System and method for drug re-purposing to target a hallmark of aging

Abstract

A system and corresponding method enable drug re-purposing. The method accesses a datastore containing machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging. The machine-readable drug data represent candidate drugs of the machine-readable list and their respective targets. The method produces a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs as a function of network proximities in a machine-readable network of nodes. The network proximities are measured between i) nodes, of the machine-readable network, that represent targets of candidate drugs of the machine-readable reduced list, and ii) a cluster of nodes, of the machine-readable network, that represent a hallmark module corresponding to the hallmark of aging targeted. The method outputs the machine-readable reduced list for re-purposing to target the hallmark of aging.

Description

System and Method for Drug Re-Purposing to Target a Hallmark of AgingRELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 647,482, filed on May 14, 2024, and U.S. Provisional Application No. 63 / 761,047, filed on February 20, 2025, and U.S. Provisional Application No. 63 / 784,871, filed on April 7, 2025. The entire teachings of the above applications are incorporated herein by reference.BACKGROUND

[0002] Interconnected cellular and molecular changes considered to be primary drivers of an aging process may be generally referred to as “hallmarks of aging.” Examples of such hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, disabled macroautophagy, chronic inflammation, and dysbiosis for non-limiting examples.SUMMARY

[0003] According to an example embodiment, a computer-based system for drug repurposing comprises at least one processor and a memory. The memory has encoded thereon a sequence of instructions which, when loaded and executed by the at least one processor, causes the computer-based system to access a datastore containing therein machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging. The machine-readable drug data represent candidate drugs of the machine-readable list of candidate drugs and their respective targets. The sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to produce a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs obtained. The filtering is configured to be performed as a function of network proximities in a machine-readable network of nodes representing a human or animal interactome. The network proximities are measured between i) nodes, of the machine-readable network of nodes representing the human or animal interactome, that represent targets of candidate drugs of the machine-readable reduced list of the candidate drugs, and ii) a cluster of nodes, of the machine-readable network of nodes, that represent a hallmark module, the hallmark modulecorresponding to the hallmark of aging targeted. The sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to output the machine-readable reduced list of the candidate drugs for re-purposing to target the hallmark of aging.

[0004] The datastore accessed may be a drug datastore and the sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer- based system to access a gene datastore containing therein a machine-readable list of genes associated with aging and longevity. The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system to identify the cluster of nodes based on the machine-readable list of genes of the gene datastore accessed. The cluster of nodes may represent a biological origin of the hallmark of aging targeted.

[0005] The datastore accessed may be a drug datastore and the sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer- based system to access a gene datastore to retrieve a machine-readable list of genes associated with aging and longevity. The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system to identify, based on the a machine-readable list of genes retrieved, a plurality of hallmark modules in the machine-readable network of nodes representing the human or animal interactome. Hallmark modules of the plurality of hallmark modules may correspond to respective hallmarks of aging. The plurality of hallmark modules identified may include the hallmark module corresponding to the hallmark of aging targeted.

[0006] The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system to perform an assessment of whether at least one gene expression change induced by re-purposing a candidate drug, of the machine-readable reduced list of the candidate drugs, reinforces or counteracts documented age-related gene expression changes associated with the hallmark of aging targeted. The at least one gene expression may correspond to at least one gene represented by the hallmark module. The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system to output an electronic representation of the assessment performed.

[0007] The computer-based system may further comprise a display device. The electronic representation output may be a visual representation. The sequence of instructions,when loaded and executed by the at least one processor, may further cause the computer- based system to display the visual representation on the display device.

[0008] The datastore accessed may be a drug datastore and the sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer- based system to access a machine-readable Connectivity Map (Cmap) datastore, retrieve machine-readable Cmap data from the Cmap datastore for a candidate drug of the machine- readable reduced list of the candidate drugs, determine a drug perturbation signature for the candidate drug based on the Cmap data retrieved, and compute a transcription-based metric for the candidate drug based on the drug perturbation signature determined. The transcription-based metric computed may quantify whether an impact of the candidate drug on gene expression reinforces or counteracts documented age-related gene expression changes.

[0009] The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system to compute the transcription-based metric based on a longevity vector. The longevity vector may encode an age-induced expression change of a plurality of genes. The transcription-based metric computed may represent an alignment between the drug perturbation signature determined and the longevity vector.

[0010] The computer-based system may further comprise a display device and the sequence of instructions may further cause the at least one processor to generate a visual representation of the cluster of nodes, employ the transcription-based metric computed to modify the visual representation generated to indicate the impact, and display, on the display device, the visual representation generated and modified.

[0011] Nodes of the machine-readable network of nodes may represent genes of the human or animal interactome. The datastore may be a drug database and the machine- readable drug data may include structured drug data.

[0012] The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system to update the structured drug data to indicate that genes represented by nodes of the cluster of nodes representing the hallmark module are drug targets for at least one candidate drug of the machine-readable reduced list of the candidate drugs.

[0013] The update may be configured to be performed responsive to receiving an input representing a positive result of a clinical trial for aging. The clinical trial for aging may beperformed on at least one subject based on the machine-readable reduced list of the candidate drugs output. The positive result may represent validation that the at least one candidate drug reinforces documented age-related gene expression changes associated with the hallmark of aging targeted.

[0014] According to another example embodiment, a computer-implemented method for drug re-purposing comprises accessing a datastore containing therein machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging. The machine-readable drug data represent candidate drugs of the machine-readable list of candidate drugs and their respective targets. The computer- implemented method further comprises producing a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs obtained. The filtering is performed as a function of network proximities in a machine-readable network of nodes representing a human or animal interactome. The network proximities are measured between i) nodes, of the machine-readable network of nodes representing the human or animal interactome, that represent targets of candidate drugs of the machine-readable reduced list of the candidate drugs, and ii) a cluster of nodes, of the machine-readable network of nodes, that represent a hallmark module. The hallmark module corresponds to the hallmark of aging targeted. The computer-implemented method further comprises outputting the machine-readable reduced list of the candidate drugs for repurposing to target the hallmark of aging.

[0015] Further alternative computer-implemented method embodiments parallel those described above in connection with the example computer-based system embodiment.

[0016] According to another example embodiment, a non-transitory computer-readable medium for drug re-purposing has encoded thereon a sequence of instructions which, when loaded and executed by at least one processor, causes the at least one processor to access a datastore containing therein machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging. The machine- readable drug data represent candidate drugs of the machine-readable list of candidate drugs and their respective targets. The sequence of instructions further causes the at least one processor to produce a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs obtained. The filtering is performed as a function of network proximities in a machine-readable network of nodes representing a human or animal interactome. The network proximities are measuredbetween i) nodes, of the machine-readable network of nodes representing the human or animal interactome, that represent targets of candidate drugs of the machine-readable reduced list of the candidate drugs, and ii) a cluster of nodes, of the machine-readable network of nodes, that represent a hallmark module, the hallmark module corresponding to the hallmark of aging targeted. The sequence of instructions further causes the at least one processor to output the machine-readable reduced list of the candidate drugs for re-purposing to target the hallmark of aging.

[0017] Further alternative non-transitory computer-readable medium embodiments parallel those described above in connection with the example computer-based system embodiment.

[0018] It should be understood that example embodiments disclosed herein can be implemented in the form of a method, apparatus, system, or computer readable medium with program codes embodied thereon.BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.

[0020] The foregoing will be apparent from the following more particular description of example embodiments, as illustrated in the accompanying drawings in which like reference characters refer to the same parts throughout the different views. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating embodiments.

[0021] FIG. l is a block diagram of an example embodiment of a computer-based system for drug re-purposing.

[0022] FIG. 2 is a flow diagram of an example embodiment of a computer-implemented method for drug re-purposing.

[0023] FIG. 3 is a schematic diagram of an example embodiment of a pro- Age (pAGE) measure.

[0024] FIGS. 4A-C are graphs of example embodiments of aging-associated genes.

[0025] FIGS. 5A is schematic diagram of an example embodiment of network characteristics of aging.

[0026] FIGS. 5B-1 through 5B-11 are graphs of example embodiments of network characteristics of aging.

[0027] FIGS. 6A-K are network diagrams of example embodiments of hallmark modules.

[0028] FIG. 6L is a network diagram of an example embodiment of a longevity module.

[0029] FIGS. 7A, 7B, 7C-1 through 7C-4, and 7D-1 through 7D-4 are diagrams disclosing example embodiments of a perturbation pAGE signature

[0030] FIG. 8A is a table of an example embodiment of values for proximity significance and pAGE for drugs currently under clinical trials for anti-aging

[0031] FIG. 8B is a legend for the table of FIG. 8 A.

[0032] FIG. 9 is a table of an example embodiment of proximity and pAGE values for drug-repurposing for hallmark-targeted drugs.

[0033] FIG. 10A is a network diagram of an example embodiment of a hallmark module and drug targets.

[0034] FIG. 10B is a network diagram of an example embodiment of a drug perturbation profile.

[0035] FIG. 10C is a network diagram of an example embodiment of a hallmark aging profile.

[0036] FIG. 10D is a network diagram of a hallmark pAGE profile.

[0037] FIG. 11 A is schematic diagram of an example embodiment of drugs being repurposed to affect a specific hallmark or multiple hallmarks, simultaneously.

[0038] FIG. 1 IB is an overview of an example embodiment of a perturbation impact of a hallmark module induced by a drug.

[0039] FIG. 11C is a graph of an example embodiment of perturbation parameters characterizing an impact of a drug on a hallmark module’s functionality.

[0040] FIG. 1 ID is a chart of an example embodiment of network proximity values used to estimate an impact of a drug on a hallmark module.

[0041] FIGS. 1 IE-1 through 1 IE-4 are network diagrams of example embodiments of perturbation impact in an altered intercellular communication hallmark module.

[0042] FIG. 12 is a graph of an example embodiment of drug targets.

[0043] FIG. 13 is a table of an example embodiment of enrichment of an OpenGenes database with other aging genomics databases.

[0044] FIG. 14 is a table of an example embodiment of enrichment of the OpenGenes database with aging-related diseases.

[0045] FIG. 15 is a graph of an example embodiment of enrichment of the hallmarks of aging with cancer.

[0046] FIG. 16 is a graph of an example embodiment of DNA repair and Progeriod syndromes genes.

[0047] FIG. 17A is a graph of an example embodiment of median degree.

[0048] FIG. 17B is a graph of an example embodiment of median betweenness.

[0049] FIG. 18 is an array of an example embodiment of values for a Jaccard index of the hallmarks of aging.

[0050] FIG. 19 is an array of an example embodiment of values for proximity of the hallmarks of aging.

[0051] FIG. 20 is an array 2000 of an example embodiment of values for separation of the hallmarks of aging.

[0052] FIGS. 21A and 21B are representations of example embodiments of a core of a longevity module.

[0053] FIGS. 22A- K are graphs of an example embodiment of a KEGG pathway analysis of the hallmarks of aging.

[0054] FIGS. 23 A-D are graphs of an example embodiment of an impact of perturbation time and dose on a perturbation magnitude and globality in a longevity module.

[0055] FIG. 24 is a graph of an example embodiment of correlation of a maximum perturbation score and a number of significantly perturbed genes.

[0056] FIG. 25 is a table of an example embodiment of a drug-repurposing score for drugs that extend life in mice.

[0057] FIGS. 26A and 26B are graphs of example embodiments of missed aging drugs.

[0058] FIGS. 27A-J are graphs of example embodiments of proximity and pAGE of drugs.

[0059] FIGS. 28A-K are graphs of example embodiments of values for proximity and pAGE of drug candidates for each of the hallmarks of aging.

[0060] FIG. 29 is a table of an example embodiment of drugs for drug-repurposing for multi -hallmark drugs ranked by a number of hallmarks.

[0061] FIG. 30 is a table of an example embodiment of drugs for drug-repurposing for multi -hallmark drugs ranked by a number of hallmarks.

[0062] FIGS. 31-33 are tables of example embodiments of a list of drugs used for comparing pAGE values between cell lines for a MCF7 cell line, WI38 cell line, and IMR90 cell line, respectively.

[0063] FIGS. 34A and 34B are graphs of example embodiments of pAGE values between cell lines.

[0064] FIG. 35 is a block diagram of an example of the internal structure of a computer in which various embodiments of the present disclosure may be implemented.DETAILED DESCRIPTION

[0065] A description of example embodiments follows.

[0066] A module, as referenced herein, may be a cluster of genes. A statistically significant number, as referenced herein, may be considered statistically significant based on a comparison to a level representing a standard level for being statistically significant.

[0067] Human aging is a progressive declining process of the human body that leads to the onset of age-related diseases such as stroke, cancer, diabetes, and Alzheimer's disease. Aging phenotypes such as lifespan (the total years lived), healthspan (years lived prior to the onset of aging-related diseases), and longevity (living to an exceptionally old age) are affected by many factors, including genetics, diet, and living environment. Studies of genetic variations of individuals, especially those with extremely long lifespan, provide genomic data for the fingerprint of aging in the interactome. Due to its significant impact on human health, searching for drugs that will slow the aging process or even stop it is of high importance.

[0068] The scientific field of network medicine aims to discover relations between human diseases and biological networks characterizing the human cell. One of the fundamental concepts in network medicine is disease modules. It was found that disease genes are not randomly spread in the interactome but rather aggregate to form a statistically significant cluster defining the disease module. Identifying disease modules allows us to examine the relation between diseases using separation and proximity methods with the possibility for therapeutic applications such as drug-repurposing.

[0069] Despite a clear genetic origin of aging, a systematic methodology for identifying the longevity module and providing a ranked drug list for drug-repurposing opportunities is missing. An example embodiment of the present disclosure provides a systematic methodology to fill this gap.

[0070] Despite thousands of genes implicated in aging, effective interventions for age- related phenotypes remain elusive, a lack of advance rooted in the multifactorial nature of longevity and the functional interconnectedness of the molecular components implicated in aging. The present disclosure introduces a network medicine framework that integrates 2,358longevity-associated genes onto the human interactome to identify existing drugs that can modulate aging processes. As disclosed herein, genes associated with each hallmark of aging were found to form a connected subgraph, or hallmark module, a discovery enabling measurement of proximity of 6,442 clinically approved or experimental compounds to each hallmark. An example embodiment of a transcription-based metric, / ?^ GE, is disclosed, which evaluates whether a drug-induced expression shifts, reinforces, or counteracts known age-related expression changes.

[0071] By integrating network proximity and pAGE, an example embodiment identifies multiple drug repurposing candidates that not only target specific hallmarks but act to reverse their aging-associated transcriptional changes. Findings disclosed herein are interpretable, revealing for each drug the molecular mechanisms through which it modulates a hallmark, offering an experimentally falsifiable framework to leverage genomic discoveries to accelerate drug repurposing for longevity. An example embodiment of a system that provides such acceleration is disclosed immediately below in reference to FIG. 1.

