A novel and preferred method for qualitatively and quantitatively evaluating the physiological activity of therapeutic or beneficial products based on natural matrices, in terms of benefit / risk, by comparing it with that of other products with pharmacological or nutritional supplemental activity based on synthetic molecules.

Abstract

This invention provides novel predictive in vitro or ex vivo methods for defining benefit / risk scores for therapeutic or beneficial products, methods based on data obtained from in vivo experiments for defining benefit / risk scores for therapeutic or beneficial products, and methods for selecting therapeutic or beneficial products for further development. [Solution] A computer implementation method for providing a benefit / risk score for a therapeutic product of interest based on in vitro transcriptional data or ex vivo transcriptional data, comprising: providing a sample of a biological substrate representing a disease or pathological condition treated by the product; extracting RNA from the sample; performing pathway enrichment analysis and functional analysis of transcriptome data from the extracted RNA; calculating a benefit score; and calculating a risk score.

Description

Technical Field

[0001] The present invention relates to a conceptual gap in modern medicine and regulation that focuses on classical API-based products rather than new therapeutic or beneficial compositions consisting of 100% natural substances with physiological mechanisms of action (opposite to pharmacological ones).

[0002] Modern medicine is evolving towards increasing specialization with respect to therapeutic products centered on isolated or synthetic pharmaceutical active ingredients (APIs) and clinical approaches that are increasingly distant from the overall patient picture. Organisms are currently often interpreted through the lens of specialized subsystems (organs, functions, receptors), losing sight of systemic interconnections. This specialization has shaped not only treatment strategies but also the entire regulatory framework governing the development and approval of therapeutic or beneficial products.

[0003] In particular, when exclusive selections and natural matrices appropriately processed by specific processes and methods are used to create end products for therapeutic purposes or for rejuvenation in order to restore or rejuvenate an organism when restoring a healthy physiological state, the developers of such new products face several regulatory challenges. In fact, developers of natural matrix-based therapeutic or beneficial products mainly face significant regulatory obstacles because the current pharmaceutical framework is designed for single compound drugs rather than complex multi-component natural formulations. Regulatory agencies such as the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and other global authorities require strict characterization, reproducibility, and clinical validation (e.g., for plant-derived therapies, criteria that are difficult to meet due to their natural variability, multi-target effects, and the impossibility of defining specific therapeutic active ingredients). Standard drug approval pathways such as US New Drug Applications (NDAs) or European Marketing Authorizations (MAs) often require clearly defined active ingredients, precise dosing, and clear pharmacokinetics, which do not match the synergistic and emergent properties of plant matrices.

[0004] The entire regulatory system, encompassing methods for evaluating the benefit / risk profiles of new therapeutics, is built around this reductionist model. In current practice, benefit / risk assessments are generally qualitative in nature, based on clinical trial results, therapeutic efficacy and side effects observed in patient populations, and are only refined later through actual post-marketing data. In particular, there are no standardized methods for predictive early-stage benefit / risk assessments based on in vitro or ex vivo data. Not to mention diagnostic methods that can identify and intercept such parameters (e.g., complete IVD treatment), nor are there methods to interpret this data through probabilistic and holistic models capable of generating measurable quantitative outputs.

[0005] As a result, many plant-based products are classified as herbal medicines, dietary supplements, or traditional pharmaceuticals, which not only limits their ability to claim therapeutic efficacy or obtain full drug approval, but also restricts the development of innovation in this field. Furthermore, one of the major gaps in the current regulatory landscape is the lack of standardized benefit / risk assessment models tailored to multi-component natural matrices. Conventional risk assessments are based on dose-response relationships, toxicity thresholds, and drug interaction studies, all of which are well-suited to synthetic molecules, but fail to capture the overall synergistic effects of plant-based therapies. Conversely, the full range of benefits, encompassing immunomodulation, multi-pathway interactions, and adaptive physiological responses, is also difficult to quantify and compare with conventional drugs under current guidelines.

[0006] This highlights the need for a new evaluation paradigm that reflects a systems-based, integrated perspective in human biology, something that modern regulations, shaped by increasingly fragmented medical thinking, have failed to incorporate.

[0007] Another significant challenge is the requirement of Good Manufacturing Practices (GMP), ensuring batch-to-batch consistency, which is a major issue when dealing with natural (e.g., plant-based) extracts that are subject to geographical, seasonal, and genetic variations. Furthermore, developers of natural matrix-based products also grapple with intellectual property (IP) complexities. This makes it difficult for companies to secure investment for research and development as they struggle to establish exclusive rights to their innovations. Moreover, compliance with global safety regulations such as Europe's REACH (Registration, Evaluation, Authorisation, and Restriction of Chemicals) or China's Traditional Herbal Medicine Registration adds another layer of complexity, often requiring lengthy toxicological studies, clinical trials, and detailed drug monitoring programs. These regulatory barriers significantly delay entry into undervalued markets, increase costs, and create uncertainty for innovators in the plant-based therapeutics field. On the other hand, the public is increasingly demanding a drift towards naturalness under the One Health principle (which recognizes the interrelationship between human health, animal health, and environmental health), and therefore demanding products in which every stage of production adheres to the aforementioned principle and the use of artificial forces or substances is not permitted.

[0008] In fact, in the field of the present invention, therapeutic or beneficial products of interest, i.e., products containing or consisting of one or more natural matrices, maintain their natural intelligence, i.e., the imprint of the biological domain to which each component of the product belongs, thereby maintaining a network that can interconnect and recognize other networks, whether natural or artificial, i.e., the original natural network that has acquired a degree of artificiality through interaction with artificial components. This interconnectivity is considered fundamental to rebalancing any disturbances in the network of events that are active in each interacting biological system.

[0009] Each network of the natural matrix contained in the product contributes to the formation of the final matrix network of the product of interest and 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 its self-assembly properties cannot be determined or verified based on small molecule chemistry protocols as it is a treated product.

[0010] Each network is characterized by the establishment of connections within the matrix of the final product and within the physiological effects exerted by the product on recipient organisms. Production validation of this type of product can be performed and verified using a probabilistic model based on the relationship between the preservation of the physiological activity profile and descriptors of the matrix itself generated using multiple biophysical analysis systems, including spectroscopy (NIR and other techniques), mass spectrometry, and paper or X-ray crystallography (fractal measurements) as disclosed in Japanese Patent No. 7616724 and PCT / IB2024 / 054526. In practice, although useful, conventional molecular chemical definitions of individual substances contained in a material cannot be used to validate this type of product because they do not represent its overall efficacy and quality.

[0011] The selection of the matrix intended 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 may need to be examined, depending on the circumstances, taking into account the effects of acoustic effects (music or other forms) and the wave-particle realm, including those of quantum nature.

[0012] With the current level of technology, it is not always possible to outline the fully explained mechanisms 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.

[0013] The present invention aims to enable humans who need it to select and provide new therapeutic or beneficial entities or products, as well as systems that enable the rebalancing, activation, or restriction of physiological functions in specific metabolic states of an organism, which are always in a continuous state of transformation.

[0014] The preparations conceived in this way can rebalance the psychoneuroendocrine immune system, which is considered a single system that controls and manages all other systems.

[0015] This invention contributes to a new, cutting-edge technology that goes beyond alchemical techniques in the medical field, whose origins can be traced back to the early 16th century, returning products and processes to the conceptual One Health goal already described. 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 materials, recognizing existing rules or discovering new ones, to ensure the composition of verifiable entities. The latter are continuously changing organisms requiring evaluation of their physiological state within defined intervals, a concept encompassed in personalized medicine today. This invention conforms to the concept of science, understood as a set of knowledge that can empirically verify the effects of theoretical mechanisms of action. Today, these methods apply to establishing interconnections between all forms of life in a situation where technological innovation is progressing at a pace that risks undermining the interconnections between human-generated (artificial) intelligence and nature.

[0016] Because the activity of therapeutic or beneficial natural matrix-based products is not currently fully encompassed by modern technology, it is necessary to consider the entire product cycle, from the end user to the relevant social context, under the One Health concept.

[0017] The operating paradigm developed within this invention is referred to herein as "Bios Physiological Health".

[0018] This paradigm aims to introduce innovative approaches to health treatment and self-management into the medical technology field by using natural matrices alone or in combination, and to rebalance the normal physiological state of various organisms, including humans, through the endogenous physiological effects induced by the products. The challenge lies in identifying, selecting, and assembling natural entities with the physiological mechanisms of action of the final product and newly emerging properties that can be verified by other methods that have evolved in recent decades.

[0019] The contextual interpretation, based on both technical and anthropological norms integrated into their cross-cutting nature, forms the basis of the proposed invention. While some properties of each matrix component of the product may already be known, the newly emerging properties of the new composition are unexpected.

[0020] Of particular relevance is the role of determining the networks that represent the natural matrix and the genetic and epigenetic aspects that determine their descriptions at the level of their specific isotopic abundances.

[0021] To satisfy the Bios Physiological Health paradigm, each stage of processing, from the selection of recycled materials to the agricultural and industrial stages, and to the methods of use, must preserve, to the greatest extent possible, the integrity of the inheritance of the native programming embedded in the natural intelligence of each created entity, at least as far as is known on a global scale. It will be essential to examine a matrix derived from an epigenetic reality similar to that of a reference, which is recognized as a reference standard for its specific newly emerging characteristics regarding the metabolism of other organisms, including humans. For example, one factor that negatively impacts epigenetic differentiation is represented by different soil conditions, with variations every 24 hours, month, and year. To maintain the properties of natural systems, which are the only ones that can claim physiological interconnection with the whole product, it is impossible to use substances derived from alchemical processes such as distillation, other synthetic or semi-synthetic processes, or products derived from genetically modified or genetically modified organisms. A new interpretation of the mystery of the natural programming that underlies the evolution of life in organic and inorganic matter is needed. The recent establishment of scientific advancements allows for a re-evaluation of the origins of progress based on reductionist determinism, building upon the development of alchemical processes from the early 16th century, which, along with Paracelsus in medicine, marked the beginning of the current evolutionary process known as the Anthropocene.

[0022] The term Anthropocene represents the current stage of human evolution and can be traced back to different periods. When considered in the context of this invention, the only significant date is 1492, which marks the end of the early Renaissance humanistic / neoplatonic period. This period was politically represented by Cosimo the Elder and Lorenzo de'Medici, along with artists and scientists such as Piero della Francesca, Luca Pacioli, Leonardo, and Durer. In the 16th century, alchemical studies, considered the possibility of human control over nature, have evolved to the present day under the protection of artificial intelligence, as opposed to that of nature, with the aim of improving the creation of natural disasters, as it is said that "man controls all creation."

[0023] 1492 is a symbolic date, as it was the year Lorenzo de' Medici and Piero della Francesca died, while Columbus discovered America. The human species abandoned the Neoplatonic path of the 15th century, followed the Judeo-Catholic path, applied Paracelsus' alchemical practices to medicine, and marked a transition to Renaissance Mannerism in the 1500s, leading to a full and irreversible sixth extinction of its inventors, which continues to this day.

[0024] While this invention demonstrates the feasibility of industrial discoveries obtained in the medical field, it is, in principle, adaptable to any production field and is intended to address shifts in evolutionary paradigms. The inventors often speak of protecting biodiversity without addressing the practical problem of billions of tons of exogenous, non-biodegradable artificial materials released into planetary systems—a problem that is clearly obscured—while the "carpe diem" approach outweighs the sense of species survival.

[0025] 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, which is largely incapable of stopping or slowing down the sixth extinction or building the foundation for alternative advances to the present. Inventor Valentino Mercati, together with his collaborator Jacopo Lucci, has chosen the path of research in the natural world that may be useful for biological systems, developing knowledge in agricultural and industrial production systems for over 40 years and 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 instrumental and diagnostic readings based on chemistry-related principles concerning physiological effects and the relationship between newly emerging properties of natural matrices and the innate defenses of individual organisms that interconnect them.

[0026] The analysis that inspired the approach disclosed herein, which was unthinkable just a few decades ago, is due to the technical impossibility of reading the genetic and epigenetic information encoded in the cells of any organism, as well as the role of atomic isotope differentiation in molecular self-assembly and the interconnectedness of all singular / individual entities with the “cosmos.” The conceptual difficulty of moving from the control parameters that provide a sense of reassurance of molecular artificiality (at least partially purified and linked by strong thermodynamic forces enabling strong bonds such as covalent bonds that act to reduce the molecular range of other organisms) to the natural matrix, which is by definition mysterious and still considered today to be a therapeutically unreliable component, is extremely high.

[0027] After five centuries of alchemical reductionism, if a new interpretation of the present invention is required for the new medical situation, this interpretation must link the farthest concepts and processes in a single field of application. This is due to the ideological legacy that questions the human condition, as already stated: Whether the human species was produced by an original vital intelligence, like all other species, for the purpose of life as far as the inventors can hypothesize, or was experimentally given different abilities from other organisms already suitably inserted into creation to constitute a new ecological niche in the service of the universe.

[0028] The answer to this dilemma does not arise in the present invention: Humanity must return to the new Platonic thought of the early Renaissance, and the experimental duality of the human species must be liberated from the spirit of domination in order to share all its unique abilities within the universe with all creations. Humanity must reconsider Leonardo da Vinci's warning "Man can only make his own descendants..." and reflect on the morbid thoughts of discerning individuals like Piero della Francesca, Luca Pacioli, and Durer regarding the impossibility of understanding and expressing the beauty of creation and decoding its mystery.

[0029] The time has come to acquire new research centers in molecular biology and cell biology, focusing on what is essential for bioinformatics and the new physical sciences. Today, the inventors can base research strategies and socio-economic applications on new therapeutic fields, particularly those of a complex and / or chronic degenerative nature, and the restoration of the metabolic balance of organisms, whether naturally or artificially disrupted, is already an essential part of the future that exists.

[0030] The present invention redefines cutting-edge medicine by introducing a holistic evaluation model that is consistent with personalized medicine and systems biology. Different from the pharmacological approach of reductionist scholars who isolate active ingredients, the present invention recognizes the complexity of self-organization in natural systems.

[0031] By establishing a rigorous scientific basis for the evaluation of natural matrix-based therapies, the present invention fills a major gap in current medical research. This addresses the limitations of the medical and regulatory frameworks built around the pharmacology of highly specialized single compounds and there is no tool for systematically evaluating complex multi-target products. It introduces for the first time quantitative and predictive methods based on in vitro or ex vivo data that can assist in early-stage decision-making and restore scientific validity to a holistic approach, integrating biophysical analysis, probabilistic modeling, and physiological impact assessment to provide a new paradigm for benefit / risk assessment. This ensures that it is natural intelligence, not artificial intelligence, that guides future progress in medicine.

[0032] According to current procedures for evaluating the benefit / risk score of medical products, regulatory agencies such as the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the World Health Organization (WHO) use a strict benefit / risk assessment framework to evaluate medical products before granting market approval. These evaluations rely on quantitative and qualitative methodologies to compare and consider the therapeutic benefits of the product against its potential risks, ensuring that the overall impact on public health is preferably justified.

[0033] In addition to regulatory approval, many countries also require health technology assessment (HTA) as part of the decision-making process for price setting, reimbursement, and market access. HTA is a multidisciplinary assessment framework that considers not only clinical effectiveness and safety but also the economic, social, and ethical implications of medical products.

[0034] Standard benefit / risk assessment methods typically include the following:

[0035] Analysis of clinical trial data: Evaluate safety and effectiveness through randomized controlled trials (RCTs) that provide a statistical comparison of the test product against a placebo or an existing standard treatment.

