Drug Repurposing: Accelerating New Therapeutic Discoveries from Old Molecules (1990 – 2025)

Drug discovery traditionally follows a linear sequence—target identification, lead optimisation, pre-clinical testing, and multistage clinical evaluation—that can take over a decade [8]. High attrition rates and escalating costs have made pharmaceutical innovation increasingly unsustainable [9]. Drug repurposing provides an alternative by re-examining known molecules for novel therapeutic use [10]. Because the compounds’ safety, tolerability, and dosage parameters are already established, the clinical development timeline can be shortened by 60–70 percent [11].

Early success stories such as sildenafil, thalidomide, and aspirin proved that existing molecules can deliver new pharmacological benefits through secondary mechanisms [12].Modern computational tools and curated databases—Drug Bank, PubChem,and ClinicalTrials.gov—enable in silico  exploration of drug–target–disease networks. [13].Artificial-intelligence algorithms integrate chemical, genomic, and  clinical datasets to predict new indications. [14].The global market for repurposed drugs is projected to surpass USD 35 billion by 2025, reflecting both academic and industrial interest [15]. In India, organisations such as the Council of Scientific and Industrial Research (CSIR), the Indian Council of Medical Research (ICMR), and academic hubs like AIIMS and NIPER have initiated national programmes to promote open-source and computational repurposing [16]. Although India lacks a dedicated regulatory pathway equivalent to the US FDA’s 505(b)(2) system, the country’s strong generics base and biomedical talent make it a promising contributor [17]. Key challenges include intellectual-property conflicts, limited access to proprietary datasets, and ethical oversight for off-label use [18]. Nevertheless, repurposing represents a critical bridge between classical pharmacology and personalised medicine [19].

Historical Dev elopment of Drug Repurposing (Pre-1990 – 2025)

1. Serendipitous Beginnings (Pre-1990 Era)

Before computational biology, most repurposing cases were discovered accidentally through clinical observation [20]. The vasodilator minoxidil was repurposed for alopecia after patients reported excessive hair growth [21].

Thalidomide, once withdrawn for teratogenic effects, was later found to have potent anti-angiogenic activity and approved for multiple myeloma [22]. Aspirin’s antiplatelet effect, discovered decades after its analgesic use, revolutionised cardiovascular prevention [23]. Such findings highlighted polypharmacology—the ability of one drug to act on multiple targets [24]. During this era, repurposing lacked structured methodology but laid conceptual foundations for future systematic approaches [25].

2. Systematic Repurposing and Bioinformatics (1990 – 2010)

The Human Genome Project and advances in bioinformatics marked the transition from chance discoveries to targeted strategies [26]. Large-scale chemical and genomic databases enabled researchers to map drug–gene–disease associations computationally [27]. Pharmaceutical companies established dedicated repurposing divisions, while the FDA’s 505(b)(2) regulation formally recognised new indications for existing drugs [28]. Prominent examples include bupropion for smoking cessation and thalidomide for oncology [29]. Government-funded collaborations, such as NIH’s National Center for Advancing Translational Sciences (NCATS), created public repositories of failed compounds for academic testing [30]. By 2010, repurposing had evolved into a mainstream research paradigm [31].

3. Artificial Intelligence and Big-Data Era (2010 – 2025)

The integration of artificial intelligence, machine learning, and real-world evidence has revolutionised repurposing [32]. Algorithms trained on multi-omics and chemical-structure datasets can predict novel therapeutic indications within weeks [33]. During the COVID-19 pandemic, global collaborations such as WHO’s Solidarity Trial tested antivirals like remdesivir and favipiravir within record timelines [34]. Indian initiatives, including CSIR-OSDD and ICMR–AIIMS joint projects, applied virtual screening to identify potential leads for tuberculosis and SARS-CoV-2 [35]. AI-driven network pharmacology now links molecular mechanisms with patient phenotypes, supporting precision repurposing [36]. By 2025, data-centric discovery platforms and real-world analytics have transformed drug repurposing into a predictive science rather than empirical observation [37].

Mechanisms and Strategies of Drug Repurposing

Drug repurposing relies on understanding that a single compound can interact with multiple biological targets [38]. These interactions may produce secondary effects that are therapeutically useful for diseases other than the original indication [39]. Modern approaches can be divided into three broad categories — computational, experimental, and clinical epidemiological methods [40].

1. Computational (In Silico) Approaches

Computational methods use bioinformatics, molecular modeling, and machine learning to predict novel drug–target–disease links [41]. Network pharmacology and systems biology integrate large datasets to map relationships between genes, proteins, and chemical structures [42]. Techniques such as molecular docking, virtual screening, and signature matching are used to identify potential repositioning candidates [43]. Artificial intelligence can analyse millions of molecules simultaneously, reducing time for lead identification from months to days [44]. Databases like DrugBank, Connectivity Map, and Open Targets provide reference frameworks for these analyses [45]. Integration of electronic health records and real-world evidence further enhances prediction accuracy [46].

