Identifying Potential Ageing-Modulating Drugs In Silico
Authors: Handan Melike Dönertaş, Matías Fuentealba, Linda Partridge, Janet M. Thornton
Journal: Trends in Endocrinology and Metabolism (2018)

The surge of high-throughput technologies over the last decade has promoted the use of computational biology as a method to integrate and interpret the multi-omics information of ageing, diseases, and drugs. Ageing-related pathways in animal models are also involved in human diseases. Thus, drugs employed to treat human diseases potentially could be repurposed to ameliorate aspects of the ageing process and hence prevent more than one age-related condition in humans. Recently, several approaches using different data sources have been developed to prioritise prolongevity compounds for humans and for testing in animal models. The development of biorepositories, longitudinal cohort studies, and electronic health records is strengthening the bridge between the clinical and molecular data, thus enabling future research on human ageing. Increasing human life expectancy has posed increasing challenges for healthcare systems. As people age, they become more susceptible to chronic diseases, with an increasing burden of multimorbidity, and the associated polypharmacy. Accumulating evidence from work with laboratory animals has shown that ageing is a malleable process that can be ameliorated by genetic and environmental interventions. Drugs that modulate the ageing process may delay or even prevent the incidence of multiple diseases of ageing. To identify novel, anti-ageing drugs, several studies have developed computational drug-repurposing strategies. We review published studies showing the potential of current drugs to modulate ageing. Future studies should integrate current knowledge with multi-omics, health records, and drug safety data to predict drugs that can improve health in late life. Increasing human life expectancy has posed increasing challenges for healthcare systems. As people age, they become more susceptible to chronic diseases, with an increasing burden of multimorbidity, and the associated polypharmacy. Accumulating evidence from work with laboratory animals has shown that ageing is a malleable process that can be ameliorated by genetic and environmental interventions. Drugs that modulate the ageing process may delay or even prevent the incidence of multiple diseases of ageing. To identify novel, anti-ageing drugs, several studies have developed computational drug-repurposing strategies. We review published studies showing the potential of current drugs to modulate ageing. Future studies should integrate current knowledge with multi-omics, health records, and drug safety data to predict drugs that can improve health in late life. Human life expectancy (see Glossary) has steadily increased since the middle of the 19th century in many parts of the world [1Oeppen J. Vaupel J.W. Demography: broken limits to life expectancy.Science. 2002; 296: 1029-1031Crossref PubMed Scopus (1562) Google Scholar] and is projected to continue to do so [2Kontis V. et al.Future life expectancy in 35 industrialised countries: projections with a Bayesian model ensemble.Lancet. 2017; 389: 1323-1335Abstract Full Text Full Text PDF PubMed Scopus (664) Google Scholar]. Longer lives are a result of improved living conditions and medical care, which have made people healthier at most ages [3Christensen K. et al.Physical and cognitive functioning of people older than 90 years: a comparison of two Danish cohorts born 10 years apart.Lancet. 2013; 382: 1507-1513Abstract Full Text Full Text PDF PubMed Scopus (263) Google Scholar, 4Zeng Y. et al.Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: a cohort study.Lancet. 2017; 389: 1619-1629Abstract Full Text Full Text PDF PubMed Scopus (301) Google Scholar]. However, the years of healthy life (healthspan) have not kept up with the overall increase in life expectancy, and there is a large and growing period of poor function and ill health at the end of life [5Crimmins E.M. Lifespan and healthspan: past, present, and promise.Gerontologist. 