[0036] Pharmacokinetic and pharmacodynamic (PK / PD) modeling: Measuring how drugs are absorbed, distributed, metabolized, and excreted, along with their biological effects at different concentrations.

[0037] Toxicology and safety profiling: Identifying potential adverse effects, contraindications, and drug interactions through preclinical studies (animals and in vitro) and clinical studies (human trials).

[0038] Structured decision-making approach: To systematically balance benefits and risks, models such as multi-criterion decision analysis (MCDA) and quantitative benefit-risk models (QBRM) are used.

[0039] Standardized risk management plans (RMPs) and post-marketing surveillance: Ensuring continuous monitoring of safety signals through drug surveillance programs.

[0040] Health economics and cost-effectiveness analysis (HTA): Evaluates whether a treatment provides sufficient value for its cost by comparing quality-adjusted life years (QALYs) and other economic indicators.

[0041] Risk Management Plans (RMPs) and Post-Marketing Surveillance: Ensuring continuous monitoring of safety signals through drug surveillance programs.

[0042] These methodologies are highly structured and reproducible, and are suitable for synthetic drugs and well-defined biologics, allowing for the precise isolation, characterization, and administration of individual active pharmaceutical ingredients (APIs).

[0043] These procedures cannot be directly applied to therapeutic or beneficial products based on natural matrices; in fact, despite their efficacy in conventional pharmaceuticals, these existing benefit / risk and HTA assessment procedures face significant limitations when applied to therapeutic or beneficial products based on natural matrices, primarily due to the inherent complexity and variability of natural substances.

[0044] Unlike conventional drugs that possess a single, clearly defined active compound, natural matrices contain multiple bioactive compounds that interact with each other as a network system, contributing synergistically to their therapeutic effects; therefore, they lack a single active ingredient. Modern medicine, and modern regulatory science as well, is a narrow-minded approach, focusing on identifying a single molecular entity involved in efficacy. However, natural matrix-based products exert their effects through complex, multi-target interactions. Current clinical and pharmacological models are not designed to capture or quantify these expressive characteristics, and standard PK / PD modeling is insufficient. Furthermore, natural matrices are inherently subject to biological, environmental, and genetic variability, resulting in batch-to-batch compositional differences.

[0045] Pharmaceutical regulations and HTA models require highly standardized compositions that are precisely administered, and natural matrix products cannot be readily provided without altering their inherent properties. Current analytical techniques struggle to measure efficacy without relying on a single molecular marker, making regulatory and HTA approval difficult. Natural matrices often contain hundreds of chemical components, some of which may have nonlinear dose-response relationships or adaptive physiological effects that are difficult to predict using standard toxicological models. Traditional safety studies are designed for synthetic compounds with clear pharmacokinetics, making it difficult to assess the risks of natural matrices where multiple components dynamically interact with the body's metabolism.

[0046] Natural matrix-based products do not follow a linear dose-response curve, and while their benefits may be long-term and preventative, it is difficult to quantify their effectiveness from an economic standpoint due to the fact that cost-effectiveness models prioritize immediate, measurable results.

[0047] In essence, this technology field requires a new benefit / risk and HTA assessment paradigm to bridge the gap between regulatory and HTA requirements and the reality of natural matrix-based therapeutics that offer new benefit / risk and health technology assessment models.

[0048] Until such technologies are developed and accepted by regulatory bodies and HTA organizations, natural matrix-based therapies will remain at a disadvantage because they cannot be adequately evaluated using current pharmacovigilance / risk and HTA models.

[0049] Ultimately, this invention forms the basis of a new paradigm for therapeutic development that not only aligns with the principles of One Health and natural intelligence, but also introduces a prospective model for benefit / risk assessment.

[0050] By enabling quantitative, initial, and probabilistic assessments of both beneficial and potentially harmful physiological effects derived from in vitro or ex vivo transcriptional data, the present invention addresses the fundamental limitations of current regulations and scientific practices. It provides a specific and measurable alternative to common qualitative methods designed around single-compound synthetic agents, and thus restores scientific integrity and regulatory feasibility for complex natural products.

[0051] This approach not merely complements the current system, but redefines it, providing a pathway to safer, more effective, and truly integrated healthcare. [Overview of the project]

[0052] This invention relates to a novel approach for quantifying differences in the benefit / risk profiles of therapeutic or beneficial products for a given pathological area of ​​interest. This approach takes into account transcriptional effects evaluated in an in vitro or ex vivo setting. The invention presents an analytical method designed as a suitable exploratory tool for early research and development (R&D) stages.

[0053] This method is based on an integrated approach utilizing transcriptome data and advanced biological pathway analysis tools (e.g., Ingenuity Pathway Analysis, e.g., IPA) to identify modifications resulting from altered gene expression and associated biological activity, addressing both therapeutic efficacy and potential adverse effects.

[0054] Specifically, the generated transcriptional profiles are analyzed to determine differential gene expression (DEGs) and key biological functions related to the ability to counteract the disease state of interest and specific side effects caused by standard reference agents for each treatment. This allows for the calculation of benefit / risk scores for different therapeutic or beneficial products based on a conceptually integrated approach of transcriptional profiles obtained from in vitro or ex vivo studies. The method of the present invention is particularly suitable for therapeutic or beneficial products containing natural matrices.

[0055] The benefit-to-risk ratio is a very useful and important parameter in understanding the differences between such important characteristics of treatment options or beneficial options. Nevertheless, such parameters are currently difficult to resummate into a single objectively calculated numerical parameter that can facilitate a first approach to comparing treatment solutions. Herein, we provide a general method that can accurately and objectively predict the risk-to-benefit ratio comparison between a new therapeutic or beneficial product of interest for the treatment of a given disease or pathological condition and a known reference agent for the treatment of the same disease or pathological condition, and the method of the present invention can also be applied based solely on transcriptome data in vitro or ex vivo.

[0056] Therefore, the object of the present invention is A computer implementation method for providing a benefit / risk score for a therapeutic product of interest based on in vitro or ex vivo transcriptional data, the method comprising the following steps: 1. Perform transcriptomics analysis as follows: 1.1 Provide a sample of a biological substrate representing a pathological or pathological state treated by the product, a. Process one or more of the aforementioned samples (hereinafter referred to as "sample a") with the aforementioned product; b. Treat one or more of the samples (hereinafter referred to as "sample b") with a reference agent for treating the aforementioned pathological condition or state; and c. One or more samples of the biological substrate (hereinafter referred to as sample c) are used as a reference control; 1.2 Extract RNA from each of the aforementioned samples a, b, and c; 1.3 Perform transcriptome raw data analysis on the RNA extracted in 1.2 to identify differentially expressed genes (DEGs) for sample c in each of samples a and b, and the changes in their expression folds for sample c, thereby obtaining the fold change values ​​for each of the DEGs; 1.4 Pathway enrichment analysis and functional analysis of transcriptome data are performed on the list obtained in 1.3, thereby obtaining numerical values ​​representing the magnitude and directionality of the biological activity related to the differential expression of DEG in each of samples a and b, normalized to sample c; 2. Determine the benefit score as follows: 2.1 From among the biological activities listed in 1.4, select the biological activity related to the therapeutic indication of the product. 2.2 Convert the numerical values ​​of the related biological activity obtained in 1.4 to absolute values. 2.3 The absolute values ​​of each of the biological activities are summed up to obtain a benefit score for the product under test. 3. Determine the risk score as follows: 3.1 To treat the aforementioned conditions, a list of known side effects associated with the reference drug is provided. 3.2 The biological activity related to the aforementioned side effects is determined by pathway analysis and functional analysis. 3.3 For samples a and b, construct an in silico model of risk using pathway analysis and functional analysis by establishing the relationship between the gene expression patterns obtained in point 1.3 and the biological activity determined in point 3.2; 3.4 Input the relevant fold change values ​​for each DEG of samples a and b obtained in 1.3 into the risk-in-silico model obtained in point 3.3, and use pathway analysis and functional analysis to obtain data representing the direction and magnitude of regulation of each biological activity determined in point 3.2, and convert this data into corresponding absolute values; 3.5 The positive values ​​obtained in 3.4 are summed up to obtain a final value representing the risk score of the product under inspection. If the final value is less than a predetermined minimum positive value, it is automatically adjusted to that minimum positive value. 4. Provide the benefit / risk score of the product of interest as the ratio of the benefit score obtained in point 2.3 to the risk score obtained in point 3.5.

[0057] Another object of the present invention is a method for selecting one or more novel therapeutic or beneficial products for clinical development, wherein benefit / risk scores for one or more therapeutic or beneficial products of interest for the treatment of a given disease or pathological condition and a reference agent for the treatment of the disease or pathological condition are provided by performing steps defined herein and in the claims (see above method), and each of the at least one therapeutic or beneficial product of interest is selected for clinical development if at least one of the benefit / risk score values ​​provided for the therapeutic or beneficial product of interest is greater than or equal to the benefit / risk score value provided for the reference agent.

[0058] A further object of the present invention is a computer implementation method for manufacturing a therapeutic or beneficial product based on in vitro or ex vivo transcriptional data, comprising determining a benefit / risk score thereof, and comprising the following steps: 1. Perform transcriptomics analysis as follows: 1.1 Provide a sample of a biological substrate representing a disease or pathological condition treated by the product, a. Process one or more of the aforementioned samples (hereinafter referred to as "sample a") with the aforementioned product; b. Treat one or more of the samples (hereinafter referred to as "sample b") with a reference agent for treating the aforementioned pathological condition or state; and c. One or more samples of the biological substrate (hereinafter referred to as sample c) are used as a reference control; 1.2 Extract RNA from each of the aforementioned samples a, b, and c; 1.3 Perform transcriptome raw data analysis on the RNA extracted in 1.2 to identify differentially expressed genes (DEGs) for sample c in each of samples a and b, and the changes in their expression folds for sample c, thereby obtaining the fold change values ​​for each of the DEGs; 1.4 Perform pathway enrichment and functional analyses on the list obtained in 1.3 to obtain numerical values ​​representing the magnitude and directionality of the biological activity associated with differential expression of the DEG in each of samples a and b, normalized to sample c (i.e., information regulation or downregulation); 2. Determine the benefit score as follows: 2.1 From among the biological activities listed in 1.4, select the biological activity related to the therapeutic indication of the product. 2.2 Convert the numerical values ​​of the related biological activity obtained in 1.4 to absolute values. 2.3 The absolute values ​​of each of the biological activities are summed up to obtain a benefit score for the product under test. 3. Determine the risk score as follows: 3.1 To treat the aforementioned conditions, a list of known side effects associated with the reference drug is provided. 3.2 The biological activity related to the aforementioned side effects is determined by pathway analysis and functional analysis. 3.3 For samples a and b, construct an in silico model of risk using pathway analysis and functional analysis by establishing the relationship between the gene expression patterns obtained in point 1.3 and the biological activity determined in point 3.2; 3.4 The relevant fold change values ​​for each DEG of samples a and b obtained in 1.3 are input into the risk-in-silico model obtained in point 3.3, and pathway analysis and functional analysis are used to obtain data representing the direction and magnitude of regulation of each biological activity determined in point 3.2, and the said data are converted into corresponding absolute values; 3.5 The positive values ​​obtained in 3.4 are summed up to obtain a final value representing the risk score of the product under inspection. If the final value is less than a predetermined minimum positive value, it is automatically adjusted to that minimum positive value.

[0059] The method of the present invention is advantageously applicable to natural matrix-based therapeutic or beneficial products that cannot be defined with respect to a single active ingredient having a measurable dose-response relationship, as demonstrated herein and in examples.

[0060] 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 required by context, singular terms shall encompass plural forms, and plural terms shall encompass singular forms.

[0061] 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.”

[0062] Benefit / risk assessment, in modern technology, is merely a qualitative measure used to evaluate the overall value of a beneficial product or medical device, intervention, or procedure by comparing its therapeutic or beneficial effects with its potential risks or harms. This score is crucial in regulatory decision-making, health technology assessment (HTA), and clinical practice, helping to determine whether a procedure offers a favorable balance between its positive effects and potential adverse effects. Therefore, this specification defines benefit / risk assessment. Score This term provides qualitative information. and Regarding quantitative scales.

[0063] In this application, “natural matrix” refers to a material consisting of a network represented by a wide number of components / components obtained directly (e.g., extracted) from members of the natural world or their naturally occurring parts (i.e., from natural raw materials) without significant treatment or synthetic alteration, and “without significant treatment or synthetic alteration” means that no modification process is used to obtain the matrix from the raw materials. In other words, the natural raw materials are treated by manual, mechanical or gravitational means, for example by dissolving them in water or other naturally occurring solvents such as water, water-alcohol solutions; by flotation; by extraction in water or other naturally occurring solvents; by steam distillation, or by heating only to remove water or any other naturally occurring solvent; or by any means, and the “natural matrix” is extracted from air untreated, under conditions that exclude the aforementioned members of the natural world “themselves.” In particular, according to the present invention, the natural matrix is ​​a 100% natural and biodegradable material consisting of natural components that have not been modified by a process for producing the matrix from starting materials, without intentionally adding synthetic products throughout the entire process. 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 effect conferred to the matrix by the presence of structural interactions (material interactions) due to its components and functional interactions (non-material 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 properties, which naturally self-assemble and determine physiological interactions with other 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 kingdom, the Protist kingdom, the Fungi kingdom, the Plant kingdom and the Animal kingdom.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. The synonyms for natural matrices or one or more natural matrices used herein are “composite natural systems” or “natural materials” as defined below.

[0064] Examples of naturally occurring parts of living organisms may be represented, for example, by roots, leaves, bark, fruits, flowers, plants or their sections, organs, or tissues.

[0065] In all parts of this specification, the natural matrix of common terms may be replaced by the following: Plant natural matrix or natural matrix obtained from plants, Animal natural matrix or natural matrix obtained from animals or 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, wherein the extraction method does not involve a denaturation step (e.g., temperature or use of a denaturing solvent).

[0066] The term "plant" is synonymous with "herb."

[0067] The term "natural" matrix emphasizes that, because no denaturing treatment is performed to obtain it, it retains the integrity and complexity of its constituent / component network as 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.

[0068] Due to the supramolecular self-assembly of the components / parts 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 is also organized as a network) in the recipient organism. Therefore, the interaction between the natural matrix and the recipient organism is not the result of a point-to-point interaction with typical pharmaceutical APIs, but rather a result of (i.e., matrix)-"recipient" network (i.e., the organism to which the matrix is ​​administered) interaction.

[0069] The term "natural matrix" may be replaced in this specification and in all parts of the claims by the complex natural system.

[0070] Nowhere in this specification or in the claims, the term "natural matrix" cannot be interpreted in itself as a "natural product," but rather, a natural matrix is ​​a product obtained from a natural organism and processed (e.g., extracted) therefrom by a technique that does not substantially alter the biological structure and the relevant supramolecular and functional interconnections between the components within the matrix, i.e., by a technique that does not employ denaturing techniques and does not involve additional isolated or synthesized molecules or classes of molecules.

[0071] The newly emerging properties described herein and in the Art define, as described herein, the properties of a natural matrix or natural material, that is, properties expressed not merely by the sum of the properties of each isolated component / component of the matrix / material, but also by both the functional and structural interactions between all the components / components of the matrix / material, which are also the result of the supramolecular self-assembly of the components / components within the matrix / material itself.

