis leaves 25% w / w Ashwagandha (Withania somnifera) root 25% w / w Filipendula vulgaris leaves and flowers 25% w / w Broccoli variety (Brassica oleracea L. botrytis cymosa) seeds 25% w / w Co-extracted in water for a total of 100% w / w.

[0196] Ingredient 2 Artichoke (Cynara scolymus L.) leaves 14.30% w / w Turmeric (Curcuma longa L.) root 42.85% w / w Feverfew (Tanacetum parthenium L.) flowers 42.85% w / w Co-extracted in water for a total of 100% w / w.

[0197] Component 3 Freeze-dried extract of sisal leaves.

[0198] All animal procedures were performed in accordance with Directive 2010 / 63 / EU of the European Parliament and of the Council of the European Union (September 22, 2010) on the protection of animals used for scientific purposes. The ethical policy of the University of Florence follows the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health (NIH Publication No. 85-23, revised 1996; University of Florence Assurance No. A5278-01). Formal approval to conduct the described experiments was obtained from the Animal Subjects Review Board of the University of Florence. Experiments involving animals were performed in accordance with the ARRIVE guidelines. All efforts were made to minimize animal suffering and reduce the number of animals used.

[0199] 2. Application of the present invention: EpigenAU / 11 natural matrix-based products As a non-limiting example of application, we applied the method proposed for the already patented EpigenAU / 11 to characterize its temporal transcriptome effects in vivo.

[0200] 2.1 Collected samples A total of 45 immunodeficient nude mice (nu / nu) were used. Animals were maintained under standard barrier facility conditions (temperature 22 ± 2°C; relative humidity 50–60%; 12 / 12 h light / dark cycle) and provided with sterile food and water ad libitum.

[0201] Experimental Group: Baseline group (0 hours, no infusion): 3 mice. Treatment group: subcutaneous injection of 400 μL of EpigenAU / 11 (50 mg / mL) per animal. Sham control group: subcutaneous injection of 400 μL saline (vehicle) per animal.

[0202] Tissue samples were collected at the following predetermined time points after injection: 0 hours, 2 hours, 6 hours, 12 hours, 18 hours, 24 hours, and 48 hours.

[0203] At each time point, six animals were sacrificed (three treated with EpigenAU / 11 and three treated with vehicle).

[0204] Biological samples collected included: Blood (500 μL, stabilized in RNAprotect tubes) injection site tissue brain liver kidney lung heart Muscle (from the injection site area)

[0205] Samples were processed and stored in RNAlater at 4°C for 24 hours, then stored at −80°C for RNA extraction.

[0206] 3. RNA Extraction and Transcriptome Profiling Tissue homogenization was performed in RLT buffer (Qiagen) containing β-mercaptoethanol and DX reagent.

[0207] Total RNA was isolated using the QIAsymphony RNA Kit and a QIAsymphony SP instrument.

[0208] RNA integrity was verified by Agilent 2100 Bioanalyzer and quantified spectrophotometrically.

[0209] Whole transcriptome analysis was performed using the Clariom™ S Pico Assay, Mouse (Thermo Fisher Scientific) as defined in the diagram section above on the GeneTitan® MC Instrument, followed by SST-RMA normalization.

[0210] 4. Data Processing and Analysis Two analytical strategies were used:

[0211] Molecular Dynamics (MDP) - Global quantification of transcriptome deviations compared to untreated controls. -Capture subtle and broad gene expression changes across the whole transcriptome.

[0212] Differential Expression Analysis (DE) or (DEA) - Identification of specific genes that show statistically significant changes between product-treated and vehicle-treated animals at each time point, thereby obtaining DEGs (differentially expressed genes).

