Methods for characterizing the onset, duration, and disappearance of the biological effects of therapeutic or beneficial products.

Abstract

The present invention provides an innovative analytical strategy specifically designed to overcome the inherent limitations of conventional ADME (absorption, distribution, metabolism, and excretion) approaches in the pharmacokinetic and pharmacodynamic characterization of natural matrix-based therapeutic or beneficial products, such as those containing or comprising multi-component plant extracts. [Solution] The present invention provides a fundamentally different approach that is not based on the molecular profile of the 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, that is, characterizing the pharmacokinetic and pharmacodynamic behavior of a therapeutic or beneficial product through its functional target response, regardless of its chemical composition. This approach is applicable to conventional drugs and beneficial products, but is particularly well-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 after administration to a subject. This method is suitable for revealing the characteristics of therapeutic or beneficial products, particularly for products based on natural matrices, i.e., compositions of substances consisting only of natural substances and having a physiological mode of action.

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

[0003] In fact, the present invention is a product that particularly addresses products containing or consisting of one or more natural matrices, and it is necessary to maintain the natural intelligence, i.e., the imprint of the domain of the organism to which each component of the product belongs. Thereby, whether natural or artificial, it is necessary to maintain the original natural network that can interconnect and recognize itself with other networks, i.e., a network that has acquired a certain degree of artificiality through interaction with artificial components. This interconnection is considered fundamental for rebalancing any disruptions in the network of events that are active in each interacting biological system. All identified matrices present biophysical specifications such that they represent the invention itself.

[0004] Each network of each natural matrix contained in the product, which contributes to the formation of the final matrix network of the product of the present invention, can be defined as an 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, because it is a product processed according to its self-assembly properties and therefore cannot be determined or verified based on small molecule chemical 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 recipient organisms. Product production validation can be performed and confirmed using a probabilistic model based on the relationship between the storage of physiological activity profiles and descriptors of the matrix itself generated using multiple biophysical analysis systems such as spectroscopy (NIR and other techniques), mass spectrometry, and paper or X-ray crystallography (fractal measurements).

[0006] The natural matrix, such as that which forms the basis of the product of the present invention, contains numerous components that, at both the structural and functional levels, interact to produce dynamic and overall biological responses. While some components may modulate the bioavailability of others, synergistic or sequential actions on multiple molecular pathways result in pharmacodynamic effects that cannot be attributed 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 action networks mediated by the natural matrix.

[0008] Therefore, conventional pharmacokinetic parameters such as Cmax, Tmax, AUC, and systemic clearance are not sufficiently representative or appropriate for characterizing these types of products. Instead, alternative analytical strategies focusing on biological-level physiological responses and inter-network communications are needed to verify the effects of the present invention.

[0009] This paradigm shift is in perfect alignment with the objectives of the biophysiological health model proposed herein, which emphasizes the rebalancing of the psychoneuroendocrine immune system through endogenous activation mechanisms rather than relying on isolated exogenous substances. Conventional molecular chemical definitions of individual substances contained within a material, while useful, do not represent its overall efficacy and quality, and therefore cannot be used to validate this type of product.

[0010] The selection of a matrix for administration must be verified according to the updated and specific current taxonomic standards of the animal, plant, and mineral kingdoms. When used in combination with natural physical phenomena, the relationship between action and efficacy should be examined, in some cases, taking into account the effects in the realm of acoustic effects (music or other forms) and wave-particles, including those of quantum nature.

[0011] With the current level of technology, it is not always possible to outline a fully explained mechanism of action. However, it is possible to examine the actions and reactions in the interconnections of each network, which have already been verified at the biophysical level.

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

[0013] The preparations conceived in this way can rebalance the psychoneuroendocrine immune system, which is considered a single system that regulates and controls all other systems.

[0014] This invention contributes to a new, cutting-edge technology that goes beyond alchemical techniques in the medical field, its origins dating back to the early 16th century, and returns the products and methods to the conceptual One Health goal already mentioned. Based primarily on the concept of verifying their effects and activity on other organisms, this invention proposes a new declination of artificial and naturally occurring self-assembly of matter, recognizing existing rules or discovering new ones, in order to ensure the formation of verifiable entities. Here, organisms are continuously transforming beings, and their physiological states must be evaluated within defined intervals; this concept is now encompassed in individualized medicine. This invention conforms to the concept of science, understood as a set of knowledge whose effects in theoretical modes of action can be empirically verified.

[0015] The activities of the present invention disclosed herein are not covered by current state-of-the-art technology. Therefore, the entire product cycle, from the end user to the relevant social context, must be considered under the concept of One Health.

[0016] Within this concept, the paradigm of operations established by the present invention is referred to herein as "Bios Physiological Health."

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

[0018] The interpretation of the context based on both technical, scientific, and humanistic norms, and its cross-cutting integration, constitutes the foundation of this invention. Some of the properties of each matrix component of the product may already be known; however, newly discovered properties of the novel composition may be unexpected.

[0019] Related to this is the role of determining the genetic and epigenetic aspects that define the networks representing the natural matrix and their depiction, at the level of their specific isotopic abundances.

[0020] To realize a biophysiological health paradigm, it is necessary to preserve, as much as possible, the completeness of all genetic traits of the native programming embedded in the natural intelligence of each creation entity, at least as far as is known on a global scale, throughout each processing phase, from the selection of recycled materials to the agricultural and industrial stages, and even to the methods of use. It will be essential to examine matrices derived from epigenetic entities similar to the reference matrices recognized as reference standards for the specific newly emerging characteristics of the metabolism of other organisms such as humans. For example, one factor that negatively influences epigenetic differentiation is represented by different soil conditions, along with circadian, lunar, and annual variations. It is impossible to use substances derived from alchemical methods such as distillation, other synthetic processes, or hemisynthesis, or products derived from genetically modified or genetically modified organisms, in order to preserve the properties of natural systems, which are the only ones that can claim physiological interconnectivity with the entire creation. A new interpretation of the mystery of the natural programming that underlies the evolution of life in organic and inorganic matter is needed. With scientific advancements becoming firmly established in recent decades, it is now possible in medicine to redefine the origins of progress based on reductionist determinism, drawing on the development of alchemical methods that began in the early 16th century, along with Paracelsus, which marked the beginning of the current evolutionary process known as the Anthropocene.

[0021] The term Anthropocene describes the current stage of human evolution and can be traced back to different periods. In the context of this invention, the only significant date is 1492, which marks the end of the humanistic / Neoplatonic era of the early Renaissance. Politically, this era was represented by Cosimo the Elder and Lorenzo de Medici, and by artists and scientists such as Piero della Francesca, Luca Pacioli, Leonardo, and Durer. In the 16th century, alchemical studies, viewed as humanity's potential for dominion over nature, have evolved to this day under the protection of artificial intelligence, in contrast to natural intelligence. This is inspired by the biblical idea that "man will have control over all creation" and aims to improve upon God's creation.

[0022] 1492 is a symbolic date, as it was the year Lorenzo de Medici and Piero della Francesca died and Columbus discovered America. Humanity departed the Neoplatonic path of the 15th century and followed the path of Judeo-Catholic thought, with the application of Paracelsus' alchemical practices to medicine, marking a transition to the Renaissance style of the 1500s, and leading to a complete and irreversible sixth mass extinction that continues to this day.

[0023] This invention demonstrates the feasibility of the resulting industrial discoveries in the medical field, but is applicable in principle to any field of production and is intended to address the paradigm shift of evolution. While it often speaks of protecting biodiversity, it never actually addresses the sinful and ambiguous real problem of billions of tons of exogenous, non-biodegradable artificial substances being released into the Earth system, which are certain to irreversibly pollute the sources of life. Meanwhile, a "live in the moment" attitude is prevailing over the instinct for species survival.

[0024] This invention is presented primarily in the context of patents and is expected to open up a new field of research that explores and shares natural intelligence rather than artificial intelligence. Artificial intelligence is largely incapable of stopping or slowing the sixth mass extinction or laying the foundation for alternative advances to those of the present. Inventor Valentino Mercati, together with collaborator Jacopo Lucci, has chosen the path of research in nature itself, which may be useful for living systems, and has developed some knowledge in agricultural and industrial production systems for over 40 years, filing numerous patent applications in accordance with this operational strategy. Patents previously filed in connection with the methods of this invention are essentially based on the interpretation of instruments and diagnostics based on chemistry-related principles concerning the relationship between physiological effects and newly emerging properties of natural matrices and the respective innate defenses of individual organisms that interconnect them.

[0025] The analysis that inspired the approaches disclosed herein was unthinkable just a few decades ago, due to the technical impossibility of reading the genetic and epigenetic information encoded within the cells of any 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 reassuring control parameters of molecular artificiality (at least partially purified and linked by strong thermodynamic forces that enable strong bonds such as covalent bonds acting on a reduction in the molecular range of other organisms) to the natural matrix, which is by definition mysterious and still considered a therapeutically unreliable component, is extremely high.

[0026] When, after five centuries of alchemical reduction, a new interpretation of the present invention is required for a new medical state, this interpretation needs to link the farthest concepts and processes in a single field of application. This is due to the ideological legacy that questions the human state, and whether the human species was created by the original vital intelligence like all other species for the purpose of life itself, as far as it can be assumed to dominate creation, or was experimentally given different abilities from other organisms that are already suitably inserted into the creation to form a new ecological niche in the service of the universe?

[0027] The answer to this dilemma is not given by the present invention. Humanity needs to return to the new Platonic thought of the early Renaissance, and the experimental duality of the human species must liberate itself from the spirit of domination in order to share all its unique abilities within the universe with all creation. Humanity needs to reconsider Leonardo da Vinci's warning that "man can only make his own descendants," understand, express, and contemplate the melancholy thought that it is impossible to decipher the mystery of the beauty of creation by sages such as Piero della Francesca, Luca Pacioli, and Durer.

[0028] The time has come to acquire new research centers in molecular biology and cell biology, and it is essential to emphasize bioinformatics and the new physical sciences. Currently, the inventor can lay the foundation for research strategies and socio-economic applications in new therapeutic fields, especially in the fields of complexity and / or chronic degeneration, and the restoration of the metabolic balance of organisms that have been naturally or artificially disrupted will already be an essential part of the future that exists.

[0029] The present invention represents a new vision of medical technology that reexamines scientific evolution from a perspective different from reductionist determinism. This alternative progress does not rely so much on artificial intelligence and technological progress, contrary to universal or earthly rules, but rather on the development of laws that govern our universe and life itself. Even from a systematic perspective using modern techniques of systems biology, the shift from artificial treatment of isolated symptoms to a holistic approach that encompasses the whole embodies the basis of current progress.

[0030] In this context, the present invention addresses the urgent need to verify, for the administration of therapeutic or beneficial products, the overall biological response of an organism, its safety, and its functional evaluation, based solely on natural matrices that operate through mechanisms that respect the inherent complexity of living systems.