[0072] Therefore, “newly emerging properties” refer to the technical effects that the interactions and relationships between the components / relationships of the natural matrix have on the receiving living system, such as therapeutic properties or homeostatic-adjuvant properties (i.e., beneficial effects). By definition, emerging properties are those that are not immediately apparent, or even predictable, based solely on the individual properties of each component / relationship of the matrix. Instead, they “emerge” when all the components / relationships of the matrix network interact with each other and with the receiving living system network in a dynamic and complex manner. Newly emerging properties are widely discussed in this field across various scientific and systems-oriented disciplines, encompassing physics, chemistry, biology, and complex systems theory.

[0073] Therefore, newly emerging properties cannot be predicted a priori by qualitative-quantitative knowledge of each component of a given composition or matrix, and as a result, they cannot be attributed to one or more specific APIs. Thus, while a multi-component composition may exhibit unpredictable synergistic effects, the properties of the composition are still attributable to the specific APIs contained therein and their amounts.

[0074] In the case of newly emerging properties characteristic of the natural matrix, the observed newly emerging properties cannot be replicated in a specific API and are maintained in different batches of a given matrix or a given mixture of matrices, regardless of the different qualitative-quantitative compositions of the batches (see below for functional elasticity).

[0075] The term "synthesis" as used herein 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 simpler chemicals to produce 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.

[0076] 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.

[0077] The hallmarks of disease or pathological or medical conditions as used herein have the meanings conventionally used in the art. They can be readily identified by those skilled in the art in specialized databases, pathway and function databases, or disease-specific databases. Disease hallmarks are known to be indicators that can mark the progression or control of a given disease or pathological or prepathological condition, and together usually represent a general pathological condition associated with a given disease. 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 summary, 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, in the case of neurodegenerative diseases (NDDs), at least eight hallmarks of NDDs are known in the art: (pathological protein) aggregation, synaptic and neuronal network (dysfunction), (abnormal) proteostasis, cytoskeleton (abnormal), (altered) energy homeostasis, DNA and RNA (defects), inflammation (increased), and neuronal cell death (increased). In cancer research, cancer hallmarks are a set of characteristic properties commonly found in cancer cells. These hallmarks include (persistent) proliferative signaling, growth inhibitors (and their evasion), cell death (and its resistance), replication immortalization (which enables it), angiogenesis (which induces it), and invasion and metastasis (which are activated).

[0078] Disease hallmarks, parameters associated with such hallmarks (e.g., biomarkers), and one or more biological activities associated with such hallmarks constitute a framework for studying diseases or pathological or medical conditions using an integrative / holistic approach.

[0079] Hallmarks of altered physiological states typically encompass observable changes in various aspects of bodily function, which may manifest through symptoms, signs, or laboratory findings.

[0080] Altered physiological states typically reflect a disruption of the body's homeostatic mechanisms, resulting in deviations from normal physiological parameters. These imbalances can involve changes in thermoregulation, fluid and electrolyte balance, acid-base balance, glucose metabolism, or other regulatory processes.

[0081] Overall, hallmarks of altered physiological states provide valuable clues for healthcare providers to identify underlying causes, assess severity, and guide appropriate interventions to restore normal function and promote recovery.

[0082] A reference drug is a drug that is generally selected or chosen as the standard or preferred treatment for a particular medical condition or disease. It is often established based on factors such as its efficacy, safety profile, cost, and clinical experience. Reference drugs serve as benchmarks for comparison with other drugs, particularly when evaluating common versions, new treatments, or alternative therapies. They are typically first-line drugs recommended by medical guidelines or healthcare providers to treat a particular condition. In this invention, reference drugs are those listed in official lists of approved and reference drugs issued by regulatory agencies such as the FDA and EMA, used as comparators in clinical trials and for general drug approval.

[0083] In this specification, pathway functional analysis or pathway enrichment and functional analysis (IPA-like analysis) refers to a bioinformatics approach that interprets large-scale omics data (e.g., transcriptomics, proteomics, metabolomics) by: Identifying differentially expressed genes (DEGs) or molecules from experimental data (e.g., RNA-seq, microarrays, mass spectrometry) and mapping these genes / proteins / metabolites to known biological pathways (e.g., signaling cascades, metabolic pathways); performing enrichment analyses to determine which biological processes or activities, molecular functions, or cellular components are most significantly affected; inferring upstream regulators (e.g., transcription factors, cytokines, microRNAs) that may explain observed expression changes, as needed; and predicting biological effects (e.g., activation or inhibition of pathways, disease-related, or drug interactions).

[0084] The Z-score is a useful statistical tool for measuring the relative position within a dataset, detecting outliers, and standardizing comparisons across different distributions. In this specification, the term Z-score (also called standard score) has the meaning commonly recognized in the art: a statistical measure, expressed in terms of standard deviation, that describes how far a data point is from the mean of a dataset. It allows for comparison of values ​​from different distributions by standardizing them on a common scale. The Z-score is calculated using the following formula: Z = X - μ / σ Here, Z = Z score X = individual data points μ = mean of the dataset σ = Standard deviation of the dataset [Brief explanation of the drawing]

[0085] [Figure 1] A flowchart illustrating the procedure for calculating benefit / risk scores based on transcription profiles obtained from in vitro or ex vivo experiments. [Figure 2] A flowchart illustrating the procedure for calculating benefit / risk scores based on transcription profiles obtained from in vivo experiments. [Figure 3]Annotated side effects of cisplatin and IPA corresponding biological activities. Column "Hallmark": Anatomical and functional areas potentially affected by cisplatin toxicity. Column "Annotated Side Effects of Cisplatin": A list of cisplatin side effects reported and classified as "common." Column "IPA Biological Activity": Side effects of cisplatin outlined according to the biological activity documented in IPA. Column "Desirable Trend": A healthy trend of the selected biological activity (upward modulation ↑ or downward modulation ↓). [Figure 4] Calculation of benefit scores based on transcriptional data of EpigenAU / 11 and cisplatin obtained from in vitro and ex vivo studies without using an arbitrary numerical coefficient (a) of importance, and calculation of benefit scores of EpigenAU / 11 and cisplatin by multiplying the obtained Z-scores by their corresponding numerical coefficients (b). [Figure 5] Calculation of EpigenAU / 11 and cisplatin risk scores based on transcriptional data obtained from ex vivo studies. [Figure 6] Benefit / risk scores obtained after 6 hours of ex vivo treatment with EpigenAU / 11 and cisplatin using only the required steps as described in (A). Benefit / risk scores obtained after 6 hours of treatment with EpigenAU / 11 and cisplatin using the required and optional steps as specified herein (i.e., multiplying the obtained Z score by the corresponding importance numerical coefficient) were performed (C). Calculation of the fold change of the score obtained using EpigenAU / 11 compared to the score obtained using cisplatin: The EpigenAU / 11 score is twice that of cisplatin, regardless of whether it is calculated using only the required step (B) or both the required and optional steps (D). [Figure 7]Benefit / risk scores obtained from in vivo treatment with EpigenAU / 11 and cisplatin in an animal model (A). Risk (intended as a side effect) was estimated by observing behavioral parameters (effects on the behavior itself, spontaneous motor system, muscle strength, and nerve reflexes) and potential weight loss in the animals (see Table in Example 8.2). Benefit (intended as therapeutic activity) was assessed by taking into account the reduction in tumor mass size compared to untreated tumors (see Table in Example 8.3). Calculation of the fold change in scores obtained with EpigenAU / 11 compared to scores obtained with cisplatin: EpigenAU / 11 scores are three times higher than cisplatin scores (D). [Figure 8] Annotated side effects of the corresponding biological activities of DIBASE and IPA. Column "Hallmark": Anatomical and functional regions potentially affected by DIBASE toxicity. Column "DIBASE Annotated Side Effects": A list of DIBASE side effects reported and classified as "Common". Column "IPA Biological Activity": Side effects of DIBASE outlined according to the biological activity documented in IPA. Column "Desirable Trend": Healthy trend of the selected biological activity (upward modulation ↑ or downward modulation ↓). [Figure 9] Calculation of benefit scores based on transcriptional data of Osteoredux and DIBASE obtained from in vitro and ex vivo studies without using an arbitrary numerical coefficient (a) of importance, and calculation of Osteoredux and DIBASE benefit scores by multiplying the obtained Z-scores by the corresponding numerical coefficient (b) of importance. [Figure 10] Calculation of Osteoredux and DIBASE risk scores based on transcriptional data obtained from in vitro studies. [Figure 11]Benefit / risk scores obtained from in vitro treatments of Osteoredux and DIBASE, having only the essential steps described in (A). Benefit / risk scores obtained from Osteoredux and DIBASE treatments after 6 hours were performed using the essential and optional steps described herein (i.e., multiplying the obtained Z score by the corresponding importance numerical coefficient) (C). Calculation of the fold change of the score obtained by Osteoredux compared to the score obtained by DIBASE: The Osteoredux score is 3 times higher than DIBASE when only the essential steps are used in the calculation (B), and 6 times higher than DIBASE when both the essential and optional steps are included (D). [Modes for carrying out the invention]

[0086] As described above, benefit / risk assessments are conceptually evaluated based on qualitative data. This invention, instead, evaluates benefit / risk Score A metric is a quantitative and qualitative measure for evaluating the overall value of a medical or beneficial product, intervention, or treatment by comparing its therapeutic or beneficial effects with its potential risks or harms. Therefore, it is expressed as a ratio between the magnitude of the benefit and the magnitude of the risk, where the benefit refers to the positive therapeutic or beneficial effect of the treatment, such as effectiveness in disease management, symptom relief, improved survival, and improved quality of life. On the other hand, the risk encompasses potential harms, including side effects, toxicity, long-term safety concerns, and contraindications.

[0087] In this technical field, the 4R approach is often applied to ensure comprehensive and adaptive evaluation: reviewing clinical data to assess benefits and risks, refining methodologies as new evidence emerges, reassessing the benefit-risk balance over time, and transparently reporting findings to stakeholders. This iterative process enhances the accuracy, reliability, and applicability of benefit / risk assessments, supporting evidence-based decision-making in healthcare. The methods provided in this invention enable in vitro or ex vivo prediction of benefit / risk scores for therapeutic or beneficial products, which is highly advantageous as it allows for early, controlled, and cost-effective assessment of product efficacy and safety before proceeding to animal models or clinical trials. By providing quantitative and purely qualitative insights into therapeutic effects and potential toxicity at the cellular level, such methods help refine candidate selection, reduce reliance on in vivo studies, and accelerate regulatory decision-making. This approach can lead to more efficient drug development, minimized late-stage complications, and optimized patient safety while streamlining the pathway to clinical application and approval.

[0088] In fact, the method of the present invention is particularly advantageous because it allows for a direct comparison between the product under test and a well-established reference drug. By applying the same standardized evaluation criteria to both (the product under test and the reference drug), the method provides a controlled quantitative assessment of efficacy and safety, enabling researchers to measure the benefits and risks of the product under test in comparison to the known profile of the reference drug.

[0089] This method is applicable to both synthetic and natural matrix-based therapeutic or beneficial products, but is particularly advantageous for the latter. Indeed, the benefits and risks of synthetic drugs are easier to assess even by classical methods because they contain a single active ingredient with a measurable dose-response relationship (known API, known receptor). On the other hand, therapeutic or beneficial products based on natural matrices involve multiple components, synergistic effects, and compositional variability, making it difficult to readily identify the specific toxicological domain of the subject of interest using classical methods.

[0090] This invention provides a novel method for calculating the benefit / risk score of therapeutic or beneficial products, which is also applicable to natural matrix-based products. The method is based on an integrated approach utilizing transcriptome data and advanced biological pathway analysis tools (e.g., Ingenuity Pathway Analysis (IPA)) to identify alterations in gene expression and associated modifications of biological activity, addressing both the therapeutic efficacy and potential adverse effects of the product.

[0091] The benefit-risk ratio is a very useful and important parameter in understanding the differences between such key characteristics of treatment options. Nevertheless, such parameters are currently difficult to resummate into a single objectively calculated numerical parameter that can facilitate a primary approach to comparing treatment solutions.

[0092] The present invention provides a method for accurately predicting and objectifying a comparison between the benefit / risk ratios of two therapeutic solutions, based exclusively on transcriptome data from in vitro and ex vivo studies. Furthermore, the present invention provides additional methods for rationalizing and summarizing the benefit / risk ratio from in vivo data obtained in animal models (see Tables 8.2 and 8.3) into a single numerical parameter.

[0093] In modern technology, drug benefit / risk assessment involves comparing the observed clinically positive effects (benefits) with the observed clinically negative effects (risks). This process typically begins with clinical trials to evaluate efficacy and safety. Benefits are assessed based on the drug's ability to effectively treat or prevent symptoms. Risks are considered by identifying side effects, toxicity, and long-term effects. Data from preclinical trials, clinical trials, and post-marketing surveillance are helpful in this assessment. The drug's safety profile, the severity of side effects, and the severity of the condition being treated are all considered. Regulatory agencies like the FDA evaluate the evidence before approval, considering whether the benefits outweigh the risks. Continuous monitoring ensures continued safety after approval. If the risks outweigh the benefits, the drug may be recalled or its use restricted.

[0094] This invention uses known reference drugs with known side effects as product controls to assess the benefits / risks of new potential drugs or beneficial products. Score This provides a new method for determining this.

[0095] As defined in the glossary, a disease reference drug (also known as a standard treatment or gold standard therapy) is an established medicine that serves as a benchmark for efficacy and safety in treating a particular condition. It is typically the most widely accepted and prescribed treatment for a disease, supported by strong clinical evidence from large-scale trials and real-world data, and is usually used as a comparator in clinical trials of new drugs. For example, regulatory agencies such as the FDA and EMA use reference drugs to evaluate whether a new drug adds value in terms of efficacy, safety, or tolerability. Therefore, those skilled in the art can easily identify reference drugs, as regulatory agencies maintain formal lists of approved and reference drugs used as comparators for clinical trials and general drug approvals. For example, reference drugs are formally listed in the FDA's Orange and Purple Books, EMA's EPARs, WHO's Essential Medicines List, and various Health Technology Assessment (HTA) agencies and clinical guidelines, and these lists ensure consistency, safety, and efficacy in treatment recommendations and drug approvals worldwide.

[0096] The method of the present invention is particularly suitable for predicting the benefit / risk ratio of a new product under evaluation, when compared to the benefit / risk ratio obtained by the same method for a reference agent for treating the same disease condition treated by the product under evaluation.