[0072] FIG. 1 is a block diagram 100 of an example embodiment of a computer-based system 110 for drug re-purposing. The computer-based system 110 may comprises at least one processor (not shown) and a memory (not shown), such as disclosed further below in reference to FIG. 35 for non-limiting example. Continuing with reference to FIG. 1, the memory has encoded thereon a sequence of instructions (not shown) which, when loaded and executed by the at least one processor, causes the computer-based system 110 to access a datastore 102 containing therein machine-readable drug data 104 to obtain a machine- readable list of candidate drugs 106 to consider for re-purposing to target a hallmark of aging. The machine-readable drug data 104 represents candidate drugs (not shown) of the machine- readable list of candidate drugs 106 and their respective targets (not shown). The sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system 110 to produce a machine-readable reduced list 108 of the candidate drugs to consider for the re-purposing by filtering (not shown) the machine-readable list of the candidate drugs 106 obtained. The filtering is configured to be performed as a function of network proximities (not shown) in a machine-readable network of nodes (not shown) representing a human or animal interactome. The network proximities are measured between i) nodes (not shown), of the machine-readable network of nodes representing the human or animal interactome, that represent targets (not shown) of candidate drugs of the machine- readable reduced list of the candidate drugs 108, and ii) a cluster of nodes (not shown), of themachine-readable network of nodes, that represent a hallmark module (not shown), the hallmark module corresponding to the hallmark of aging targeted. The sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system 110 to output the machine-readable reduced list of the candidate drugs 108 for re-purposing to target the hallmark of aging.

[0073] The datastore 102 accessed may be a drug datastore and the sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to access a gene datastore (not shown) containing therein a machine-readable list of genes (not shown) associated with aging and longevity. The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to identify the cluster of nodes based on the machine- readable list of genes of the gene datastore accessed. The cluster of nodes may represent a biological origin of the hallmark of aging targeted.

[0074] The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to access the gene datastore to retrieve a machine-readable list of genes (not shown) associated with aging and longevity and to identify, based on the a machine-readable list of genes retrieved, a plurality of hallmark modules, such as disclosed further below in reference to FIGS. 6A-K, in the machine- readable network of nodes representing the human or animal interactome. Hallmark modules of the plurality of hallmark modules may correspond to respective hallmarks of aging. The plurality of hallmark modules identified may include the hallmark module corresponding to the hallmark of aging targeted.

[0075] Continuing with reference to FIG. 1, the sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to perform an assessment of whether at least one gene expression change (not shown) induced by re-purposing a candidate drug (not shown), of the machine-readable reduced list of the candidate drugs 108, reinforces or counteracts documented age-related gene expression changes associated with the hallmark of aging targeted. The at least one gene expression may correspond to at least one gene represented by the hallmark module. The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to output an electronic representation of the assessment performed, such as disclosed below.

[0076] The computer-based system 110 may further comprise a display device 112. The electronic representation output may be a visual representation 114 for non-limiting example. The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to display the visual representation 114 on the display device 112 for a user 116 to view. The visual representation 114 may represent a hallmark pAGE profile, such as disclosed further below with regard to FIG. 10D.

[0077] Continuing with reference to FIG. 1, the visual representation 114 may indicate, visually, how a specific drug will act in a biological manner to explain its impact on aging, as disclosed further below. For example, continuing with reference to FIG. 1, the visual representation 114 may employ color to represent an increase pAGE value 118 and decrease pAGE value 122 of genes, represented by nodes in a hallmark pAGE profile for non-limiting example. It should be understood, however, that patterns may be employed to distinguish such values instead of color, for non-limiting example. The increase pAGE value 118, however represented to distinguish from the decrease pAGE value 122, may indicate that a respective gene is affected by a drug in a direction which is in an opposite direction of aging, that is, such drug is beneficial for aging perturbation. It should be understood that the electronic representation is not limited to the visual representation 114 and may be an electronic file for non-limiting example.

[0078] The datastore 102 accessed may be a drug datastore and the sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to access a machine-readable Connectivity Map (Cmap) datastore (not shown), retrieve machine-readable Cmap data (not shown) from the Cmap datastore for a candidate drug of the machine-readable reduced list of the candidate drugs 108, determine a drug perturbation signature (not shown) for the candidate drug based on the Cmap data retrieved, and compute a transcription-based metric ( .e. AGE) for the candidate drug based on the drug perturbation signature determined. The transcription-based metric computed may quantify whether an impact of the candidate drug on gene expression reinforces or counteracts documented age-related gene expression changes. Such quantification may be represented in the visual representation 114, as disclosed above.

[0079] The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to compute the transcriptionbased metric based on a longevity vector, such as the longevity vector disclosed further below. The longevity vector may encode an age-induced expression change of a plurality ofgenes. The transcription-based metric computed may represent an alignment between the drug perturbation signature determined and the longevity vector, such as disclosed further below.

[0080] The sequence of instructions may further cause the at least one processor to generate the visual representation 114 that may be include the cluster of nodes, employ the transcription-based metric computed to modify the visual representation generated to indicate the impact, and display, on the display device, the visual representation 114 generated and modified.

[0081] Nodes of the machine-readable network of nodes may represent genes of the human or animal interactome (not shown). The datastore 102 may be a drug database and the machine-readable drug data 104 may include structured drug data. The sequence of instructions, when loaded and executed by the at least one processor, may further cause the computer-based system 110 to update the structured drug data to indicate that genes represented by nodes of the cluster of nodes representing the hallmark module are drug targets for at least one candidate drug of the machine-readable reduced list of the candidate drugs 108.

[0082] The update may be configured to be performed responsive to receiving an input (not shown) representing a positive result (not shown) of a clinical trial (not shown) for aging. The clinical trial for aging may be performed on at least one subject (not shown) based on the machine-readable reduced list of the candidate drugs 108 output. The positive result may represent validation that the at least one candidate drug reinforces documented age-related gene expression changes associated with the hallmark of aging targeted. An example embodiment of a computer-implemented method for drug re-purposing that may be implemented by the computer-based system 110 is disclosed below in reference to FIG. 2.

[0083] FIG. 2 is a flow diagram of an example embodiment of a computer-implemented method for drug re-purposing (200). The computer-implemented method begins (202) and comprises accessing a datastore containing therein machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging (204). The machine-readable drug data represent candidate drugs of the machine- readable list of candidate drugs and their respective targets. The computer-implemented method further comprises producing a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs obtained (206). The filtering is performed as a function of network proximities in a machine-readable network of nodes representing a human or animal interactome. The network proximities are measured between i) nodes, of the machine-readable network of nodes representing the human or animal interactome, that represent targets of candidate drugs of the machine-readable reduced list of the candidate drugs, and ii) a cluster of nodes, of the machine-readable network of nodes, that represent a hallmark module. The hallmark module corresponds to the hallmark of aging targeted. The computer-implemented method further comprises outputting the machine-readable reduced list of the candidate drugs for repurposing to target the hallmark of aging (208) and the computer-implemented method thereafter ends (210) in the example embodiment.

[0084] Further alternative computer-implemented method embodiments parallel those described above in connection with the example embodiment of the computer-based system 110 of FIG. 1.

[0085] Further technical details are disclosed below.

[0086] Comprehensive genetic surveys (Joao Pedro De Magalhaes, Joao Curado, and George M Church. “Meta-analysis of age-related gene expression profiles identifies common signatures of aging”. In: Bioinformatics 25.7 (2009), pp. 875-881; Anne B Newman et al. “A meta-analysis of four genome-wide association studies of survival to age 90 years or older: the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium”. In: Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 65.5 (2010), pp. 478-487; Paul RHJ Timmers et al. “Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances”. In: elife 8 (2019), e39856; Paul RHJ Timmers, James F Wilson, Peter K Joshi, and Joris Deelen. “Multivariate genomic scan implicates novel loci and haem metabolism in human ageing”. In: Nature communications 11.1 (2020), p. 3570; Aleksandr Zenin et al. “Identification of 12 genetic loci associated with human healthspan;” In: Communications biology 2.1 (2019), pp. 1-11) and systematic animal experiments (Leonard Guarente and Cynthia Kenyon. “Genetic pathways that regulate ageing in model organisms”. In: Nature 408.6809 (2000), pp. 255- 262); Sarah J Mitchell, Morten Scheibye-Knudsen, Dan L Longo, and Rafael de Cabo. “Animal models of aging research: implications for human aging and age-related diseases”. In: Annu. Rev. Anim. Biosci. 3.1 (2015), pp. 283-303) have implicated thousands of human genes in age-related phenotypes, offering unprecedented opportunities to dissect the molecular basis of longevity (Caleb E Finch and Gary Ruvkun. “The genetics of aging”. In: Annual review of genomics and human genetics 2.1 (2001), pp. 435-462; Caleb E Finch andRudolph E Tanzi. “Genetics of aging”. In: Science 278.5337 (1997), pp. 407-411; Angela R Brooks-Wilson. “Genetics of healthy aging and longevity”. In: Human genetics 132 (2013), pp. 1323-1338; Uri Alon. Systems medicine physiological circuits and the dynamics of disease. CRC Press, 2023). Despite the sheer scale of these discoveries, treatments and interventions capable of modulating specific aging processes continue to be lacking. This shortfall potentially stems from the multifactorial nature of aging and the functional and mechanistic interconnectedness of the molecular and genetic processes implicated in longevity, limiting the impact of any single intervention.

[0087] The multifactorial nature of longevity is well captured by the “hallmarks of aging,” which demarcate multiple distinct age-related mechanisms, ranging from genomic instability to cellular senescence (Carlos Lopez-Otin, Maria A Blasco, Linda Partridge, Manuel Serrano, and Guido Kroemer. “The hallmarks of aging”. In: Cell 153.6 (2013), pp. 1194-1217; Carlos Lopez-Otin, Maria A Blasco, Linda Partridge, Manuel Serrano, and Guido Kroemer. “Hallmarks of aging: An expanding universe”. In: Cell (2023); FrankWPun et al. “Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics Al-powered discovery engine”. In: Aging (Albany NY) 14.6 (2022), p. 2475), Although each hallmark is intended to represent a distinct biological dimension, extensive cross-talk and synergy exist among them. Yet, current therapeutic interventions in clinical trials typically target only one or at most a few facets of aging. A comprehensive strategy for promoting longevity will likely rely on multiple interventions, each addressing different mechanisms (or hallmarks) of aging. Developing novel compounds to achieve this is a lengthy endeavor requiring a decade or more to reach clinical practice. An attractive alternative is to repurpose from the pool of over 6,000 clinically approved or experimental drugs, as some of these agents might effectively target specific aging processes )Nir Barzilai, Derek M Huffman, Radhika H Muzumdar, and Andrzej Bartke. “The critical role of metabolic pathways in aging”. In: Diabetes 61.6 (2012), pp. 1315-1322; Kang Wang et al. “Epigenetic regulation of aging: implications for interventions of aging and diseases”. In: Signal transduction and targeted therapy 7.1 (2022), p. 374; Sheng Fong, Kamil Pabis, Djakim Latumalea, Nomuundari Dugersuren, Maximilian Unfried, Nicholas Tolwinski, Brian Kennedy, and Jan Gruber. “Principal component based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention”. In: Nature Aging 4.8 (2024), pp. 1137-1152). Indeed, most of these compounds have already undergone toxicity screening and possess well-characterized targets (and side effects), allowing for more rapid testing andclinical development. The key challenge is to identify the compounds that can influence longevity— and specifically, the hallmark they target and the relevant molecular mechanism.

[0088] To address this bottleneck in aging research, an example embodiment disclosed herein introduces a network medicine framework (Jorg Menche et al. “Uncovering diseasedisease relationships through the incomplete interactome”. In: Science 347.6224 (2015), p. 1257601; Albert-Laszlo Barabasi, Natali Gulbahce, and Joseph Loscalzo. “Network medicine: a network-based approach to human disease”. In: Nature Reviews Genetics 12.1 (2011), pp. 56-68; Jing-Dong Jackie Han. “Understanding biological functions through molecular networks”. In: Cell research 18.2 (2008), pp. 224-237; Alan A Cohen, Luigi Ferrucci, Tamas Fulop, Dominique Gravel, Nan Hao, Andres Kriete, Morgan E Levine, Lewis A Lipsitz, Marcel G Molde Rikkert, Andrew Rutenberg, et al. “A complex systems approach to aging biology”. In: Nature aging 2.7 (2022), pp. 580-591) that allows for integration of data on thousands of aging-associated genes, along with their network relationships, and the targets of all approved and experimental drugs, aiming to identify potential interventions that could affect longevity. Specifically, an example embodiment may begin with a library of 2,358 previously identified longevity-associated genes, which are mapped onto the human interactome. Strikingly, it was found that specific hallmark associated genes aggregate into a connected subgraph, forming distinct and statistically significant hallmark modules. The discovery of these modules is a first discovery, enabling an example embodiment disclosed herein to apply established network medicine approaches for drug re-purposing (Emre Guney, Jorg Menche, Marc Vidal, and Albert-Laszlo Barabasi. “Network-based in silico drug efficacy screening”. In: Nature communications 7.1 (2016), p. 10331; Julia Guthrie, Sevgi Kostel Bal, Salvo Danilo Lombardo, Felix Muller, Celine Sin, Christiane VR Hutter, Jorg Menche, and Kaan Boztug. “AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation”. In: Science Advances 9.35 (2023), eadg6375; Deisy Morselli Gysi, Italo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, JJ Patten, Robert A Davey, Joseph Loscalzo, et al. “Network medicine framework for identifying drug-repurposing opportunities for COVID-19”. In: Proceedings of the National Academy of Sciences 118.19 (2021), e2025581118) and to evaluate the proximity of 6,442 clinically approved or experimental compounds to each hallmark module, thereby identifying candidates capable of perturbing specific aging phenotypes.