[0031] Rather than relying on conventional measurement criteria for pharmacokinetics designed for simple molecules, in the present invention, its effectiveness is verified through the preservation and regulation of physiological network activities across the entire psychoneuroendocrine-immune system.

[0032] The biopsychophysiological health paradigm proposed herein establishes a new scientific criterion for measuring the success of health interventions by the restored ability of an organism to maintain coherence within its internal network and re-establish its dynamic balance with its environment, rather than by isolated chemical substance concentrations.

[0033] Therefore, the present invention focuses on a deep understanding of natural intelligence and its synergistic interaction with organisms, provides a genuine alternative to current models, and aims to contribute to the survival and regeneration of living organisms in an era of profound ecological and biological disruption, opening up a new frontier of research.

[0034] Background of the Invention In pharmaceutical science, characterizing the pharmacokinetic profiles of compounds traditionally follows the ADME paradigm (absorption, distribution, metabolism, and excretion). This analytical framework has facilitated the development and regulatory approval of numerous pharmacological drugs by enabling the definition of key parameters such as half-life, peak plasma concentration (Cmax), time to peak concentration (Tmax), area under the curve (AUC), and systemic clearance.

[0035] Such metrics are derived from the direct quantification of active components in plasma or tissue over time, and they 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 activity whose biological activity correlates closely with measurable systemic exposure. In these situations, the underlying assumption is that therapeutic effects are closely related, both temporally and quantitatively, to the presence of the drug or its metabolites in the bloodstream and target tissues in a detectable state.

[0036] This assumption becomes problematic when applied to multi-component plant extracts or natural plant matrices that are inherently structurally and functionally heterogeneous. Such extracts typically contain tens to hundreds of different chemical components. These compounds can act synergistically, antagonistically, or sequentially across multiple biological targets and pathways. These interactions can occur at the structural level, where different molecules potentially modulate each other's absorption, bioavailability, or metabolic fate through complex formation, micelle formation, or competition for transporters, and at the functional level, where combined effects arise from the activation or inhibition of multiple cellular or molecular pathways, creating a pharmacodynamic footprint not attributable 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 biological effects indirectly by modulating endogenous signaling cascades or by generating active metabolites. Therefore, defining the "effective" concentration of plant natural matrix based on plasma levels of several arbitrarily selected marker compounds is an oversimplification that ignores the holistic and interactive nature of the product. Moreover, plant matrices act not on isolated molecular targets but on the entire pathological state, contributing to the restoration of the organism's dynamic physiological balance not purely through pharmacological inhibition or stimulation, but through endogenous regulatory mechanisms. From this perspective, following the ADME parameters of individual compounds within natural matrices such as plant natural matrix is ​​inherently insufficient. The absorption, distribution, metabolism, and excretion of isolated molecules are inherently insufficient because they cannot represent the overall systemic effects and physiological rebalancing caused by the natural matrix as a whole.

[0038] The implications of these conceptual and methodological constraints are concrete. When traditional ADME strategies are applied to complex plant 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 presumed active ingredient cannot be measured in plasma or tissue. Such findings are frequently dismissed as "abnormal" or due to analytical shortcomings, but they may reflect purely biological phenomena inherent in the multifaceted actions of complex natural products.

[0039] Importantly, this discrepancy between detectable pharmacokinetic exposure and observed biological effects is not unique to therapeutic or beneficial products based on natural matrices. Even with conventional drugs whose pharmacokinetic behavior can be accurately characterized, there have been cases where long-term or sustained effects were observed even when no detectable drug was present in systemic circulation. These phenomena challenge the simple correlation between drug presence and pharmacological effect and highlight the existence of mechanisms that can maintain biological activity far beyond drug clearance.

[0040] Some conventional drugs exhibit pharmacodynamic effects that persist beyond measurable levels in plasma, clearly demonstrating that conventional ADME assessments only provide a partial understanding of their biological mechanisms. A well-known example is the post-antibiotic effect (PAE), where bacterial growth remains inhibited even after antibiotic concentrations fall below detectable levels. Reported for several classes of antimicrobial agents, including aminoglycosides and fluoroquinolones, this phenomenon is attributed to persistent non-lethal bacterial damage and 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] Sustained effects are also observed with drugs that irreversibly bind to biological targets. For example, aspirin permanently acetylates platelet cyclooxygenase-1 (COX-1), resulting in inhibition of thromboxane A2 synthesis throughout the lifespan of platelets, even though aspirin itself is removed from the bloodstream within hours. This long-lasting antiplatelet effect is central to its cardioprotective use and forms 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, resulting in acid suppression that lasts longer than 24 hours despite a plasma half-life of approximately one hour, an effect 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-established pharmacodynamic effects is 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 decreased. Animal studies have demonstrated that altered gene expression can persist for up to 72 hours after drug elimination, supporting clinical observations of prolonged anti-inflammatory or immunosuppressive effects even after corticosteroid discontinuation (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 discrepancy between plasma pharmacokinetics and clinical efficacy. These agents, such as SSRIs and SNRIs, have shown a delayed onset of therapeutic effect and can exert sustained effects even after discontinuation. Neuroadaptive mechanisms, such as receptor modulation and epigenetic modifications affecting genes like BDNF, contribute to the persistence of these effects, strengthening the idea that the therapeutic benefits of antidepressants stem not from the immediate presence of the drug, but from the permanent changes in neuronal function (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 demonstrate remarkable examples of biological effects that persist far longer than drug elimination. Rituximab, an anti-CD20 monoclonal antibody, depletes B cells for 6–12 months after administration, even though the antibody itself is eliminated within weeks. Similarly, immune checkpoint inhibitors can induce persistent antitumor immune responses that continue even after treatment termination due to the establishment of 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, even with conventional drugs, there are several examples of well-known sustained pharmacodynamic effects in conventional drugs, demonstrating that conventional ADME provides only partial information about the sustained effects of drugs in the body. In these examples across diverse pharmacological classes, critical points are identified, and the temporal relationship between plasma drug levels and bioactivity is often complex and nonlinear. While conventional ADME profiling is extremely useful, it captures only a portion of the actual drug effects in the body. Therefore, understanding the persistence of pharmacodynamic effects is important, especially when evaluating complex therapeutic approaches that rely on systemic reprogramming and physiological rebalancing rather than transient chemical presence.

[0046] All of these examples identify a “limit” that tightly correlates the duration of a substance’s action with its plasma concentration or chemical half-life, indicating the need for alternative analytical strategies that can capture the biological response at the biological level to natural matrix-based products.

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

[0048] The present invention offers a fundamentally different approach that is not based on the molecular profile of administered therapeutic or beneficial products (i.e., an approach unrelated to 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, that is, characterizes the pharmacokinetic and pharmacodynamic behavior of a therapeutic or beneficial product through its 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 containing plant natural matrices, such as those derived from natural matrices, e.g., plant extracts, parts, fractions, etc.

[0049] The functional evaluation of an organism's overall biological response to the administration of therapeutic or beneficial products based on natural matrices is achieved through systematic, organ-specific, and time-disappearance transcriptome analyses that can capture comprehensive gene expression profiles related to both transient and sustained pharmacodynamic effects. These improvements over standard transcriptome analyses provide direct, quantitative, and integrated measures of the manifestation, persistence, and disappearance (with respect to transcriptome variability of gene expression) of the biological activity induced by the product under consideration, regardless of whether its original chemical components are detectable in plasma or other tissues. Furthermore, this improved method provides information on the temporal trend of the magnitude of the underlying transcriptome variability of biological activity.

[0050] Importantly, this method is applicable to any therapeutic or beneficial product, particularly natural matrix-based products, i.e., products containing or consisting of one or more natural matrices, including but not limited to plant extracts and / or parts, animal extracts and / or parts, natural mineral sources such as eggshells, fossils, fungal extracts and / or parts, etc., and conventional ADMEs cannot capture the relevant information.

[0051] The analytical method of the present invention includes the following: 1. To collect 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 as the treatment group, e.g., with the active therapeutic component subtracted from the same handling, same route and frequency of administration, same sampling time points, e.g., animals where the vehicle is administered the same route and timing as the product of the intent) under standardized experimental conditions in which a therapeutic or beneficial product or sham treatment based on a natural matrix was administered. 2. Perform whole-transcriptome analysis of collected tissues using an array-based or sequencing-based platform to evaluate changes in gene expression over time for each tissue at each time point. This transcriptome analysis will detect changes in gene expression in key organs and provide a "biological signature" of the product's effect. 3. Differential expression analysis (DEA) to detect statistically significant changes in the expression levels of individual genes, and, at an optional rate, • Molecular variability (MDP) to quantify the overall deviation of the transcriptome from its reference physiological state. Perform bioinformatics and statistical processing of transcriptome data using tools such as those mentioned above to identify fluctuation patterns. 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, if applicable, untreated baseline animals.

[0052] This allows for the identification of both transient (immediate response) and persistent (long-term biological imprinting) effects induced by the product being tested, compared to a control that does not require tracking of chemical markers. [Brief explanation of the drawing]