2015; 55: 901-911Crossref PubMed Scopus (266) Google Scholar]. Ageing is the major risk factor for illness, including the major chronic and killer diseases: cancer, metabolic and cardiovascular disease, and dementia [6Niccoli T. Partridge L. Ageing as a risk factor for disease.Curr. Biol. 2012; 22: R741-R752Abstract Full Text Full Text PDF PubMed Scopus (832) Google Scholar, 7Partridge L. et al.Facing up to the global challenges of ageing.Nature. 2018; 561: 45-56Crossref PubMed Scopus (425) Google Scholar]. The increasing proportion of unhealthy old people, often with two or more diseases (multimorbidity) [8Violan C. et al.Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies.PLoS One. 2014; 9e102149Crossref PubMed Scopus (538) Google Scholar, 9Kingston A. et al.Projections of multi-morbidity in the older population in England to 2035: estimates from the Population Ageing and Care Simulation (PACSim) model.Age Ageing. 2018; 47: 374-380Crossref PubMed Scopus (276) Google Scholar, 10Marengoni A. et al.Aging with multimorbidity: a systematic review of the literature.Ageing Res. Rev. 2011; 10: 430-439Crossref PubMed Scopus (1605) Google Scholar], and associated problems from the use of multiple drugs for their treatment (polypharmacy) [11Guthrie B. et al.The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995-2010.BMC Med. 2015; 13: 74Crossref PubMed Scopus (440) Google Scholar, 12Gu Q. et al.Prescription drug use continues to increase: U.S. prescription drug data for 2007-2008.NCHS Data Brief. 2010; 42: 1-8PubMed Google Scholar, 13Bushardt R.L. et al.Polypharmacy: misleading, but manageable.Clin. Interv. Aging. 2008; 3: 383-389Crossref PubMed Google Scholar, 14Nair N.P. et al.Hospitalization in older patients due to adverse drug reactions - the need for a prediction tool.Clin. Interv. Aging. 2016; 11: 497-505Crossref PubMed Scopus (88) Google Scholar], is posing an increasing burden on elderly people, their carers and social networks, and healthcare systems. Reducing the impact of ill health at the end of life is, therefore, a high priority for national governments and international health organisations [15WHO World Report on Ageing and Health 2015. World Health Organization, 2015Google Scholar]. Since ageing is the major risk factor for poor functioning and disease, intervening to ameliorate its effects could also prevent multiple age-related conditions simultaneously. There is growing evidence for the feasibility of this approach. People who die when they are very old (100, 105, 110) show progressively less multimorbidity at the end of their lives [16Christensen K. et al.Exceptional longevity does not result in excessive levels of disability.Proc. Natl. Acad. Sci. U. S. A. 2008; 105: 13274-13279Crossref PubMed Scopus (181) Google Scholar, 17Andersen S.L. et al.Health span approximates life span among many supercentenarians: compression of morbidity at the approximate limit of life span.J. Gerontol. A Biol. Sci. Med. Sci. 2012; 67: 395-405Crossref PubMed Scopus (242) Google Scholar]. Thus, a healthy ageing phenotype in humans can be achieved, and if we could understand the mechanisms leading to it, we might be able to extend it to the general population. Additionally, work over the past ∼20 years has shown that environmental, genetic, and pharmacological interventions in animals can extend both their lifespan and their healthspan [18Kenyon C.J. The genetics of ageing.Nature. 2010; 464: 504-512Crossref PubMed Scopus (1991) Google Scholar, 19Fontana L. et al.Extending healthy life span-from yeast to humans.Science. 2010; 328: 321-326Crossref PubMed Scopus (2104) Google Scholar]. Dietary restriction (DR), a reduction in food intake that avoids malnutrition, can extend lifespan and induce a marked improvement in health during ageing in diverse organisms [20Fontana L. Partridge L. Promoting health and longevity through diet: from model organisms to humans.Cell. 2015; 161: 106-118Abstract Full Text Full Text PDF PubMed Scopus (745) Google Scholar, 21Kapahi P. et al.Dietary restriction and lifespan: lessons from invertebrate models.Ageing Res. Rev. 2017; 39: 3-14Crossref PubMed Scopus (183) Google Scholar] including rodents. Two studies of rhesus monkeys subjected to DR found that the animals had lowered plasma triglycerides, diabetes, cardiovascular disease, sarcopenia, incidence of neoplasms, and brain atrophy, all features of ageing in humans [22Colman R.J. et al.Caloric restriction reduces age-related and all-cause mortality in rhesus monkeys.Nat. Commun. 