[0089] A second key advance of the present disclosure is an introduction of a novel transcription-based metric, GE, that enables an assessment of whether the expression changes induced by a re-purposable drug reinforces or counteracts known age-related expression changes, enabling beneficial interventions to be distinguished from those that may accelerate aging. This advance was missing in prior repurposing efforts (Emre Guney, Jorg Menche, Marc Vidal, and Albert-Laszlo Barabasi. “Network-based in silico drug efficacy screening”. In: Nature communications 7.1 (2016), p. 10331; Julia Guthrie, Sevgi Kostel Bal, Salvo Danilo Lombardo, Felix Muller, Celine Sin, Christiane VR Hutter, Jorg Menche, and Kaan Boztug. “AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation”. In: Science Advances 9.35 (2023), eadg6375; Deisy Morselli Gysi, Italo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, JJ Patten, Robert A Davey, Joseph Loscalzo, et al. “Network medicine framework for identifying drug-repurposing opportunities for COVID-19”. In: Proceedings of the National Academy of Sciences 118.19 (2021), e2025581118) that only predicted the drug impact but not the directionality of its action. Ultimately, multiple drugs were found that successfully reverse the expression changes observed during aging in specific hallmarks, representing promising repurposing candidates. An example embodiment provides predictions that are interpretable, revealing the precise molecular mechanisms by which each drug-repurposing candidate modulates the specific hallmark of aging, thereby providing experimentally falsifiable hypotheses. Together, the present disclosure offers a principled, integrative route to leverage the vast body of aging-related knowledge to identify drugs that can address the multifactorial nature of aging.Results:The genetic roots and the interconnectivity of the hallmarks of aging

[0090] An example embodiment may begin by querying the OpenGenes Database (Ekaterina Rafikova et al. “Open Genes — a new comprehensive database of human genes associated with aging and longevity”. In: Nucleic Acids Research (2023), gkad712), which curates gene-level annotations that link 2,358 genes to longevity, age-associated diseases, or pathways implicated in aging, also offering a confidence level for each association (FIG. 4A and Supplemental Section SI), ranging from 1 (highest) to 5 (lowest). From this resource, the genes that are explicitly linked to at least one of the 11 hallmarks of aging (Carlos Lopez - Otin, Maria A Blasco, Linda Partridge, Manuel Serrano, and Guido Kroemer. “The hallmarksof aging”. In: Cell 153.6 (2013), pp. 1194-1217; Carlos Lopez-Otin, Maria A Blasco, Linda Partridge, Manuel Serrano, and Guido Kroemer. “Hallmarks of aging: An expanding universe”. In: Cell (2023); FrankWPun et al. “Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics Al-powered discovery engine”. In: Aging (Albany NY) 14.6 (2022), p. 2475. (FIG. 4B). Among the 2,358 longevity-associated genes, 1,250 can be associated to specific hallmarks of aging: 860 are exclusive to a single hallmark, and 390 span multiple hallmarks (FIG. 4C). The remaining 1,108 genes, while linked to aging, could not be associated with a specific hallmark. The 390 multi-hallmark genes support a hypothesis of the present disclosure that, at the molecular level, the hallmarks of aging are not independent entities. Further support is offered by pairwise comparisons using the Jaccard index (Paul Jaccard. “Etude comparative de la distribution florale dans une portion des Alpes et des Jura”. In: Bull Soc Vaudoise Sci Nat 37 (1901), pp. 547-579), revealing statistically significant gene overlap among 47 of the 55 hallmark pairs (FIG. 18 and Supplemental Section Sill).

[0091] Although the OpenGenes database offers evidence extracted from observational and experimental studies, that link each gene to its assigned hallmark of aging, confirmation of whether the collective set of 1,250 hallmark-associated genes is broadly relevant to longevity was deemed useful. To this end, multiple validation tests were performed (Supplemental Section SII), finding that the 1,250 gene set shows significant enrichment in (i) age-related KEGG pathways (Minoru Kanehisa and Susumu Goto. “KEGG: kyoto encyclopedia of genes and genomes”. In: Nucleic acids research 28.1 (2000), pp. 27-30) (FIGS. 22 A- K), (ii) genes implicated in aging by seven large-scale aging studies (Paul RHJ Timmers et al. “Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances”. In: elife 8 (2019), e39856; Paul RHJ Timmers, James F Wilson, Peter K Joshi, and Joris Deelen. “Multivariate genomic scan implicates novel loci and haem metabolism in human ageing”. In: Nature communications 11.1 (2020), p. 3570; Aleksandr Zenin et al. “Identification of 12 genetic loci associated with human healthspan”; In: Communications biology 2.1 (2019), pp. 1-11. Kaiwen Jia et al. “An analysis of aging-related genes derived from the genotypetissue expression project (GTEx)”. In: Cell death discovery 4.1 (2018), p. 91; Marj olein J Peters et al. “The transcriptional landscape of age in human peripheral blood”. In: Nature communications 6.1 (2015), pp. 1- 14; “Aging Atlas: a multi-omics database for aging biology”. In: Nucleic acids research 49. DI (2021), pp. D825-D830; Paola Sebastiani et al. “Protein signatures of centenarians andtheir offspring suggest centenarians age slower than other humans”. In: Aging cell 20.2 (2021), el3290.] (FIG. 13, table 1300), (iii) five aging-related diseases (FIG. 14, table 1400), (iv) eight distinct cancer types whose incidence increasing significantly with age (FIG. 15), and (v) genes involved in DNA repair or progeroid syndromes (Alex A Freitas, Olga Vasieva, and Joao Pedro de Magalhaes. “A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related”. In: BMC genomics 12 (2011), pp. 1-11; David Kipling, Terence Davis, Elizabeth L Ostler, and Richard GA Faragher. “What can progeroid syndromes tell us about human aging?” In: Science 305.5689 (2004), pp. 1426-1431; Romina Burla, Mattia La Torre, Chiara Merigliano, Fiammetta Verm, and Isabella Saggio. “Genomic instability and DNA replication defects in progeroid syndromes”; In: Nucleus 9.1 (2018), pp. 368-379) (FIG. 16). These enrichments lend additional support to the aging relevance of this gene set, serving as the foundation for subsequent work disclosed herein.The hallmark modules of aging

[0092] To capture the network-level organization of aging, the 1,250 hallmark-associated genes were mapped onto the human interactome— a comprehensive catalog of 524,156 experimentally validated binding interactions among 18,223 proteins. For many diseases and phenotypes, the genes associated with the disease are known to coalesce in the interactome to form a disease module, formally defined as the largest connected component (LCC) formed by the disease genes (Jorg Menche et al. “Uncovering disease-disease relationships through the incomplete interactome”. In: Science 347.6224 (2015), p. 1257601). While disease modules were validated for multiple isolated traits (Amitabh Sharma et al. “A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma”. In: Human molecular genetics 24.11 (2015), pp. 3005-3020; Italo F do Valle, Harvey G Roweth, Michael W Malloy, Sofia Moco, Denis Barron, Elisabeth Battinelli, Joseph Loscalzo, and Albert-Laszlo Barabasi. “Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols”. In: Nature Food 2.3 (2021), pp. 143-155; Jonah Spector, Andres Aldana, Michael Sebek, Joey Ehlert, Christian De Frondeville, Susan Dina Ghiassian, and Albert- Laszlo Barabasi. “Transformers Enhance the Predictive Power of Network Medicine”. In: medRxiv (2025), pp. 2025-01; Johannes Kersting, Quirin Manz, Joaquim Aguirre-Plans, Chloe Bucheron, Lisa Spindler, Tanja Pock, Fernando Miguel Delgado-Chaves, Emre Guney, and Markus List. “A Nextflow Pipeline for Network-Based Disease Module Identificationand Validation”. In: RExPO24 Conference. REP04EU. 2024), aging comprises multiple hallmarks that may each behave as distinct, yet interrelated, phenotypes. It is thus unclear whether the different hallmark genes form independent modules, and if these modules reside in the same network neighborhood (FIG. 5A).

[0093] To answer these questions, each hallmark was examined separately, finding that in nine of the 11 hallmarks, associated genes cluster into a statistically significant LCC (z-score > 1.96, FIGS. 5B-1 through 5B-11). The remaining two hallmarks — Loss of proteostasis (z- score = 1.74) and Epigenetic alterations (z-score = 1.67) — also show a marginal significance, indicating that their gene sets are characterized by non-random connectivity. In other words, the hallmark-associated genes reside in narrowly defined network neighborhoods, each representing a distinct and identifiable hallmark module within the global interactome (FIGS. 6A-K). This finding represents a first key discovery of the present disclosure, establishing that for each hallmark, the hallmark genes form well-defined and statistically significant network modules.

[0094] Next an examination into whether these hallmark modules overlap was performed, aiming to reveal functional relationships among them. To do so, two complementary measures were used: separation (Jorg Menche et al. “Uncovering disease-disease relationships through the incomplete interactome”. In: Science 347.6224 (2015), p.1257601). and proximity (Emre Guney, Jorg Menche, Marc Vidal, and Albert-Laszlo Barabasi. “Network-based in silico drug efficacy screening”. In: Nature communications 7.1 (2016), p. 10331) (FIG. 5A, see also Supplemental Section SIV), finding that the hallmarks of aging are located in the same neighborhood of the interactome, together forming a broader “longevity module" (FIG. 6L and Supplemental Section SIV). Next, we the existence of the individual hallmark modules was leveraged to identify drug repurposing candidates that target specific hallmarks.Network-based identification of hallmark-specific drug-repurposing candidates

[0095] The existence of distinct hallmark modules offers the opportunity to apply network-based drug-repurposing methods, originally designed for single-disease modules, to the more complex and multifactorial context of aging (Emre Guney, Jorg Menche, Marc Vidal, and Albert-Laszlo Barabasi. “Network-based in silico drug efficacy screening”. In: Nature communications 7.1 (2016), p. 10331; Julia Guthrie, Sevgi Kostel Bal, Salvo Danilo Lombardo, Felix Muller, Celine Sin, Christiane VR Hutter, Jorg Menche, and Kaan Boztug.“AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation”. In: Science Advances 9.35 (2023), eadg6375; Deisy Morselli Gysi, Italo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, JJ Patten, Robert A Davey, Joseph Loscalzo, et al. “Network medicine framework for identifying drug-repurposing opportunities for COVID-19”. In: Proceedings of the National Academy of Sciences 118.19 (2021), e2025581118). To this end, 6,442 approved or clinically tested compounds were compiled from DrugBank (David S Wishart, Craig Knox, An Chi Guo, Savita Shrivastava, Murtaza Hassanali, Paul Stothard, Zhan Chang, and Jennifer Woolsey. “DrugBank: a comprehensive resource for in silico drug discovery and exploration”. In: Nucleic acids research 34. suppl 1 (2006), pp. D668-D672). An approach of the present disclosure rests on the premise that drugs whose targets lie in the network proximity to a disease (or hallmark) module are poised to perturb that disease (or hallmark) with potential therapeutic outcome, a hypothesis that has been experimentally supported across multiple diseases, from asthma to heart disease, and has been experimentally validated for 6,710 drugs, successfully predicting their potential role in treating COVID-19 infection (Deisy Morselli Gysi, Italo Do Valle, Marinka Zitnik, Asher Ameli, Xiao Gan, Onur Varol, Susan Dina Ghiassian, JJ Patten, Robert A Davey, Joseph Loscalzo, et al. “Network medicine framework for identifying drug-repurposing opportunities for COVID-19”. In: Proceedings of the National Academy of Sciences 118.19 (2021), e2025581118., Amitabh Sharma et al. “A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma”. ImHuman molecular genetics 24.11 (2015), pp. 3005- 3020.; Italo F do Valle, Harvey G Roweth, Michael W Malloy, Sofia Moco, Denis Barron, Elisabeth Battinelli, Joseph Loscalzo, and Albert-Laszlo Barabasi. “Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols”. In: Nature Food 2.3 (2021), pp. 143-155; JJ Patten et al. “Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization”. In: Iscience 25.9 (2022).

[0096] An example embodiment may begin by measuring each drug’s network proximity to every hallmark module for five sets of hallmark genes, stratified by confidence level (FIG. 4A). For each hallmark, drugs may be by the significance of their proximity (z-score< -1.96). For example, 26 compounds that displayed significant proximity to the Cell senescence hallmark at every confidence level were found (FIG. 9, Table 900). The top-ranked candidate in this list, pimasertib, is a MEK1 / 2 inhibitor known to induce apoptosis and senescence(Aaron N Hata, Steve Rowley, Hannah L Archibald, Maria Gomez-Caraballo, FariaM Siddiqui, Fei Ji, Joonil Jung, Madelyn Light, Joon Sang Lee, Laurent Debussche, etal:, “Synergistic activity and heterogeneous acquired resistance of combined MDM2 and MEK inhibition in KRAS mutant cancers”. In: Oncogene 36.47 (2017), pp. 6581-6591), aligning with its predicted effect. Another high-ranking compound, selisistat, a SIRT1 inhibitor, also promotes senescence (Changyong Fu, Dong Yuan, Yaona Jiang, Yaqing Li, et al. “SRT1720 Protects Against CSE-Induced Cellular Senescence via Accelerates of FOXO3-PINK1- mediated Mitophagy”. In: (2021)). These examples demonstrate that proximity successfully detects compounds previously implicated in senescence. Yet they also show that proximity alone does not imply a beneficial (anti-senescence) effect.

[0097] The network-based approach relies on undirected protein interactions, which can successfully establish a compound’s ability to perturb a module but carries no information on whether the perturbation is beneficial or detrimental. To address this limitation, an example embodiment disclosed herein introduces a metric called Pro- Age (pAGE), which quantifies whether drug induced changes in gene expression reinforce, or counter documented age- related expression shifts. Specifically, if a drug up-regulates a gene that is known to be up- regulated with age, one might anticipate a potentially adverse effect on longevity, whereas downregulating the same gene may be advantageous, as disclosed below.Pro-Age (pA GE) Measure

[0098] FIG. 3 is a schematic diagram 300 of an example embodiment of a pAGE measure (metric) 338. In the schematic diagram 300, an aging signature 331 marks genes that are up- regulated 332 and down-regulated 334 with age. The network medicine framework relies on undirected protein interactions, which capture a drug’s ability to perturb a disease module but lack information about a direction of the induced change, or whether a drug-induced perturbation is beneficial or detrimental for the studied phenotype. To overcome this limitation, an example embodiment introduces the pAGE metric (Eq. (1)), which quantifies whether a drug’s impact on gene expression reinforces or counteracts documented age-related shifts. Comparing the aging signature 331 to the drug signature, that is, the perturbation signature 336, allows for calculating the pAGE metric (Eq. (1)) of drugs to each of the hallmarks of aging, predicting drugs that are beneficial (pAGE > 0, green, 342) or deficient (pAGE < 0, orange, 343) for aging and their respective significance.

[0099] The pAGE metric is defined as follows: (i) a longevity vector 0 = {o'#}9EA, is defined that encodes the age-induced expression change of 2,025 genes, where a'g = +1 for 995 genes that are up-regulated 332 with age (blue, left panel 337) according to the OpenGenes database and a'g = -1 for the 1,030 genes that are down-regulated 334 with age (red, left panel 337). (ii) an example embodiment defines a drug’s perturbation signature 336 T = {<5g}ge , for a set of genes A, capturing each gene’s changes in expression following exposure to the drug according to the Connectivity Map (CMap) (Aravind Subramanian, Rajiv Narayan, StevenMCorsello, David D Peck, Ted E Natoli, Xiaodong Lu, Joshua Gould, John F Davis, Andrew A Tubelli, Jacob K Asiedu, et al. “A next generation connectivity map: L1000 platform and the first 1,000,000 profiles”. In: Cell 171.6 (2017), pp. 1437-1452). Specifically, ? E {-1, 0, +1 } in T indicates whether drug exposure decreases (red, right panel 339), leaves unchanged (white) (z.e., unregulated 335), or increases (blue) expression of gene g. (iii) Finally, to assess the alignment be-tween a drug’s perturbation profile and the longevity vector 0, the pAGE measure is introduced, defined as:where A0 = {z | ozo'z 0} is a normalization factor that constrains pAGE to vary between -1 and +1 (bottom panel). Whenever a drug’s induced expression changes match an age-related directional shift ( and o'z share the same sign), the term ozo'z is positive, pushing pAGE toward negative values (orange). Conversely, if the drug induces down-regulation of a gene that is typically up-regulated with age, it turns ozo'z negative, increasing pAGE (green). Thus, pAGE > 0 indicates that a drug attenuates age-related expression changes (pro-longevity compound), whereas pAGE < 0 implies that it may exacerbate them (age-accelerating compound). The statistical significance of the pAGE value is evaluated by comparing it to a control group of random drug signatures (see Supplemental Section SVII).