[0053] [Figure 1] Molecular variability in liver using all genes in the dataset: Molecular variability (MDP) is shown as the variability score of liver samples obtained from product-treated mice and sham-control 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, where white boxes represent the untreated control group at time point 0, gray boxes represent the sham control group, and boxes in a grid pattern represent the product-treated group. Comparing the product-treated group to the sham control group, transcriptome variability in product-treated samples is clearly increased at approximately 2 hours post-administration, peaking at 6 hours and ending virtually identically at 48 hours post-administration. [Figure 2]Molecular variability of liver using only ADME-related genes: Molecular variability (MDP) is shown as the variability score of liver samples from both EpigenAU / 11-treated mice and sham-control-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 only ADME-related genes available with the latest technology (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, where white boxes represent the untreated control group at time point 0, gray boxes represent the sham-control group, and boxes in a grid pattern represent the product-treated group. Comparing the product-treated group and the sham-control group, transcriptome variability in product-treated samples is significantly increased approximately 6 hours after administration, further distinguishing product-treated mice from sham-control-treated mice. This fluctuation gradually decreases and essentially ends without any difference 48 hours after administration. [Figure 3]Molecular Degree of Perturbation (MDP) in kidneys using all genes in the dataset: Molecular Degree of Perturbation (MDP) is presented as a variability score for kidney samples obtained from both product-treated mice 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, mouse (Applied Biosystems, ThermoFisher Scientific). Overall variability is represented by a box plot, where white boxes represent the untreated control group at time point 0, gray boxes represent the sham-treated group, and boxes with a grid pattern represent the product-treated group. Comparing the product-treated group to the sham-treated group, transcriptome variability in product-treated samples is significantly increased at approximately 2 hours post-administration, which gradually decreases and ends with virtually no difference at 48 hours post-administration. [Figure 4]Molecular variability in kidneys using only ADME-related genes: Molecular variability (MDP) of kidney samples from both product and sham-control 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 only ADME-related genes available with state-of-the-art technology (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 a box plot, where white boxes represent the untreated control group at time point 0, gray boxes represent the sham-control treated group, and boxes in a grid pattern represent the product-treated group. Comparing the product-treated group with the sham control group, the transcriptome variability in the product-treated samples increased significantly approximately 6 hours after administration, further distinguishing the product-treated mice from the sham control mice. This variability gradually decreased and ended with virtually no difference after 48 hours post-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-controlled 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 Clariom® S Pico Assay, mice (Applied Biosystems, ThermoFisher Scientific). Overall variability is represented by a box plot, where white boxes represent the untreated control group at time point 0, gray boxes represent the sham control group, and boxes in a grid pattern represent the product-treated group. Except for the 18-hour time point, where samples from the sham control group showed higher transcriptome variability, there was no clear difference in variability between the product-treated and sham-controlled groups. [Figure 6]Molecular variability in the brain using all genes in the dataset: Molecular variability (MDP) of brain samples from both product and sham control 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® Pico Assay, mouse (Applied Biosystems, ThermoFisher Scientific) as defined above. Overall variability is represented by a box plot, where white boxes represent the untreated control group at time point 0, gray boxes represent the sham control group, and boxes in a grid pattern represent the product-treated group. When comparing the product-treated group and 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. It is also possible to see these group differences at 24 hours, although they appear to decrease at 48 hours. [Figure 7] Line plot of differentially expressed genes in the liver: The number of genes whose expression in the product treatment 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 upregulatory genes at each time point (expression in the treatment group is higher than in the sham control group), and the black line represents the total number of downregulatory genes. On the other hand, the dashed line represents the number of differentially expressed genes related to ADME, according to the same color coding (gray-up and black-down adjustment). In fact, there is a peak in DEG at 6 hours post-administration and almost no DEG at 48 hours (no evidence of significant change in ADME-related genes). [Figure 8]Line plot of differentially expressed genes in the kidney: The number of genes whose expression in the product treatment 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 upregulatory genes at each time point (expression in the treatment group is higher than in the sham control group), and the black line represents the total number of downregulatory genes. On the other hand, the dashed line represents the number of differentially expressed genes that are ADME-related, according to the same color coding (gray-up and black-down adjustment). There is a peak in DEG at 2 hours post-administration and almost no DEG at 48 hours (no evidence of significant change in ADME-related genes). [Figure 9] Line plot of differentially expressed genes in blood: the number of genes whose expression in the product treatment group is statistically different from that in the sham control group (adjusted p-value < 0.05). The gray line represents the total number of upregulatory genes at each time point (expression in the treatment group is higher compared to the sham control group), and the black line represents the total number of downregulatory genes. There is a peak in DEG at 0 hours post-administration, which may reflect an immediate effect on the blood transcriptome. A second peak in the number of DEGs occurs at 18 hours post-administration, which is also influenced by the higher variability of the sham control sample observed in molecular variability analysis. [Figure 10] Line plot of differentially expressed genes in the brain: the number of genes whose expression in the product treatment group is statistically different from that in the sham control group (adjusted p-value < 0.05). The gray line represents the total number of upregulatory genes at each time point (expression in the treatment group is higher than in the sham control group), and the black line represents the total number of downregulatory genes. There is a peak in DEG 2 hours after administration, which follows the same pattern as observed in other organs such as the kidneys and liver. The variation decreases sharply in a small number of DEGs at 6 hours post-administration, and there is no significant variation at 48 hours. [Figure 11]Line plot of the number of differentially expressed genes from physiological solution-treated samples to untreated samples at the 0-hour time point: the number of genes whose expression in the sham control group is statistically different from that in the untreated control group (adjusted p-value < 0.05). The gray line represents the total number of upregulatory genes at each time point (expression in the sham control group is higher than in the untreated control group), and the black line represents the total number of downregulatory genes. Blood shows similar peaks of variation at 0 and 18 hours. The brain showed a small number of downregulatory genes at 18 hours. In the kidney, some DEG variation occurred at 6 hours, peaking at 12 hours, after which the variation decreased. In the liver, several genes were statistically varied at 6–18 hours post-treatment. This figure shows that the sham treatment itself induces variation, and therefore a correct transcriptome comparison can be made between the product-treated group and the sham control group, and that the circadian cycle also affects transcriptome variation, and therefore the same time points between the product-treated group and the sham-treated group should be compared. [Figure 12] This figure shows the Area Under the Effect Curve (AUEC). This figure shows the total for all different biological samples tested (i.e., liver, kidney, brain, blood) (see Figures 1-10 and examples). It shows the transcriptome variation induced by the product under test (Epigen11 / AU) over time (hours). The 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 shaded area represents the AUEC.

[0054] term Unless otherwise defined herein, scientific and technical terms used in connection with the present invention shall have meanings generally understood by those skilled in the art. Furthermore, unless otherwise necessary in context, a singular form of a word shall include its plural form, and a plural form of a word shall include its singular form.

[0055] In any part of this specification or the claims, the expression “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 number of components / components obtained (e.g., extracted) directly from members of the natural world or their naturally occurring parts (i.e., from natural raw materials) without significant processing or synthetic alteration, and “without significant processing or synthetic alteration” means that no modification process is used to obtain the matrix from the raw materials. In other words, the natural raw material source is processed only by manual, mechanical or gravitational means, for example, by suspension, extraction with water or other naturally occurring solvents, steam distillation, heating only to remove water or any other naturally occurring solvent, or by extraction from air by any means, provided that the “natural matrix” excludes the members of the natural world “itself,” 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 a process for producing the matrix from starting materials, without intentionally adding synthetic products along the entire method. In this specification, 100% biodegradability is considered to be “easily biodegradable” according to the OECD biodegradability test. These characteristics ensure the maintenance of the matrix's effects through the presence of structural interactions (material interactions) by its constituent components and functional interactions (intangible interactions) that become apparent upon exposure of biological systems to the natural matrix. In other words, a natural matrix or mixture of natural matrices is a material obtained from entities that have been processed to preserve their natural biophysical characteristics, which self-assemble in nature and determine physiological interactions with other living organisms such as human organisms. Their newly emerging properties may manifest by contributing to the rebalancing of metabolic processes or states of recipient organisms and / or certain organs or tissues, along with physiological effects that are activated in their respective specific circumstances. According to the present invention, a natural matrix may be derived from materials obtained from any source in the biological kingdom, i.e., the Monera, Protistia, Fungi, Plant, and Animalia kingdoms.Therefore, this term encompasses plant natural matrices, animal natural matrices, fungal natural matrices, protist (archaea or bacteria) natural matrices, and Monera natural matrices. Natural matrices may also include natural inorganic materials such as minerals obtained from natural raw materials. In this specification, the synonyms for natural matrices or one or more natural matrices are “complex natural systems” or “natural materials” as defined below.

[0057] Examples of naturally occurring parts of living organisms may be represented, for example, by the roots, leaves, bark, fruits, flowers, or their sections, organs, and tissues.

[0058] In any part of the explanation, the general term "natural matrix" can be substituted with the following: Plant natural matrix or natural matrix obtained from plants, Animal natural matrix, or natural matrix obtained from animals or from animal products such as eggs or milk, Natural fungal matrix or natural matrix obtained from fungi, Protist natural matrix or natural matrix obtained from protists, Monera natural matrix or natural matrix obtained from Monera, or plant materials and / or extracts, extracts from animal tissues or organs, fungi and / or fungal extracts, or mixtures thereof. The extraction method does not involve any denaturation steps (e.g., temperature or use of denaturing solvents).

[0059] The term "plant" is synonymous with "herb."

[0060] The term "natural" matrix emphasizes that, because no denaturing treatment is performed to obtain it, it retains the integrity and complexity of its component / constituent network, just like the original natural source. Therefore, a natural matrix does not contain compositions of natural origin that are rich in specific molecules of artificial synthesis or isolated from natural raw materials. Furthermore, a natural matrix can only be obtained through 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 does not interact with a single target molecule, but rather behaves as a complex network that interacts with a network of receptors (which are also organized as a network) in the recipient organism. Therefore, the interaction between the natural matrix and the recipient organism does not result from a one-to-one point interaction, as with typical drug ingredients (APIs), but rather from a network interaction between the "interactor" network (i.e., the matrix) and the "receiver" network (i.e., the organism to which the matrix is ​​administered).

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

[0063] In this specification and in the art, “newly emerging properties” is a term used to define properties of natural matrices or natural materials, that is, properties expressed not merely as the sum of the properties of each isolated component / constituent of the matrix / material, but as a result of both the functional and structural interactions between all components / constituents of the matrix / material. This is also a result of the supramolecular self-assembly of the constituent components / constituents within the matrix / material itself.

[0064] Therefore, “newly emerging properties” refer to the technical effects that the interactions and relationships between the components / constituents of the natural matrix have on the receptor biosystem, such as therapeutic properties or homeostatic-adjuvant properties (i.e., beneficial effects). Newly emerging properties are those that are not immediately apparent, or even predictable, based solely on the individual characteristics of each component / constituent of the matrix. Instead, they “emerge” when all the components / constituents of the matrix network interact with each other and with the biosystem receptor network in a dynamic and complex way. Newly emerging properties are widely discussed in this field in various scientific and system-oriented disciplines, including physics, chemistry, biology, and complex systems theory.

[0065] Therefore, the newly arising properties cannot be predicted a priori from the qualitative and quantitative information of each component of the composition or matrix, and as a result, these properties cannot be attributed to one or more specific APIs. Consequently, although a composition containing multiple agents may exhibit unpredictable synergistic effects, the properties of the composition must remain determined by the specific APIs included and their quantities.

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

[0067] As used herein, “synthesis” has the conventionally accepted meaning in chemistry. Conventionally in chemistry, the term “synthesis” refers to the origin or source of a material or substance. Synthetic substances or materials are produced by humans through artificial synthesis, that is, by laboratory chemical reactions, which typically involve reacting simple chemicals to create more complex chemicals through processes that often use different pathways, temperature conditions, pressure conditions, energy sources and / or catalysts than those used by living organisms. Examples: Synthetic substances or materials include plastics, pharmaceuticals, and many industrial chemicals. For example, nylon is a synthetic polymer produced by chemical synthesis, and aspirin is a synthetic drug produced by a specific chemical reaction.

[0068] In this specification, “functional resilience” (also spelled “redundancy”) is intended to mean the therapeutic or beneficial (homeostatic adjuvant) persistence of a therapeutic or beneficial product containing or comprising one or more natural matrices, and this term means that the therapeutic or beneficial properties of a particular product containing or comprising one or more natural matrices are maintained in different batches despite the (inevitable) qualitative and quantitative compositional differences between batches that are inherently present in a product containing or comprising one or more natural matrices. As is known to those skilled in the art, each time different batches of starting materials are used, the resulting natural matrices will have a unique qualitative and quantitative composition. This is common at the molecular level, which is typical individual diversity among organisms of the same species.