[0097] Accordingly, the present invention provides a computer implementation method for providing a benefit / risk score for a therapeutic product of interest based on in vitro or ex vivo transcriptional data, the method comprising the following steps. 1. Perform transcriptomics analysis as follows: 1.1 Provide a sample of a biological substrate representing a disease or pathological condition treated by the product, a. Process one or more of the aforementioned samples (hereinafter referred to as "sample a") with the aforementioned product; b. Treat one or more of the samples (hereinafter referred to as "sample b") with a reference agent for treating the aforementioned pathological condition or state; and c. One or more samples of the biological substrate (hereinafter referred to as sample c) are used as a reference control; 1.2 Extract RNA from each of the aforementioned samples a, b, and c; 1.3 Perform transcriptome raw data analysis on the RNA extracted in 1.2 to identify differentially expressed genes (DEGs) for sample c in each of samples a and b, and the changes in their expression folds for sample c, thereby obtaining the fold change values ​​for each of the DEGs; 1.4 Perform pathway enrichment and functional analyses on the list obtained in 1.3 to obtain numerical values ​​representing the magnitude and directionality of the biological activity associated with differential expression of the DEG in each of samples a and b, normalized to sample c (i.e., information regulation or downregulation); 2. Determine the benefit score as follows: 2.1 From among the biological activities listed in 1.4, select the biological activity related to the therapeutic indication of the product. 2.2 Convert the numerical values ​​of the related biological activity obtained in 1.4 to absolute values. 2.3 The absolute values ​​of each of the biological activities are summed up to obtain a benefit score for the product under test. 3. Determine the risk score as follows: 3.1 To treat the aforementioned conditions, a list of known side effects associated with the reference drug is provided. 3.2 The biological activity related to the aforementioned side effects is determined by pathway analysis and functional analysis. 3.3 For samples a and b, construct an in silico model of risk using pathway analysis and functional analysis by establishing the relationship between the gene expression patterns obtained in point 1.3 and the biological activity determined in point 3.2; 3.4 The relevant fold change values ​​for each DEG of samples a and b obtained in 1.3 are input into the risk-in-silico model obtained in point 3.3, and pathway analysis and functional analysis are used to obtain data representing the direction and magnitude of regulation of each biological activity determined in point 3.2, and the said data are converted into corresponding absolute values; 3.5 The positive values ​​obtained in 3.4 are summed to obtain a final value representing the risk score of the product under inspection. If the final value is less than a predetermined minimum positive value, it is automatically adjusted to that minimum positive value. 4. Provide the benefit / risk score of the product of interest as the ratio of the benefit score obtained in point 2.3 to the risk score obtained in point 3.5.

[0098] Therefore, in the method of the present invention, transcriptional data is generated in vitro or ex vivo using, for example, a suitable biological substrate that is a representative model of the disease state or pathological condition of interest.

[0099] Those skilled in the art will be able to readily select suitable biological substrates from well-known in vitro or ex vivo models to be treated with the product of interest or its relative reference agent in order to generate the required transcriptional data according to the method of the present invention. Non-limiting examples of such in vitro models include cultured cells, primary cells, human or animal cells derived from relevant tissues (e.g., lung epithelial cells for respiratory pathologies), patient-derived primary cells (e.g., cancer cell lines), immortalized cell lines, stem cell-derived models (e.g., iPSC-derived neurons for neurodegenerative treatment), organ-specific cell lines (e.g., HepG2 for hepatic metabolic studies), organoids (e.g., organoids such as the intestine, brain, or kidney mimicking in vivo conditions), spheroids (e.g., 3D cancer or stem cell models), and microfluidic "organ on a chip" systems that simulate tissue-level responses. In more non-limiting examples, the ex vivo model may be a tissue-based system such as an exgraft (organ-type section), a human or animal tumor biopsy (e.g., for testing an anticancer product), a liver or kidney section, a brain section, or a patient-derived xenograft (PDX model). The relevant control c. according to the present invention is selected by those skilled in the art to represent a pathological condition without treatment with the product of interest or a reference agent.

[0100] The method of the present invention is generally applicable to therapeutic or beneficial products, and the appropriate biological sample will vary with necessary modifications depending on the therapeutic or beneficial purpose of the product under test, as is evident from this specification and examples. This specification and examples demonstrate the applicability of the method of the present invention to products with very different therapeutic purposes, and therefore, the method should not be limited to a specific pathological setting, as it can be readily applied to a number of pathological settings by those skilled in the art. In fact, the method of the present invention was developed to provide a general procedural protocol that enables developers to evaluate benefit / risk scores for different therapeutic or beneficial products and compare said scores to one of the selected reference agents for said products. Furthermore, the method disclosed herein is based on in vitro or ex vivo systems and enables developers to select the most promising candidates from among new therapeutic or beneficial products under development by providing rapid and reliable benefit / risk scores. The reference agents in this method are as defined in the glossary, and additional research and evaluation may be conducted using additional agents used as references as needed.

[0101] Transcriptome analysis can be performed according to any prior art available to those skilled in the art that is suitable for evaluating whole transcriptome expression profiles, such as high-throughput RNA sequencing, qRT-PCR, and microarrays. According to the present invention, path and function analysis tools can be run using commercially available or open-source tools, one of the most well-known being QIAGEN Ingenuity® Pathway Analysis (IPA®), and other known commercially available alternatives to IPA include MetaCore® (Clarivate Analytics), GeneGo® (by MetaCore®), QIAGEN OmicSoft®, lsevier® Pathway Studio®, and Ingenuity® Variant Analysis (IVA®) (QIAGEN). Open-source tools include DAVID (Database for Annotation, Visualization, and Integrated Discovery), GSEA (Gene Set Enrichment Analysis), STRING® (Search Tool for the Retrieval of Interacting Genes / Proteins), KEGG Pathway Analysis, Metascape®, and Cytoscape® with enrichment plugins (e.g., ClueGO®, ReactomeFI). Unless otherwise specified, this specification refers to the final version of each of the above-described tools available at the time of filing the application of the invention.

[0102] In preferred embodiments of the present invention, when pathway and functional analysis is referred to in the steps of the methods disclosed herein, IPA may be used, and in particular, when transcriptome pathway enrichment and functional analysis are referred to (e.g., step 1.4), core analysis by IPA may be used.

[0103] According to the present invention, the numerical value in 1.4 indicates both magnitude (e.g., fold change relative to control) and directionality (e.g., upward regulation by positive values ​​and downward regulation by negative values). The numerical value can be expressed as a Z-score. For example, IPA, MetaCore, GSEA, and Enrichr provide results for Z-score-based gene regulation analysis. The fold change value obtained in step 1.4 can be expressed as a Z-score value for normalizing gene expression changes across different datasets or experimental conditions.

[0104] If the DEG analysis in 1.4 is performed using tools that provide results in terms of visual representations such as color-coded heatmaps, network diagrams, and enrichment plots, then, since these representations are derived from quantitative data, they can be readily converted into numerical values ​​by those skilled in the art using readily available common statistical and computational methods.

[0105] For example, when referring to variations in magnitude and direction relative to a control (e.g., sample c), in 1.4, for example, the variations are evaluated in terms of folding (magnitude) and vertical adjustment (direction), with vertical adjustment represented by a positive value and the number of such values ​​representing the fold change, and vertical adjustment represented by a negative value and the number of such values ​​representing the fold change.

[0106] Whole transcriptome expression profiles can be prepared according to any method commonly used by those skilled in the art, and non-limiting examples include the use of Human Clariom® S Pico Assay HT (Applied Biosystems, ThermoFisher Scientific) in GeneTitan MC Instrument (Applied Biosystems, ThermoFisher Scientific) according to the manufacturer's instructions. CEL intensity files can be prepared by Affymetrix GeneChip Command Console Software (AGCC, ThermoFisher Scientific). Data analysis can be performed using Transcriptomic Analysis Console Software (TAC, ThermoFisher Scientific) (Limma Bioconductor package), which provides quality control analysis, normalization and summarization based on the Signal Space Transformation-Robust Multi-Chip Analysis (SST-RMA) analysis algorithm and provides a list of differentially expressed genes. Alternative commercial and / or open-source tools known to those skilled in the art can be used in any part of the preparation of the transcriptome expression profiles exemplified above. This step is performed to obtain a list of differentially expressed genes (DEGs) identified based on their expression fold changes against relevant control experimental conditions (i.e., tumor volume without treatment, cell lines not treated with the product of interest, etc.).

[0107] According to one aspect of the present invention, transcriptional profile analysis can be performed using IPA as described above. The use of IPA makes it possible to easily estimate how and to what extent the modulation of gene expression in a biological system affects the biological activity related to the disease state of interest.

[0108] Next, the obtained transcriptional modifications profile is subjected to functional pathway enrichment analysis. One commercially available tool that can be used, and used in the examples provided in this application, is Ingenuity Pathway Analysis (IPA version 94302991, Qiagen) [Kramer et al. (2014)]. When IPA is used, a list of differentially expressed genes and corresponding data measurements identified under different experimental conditions is uploaded to the application, and then the available identifiers are mapped to their corresponding entities in the QIAGEN Knowledge Base.

[0109] By initiating "core analysis," highly perturbed genes called Network Eligible molecules are overlaid onto a global molecular network developed from information contained in the QIAGEN Knowledge Base. Subsequently, Networks of Network Eligible Molecules are algorithmically generated based on their connectivity.

[0110] The "core" analysis provides a comprehensive list of approximately the top 500 biological activities derived from the constructed network. The association between biological activity (also referred to herein as biological activity) and gene is always supported by annotations corresponding to scientific peer-reviewed publications, demonstrated through automatic association to Z-scores [Kramer et al. (2014)] representing the direction and magnitude of the modulation of the calculated biological activity. Essentially, this value represents a statistical metric that assesses the similarity between the observed pattern of differentially expressed genes (DEGs) and the expected pattern based on existing literature for a given annotation.

[0111] Biological activities associated with a specific pathological condition under investigation can be selected by examining appropriate AIs, or the selection may be based on relevant modern technologies. The selection of relevant biological activities is based on identified hallmarks of the pathological condition of interest and applies a set of Z-score thresholds with statistical significance indicated by a p-value of 0.05 or less.

[0112] The hallmark of a given pathological condition can be readily identified by those skilled in the art in specialized databases, either through pathway and function databases or through disease-specific databases.

[0113] Next, the direction and magnitude of modulation for each biological function are shown using the relevant Z-score values ​​(Figures 4 and 9).

[0114] If the IPA “core analysis” (or its equivalent analysis using different open-source or commercial tools) does not provide sufficient relevant information, an alternative available QIAGEN IPA approach called “overlay analysis” (or its equivalent analysis using different open-source or commercial tools) can be used. This analysis focuses on biological activity identified by “in silico models of pathophysiological states.” The selection of biological activity can be built upon identified hallmarks of the pathological state of interest and can be done by examining appropriate AIs or based on the latest technologies.

[0115] "Overlay analysis" establishes the relationship between the pattern of differentially expressed genes and selected biological activity (always supported by annotations corresponding to scientific peer-reviewed publications that demonstrate the direction and magnitude of the modulation of biological activity).

[0116] For example, this can be done using the following steps:

[0117] A set of biological activities selected from in silico models of pathophysiological states is imported into a new sheet called "my pathway".

[0118] Use the "Construction Tools" and "Growth Tools" to identify differentially expressed genes (DEGs) that belong to the transcriptome profile under investigation and are associated with the regulation of biological activity selected in the previous step.

[0119] The "Overlay" and "Molecule Activity Predictor" (MAP) tools are used to determine the expected computational effects of such experimentally observed gene expression modulations on biological activity. The "Prediction" function is activated within the MAP tool to calculate the expected modulation resulting from the biological activity. (Note that QUIAGEN IPA defines biological activity as "disease and biological function").

[0120] In Step 2.1, any obvious biological activity that contradicts the observed therapeutic or biological effect of the selected reference drug, if present, will not be selected. An example is increased tumorigenesis caused by antitumor drugs.

[0121] According to this specification and the claims, step 2.2, which converts the numerical values ​​obtained in 1.4 to absolute values, means that for each value obtained in 1.4, the modulus of the value, i.e., the distance of the number (value) from zero on the number line regardless of direction, is taken into consideration. Absolute values ​​are not always negative.

[0122] Summing each value means performing the sum of each value in 2.2.

[0123] According to one aspect of the present invention, this method is Step 2.1 further includes clustering the relevant biological activities into hallmarks of disease or pathological conditions treated with the product of interest, and providing a numerical coefficient of importance for each of the hallmarks.

[0124] In fact, to further refine the benefit score, in one aspect of the present invention, hallmarks of the disease state of interest can be searched and assigned a “numerical importance coefficient” based on their importance in disease progression and the impact of treatment. By clustering the relevant biological activities selected in 2.1 to the hallmarks and assigning a numerical importance coefficient to each hallmark, a more precise calculation of the relevance of each of the biological activities becomes possible. A higher coefficient indicates that targeting the hallmark is important for effective treatment, while a lower coefficient suggests an indirect or newly emerging role.

[0125] Preferably, established hallmarks, i.e., features officially recognized in the art, are used. Hallmarks for pathological conditions or states can be easily retrieved from the latest technology, or they can be retrieved by computational methods or by querying appropriate databases, AI (e.g., ChatGPT) or dedicated computer programs.

[0126] For example, hallmarks of disease states can be retrieved using computational methods that combine bioinformatics tools, machine learning, network analysis, and natural language processing (NLP).

[0127] Non-limiting examples of importance coefficients according to the present invention are provided below. Generally, hallmarks can be divided into two main categories, with importance coefficients indicated in parentheses: High-relevance hallmarks (2): A therapeutic or beneficial product must target the hallmark for meaningful therapeutic benefit.

[0128] Low-relevance hallmarks (1): Useful for complementary effects, but not useful as primary efficacy markers.

[0129] Below, we provide non-limiting examples of hallmarks for different pathological conditions, along with importance factors suitable for the method of the present invention:

[0130] [Table 1] [Table 2] [Table 3] [Table 4] [Table 5]

[0131] The hallmark and importance coefficient of a given pathological condition can be easily retrieved by a person skilled in the art by appropriately examining available AIs that include ChatGPT.

[0132] When a Hallmark is found, a dedicated program or statistical or machine learning techniques can be used to group biological activity into Hallmarks using a clustering algorithm.

[0133] Clustering relevant biological activities to hallmarks of a given disease state can be easily done by those skilled in the art by integrating biological data, applying computational methods, and utilizing existing biological knowledge (such as pathways or gene ontologies).

[0134] As described above, clustering biological activity to hallmarks of the disease state or pathological condition of interest can be done by applying computational methods and integrating biological data by leveraging existing biological knowledge (such as pathways or gene ontologies). Numerical importance coefficients are typically determined based on biological and / or clinical relevance, or on feature importance based on machine learning.

[0135] Furthermore, the method may further include, in step 2.2, multiplying each of the numerical values ​​of the relevant biological activity obtained in 1.4 by the clustered Hallmark importance numerical coefficient provided in 2.1.a before converting the numerical values ​​to absolute values. The Hallmark importance numerical coefficient enables a quantitative assessment of its relevance to a given disease state and allows for a more accurate and objective calculation of the benefit score. This approach helps prioritize treatments or interventions by weighting effects according to the biological significance of Hallmark, ensuring that treatments targeting the most important disease mechanisms receive greater emphasis. It also facilitates individualized treatment strategies, standardized comparisons across studies, and efficient resource allocation, ultimately enhancing decision-making in research, clinical practice, and drug development.

[0136] According to the present invention, the side effects considered in 3.1 are those indicated in the reference drug leaflet as very common (affected by more than 1 in 10 patients) and common / frequent side effects (affected by more than 1 in 100 patients) or adverse drug reactions.

[0137] Notably, drug side effects are reported and therefore officially registered in accordance with the global drug surveillance system, and thus, those skilled in the art can simply rely on the leaflet accompanying the reference drug for an official list of very common and common side effects when implementing the present invention.

[0138] In step 3.2, the pathway and functional analysis biological activity related to the adverse event can be determined by inputting the adverse event into the pathway and functional analysis program and searching for the associated biological activity.

[0139] In step 3.3, for samples a and b, an in silico model of risk using pathway and functional analysis can be determined by establishing the relationship between the gene expression patterns obtained in point 1.3 and the biological activity determined in point 3.2, using the tools of the selected pathway enrichment analysis software. For example, if IPA is used, the growth tools are suitable for constructing an in silico model of risk.