[0100] In summary, a SHARP (Systematic Hallmark-based Aging Repurposing Pipeline) is disclosed herein which includes: (1) relying on network proximity to identify compounds whose targets lie in the proximity of specific hallmark-related subgraphs, thereby filtering out thousands of compounds that target regions not associated with longevity. (2) measuring each proximal compound’s pAGE parameter to determine whether it reinforces or counteracts aging-related transcriptional changes, enabling an example embodiment to distinguish potential "pro-longevity" drugs from “age-accelerating” agents.Validation of the SHARP Repurposing Pipeline

[0101] To evaluate the validity and limitations of an example embodiment of a drugrepurposing pipeline, SHARP, such pipeline was first assessed for its ability to predict drugs whose relevance to aging has been supported by experimental and clinical evidence. Specifically, SHARP was identified to example whether such pipeline can identify the 17 drugs currently in clinical trials for longevity and the 11 compounds that have been experimentally shown to extend lifespan in mice.Validation 1: Drugs under clinical trials for humans.

[0102] An example embodiment of a drug-repurposing pipeline was first tested against a curated list of 17 compounds currently under clinical trial for healthy longevity (Leonard Guarente, David A Sinclair, and Guido Kroemer. “Human trials exploring anti-aging medicines”. In: Cell Metabolism (2023)., a list that included well-known candidates such as metformin and sirolimus (rapamycin)) (FIG. 8A, table 800, and FIG. 29, table 2900). Of these 17, 11 were found to display significant proximity to at least one hallmark (z-score< -1.96). For instance, Aspirin is predicted to influence six hallmarks, and dasatinib is predicted to affect five, whereas sirolimus (rapamycin) affects only one hallmark, specifically the Intercellular communication hallmark module. The six drugs under clinical trials not captured by the pipeline had targets located relatively far from hallmark modules (proximity> 1.6, FIGS. 26A-B). Even so, three of the six compounds acarbose, metformin, and quercetin, exhibited marginally significant proximity (z-score < -1.645), illustrating that partial alignment with a hallmark can still be detected for these compounds.

[0103] Finally, the pAGE parameter was measured for 9 of the 17 drugs in clinical trials for aging or longevity for which Connectivity Map (CMap) data are available, finding that all nine displayed positive pAGE for at least three hallmarks (see FIG. 8A, table 800, and FIG. 29, table 2900), indicating that they alleviate age-related expression changes.Validation 2: Drugs extending lifespan in mice (ITP).

[0104] Next 11 drugs experimentally tested by the Intervention Testing Program (ITP) (Richard A Miller, David E Harrison, Clinton M Astle, Robert A Floyd, Kevin Flurkey, Kenneth L Hensley, Martin A Javors, Christiaan Leeuwenburgh, James F Nelson, Ennio Ongini, et al. “An aging intervention testing program: study design and interim report”. In:Aging cell 6.4 (2007), pp. 565-575) were examined, finding that they prolong lifespan in mice (see FIG. 8A, table 800 and FIG. 25, table 2500). Three of these (sirolimus, acarbose, and metformin) overlap with the 17 compounds in the human clinical trials set above. Of the 11, six displayed significant proximity (z-score< -1.96), and four had marginal proximity (z- score< -1.645) to at least one hallmark.

[0105] Finally, the pAGE parameter was measured for 8 of the 11 ITP-confirmed lifespan-extending drugs (Richard A Miller, David E Harrison, Clinton M Astle, Robert A Floyd, Kevin Flurkey, Kenneth L Hensley, Martin A Javors, Christiaan Leeuwenburgh, James F Nelson, Ennio Ongini, et al. “An aging intervention testing program: study design and interim report”. In: Aging cell 6.4 (2007), pp. 565-575) with CMap data, finding that all eight have positive pAGE for at least three hallmarks (see FIG. 8A, table 800, and FIG. 25, table 2500).

[0106] Taken together, it was found that SHARP captured 82.4% of clinically tested compounds and 90.9% of mouse lifespan-extending compounds, counting both strong and marginal hits, and that the pAGE measure indicates that each drug under clinical trials or with impact on mice lifespan induces expression changes that act to restore the age-induced changes in the respective hallmark module. These findings not only confirm the predictive power of such approach but offer confidence in the novel predictions of drug repurposing candidates described below.Identifying hallmark-targeted drug repurposing candidates

[0107] Encouraged by the positive validation above, SHARP was next applied to identify drug repurposing opportunities for each hallmark. Specifically, all approved and experimental compounds with significant proximity to specific aging modules were identified, as identified by a network analysis disclosed herein, and a positive pAGE value, if there is available CMap data for the compound. This process allows pro-longevity compounds to be identified that can successfully perturb a hallmark module, inducing pro-longevity expression changes. Ageaccelerating compounds that can also perturb a hallmark were identified, but the induced expression changes are more consistent with pro-aging effects.

[0108] The network-based repurposing pipeline disclosed herein has enabled a total of 370 drugs to be identified (FIG. 9, Table 900), each showing statistically significant proximity to one or more hallmarks of aging hence potentially capable of modulating longevity. Of these, for 60 drugs CMap expression profiles are known, allowing their pAGEparameter to be computed. Among these 60 drugs, 14 display positive pAGE, indicating that they represent pro-longevity drugs, and another 14 display negative pAGE, representing potential age-accelerating compounds.

[0109] The remaining 32 drugs show inconsistent pAGE values across the 5 confidence levels, hence further data would be used to evaluate their impact on longevity. Below, the identified candidate drugs in the context of each hallmark are summarized.

[0110] Exhaustion of stem cells: 113 drugs were identified with significant proximity (z- score <-1.96) to this hallmark at confidence levels 3-5, 19 of which CMap data was known. Four of 19 exhibit positive pAGE across all tested levels (guanadrel, nisoxetine, amineptine, and amlexanox), and 5 are predicted to be age-accelerating compounds (protriptyline, iobenguane, enalaprilat, doramapimod, and benzatropine).

[0111] Altered intercellular communication: 61 drugs are significantly proximal to this hallmark across all five confidence levels, of which 25 have CMap data. Seven exhibit positive pAGE across the board (oxymetazoline, metaraminol, terazosin, tamsulosin, tetryzoline, cirazoline, and synephrine) and 7 are age-accelerating compounds (niguldipine sertindole, doxazosin, naphazoline, linsitinib, bms-754807, and dequalinium).

[0112] Epigenetic alterations: Of 52 drugs with significant proximity across all five confidence levels, only five have CMap data. Of these, clinofibrate has positive pAGE across all levels, while pilaralisib is predicted to be an age-accelerating compound.

[0113] Mitochondrial dysfunction: 21 drugs exhibit significant proximity across all five confidence levels, but none have CMap data. If drugs that show proximity for four out of five confidence levels are also considered, three drugs with CMap data are found (navitoclax, al sterpaullone, and pyrazol anthrone). Among them, pyrazol anthrone has positive pAGE across all levels.

[0114] Loss of proteostasis: 6 drugs have significant proximity at all five confidence levels but CMap data was not known for any of them. Considering those display proximity at four out of five confidence levels, minocycline with CMap data was found, however, its pAGE values are inconsistent at the different confidence levels.

[0115] Changes in the extracellular matrix structure: As no genes from the OpenGenes database have confidence levels 1 or 2 for this hallmark, levels 3-5 were examined, identifying significantly proximal drugs. Among these, two drugs have CMap data: marimastat with a positive pAGE and captopril with a negative pAGE.

[0116] Deregulated nutrient sensing: 52 drugs reach significance (z-score< -1.96) across all five confidence levels, four of which have CMap data. While none show positive pAGE at all five levels, three drugs exhibit positive pAGE in four significance levels (bms-754807, pilaralisib, and linsitinib).

[0117] Genomic instability: 4 drugs achieve significant proximity at all five confidence levels, yet none have CMap data. Adding those significant at four levels yields three drugs with CMap data (gsk-1059615, paricalcitol, and pimecrolimus), of which gsk-1059615 and pimecrolimus are age-accelerating compounds exhibiting negative pAGE across all levels.

[0118] Cell senescence: Twenty-seven drugs are significant at all five levels; three of them-biotin, linsitinib and bms-754807 have CMap data, but show inconsistent pAGE values.

[0119] Disabled macroautophagy: With no confidence level 1 genes available, levels 2-5 were analyzed, identifying seven drugs that reach significance for all four. Two, monobenzone and imexon, have CMap data and imexon also has a positive pAGE value.

[0120] Telomere attrition: Two drugs prove significant across all five levels, though neither has CMap data.

[0121] To summarize, 370 drugs were found that exhibit significant proximity to at least one hallmark of aging. Of the 370 drugs, 60 have CMap data, enabling their pAGE to be computed; of these 14 have a positive pAGE across all five confidence levels, making them prime candidates for experimental testing in animal models. An additional 14 compounds were also found with negative pAGE, indicative of potential age-accelerating effects.

[0122] It is emphasized that 310 of the drug-repurposing predictions currently lack CMap data. One can rely, therefore, on expression profiling to determine their pAGE value, and assess their directionality. Extrapolating from previous data, it is anticipated that 23.3% (or about 72 drugs) of these candidates may benefit longevity.Proximity and pAGE predict therapeutic effects

[0123] The integrated network-based pipeline, augmented by the pAGE metric, is not only capable of identifying promising drug-repurposing candidates, but can also yield falsifiable predictions pertaining to the drug’s mechanism of action. Such was demonstrated on oxymetazoline for non-limiting example, a repurposing candidate that, according to a pipeline of the present disclosure, impacts the Altered Intercellular Communication hallmark (FIG. 9, Table.900). Oxymetazoline is an adrenergic al- and a2-agonist and a direct acting sympathomimetic drug and is available in various formulations with a wide variety of clinicalimplications, including nasal congestion, allergic reactions of the eye, and facial erythema associated with rosacea (Nupur U Patel, Shweta Shukla, Jessica Zaki, and Steven R Feldman. “Oxymetazoline hydrochloride cream for facial erythema associated with rosacea”. In: Expert review of clinical pharmacology 10.10 (2017), pp. 1049-1054), oxymetazoline targets the proteins ADRA1 A, ADRA1B, and ADRA1D as part of the al-adrenergic receptor protein group, and the proteins HTR1 A, HTR1B and HTR1D as part of the serotonin receptor protein group (FIG. 10A).

[0124] Of these, ADRA1 A is a hallmark gene (confidence level 1), as mice expressing a constitutively active mutant ADRA1 A lived significantly longer (Van A Doze, Robert S Papay, Brianna L Goldenstein, Manveen K Gupta, Katie M Collette, Brian W Nelson, Mariaha J Lyons, Bethany A Davis, Elizabeth J Luger, Sarah G Wood, et al. “Long-term al A-adrenergic receptor stimulation improves synaptic plasticity, cognitive function, mood, and longevity”. In: Molecular pharmacology 80.4 (2011), pp. 747-758). While the potential impact of oxymetazoline on longevity is unknown, perturbing the activity of ADRA1 A has the potential to extend lifespan by altering molecular mechanisms related to insulin signaling, the AMPK and TOR pathways, and chronic inflammation (Francisco Alejandro Lagunas- Rangel. “G protein-coupled receptors that influence lifespan of human and animal models”. In: Biogerontology 23.1 (2022), pp. 1-19).

[0125] To understand how the perturbation induced by oxymetazoline propagates through the hallmark module, the gene perturbation signature in the vicinity of its targets was examined: the al-adrenergic receptor protein group ADRA1A, ADRA1B, and ADRA1D, and the serotonin receptor protein group HTR1 A, HTR1B, and HTR1D. The al-adrenergic receptor protein group directly connects to the module by the ALB and NR3C1 genes, while the serotonin receptor protein group directly connects to the module by the TGFB1 gene. The ALB, NR3C1 and TGFB1 proteins are direct interacting partners of oxymetazoline’s targets in the hallmark module. Yet, the drug-induced perturbation signatures of the hallmark proteins ALB, NR3C1, and TGFB1 are weak (z-score = 0.39 for ALB, z-score = -0.18 for NR3C1, and z-score = 0.36 for TGFB1). This suggests that the drug induced perturbation propagates through the ACKR3 protein, a non-hallmark protein, which has some of the highest perturbation scores of the targets’ neighbors (z-score = -0.66, FIG. 10B). The ACKR3 protein interacts with the hallmark proteins NFKB1 (z-score = 0.77), TP53 (z-score = 1.14), and AKT1 (z-score = -0.79), each displaying significant perturbation signature.

[0126] While the expression patterns of the al -adrenergic receptor proteins do not change with age (FIG. 10C), it is predicted that targeting them with oxymetazoline leads to a perturbation signature that affects the expression patterns of multiple genes in the Altered intercellular communication hallmark module (FIG. 10B) resulting in a statistically significant pAGE = 0.46 (z-score = 2.35, see Supplemental Section SVIII). Specifically, the expression pattern of genes CCL5, NFE2L2, AGER, RELA, CEBPB, C3, MIF, NFKBIA, HDAC4, PTGS2, HLA-DRB1 that are involved in the aging mechanism of sterile inflammation, are perturbed by oxymetazoline in the direction that corrects the aging-induced expression changes (FIGS. 10B and 10C), increasing the pAGE value (FIG. 10D). Similarly, the expression of CCL5, TP63, NFE2L2, FOXO3, CDKN2B, IGF1R, and SIRT1 genes involved in the aging mechanism of intercellular communication impairment, are also perturbed by oxymetazoline, opposing their aging-induced expression changes and further increasing the pAGE value (FIGS. 10B-D).