[0069] A "healthy physiological state" refers to a state in which the body, organs, apparatus, systems, or regions of an organism and their associated internal processes function within optimal and normal parameters for that individual, i.e., a state toward homeostasis. In the context of one or more biological activities known to contribute to a hallmark of a particular (given) disease or pathological state or altered physiological state, a healthy physiological state refers to a state in which one or more biological activities operate within optimal and normal (healthy) parameters. This state is characterized by the absence of significant abnormal cellular processes or cellular processes associated with the particular disease under consideration. If alteration orientations of one or more biological activities corresponding to a pathological prepathological state are known, a healthy physiological state can be considered to be represented by the opposite alteration orientation for each of the activities. This term takes into account the hallmarks of a particular disease, which are characteristic functions or features 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 significant role in the onset or progression of the disease.

[0070] In summary, a healthy physiological state in the context of a specific disease or pathological / altered condition is a state in which one or more biological activities associated with the known hallmark of that disease or pathological condition are regulated in a direction consistent with the non-pathological / altered state, in other words, in the opposite direction to the pathological / altered state.

[0071] Therefore, the healthy physiological state according to the present invention also indicates a direction of regulation of one or more biological activities that is a known hallmark of pathological states in homeostasis, i.e., a homeostatic direction of regulation of one or more biological activities attributable to a specific system, region, apparatus or organ of a healthy organism prior to the manifestation of a pathological state.

[0072] "Altered physiological state" and "altered homeostasis" are closely related concepts that describe deviations from the 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 functioning of the body, including disruption of organ systems, biochemical processes, and cellular functions. Altered physiological states can result from various factors such as disease, injury, medication, environmental factors, and psychological stress. Examples include fever, inflammation, hormonal imbalance, and organ dysfunction.

[0074] Homeostasis (and its alteration): Homeostasis refers to the body's ability to maintain a stable internal environment despite external or pre-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 a narrow range. When these regulatory mechanisms fail to maintain balance and deviations occur from the body's normal setpoints, a change (impairment) in homeostasis occurs. These deviations can be temporary or chronic and may include compensatory mechanisms to restore balance.

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

[0076] As a disruption of homeostatic mechanisms that can lead to physiological imbalances and the manifestation of disease or dysfunction, changes in homeostasis underlie changes in the physiological state. Products that support the maintenance of homeostasis are products that help the body restore stability to its internal environment when it has changed.

[0077] As used herein, “disease hallmarks” or “pathological or medical conditions” have the meanings 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 prepathological condition, and together they usually represent a general pathological condition associated with a particular pathology. These hallmarks (also called “key indicators”) are typically a set of features or patterns that a physician monitors over time to track the onset, progression, or regression of a particular disease. In short, disease hallmarks are defining features or characteristics whose alterations indicate a given premedical or medical condition and help in its identification, diagnosis, monitoring, and understanding. For example, for neurodegenerative diseases (NDDs), at least eight hallmarks of NDDs are known in the art: (pathological protein) aggregation, synapses and neuronal networks (dysfunction), (abnormal) proteostasis, cytoskeleton (abnormal), energy homeostasis (alteration), DNA and RNA (defects), inflammation (increase), and neuronal cell death (increase). For example, in cancer research, cancer hallmarks are a set of distinctive features commonly found in cancer cells. These hallmarks include (persistence of) proliferative signaling, (evasion of) growth inhibitors, (resistance to) cell death, (enabling) replication immortalization, (inducing) angiogenesis, and (activation of) invasion and metastasis. Disease hallmarks, hallmark-related parameters (e.g., biomarkers), and one or more bioactivities associated with hallmarks constitute a framework for studying diseases, pathological conditions, or medical conditions using an integrative / holistic approach.

[0078] Hallmarks of changes in physiological state typically include observable changes in various aspects of bodily function, which may manifest through symptoms, signs, or laboratory findings.

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

[0080] Overall, hallmarks of changes in physiological status provide important clues (suggestions) for healthcare providers to identify the underlying cause, determine the severity, and guide appropriate interventions to restore normal function and promote recovery.

[0081] Since the natural matrix is ​​already known and therefore endogenous, native natural intelligence represents the intrinsic ability of the natural matrix to store and transmit the biological and physicochemical information necessary to interact with and integrate with other biological networks, using logic specific to the organism receiving them. This intelligence is the expression of natural autopoiesis, i.e., the ability to self-organize and adapt to environmental stimuli without artificial intervention, communicating messages according to a point-like logic and through mediating factors that are unknown to the organism receiving them and therefore exogenous.

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

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

[0084] When referring to subjects requiring beneficial or therapeutic treatment, this description pertains to individuals who are suffering from or at risk of developing a pathological condition.

[0085] ADME, as used herein in the sense commonly used in the art, is an acronym representing absorption, distribution, metabolism, and excretion, and refers to the fundamental pharmacokinetic processes that determine the fate of a drug or other substance in the body after administration. This includes the entry of the substance into the bloodstream (absorption), its diffusion into body fluids and tissues (distribution), its chemical transformation by enzymatic systems, mainly in the liver (metabolism), and its elimination from the body, typically via the kidneys or bile pathways (excretion). ADME research is important in drug development for understanding the bioavailability, pharmacokinetic profile, efficacy, toxicity, and appropriate administration strategies of substances.

[0086] Product A or EpigenAU / 11 as defined herein are therapeutic products based on natural matrix as defined in the Examples section. Conventional definition of ADME pharmacokinetic parameters:

[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 indicates the peak exposure to a drug and is often related to the intensity of its pharmacological effect or potential toxicity.

[0089] Definition of Tmax (Time to Maximum Concentration): The time required to reach Cmax after drug administration. Tmax reflects the absorption rate; a shorter Tmax means the drug is absorbed more quickly.

[0090] Definition of AUC (Area Under Curve): The area under the plasma concentration-time curve, which represents the total drug exposure over time, used to determine the degree of absorption and bioavailability. A larger AUC indicates that more of the drug is entering the systemic circulation.

[0091] MRT (Mean Residence Time): Reflects the average duration that molecules remain in the system.

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

[0093] Clearance (CL) is defined as the volume of plasma (e.g., mL / min or L / hour) in which a drug is completely removed per unit time, reflecting the efficiency of drug elimination through metabolism and excretion. This 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, a sham control animal is an animal that undergoes 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, according to the formal definition in the scientific literature. The sham group serves as a control to isolate the specific effect of the experimental treatment from the general treatment effect. In summary, a sham procedure in animal testing is a control method designed to reproduce all aspects of the experimental intervention, excluding the active ingredient / product, in order to accurately determine the specific effect of the intervention.

[0095] The definitions of these terms are intended to serve as a basis for paraphrasing or clarifying the claims or description.

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

[0097] The conventional ADME (absorption, distribution, metabolism, and excretion) framework 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 pharmacokinetic effects, based on reductionism and a molecular-centered strategy. 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 mentioned 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] For example, natural matrices such as plant extracts are inherently variable and complex mixtures of structurally diverse secondary metabolites (polyphenols, flavonoids, terpenoids, alkaloids, glycosides, etc.) with vastly different 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 unstable components may lack validated analytical standards, making their accurate quantification impossible. Furthermore, the presence of matrix effects (interference from other components in the extract) frequently complicates or hinders accurate measurements, even when advanced analytical platforms are employed.

[0099] Furthermore, conventional analytical techniques typically focus on a pre-selected subset of "marker" compounds, chosen based on chemical abundance or ease of detection rather than biological relevance. This introduces inherent bias, potentially leading to incomplete or distorted judgments about the true pharmacokinetic and pharmacodynamic profiles of the extract.

[0100] A fundamental limitation of molecular approaches (e.g., ADME) is their inability to capture the functional and expressive properties of natural matrix-based products. As is already known, natural matrices, such as plant extracts, exert their biological effects not as a simple sum of their individual components, but through multilayer interactions. These include synergistic interactions (where multiple molecules enhance each other's bioavailability, stability, or pharmacodynamic effects), 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 different but interconnected molecular targets or pathways).

[0101] These multilayer interactions typically 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 through metabolic transformation, microbiome activity, or conjugation processes, further complicating any attempt at direct analytical correlation. A further significant limitation of conventional approaches is the temporal dissociation between the pharmacokinetics of detectable components and the duration of their biological effects. Often, the therapeutic or physiological effects of natural matrix-based products persist far beyond the timeframe in which the original compound or its known metabolites are detectable in plasma or tissue. This can be due to tissue accumulation of certain compounds or their derivatives with delayed release or activity. Epigenetic modifications, transcriptional reprogramming, or other long-term biological adaptations are triggered by short-term exposure to and regulation of endogenous regulatory systems (e.g., immune, endocrine, or metabolic pathways) that remain altered even after the compound has been metabolized and removed.

[0102] Under these circumstances, conventional ADME metrics cannot provide reliable estimates of the biological persistence, therapeutic efficacy, or safety profile of extracts.

[0103] The analytical and conceptual challenges described above are not hypothetical but are routinely encountered in the pharmacokinetic assessment of numerous natural matrices. Typical examples include plant-based natural matrices containing both rapidly eliminated and persistent components, such as flavonoids and terpenes, which often exhibit significantly different half-lives and 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 remarkably different pharmacokinetic properties, particularly elimination half-lives. Furthermore, naturally occurring metabolites can often be metabolized to become endogenous metabolites. This further reinforces 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), where polyphenol-rich extracts, anthocyanins, and catechins (e.g., in berries or green tea) typically exhibit a T1 / 2 of 2–3 hours, while 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); and triterpenoid saponins (Saikosaponin A vs. D). Here, saikosaponins A and D differ only slightly in their glycoside positions, but in comparative pharmacokinetic studies, saikosaponin A has an elimination half-life of approximately 4.5 hours and saikosaponin D has an elimination half-life of approximately 8.2 hours in rats, with type D consistently showing longer persistence (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 close analog, levistilide A, are significantly different.Specifically, the incubation period is approximately 14 hours for Z-ligustilide and approximately 24 hours for levistilide 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-derived natural matrices undergo metabolic transformation by liver enzymes or the gut microbiota to produce bioactive metabolites in vivo, generating compounds with higher stability, greater bioactivity, or different pharmacological properties compared to the original components, resulting in long-lasting biological effects even after the measurable compounds have been eliminated from plasma or tissues. Furthermore, there is an objective lack of purified reference standards, and in fact, some trace components of the natural matrix that have potential biological relevance are either not commercially available or difficult to handle analytically, making it impossible to quantify and pharmacokinetically profile them.