[0140] In 3.4, the relevant fold change value may be a Z-score value, as described above. The representation of the Z-score value provided above is applied to step 3.4 of this method with any necessary modifications.

[0141] Regarding 2.2, the data is also converted to absolute values ​​in 3.4.

[0142] In events where the achieved modulation of biological activity does not indicate the activation of a side effect, the corresponding value is set to 0. Conversely, if the achieved modulation of biological activity does indicate the activation of a side effect, the value is converted to its absolute value. In the proposed computer implementation method for determining the benefit / risk score of a therapeutic or beneficial product, step 3.5 ensures that the risk score has a predetermined positive minimum value, even if the calculated risk score is initially zero or very low. The reason for this adjustment is that the benefit score must always be higher than the lowest biological activity value, which serves several important purposes.

[0143] To prevent a mathematically undefined benefit / risk ratio when the risk score is zero (i.e., no risk-related biological activity is detected), a predetermined positive minimum value of 3.5 is introduced, which results in a mathematically undefined expression due to division by zero. This prevents a meaningful interpretation of the results. By setting a minimum positive risk score, the system allows for differentiation between products with different safety profiles while avoiding partitioning errors.

[0144] Furthermore, if the risk score is very low but not zero, the ratio can be artificially inflated, potentially giving the impression of an unrealistically high benefit / risk score. Normalizing the risk score can prevent overestimation.

[0145] In practical applications, comparing multiple drugs or test compounds requires standardized benefit-risk calculations. If risk scores approach zero for some products, their benefit-risk scores will be disproportionately high compared to others, even if their actual clinical outcomes are similar. By enforcing a minimum positive risk value, this method ensures a fair and standardized comparison between different treatment candidates.

[0146] Furthermore, the methods of the present invention take into account that therapeutic or beneficial products are generally not completely risk-free, and therefore, even if in vitro or ex vivo transcriptional analysis does not detect potent activation of pathways associated with known side effects, this does not guarantee that the product is completely risk-free in real-world use.

[0147] By setting a minimum positive risk value, the system recognizes inherent uncertainties and ensures that even highly favorable drugs have a finite, quantifiable risk component.

[0148] Finally, this method relies on an in silico risk model (step 3.3) that predicts risk based on pathway and functional analysis. While this model is robust, it cannot capture all possible adverse effects due to potential toxicities that may not be detected by transcriptional events, potential limitations in the current pathway database, and the lack of knowledge regarding all molecular interactions and differences between in vitro / in vivo conditions and actual patient responses.

[0149] Enforcing the smallest positive risk score helps correct potential risk underestimation due to model limitations and prevents overconfidence in drug safety.

[0150] Therefore, according to the method of the present invention, the predetermined minimum positive value should depend on the scale of the measurement and the type of numerical transformation applied to the risk-related biological activity. The selected value should strike a balance between preventing splitting errors (avoiding infinitely or artificially high benefit / risk ratios) while still allowing meaningful distinction between drugs.

[0151] According to one aspect of the present invention, the predetermined minimum positive value may be a value between 0.001 and 1.0.

[0152] For example, if a risk score is calculated as a sum of positive values ​​(e.g., a pathway activation score, Z-score, or weighted functional annotation), a reasonable predetermined minimum positive value should be small but not negligible.

[0153] According to one embodiment, for example, if the sum of the pathway activity scores is generally in the range of 0 to 10, the predetermined minimum positive value may be 0.1, while if the sum is in the range of 1 to 100, the predetermined minimum positive value may be 1.0.

[0154] Generally, a person skilled in the art can select the predetermined minimum positive value between 1 and 10%, for example, 5% of the median of the observed risk scores.

[0155] When IPA-like path analysis is used, the predetermined minimum positive value is preferably selected from 0.5 to 1.0.

[0156] The predetermined minimum positive value can also be selected in relation to the benefit score, for example, from 1 to 10% of the obtained benefit score, for example, 5%.

[0157] Those skilled in the art who perform the method of the present invention know that the benefit score in a quantitative benefit / risk (B / R) assessment represents the total effect of the treatment-related biological activity induced by the drug. To ensure that the drug produces a meaningful therapeutic effect, its benefit score must always be higher than the lowest measurable biological activity value. If this condition is not met, proceeding with the B / R assessment is not justified. This also applies to the method of the present invention.

[0158] Furthermore, in one aspect of the present invention, an in silico model of risk may also be integrated to assign a numerical coefficient of importance based on the severity of each adverse event, based on its clinical relevance, severity, frequency, and impact on the patient's quality of life, to each adverse event associated with the reference drug provided in 3.1.

[0159] For example, the FDA, EMA, and WHO standards classify side effects by their severity, for instance, Classification by Severity (based on FDA, EMA, and WHO standards) is used. Mild → No serious health impact; in most cases, recovery occurs without treatment. Non-specific examples: nausea, dry mouth, mild headache, drowsiness Moderate → Interferes with daily activities and may require medical intervention. Non-specific examples: dizziness, muscle pain, skin rash, gastrointestinal discomfort Severe → Serious or life-threatening; requires medical attention. Non-specific examples: severe allergic reaction (anaphylaxis), liver failure, cardiac arrhythmia. Lethal → Causes death or significantly contributes to a fatal condition. Non-limiting examples: severe drug-induced liver injury (DILI), toxic epidermal necrolysis (TEN)

[0160] Numerical coefficients for the importance of side effects associated with a reference drug can be provided, for example, based on a standard clinical classification system (e.g., Common Terminology Criteria for Adverse Events from CTCAE-NCI).

[0161] As an example of assigning severity scores based on grade: Grade 1 (mild): Coefficient = 1 Grade 2 (Intermediate): Coefficient = 2 Grade 3 (Severe): Coefficient = 3 Grade 4 (Life-threatening): Coefficient = 4 Grade 5 (death): coefficient = 5, or can be calculated based on the incidence of each adverse event in clinical trials or real-world data, for example, common (incidence > 10%): coefficient = 1.0 Infrequent (occurrence rate of 1-10%): coefficient = 0.75 Rare (incidence rate less than 1%): coefficient = 0.5

[0162] For example, adjusting risk scores can be easily done with the help of the following computer software: Based on the severity, frequency, impact on quality of life, and duration described above, a numerical coefficient of importance is assigned to each adverse event. We calculate the risk contribution using changes in gene expression folds. The overall risk score is calculated, and a predetermined minimum value is applied. The benefit / risk score is generated by dividing the benefit score by the risk score.

[0163] Display a summary table containing all relevant data.

[0164] In other words, the method of the present invention further includes the following: Assign a numerical importance coefficient to each adverse event determined in step 3.1, and multiply the fold change value obtained in step 1.3 associated with each adverse event by the corresponding numerical importance coefficient.

[0165] As described above, the numerical coefficient of importance is preferably calculated using a computer-aided method as disclosed above, based on one or more of the following parameters: Severity of adverse events according to the clinical classification system, frequency of occurrence in clinical data, impact on patient-reported quality of life, and duration of adverse events.

[0166] According to the present invention, steps 2.1.a and / or 3.2 can be performed using a machine learning model, such as a neural network or support vector machine, that has been trained on a known transcriptome dataset.

[0167] It should be noted that in this specification, mathematically and in this specification, the value 0 is not considered a positive or negative number. Therefore, the correction to the minimum positive value is necessarily applied when a value of 0 is obtained in the risk score value provided by the method disclosed herein.

[0168] Preferably, the method of the present invention is also performed on a reference drug, thereby providing benefit / risk scores for the product of interest and the reference drug, thereby enabling a direct comparison of the benefit / risk scores of the product of interest and its reference drug. Advantageously, the benefit / risk score of the reference drug can be used as a benchmark for evaluating the therapeutic efficacy of the product of interest.

[0169] Accordingly, the present invention also refers to a method for selecting one or more new therapeutic or beneficial products for clinical development, wherein benefit / risk scores for one or more therapeutic or beneficial products of interest for the treatment of a given disease or pathological condition, and a reference agent for the treatment of the disease or pathological condition, are provided by performing the steps defined above and in the claims, and each of the at least one therapeutic or beneficial product of interest is selected for clinical development if the benefit / risk score value provided for at least one of the therapeutic or beneficial products of interest is greater than or equal to the benefit / risk score value provided for the reference agent.

[0170] The present invention further discloses a method for providing a benefit / risk score for a therapeutic product of interest for the treatment of a given disease condition, based on data obtained from an in vivo animal model, which can be performed in addition to the in vitro or ex vivo method of the present invention, in order to validate the results obtained by the in vitro or ex vivo method disclosed herein, based on data obtained in vivo.

[0171] The further method for providing a benefit / risk score for a therapeutic product of interest for the treatment of a given disease condition, based on previously obtained data from in vivo animal models, includes: I. To provide numerical values ​​obtained from the following group of animal models that represent the aforementioned pathological conditions. a. Groups treated with the above product and b. The group treated with a reference drug for the treatment of the aforementioned pathological condition; and c. Related control group The aforementioned values ​​represent each of the following parameters: the therapeutic effect observed in groups a and b, normalized relative to the control group c; changes in animal behavior indicating distress; and weight loss indicating animal distress. II. The benefit score is determined by summing the values ​​of the treatment effectiveness mentioned above and thereby obtaining the benefit score value of the product under test. III. Determine the risk score, In III.1 1, the numerical values ​​indicated for changes in animal suffering and weight loss are identified as risk parameters. III.2 The numerical values ​​from III.1 (for group a) are summed to obtain a final value representing the risk score of the product under test; if the final value is less than a predetermined minimum positive value, it is automatically adjusted to that minimum positive value. IV. Provide the benefit / risk score of the product of interest as the ratio of the benefit score obtained in Point II to the risk score obtained in Point III.2.

[0172] The above method can be carried out in addition to the in vitro or ex vivo-based method of the present invention.

[0173] Preferably, the above method is based on existing data obtained from in vivo animal models, and this method uses only ethically approved, preferably existing animal research data or data obtained from publicly available scientific databases.

[0174] Therefore, the method according to the present invention can be interpreted as follows: A computer implementation method for providing a benefit / risk score for a therapeutic or beneficial product of interest for treating a given disease condition, based on existing data obtained from in vivo animal models.

[0175] I. To provide numerical values ​​derived from previously obtained data of in vivo animal models representing the aforementioned pathological conditions. a. The group treated with the above product; b. The group treated with the reference drug for the treatment of the aforementioned pathological condition; and c. Related control group Includes; Here, the aforementioned value is: i. Treatment effectiveness; ii. Changes in behavior indicating the effectiveness of the treatment; and iii. Weight changes observed in groups a and b, normalized relative to control group c. To represent, to provide, II. Determine the benefit score by summing the aforementioned treatment effectiveness values, and thereby obtain the risk score of the product under examination. III. Determining the risk score: III.1. Identifying risk parameters using the aforementioned numerical values ​​in relation to behavioral changes and weight changes, and III.2. The numerical values ​​related to the adverse effects of group a (as identified in III.1) are summed to obtain a final value representing the risk score of the product under test. If the final value falls below a predetermined minimum positive threshold, it is adjusted to that minimum value. IV. Provide a benefit / risk score by calculating the ratio between the benefit score (obtained in II) and the risk score (obtained in III.2).

[0176] When animal data is used, only data obtained from animals treated according to ethically approved protocols will be used.

[0177] As mentioned above, advantageously, methods based on data obtained from in vivo models can be combined with methods based on in vitro or ex vivo data to further validate the predictive results obtained therefrom.

[0178] The data obtained from the following example in vivo models, which validate the results obtained in vitro or ex vivo using the method of the present invention, demonstrate that the in vitro or ex vivo method of the present invention actually provides a reliable benefit / risk score, thereby eliminating unnecessary in vivo experiments.

[0179] Furthermore, methods disclosed herein for selecting one or more new therapeutic or beneficial products for clinical development may further include calculating benefit / risk scores for each test product and reference drug using methods based on data obtained from in vivo models disclosed above, such that each of the at least one therapeutic or beneficial product of interest is selected for clinical development if the benefit / risk score values ​​provided to at least one of the therapeutic or beneficial products of interest by both methods (based on data obtained from in vitro or ex vivo and in vivo models) are greater than or equal to both of the relative benefit / risk score values ​​provided to the reference drug (based on data obtained from in vitro or ex vivo and in vivo models).

[0180] Equal to or higher than means, as specified herein, that the value is numerically higher, i.e., the benefit / risk score value of the tested product is numerically equal to or higher than the benefit / risk score of the reference drug, preferably, that it is even statistically significant.

[0181] All further steps disclosed in the in vitro or ex vivo-based methods described above are applicable mutatis mutandis to methods based on data obtained from in vivo models disclosed herein, with necessary modifications. The therapeutic efficacy parameters depend on the disease state of interest; for example, in the case of anticancer agents, the parameters are primarily expressed by tumor burden reduction, and optionally by the presence or absence of metastasis; in the case of antidepressants, the parameters may include behavioral changes such as improvement in the severity of depression. Thus, the parameters are those that generally correlate with the evaluation of therapeutic efficacy in treating a given disease state.

[0182] Parameters indicating changes in animal distress are those coded by standard tests commonly used in the art, such as the Irwin test, which are used in formal protocols for evaluating animal distress in preclinical studies or in captivity.

[0183] Even in this case, a benefit / risk score can be provided for the reference drug as well.

[0184] As described above, preferably, where applicable, both methods are performed to verify the results obtained from either one of them.

[0185] In preferred embodiments of the present invention, the therapeutic or beneficial product of interest in the methods described herein is a product comprising one or more natural matrices, e.g., eukaryotic sources, prokaryotic sources, e.g., plant sources, marine sources, bacterial sources, fungal sources, yeast sources, animal sources, or natural sources; for example: a product comprising one or more of the following: cut or crushed plant parts, plant extracts, fractions of such extracts, microorganisms, honey, propolis, silk, wax, plant resins, plant gums, plant exudates, vegetable oils, plant essential oils, animal tissue lysates, or plant liquids or animal body fluids.

[0186] In this specification and in all parts of the claims, the term "comprising" may be replaced with the term "consisting of".

[0187] Where calculations are performed in any part of the specification or claims, these may be performed in computer implementation mode.

[0188] Wherever an Internet address or URL is provided in any part of this specification, it relates to information retrieved from said Internet address available as of the filing date of this application, i.e., from the latest version of the content at said Internet address as of the filing date of this application.

[0189] Wherever a product on the market is referenced in this specification or in the claims, the symbols ™ (trademark) or (registered trademark) are considered implicit and may be added to each of such products at any time.

[0190] Wherever the phrase "computer implementation method for providing a benefit / risk score" is used in this specification or in any part of the claims, the phrase may be replaced with "computer implementation method for manufacturing a therapeutic or beneficial product, comprising determining its benefit / risk score."

[0191] Examples are reported below for the purpose of better illustrating the embodiments disclosed herein, and such examples should not be considered in any way as limitations of the foregoing specification and subsequent claims. Furthermore, the following examples report all the research performed on the products of the present invention and support all the subject matter claimed.