[0127] In summary, the integration of the network module (FIG. 10 A), the drug’s perturbation profile (FIG. 10B), and age-associated expression changes (FIG. 10C) unveils the molecular mechanism by which a repurposable drug is expected to modulate a hallmark module. The predicted mechanism can then be validated in cell-based assays (Amitabh Sharma et al. “A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma”. ImHuman molecular genetics 24.11 (2015), pp. 3005-3020; Italo F do Valle, Harvey G Roweth, Michael W Malloy, Sofia Moco, Denis Barron, Elisabeth Battinelli, Joseph Loscalzo, and Albert-Laszlo Barabasi. “Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols”. In: Nature Food 2.3 (2021), pp. 143— 155). and in appropriate animal models. It is emphasized, however, that a focus on oxymetazoline is intended to serve as an illustrative example; the integration of the pAGE metric with the network structure of the respective hallmark (FIGS. 6A-K) enables an example embodiment to generate similarly detailed mechanistic predictions for each drug in FIG. 9, table 900, that is predicted to modulate longevity for non-limiting examples.Additional Analysis

[0128] A central debate in aging research revolves around the need to focus on the underlying why of aging, a question addressed by long-standing theories positing DNA damage (Helen L Gensler and Harris Bernstein. “DNA damage as the primary cause ofaging”. In: The Quarterly review of biology 56.3 (1981), pp. 279-303; Bjorn Schumacher, George A Garinis, and Jan HJ Hoeijmakers. “Age to survive: DNA damage and aging”. In: Trends in Genetics 24.2 (2008), pp. 77-85) and epigenetic alterations (Sangita Pal and Jessica K Tyler. “Epigenetics and aging”. In: Science advances 2.7 (2016), el600584) as principal causal factors — or on the how of aging, as encapsulated by the so-called “hallmarks” (David Gems and Joao Pedro De Magalhaes. “The hoverfly and the wasp: A critique of the hallmarks of aging as a paradigm”. In: Ageing research reviews 70 (2021), p. 101407). An example embodiment of a longevity module disclosed herein reveals that from a network perspective, both the causes (e.g., DNA damage and epigenetic alterations) and the how "hallmarks" of aging are located in the same network neighborhood. This implies that therapeutic interventions designed to target either the drivers of aging or its hallmark processes should ultimately focus on the same well-localized neighborhood of the sub-cellular network, defined by the longevity module (FIG. 6L). Consequently, from a network perspective, the traditional distinction between targeting “theories” or “hallmarks” may be less critical, given the realization that both involve the same network neighborhood. Ultimately, findings disclosed herein underscore the potential of leveraging the extensive hallmark associated genetic evidence to identify drug-repurposing candidates for healthy longevity. Although the evidence presented herein may be primarily computational, it is supported by genetics and expression-based evidence, offering a principled basis for subsequent in vitro and in vivo validations, culminating in animal studies and eventually clinical trials.

[0129] An example embodiment also introduces a key methodological advance, the pAGE metric, that helps gauge whether a drug reinforces or counteracts aging-related transcriptional changes. Consequently, an example embodiment of a pipeline disclosed herein uncovers both compounds that can act as pro-longevity compounds, as well as compounds that likely serve as “age-accelerating” agents. These are also valuable for validating the genetic nexus of aging, illuminating further molecular targets, and identifying previously unknown side-effects of existing drugs, helping clinicians to avoid unintended adverse effects on lifespan.

[0130] A focus disclosed herein has been on compounds that can modulate individual hallmarks of aging. However, given that aging is a multifactorial phenomenon, it is unlikely that a single drug can successfully perturb and alter all its signatures. Instead, multiple interventions potentially involving combination therapies — are useful. Notably, an example embodiment disclosed herein also identifies drugs that perturb several hallmarks, therebyoffering a framework for multi-target strategies (FIG. 30, table 3000, and FIG. 31, table 3100). Future work should also explore rational combinations of drugs, resulting in therapeutic cocktails that can target multiple hallmarks, simultaneously. Network-based models that factor in drug-drug interactions, synergy, and toxicity could inform such combination therapies (Feixiong Cheng, Istvan A Kovacs, and Albert-Laszlo Barabasi. “Network-based prediction of drug combinations”. In: Nature communications 10.1 (2019), p. 1197).

[0131] An approach disclosed herein may have several limitations. First, aging processes vary substantially across tissues and cell types (Ludger JE Goeminne, Anastasiya Vladimirova, Alec Eames, Alexander Tyshkovskiy, M Austin Argentieri, Kejun Ying, Mahdi Moqri, and Vadim N Gladyshev. “Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems”. In: Cell Metabolism (2024)), necessitating the integration of tissue-specific or single-cell data to improve hallmark definition and refine the tissue-level pAGE metric. Such tissue dependence can be systematically integrated into an example embodiment of a repurposing pipeline disclosed herein by leveraging the GTEx (Genotype-Tissue Expression) database (John Lonsdale, Jeffrey Thomas, Mike Salvatore, Rebecca Phillips, Edmund Lo, Saboor Shad, Richard Hasz, Gary Walters, Fernando Garcia, Nancy Young, et al. “The genotype-tissue expression (GTEx) project”. In: Nature genetics 45.6 (2013), pp. 580-585). This enables an example embodiment to filter out proteins not expressed in specific cell types and re-assess a drug’s potential as a perturbant within a more biologically relevant network context (Ludger JE Goeminne, Anastasiya Vladimirova, Alec Eames, Alexander Tyshkovskiy, M Austin Argentieri, Kejun Ying, Mahdi Moqri, and Vadim N Gladyshev. “Plasma protein-based organ-specific aging and mortality models unveil diseases as accelerated aging of organismal systems”. In: Cell Metabolism (2024)).

[0132] With reference to the pAGE parameter, it is noted that it does not yet account for dosage, non-linear responses, or the possibility that a single compound may confer beneficial effects in one tissue and detrimental effects in another. Future work could help improve its predictive value by implementing these features.

[0133] Capturing the progression of hallmark modules at different chronological ages in humans or model organisms could also reveal critical windows when interventions are most effective, guiding stage-specific therapeutics for healthy aging. Finally, combining patient stratification (e.g., genetic background, lifestyle factors) with network-derived hallmarksignatures might help tailor interventions to individual aging trajectories, moving the field toward personalized anti-aging strategies and opening the doors for Precision Geroscience.The human interactome

[0134] The human PPI is assembled using experimentally validated protein interactions including (i) binary interactions, derived from high-throughput yeast two-hybrid experiments, three-dimensional protein structures; (ii) interactions identified by affinity purification followed by mass spectrometry; (iii) kinase substrate interactions; (iv) signaling interactions; and (v) regulatory interactions. The final PPI used in a study of the present disclosure includes 18,223 proteins connected by 524,156 binding interactions.LCC statistical significance

[0135] A set of genes A with a degree distribution PA(R) forms the largest connected component ZCC (A) EA in the interactome. The degree distribution of the interactome Po(k) is sampled using log-binning with a bin size of 100 nodes. The statistical significance of LCC (A) is measured by random sampling 1000 gene sets in the interactome with size (4| and degree distribution PA(k) and measuring the expected distribution of the LCC sizes resulting in a z-score for the LCC(A) statistical significance.Network-based separation

[0136] The network-based separation S(A, B) between pairs of genes set A and B is calculated using the separation measurement introduced in Jorg Menche et al. “Uncovering disease-disease relationships through the incomplete interactome”. In: Science 347.6224 (2015), p. 1257601), namely:where <ZLB) is the average shortest path between proteins in different gene sets and (BAA) and BBB) are the average shortest path between proteins within the same gene set.Network-based proximity

[0137] The network-based proximity P(S, T) between pairs of genes set S and T (pair of hallmarks of aging, or a hallmark of aging a drug targets) is calculated using the proximity measurement introduced in Emre Guney, Jorg Menche, Marc Vidal, and Albert-Laszlo Barabasi. “Network-based in silico drug efficacy screening”. In: Nature communications 7.1 (2016), p. 10331, namely:where dfs, t) is the shortest path length between nodes 5 and t in the network. The statistical significance of the proximity is obtained by comparing P(S, T) to the distribution of proximity values of 1000 random selections of two sets of genes with size and degree distribution similar to S and T.Analysis of gene expression and perturbation parameters

[0138] The perturbation signatures of genes in the hallmark modules are retrieved from the Connectivity Map (CMap) database ( / / clue. io / ) for the MCF7 cell line after treatment with all drugs in the CMap database. These signatures reflect the perturbation of the gene expression profile in the hallmark modules caused by the treatment with that particular drug relative to a reference population, which comprises all other treatments in the same experimental plate (Aravind Subramanian, Rajiv Narayan, Steven MCorsello, David D Peck, Ted E Natoli, Xiaodong Lu, Joshua Gould, John F Davis, Andrew A Tubelli, Jacob K Asiedu, et al. “A next generation connectivity map: L1000 platform and the first 1,000,000 profiles”. In: Cell 171.6 (2017), pp. 1437-1452). For drugs having more than one experimental instance (such as time of exposure, cell line, and dose), the one with the highest distil_cc_q75 value is selected (75th quantile of pairwise Spearman correlations in landmark genes, / / clue.io / connectopedia / glossary).Gene disease associations

[0139] By surveying over 120 databases with Gene-Disease-Associations (GDA), sources were selected that i) were not compiled from other data sources, and ii) provided at least one kind of evidence type classified as Strong (functional evidence using an experimental essay);Weak (GWAS evidence but no experimental validation); Inferred (relying on bioinformatics or SNPs from imputation in GWAS); not compatible [(I)ncRNA, miRNA andother transcripts with or without experimental validation]. For each database we kept the disease name, gene converted to HGNC names (HUGO Gene Nomenclature Committee), and evidence level. Finally, the following data sources were combined: GWAS from ClinGen, ClinVar, CTD, Disease Enhancer, DisGeNET, GWAS Catalog, HMDD45, IneBook, LncRNA disease, LOVD, Monarch, OMIM, Orphanet, PheGenl, and PsyGeNet. All types of association were used for purposes of the present disclosure.

[0140] As such, an example embodiment disclosed herein may employ several aging genes databases including GWAS studies, transcriptional landscape, GTEx, and TWMR causal genes as well as aging-related diseases databases to identify and characterize a longevity module. In an example embodiment, the aging genes were filtered using their p- value across all databases and were found to characterize a significant module. To establish the longevity module, an example embodiment may apply enrichment analysis with all aging databases and aging-related diseases as well as proximity and separation techniques from a network medicine toolbox.

[0141] Once the longevity module is well established, an example embodiment may develop a systematic methodology to create a ranked drug list for drug-repurposing opportunities. An example embodiment may include scanning all FDA-approved drugs that could affect aging phenotypes. The ranked list may be established using a multi-modal methodology of (i) Diffusion state distance approach, (ii) Al-based methods where Al is used to predict drugs for a particular disease based on drug-target associations, disease-protein associations, and drug-disease indications, and (iii) proximity-based methods where Disease- Genes proximity is calculated to estimate the possibility of drug-repurposing.

[0142] An example embodiment disclosed herein enables features such as longevity module identification, multi-disease module identifications, and drug-repurposing methodology for the longevity module for non-limiting examples. An example embodiment disclosed herein may improve disease module identification for large modules, improve disease module identification for multi-disease modules, improves the drug-repurposing methodology for large modules, and improve the drug-repurposing methodology for multidisease modules for non-limiting examples. Non-limiting example uses of an example embodiment herein include providing methods to identify relations between aging and other diseases, providing methods for drug-repurposing of existing drugs to slow the aging process, providing methods for drug-repurposing of existing drugs to affect specific aging phenotypes.

[0143] FIGS. 4A-C are graphs (400-A, 400-B, 400-C) of example embodiments of aging-associated genes. In the graph 400-A, the OpenGenes database 401 is shown as containing 2,358 aging-associated genes, each gene being assigned a confidence level ranging from 1 (Highest) to 5 (Lowest) based on the existing evidence of its association with lifespan and longevity (see Supplemental Section SI, disclosed further below, for classification protocol). While only 26 genes have the highest confidence level, indicating that changes in their activity extend mammalian lifespan, most genes have low confidence and show a weak association with aging. In the graph 400-B, the number of genes 451 associated with each of the hallmarks of aging 452 are shown. 1,250 genes are associated by the OpenGenes database with one or more hallmarks of aging based on their biological role, while the remaining 1,108 genes are unclassified. Stars indicate the number of genes with a specific confidence level (1- 5) associated with each hallmark. Note that the Exhaustion of stem cells and Changes in the extracellular matrix structure hallmarks are not associated with any of the genes in levels 1 and 2 while the Disabled macroautophagy hallmark is not associated with any of the genes in level 1. In the graph 400-C, the number of genes 453 associated with multiple hallmarks are shown by way of the number of hallmarks 455. Reflecting the interconnectedness between the hallmarks, some of the genes are associated with multiple hallmarks. 1,108 genes are not linked to any hallmark, 860 genes are linked to a single hall-mark, and 390 genes are shared by multiple hallmarks. The TP53 gene is associated with the most (7) hallmarks, reflecting its critical roles in various essential cellular processes such as DNA repair and apoptosis.

[0144] FIGS. 5A is schematic diagram 500-A of an example embodiment of network characteristics of aging.

[0145] FIGS. 5B-1 through 5B-11 are graphs (500-B-l through 500-B-l 1) of example embodiments of network characteristics of aging. In the schematic diagram 500-A, genes associated with similar biological mechanisms often form connected components (Jorg Menche et al. “Uncovering disease-disease relationships through the incomplete interactome”. In: Science 347.6224 (2015), p. 1257601). The largest connected component (LCC) characterizes the module of the gene set. The network structure allows one to analyze the network separation 546 and network proximity 547 between different gene sets. Negative separation indicates overlapping modules while positive separation de-notes distinct, topologically non-overlapping modules. Network proximity estimates the network-based distance between modules utilizing the shortest paths between pairs of genes in different modules. The proximity allows for estimating modules in close neighborhoods compared todistant ones. The graphs 500-B-l through 500-B-l 1 indicate the LCC size and significance of each hallmark of aging compared to the distribution of LCCs formed by a randomized control group. The LCC formed by the genes of each hallmark defines the hallmark module, characterizing the network representation of the hallmark. The genes of each hallmark of aging form a statistically significant LCC (defined as z-score > 1.96) compared to the control group. The only two exceptions are the Loss of proteostasis hallmark (z-score = 1.74) and the Epigenetic alterations hallmark (z-score = 1.67) display marginal significance (defined as z- score > 1.645).

[0146] FIGS. 6A-K are network diagrams of example embodiments of hallmark modules (600A, 600B, 600C, 600D, 600E, 600F, 600G, 600H, 6001, 600J, 600K).

[0147] FIG. 6L is a network diagram of an example embodiment of a longevity module 660L.

[0148] In FIGS. 6A-K, genes associated with each of the hallmarks of aging are not randomly distributed in the human interactome but agglomerate in specific network neighborhood, forming a statistically significant LCC. These LCCs are the hallmark modules - sub-graphs of the human interactome representing the biological origin of the hallmarks of aging. Each hallmark module is shown separately in the figure with a distinct color. Genes associated with more than a single hallmark are shown with a label. FIG. 6 A, epigenetic alterations (89 genes). FIG. 6B, Altered intercellular communication (53 genes). FIG. 6C, Genomic instability (49 genes). FIG. 6D, Mitochondrial dysfunction (40 genes). FIG. 6E, Deregulated nutrient sensing (34 genes). FIG. 6F, Cell senescence (27 genes). FIG. 6G, Loss of proteostasis (27 genes). FIG. 6H, Exhaustion of stem cells (10 genes). FIG. 61, Disabled macroautophagy (7 genes). FIG. 6J, Telomere attrition (16 genes). FIG. 6K, Changes in the extra-cellular matrix structure (7 genes). FIG. 6L, The 11 hallmark modules were found to be in the same network neighborhood and when agglomerated together form the longevity module shown here. Genes associated with a single hallmark module are colored accordingly while genes associated with multiple hallmarks are colored in black with the node’s edges colored based on their hallmark associations. The size of each node reflects its number of hallmark associations. The labels of genes associated with five or more hallmarks are shown.