[0213] 4.1 DE analysis: Transcriptome data were processed using the Transcriptome Analysis Console software (ThermoFisher Scientific), which applies SST-RMA normalization (a method developed by this software to adjust for technical variability and background noise and ensure gene expression values ​​are comparable across all samples) (https: / / assets.thermofisher.com / TFS-Assets / GSD / Reference-Materials / white-paper-microarray%20-normalization-using-sst-with-probe-guanine-cytosine-count-correction.pdf). The Transcriptome Analysis Console software also performs differential expression (DE) analysis (based on a linear model from the Limma Bioconductor package). This analysis uses a linear model designed specifically for the microarray platform to estimate whether differences in gene expression between groups (e.g., EpigenAU / 11 vs. physiological solution or physiological solution vs. untreated) are statistically significant (Phipson, B, Lee, S, Majewski, IJ, Alexander, WS, and Smyth, GK (2016). Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression. Annals of Applied Statistics 10(2), 946-963).

[0214] When testing the significance of many genes (thousands of hypotheses at once), there is a risk of obtaining some false positives by chance. To avoid this problem, the Limma package adjusts for multiple testing by controlling the false discovery rate (FDR) with the BH method (Benjamini, Yoav & Hochberg, Yosef. (1995). Controlling the False Discovery Rate - A Practical And Powerful Approach To Multiple Testing. J. Royal Statist. Soc., Series B. 57. 289 - 300. 10.2307 / 2346101). The FDR is the expected proportion of false positives among genes declared "significant." This procedure begins by collecting all p-values ​​from individual tests and sorting them in ascending order. Each p-value is assigned a rank (i) based on its position in the sorted list: the smallest p-value is assigned rank 1, the second smallest p-value is assigned rank 2, and so on, up to the total number of tests (m). The false discovery rate (FDR) threshold is set at 0.05, and for each ranked p-value, the corresponding threshold is calculated by multiplying the rank by the FDR threshold and dividing by the total number of trials.

number

[0215] Genes were considered differentially expressed (DEGs) if their adjusted p-value was less than 0.05, with lower values ​​corresponding to a lower expected rate of false discoveries among genes declared significant and indicating stronger evidence of true differential expression.

[0216] 4.2 MDP The normalized data was used to calculate the molecular variability (MDP) of samples from both the EpigenAU / 11 and sham control groups compared to the untreated control group. This analysis provides a global picture of transcriptome changes at the sample level. This method does not look at individual genes; it asks how globally "different" the sample is from a typical healthy control.

[0217] MDP is derived from the concept of Molecular Distance to Health, which quantifies sample heterogeneity by assessing how much the expression profile of a particular sample deviates from that of a healthy reference. The MDP package calculates a variability score for each sample j by:

[0218] 1. Compute a Z-score for each gene, a measure of how different the expression of the gene in each sample is from the typical value in healthy controls (expression value for each gene i minus the median value for the same gene in control samples (Ci) divided by the variability of this gene in control samples (Vi)).

[0219] 2. Only genes with significant deviations (absolute Z-score ≧2) are computed.

[0220] 3. The variability score for a sample is the average of these significant deviations, representing how "variable" its overall gene expression is compared to the reference.

[0221] MDP formula

number

number

number

number

number

number

[0222] Each gene j is assumed to be included in the analysis if and only if its absolute Z-score value is 2 or greater.

[0223] Therefore, the variation per sample (MDPi) is calculated by averaging the Z-scores (Z-scores with absolute values ​​≥ 2) of significant genes for each sample i, then dividing by the total number of genes in the input data. The Z-score is calculated using the difference in expression level of sample i from the mean of the genes in the reference group divided by the corresponding standard deviation. Essentially, this represents the number of standard deviations from the reference (1).

[0224] The dual approach ensures both the detection of systemic transcriptome shifts and the identification of key driver genes involved in the biological response.

[0225] Importantly, the sham-treated samples also showed transcriptome variation compared to the untreated control samples. This reflects not only variations in gene expression throughout the day, including circadian rhythms, but also other influences, such as stress caused by the injection (Figure 11). For this reason, DE analysis was performed by comparing the EpigenAU / 11-treated group at each time point with the corresponding group treated with physiological solution. This approach was designed to minimize the confounding effects of gene expression variations throughout the day.