[0105] In such situations, the absence of measurable plasma levels of one or more "marker" compounds does not necessarily indicate the absence of pharmacodynamic effects. Under such conditions, applying conventional ADME parameters to a natural matrix can be misleading and undeniably incomplete regarding the pharmacodynamic reality. Practical examples support these challenges. Plant extracts often contain compounds with fundamentally different half-lives, resulting in asynchronous concentration profiles.

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

[0107] Surprisingly, as is directly evident from Figure 11, the inventors found that, in the experimental design of this method, because the sham treatment alters the transcriptome, samples from untreated animals are unsuitable as controls for analyzing transcriptome changes. Therefore, they also found that transcriptome data from treated animals should and must be compared with the transcriptome data of the 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 influenced by the animal's circadian rhythm are also observed with respect to untreated controls at t0. To perform reliable and useful differential expression analysis, compared to untreated animals, the treated product cannot eliminate differentially expressed genes (DEGs) induced by the experimental design itself. Furthermore, comparisons between the treated product and the sham treatment group must be performed at two or more time points to define the appearance and disappearance of variations. Multiple time points allow us to provide further relevant information, such as the precise disappearance time, the duration of the variation, and the ADME equivalent parameters, as shown in the table below.

[0108] Furthermore, the inventors have also found that transcriptome analysis of DEG ADME-related genes is insufficient to show the desired functional characteristics of the expression, duration, and disappearance of the biological effects of the analyzed therapeutic or beneficial product (preferably a natural matrix-based product) provided by the method of the present invention for these organs (e.g., the liver and kidney) in which ADME-related genes are disclosed in the Art. In practice, as is evident from Figures 7 and 8, analysis focusing solely on ADME-related genes provides very useful information, such as information on secretion time when renal ADME-related genes are analyzed, but it cannot provide line plots that imply upregulation or downregulation related to the genes induced by the administration of the therapeutic or beneficial product under examination.

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

[0110] The objective of the present invention is, A method for functionally characterizing the onset, duration, and disappearance of the biological effects of a therapeutic or beneficial product, preferably containing one or more natural matrices, after administration to a living organism, wherein the method is: (a) Extracting RNA from biological samples derived from different tissues, i.e., treated samples, obtained at multiple time points after administering a natural matrix-based product to a living organism, and also extracting RNA from corresponding biological samples from a sham control living organism obtained at the same time point (e.g., using the same administration and handling protocol as the vehicle / placebo), i.e., sham control samples; (b) Perform transcriptome analysis, including gene expression profiling, on RNA extracted from the treated samples and sham control samples; and identify transcriptome changes based on differentially expressed genes (DEGs) that are significantly differentially expressed in the treated samples at each time point compared to the corresponding sham control samples; (c) Define the expression, duration, and elimination profiles of the biological activity induced by the administration of the product, Here, The time point of expression corresponds to the time point at which significant transcriptome changes were first observed in at least one of the treated samples with respect to the corresponding false sample. The time point of disappearance corresponds to the time point at which a significant transcriptome change is last observed in the treated sample compared to the corresponding sham sample. The aforementioned duration corresponds to the time interval from the time point of appearance 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 a treated sample obtained from that particular organ, and the Pmax calculation can be extended to a whole-body level.

[0113] The fTmax of a product for a specific organ is the time point at which the highest significant transcriptome variation peak was observed in the processed sample obtained from that specific organ, while the fTmax of the overall product corresponds to the time point at which the maximum total number of significant transcriptome variations was observed among all processed samples from all organs. The AUEC of a product is the numerical integral of the DEG or MDP area with respect to time.

[0114] The pharmacokinetic concept of half-life indicates the time required for a concentration to be halved, and there is no direct equivalent in the transcriptome response. This is because the transcriptome response does not typically decay linearly (as can be seen in Figure 12, a second, less intense peak follows the first peak). A more appropriate analogue, therefore, is the "functional mean residence time (fMRT)," which reflects the average duration that the transcriptome fluctuations remain in the system. In the art, MRT is calculated by the AUMC / AUC ratio according to the formula shown in the detailed description, and the elimination time is the first time point where there are no further significant transcriptome fluctuations (end of fluctuations), and according to the present invention, fMRT is calculated as the AUMC / AUEC ratio.

[0115] The organ-specific elimination time (fTres) corresponds to the time point at which the basal transcriptome is restored by the administration of a therapeutic or beneficial product (the end of transcriptional variation is defined as a transcriptome variation of less than 5%Pmax for all tissue samples except blood, and the end of transcriptome variation is defined as less than 12%Pmax, preferably less than 5%Pmax). As can be expected, the transcriptome variation in blood samples is more unstable because blood flow is susceptible to changes due to fluctuations such as blood glucose peaks immediately after feeding and the change of day and night. Taking a systemic perspective into consideration, fTres is defined as the first time point at which all tested organs, in organ-specific terms, reach a transcriptome variation of less than 5%Pmax for the eliminated blood, and the end of transcriptome variation is defined as less than 12%Pmax, preferably less than 5%Pmax.

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

[0117] As is evident from the figure, the method of the present invention provides a period-specific line plot showing the onset, duration, and disappearance time frames of the biological effects (represented by measurable transcriptome fluctuations) induced by the administration of the tested product. Organ-specific data can then be compared and refined to provide information at the biological level. In other words, the method of the present invention provides a biological persistence and clearance profile of a product based on the functional transcriptome response at the biological level, without relying on the direct quantification of the individual chemical components of the product. The biological-level profile provided by the method of the present invention is obtained by an accompanying analysis of transcriptome fluctuations in a biologically representative selection of tissues. Furthermore, analysis of transcriptome fluctuation data obtained from either a biologically representative sample or a selected organ-tissue sample according to the present invention can provide ADME equivalent functional parameters summarized in the table above.

[0118] The method of the present invention advantageously enables 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 if the original compound is, in principle, no longer measurable in plasma, data that cannot be obtained by classical ADME assessment), and to obtain a reliable indicator of any persistent or delayed potential toxic effects. In other words, the method of the present invention can provide a reliable measure of the effect of a therapeutic or beneficial product when administered to a subject, even when classical ADME assessment is not technically or economically feasible.

[0119] In fact, ADME evaluation for products where the active compound cannot be determined, such as products based on natural matrices, requires the precise determination of every single compound in the product. It should be noted that ADME evaluation is necessary for each and every compound in the product, precisely because it is impossible to determine the "active compound." Similar to natural matrices such as plant extracts, the matrix contains hundreds or even thousands of compounds on average, making a complete ADME cost impractical. Indeed, market analysis estimates that preclinical pharmacological research, including ADME / PK profiling, accounts for approximately 10% of the total preclinical development budget for new drugs. Given an average total preclinical investment of approximately $6.2 million, this translates to an average ADME / PK cost of approximately $600,000 per candidate compound.

[0120] In practice, a complete DMPK preclinical package (including method development, plasma stability, protein binding, and multi-PK testing) typically costs between $150,000 and $235,000. A single in vivo ADME study, such as a radiolabeled mass balance experiment in a single animal model, is typically priced in the lower range of tens of thousands per type. These figures apply to both the US and Europe, and pharmaceutical companies often outsource to established CROs for regulatory compliance and improved data quality. Thus, it is clear that ADME evaluation is not a technically or economically viable tool for such products. Furthermore, as already mentioned in the explanation of the background technology, some drugs have been reported to exhibit relevant effects even after excretion, and therefore ADME lacks essential information for these types of drugs, i.e., it does not inform developers and users 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 beyond the excretion time of the analyzed product, is advantageously suited to all therapeutic products, including natural matrix-based products, and provides a functional and holistic measurement of the biological response to the product under examination, independent of the quantification of chemical markers (and therefore independent of the batch differences typical of these products), enables the identification of both transient and persistent biological effects (persistent effects are generally indicated by the persistence of transcriptome changes in one or more organ samples when the transcriptome changes induced by the product disappear in most other organ samples), and safety-related molecular changes, providing an objective characterization of the pharmacokinetic and pharmacodynamic profiles in natural matrix-based products.

[0122] In short, the present invention is significantly more advantageous than conventional ADME for many reasons. The present invention makes it possible to monitor the actual response of an organism rather than tracking individual molecules. The method of the present invention also enables the identification of temporal evidence and sustained effects of beneficial compound-host interactions, even when the components of individual natural matrix-based therapeutic or beneficial products cannot be measured in plasma. Therefore, it becomes possible to rapidly observe changes in gene expression that persist beyond the expected duration of action, even without any "chemical traces" of the compound.

[0123] The method of the present invention also provides an improved safety assessment method. Slow regression to baseline demonstrates batch-to-batch robustness (reproducibility) along with transient effects rather than evidence of constantly occurring molecular-level variations. This is because transcriptomics reduces the impact of chemical variations between different lots by detecting the "functional" effect of the extract in vivo.

[0124] Furthermore, the present invention enables even more robust and useful risk management: by evaluating the actual biological (transcriptome) response, more reliable parameters are provided for assessing safety and effectiveness, especially when lot-to-lot variability exists.

[0125] The present invention is a method for functionally characterizing the onset, duration, and disappearance of the biological effects of a therapeutic or beneficial product after administration to a subject, and this method is (a) Extracting RNA from biological samples of different tissues obtained from living organisms (i.e., treated samples) and from sham control living organisms (i.e., those administered an appropriate placebo or vehicle according to the same administration route, dosage, etc., as used for the product under test) at multiple time points after administration of a natural matrix-based product, and from corresponding biological samples obtained at the same time points (i.e., sham control samples), (b) Perform transcriptome analysis, including gene expression profiling, on RNA extracted from the treated samples and sham control samples; and identify transcriptome changes based on differentially expressed genes (DEGs) that are significantly differentially expressed in the treated samples at each time point compared to the corresponding sham control samples; (c) Defining the onset, duration, persistence, and temporal disappearance of transcriptome variations for each of different tissues, based on the variations of each transcriptome identified in b) for samples of the same tissue, This method provides profiles of the onset, duration, and disappearance of the biological effects of a product without relying on the direct quantification of the individual chemical components of the product.

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

[0127] Therefore, 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 other than humans.

[0128] It is clear that the sham and product-treated animals, as well as the untreated control animals, are all the same species with the same genotype.

[0129] According to the method of the present invention, the appropriate control group for observing transcriptome perturbation induced by product administration is a sham control, not an untreated control. The inventors have found that transcriptome perturbation caused by circadian rhythm and / or treatment-related stress (e.g., injection) presents a confounding effect when comparing treated and untreated samples (see Figure 11). Therefore, the analysis of transcriptome perturbation should be performed on product-treated and sham control-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 derived from different tissues (multiple tissues) selected from among the most representative of the whole organism's response to the administration of the product, and in one non-limiting aspect of the present invention, the tissue can be selected from one or more of the following: blood, liver, kidney, brain, hypothalamus, lung, heart, spleen, testes, stomach, intestine, gallbladder, pancreas, various glands, ovaries, and muscle tissue (for example, the injection site when administration is performed by intramuscular injection). In a preferred embodiment, the tissue includes at least blood, liver, kidney, brain, lung, and heart tissue.