2014; 5: 3557Crossref PubMed Scopus (487) Google Scholar, 23Mattison J.A. et al.Impact of caloric restriction on health and survival in rhesus monkeys from the NIA study.Nature. 2012; 489: 318-321Crossref PubMed Scopus (796) Google Scholar, 24Vaughan K.L. et al.Caloric restriction study design limitations in rodent and nonhuman primate studies.J. Gerontol. A Biol. Sci. Med. Sci. 2017; 73: 48-53Crossref PubMed Scopus (40) Google Scholar]. However, compliance with DR regimes in humans is low, and for this reason, it is not a practical public health intervention. Changes in diet are monitored by many nutrient-sensing systems, including the insulin/insulin-like growth factor and target of rapamycin (mTOR) signalling network. This highly conserved network senses nutrients, growth factors, and stress status, and modulates the costly activities of the organism, such as metabolism, growth, and reproduction, accordingly. Genetic interventions that reduce the activity of the network have proved to extend lifespan in nematode worms, fruit flies, and mice [18Kenyon C.J. The genetics of ageing.Nature. 2010; 464: 504-512Crossref PubMed Scopus (1991) Google Scholar, 19Fontana L. et al.Extending healthy life span-from yeast to humans.Science. 2010; 328: 321-326Crossref PubMed Scopus (2104) Google Scholar, 25Pan H. Finkel T. Key proteins and pathways that regulate lifespan.J. Biol. Chem. 2017; 292: 6452-6460Crossref PubMed Scopus (139) Google Scholar]. These long-lived mutants are protected against many natural pathologies of old age and also those associated with genetic models of age-related diseases. Genetic variants in orthologous genes in humans are associated with survival to advanced ages [26Pawlikowska L. et al.Association of common genetic variation in the insulin/IGF1 signaling pathway with human longevity.Aging Cell. 2009; 8: 460-472Crossref PubMed Scopus (276) Google Scholar, 27Deelen J. et al.Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited.Aging Cell. 2011; 10: 686-698Crossref PubMed Scopus (222) Google Scholar, 28Passtoors W.M. et al.Gene expression analysis of mTOR pathway: association with human longevity.Aging Cell. 2013; 12: 24-31Crossref PubMed Scopus (88) Google Scholar]. Mechanisms of ageing are highly conserved during evolution, and the process shows a set of characteristic hallmarks of ageing [29López-Otín C. et al.The hallmarks of aging.Cell. 2013; 153: 1194-1217Abstract Full Text Full Text PDF PubMed Scopus (7742) Google Scholar], which are also present in the aetiology of age-related diseases [7Partridge L. et al.Facing up to the global challenges of ageing.Nature. 2018; 561: 45-56Crossref PubMed Scopus (425) Google Scholar, 29López-Otín C. et al.The hallmarks of aging.Cell. 2013; 153: 1194-1217Abstract Full Text Full Text PDF PubMed Scopus (7742) Google Scholar]. Interventions that improve health during ageing and increase lifespan in laboratory animals do so by reducing the impact of one or more of these hallmarks. Increasingly, attention is turning to the possibility of pharmacological intervention into the ageing process, with a view to preventing age-related diseases. Although, ageing is becoming popular amongst the conditions under study in clinical trials, which aim, for example, to find drugs that restore gene expression levels to that of young healthy subjects (ClinicalTrials.gov identifier: NCT02432287) or reduce ageing-related biochemical parameters (NCT03451006), de novo drug development for ageing poses major challenges. We lack good biomarkers of ageing that could give a rapid prediction of the outcome of drug treatment, and a clinical trial with a potentially long-term treatment of an initially healthy population would be both prohibitively costly and would require drugs that are almost completely safe and free of side-effects. However, unsurprisingly, many of the gene products identified as potential targets to reduce the impact of ageing are already the targets of drugs licensed to treat specific age-related conditions. For instance, the licensed drug sirolimus (rapamycin), an inhibitor of the mTOR Complex 1 (TORC1), is used to prevent rejection of tissues after transplant, restenosis after cardiac surgery, and to treat cancer. This drug also extends lifespan in yeast, nematode worms, flies, and mice, and prevents many age-related conditions in ageing mice [30Johnson S.C. et al.MTOR is a key modulator of ageing and age-related disease.Nature. 