[0149] FIGS. 7A-B, the network proximity identifies drugs with close targets to a specific hallmark module (z-score < -1.96), indicating a strong impact while the pAGE values predict the direction of the impact to be beneficial (pAGE > 0) or deficient (pAGE < 0) for aging.The data points are all 1346 drugs that appear in both the DrugBank and CMap databases andtheir respective proximity z-score and pAGE values for the Altered intercellular communication and Mitochondrial dysfunction hallmarks confidence level 4. The grey area indicates a lack of significance for proximity (z-score > -1.96). FIGS. 7C-1 through 7C-4, and FIGS. 7D-1 through 7D-4, compare the aging signature to the drug signature allows for calculating the pAGE value (Eq. (1)) of drugs to each of the hallmarks of aging, predicting drugs that are beneficial (pAGE > 0, green) or deficient (pAGE < 0, orange) for aging and their respective significance.

[0150] FIG. 8A is a table 800 of an example embodiment of values for proximity significance and pAGE for drugs currently under clinical trials for anti-aging (and from the ITP project). 25 drugs currently under clinical trials for anti-aging medicine for humans (C) (Leonard Guarente, David A Sinclair, and Guido Kroemer. “Human trials exploring antiaging medicines”. In: Cell Metabolism (2023)) or found to extend lifespan in mice from the ITP project (I) (Richard A Miller, David E Harrison, Clinton M Astle, Robert A Floyd, Kevin Flurkey, Kenneth L Hensley, Martin A Javors, Christiaan Leeuwenburgh, James F Nelson, Ennio Ongini, et al. “An aging interventions testing program: study design and interim report”. In: Aging cell 6.4 (2007), pp. 565-575). Among them, 16 showed statistically significant proximity (z-score < -1.96) for at least one hallmark. Additional five shows marginal significance (z-score < -1.645). All drugs show positive pAGE for at least four hallmarks. Statistically significant proximity is shown with full color and marginal significance proximity with transparent color. Non-significant are shown in white. Arrows indicate the pAGE directionality (positive - up or negative - down). Proximity and pAGE are measured across all confidence levels and the most significant result is shown.

[0151] FIG. 8B is a legend 805 for the table 800 of FIG. 8 A.

[0152] FIG. 9 is a table 900 of an example embodiment of proximity and pAGE values for drug-repurposing for Hallmark-targeted drugs. For each hallmark, top candidates (partial list) with statistically significant proximity to each of the hallmarks of aging are shown. The proximity value and z-score are shown for level 4. Positive pAGE values are shown as up and negative values as down. Mixed pAGE signature across the confidence levels shown as both up and down.

[0153] FIG. 10A is a network diagram 1000A of an example embodiment of a hallmark module and drug targets (1000a, 1000b).

[0154] FIG. 10B is a network diagram 1000B of an example embodiment of a drug perturbation profile.

[0155] FIG. 10C is a network diagram 1000C of an example embodiment of a hallmark aging profile.

[0156] FIG. 10D is a network diagram lOOOD of a hallmark pAGE profile.

[0157] FIGS. 10A-D disclose that proximity and pAGE predict mechanism of action. In FIG. 10 A, oxymetazoline is one of the candidates for drug-repurposing for the Altered intercellular communication hallmark (shown in purple). Oxymetazoline targets the proteins ADRA1 A, ADRA1B, and ADRA1D as part of the al-adrenergic receptor protein group, that is, the target 1000a, and the proteins HTR1 A, HTR1B and HTR1D as part of the serotonin receptor protein group, that is, the target 1000b. The al-adrenergic receptor protein group directly connects to the hallmark module by the ALB and NR3C1 genes, while the serotonin receptor protein group directly connects to the hallmark module by the TGFB1 gene. The gene ADRA1 A is both a drug target and a hallmark gene (green) while ADRA1B and HTR1B (blue) are nearest neighbors of the hallmark module, leading to a statistically significant proximity of 1.5. FIG. 10B, discloses that by perturbing the MCF7 cell line with oxymetazoline, the drug signature up- (green) and down- (red) regulates genes in the module. The color bar shows the z-score of the perturbation signature for each gene. The perturbation follows a detour path starting from the al -adrenergic receptor protein group and does not follow the shortest path to the module through the immediate target neighbors ALB, and NR3C1. Instead, the target’s neighbor ACKR3 (not a hallmark gene) is perturbed and transmits the information to the module. In FIG. 10C, the aging signature marks genes that are up-regulated (green) and down-regulated (red) with age. In FIG. 10D, the pAGE value is measured according to Eq. (1). By comparing the aging signature and the drug signature, genes with opposite signs (green) increase the pAGE value, while genes with similar signs (orange) decrease it, resulting in a statistically significant pAGE = 0.46.Supplemental InformationSI The OpenGenes database confidence level system

[0158] The open genes database has a strict confidence level system for classifying aging genes (Ekaterina Rafikova et al. “Open Genes — a new comprehensive database of human genes associated with aging and longevity”. In: Nucleic Acids Research (2023), gkad712) 11 different aging-related criteria are considered for the classification of genes into 5 groups: Group A: Changes in gene activity extend mammalian lifespan Group B: Changes in gene activity extend non-mammalian lifespanGroup C: Association of genetic variants and gene expression levels with longevityGroup D:• Association of the gene with accelerated aging in humans• Changes in gene activity reduce mammalian lifespan• Changes in gene activity reduce non-mammalian lifespan• Changes in gene activity protect against age-related impairment• Age-related changes in gene expression, methylation or protein activity in humans Group E:• Age-related changes in gene expression, methylation or protein activity in nonmammals• Changes in gene activity enhance age-related deterioration• Regulation of genes associated with aging

[0159] Based on the above groups the confidence level of a gene is determined:Highest (26 genes): A + C A gene meets selection criteria in both groups^ and C High (52 genes): A - C: A gene meets selection criteria in group A but not group C Moderate (88 genes): B: A gene meets selection criteria in group BLow (120 genes): C, D > 2: A gene meets at least two selection criteria in groups C or DLowest (2072 genes): C, D = 1 or A: A gene meets no more than a single selection criterion n groups C or D or a selection criteria in group ESII Validation of the aging genes

[0160] While the OpenGenes database offers substantial individual evidence for each of the aging genes. To strengthen our confidence in our results, we perform a series of validation tests. We begin by measuring the enrichment of the aging genes in 7 aging-related high throughput studies: 3 GWAS studies (Paul RHJ Timmers et al. “Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances”. In: elife 8(2019), e39856, Paul RHJ Timmers et al. “Multivariate genomic scan implicates novel loci and haem metabolism in human ageing”. In: Nature communications 11.1 (2020), p. 3570, Aleksandr Zenin et al. “Identification of 12 genetic loci associated with human healthspan”. In: Communications biology 2.1 (2019), pp. 1-11), GTEx study (Kaiwen Jia et al. “Ananalysis of aging-related genes derived from the genotype-tissue expression project (GTEx)”. In: Cell death discovery 4.1 (2018), p. 91), transcriptional landscape (TL) of age in human peripheral blood gene expression meta-analysis study (Marjolein J Peters et al. “The transcriptional landscape of age in human peripheral blood”. In: Nature communications 6.1 (2015), pp. 1-14), the Aging Atlas database (“Aging Atlas: a multi-omics database for aging biology”. In: Nucleic acids research 49. DI (2021), pp. D825-D830) and a centenarians protein signatures study (Paola Sebastiani et al. “Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans”. In: Aging cell 20.2 (2021), el3290) (see FIG. 13, table 1300). We hypothesize that the genes identified by these studies should show a strong, statistically significant overlap with multiple hallmark genes. In line with this hypothesis, we find that each GWAS study shows statistically significant overlap (p- value< 0.05) with at least 5 different hallmarks. The GTEx study shows enrichment only with the Mitochondrial dysfunction and Loss of proteostasis hallmarks. The TL study shows enrichment with all hallmarks except Changes in the extracellular matrix structure and Telomere attrition. The Aging Atlas database shows enrichment with all the hallmarks and the centenarians protein study shows enrichment with all the hallmarks except with Exhaustion of stem cells and Mitochondrial dysfunction.

[0161] As a second validation, we measured the overlap of the aging gene list with the genes associated with 5 aging-related diseases. We find that genes associated with Stroke and Coronary artery disease show enrichment with 4 different hallmarks, genes involved in Diabetes mellitus type 2 show enrichment with 5 different hallmarks, Alzheimer’s disease genes with 3 hallmarks, and Pulmonary disease chronic obstructive genes with 2 hallmarks (FIG. 14, table 1400). Finally, the onset rate of many cancers increases with age. We therefore measured the enrichment of the aging genes with 8 different types of cancer: breast neoplasms, carcinoma non-small cell lung, lung neoplasms, prostatic neoplasms, ovarian neoplasms, skin neoplasms, stomach neoplasms, and colonic neoplasms. As shown in FIG. 15, the hallmarks Epigenetic alterations and Mitochondrial dysfunction are enriched with all 8 types of cancer genes while the other hallmarks are enriched with some of the cancer types. Finally, a KEGG pathway analysis (Minoru Kanehisa and Susumu Goto. “KEGG: kyoto encyclopedia of genes and genomes”. In: Nucleic acids research 28.1 (2000), pp. 27-30) was performed, finding that genes associated with the hallmarks of aging are enriched in longevity-related pathways, type 2 diabetes mellitus pathways, Cellular senescence pathways, and multiple cancer pathways (FIGS. 22A- K).

[0162] Furthermore, 143 genes associated with DNA repair (Alex A Freitas, Olga Vasieva, and Joao Pedro de Magalhaes. “A data mining approach for classifying DNA repair genes into ageing-related or non-ageing-related”. In: BMC genomics 12 (2011), pp. 1-11) were collected and measured their enrichment with the genes of the hallmarks of aging. As expected, the Genomic instability hallmark shows the highest enrichment, however, an additional 5 out of the 11 hallmarks of aging are also enriched (p-value < 0.01, FIG. 16), showing the common genetic origin of both DNA repair mechanism and the hallmarks of aging.

[0163] DNA damage is the main source of Progeroid syndromes (David Kipling et al. “What can progeroid syndromes tell us about human aging?” In: Science 305.5689 (2004), pp. 1426-1431, Romina Burla et al. “Genomic instability and DNA replication defects in progeroid syndromes”. In: Nucleus 9.1 (2018), pp. 368-379) also known as premature aging syndrome (Mattheus Xing Rong Foo, Peh Fem Ong, and Oliver Dreesen. “Premature aging syndromes: From patients to mechanism”. In: Journal of Dermatological Science 96.2 (2019), pp. 58-65) or accelerated aging (George M Martin. “Syndromes of accelerated aging”. In: Natl Cancer Inst Monogr 60 (1982), pp. 241-247). While Progeroid syndromes are defined by their genetic origin, there is a strong overlap between their phenotype and the hallmark of aging (Dido Carrero, Clara Soria-Valles, and Carlos Lopez-Otin. “Hallmarks of progeroid syndromes: lessons from mice and reprogrammed cells”. In: Disease models & mechanisms 9.7 (2016), pp. 719-735). Hence, we hypothesize that the genes associated with Progeroid symptoms should show a strong overlap with the pool of aging genes. To test this hypothesis, we collected 40 genes associated with 20 different Progeroid syndromes (Dido Carrero, Clara Soria-Valles, and Carlos Lopez-Otin. “Hallmarks of progeroid syndromes: lessons from mice and reprogrammed cells”. In: Disease models & mechanisms 9.7 (2016), pp. 719-735), finding that the genes associated with the DNA repair hallmark are significantly enriched with the Progeroid syndromes genes (p-value = 3.52 x 10-52), and also finding significant overlap with 5 out of the 11 hallmarks of aging (FIG. 16).Sill Network properties of aging genes

[0164] Proteins interacting with many other proteins were shown to be more likely to play important roles in physiological processes (Michael Caldera et al. “Interactome-based approaches to human disease”. In: Cur-rent Opinion in Systems Biology 3 (2017), pp. 88-94). We find that the genes implicated in aging also fit this pattern, having a 7 times highermedian degree (FIG. 17 A) and an order of magnitude higher median betweenness (FIG. 17B) compared to genes not involved in aging.SIV The core of the longevity module

[0165] While the overlap of many pairs of hallmarks indicates a common genetic origin, it is yet unclear if it is a result of a genetic core shared by all the hallmarks of aging or if each pair of hallmarks is overlapped separately. For example, three hallmark modules can overlap without having a common genetic origin where each pair overlapping in a separated network neighborhood or the overlap of all three hallmarks be in the same area (FIGS. 21 A-B). To counter this problem we noticed that if each pair of modules overlaps separately, the intersections of gene sets of each pair will not overlap while if the overlap of the modules is in the same area, the gene sets intersections are expected to overlap (FIGS. 21 A-B). Therefore, we measured the separation value of the intersections of each pair of hallmarks (FIGS. 21 A-B) and found that most of them overlap. This indicates that not only are the hallmarks of aging located in the same network neighborhood and form the longevity module, but the longevity module itself has a core where all the hallmarks of aging overlap. We hypothesize that the genes in the core of the longevity module have a more central role in aging. An obvious example is TP53 associated with 7 hallmarks, its role in aging being rooted in DNA damage response (Sara Nicolai et al. “DNA repair and aging: the impact of the p53 family”. In: Aging (Albany NY) 7.12 (2015), p. 1050), a process central to many aging processes. The gene FOXO1, involved in 6 hallmarks, is also known to play multiple cellular roles including insulin signaling in metabolic pathways, and is also highly associated with aging (Wanbao Yang et al. “Suppression of FOXO1 attenuates inflamm-aging and improves liver function during aging”. In: Aging Cell 22.10 (2023), el3968). SIRT1 (6 hallmarks) has a strong relation to multiple aging-related functions (Yujia Yuan et al.“Regulation of SIRT1 in aging: roles in mitochondrial function and biogenesis”. In: Mechanisms of ageing and development 155 (2016), pp. 10-21, Cui Chen et al. “SIRT1 and aging related signaling pathways”. In: Mechanisms of ageing and development 187 (2020), p. 111215). AKT1 (5 hallmarks) is associated with osteosarcopenia and impact on mice lifespan (Takayoshi Sasako et al. “Deletion of skeletal muscle Aktl / 2 causes osteosarcopenia and reduces lifespan in mice”. In: Nature Communications 13.1 (2022), p. 5655). The enrichment of the longevity module with cancer and DNA repair genes (FIG. 15 and FIG. 16) results in cancer and DNA repair-associated genes such as the ATM gene (Mei Hua Jin and Do-YounOh. “ATM in DNA repair in cancer”. In: Pharmacology & therapeutics 203 (2019), p. 107391, CA Cremona and A Behrens. “ATM signalling and cancer”. In: Oncogene 33.26 (2014), pp. 3351-3360) (5 hallmarks) as an interconnected gene in the module. The PARP1 gene (5 hallmarks) plays a main role in neurodegenerative diseases (Kanmin Mao and Guo Zhang. “The role of PARP1 in neurodegenerative diseases and aging”. In: The FEBS journal 289.8 (2022), pp. 2013-2024) and longevity (Aswin Mangerich, Alexander Biirkle, et al. “Pleiotropic cellular functions of PARP1 in longevity and aging: genome maintenance meets inflammation”. In: Oxidative medicine and cellular longevity 2012 (2012).SV Proximity predicts perturbation impact

[0166] We retrieved the perturbation signature of each drug in the MCF7 cell line from the Connectivity Map (CMap) database (Aravind Subramanian et a\. “A next generation connectivity map: L1000 platform and the first 1,000,000 profiles”. In: Cell 171.6 (2017), pp. 1437-1452), allowing us to test if and to what degree the drug perturbs each hallmark module. We use two parameters to characterize the perturbation impact of a drug (see FIG.1 IB).