[0131] Depending on the estimated therapeutic or beneficial effects of the product subjected to the test, and the class of patients for whom the product is intended, a person 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 example and reported in the figure is an anti-cancer product (also defined herein as EpigenAU / 11, or Product A). When referring to Clariom® S Pico Assay, Mouse (Applied Biosystems, ThermoFisher Scientific) and its gene set, the gene set is the set annotated by the manufacturer on July 30, 2018.

[0132] Therefore, it is clear that the method of the present invention can encompass local effects in tissues from numerous organs and / or systems of the treated subject, and thus can provide relevant information regarding the biological effects of the analyzed product, and can thus provide a much more comprehensive profile of the product's biological effects at the "biological level" over time.

[0133] The sample should preferably have been treated with a tissue and RNA stabilization reagent to preserve RNA integrity before storage. Any suitable commercially available product can be used, such as RNAlater®, NA / RNA Shield® (Zymo Research), Allprotect Tissue Reagent (QIAGEN), or RNAprotect® Tissue Reagent (QIAGEN) by Thermo Fisher Scientific, following the manufacturer's instructions.

[0134] According to the present invention, the multiple time points are multiple post-administration time points for tracking the effects and influences over time, and include T0, which represents the time immediately after administration of the product under test, and at least one, preferably at least two, and 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 at or above the time point of elimination.

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

[0136] If the disappearance time, i.e., the time at which significant transcriptome fluctuations can no longer be detected, is 48 hours after T0, then Tn will include time points where n is greater than 48, for example, 72.

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

[0138] If T48 is found to be insufficient for the disappearance of the transcriptome fluctuations induced by the administration of the tested product, the method of the present invention can be repeated at an additional time point where n is > 48.

[0139] If transcriptome fluctuations have disappeared in most test organs or tissues, but persist in one organ, this generally indicates that the effects of the tested product are persisting in that organ, or that damage induced by the tested product has occurred in that organ.

[0140] Therefore, in contrast to ADME, the method of the present invention not only provides an overall 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 a sample by any generally known method or using any suitable commercially available kit.

[0142] Transcriptome profiling can be performed using procedures commonly used in this 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), sequenced, or labeled and hybridized on a commercially available or prepared microarray using pre-designed probes, or profiling can be performed by directly hybridizing target RNA to a fluorescent barcode such as NanoString nCounter, or by qPCR-based arrays (e.g., TaqMan, Biomark Fluidigm).

[0143] One aspect of the present invention involves processing transcriptome data to ensure accuracy and comparability between samples. Generally, data processing includes normalization to correct for technical variability and background noise. Preferably, a normalization method such as signal-space transformation-robust multi-array mean (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 Roes normalization, may also be used. A suitable tool for such processing is, for example, transcriptomics analysis console software (ThermoFisher Scientific) that applies SST-RMA normalization. Alternatively, those skilled in the art may utilize Affymetrix Power Tools (APT), an R / Bioconductor environment (e.g., the "Oligo" or "Affine" package), a custom preprocessing pipeline that replicates SST adjustment followed by RMA normalization, or other equivalent software capable of performing signal spatial transformation correction in combination with robust multi-array averaging.

[0144] After normalization, differential expression (DE) analysis is performed to identify genes showing 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 be used as alternatives. The analysis involves estimating whether the differences in gene expression between treatment groups (e.g., treatment group vs. sham control or untreated control) are statistically significant using a linear model specifically designed for microarrays (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 detection rate (FDR) and the risk of false positives across multiple tests. Preferably, the well-known Benjamini-Hochberg (BH) procedure is used (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 the Bonferroni correction may be applied. Genes are considered differentially expressed when their adjusted p-values ​​fall below a given threshold, typically 0.05, providing strong evidence of true differential expression.

[0146] According to a non-limiting example of the present invention, as described above, when testing significance for many genes (thousands of hypotheses at once), it is important to control for multiple tests in order to avoid false positives. This can be done, for example, using the Limma package “Linear Models for Microarray Data”, a widely used biostatistical software package that uses BH to control for FDR (False Detection Rate) (3), which 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, with the smallest p-value being rank 1, the second smallest p-value being rank 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 this invention, genes are considered differentially expressed (DEG) if their adjusted p-values ​​are less than 0.05. Lower values ​​correspond to a lower expected proportion of false discoveries between genes declared significant and provide stronger evidence of true differential expression.

number

[0148] Furthermore, molecular variability (MDP) analysis can be used to determine overall transcriptome variability at the sample level. MDP analysis is based on calculating a Z-score for each gene, reflecting the deviation from the median expression of a reference (preferably an untreated control sample). Alternative statistical measures such as t-scores or multiplier thresholds can also be considered for similar analysis. The variability score for each sample is calculated by computer using only genes with significant deviations, i.e., those with an absolute Z-score of ≥ 2. The variability score represents the mean of significant gene expression deviations, thereby quantifying the degree of transcriptome disruption.

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

[0150] This method is independent of the chemical composition because it is based on profiling the overall effect of the product and therefore does not require specific chemical markers.

[0151] As is evident from the examples, including the embodiments, the method of the present invention provides consistent results for each tissue analyzed. The 48-hour time frame for product effect clearance observed in the examples is relevant to the specific product tested, and it will be apparent to those skilled in the art that the time for the disappearance of transcriptome changes may differ from product to product. If different tissue samples show different disappearance times for transcriptome changes induced by the administration of the tested product, when determining the systemic disappearance time, the disappearance time corresponds to the first period in which no further significant changes are observed when all tissues tested are faced with the corresponding treated product and sham control samples.

[0152] Furthermore, this method can further include MDP analysis. While DEG analysis identifies specific genes with significant expression changes and shows the overall development of transcriptome changes over time, MDP analysis provides an overall measure of transcriptome shifts, as also shown herein (see Figures 1-6 as “Variation Scores”). This dual approach enables a comprehensive characterization of the biological response to treatment.

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

[0154] Furthermore, using normalized data, molecular variability (MDP) can be calculated, expressed as a "variability score" for samples from both the product-treated and sham-treated groups relative to an additional untreated control group. This analysis provides a comprehensive 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 is overall from a typical healthy control (untreated control sample) used as a baseline. In this specification, claims, and drawings, "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 evaluating the degree to which the expression profile of a particular sample deviates from the expression profile of a healthy reference. Those skilled in the art can use commonly available software, such as an MDP package, to calculate the variability score for each sample j by:

[0156] 1. Calculate the Z-score for each gene. This is a measure of how much the expression of a gene in each sample differs from the typical value in a healthy control (the expression value i for each gene is subtracted from the median (Ci) of the same gene in the control sample, and divided by the variability (Vi) of this gene in the control sample).

[0157] 2. Only genes with a significant deviation (absolute Z-score value ≥ 2) are analyzed by computer.

[0158] 3. The sample variation score is the mean of these significant deviations and represents how "variable" overall gene expression is compared to the untreated reference.

[0159] MDP format:

number

number

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

[0161] Therefore, the MDPi for each sample i is calculated by taking the average of 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, obtained by dividing the mean of the genes in the reference group 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 is evident from the following examples and figures, both DEG and MDP analyses revealed that the transcriptome fluctuations for the tested product peaked shortly after administration and subsided within 48 hours, showing a trend towards recovery over time. Furthermore, in certain preferred embodiments, particular attention is paid to analyzing known ADME-related gene sets in liver and kidney tissue. In Figures 2 and 4, differentially expressed ADME genes are identified, and in MDP analysis, fluctuation scores are computer-calculated using only ADME-related genes, providing a focused assessment of metabolic and excretory functions.

[0163] Furthermore, as described above, this method provides various ADME-like functional parameters as follows. [Table 2]

[0164] Therefore, according to one embodiment, the method disclosed herein is a. To provide transcriptome peak Pmax values ​​of the product for one or more organs, representing the maximum intensity of transcriptome changes induced by administration of the product in those organs. b. To provide the functional Tmax, fTmax, and values ​​of the product for one or more organs, representing the time point at which the maximum peak of transcriptome variation induced by the administration of the product in an organ is observed; and c. To provide the area under the systemic effect 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 that defines the average effect / variation time induced by product processing, wherein fMRT = AUMC / AUEC, where AUMC is the area under the first moment curve and AUEC is the area under the effect curve defined in c.

[0165] MRT General Formula MRT = AUMC / AUC

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

number

[0167] According to the present invention and the latest technology, integrals can be calculated using trapezoidal estimation, and therefore the above formula can be extended as follows.

number

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

[0169] In this invention, AUC corresponds to the area under its 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, and this is a precise 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 the time multiplied by the effect / variation. In contrast, AUEC is also the area under the curve, but here the x-axis represents both time and the y-axis represents only the effect / variation.

[0172] In classical pharmacokinetics, MRT represents the average time a molecule remains in the body. As already stated, the method of the present invention does not depend on parameters related to molecules or molecular concentrations; therefore, the parameters provided herein are computer-calculated as the average effect / variation time of the analyzed treatment.

[0173] A therapeutic or beneficial product may be any therapeutic or beneficial product, and in a preferred embodiment, the product consists of or includes 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 plants or animals (e.g., coral skeletons, eggshells, etc.), or a fraction of an extract. In particular, an extract that does not alter the original components of the natural source used, such as an aqueous extract, is preferred.

[0174] Non-limiting examples of plant matrices include plant-derived matrices, e.g., medicinal plants 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 gum; fungal matrices, e.g., mushroom mycelium, reishi extract, cordyceps extract, and chaga extract; marine-derived products, e.g., fish collagen, krill oil, whole sea cucumber extract, brown algae extract, algae extract or part thereof; mother of pearl Pearl powder, animal-derived materials such as bovine or porcine collagen, placental extract, lanolin from 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 concentrate and colostrum fraction; insect-derived materials such as bee venom, propolis, royal jelly, silk fibroin from silkworm cocoons, chitosan from insect or crustacean shells; microbial products such as bacterial cellulose derived from Acetobacter, whole-cell probiotics, fermented plant extracts, and microbial matrices rich in exopolysaccharides; natural clays and mineral matrices concentrated with bioactive plant or microbial components, such as montmorillonite clay filled with herbal extracts.

[0175] For the present invention to be meaningful, these materials are typically used in their complex, multi-component forms.

[0176] A specific, non-limiting example of a therapeutic product based on a natural matrix is ​​the EpigenAU / 11 product disclosed in the examples. As is evident from this specification and the examples, the method of the present invention offers the following key advantages over conventional ADME-based strategies.