2013; 493: 338-345Crossref PubMed Scopus (1114) Google Scholar, 31Blenis J. TOR, the gateway to cellular metabolism, cell growth, and disease.Cell. 2017; 171: 10-13Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar, 32Bitto A. et al.Transient rapamycin treatment can increase lifespan and healthspan in middle-aged mice.eLife. 2016; 5e16351Crossref PubMed Scopus (221) Google Scholar]. Inhibitors of mTOR can also potentiate the weak response to immunisation against influenza in ageing mice and humans [33Mannick J.B. et al.mTOR inhibition improves immune function in the elderly.Sci. Transl. Med. 2014; 6: 268ra179Crossref PubMed Scopus (450) Google Scholar, 34Mannick J.B. et al.TORC1 inhibition enhances immune function and reduces infections in the elderly.Sci. Transl. Med. 2018; 10: eaaq1564Crossref PubMed Scopus (224) Google Scholar]. Metformin, a drug used to treat type 2 diabetes, also targets the nutrient-sensing network. This drug has been shown to reduce all-cause mortality in diabetic patients when compared with those receiving nonmetformin therapies, and even non-diabetics in multiple studies [35Campbell J.M. et al.Metformin reduces all-cause mortality and diseases of ageing independent of its effect on diabetes control: a systematic review and meta-analysis.Ageing Res. Rev. 2017; 40: 31-44Crossref PubMed Scopus (207) Google Scholar], and is under trial for protection against the effects of ageing [36Barzilai N. et al.Metformin as a tool to target aging.Cell Metab. 2016; 23: 1060-1065Abstract Full Text Full Text PDF PubMed Scopus (551) Google Scholar, 37Espeland M.A. et al.Clinical trials targeting aging and age-related multimorbidity.J. Gerontol. A Biol. Sci. Med. Sci. 2017; 72: 355-361PubMed Google Scholar]. Repurposing of drugs, to protect against the effects of ageing and hence delay or prevent age-related diseases, is thus an increasingly realistic prospect. Indeed, some existing drugs could already be viewed as being used in this way. Both statins and drugs that lower blood pressure are widely used to prevent cardiovascular disease [38Ettehad D. et al.Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis.Lancet. 2016; 387: 957-967Abstract Full Text Full Text PDF PubMed Scopus (1886) Google Scholar, 39Collins R. et al.Interpretation of the evidence for the efficacy and safety of statin therapy.Lancet. 2016; 388: 2532-2561Abstract Full Text Full Text PDF PubMed Scopus (1127) Google Scholar], for which age is the major risk factor. Non-compliance with preventive drug regimens will always limit their efficacy, but there is at least the prospect that many age-related conditions could be warded off pharmacologically by those who wish to do so. There is, therefore, growing interest in identifying and prioritising such potentially geroprotective drugs. In this short review, we discuss the available data and some of the bioinformatics methods that are being used to this end. It is important to stress that these are methods to prioritise drugs for experimental testing. They provide the first step of many required for successful drug repurposing. Here we review 12 recent publications, summarised in Table 1 and Figure 1, aiming to identify and prioritise prolongevity drugs for animal models and humans. All such studies have been enabled by the development of powerful databases for the annotation and curation of genes/proteins (Ensembl [40Herrero J. et al.Ensembl comparative genomics resources.Database. 2016; 2016: bav096Crossref PubMed Scopus (265) Google Scholar], UniProt [41Uniprot Consortium UniProt: the universal protein knowledgebase.Nucleic Acids Res. 2017; 45: D158-D169Crossref PubMed Scopus (3122) Google Scholar]), their associated functions and pathways (Gene Ontology [42Ashburner M. et al.Gene ontology: tool for the unification of biology.Nat. Genet. 