[0167] (i) The perturbation magnitude M G [0, 1] measures the maximal perturbation score of genes in a specific hallmark, normalized by the maximal perturbation score in the whole network. We find that AT increases with the perturbation dose (p-value = 1.91 x 10-10, dose of 0.1 pA7 compared to lOpAT for the MCF7 cell line) but it is unaffected by the perturbation time (p-value = 0.44, perturbation time of 6h compared to 48A for the MCF7 cell line), see FIGS. 23 A-D.

[0168] (ii) Perturbation globality G G [0, 1], is the fraction of statistically significant (|z- score| 2) perturbed genes in the module. While M and G are correlated (Pearson correlation coefficient 0.65, FIG. 24) they offer different insights into the perturbation signature (FIGS. 11C and 1 IE-1 through 1 IE-4). For example, Perospirone strongly perturbs a few genes in a module, hence we observe a high magnitude AT —> 1 along with a low globality G —> 0. In contrast, Lestaurtinib perturbs most of the genes in the module just above the significance cutoff, resulting in high globality G —> 1 but intermediate magnitude M « 1 (FIGS. 11C and 11E-1 through 11E-4).

[0169] We can measure the perturbation parameters (A / , G) for 9 out of the 17 drugs under clinical trials for humans that were tested in the CMap database (see FIG. 9, table 900). Dasatinib, Sirolimus, and Mesalazine have significant magnitude (M > 0.2) for all thehallmarks of aging while others (e.g., Aspirin, Acarbose, Metformin) significantly modulate four of them, a characteristic which can be affected by the dose level (FIGS. 23A-D). Despite the correlation between Aland G, high globality is observed only for Dasatinib and Mesalazine and only for the Telomere attrition hallmark, indicating that longevity drugs tend to have a local effect on few genes and not the entire modules.SVI pAGE value of all drugs

[0170] Drugs tend to have positive pAGE for the hallmarks of aging. The exceptions are the Genomic instability and Deregulated nutrient sensing hallmarks, where the majority of tested drugs (95% and 78% respectively) have negative pAGE (FIGS. 27A-J). Since aging signature is shared with many diseases and hence drugs are developed to counter the disease’s effect leading to a general trend of positive pAGE with aging.SVII pAGE variability across cell lines

[0171] Drug signatures vary between cell lines. To characterize this variability and validate the robustness of the pAGE metric, we collected the signatures of drugs of the lung cell lines WI38 and IMR90 (in addition to the MCF7 cell line presented in the main manuscript). Since the available data for these cell lines is limited in the CMap database, we extended our criteria to include drugs with significant proximity in few of the confidence levels (see FIG. 31, table 3100, FIG. 32, table 3200, and FIG. 33, table 3300, for the MCF7, WI38 and IMR90 cell lines, respectively). As shown in Fig. SI 6a, the majority of pAGE signatures show a very low change between cell lines. The cell lines WI38 and IMR90 show even less change, since both are lung cell lines. We also measured the Pearson correlation between the pAGE values of the cell lines (Fig. SI 6b). The WI38 and IMR90 show a very strong correlation of 0.826 as expected since both are lung cells. The MCF7 cell line is a human breast cancer cell line, but still shows a moderate correlation with WI38 and IMR90 of 0.505 and 0.508 respectively.SVIII Drug predictions with marginal significance

[0172] When extending our criteria to include marginal significance (-1.96 < z-score < -1.645) we identify additional 206 drugs. Among these, 27 have Cmap data and 12 with positive pAGE should also be considered as candidates.

[0173] Exhaustion of stem cells: The OpenGenes database contains no genes with confidence levels 1 and 2 for this hallmark, limiting measurements to levels 3-5. We find anadditional 86 drugs with marginal significance, of which 11 drugs with CMap data. Four of these, Lenalidomide, Cyclizine, Pseudoephedrine, and Fostamatinib show positive pAGE across all levels.

[0174] Altered intercellular communication: We find an additional 21 drugs with marginal significance, of which 7 drugs with CMap data. Four of these, Clonidine Dobutamine, Isometheptene, and Pizotifen show positive pAGE across all levels.

[0175] Epigenetic alterations: We find an additional 7 drugs with marginal significance, of which only Aspirin has CMap data and shows positive pAGE across all levels.

[0176] Mitochondrial dysfunction: We find an additional 16 drugs with marginal significance, of which only one has CMap data but shows negative pAGE.

[0177] Loss of proteostasis'. We find an additional 5 drugs with marginal significance, of which only one has CMap data but shows negative pAGE.

[0178] Changes in the extracellular matrix structure: The OpenGenes database contains no genes with confidence levels 1 and 2 for this hallmark, limiting measurements to levels 3- 5. We find an additional 8 drugs with marginal significance, none of them has CMap data.

[0179] Deregulated nutrient sensing: We find an additional 19 drugs with marginal significance, of which two drugs with CMap data and only Staurosporine show positive pAGE across all levels.

[0180] Genomic instability: We find an additional 2 drugs with marginal significance, of which only one has CMap data but shows negative pAGE.

[0181] Cell senescence: We find an additional 26 drugs with marginal significance, of which only one has CMap data but shows negative pAGE.

[0182] Disabled macroautophagy. No genes with confidence level 1 are available for this hallmark; hence, we use confidence levels 2-5. We find an additional 14 drugs with marginal significance, of which only Navitoclax and Hexylresorcinol with CMap data, and both show positive pAGE.

[0183] Telomere attrition: We find an additional two drugs with marginal significance, none of them has CMap data.

[0184] FIG. 11 A is schematic diagram 1100A of an example embodiment of drugs being re-purposed to affect a specific hallmark or multiple hallmarks simultaneously.

[0185] FIG. 1 IB is an overview 1100B of an example embodiment of the perturbation impact of a hallmark module induced by a drug, which can be classified by two parameters.

[0186] FIG. 11C is a graph 1100C of an example embodiment perturbation parameters (A / , G) characterizing an impact of a drug on a hallmark module’s functionality.

[0187] FIG. 1 ID is a chart HOOD of an example embodiment of network proximity used to estimate an impact of a drug on a hallmark module.

[0188] FIGS. 1 IE-1 through 1 IE-4 are network diagrams (1100E-1, 1100E-2, 1100E-3, 1100E-4) of four examples of perturbation impact in Altered intercellular communication hallmark module.

[0189] FIGS. 11 A-D and 1 IE-1 through 1 IE-4 show an example embodiment of perturbation impact of drugs. FIG. 11 A shows that drugs can be repurposed to affect a specific hallmark or multiple hallmarks simultaneously. FIG. 1 IB shows the perturbation impact of a module induced by a drug can be classified by two parameters. The perturbation magnitude (M) characterizes the maximal perturbation score, and perturbation globality (G) characterizes the fraction of the module with a statistically significant perturbation score (|z- score| > 2). FIG. 11C shows that the perturbation parameters (A / , G) characterize the impact of a drug on the hallmark module functionality. Low values of A indicate weak impact and high-value strong im-pact. Low values of G imply local impact and high-value global impact. The data points are all the drug in the DrugBank and their perturbation parameters (A / , G) for the Altered intercellular communication hallmark confidence level 4. FIG. 1 ID is a chart HOOD that shows that network proximity can estimate the impact of a drug on a hallmark module. Sertindole is one of the candidates for drug-repurposing for the Altered intercellular communication hallmark. When compared to distant disease modules (proximity > 2), the perturbation magnitude in the hallmark module is higher. FIGS. 1 IE-1 through 1 IE-4 are network diagrams (1100E-1, 1100E-2, 1100E-3, 1100E-4) of four examples of perturbation impact in Altered intercellular communication hallmark module. Acemetacin shows both weak and local impact. Perospirone shows a strong impact but is local. Lestaurtinib shows intermediate impact and global. Finally, Fingolimod shows a strong and global impact.

[0190] FIG. 12 is a graph 1200 of an example embodiment of drug targets. The graph 1200 includes data of 6442 drugs with at least one target. The distribution of drug targets is characterized by a median of 1 and a mean of 4.024. While most drugs have a low number of targets, some drugs can have a high number of targets.

[0191] FIG. 13 is a table 1300 of an example embodiment of enrichment of the OpenGenes database with other aging genomics databases. The following data sets are considered: GWAS studies (Paul RHJ Timmers et al. “Genomics of 1 million parent lifespansimplicates novel pathways and common diseases and distinguishes survival chances”. In: elife 8 (2019), e39856, Paul RHJ Timmers et al. “Multivariate genomic scan implicates novel loci and haem metabolism in human ageing”. In: Nature communications 11.1 (2020), p. 3570, Aleksandr Zenin et al. “Identification of 12 genetic loci associated with human healthspan”. In: Communications biology 2.1 (2019), pp. 1-11), GTEx (Kaiwen Jia et al. “An analysis of aging-related genes derived from the genotype-tissue expression project (GTEx)”. In: Cell death discovery 4.1 (2018), p. 91), blood gene expression meta-analysis studies (Maij olein J Peters et al. “The transcriptional landscape of age in human peripheral blood”. In: Nature communications 6.1 (2015), pp. 1-14), Aging Atlas (“Aging Atlas: a multi-omics database for aging biology”. In: Nucleic acids research 49. DI (2021), pp. D825-D830) and centenarians protein signatures (Paola Sebastiani et al. “Protein signatures of centenarians and their offspring suggest centenarians age slower than other humans”. In: Aging cell 20.2 (2021), el3290).

[0192] FIG. 14 is a table 1400 of an example embodiment of enrichment of the OpenGenes database with aging-related diseases. The aging-related diseases Stroke, Diabetes mellitus type 2, Alzheimer’s disease, Coronary artery disease, and Pulmonary disease chronic obstructive are enriched with various hallmarks of aging.

[0193] FIG. 15 is a graph 1500 of an example embodiment of enrichment of the hallmarks of aging with cancer. The rate of cancer diagnostics increases with age for different types of cancer. Therefore, most of the hallmarks of aging are enriched with genes associated with different types of cancer. Breast neo-plasms enriched with 8 out of 11 hallmarks, Carcinoma non-small cell lung with 8, Lung neoplasms with 7, Prostatic neoplasms with 9, Ovarian neoplasms with 8, Skin neoplasms with 8, Stomach neoplasms with 5 and Colonic neoplasms with 5 out of 11 different hallmarks.

[0194] FIG. 16 is a graph 1600 of an example embodiment of DNA repair and Progeriod syndromes genes. Some of the hallmarks of aging are enriched with DNA repair genes and Progeroid syndromes genes. Here, aging-associated genes with confidence levels 1-4 were used.

[0195] FIG. 17A is a graph 1700-A of an example embodiment of median degree.

[0196] FIG. 17B is a graph 1770-B of an example embodiment of median betweenness.With reference to FIGS. 17A and 17B, the aging genes show high centrality both in (FIG. 17 A) median degree and (FIG. 17B) median betweenness across all the hallmarks of agingcom-pared to the entire network (black dashed line). Stars indicate the confidence level considered (1-5).

[0197] FIG. 18 is an array 1800 of an example embodiment of values for a Jaccard index of the hallmarks of aging. The Jaccard index measures the inter-connectivity between pairs of hallmarks of aging. The hallmarks of Genomic instability, cell senescence, and Telomere attrition show the highest Jaccard value (center) while the Changes in the extracellular matrix structure hallmark show almost no overlap with the other hallmarks.

[0198] FIG. 19 is an array 1900 of an example embodiment of values for proximity of the hallmarks of aging. The z-score of the network-based proximity measurement draws a similar picture where most hallmarks show statistically significant proximity (z-score < -2) except for the Changes in the extracellular matrix structure hallmark.

[0199] FIG. 20 is an array 2000 of an example embodiment of values for separation of the hallmarks of aging. The network-based separation measurement between pairs of hallmarks of aging. While the separation of the Changes in the extracellular matrix structure hallmark is positive with the other hallmarks (5 > 0) many other pairs of the hallmarks are overlapping (5 < 0).

[0200] FIGS. 21A and 21B are representations (2100-A, 2100-B) of example embodiments of the core of the longevity module. Multiple hallmarks may overlap pairwise but will not have a common origin to all of them. The separation of the gene sets intersections unveils that the overlap of the modules is at the same network neighborhood, suggesting the longevity module has a core of genetic origin to all hallmarks of aging.

[0201] FIGS. 22A- K are graphs (2200-A, 2200-B, 2200-C, 2200-D, 2200-E, 2200-F, 2200-G, 2200-H, 2200-1, 2200-J, and 2200-K) of an example embodiment of a KEGG pathway analysis of the hallmarks of aging.

[0202] FIGS. 23 A-D are graphs (2300-A, 2300-B, 2300-C, 2300-D) of an example embodiment of an impact of perturbation time and dose on the perturbation magnitude and globality in the longevity module for the MCF7 cell line. While both Aland G increase with the dose, the perturbation time remains statistically unaffected.

[0203] FIG. 24 is a graph 2400 of an example embodiment of correlation of the maximum perturbation score and the number of significantly perturbed genes. For all the drugs in the DrugBank we measured both the maximum perturbation score and the number of statistically significant perturbed genes in the longevity module. Both measurements are correlated with a Pearson correlation co-efficient 0.65.

[0204] FIG. 25 is a table 2500 of an example embodiment of a drug-repurposing score for drugs that extend life in mice from the Interventions Testing Program (ITP) database. Among the 11 drugs that extend lifespan in mice from the ITP database 6 drugs show statistically significant proximity (z-score < -1.96) for at least one hallmark. Additional four show marginal significance (z-score < -1.645). The number of significant levels for different hallmarks is shown (score, full color) and marginal significance is shown in parenthesis (transparent color). Non-significant are shown in white. For each hallmark, drugs are shown with a specific confidence level that the proximity is statistically significant as well as pAGE value.

[0205] FIGS. 26A and 26B are graphs (2600-A, 2600-B) of example embodiments of missed aging drugs. In FIG. 26A, among the 17 drugs currently under clinical trials, our pipeline missed 6 with zero significant hallmarks: Dulaglutide, Metformin, Quercetin, Niacin, Semaglutide, and Acarbose. The targets of these drugs are distant from the hall-mark modules with an average proximity higher than 1.6. Indeed, other drugs are also distant and still have statistically significant proximity to some hallmarks (e.g., Everolimus with an average proximity of 1.96 and 1 significant hallmark), this difference is due to marginal z- score. While Metformin shows z-score = -1.87 > -2 for mitochondrial dysfunction level 2, Everolimus shows z-score = -2.05 < -2 for Altered intercellular communication level 4.Hence, the difference results from statistical error near marginal significance (z-score = -2) or due to the difference in the degrees of the drug targets. In FIG. 26B, among the 11 drugs that increase lifespan in mice from the ITP database, our pipeline missed 5 with zero significant hallmarks: Metformin, Glycine, Acarbose, Estradiol, and Meclizine. The targets of these drugs are distant from the hallmark modules with an average proximity higher than 1.8.