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

[0178] By effectively bridging the gap between the measurable presence of individual molecular components and the persistence of pharmacological or biological effects, it provides functional and holistic measurements of biological responses, independent of the quantification of chemical markers.

[0179] In fact, by capturing the dynamic changes in gene expression over time, it becomes possible to detect and characterize functionally relevant biological responses even when the original matrix components are no longer detectable in plasma or tissue. Furthermore, by analyzing not only ADME-related genes but the entire protein-coding transcriptome, the method of the present invention reliably captures all relevant biological signals, whether they arise from adaptive, beneficial, toxicological, indirect, or systemic mechanisms of action.

[0180] In this way, an accurate, time-resolved mapping of the onset, peak, and elimination stages of the product's pharmacodynamic activity is provided. In fact, analysis of gene expression patterns according to the present invention reveals a temporal dynamic profile of biological activity, enabling the detection of pharmacodynamic effects even when the original chemical components are no longer detectable in plasma or tissue. This method provides a detailed time-resolved map of the product's activity in multiple organs, highlighting the sequential development of metabolism (liver), excretion (kidney), and systemic responses.

[0181] Temporal resolution allows for a clear distinction between early and late biological effects. This supports a comprehensive understanding of the pharmacokinetic and pharmacodynamic profiles of the tested product.

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

[0183] Furthermore, it enables the identification of safety-related molecular fluctuations. This is because the gradual return of the transcriptome signature to baseline values ​​over time is a robust and reliable indicator that biological recovery and the absence of chronic molecular fluctuations are present.

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

[0185] As discussed, demonstrated, and illustrated above, 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 reveals biologically relevant parameters for evaluating the safety, efficacy, and consistency of multi-component plant products, thereby improving the robustness of preclinical and regulatory documentation.

[0186] Ultimately, the present invention provides a significant methodological advance in the pharmacokinetic and pharmacodynamic determination 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 conventional ADME methodologies, and is directly applicable to preclinical development, safety evaluation, efficacy profiling, regulatory submission preparation, and product standardization strategies for plant-based and natural matrix-based therapies.

[0188] This method enables unprecedented functional profiling of the natural matrix, overcoming the inherent limitations of conventional ADME approaches while providing ADME-like information and thus being applicable to multiple therapeutic and regulatory situations.

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

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

[0191] Wherever an Internet address or URL is indicated in this specification, these refer to an Internet address available as of the filing date of this application, i.e., information retrieved from the most recent version of the content at that Internet address as of the filing date of this application.

[0192] Wherever a product on the market is referenced in this specification or in the claims, the ™ (trademark) or © (copyright) symbol is considered implicit and may be added to each product from time to time.

[0193] The following examples illustrate, but are not limited to, the foregoing description, and such examples should not be considered to limit the foregoing description or the subsequent claims. Furthermore, the following examples report all studies conducted on the products of the present invention and support all the subject matter claimed.

[0194] Example (Example) 1. Composition of the product under test Product A in this specification 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 Product A contains 0.89% by weight of freeze-dried component 3, totaling 100% w / w.

[0195] Ingredient 1 Bay laurel (Laurus nobilis) leaves 25% w / w Ashwagandha (Withania somnifera) roots 25% w / w Leaves and flowers of Filipendula vulgaris (25% w / w) Broccoli variety (Brassica oleracea L.botrytis cymosa) seeds 25% w / w Co-extraction was performed 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 Tanacetum parthenium L. flowers 42.85% w / w Co-extraction was performed in water for a total of 100% w / w.

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

[0198] All animal manipulations were carried out in accordance with European Parliament and Council Directive 2010 / 63 / EU (September 22, 2010) concerning the protection of animals used for scientific purposes. The University of Florence's ethical policy follows the guidelines for the management and use of laboratory animals of the U.S. National Institutes of Health (NIH Publication No. 85-23, revised 1996; University of Florence Assurance No.: A5278-01). Formal approval for conducting the described experiments was obtained from the Animal Subjects Review Board of the University of Florence. Experiments involving animals were conducted in accordance with the ARRIVE guidelines. All efforts were made to minimize animal suffering and reduce the number of animals used.

[0199] 2. Examples of applications of the present invention: Products based on EpigenAU / 11 natural matrix As a non-limiting example of its 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. The animals were maintained under standard barrier facility conditions (temperature 22±2°C; relative humidity 50-60%; 12 / 12 hour light-dark cycle) and were given free access to sterile food and water.

[0201] Experimental group: Baseline group (0 hours, no infusion): 3 mice. Treatment group: Subcutaneous injection of 400 μL of Epigen AU / 11 (50 mg / mL) per animal. Pseudo-control group: Subcutaneous injection of 400 μL of physiological 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 slaughtered (three were treated with EpigenAU / 11 and three with vehicles).

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

[0205] The 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 the QIAsymphony SP instrument.

[0208] RNA integrity was verified using an Agilent 2100 Bioanalyzer and quantified by spectrophotometric analysis.

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

[0210] 4. Data Processing and Analysis We used two analytical strategies:

[0211] Molecular variability (MDP) - Overall quantification of transcriptome deviation compared to untreated control. - Captures subtle and widespread gene expression changes across the entire transcriptome.

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

[0213] 4.1 DE analysis: Transcriptome data were processed using transcriptome analysis console software (ThermoFisher Scientific) with SST-RMA normalization applied (SST-RMA normalization is a method developed by this software to adjust for technical variability and background noise and ensure that gene expression values ​​are equivalent 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 specifically designed for the microarray platform to estimate whether the difference in gene expression between groups (e.g., EpigenAU / 11 vs. physiological solution or physiological solution vs. untreated) is 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 significance for many genes (thousands of hypotheses at once), there is a risk of obtaining some false positives by chance. To avoid this problem, it is important that the lymma package coordinates multiple tests performed by controlling the FDR (false discovery rate) using 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, with the smallest p-value being rank 1, the second smallest being rank 2, and so on, up to the total number of tests (m). The false detection rate (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 trials.

number

[0215] A gene was considered differentially expressed (DEG) if the adjusted p-value was less than 0.05. Lower values ​​correspond to a lower expected proportion of false discoveries between genes declared significant, and indicate stronger evidence of true differential expression.

[0216] 4.2 MDP Using normalized data, molecular variability (MDP) was calculated for samples from both the EpigenAU / 11 and sham control groups compared to the untreated control group. This analysis provides a comprehensive picture of transcriptome changes at the sample level. This method does not focus on individual genes: it determines how globally "different" this sample is from a typical healthy control.

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

[0218] 1. The Z-score for each gene is calculated by computer to measure how much the gene expression in each sample differs from the typical value in a healthy control (calculated by subtracting the median (Ci) of the same gene in the control sample from the expression value of each gene i, and dividing by the variability (Vi) of this gene in the control sample).

[0219] 2. Only genes with a significant deviation (absolute Z-score ≥ 2) are analyzed by computer.

[0220] 3. The sample variation score is the mean of these significant deviations and represents how "variable" overall gene expression is compared to the reference.

[0221] MDP type

number

number

number

number

number

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[0222] Assuming each gene j is included in the analysis, this applies only if its absolute Z-score is 2 or greater.

[0223] Therefore, the sample variation (MDPi) for each sample i is calculated by averaging the Z-scores (Z-scores with an absolute value of ≥ 2) of significant genes for each sample i, 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, obtained by dividing the mean of the genes in the reference group 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 biological responses.

[0225] Importantly, the sham control samples also showed transcriptome variability compared to the untreated control samples. This reflects not only circadian rhythms and variations in gene expression throughout the day, but also other influences such as stress caused by 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 a physiological solution. This approach was designed to minimize confounding effects of circadian gene expression variations.

[0226] The molecular variability (MDP) approach assesses the variability of the entire transcriptome in each sample at the sample level by quantifying the deviation of gene expression profiles up to the median of the untreated control group, without performing statistical tests on individual genes. Differential expression (DE) analysis, on the other hand, 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 overall shifts in the transcriptome for all genes or specific subsets. DE analysis identifies different genes that may be key to the biological response and looks for group differences between EpigenAU / 11 treatments relative to sham treatments at each time point. Therefore, the same results are not always obtained using either method. Importantly, both suggest that the peak of variability caused by EpigenAU / 11 in different organs decreases by 48 hours post-administration.

[0227] For the liver and kidneys, both approaches further focused on genes potentially associated with ADME methods, based on datasets described for each organ (for the liver: 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 kidneys: 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). DE analysis identified the number of DEGs that were part of the ADME-specific list for these organs. For MDP, a full analysis was performed using only these target genes.

[0228] 5. Results and Discussion 5.1 Summary of Observed Biological Responses Applying organ-specific transcriptome analysis to EpigenAU / 11 revealed clear time-dependent biological responses in multiple organs in vivo. We evaluated product-induced transcriptome changes using two complementary analytical strategies:

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

[0230] Molecular variability (MDP), used to quantify the overall transcriptome deviation of each sample from untreated controls considered as reference physiological states, is an overall and integrated measure of gene expression variation.

[0231] This dual analysis framework enables comprehensive characterization of the biological responses to EpigenAU / 11 administration, allowing both a broad / wide-ranging assessment of the transcriptome landscape and targeting of metabolism-specific processes (ADME genes) for focused attention.

[0232] 5.2 Liver: Metabolism activation and detoxification The liver showed the strongest transcriptome response after EpigenAU / 11 administration.

[0233] As demonstrated by MDP analysis (Figure 1) and confirmed by DEA (Figure 7), the first increase in gene expression variation was observed approximately 2 hours after administration and reached a peak at 6 hours.

[0234] This approach was also applied to a subset of the dataset, selecting only genes related to the ADME (absorption, distribution, metabolism, and excretion) process according to 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 particularly on ADME-related genes (Figure 2), a similar temporal pattern was observed, but the fluctuations at 2 hours were small, consistent with the activation of hepatic metabolism and detoxification pathways. The difference between the physiological solution and EpigenAU / 11 treatment became clearer.

[0236] Importantly, 48 hours after administration, both overall and ADME-specific variability levels returned to levels comparable to the vehicle-treated group (Figures 1 and 7), indicating a complete cessation of the hepatic response.

[0237] 5.3 Kidney: Excretion dynamics and biphasic response In the kidneys, transcriptome changes, when considering all genes, first peaked 2 hours after administration (Figure 3).

[0238] Here, MDP was used to determine both overall transcriptome variability and specific variability related to the ADME process. The gene set selected for the 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 (genes found by GWAS and generally known to be involved in metabolic pathways) were selected.

[0239] MDP analysis of ADME-related genes (Figure 4) revealed a distinct second peak in the fluctuations approximately 18 hours after administration, likely reflecting the renal excretion process.

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

[0241] 5.4 Blood: Minimum fluctuation due to transient signals Blood samples showed generally low variability scores over time (Figure 5).