2000; 25: 25-29Crossref PubMed Scopus (27005) Google Scholar], KEGG [43Kanehisa M. et al.KEGG as a reference resource for gene and protein annotation.Nucleic Acids Res. 2016; 44: D457-D462Crossref PubMed Scopus (3609) Google Scholar], Reactome [44Fabregat A. et al.The reactome pathway knowledgebase.Nucleic Acids Res. 2018; 46: D649-D655Crossref PubMed Scopus (1488) Google Scholar]) and chemical ligands and drugs interacting with them (ChEMBL [45Gaulton A. et al.The ChEMBL database in 2017.Nucleic Acids Res. 2017; 45: D945-D954Crossref PubMed Scopus (1195) Google Scholar], DrugBank [46Law V. et al.DrugBank 4.0: shedding new light on drug metabolism.Nucleic Acids Res. 2014; 42: D1091-D1097Crossref PubMed Scopus (1536) Google Scholar], STITCH [47Szklarczyk D. et al.STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data.Nucleic Acids Res. 2016; 44: D380-D384Crossref PubMed Scopus (752) Google Scholar], drug gene interaction database; DGIdb [48Griffith M. et al.DGIdb: mining the druggable genome.Nat. Methods. 2013; 10: 1209-1210Crossref PubMed Scopus (330) Google Scholar], protein data bank; PDB [49Berman H.M. et al.The protein data bank.Nucleic Acids Res. 2000; 28: 235-242Crossref PubMed Scopus (27325) Google Scholar]) or affecting their expression (Connectivity Map [50Lamb J. et al.The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease.Science. 2006; 313: 1929-1935Crossref PubMed Scopus (3508) Google Scholar], CREEDS [51Wang Z. et al.Extraction and analysis of signatures from the Gene Expression Omnibus by the crowd.Nat. Commun. 2016; 7: 12846Crossref PubMed Scopus (152) Google Scholar]), as well as drugs (DrugAge [52Barardo D. et al.The DrugAge database of aging-related drugs.Aging Cell. 2017; 16: 594-597Crossref PubMed Scopus (78) Google Scholar], Geroprotectors.org [53Moskalev A. et al.Geroprotectors.org: a new, structured and curated database of current therapeutic interventions in aging and age-related disease.Aging (Albany NY). 2015; 7: 616-628Crossref PubMed Scopus (70) Google Scholar]) and targets (GenAge [54Tacutu R. et al.Human ageing genomic resources: new and updated databases.Nucleic Acids Res. 2017; 46: D1083-D1090Crossref Scopus (269) Google Scholar], Aging Clusters [55Blankenburg H. et al.A network-based meta-analysis for characterizing the genetic landscape of human aging.Biogerontology. 2018; 19: 81-94Crossref PubMed Scopus (13) Google Scholar]) implicated in ageing and age-related disease mechanisms (Figure 1).Table 1Published Studies of Drug-Repurposing to Target AgeingStudySource organismaOrganism from which the ageing information was acquired.Source of databSource of ageing knowledge (e.g., ageing databases or age-related expression data).MethodcSee Box 1 for the detailed information.Target organismdOrganism in which the drugs identified in the study should have an effect.Additional dataeAdditional information used in the method.RefsVirtual screening against known ageing genes1. Snell (2016)RotiferSpecific genesfTRP7, S6K, FhBC.Virtual screeningRotifer* Drugs (FDA approved in DrugBank & ZINC8 76Irwin J.J. Shoichet B.K. ZINC – a free database of commercially available compounds for virtual screening.J. Chem. Inf. Model. 2005; 45: 177-182Crossref PubMed Scopus (3026) Google Scholar)* Protein structure (PDB)56Snell T.W. et al.Repurposing FDA-approved drugs for anti-aging therapies.Biogerontology. 2016; 17: 907-920Crossref PubMed Scopus (23) Google Scholar2. Snell (2018)Rotifer orthologues of yeast, worms, flies, miceGenAgeVirtual screeningRotifer* Drugs (DrugBank, ZINC8 76Irwin J.J. Shoichet B.K. ZINC – a free database of commercially available compounds for virtual screening.J. Chem. Inf. Model. 2005; 45: 177-182Crossref PubMed Scopus (3026) Google Scholar)* Protein structure (PDB)57Snell T.W. et al.Repurposed FDA-approved drugs targeting genes influencing aging can extend lifespan and healthspan in rotifers.Biogerontology. 2018; 19: 145-157Crossref PubMed Scopus (14) Google Scholar3. Mofidifar (2018)–Specific genesgAMPK.Virtual screening and molecular dynamicsHuman* Drugs (FDA approved in DrugBank)* Protein structure (PDB)59Mofidifar S. et al.Repurposing existing drugs for new AMPK activators as a strategy to extend lifespan: a computer-aided drug discovery study.Biogerontology. 