[0226] The molecular variability (MDP) approach determines the variation of each sample's entire transcriptome at the sample level by quantifying the deviation of gene expression profiles relative to the median of the untreated control group without performing statistical tests on individual genes. On the other hand, differential expression (DE) analysis applies statistical tests to identify specific genes whose expression changes significantly between conditions. These two methods are complementary. MDP compares each sample to the untreated control group at time point 0 to capture subtle global shifts in the transcriptome for all genes or specific subsets. DE analysis identifies differential genes that may be key to the biological response and explores group differences between EpigenAU / 11 treatment and sham treatment at each time point. Therefore, both methods do not always yield the same results. Importantly, both suggest that the peak variation caused by EpigenAU / 11 in different organs decreases by 48 hours post-administration.

[0227] For the liver and kidney, both approaches focused on genes potentially relevant to ADME methods based on the datasets described for each organ (for the liver, see Hu DG, Mackenzie PI, Nair PC, McKinnon RA, Meech R. The Expression Profiles of ADME Genes in Human Cancers and Their Associations with Clinical Outcomes. Cancers (Basel). 2020 Nov 13;12(11):3369. doi: 10.3390 / cancers12113369. PMID: 33202946; PMCID: PMC7697355; and for the kidney, see Schlosser, P., Li, Y., Sekula, P. et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet 52, 167-176 (2020)). https: / / doi.org / 10.1038 / s41588-019-0567-8). The DE analysis identified a number of DEGs that were part of the ADME-specific list for these organs. For MDP, the full analysis was performed using only these genes of interest.

[0228] 5. Results and Discussion 5.1 Summary of observed biological responses Applying organ-specific transcriptome analysis to EpigenAU / 11 revealed distinct time-dependent biological responses in multiple organs in vivo. Two complementary analytical strategies were used to assess product-induced transcriptome changes:

[0229] Differential expression analysis (DEA) was performed to identify statistically significant gene expression changes between EpigenAU / 11-treated animals and appropriate sham controls. Following this analysis, differentially expressed genes (DEGs) were further annotated to specifically identify genes belonging to ADME-related categories, if relevant.

[0230] Molecular Divergence Point (MDP), used to quantify the overall transcriptome deviation of each sample from the untreated control, considered the reference physiological state, provides a global and integrated measure of gene expression variation.

[0231] This dual analytical framework enabled comprehensive characterization of the biological response to EpigenAU / 11 administration, allowing both a broad / extensive assessment of the transcriptome landscape and targeting and focusing on metabolic-specific processes (ADME genes).

[0232] 5.2 Liver: metabolic activation and detoxification The liver showed the strongest transcriptomic response following EpigenAU / 11 administration.

[0233] As demonstrated by MDP analysis (Figure 1) and confirmed by DEA (Figure 7), an initial increase in gene expression variability was observed approximately 2 hours after administration, peaking at 6 hours.

[0234] This approach was also applied to a subset of the dataset to select only genes related to ADME (absorption, distribution, metabolism and excretion) processes, following the gene set used in a previous study by Dong Gui Hu et al. (Dong Gui Hu et al. The Expression Profiles of ADME Genes in Human Cancers and Their Associations with Clinical Outcomes Cancers 2020, 12(11), 3369).

[0235] Focusing specifically on ADME-related genes (Figure 2), a similar temporal pattern was observed, although there was less variation at 2 hours, consistent with activation of hepatic metabolic and detoxification pathways. Differences between physiological solution and EpigenAU / 11 treatment became more apparent.

[0236] Importantly, 48 hours after administration, both global and ADME-specific fluctuation levels returned to values ​​comparable to the vehicle-treated group (Figs. 1 and 7), indicating a complete abolition of the hepatic response.

[0237] 5.3 Kidney: Excretion kinetics and biphasic response In the kidney, transcriptome variations first peaked at 2 hours post-administration when all genes were considered (Fig. 3).