[0242] However, two notable findings emerged: Eighteen hours after administration, vehicle-treated samples showed higher levels of variability than EpigenAU / 11-treated samples, likely reflecting stress-related or circadian influences.

[0243] In one example of an EpigenAU / 11 processed sample, a small number of outliers were detected after 48 hours.

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

[0245] 5.5 Brain: Detection of indirect and systemic effects on the CNS Transcriptome changes in the brain were temporally aligned with fluctuation signals observed in peripheral metabolic organs such as the liver and kidneys and could be detected approximately 2 hours after administration of EpigenAU / 11 (Figure 6).

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

[0247] This finding is particularly important because it demonstrates that the proposed transcriptome 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 reach or accumulate directly in the central nervous system (CNS).

[0248] Such performance in detecting CNS-related transcriptome changes (arising from whole-body regulation, peripheral signaling, or pathways via metabolites) is an important advantage of this method, especially considering that conventional pharmacokinetic strategies typically cannot capture these indirect effects.

[0249] This performance in monitoring fine regulation of brain gene expression, in addition to the compound's direct permeation across the blood-brain barrier, enables even compounds that are classically considered CNS-inactive to yield valuable insights into the broad systemic effects of natural matrix-based products, potential CNS-related efficacy signals, and early detection of safety-related changes in the brain transcriptome.

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

[0251] 5.6 Disappearance stages in all organs By 48 hours after administration, the transcriptome profiles in all examined organs, such as the liver, kidney, blood, and brain, had returned to levels comparable to those of the sham controls.

[0252] This clearance phase follows the expected sequence of liver metabolic processes, [[ID=3(5]] renal excretion, and whole-body recovery. ​This is consistent with the findings. Typically, it is associated with the disappearance 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 action.

[0254] 5.7 Safety and Tolerability of EpigenAU / 11 A key observation from this analysis is the absence of sustained or chronic molecular changes after administration of EpigenAU / 11. Specifically, it is as follows:

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

[0256] No persistent transcriptome changes were detected, suggesting that the product was effectively processed and eliminated within a reasonable and predictable timeframe.

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

[0258] It is important to emphasize 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, conjugate enzymes, or membrane transporters).

[0259] This comprehensive strategy ensures that all potential biological effects, whether metabolic, inflammatory, adaptive, or toxicological, can be reliably detected and determined.

[0260] This approach captures both direct and indirect effects, including those potentially arising from metabolites or systemic regulation, to provide a uniquely broad and reliable assessment of safety and pharmacodynamic behavior.

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

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

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

[0264] 6. ADME Function Parameters The functional / equivalent ADME parameters defined in the above and related tables were calculated.

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

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

[0267] [Table 3]

[0268] Notably, T0, which is a few minutes after the injection of EpigenAU / 11, indicates that blood levels are likely to be affected by the administration procedure alone, which could cause transcriptome fluctuations almost immediately.

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

[0270] 6.1 Pmax The maximum variation (Pmax) of a particular organ is defined as the maximum number of observed DEGs in the treated sample compared to a spurious control, regardless of whether the gene is upregulated or downregulated.

[0271] As is clear from the table above, in blood, the peak levels are 801 DEG in the brain, 58,666 in the kidneys, and 1174 DEG in the liver.

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

[0273] 6.2 fTmax The functional maximum time to effect (fTmax) value for one or more organs is the time point at which the peak change in the maximum transcriptome induced by the administration of the product in the organ occurs, and the systemic fTmax value is the time point at which the maximum number of DEGs is observed when all organ-specific DEGs are considered. As is clear from the table above, after administration of EpigenAU / 11, the most significant change (Pmax) in the liver was characterized by 1174 DEGs, which was observed at 6 hours (fTmax = 6 hours). In the blood, kidneys, and brain, the fTmax was 18 hours, and the systemic fTmax defines the time point at which the greatest overall change is observed, and as is clear from the table above, the systemic fTmax was 2 hours.

[0274] 6.3 AUEC To evaluate the overall temporal variation profile, the area under the effect curve (AUEC), i.e., the area under the curve representing the overall effect / variation induced by product processing over time, was calculated by plotting the number of DEGs (y-axis) as a function of time (x-axis) and calculating the area under the curve obtained using the trapezoidal rule. The 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] The following formula was used to calculate whole-body AUEC.

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

[0277] For example, in the liver, the calculation would be as follows (using the trapezoidal rule described on page 44): [Table 4] AUC in liver ≈ 753 + 3854 + 4389 + 2004 + 1866 + 3252 = 16118

[0278] After making the necessary changes, all calculations were performed for the other organs.

[0279] The calculated whole-body AUEC, representing the overall (whole-organ) effect of the variation, was 28820. When the effect magnitude was measured for a single organ, the values ​​obtained were 16118 for the liver, 4668 for the kidney, 7743 for the blood, and 291 for the brain. Calculating AUEC for separate tissue samples can reveal relevant differences in effect magnitude across tissues. For example, the above data shows that the effect magnitude of EpigenAU / 11 administration is significantly greater in the liver than in the brain.

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

[0281] While this calculation of mean variability time is closely related to systemic effects, it can also be calculated for a single organ in the case of specific organs. 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 systemic mean residence time of our product was 14.29 hours.

[0282] The formula used

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

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

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[0285] After making the necessary changes, all calculations were performed for the other organs.

[0286] 6.5 fTres Organ-specific functional elimination 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 fluctuation is defined as a transcriptome fluctuation of ≤5%Pmax for all tissue samples except blood, and the end of transcriptome fluctuation is defined as ≤12%Pmax, preferably ≤5%Pmax). As can be expected, the transcriptome fluctuation in blood samples is more unstable because blood flow is susceptible to changes due to fluctuations such as blood glucose peaks immediately after feeding and the change of day and night. In this case, fTres may be organ-specific or systemic. Considering a systemic perspective, fTres is defined as the first time point at which all tested organs, organ-specifically, reach a transcriptome fluctuation of ≤5%Pmax for the excluded blood, and the end of transcriptome fluctuation is defined as ≤12%Pmax, preferably ≤5%Pmax.

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

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

Claims

1. A method for functionally characterizing the onset, duration, and resolution of the biological effects of a therapeutic or beneficial product, wherein the product preferably comprises one or more natural matrices, and the method is performed after administration of the product to a subject: (a) After administering the natural matrix-based product to a subject, RNA is extracted from subject samples derived from different tissues, i.e., treated samples, obtained at multiple time points, and RNA is extracted from corresponding subject samples, i.e., sham control samples, obtained at the same time points; (b) Perform transcriptome analysis, including gene expression profiling, on RNA extracted from the treated samples and sham control samples; and identify transcriptome changes based on differentially expressed genes (DEGs) that are significantly differentially expressed in the treated samples at each time point compared to the corresponding sham control samples; (c) To identify the profiles of the expression, duration, and elimination of biological activity induced by the administration of the product; Here: The onset time point corresponds to the time point at which a significant transcriptome change was first observed in at least one of the treated samples compared to the corresponding sham control sample; The resolution time point corresponds to the time point at which a significant transcriptome change is last observed in the treated sample compared to the corresponding sham control sample; The duration corresponds to the time interval from the time point of onset to the time point of disappearance; The method provides profiles of the biological effects of the product, including profiles of its expression, duration, and disappearance, without relying on the direct quantitative determination of the individual chemical components of the product.

2. The method according to claim 1, further comprising one or more of the following: a. To provide the transcriptome peak Pmax value of the said product as a value representing the maximum intensity of the transcriptome fluctuation induced in one or more organs by administration of the said product in said organ. b. To provide a functional Tmax value, i.e., an fTmax value, as a time point for one or more organs, wherein the time point is the time point at which the maximum peak of the transcriptome fluctuation induced by administration of the product in the organ is observed. c. To provide the area under the overall effect curve, wherein the area under the overall effect curve represents the area under the curve over time of the overall effect / variation induced by the processing of the product. d. To provide a whole-body functional mean residence time fMRT, wherein fMRT is defined as the average effect / variation time induced by the processing of the product, and fMRT = AUMC / AUEC, where AUMC is the area under the first moment curve and AUEC is the area under the effect curve defined in c. e. To provide a functional elimination time fTres, wherein fTres is defined as the time point where the number of DEGs is ≤ 5% Pmax for all tissue samples except blood, and corresponds to the time point at the end of transcriptome variation, where the blood end of transcriptome variation is defined as the time point where the number of DEGs is ≤ 12% Pmax, preferably ≤ 5% Pmax.

3. The method according to claim 1 or 2, wherein the different tissues are selected from blood, liver, kidney, brain, hypothalamus, lung, heart, spleen, testes, stomach, intestine, gallbladder, pancreas, glands, ovaries, muscle tissue, preferably at least one or more tissues from blood, liver, kidney, brain, lungs, and heart.

4. The method according to any one of claims 1 to 3, wherein the plurality of time points include T0 representing the time immediately after administration of the product, and include at least one, preferably at least two, and more preferably at least three different Tn, where n represents the time after administration of the product and is ≥ 1, preferably ≥ 2, and one of the Tn is ≥ the time point of disappearance.

5. The method according to claim 4, wherein the time points of Tn include T2 and T48, and preferably the time points of Tn include at least three of T2, T6, T18, T24 and T48.

6. The method according to any one of claims 1 to 5, wherein the transcriptome analysis is performed using RNA sequencing or hybridization to a gene expression microarray.

7. The method according to any one of claims 1 to 6, wherein the transcriptome analysis includes the following: a) Perform transcriptome raw data analysis from the RNA extracted in step a), and Identify the genes (DEGs) that are significantly differentially expressed at each time point for each treated sample, relative to the corresponding sham control sample, identify the changes in their expression levels relative to the corresponding sham control sample, and thereby identify the number and intensity of significant transcriptome fluctuations for each treated sample relative to the corresponding sham control sample at each time point.

8. The method according to any one of claims 1 to 7, further comprising: Extracting RNA from target samples of different tissues obtained from untreated control organisms, i.e., untreated control samples as described below. Performing transcriptome analysis; said transcriptome analysis includes gene expression profiling of the extracted RNA from each of the untreated control samples, followed by molecular degree of perturbation (MDP) analysis, for the corresponding product-treated samples and corresponding sham-treated control samples, thereby obtaining an MDP perturbation score.

9. The method according to claim 8, wherein the MDP variation score is calculated according to the following formula: [Math 1] Here, MDPi: A variation score that defines the degree of molecular variation for target i. n: Total number of genes used in the analysis x ij : Value of gene j in subject i μ j (ref): Mean of gene j in the reference group (untreated subjects) [Math 2] And, Here, each gene j is included in the analysis only if the absolute value of its Z-score is ≥ 2.

10. The method according to any one of claims 1 to 9, 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 resin, plant gum, plant exudate, plant oil, plant essential oil, animal tissue lysate, or plant or animal bodily fluids, or animal mineral matrix.

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