[0192] example 1. Composition of the test product Product A, also known as Osteoredux in this specification, for the treatment of bone fragility. Coral skeleton powder 32% w / w Bird eggshell powder 30.2%w / w Dried coral skeleton + lemon juice (containing calcium citrate) powder 13% w / w Agaricus bisporus powder 4.65% w / w Horsetail (Equisetum arvene) flower apex dried extract 2% w / w Acerola (Malpighia punicifolia) supported with 2.0% w / w inulin dry extract. Icelandic moss (Cetraria islandica) powder 2% w / w Agave sisalana leaf powder 12% w / w Acacia senegal powder 2.15% w / w

[0193] Product B 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 0.89% by weight of freeze-dried component 3. Ingredient 1 Bay laurel (Laurus nobilis) leaves 25% w / w Ashwagandha (Whitania somnifera) root 25% w / w Leaves and flowers of Filipendula vulgaris, 25% w / w Broccoli (Brassica oleracea L.botrytis cymosa) seeds, 25% w / w Co-extraction in water 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 in water Ingredient 3 Freeze-dried extract of sisal leaves (Agave sisalana).

[0194] 2. Overview of the protocols used [Table 6-1] [Table 6-2] [Table 6-3] [Table 6-4]

[0195] 3. Techniques and settings for calculating the benefit / risk scores of products A and B. 3.1 In vitro or ex vivo sample treatment Bone vulnerability: Product A-Osteoredux This study used human adipose-derived mesenchymal stem cell lines (hADMSCs) that can differentiate into osteoblasts and mineralize the extracellular matrix (ECM). These cells were obtained during systemic surgery from three differently described patients (PA42, PA59, and PA69) (Romagnoli et al., "In Vitro Behavior of Human Adipose Tissue-Derived Stem Cells on Poly(ε-caprolactone)Film for Bone Tissue Engineering Applications," BioMed Research International, Vol. 2015, Article ID 323571, p. 12, 2015. https: / / doi.org / 10.1155 / 2015 / 323571). These cell lines have been characterized in terms of key stem cell markers for mesenchymal stem cells (CD44, CD105, and STRO1), and by studying their pluripotency for osteogenic phenotypes in the Department of Surgery and Translational Medicine at the University of Florence.

[0196] hADMSCs were cultured in growth medium (GM) and grown to 70-80% confluence. The cells were then seeded into 24-well plates at a concentration of 1 × 10⁵ cells / well. After one week, the GM was replaced with osteogenic medium (OM) containing 1 μg / mL of fluorophocalcein, and the cells were incubated for 28 days with or without the use of Osteoredux or the reference drug DiBase. The medium, with or without product C, was refreshed twice a week.

[0197] Cancer field: Product B-EpigenAU / 11 To perform the experiment, ex vivo tumor masses were generated from the FaDu head and neck squamous cell carcinoma cell line transplanted into immunosuppressed mice. Once the tumors reached a suitable size, they were excised, divided into 40 mg portions, and treated with EpigenAU / 11 or the reference agent Cisplatin in triplicate for 6 hours. The masses were then lysed, and RNA was extracted for transcriptional analysis.

[0198] 3.2 Transcriptome Raw Data Analysis The entire transcriptome expression profile was evaluated. In particular, the Human Clariom® S Pico Assay HT (Applied Biosystems, ThermoFisher Scientific) was used with the GeneTitan MC Instrument (Applied Biosystems, ThermoFisher Scientific) as instructed by the manufacturer. CEL intensity files were created using Affymetrix GeneChip Command Console Software (AGCC, ThermoFisher Scientific). Data analysis was performed using Transcriptomic Analysis Console Software (TAC, ThermoFisher Scientific) (Limma Bioconductor package), which provides quality control analysis, normalization, and summarization based on the Signal Space Transformation-Robust Multi-Chip Analysis (SST-RMA) analysis algorithm, and provides a list of differentially expressed genes. This step allows obtaining a list of differentially expressed genes (DEGs) identified based on changes in their expression folds against relevant control experimental conditions (i.e., untreated tumor volume, cell lines not treated with the product of interest, etc.). Detailed protocols are shown in the table above.

[0199] 3.3 Ingenuity Pathway Analysis of Transcription Profiles: IPA (QUIAGEN IPA) Analysis The use of IPA makes it possible to estimate how and to what extent the modulation of gene expression in a biological system affects the biological activity related to the disease state of interest.

[0200] The obtained transcriptional modification profiles were subjected to functional pathway enrichment analysis. Ingenuity Pathway Analysis (IPA version 94302991, Qiagen) [Kramer et al. (2014)] was used. For each benefit / risk assessment (e.g., product A, product B), a list of differentially expressed genes and corresponding data measurements identified under different experimental conditions were uploaded to the application.

[0201] The available identifiers were mapped to their corresponding entities in the QIAGEN Knowledge Base.

[0202] By initiating the IPA "core analysis," highly perturbed genes called Network Eligible molecules are overlaid onto a global molecular network developed from information contained in the QIAGEN Knowledge Base. Subsequently, Networks of Network Eligible Molecules were algorithmically generated based on their connectivity.

[0203] The "core" analysis provides a comprehensive list of approximately the top 500 biological activities derived from the constructed network. Biological activity-gene IPA associations are always supported by annotations corresponding to scientific peer-reviewed publications that demonstrate the direction and magnitude of the modulation of the calculated biological activity through automated association of z-scores [Kramer et al. (2014)] (wherein the present invention refers to biological activity, the designation of IPA is "biological activity"). Essentially, the obtained values ​​represent a statistical metric that evaluates the similarity between the observed pattern of differentially expressed genes (DEGs) and the expected pattern based on existing literature for a given annotation.

[0204] The biological activities associated with the specific pathological conditions under investigation were selected based on relevant cutting-edge technologies, but the same can be done by examining appropriate AI. The selection of biological activities is based on identified hallmarks of the pathological conditions of interest, and a set of Z-score thresholds with statistical significance indicated by a p-value of 0.05 or less is applied.

[0205] Next, the direction and magnitude of modulation for each biological function were shown using the relevant Z-score values ​​(Figures 4 and 9).

[0206] If the IPA core analysis did not yield sufficient relevant information, an alternative approach called "overlay analysis" was used. This analysis focused on biological activity identified by an "in silico model of the pathophysiological state." The selection of biological activity was built upon identified hallmarks of the pathological state of interest. This could be done by examining appropriate AI or based on state-of-the-art technology.

[0207] "Overlay analysis" is constructed by establishing a relationship between the pattern of differentially expressed genes and selected biological activity (always supported by annotations corresponding to scientific peer-reviewed publications that demonstrate the direction and magnitude of the modulation of biological activity).

[0208] This was done using the following procedure:

[0209] A set of biological activities (called "functions" in IPA) selected from in silico models of pathophysiological states was imported into a new sheet called "my pathway".

[0210] The "construction tool" and "growth tool" were used to identify differentially expressed genes (DEGs) belonging to the transcriptome profile under investigation and associated with the regulation of biological activity selected in the previous step.

[0211] The "Overlay" and "Molecule Activity Predictor" (MAP) tools were used to determine the expected computational effects of such experimentally observed gene expression modulations on biological activity. The "Prediction" function was activated within the MAP tool to calculate the expected modulation resulting from the biological activity.

[0212] 3.4 Image analysis of color intensity obtained for biological activity The "overlay analysis" does not directly calculate the Z-score for each biological activity (function in IPA). Therefore, the intensity of the modulation signal was quantified. This was achieved by using the dedicated application: "IPAmmap_Parser" (version 2.1-1).

[0213] The app is a web port application for Pipeline Pilot designed to assign scores called z-scores to genes and biological functions based on coloring within biological pathways generated by QIAGEN's Ingenuity Pathway Analysis software. A key step in the algorithm is the conversion from an RGB color model to a LAB [https: / / www.xrite.com / it-it / blog / lab-color-space] model, which is colorimetric coding that enables not only RGB synthesis but also the recording of color intensity. This conversion is performed within the "components" of Pipeline Pilot, utilizing procedures written in R software and relying on specific functionality of the color space package [https: / / cran.r-project.org / web / packages / colorspace / index.html].

[0214] 4. Calculation of Risk Score 4.1 Definition of side effects Officially recorded very common or common (more than 1 in 10 patients or more than 1 in 100 patients) side effects were identified by examining specific sources: -https: / / www.torrinomedica.it -https: / / www.drugs.com / -Specific leaflet for standard products Specific sources in the area of ​​interest Osteoarthritis: -https: / / www.torrinomedica.it / schede-farmaci / kenacort / -https: / / www.drugs.com / sfx / triamcinolone-side-effects.html Bone fragility: -https: / / www.torrinomedica.it / schede-farmaci / dibase / -https: / / www.drugs.com / sfx / cholecalciferol-side-effects.html Cancer area: -https: / / www.torrinomedica.it / schede-farmaci / cisplatino-3 / -https: / / www.drugs.com / sfx / cisplatin-side-effects.html

[0215] 4.2 Identifying IPA Biological Functions by Browsing Terminology Used to Annotate Side Effects The information found in the aforementioned resources is used to identify the BF (i.e., biological activity) associated with the identified side effect and to investigate the IPA using the following procedure: I entered each adverse effect term into the "Disease and Function" query box, and then started the search.

[0216] The resulting re-analysis of the tables was filtered to identify diseases / functions derived from a wide range of evidence. The source of relationships was the Ingenuity Knowledge Base, encompassing journal articles, OMIM, JAX, and curated content from ClinicalTrials.gov.

[0217] The in-silico model was limited to genes and mRNAs. Thus, the tool was able to associate each biological activity with a defined number of genes whose modulation could affect the modulation of the biological activity itself (which could represent side effects in this particular case).

[0218] 4.3 Overlay analysis The overlay analysis focuses on the biological activities identified by the "in-silico model of side effects". The selection of biological activities is constituted based on the side effects of the specified reference treatment.

[0219] The "overlay analysis" is constructed by establishing the relationship between the pattern of expression-variable genes and the selected biological activities using the following procedure (always supported by annotations corresponding to scientific peer-reviewed publications demonstrating the directionality and magnitude of the modulation of biological activities):

[0220] A set of biological activities selected from the in-silico model of the SIDE EFFECT PROFILE was imported into a new sheet called "my pathway".

[0221] The "construction tool" and "growth tool" were used to identify the expression-variable genes (DEGs) belonging to the transcriptome profile under investigation and related to the regulation of the biological activities selected in the previous step.

[0222] The "Overlay" and "Molecule Activity Predictor" tools (MAP) are used to determine the expected calculated impact of such experimentally observed modulation of gene expression on the biological activity. The "Prediction" function is activated within the MAP tool to calculate the expected modulation resulting from the biological activity.

[0223] 4.4 Image analysis of the color intensity obtained for the biological activity As mentioned above, since "overlay analysis" does not directly calculate the Z-score for each biological function, we used the dedicated application "IPAtmap_Parser" to convert the intensity and direction of each modulation from a color heatmap to numerical values ​​in order to obtain the Z-score representing the intensity and direction of each modulation.

[0224] 4.5 Calculation of Global Risk Score Next, the risk score was calculated by summing each positive Z-score value obtained from the overlay analysis (to show that the given side effect was induced by the treatment).

[0225] 5. Calculation of the benefit score 5.1 Core analysis, selection of biological activity, and export Using core analysis, appropriate biological activities (biological functions for use in IPA language) were selected, taking into account trends that match the therapeutic indications of the product under investigation. Biological activities relevant to specific treatments can be identified by pathway enrichment analysis using AI suitable for literature searches or disease-specific databases.

[0226] For each pathological region examined, the following biological activities were selected and used in conjunction with IPA. cancer field We examined the changes in the known characteristics of healthy physiological states related to cancer, paying particular attention to the following areas: cell damage Energy metabolism and insulin sensitivity Epithelial-mesenchymal transition growth factors inflammation Regulation of mitosis / proliferation pace Using the above, the outputs of the IPA core analysis were selected and classified. Bone fragility We examined the changes in the publicly known characteristics of "bone fragility" in a healthy physiological state, paying particular attention to the following areas: -Bone remodeling - Osteoporosis - Differentiation of osteoblasts - Mineralization - Reduction of inflammation - Reduction of bone adipose tissue

[0227] The above were used to examine IPA using the "IPA Bioprofiler" tool with the following keywords: osteoporosis, postmenopausal osteoporosis, bone mineralization, differentiation of osteoblasts and osteoclasts, bone mineral density.

[0228] BF was grouped by the hallmark of activity (optionally, each hallmark is given a weighting factor based on its importance in the pathogenesis of interest). Therefore, the BF Z-score values were converted to absolute values, and the list thus obtained was exported to a table reporting the specifications of biological functions and their relative regulatory values. The sum of each value for each treatment is considered the benefit score.

[0229] 6. Calculation of benefit / risk score When the risk score and benefit score were calculated, they were applied to the following formula: (Benefit score) / (Risk score)

[0230] Therefore, the higher the value of the ratio, the higher the safety of the administered treatment must be interpreted.

[0231] 7. New benefit / risk assessment product A (computer implementation / support) 7.1 Data Assembly 7.1.1 Ex vivo sample treatment and RNA extraction (Steps 1.1 and 1.2) To perform the experiment, ex vivo tumor masses were generated from the FaDu head and neck squamous cell carcinoma cell line transplanted into immunosuppressed mice. Once the tumors reached a suitable size, they were excised, divided into 40 mg portions, and treated in triplicate with EpigenAU / 11 (sample a) or cisplatin (sample b), or left untreated for 6 hours (sample c). The masses were then lysed, and RNA was extracted according to a standard protocol for transcriptional analysis.

[0232] 7.1.2 Transcriptome Raw Data Analysis (Step 1.3) The whole transcriptome expression profile was evaluated. Following the manufacturer's instructions, a Human Clariom® S Pico Assay HT (Applied Biosystems, ThermoFisher Scientific) was used with the GeneTitan MC Instrument (Applied Biosystems, ThermoFisher Scientific). CEL intensity files were created using Affymetrix GeneChip Command Console Software (AGCC, ThermoFisher Scientific). Data analysis was performed using Transcriptomic Analysis Console Software (TAC, ThermoFisher Scientific) (Limma Bioconductor package), which provides quality control analysis, normalization, and summarization based on the Signal Space Transformation-Robust Multi-Chip Analysis (SST-RMA) analysis algorithm, and provides a list of differentially expressed genes. This step allows obtaining a list of differentially expressed genes (DEGs) identified based on their expression fold changes relative to relevant control experimental conditions (i.e., untreated tumor volume).

[0233] 7.1.3 IPA analysis of transcription profile (on the implemented computer) (Step 1.4) The use of IPA allows the inventors to estimate how and to what extent the modulation of gene expression in a biological system affects the biological activity related to the disease state of interest.

[0234] The transcriptional modification profiles obtained in this manner are then subjected to functional pathway enrichment analysis. One commercially available tool that can be used is Ingenuity Pathway Analysis (IPA version 94302991, Qiagen) [Kramer A et al., Causal Analysis Approach in Ingenuity Pathway Analysis 2014]. A list of differentially expressed genes and corresponding data measurements (fold changes related to "untreated tumor masses") identified under different experimental conditions were uploaded to the application.

[0235] Differentially expressed genes and their corresponding fold changes undergo a filtering process to select only those genes that are significantly perturbed, as indicated by their fold changes compared to "untreated tumor masses."

[0236] The fold change threshold is set to include values ​​between -2 and +2, and is accompanied by statistical significance indicated by a p-value of 0.05 or less.

[0237] The available identifiers were mapped to their corresponding entities in the QIAGEN Knowledge Base.