[0238] Again, MDP was used to determine both global transcriptome variation and specific variation associated with ADME processes. The gene set selected for kidney was described by genome-wide association studies (GWAS) of metabolite concentrations (Schlosser, P., Li, Y., Sekula, P. et al. Genetic studies of urinary metabolites illuminate mechanisms of detoxification and excretion in humans. Nat Genet 52, 167-176 (2020). https: / / doi.org / 10.1038 / s41588-019-0567-8), and 63 genes (discovered by GWAS and generally known to be involved in metabolism-related pathways) were selected.

[0239] MDP analysis of ADME-related genes (Fig. 4) revealed a clear second peak of variation around 18 h post-dose, likely reflecting the renal excretion process.

[0240] DEA results confirmed this biphasic pattern, showing a first peak of fluctuation at 2 hours and a second peak of fluctuation at 18 hours (Figure 8). By 48 hours post-dose, the renal transcriptome profile returned to baseline levels.

[0241] 5.4 Blood: Minimal fluctuations due to transient signals Blood samples showed an overall low variability score over time (Figure 5).

[0242] However, two notable findings emerged: At 18 hours post-dose, vehicle-treated samples showed higher levels of variability than EpigenAU / 11-treated samples, possibly reflecting stress-related or circadian effects.

[0243] In one example, an EpigenAU / 11 treated sample, a small outlier was detected at 48 hours.

[0244] Despite these exceptions, DEA analysis revealed a significant number of differentially expressed genes (DEGs) at 18 h ( Figure 9 ), indicating dynamic transcriptome changes in blood that were not directly attributable to EpigenAU / 11 exposure alone.

[0245] 5.5 Brain: Detecting indirect and systemic effects on the CNS Transcriptome changes in the brain were detectable approximately 2 hours after administration of EpigenAU / 11, temporally aligned with the change signals observed in peripheral metabolic organs such as the liver and kidney (Figure 6).

[0246] At subsequent time points (24 and 48 h), MDP analysis continued to show small but measurable deviations from baseline gene expression in brain tissue, despite the absence of significant differentially expressed genes (DEGs) identified by DEA after 6 h (Figure 10).

[0247] This finding is particularly important because it demonstrates that the proposed transcriptomic analysis method can capture indirect, systemic, or secondary biological effects occurring in the brain, even in situations where the administered natural matrix-based product or its detectable metabolites cannot directly reach or accumulate in the central nervous system (CNS).

[0248] This ability to detect CNS-related transcriptome changes (arising from systemic regulation, peripheral signaling, or metabolite-mediated pathways) is a key advantage of the present method, especially considering that traditional pharmacokinetic strategies typically fail to capture these indirect effects.

[0249] This ability to monitor the fine regulation of brain gene expression, as well as the direct penetration of compounds across the blood-brain barrier, allows even compounds classically considered CNS inactive to be The widespread systemic effects of products based on natural matrices, Potential CNS-related efficacy signals, and early detection of safety-related changes in the brain transcriptome, This will provide useful insights.

[0250] This feature is particularly advantageous for safety profiling, pharmacodynamic evaluation, and risk characterization of plant products or xenobiotics that are not expected to act directly within the CNS compartment.

[0251] 5.6 Elimination stage in all organs By 48 hours after administration, transcriptome profiles in all organs examined, including liver, kidney, blood, and brain, returned to levels comparable to sham controls.

[0252] The disappearance steps occur in the following expected order: Hepatic metabolic processing, Renal excretion, and full body recovery Typically associated with the elimination of xenobiotics.

[0253] The dual analytical strategy applied here (combining ADME-specific gene analysis with whole transcriptome profiling) provides a uniquely detailed and comprehensive view of EpigenAU / 11 metabolism and biological actions.