2018; 19: 133-143Crossref PubMed Scopus (8) Google ScholarSimilarity-based approachesFinding drugs that target known ageing-related genes4. Fernandes (2016)Human orthologues of model organism genesGenAgeGene-set overlap analysisHuman* Protein–drug interaction network (DGIdb)* Orthologues (Ensembl Compara)60Fernandes M. et al.Systematic analysis of the gerontome reveals links between aging and age-related diseases.Hum. Mol. Genet. 2016; 25: 4804-4818PubMed Google Scholar5. Fuentealba (2018)HumanAgeing ClustersGene-set overlap analysisHuman* Protein–drug interaction network (STITCH)* Protein–protein interactions (STRING 77von Mering C. et al.STRING: known and predicted protein-protein associations, integrated and transferred across organisms.Nucleic Acids Res. 2005; 33: D433-D437Crossref PubMed Scopus (1146) Google Scholar)* Functional annotations (GO)* Pathways (KEGG & Reactome)61Fuentealba M. et al.Using the drug-protein interactome to identify anti-ageing compounds for humans.bioRxiv. 2018; (Published online October 8, 2018)https://doi.org/10.1101/438234Crossref Google ScholarFinding drugs similar to known prolongevity drugs6. Liu (2016)WormHigh-throughput drug screening in C. elegans 63Ye X. et al.A pharmacological network for lifespan extension in Caenorhabditis elegans.Aging Cell. 2014; 13: 206-215Crossref PubMed Scopus (81) Google Scholar and GenAgeMachine learningWorm* Protein–drug interaction network (STITCH)62Liu H. et al.Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks.BMC Syst. Biol. 2016; 10: 509-519Crossref Scopus (12) Google Scholar7. Barardo (2017)WormDrugAge and High-throughput drug screening in C. elegans 63Ye X. et al.A pharmacological network for lifespan extension in Caenorhabditis elegans.Aging Cell. 2014; 13: 206-215Crossref PubMed Scopus (81) Google ScholarMachine learningWorm* Drugs (DGIdb)* Functional annotations (GO)* Chemical descriptors (MOE)64Barardo D.G. et al.Machine learning for predicting lifespan-extending chemical compounds.Aging (Albany NY). 2017; 9: 1721-1737Crossref PubMed Scopus (22) Google ScholarComparing transcriptome signatures of ageing and drugs8. Calvert (2016)Human orthologues forrat and macaque genesCaloric restriction expression signature 78De Cabo R. et al.An in vitro model of caloric restriction.Exp. Gerontol. 2003; 38: 631-639Crossref PubMed Scopus (97) Google ScholarGene-set enrichment analysisHuman* Drug-induced expression profile (CMap)65Calvert S. et al.A network pharmacology approach reveals new candidate caloric restriction mimetics in C. elegans.Aging Cell. 2016; 15: 256-266Crossref PubMed Scopus (62) Google Scholar9. Dönertaş (2018)HumanAge series expressionGene-set enrichment analysisHuman* Drug-induced expression profile (CMap)66Dönertaş H.M. et al.Gene expression-based drug repurposing to target aging.Aging Cell. 2018; 17e12819Crossref PubMed Scopus (31) Google Scholar10. Yang (2018)HumanYoung and old expressionGene-set enrichment analysisHuman* Drug-induced expression profile (CREEDS)67Yang J. et al.A novel approach of human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases.bioRxiv. 2018; (Published online May 18, 2018)https://doi.org/10.1101/326264Crossref Google ScholarApproaches to prioritise drugs for testing11. Aliper (2016)HumanYoung and old expressionPathway similarityHuman* Anti-ageing drugs and their targets (http://www.geroprotectors.org)* Ageing-related pathways68Aliper A. et al.In search for geroprotectors: in silico screening and in vitro validation of signalome-level mimetics of young healthy state.Aging (Albany NY). 2016; 8: 2127-2152Crossref PubMed Scopus (40) Google Scholar12. Ziehm (2017)HumanGenAge and GO ageing term (GO:0007568) 42Ashburner M. et al.Gene ontology: tool for the unification of biology.Nat. Genet. 2000; 25: 25-29Crossref PubMed Scopus (27005) Google ScholarEmpirical scoring functionWorm and fly* Protein structure (PDB)* Sequence (Uniprot)* Binding affinity (RF-Score 79Ain Q.U. et al.Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening.Wiley Interdiscip. Rev. Comput. Mol. Sci. 2015; 5: 405-424Crossref PubMed Scopus (184) Google Scholar)* Bioavailability assay 80Burns A.R. et al.A predictive model for drug bioaccumulation and bioactivity in Caenorhabditis elegans.Nat. Chem. Biol. 2010; 6: 549-557Crossref PubMed Scopus (135) Google Scholar* Purchasability (ZINC 76Irwin J.J. Shoichet B.K. ZINC – a free database of commercially available compounds for virtual screening.J. Chem. Inf. Model. 2005; 45: 177-182Crossref PubMed Scopus (3026) Google Scholar, eMolecules; https://www.emolecules.com)* Drug approval (ChEMBL, DrugBank)* Orthologues (Ensembl Compara)69Ziehm M. et al.Drug repurposing for aging research using model organisms.Aging Cell. 2017; 16: 1006-1015Crossref PubMed Scopus (24) Google ScholarGO, Gene ontology; MOE, Molecular Operating Environment [http://www.chemcomp.com/MOE-Molecular_Operating_Environment.htm].a Organism from which the ageing information was acquired.b Source of ageing knowledge (e.g., ageing databases or age-related expression data).c See Box 1 for the detailed information.d Organism in which the drugs identified in the study should have an effect.e Additional information used in the method.f TRP7, S6K, FhBC.g AMPK. Open table in a new tab GO, Gene ontology; MOE, Molecular Operating Environment [http://www.chemcomp.com/MOE-Molecular_Operating_Environment.htm]. Although the published studies of drug repurposing to target ageing use different strategies and sources of data, they can be classified into two main categories: methods employing the structural information to predict drugs potentially interacting with proteins already identified as being involved in ageing, and methods based on the similarity between ageing-related drugs or genes based on molecular structure, interactions, pathways, or networks (Table 1). The concept here is to find drugs which are known to target those genes that have been implicated in ageing. Two studies adopted methods based on the hypothesis that proteins or ligands with similar structures are likely to bind similar ligands or proteins, respectively, to predict drug-target interactions. The first of these (Study #1) [56Snell T.W. et al.Repurposing FDA-approved drugs for anti-aging therapies.Biogerontology. 2016; 17: 907-920Crossref PubMed Scopus (23) Google Scholar] aimed to identify novel drugs targeting three specific temperature-sensing proteins implicated in ageing in the rotifer Brachionus manjavacas (TRP7, S6P, FhBC). The authors used a virtual screening (Box 1) software called FINDSITEcomb that combines protein modelling with sophisticated threading approaches to model the target. The pockets in the model are then compared with the pockets in experimentally determined structures of proteins with ligands or modelled structures with known binders. The ligands of the top 100 ranked pockets are then compared with a library of screened ligands and ranked by ligand similarity. The authors screened 1347 FDA approved drugs in silico and tested four drugs for each target experimentally in the rotifers for their effects on lifespan and healthspan. Out of the 12 compounds tested, 5 significantly increased the rotifers’ lifespan. Changes in healthspan, approximated by swimming speed, reproduction, and mitochondrial activity, were also observed. In a subsequent study by the same authors (Study #2) [57Snell T.W. et al.Repurposed FDA-approved drugs targeting genes influencing aging can extend lifespan and healthspan in rotifers.Biogerontology. 2018; 19: 145-157Crossref PubMed Scopus (14) Google Scholar], the number of proteins analysed was expanded to include a set of ageing-related genes found in other animal models that are orthologous to genes in rotifers. This time a total of 94 targets were screened in silico using the FINDSITEcomb software. The top 1% binding compounds for each target were further ranked by their cumulative lifespan extension achieved by genetic interventions into their targets as taken from experimental model organism data, and filtered according to availability and previously predicted side effects [58Zhou H. et al.Comprehensive prediction of drug-protein interactions and side effects for the human proteome.Sci. Rep. 2015; 5: 11090Crossref PubMed Scopus (85) Google Scholar]. From the 31 drugs experimentally tested in ro