[0238] By initiating "core analysis," highly perturbed DEGs called Network Eligible molecules were overlaid onto a global molecular network developed from information contained in the QIAGEN Knowledge Base. Subsequently, Networks of Network Eligible Molecules were algorithmically generated based on their connectivity.

[0239] The "core" analysis provides a comprehensive list of approximately the top 500 biological activities derived from the constructed network. Biological activity-gene associations are always supported by annotations corresponding to scientific peer-reviewed publications that demonstrate the direction and magnitude of the modulation of the calculated biological activity through automated association of z-scores [Kramer et al. (2014)]. Essentially, this value represents a statistical metric that assesses the similarity between the observed pattern of differentially expressed genes (DEGs) and the expected pattern based on existing literature for a given annotation.

[0240] Step 2.1: Selection of biological activities associated with the specific pathological condition under investigation. The selection of biological activities is based on the identified hallmarks of the pathological condition of interest, and a Z-score threshold set is applied to include values ​​between -2 and +2, with statistical significance indicated by a p-value of 0.05 or less.

[0241] Step 2.2 The relevant Z-score values ​​(Figure 4 (values ​​in cluster a of the columns)) were used to indicate the direction and magnitude of modulation for each biological function, the values ​​were converted to absolute values, and summed up to obtain the benefit score (Step 2.3).

[0242] 7.2 Calculation of Risk Score 7.2.1 Definition of Cisplatin Side Effects (Step 3.1) Officially documented side effects of cisplatin were identified by examining specific sources: -https: / / www.torrinomedica.it / schede-farmaci / cisplatino-3 / (document made available by AIFA on 07 / 13 / 21) -https: / / www.drugs.com / sfx / cisplatin-side-effects.html as last updated on March 17,2024

[0243] 7.2.2 Identification of IPA biofunctions from terminology used to annotate side effects (Step 3.2) Using the information found in the aforementioned resources, identify the biological activity associated with the identified side effects (the IPA source used is the Ingenuity Knowledge Base, which includes journal articles, curation from OMIM, JAX, and ClinicalTrials.gov), and examine the IPA using the following procedure.

[0244] Each adverse effect term is entered one by one into the "Disease and Function" query box, and then the search begins.

[0245] The resulting restart table allowed us to filter diseases / functions arising from a large amount of evidence (making it possible to understand the correspondence between IPA biological activity and annotated side effects, as shown in Figure 3). For each biological function, we created in silico models to represent a predetermined number of genes whose modulation can influence the modulation of the biological function itself (in this particular case, these can represent side effects).

[0246] 7.2.3 Overlay analysis and construction of the in silico model (Step 3.3) The overlay analysis focuses on the biological activity identified by the "in silico model of side effects." The selection of biological activity is based on the identified cisplatin side effects.

[0247] "Overlay analysis" is constructed by establishing the relationship between the pattern of differentially expressed genes and selected biological activity using the following procedure (always supported by annotations corresponding to scientific peer-reviewed publications demonstrating the direction and magnitude of the modulation of biological activity):

[0248] The set of biological activities selected from the in silico model of the cisplatin side effect profile is imported into a new sheet called "my pathway".

[0249] Using the "Construction Tools" and "Growth Tools," we identify differentially expressed genes (DEGs) belonging to the transcriptome profile under investigation (cisplatin and EpigenAU / 11) and associate them with the regulation of the biological activity selected in the previous step.

[0250] 7.2.4 Image analysis of the obtained color intensity of biological activity (3.4) The identified DEG modulation is represented using green (indicating downward modulation) and red (indicating upward modulation).

[0251] To determine the expected and calculated effects of such experimentally observed gene expression modulations on biological activity, the "Overlay" and "Molecule Activity Predictor" (MAP) tools are used. The "Prediction" function is activated within the MAP tool to calculate the expected modulation resulting from the biological activity. Thus, a color coding is established: - Orange: Increased activity -Blue: Decreased activity -White: unattainable / unpredictable

[0252] "Overlay analysis" does not directly calculate the Z-score for each biological function. Therefore, it is necessary to convert the color intensity of the modulation signal into numerical values. This is achieved by using the dedicated application: "IPAmmap_Parser" (version 2.1-1).

[0253] The app is a web port application for Pipeline Pilot designed to assign scores called z-scores to genes and biological functions based on coloring within biological pathways generated by QIAGEN's Ingenuity Pathway Analysis software. A key step in the algorithm is the conversion from an RGB color model to a LAB [https: / / www.xrite.com / it-it / blog / lab-color-space] model, which is colorimetric coding that enables not only RGB synthesis but also the recording of color intensity. This conversion is performed within the "components" of Pipeline Pilot, utilizing procedures written in R software and relying on specific functionality of the color space package [https: / / cran.r-project.org / web / packages / colorspace / index.html].

[0254] Figures 33A and 33B show the final list of biological activity and corresponding values ​​obtained for each treatment under investigation.

[0255] 7.2.5 Calculation of Global Risk Score (Step 3.5) Therefore, the risk score is calculated by summing each positive value obtained from the overlay analysis (to show that a given side effect is induced by the treatment).

[0256] Calculation of benefit scores for EpigenAU / 11 and cisplatin treatments after 6 hours of treatment (Figure 33C shows the risk score obtained by summing the annotated biological activity values ​​for each treatment under investigation).

[0257] The benefit / risk score was calculated by dividing the obtained benefit score by the obtained risk score (see Figures 6A and 6B).

[0258] The benefit / risk score for product A was calculated using an additional step: the introduction of a numerical coefficient for Hallmark importance.

[0259] 7.3 Calculation of the Benefit Score 7.3.1 Selection and Export of Biological Functions through Core Analysis Using the core analysis obtained in 7.1.3, biological activities were selected that considered trends consistent with the therapeutic indications of the product under investigation (i.e., antitumor activity). Biological activities were grouped into activity hallmarks, and each hallmark was assigned a weighting coefficient based on its importance in the pathogenesis of the subject of interest. Thus, the biological activity Z-score values ​​were converted to absolute values, and the resulting list was exported to a table reporting the specifications of the biological functions and their relative modulo values. In this process, the biological activity values ​​were multiplied by the importance numerical coefficients corresponding to the biological activity values ​​according to step 2.1a, thereby obtaining the final list values ​​used in the final calculation of the benefit score (Figure 4 (values ​​of cluster b in column)).

[0260] The sum of the values ​​for each treatment provided a benefit score (Figure 6).

[0261] The risk score was evaluated as previously shown.

[0262] 7.4 Calculation of the Benefit / Risk Score Once the risk score and benefit score are calculated, they are applied to the following formula: JPEG2026131540000011.jpg12165 (Benefit score / Risk score) (See Figures 6C and 6D).

[0263] Therefore, the ratio value must be interpreted as follows: the larger the obtained value, the safer the treatment applied, because the benefit outweighs the risk.

[0264] Figure 6 summarizes the benefit / risk score results obtained after 6 hours with EpigenAU / 11 and cisplatin treatment (A). Calculation of the fold change in the score obtained with EpigenAU / 11 compared to the score obtained with cisplatin: The EpigenAU / 11 score is double the score obtained with cisplatin (B).

[0265] When calculating the benefit / risk score considering any of the above steps (numerical coefficient of importance), the results are shown in Figure 6C, and the calculation of the change in the score obtained with EpigenAU / 1 compared to the score obtained with cisplatin is shown: the EpigenAU / 11 score is still double that of cisplatin (Figure 6D).

[0266] Therefore, the calculated benefit / risk scores, regardless of whether or not there is an optional step, are consistent with each other.

[0267] 8. Verification of the proposed transcriptome-based "Method A" by comparison with "Method B," intended as a general assessment of the benefit / risk ratio profile associated with in vivo administration of EpigenAU / 11 and cisplatin. 8.1. In vivo assay (Step 1) EpigenAU / 11(DoE2) and cisplatin were administered in vivo to a mouse model. Risks (intended side effects) were estimated by observing behavioral parameters (effects on behavior itself, spontaneous motor system, muscle strength, and neural reflexes) and by assessing potential weight loss in the animals.

[0268] 8.2 Comparison of benefit / risk ratios of EpigenAU / 11 and Cisplatin based on data obtained from in vivo experiments 8.2.1 Calculation of Risk Score The data obtained from animal studies that are considered in relation to risk include behavioral parameters and weight loss at the end of the experiment.

[0269] The monitored behavioral parameters are listed below: 1. Loss of spontaneous activity 2. Loss of cleanliness 3. Loss of curiosity 4. Loss of responsiveness 5. Loss of corrective reflexes 6. Loss of physical strength 7. Impairment of eyelid opening 8. Lid reflex 9. Tremor 10. Pallor 11. Stereotypes 12. Passivity

[0270] The values ​​of parameters 1 through 8 are normalized as "untreated animal values" (always equal to 0) + "treated animal values".

[0271] The values ​​of parameters 9-12 are normalized as "untreated animal values" + "treated animal values". Potential weight loss in treated animals was also evaluated. The following formula was used to process the data: 1-["Body weight of treated animals (observation day: 27) / Body weight of untreated animals (observation day: 19)"]

[0272] The following table summarizes the calculation of EpigenAU / 11 and cisplatin treatment risk scores using the total data obtained from in vivo experiments.

[0273] The table below shows experimental values ​​representing in vivo adverse events that have not yet been reprocessed for the purpose of calculating risk scores. [Table 7]

[0274] 8.3 Calculation of the Benefit Score (See Step 2 of Method B) The data obtained from animal studies that are considered in relation to benefit is the size of the tumor mass reached at the end of the in vivo trial for each treatment.

[0275] The data was calculated as "reduction in tumor mass size" using the following formula: 1-["Size of tumor mass collected from treated animals (Observation date: 27) / Size of tumor mass collected from untreated animals (Observation date: 19)"]

[0276] The calculation of benefit scores for EpigenAU / 11 and cisplatin treatment using data obtained from in vivo experiments is reported below.

[0277] Instead, the benefit was evaluated by taking into account the reduction in tumor mass size compared to untreated tumors (see Table in Example 8.3).

[0278] For this purpose, the tumor is 100 mm 3 Once the volume reached a certain level, immunocompromised mice carrying xenografted FaDu head and neck squamous cell carcinoma cells were included in the study. The mice received intratumoral injections of EpigenAU / 11 and intraperitoneal cisplatin daily, every other day.

[0279] The table below shows experimental values ​​representing in vivo benefit scores that have not yet been reprocessed for the purpose of calculating benefit scores. [Table 8]

[0280] The obtained values ​​indicate that the standardized benefit / risk ratio profile associated with EpigenAU / 11 is more favorable than that associated with cisplatin. Therefore, the data generated using standard in vivo observations confirm the predictability of the novel method reported herein based on ex vivo transcriptomics and demonstrate its validation.

[0281] Alternatively, data previously obtained from in vivo experiments and separately obtained data can be used in the method of the present invention.

[0282] 8.4. Calculation of Benefit / Risk Score Once the risk score and benefit score are calculated, they are applied to the following formula: JPEG2026131540000014.jpg12165

[0283] Therefore, the ratio values ​​must be interpreted as follows: The larger the value obtained, the safer the treatment applied is, because the benefits outweigh the risks. .

[0284] Figure 7 shows the benefit / risk scores obtained with in vivo EpigenAU / 11 and cisplatin treatment (A). Calculation of the fold change in the score obtained with EpigenAU / 11 compared to the score obtained with cisplatin: The EpigenAU / 11 score is three times that of the cisplatin (B) score.

[0285] Two methods disclosed in Examples 7 and 8 (with or without the optional step) have proven useful for objectively comparing the benefit / risk profiles of two anticancer treatments. The first method is based on ex vivo transcriptome data and has been proven to accurately predict the favorability of the EpigenAU / 11 profile over that of cisplatin, and has been validated by comparison with results generated using a standard qualitative approach based on in vivo data. The second method allows for summarizing such in vivo data into a single parameter, which facilitates and makes more easily accessible the comparison of benefit / risk profiles of treatment options. Both methods yield similar results demonstrating the superiority of the EpigenAU / 11 benefit / risk score over that of cisplatin (Figure 36), thus demonstrating the consistency of the ex vivo method with those generated using a standard in vivo approach, thereby representing the favorable profile of EpigenAU / 11 over that of cisplatin and returning results consistent with those generated using a standard in vivo approach.

[0286] 9. New Benefit / Risk Assessment Product B (Computer-Implemented / Supported) 9.1 Data Assembly 9.1.1 In vitro sample treatment and RNA extraction (Steps 1.1 and 1.2) This study used human adipose-derived mesenchymal stem cell lines (hADMSCs) that can differentiate into osteoblasts and mineralize the extracellular matrix (ECM). These cells were obtained from three different patients (PA42, PA59, and PA69) during systemic surgery (Romagnoli et al., "In Vitro Behavior of Human Adipose Tissue-Derived Stem Cells on Poly(ε-caprolactone)Film for Bone Tissue Engineering Applications," BioMed Research International, Vol. 2015, Article ID 323571, p. 12, 2015. https: / / doi.org / 10.1155 / 2015 / 323571). These cell lines have been characterized in terms of key stem cell markers for mesenchymal stem cells (CD44, CD105, and STRO1), and by studying their pluripotency for osteogenic phenotypes in the Department of Surgery and Translational Medicine at the University of Florence.

[0287] hADMSCs were cultured in growth medium (GM) and grown to 70-80% confluence. The cells were then seeded into 24-well plates at a concentration of 1 × 10⁵ cells / well. After one week, the GM was replaced with osteogenic medium (OM) containing 1 μg / mL of fluorophocalcein, and the cells were incubated for 28 days with or without Osteoredux or DiBase. The medium, with or without Product C, was refreshed twice a week.

[0288] 9.1.2 Transcriptome Raw Data Analysis (Step 1.3) The whole transcriptome expression profile was evaluated. Following the manufacturer's instructions, a Human Clariom® S Pico Assay HT (Applied Biosystems, ThermoFisher Scientific) was used with a GeneTitan MC Instrument (Applied Biosystems, ThermoFisher Scientific). CEL intensity files were created using Affymetrix GeneChip Command Console Software (AGCC, ThermoFisher Scientific). Data analysis was performed using Transcriptomic Analysis Console Software (TAC, ThermoFisher Scientific) (Limma Bioconductor package), which provides quality control analysis, normalization, and summarization based on the Signal Space Transformation-Robust Multi-Chip Analysis (SST-RMA) analysis algorithm, and provides a list of differentially expressed genes. This step allows the inventors to obtain a list of differentially expressed genes (DEGs).

[0289] 9.1.3 IPA analysis of transcription profile (on the implemented computer) (Step 1.4) The use of IPA allows the inventors to estimate how and to what extent the modulation of gene expression in a biological system affects the biological activity related to the disease state of interest.

[0290] The transcriptional modification profiles obtained in this manner are then subjected to functional pathway enrichment analysis. One commercially available tool that can be used is Ingenuity Pathway Analysis (IPA version 94302991, Qiagen) [Kramer A et al., Causal Analysis Approach in Ingenuity Pathway Analysis 2014]. A list of differentially expressed genes and corresponding data measurements identified under different experimental conditions were uploaded to the application.

[0291] Differentially expressed genes and their corresponding fold changes undergo a filtering process to select only those genes that are significantly perturbed, as indicated by their fold changes compared to positive controls.

[0292] The fold change threshold is set to include values ​​between -1.5 and +1.5, and is statistically significant, as indicated by a p-value of 0.05 or less.