[0254] 5.7 Safety and Tolerability of EpigenAU / 11 An important observation that emerged from this analysis was the lack of persistent or chronic molecular changes following EpigenAU / 11 administration. Specifically:

[0255] Gene expression profiles in both liver and kidney returned to baseline levels within 48 hours after administration.

[0256] No permanent transcriptome changes were detected, suggesting that the product was effectively processed and cleared within a reasonable and predictable time frame.

[0257] Under physiological conditions (healthy animal models), no molecular signatures indicative of chronic toxicity, stress response, or irreversible damage were observed.

[0258] We emphasize the importance that transcriptome analysis was performed on the entire protein-coding expression profile, without limiting the investigation to canonical ADME-related genes (e.g., cytochrome P450 enzymes, conjugated enzymes, or membrane transporters).

[0259] With this comprehensive strategy, any potential biological effect, whether metabolic, inflammatory, adaptive, or toxicological, can be reliably detected and assessed.

[0260] This approach provides a uniquely broad and reliable determination of safety and pharmacodynamic behavior by capturing both direct and indirect effects, including those potentially arising from metabolites or systemic regulation.

[0261] Organ-specific transcriptome analysis applied to the administration of EpigenAU / 11 clearly demonstrates the feasibility, robustness, and added value of this innovative approach in characterizing the pharmacokinetic and pharmacodynamic behavior of a complex plant extract.

[0262] This methodological strategy addresses several long-standing limitations inherent in traditional ADME-based analyses and provides a functionally relevant, integrative biological framework applicable to the study of other multicomponent natural matrix-based products.

[0263] In particular, while targeted ADME genetic analysis provides clear information on the pharmacokinetic disposition of compounds, transcriptomic approaches capture broader systemic responses, indirect effects, and tissue-specific adaptations, providing a true holistic assessment of complex natural matrix-based product behavior in vivo.

[0264] 6. ADME-like functional parameters Calculations of functional / equivalent ADME parameters were performed as defined above and in the associated tables.

[0265] Functional parameters were calculated based on the degree of biological response defined by the total number of differentially expressed genes (DEGs) observed when comparing treated samples with their corresponding sham control groups.

[0266] The table below shows the results of the DE analysis showing the number of significant DEGs detected at each time point for each tissue sample and the overall (whole body) number of significant DEGs at each time point.

[0267] [Table 3]

[0268] Notably, T0, minutes after injection of EpigenAU / 11, indicates that at the blood level, the mere administration procedure likely causes almost immediate perturbations in the transcriptome.

[0269] At each evaluation time point, all calculations were performed separately for each organ, allowing for organ-specific assessment of molecular variability. Furthermore, an overall systemic variability profile was derived by summing the number of DEGs across all organs evaluated at each individual time point, providing a complete picture of the treatment-induced biological response over time.

[0270] 6.1 Pmax The maximum variation (Pmax) for a particular organ is defined as the maximum number of observed DEGs observed in treated samples compared to sham controls, regardless of whether the genes are up- or down-regulated.

[0271] As can be seen from the table above, in the blood, the peaks are 801 DEG in the brain, 58,666 in the kidney, and 1174 DEG in the liver.

[0272] For systemic effects, Pmax is the overall maximum number of DEGs observed at a particular time point, which is 1477.

[0273] 6.2 fTmax The time to maximum functional effect (fTmax) value for one or more organs is the time point of the peak of the maximum transcriptome change induced by the administration of the product in that organ, and the fTmax whole-body value is the time point at which the maximum number of DEGs is observed when all organ-specific DEGs are considered. As can be seen from the table above, after administration of EpigenAU / 11, the most significant change in the liver (Pmax) was characterized by 1,174 DEGs, which was observed after 6 hours (fTmax = 6 hours). In the blood, kidney, and brain, fTmax was 18 hours, and the fTmax at the whole-body level defines the time point at which the greatest overall change is observed; as can be seen from the table above, the whole-body fTmax was 2 hours.