[0206] FIGS. 27A-J are graphs (2700-A, 2700-B, 2700-C, 2700-D, 2700-E, 2700-F, 2700-G, 2700-H, 2700-1, 2700-J) of example embodiments of proximity and pAGE of drugs from the DrugBank with CMap data in the hallmarks of aging. Most drugs have positive pAGE in the hallmarks of aging. The only exceptions are the Genomic instability and the Deregulated nutrient sensing hallmarks where more drugs have negative pAGE than positive pAGE.

[0207] FIGS. 28A-K are graphs (2800-A, 2800-B, 2800-C, 2800-D, 2800-E, 2800-F, 2800-G, 2800-H, 2800-1, 2800-J, 2800-K) of example embodiments of values for proximity and pAGE of drug candidates for each of the hallmarks of aging. Candidates without Cmap data are shown with pAGE = 0 and empty marker. A full marker shows drugs with Cmap dataand top candidates are shown with a line connecting their proximity z-score and pAGE values. The size of the symbol characterizes the maximum perturbation score.

[0208] FIG. 29 is a table 2900 of an example embodiment of drugs for drug-repurposing for multi-hallmark drugs ranked by the number of hallmarks. Top candidates are ranked by the number of hallmarks and levels of confidence that are statistically significant with a maximum score of 55 for all 5 confidence levels across all 11 hallmarks. These network drug predictions do not have Cmap data and are predicted only based on the network structure. Marginal significant count is in parenthesis and shown by transparent color.

[0209] FIG. 30 is a table 3000 of an example embodiment of drugs for drug-repurposing for multi-hallmark drugs ranked by the number of hallmarks with pAGE value. Top candidates are ranked by the number of hallmarks and levels of confidence that are statistically significant with a maximum score of 55 for all 5 confidence levels across all 11 hallmarks. The pAGE value is shown. Marginal significant count is in parenthesis and shown by transparent color.

[0210] FIG. 31 is a table 3100 of an example embodiment of a list of drugs used for comparing pAGE values between cell lines. Here the pAGE values of the MCF7 cell line are shown. Drugs with positive pAGE across all confidence are colored in blue and red otherwise.

[0211] FIG. 32 is a table 3200 of an example embodiment of a list of drugs used for comparing pAGE values between cell lines. Here the pAGE values of the WI38 cell line is shown. Drugs with positive pAGE across all confidence are colored in blue and red otherwise.

[0212] FIG. 33 is a table 3300 of an example embodiment of a list of drugs used for comparing pAGE values between cell lines. Here the pAGE values of the IMR90 cell line is shown. Drugs with positive pAGE across all confidence are colored in blue and red otherwise.

[0213] FIGS. 34A and 34B are graphs (3400-A, 3400-B) of example embodiments of the pAGE values between cell lines for comparison. The graph 3400-A plots and example embodiment of the distribution of the difference of the pAGE values between the cell lines MCF7, WI38, and IMR90. As shown, the majority of pAGE values show very small difference between cell lines. In the graph 3400-B, the Pearson correlation between the pAGE in different cell lines. Both WI38 and IMR90 are lung cell and show very strong Pearson correlation of 0.826. The MCF7 is a human breast cancer cell line but still show a moderate correlation with the WI38 and IMR90 of 0.505 and 0.508 respectively.

[0214] FIG. 35 is a block diagram of an example of an internal structure of a computer 3500 in which various embodiments of the present disclosure may be implemented. Thecomputer 3500 contains a system bus 3518, where a bus is a set of hardware lines used for data transfer among the components of a computer or digital processing system. The system bus 3518 is essentially a shared conduit that connects different elements of a computer system (e.g., processor, disk storage, memory, input / output ports, network ports, etc.) that enables the transfer of information between the elements. Coupled to the system bus 3518 is an I / O device interface 3503 for connecting various input and output devices (e.g., keyboard, mouse, display monitors, printers, speakers, microphone, etc.) to the computer 3500. A network interface 3507 allows the computer 3500 to connect to various other devices attached to a network (e.g., global computer network, wide area network, local area network, etc.). Memory 3509 provides volatile or non-volatile storage for computer software instructions 3511 and data 3517 that may be used to implement embodiments (e.g., method 200) of the present disclosure, where the volatile and non-volatile memories are examples of non-transitory media. Disk storage 3574 also provides non-volatile storage for the computer software instructions 3511 and data 3517 that may be used to implement embodiments (e.g., method 200) of the present disclosure. A central processor unit 3572 is also coupled to the system bus 3518 and provides for the execution of computer instructions.

[0215] Example embodiments disclosed herein may be configured using a computer program product. Further example embodiments may include a non-transitory computer- readable medium that contains instructions that may be executed by a processor, and, when loaded and executed, cause the processor to complete methods described herein.

[0216] In addition, the elements described herein may be combined or divided in any manner in software, hardware, or firmware. If implemented in software, the software may be written in any language that can support the example embodiments disclosed herein. The software may be stored in any form of computer readable medium, such as random-access memory (RAM), read-only memory (ROM), compact disk read-only memory (CD-ROM), and so forth.

[0217] The teachings of all patents, published applications, and references cited herein are incorporated by reference in their entirety.

[0218] While example embodiments have been particularly shown and described, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the embodiments encompassed by the appended claims.

Claims

CLAIMSWhat is claimed is:

1. A computer-based system for drug re-purposing, the computer-based system comprising: at least one processor and a memory, the memory having encoded thereon a sequence of instructions which, when loaded and executed by the at least one processor, causes the computer-based system to: access a datastore containing therein machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging, the machine-readable drug data representing candidate drugs of the machine-readable list of candidate drugs and their respective targets; produce a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs obtained, the filtering configured to be performed as a function of network proximities in a machine-readable network of nodes representing a human or animal interactome, the network proximities measured between i) nodes, of the machine-readable network of nodes representing the human or animal interactome, that represent targets of candidate drugs of the machine-readable reduced list of the candidate drugs, and ii) a cluster of nodes, of the machine-readable network of nodes, that represent a hallmark module, the hallmark module corresponding to the hallmark of aging targeted; and output the machine-readable reduced list of the candidate drugs for repurposing to target the hallmark of aging.

2. The computer-based system of Claim 1, wherein the datastore accessed is a drug datastore and wherein the sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to: access a gene datastore containing therein a machine-readable list of genes associated with aging and longevity; andidentify the cluster of nodes based on the machine-readable list of genes of the gene datastore accessed, the cluster of nodes representing a biological origin of the hallmark of aging targeted.

3. The computer-based system of Claim 1, wherein the datastore accessed is a drug datastore and wherein the sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to: access a gene datastore to retrieve a machine-readable list of genes associated with aging and longevity; and identify, based on the a machine-readable list of genes retrieved, a plurality of hallmark modules in the machine-readable network of nodes representing the human or animal interactome, wherein hallmark modules of the plurality of hallmark modules correspond to respective hallmarks of aging, and wherein the plurality of hallmark modules identified includes the hallmark module corresponding to the hallmark of aging targeted.

4. The computer-based system of Claim 1, wherein the sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to:: perform an assessment of whether at least one gene expression change induced by re-purposing a candidate drug, of the machine-readable reduced list of the candidate drugs, reinforces or counteracts documented age-related gene expression changes associated with the hallmark of aging targeted, the at least one gene expression corresponding to at least one gene represented by the hallmark module; and output an electronic representation of the assessment performed.

5. The computer-based system of Claim 4, further comprising a display device, wherein the electronic representation output is a visual representation, and wherein the sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to display the visual representation on the display device.

6. The computer-based system of Claim 1, wherein the datastore accessed is a drug datastore and wherein the sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to: access a machine-readable Connectivity Map (Cmap) datastore; retrieve machine-readable Cmap data from the Cmap datastore for a candidate drug of the machine-readable reduced list of the candidate drugs; determine a drug perturbation signature for the candidate drug based on the Cmap data retrieved; and compute a transcription-based metric for the candidate drug based on the drug perturbation signature determined, the transcription-based metric computed quantifying whether an impact of the candidate drug on gene expression reinforces or counteracts documented age-related gene expression changes.

7. The computer-based system of Claim 6, wherein the sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to compute the transcription-based metric based on a longevity vector, wherein the longevity vector encodes an age-induced expression change of a plurality of genes, and wherein the transcription-based metric computed represents an alignment between the drug perturbation signature determined and the longevity vector.

8. The computer-based system of Claim 7, further comprising a display device and wherein the sequence of instructions further causes the at least one processor to: generate a visual representation of the cluster of nodes; employ the transcription-based metric computed to modify the visual representation generated to indicate the impact; and display, on the display device, the visual representation generated and modified.

9. The computer-based system of Claim 1, wherein nodes of the machine-readable network of nodes represent genes of the human or animal interactome, wherein the datastore is a drug database, and wherein the machine-readable drug data includes structured drug data.

10. The computer-based system of Claim 9, wherein the sequence of instructions, when loaded and executed by the at least one processor, further causes the computer-based system to update the structured drug data to indicate that genes represented by nodes of the cluster of nodes representing the hallmark module are drug targets for at least one candidate drug of the machine-readable reduced list of the candidate drugs.

11. The computer-based system of Claim 10, wherein the update is configured to be performed responsive to receiving an input representing a positive result of a clinical trial for aging, the clinical trial for aging performed on at least one subject based on the machine-readable reduced list of the candidate drugs output, the positive result representing validation that the at least one candidate drug reinforces documented age-related gene expression changes associated with the hallmark of aging targeted.

12. A computer-implemented method for drug re-purposing, the computer-implemented method comprising: accessing a datastore containing therein machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging, the machine-readable drug data representing candidate drugs of the machine-readable list of candidate drugs and their respective targets; producing a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs obtained, the filtering performed as a function of network proximities in a machine- readable network of nodes representing a human or animal interactome, the network proximities measured between i) nodes, of the machine-readable network of nodes representing the human or animal interactome, that represent targets of candidate drugs of the machine-readable reduced list of the candidate drugs, and ii) a cluster of nodes, of the machine-readable network of nodes, that represent a hallmark module, the hallmark module corresponding to the hallmark of aging targeted; and outputting the machine-readable reduced list of the candidate drugs for repurposing to target the hallmark of aging.

13. The computer-implemented method of Claim 12, wherein the datastore accessed is a drug datastore and wherein the computer-implemented method further comprises:accessing a gene datastore containing therein a machine-readable list of genes associated with aging and longevity; and identifying the cluster of nodes based on the machine-readable list of genes of the gene datastore accessed, the cluster of nodes representing a biological origin of the hallmark of aging targeted.

14. The computer-implemented method of Claim 12, wherein the datastore accessed is a drug datastore and wherein the computer-implemented method further comprises: accessing a gene datastore to retrieve a machine-readable list of genes associated with aging and longevity; and identifying, based on the a machine-readable list of genes retrieved, a plurality of hallmark modules in the machine-readable network of nodes representing the human or animal interactome, wherein hallmark modules of the plurality of hallmark modules correspond to respective hallmarks of aging, and wherein the plurality of hallmark modules identified includes the hallmark module corresponding to the hallmark of aging targeted.

15. The computer-implemented method of Claim 12, further comprising: performing an assessment of whether at least one gene expression change induced by re-purposing a candidate drug, of the machine-readable reduced list of the candidate drugs, reinforces or counteracts documented age-related gene expression changes associated with the hallmark of aging targeted, the at least one gene expression corresponding to at least one gene represented by the hallmark module; and outputting an electronic representation of the assessment performed.

16. The computer-implemented method of Claim 15, the electronic representation output is a visual representation and wherein computer-implemented method further comprises displaying the visual representation on a display device.

17. The computer-implemented method of Claim 12, wherein the datastore accessed is a drug datastore and wherein the computer-implemented method further comprises: accessing a machine-readable Connectivity Map (Cmap) datastore;retrieving machine-readable Cmap data from the Cmap datastore for a candidate drug of the machine-readable reduced list of the candidate drugs; determining a drug perturbation signature for the candidate drug based on the Cmap data retrieved; and computing a transcription-based metric for the candidate drug based on the drug perturbation signature determined, the transcription-based metric computed quantifying whether an impact of the candidate drug on gene expression reinforces or counteracts documented age-related gene expression changes.

18. The computer-implemented method of Claim 17, further comprising computing the transcription-based metric based on a longevity vector, wherein the longevity vector encodes an age-induced expression change of a plurality of genes, and wherein the transcription-based metric computed represents an alignment between the drug perturbation signature determined and the longevity vector.

19. The computer-implemented method of Claim 18, further comprising: generating a visual representation of the cluster of nodes; employing the transcription-based metric computed to modify the visual representation generated to indicate the impact; and displaying, on a display device, the visual representation generated and modified.

20. The computer-implemented method of Claim 12, wherein nodes of the machine- readable network of nodes represent genes of the human or animal interactome, wherein the datastore is a drug database, and wherein the machine-readable drug data includes structured drug data.

21. The computer-implemented method of Claim 20, further comprising updating the structured drug data to indicate that genes represented by nodes of the cluster of nodes representing the hallmark module are drug targets for at least one candidate drug of the machine-readable reduced list of the candidate drugs.

22. The computer-implemented method of Claim 21, wherein the updating is performed responsive to receiving an input representing a positive result of a clinical trial for aging, the clinical trial for aging performed on at least one subject based on themachine-readable reduced list of the candidate drugs output, the positive result representing validation that the at least one candidate drug reinforces documented age-related gene expression changes associated with the hallmark of aging targeted.

23. A non-transitory computer-readable medium for drug re-purposing, the non-transitory computer-readable medium having encoded thereon a sequence of instructions which, when loaded and executed by at least one processor, causes the at least one processor to: access a datastore containing therein machine-readable drug data to obtain a machine-readable list of candidate drugs to consider for re-purposing to target a hallmark of aging, the machine-readable drug data representing candidate drugs of the machine-readable list of candidate drugs and their respective targets; produce a machine-readable reduced list of the candidate drugs to consider for the re-purposing by filtering the machine-readable list of the candidate drugs obtained, the filtering performed as a function of network proximities in a machine- readable network of nodes representing a human or animal interactome, the network proximities measured between i) nodes, of the machine-readable network of nodes representing the human or animal interactome, that represent targets of candidate drugs of the machine-readable reduced list of the candidate drugs, and ii) a cluster of nodes, of the machine-readable network of nodes, that represent a hallmark module, the hallmark module corresponding to the hallmark of aging targeted; and output the machine-readable reduced list of the candidate drugs for repurposing to target the hallmark of aging.

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