[0293] The available identifiers were mapped to their corresponding entities in the QIAGEN Knowledge Base.

[0294] By initiating "core analysis," highly perturbed DEGs called Network Eligible molecules were overlaid onto a global molecular network developed from information contained in the QIAGEN Knowledge Base. Subsequently, Networks of Network Eligible Molecules were algorithmically generated based on their connectivity.

[0295] The "core" analysis provides a comprehensive list of approximately the top 500 biological activities derived from the constructed network. Biological activity-gene associations are always supported by annotations corresponding to scientific peer-reviewed publications that demonstrate the direction and magnitude of the modulation of the calculated biological activity through automated association of z-scores [Kramer et al. (2014)]. Essentially, this value represents a statistical metric that assesses the similarity between the observed pattern of differentially expressed genes (DEGs) and the expected pattern based on existing literature for a given annotation.

[0296] Step 2.1: Selection of biological activities associated with the specific pathological condition under investigation. Biological activities were selected based on identified hallmarks of the pathological condition of interest, by applying a set of fold change thresholds to include values ​​between -1.5 and +1.5, with statistical significance indicated by a p-value of 0.05 or less.

[0297] Step 2.2 The relevant Z-score values ​​(Figure 9 (values ​​in cluster a of the columns)) were used to indicate the direction and magnitude of modulation for each biological function, the values ​​were converted to absolute values, and summed up to obtain the benefit score (Step 2.3).

[0298] 9.2 Calculation of Risk Score 9.2.1 Definition of DIBASE side effects (Step 3.1) The officially documented side effects of DIBASE were identified by examining specific sources: -https: / / www.torrinomedica.it / schede-farmaci / dibase / (document made available by AIFA on 07 / 13 / 21) -https: / / www.drugs.com / sfx / cholecalciferol-side-effects.html as last updated on March 17,2024

[0299] 9.2.2 Identification of IPA biofunctions from terminology used to annotate side effects (Step 3.2) Using the information found in the aforementioned resources, identify the biological activity associated with the identified side effects (the IPA source used is the Ingenuity Knowledge Base, which includes journal articles, curation from OMIM, JAX, and ClinicalTrials.gov), and examine the IPA using the following procedure. Each adverse effect term is entered one by one into the "Disease and Function" query box, and then the search begins.

[0300] The resulting restart table allowed us to filter diseases / functions arising from a large amount of evidence (making it possible to understand the correspondence between IPA biological activity and annotated side effects, as shown in Figure 8). For each biological function, we created in silico models to represent a predetermined number of genes whose modulation can influence the modulation of the biological function itself (in this particular case, these can represent side effects).

[0301] 9.2.3 Overlay analysis and construction of the in silico model (Step 3.3) The overlay analysis focuses on the biological activity identified by the "in silico model of side effects." The selection of biological activity is based on the identified DIBASE side effects.

[0302] "Overlay analysis" is constructed by establishing the relationship between the pattern of differentially expressed genes and selected biological activity using the following procedure (always supported by annotations corresponding to scientific peer-reviewed publications demonstrating the direction and magnitude of the modulation of biological activity): Import the set of biological activities selected from the in silico model in DIBASE's SIDE EFFECT PROFILE into a new sheet called "my pathway".

[0303] Using the "construction tool" and "growth tool," we identify differentially expressed genes (DEGs) belonging to the transcriptome profiles under investigation (DIBASE and Osteoredux) and associate them with the regulation of biological activity selected in the previous step.

[0304] 9.2.4 Image analysis of the obtained color intensity of biological activity (3.4) The identified DEG modulation is represented using green (indicating downward modulation) and red (indicating upward modulation).

[0305] To determine the expected and calculated effects of such experimentally observed gene expression modulations on biological activity, the "Overlay" and "Molecule Activity Predictor" (MAP) tools are used. The "Prediction" function is activated within the MAP tool to calculate the expected modulation resulting from the biological activity. Thus, a color coding is established: - Orange: Increased activity -Blue: Decreased activity -White: unattainable / unpredictable

[0306] "Overlay analysis" does not directly calculate the Z-score for each biological function. Therefore, it is necessary to convert the color intensity of the modulation signal into numerical values. This is achieved by using the dedicated application: "IPAmmap_Parser" (version 2.1-1).

[0307] The app is a web port application for Pipeline Pilot designed to assign scores called z-scores to genes and biological functions based on coloring within biological pathways generated by QIAGEN's Ingenuity Pathway Analysis software. A key step in the algorithm is the conversion from an RGB color model to a LAB [https: / / www.xrite.com / it-it / blog / lab-color-space] model, which is colorimetric coding that enables not only RGB synthesis but also the recording of color intensity. This conversion is performed within the "components" of Pipeline Pilot, utilizing procedures written in R software and relying on specific functionality of the color space package [https: / / cran.r-project.org / web / packages / colorspace / index.html].

[0308] Figure 9 shows the final list of biological activity and corresponding values ​​obtained for each treatment under investigation.

[0309] 9.2.5 Calculation of Global Risk Score (Step 3.5) Therefore, the risk score is calculated by summing each positive value obtained from the overlay analysis (to show that a given side effect is induced by the treatment).

[0310] Calculation of benefit scores for Osteoredux and DIBASE treatments (Figure 10 shows the risk score obtained by summing the annotated biological activity values ​​for each treatment under investigation).

[0311] The benefit / risk score was calculated by dividing the obtained benefit score by the obtained risk score (see Figures 11A and 11B).

[0312] The benefit / risk score for product B was calculated using an additional step: the introduction of a numerical coefficient for Hallmark importance.

[0313] 9.3 Calculation of Benefit Score 9.3.1 Selection and Export of Biological Functions through Core Analysis Using the core analysis obtained in 7.1.3, we selected biological activities whose trends were consistent with the therapeutic indications of the product under investigation (i.e., bone remodeling). The biological activities were grouped into activity hallmarks, and each hallmark was assigned a weighting coefficient based on its importance in the pathogenesis of interest. Thus, the biological activity Z-score values ​​were converted to absolute values, and the resulting list was exported to a table reporting the specifications of the biological functions and their relative modulo values. In this process, the biological activity values ​​were multiplied by the importance numerical coefficients corresponding to the biological activity values ​​according to step 2.1a, thereby obtaining the final list values ​​used in the final calculation of the benefit score (Figure 9 (values ​​in cluster b of column)).

[0314] The sum of the values ​​for each treatment provided a benefit score (Figure 9).

[0315] The risk score was evaluated as previously shown.

[0316] 9.4 Calculation of the Benefit / Risk Score Once the risk score and benefit score are calculated, they are applied to the following formula: JPEG2026131540000015.jpg12165 (See Figures 11C and D).

[0317] Therefore, the ratio value must be interpreted as follows: the larger the obtained value, the safer the treatment applied, because the benefit outweighs the risk.

[0318] Figure 11 summarizes the benefit / risk score results obtained with Osteoredux and DIBASE treatments (A). Calculation of the fold change in the score obtained with Osteoredux compared to the score obtained with DIBASE: The Osteoredux score is three times that of DIBASE (B).

[0319] When calculating the benefit / risk score considering any of the above steps (numerical coefficients of importance), the results are shown in Figure 11C, and the calculation of the fold change in the score obtained with Osteoredux compared to the score obtained with DIBASE is shown: the Osteoredux score is 6 times that of DIBASE (Figure 11D).

[0320] Therefore, the calculated benefit / risk scores, with or without the optional step, are mutually consistent.

Claims

1. A computer implementation method for providing a benefit / risk score for a therapeutic product of interest, or a computer implementation method for manufacturing a therapeutic product of interest, comprising the steps of determining the benefit / risk score based on in vitro transcriptional data or ex vivo transcriptional data:

1. Perform transcriptomics analysis as follows: 1.1 Provide a sample of a biological substrate representing a pathological or pathological state treated by the product, a. Treat one or more of the aforementioned samples (hereinafter referred to as "sample a") with the aforementioned product; b. Treat one or more of the samples (hereinafter referred to as "sample b") with a reference agent for treating the aforementioned pathological condition or state; and c. Using one or more samples of the biological substrate (hereinafter referred to as sample c) as a reference control; 1.2 RNA is extracted from each of the samples a, b, and c; 1.3 Perform transcriptome raw data analysis on the RNA extracted in 1.2 to identify differentially expressed genes (DEGs) related to sample c for each of samples a and b, identify the changes in their expression folds for sample c, and thereby obtain the fold change values ​​for each DEG; 1.4 Pathway enrichment analysis and functional analysis of transcriptome data are performed on the list obtained in 1.3, thereby obtaining numerical values ​​representing the variation in the magnitude and directionality of the biological activity related to the differential expression of DEG in each of samples a and b, which are normalized to sample c; 2. Determine the benefit score as follows: 2.1 From the biological activities listed in 1.4, select the biological activity relevant to the therapeutic indication of the product. 2.2 Convert the numerical values ​​of the relevant biological activity obtained in 1.4 to absolute values. 2.3 The absolute values ​​of each biological activity are summed up to obtain the benefit score of the product being tested.

3. Determine the risk score as follows: 3.1 To treat the aforementioned conditions, a list of known side effects associated with the reference drug is provided. 3.2 The biological activity related to the aforementioned side effects is determined by pathway analysis and functional analysis. 3.3 Construct an in silico model of risk using pathway analysis and functional analysis; by establishing the relationship between the gene expression patterns obtained in point 1.3 and the biological activity determined in point 3.2 for samples a and b; 3.4 The relevant fold change values ​​for each DEG of samples a and b obtained in 1.3 are input into the in silico model of the risk obtained in point 3.3, and pathway analysis and functional analysis are used to obtain data representing the direction and magnitude of regulation of each biological activity determined in point 3.2, and this data is converted into the corresponding absolute values; 3.5 The absolute values ​​obtained in 3.4 are summed to obtain a final value representing the risk score of the product being tested; if the final value is less than a predetermined minimum positive value, it is automatically adjusted to that minimum positive value.

4. The benefit / risk score of the product of interest is provided as the ratio of the benefit score obtained in point 2.3 to the risk score obtained in point 3.

5.

2. The method according to claim 1, wherein the pathway enrichment and functional analysis of transcriptomics is performed using a proprietary or open-source bioinformatics tool, such as QIAGEN Ingenuity Pathway Analysis (IPA), MetaCore (Clarivate Analytics), GeneGo (MetaCore), Qiagen OmicSoft, Elsevier Pathway Studio, Ingenuity Variant Analysis (IVA, Qiagen), DAVID (Database for Annotation, Visualization, and Integrated Discovery), GSEA (Gene Set Enrichment Analysis), STRING (Search Tool for the Retrieval of Interacting Genes / Proteins), KEGG Pathway Analysis, Metascape, Cytoscape and enrichment plugins (e.g., ClueGO, ReactomeFI), and preferably IPA.

3. The method according to claim 1 or 2, wherein the fold change value obtained in 1.4 is expressed as a Z-score value.

4. Furthermore, the method according to any one of claims 1 to 3, including the following: Step 2.1.a in Step 2.1 is a step of clustering the relevant biological activity into hallmarks of disease or pathological conditions treated by the product of interest, and providing a numerical coefficient of importance for each of the hallmarks; and In step 2.2, each of the numerical values ​​of the relevant biological activity obtained in 1.4 is multiplied by the clustered Hallmark numerical coefficient provided in 2.1.a, and then the numerical values ​​are converted to absolute values.

5. The method according to claim 4, wherein the numerical coefficient of importance is determined based on the frequency of association with a disease state, statistical association, or feature importance based on machine learning.

6. A method according to any one of claims 1 to 5, wherein step 2.1.a and / or step 3.2 is performed using a machine learning model trained on a transcriptome dataset, such as a neural network or support vector machine trained on a known transcriptome dataset.

7. Furthermore, the method according to any one of claims 1 to 6, including the following: Assign a numerical importance coefficient to each adverse event determined in step 3.1, and multiply the fold change value obtained in step 1.3 associated with each adverse event by the corresponding numerical importance coefficient.

8. The method according to claim 7, wherein the numerical coefficient of importance is calculated based on one or more of the following parameters: severity of the adverse event, impact on the patient's quality of life, and duration of the adverse event, according to a clinical classification system.

9. The method according to any one of claims 1 to 6, further comprising performing steps 1 to 4 for a reference drug, thereby providing its benefit score and risk score, as well as its benefit / risk score value.

10. A method according to any one of claims 1 to 6, further comprising providing a benefit / risk score for a therapeutic product of interest for the treatment of a predetermined pathology, based on data previously obtained from an in vivo animal model: I. Provide numerical values ​​obtained from the following animal model groups that represent the disease state in question. a. The group treated with the product and b. The group treated with a reference drug for the treatment of the condition, and c. Related control group The aforementioned values ​​represent each of the following parameters: therapeutic effect, changes in animal behavior indicating distress, and weight loss indicating animal distress, and are the values ​​observed in each of groups a and b, normalized relative to the control group c. II. The values ​​of the treatment effects are summed up to determine the benefit score, and the benefit score value of the product being tested is obtained. III. Determining the Risk Score In III.1 I., we identified the changes in animal suffering and weight loss, which were numerically represented, as risk parameters. III.2 The values ​​from III.1 are summed (for group a) to obtain a final value representing the risk score of the product being tested; if the final value is less than a predetermined minimum positive value, it is automatically adjusted to that minimum positive value. IV. The benefit / risk score of the product of interest is provided as the ratio of the benefit score obtained in point II to the risk score obtained in point III.

2.

11. The method according to claim 10, wherein the numerical values ​​provided in I. are based on existing data obtained from in vivo animal models, and the numerical values ​​provided in I. are derived from previously obtained data for in vivo animal models.

12. A method according to claim 10 or 11, further comprising performing steps I to IV for a reference drug, thereby providing its benefit score and risk score, as well as its benefit / risk score value.

13. A method for selecting one or more new therapeutic or beneficial products for clinical development, The process includes providing a benefit / risk score for one or more therapeutic or beneficial products of interest for the treatment of a given disease or pathological condition, and a reference agent for the treatment of the disease or pathological condition, by performing the steps defined in any one of claims 1 to 9. A method in which, if the benefit / risk score value provided for at least one of the therapeutic or beneficial products of interest is greater than or equal to the benefit / risk score value provided for a reference drug, each of the at least one therapeutic or beneficial product of interest is selected for clinical development.

14. A method according to claim 13, further comprising providing a benefit / risk score for the one or more therapeutic or beneficial products of interest and the reference agent by performing the steps defined in any one of claims 10 to 12, A method wherein each of the at least one therapeutic or beneficial product of interest is selected for clinical development if both benefit / risk score values ​​provided for at least one of the therapeutic or beneficial products of interest are greater than or equal to both relative benefit / risk score values ​​provided for a reference drug.

15. A method for manufacturing or screening a therapeutic product or a beneficial product, comprising performing the method described in any one of claims 1 to 12 to evaluate its benefit / risk score.

16. A method according to any one of claims 1 to 15, wherein the therapeutic or beneficial product of interest is a product comprising one or more natural matrices, such as plants, marine organisms, bacteria, fungi, yeasts, animals, and natural sources of eukaryotes and prokaryotes.

17. A method according to claim 16, wherein the natural matrix is ​​selected from one or more of the following: cut or crushed plant parts, plant extracts, fractions of the extracts, microorganisms, honey, propolis, silk, wax, plant resins, plant gums, plant exudates, plant oils, plant essential oils, animal tissue lysates, or plant or animal bodily fluids.

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