[0274] 6.3 AUEC To evaluate the overall temporal variation profile, we calculated the area under the effect curve (AUEC), i.e., the area under the curve of the overall effect / variation induced by product treatment over time, by plotting the number of DEGs (y-axis) as a function of time (x-axis) and using the trapezoidal rule to calculate the area under the curve. Implementation was performed via the trapz() function from the pracma package in R. This method ensures numerical accuracy when approximating the area under the curve based on discrete time series data.

[0275] To calculate the whole body AUEC, the following formula was used:

number

[0276] During the ceremony, AUEC: Area under the effect curve t: time (hours) E(t): Effect / variation at time t (number of DEGs)

[0277] In the liver, for example, the calculation would be (using the trapezoidal rule described on page 44): [Table 4] AUC Liver ≒ 753 + 3854 + 4389 + 2004 + 1866 + 3252 = 16118

[0278] All calculations were performed for other organs mutatis mutandis.

[0279] The calculated whole-body AUEC, which represents the overall (whole-organ) effect size of variation, was 28,820. When effect sizes were measured for single organs, the values ​​obtained were 16,118 for liver, 4,668 for kidney, 7,743 for blood, and 291 for brain. Calculation of AUEC for separate tissue samples can indicate relevant differences in effect size per tissue. As an example, the data above show that the effect size of EpigenAU / 11 administration was significantly greater in the liver than in the brain.

[0280] 6.4 fMRT The functional mean residence time (fMRT), which defines the average effect / time of change induced by product treatment, was calculated as the ratio of the area under the moment curve (AUMC) to the AUEC. AUMC was derived by calculating the area under the curve where the y-axis represents the mathematical product of the time point and the corresponding number of DEGs (i.e., time × effect), again using the trapz() function for trapezoidal approximation.

[0281] This mean fluctuating time calculation is closely related to systemic effects, but can also be calculated for single organs if specific. EpigenAU / 11 administration resulted in organ-specific fMRTs of 12.72 hours in the liver, 10.86 hours in the kidneys, 19.89 hours in the blood, and 7.63 hours in the brain. The functional mean systemic residence time of our product was 14.29 hours.

[0282] Formula used

number

[0283] Calculate the AUEC as shown on page I.60.

number

[0284] II. Calculate AUMC as the above formula. [Table 5] AUMC Liver = 1506 + 17100 + 31536 + 30870 + 37962 + 86112 = 205086

number

[0285] All calculations were performed for other organs mutatis mutandis.

[0286] 6.5 fTres The organ-specific functional loss time fTres corresponds to the time point at which the recovery of the basal transcriptome is induced by the administration of a therapeutic or beneficial product (the end of transcriptome excursion is defined as transcriptome excursion of 5% Pmax or less for all tissue samples except blood, and the end of transcriptome excursion is defined as 12% Pmax or less, preferably 5% Pmax or less). As can be expected, transcriptome excursion in blood samples is more unstable because blood flow is subject to fluctuations such as blood glucose peaks immediately after eating and day-night changes. In this case, fTres can also be organ-specific or systemic. Considered from a systemic perspective, fTres is the first time point at which all tested organs reach a transcriptome excursion of 5% Pmax or less for excluded blood, organ-specifically, and the end of transcriptome excursion is defined as 12% Pmax or less, preferably 5% Pmax or less.

[0287] In this study, fTres was 48 hours for all tissue samples and whole body values ​​tested.

[0288] The table below summarizes the calculated ADME-like functional parameters obtained with the method of the present invention after administration of EpigenA1 / 11. [Table 6]

Claims

1. 1. A method for functionally characterizing the onset, duration, and resolution of a biological effect of a therapeutic or beneficial product, said product comprising one or more natural matrices, performed after administration of said product to a subject, the method comprising: (a) extracting RNA from biological samples of different tissues obtained from an organism, i.e., treatment samples, taken at multiple time points after administration of the natural matrix-based product to the organism, and extracting RNA from corresponding biological samples from a sham control organism, i.e., sham control samples, taken at the same time points; (b) performing transcriptome analysis, including gene expression profiling, on RNA extracted from the treated and sham control samples; and identifying transcriptome variations based on differentially expressed genes (DEGs) for the treated samples at each time point compared to the corresponding sham control samples; (c) identifying the onset profile, duration profile, and offset profile of the biological activity induced by administration of said product, by time point of onset, duration of duration, and time point of offset, respectively; Where: the onset time point corresponds to the time point at which significant transcriptomic variation is first observed in at least one of the treated samples compared to a corresponding sham control sample; the resolution time point corresponds to the time point at which significant transcriptomic variation is last observed in a treated sample compared to a corresponding sham control sample; the duration corresponds to the time interval from the onset time point to the disappearance time point; The method does not rely on direct quantitation of the individual chemical components of the product, but provides onset, duration, and offset profiles of its biological effects.

2. 10. The method of claim 1, further comprising one or more of the following: a. providing a transcriptome peak Pmax value of said product for one or more organs as a value representing the maximum magnitude of the transcriptome perturbation induced by administration of said product in said organ; b. Providing functional Tmax values, i.e., fTmax values, for one or more organs as time points at which the maximum peak of transcriptome variation induced by administration of the product in the organ is observed; c. Providing a systemic effect area under the curve, wherein the systemic effect area under the curve represents the area under the curve of the overall effect / variation over time induced by treatment with the product; d. Providing a whole body functional mean residence time (fMRT), wherein the fMRT defines the mean effect / change time induced by treatment with the product, where fMRT = AUMC / AUEC, where AUMC is the area under the first moment curve and AUEC is the area under the effect curve defined by c; e. Providing a functional loss time fTres, where fTres is defined as the time point at which the number of DEGs is ≦5% Pmax for all tissue samples except blood, corresponding to the time point at which the end of transcriptome excursions, and the blood end of transcriptome excursions is defined as the time point at which the number of DEGs is ≦12% Pmax.

3. 3. The method of claim 1 or 2, wherein the different tissues are selected from one or more of blood, liver, kidney, brain, hypothalamus, lung, heart, spleen, testis, stomach, intestine, gallbladder, pancreas, gland, ovary, muscle tissue.

4. 2. The method of claim 1, wherein the plurality of time points includes a TO representing a time point immediately after administration of the product, and at least one different Tn, where n represents a time after administration of the product and is ≧1, and one of the Tn is ≧ the time point of elimination.

5. 5. The method of claim 4, wherein the Tn time points include T2 and T48.

6. 2. The method of claim 1, wherein the transcriptome analysis is performed using RNA sequencing or hybridization to a gene expression microarray.

7. 10. The method of claim 1, wherein the transcriptome analysis comprises: (a) performing transcriptome raw data analysis from the extracted RNA; and Identifying significantly differentially expressed genes (DEGs) at each time point for each treated sample relative to its corresponding sham control sample and their expression fold changes relative to the corresponding sham control sample, thereby identifying the number and intensity of significant transcriptomic variations for the treated sample relative to the corresponding sham control sample at each time point.

8. 10. The method of claim 1 further comprising: Extracting RNA from biological samples of different tissues obtained from control untreated organisms, i.e. untreated control samples in the following: performing a transcriptome analysis, which comprises gene expression profiling on the extracted RNA from each of the untreated control samples, and subsequent Molecular Degree of Perturbation (MDP) analysis on the corresponding product-treated samples and the corresponding sham-treated control samples, thereby obtaining an MDP perturbation score.

9. 9. The method of claim 8, wherein the MDP variability score is calculated according to the following formula: [Equation 1] where: MDPi: Variability score defining molecular variability for subject i n: total number of genes used in the analysis x ij : the value of gene j in target i μ j (ref): Average of gene j in the reference group (untreated control) [Equation 2] and Here, each gene j is included in the analysis only if the absolute value of its Z-score is ≧2.

10. 10. The method of claim 1, 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 body fluids, or animal mineral matrices.

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