2. Experimental (In Vitro and In Viv o) Approaches

Repurposing hypotheses generated computationally require laboratory validation [47]. High-throughput phenotypic screening and cell-based assays identify unexpected drug activities [48]. Animal models help verify pharmacodynamics and toxicity for new indications [49]. This approach bridges computational predictions with clinical translation [50].

3. Clinical and Epidemiological Approaches

Analysis of clinical data sets and pharmacovigilance databases can reveal beneficial off-target effects [51]. Retrospective studies of electronic medical records help identify unexpected correlations between drug exposure and disease outcomes [52]. Such data provide real-world validation before prospective trials are initiated [53].

Global Success Stories in Drug Repurposing

Drug repurposing has produced numerous marketed therapies across diverse disease areas [54]. Notable examples include sildenafil for pulmonary hypertension, thalidomide for oncology, and metformin for metabolic and oncological applications [55].

Several marketed drugs stand as classic examples of successful repurposing initiatives. Sildenafil, originally designed as an antihypertensive and anti-anginal drug, was later developed and approved in 1998 for erectile dysfunction and pulmonary hypertension after clinical studies revealed its vasodilatory effect on penile arteries [56]. Thalidomide, once introduced as a sedative and withdrawn for teratogenicity, was rediscovered for its anti-angiogenic and immunomodulatory activity and approved in 1998 for multiple myeloma and erythema nodosum leprosum [57].

Minoxidil, an oral antihypertensive, was repurposed in 1988 for alopecia due to its ability to prolong the anagen phase of hair follicles [58]. The classical analgesic aspirin demonstrated potent antiplatelet effects, leading to its use in the prevention of atherothrombosis and cardiovascular disorders during the 1980s [59]. Metformin, a long-established antidiabetic agent, showed emerging benefits in oncology and anti-aging research, becoming a widely studied repositioning candidate after 2015 [60]. The antiviral remdesivir, initially developed against Ebola, was rapidly repurposed and approved for COVID-19 treatment under emergency use authorization in 2020 [61].

These cases illustrate the broad applicability of drug repurposing across therapeutic areas. Each success story emphasises how established pharmacokinetic data and clinical safety profiles help reduce development risks [62]. Pharmaceutical companies gain extended patent life and cost-effective innovation opportunities [63], while public-health systems benefit from faster and more affordable access to effective therapies [64]. Consequently, repurposing is now endorsed by major global organisations such as the World Health Organization and the US National Institutes of Health, both of which promote collaborative frameworks to expand repositioning efforts for rare and neglected diseases [65].

Indian Perspectiv e on Drug Repurposing

India’s pharmaceutical ecosystem is increasingly integrating repurposing into its research and regulatory framework [66]. The Council of Scientific and Industrial Research (CSIR) launched the Open Source Drug Discovery (OSDD) programme to identify new uses for existing molecules against tuberculosis, malaria, and COVID-19 [67]. The Indian Council of Medical Research (ICMR) and AIIMS collaborated to screen clinically approved anti-inflammatory and cardiovascular drugs for neurodegenerative conditions [68]. Several Indian start-ups, including ReaGene Biosciences and Bugworks Research, are using AI and computational chemistry for repositioning projects [69].

Regulatory Environment

The Central Drugs Standard Control Organisation (CDSCO) currently evaluates repurposed drugs under Schedule Y of the Drugs and Cosmetics Rules as “new indication for existing drug” [70]. Experts recommend establishing a distinct pathway similar to the US FDA’s 505(b)(2) model to streamline approvals [71]. During the COVID-19 pandemic, India authorised emergency use of repurposed drugs like favipiravir and itolizumab based on accelerated clinical data [72]. These experiences demonstrated the country’s capacity for rapid translational research [73].

Academic and Industrial Collaborations

Joint ventures between academic institutes and industry partners have expanded the scope of repurposing in India [74]. National missions on interdisciplinary cyber-physical systems support AI-driven drug design [75]. NIPER and IITs are developing databases to link traditional Ayurvedic formulations with modern molecular targets for possible repurposing [76]. Such efforts bridge India’s heritage of natural medicine with contemporary pharmacology [77].

Socio-economic Impact

Repurposing also supports India’s public-health goals by reducing cost burdens on patients and governments [78]. Affordable repurposed therapies can address communicable and non-communicable diseases where new drug development is financially unviable [79]. Integrating repurposing into national innovation policy could enhance India’s global competitiveness in pharmaceutical research [80].

Computational Tools, Challenges, and Future Prospects of Drug Repurposing

Computational Platforms and Databases in Drug Repurposing

The rise of computational tools has transformed drug repurposing from trial-and-error observation into a predictive science [80]. Bioinformatics, cheminformatics, and artificial intelligence (AI) integrate vast molecular datasets to identify new drug–target–disease associations [81]. Several open-access platforms have become essential for researchers and industries pursuing systematic repositioning [82].

DrugBank is one of the most comprehensive resources, combining detailed drug data with molecular-target information, enabling both ligand-based and target-based screening [83]. The Connectivity Map (CMap) developed by the Broad Institute compares disease gene-expression signatures with drug-induced transcriptional profiles to predict potential therapeutic matches [84]. The Open Targets Platform links genetic, pharmacological, and clinical data to assess target–disease relationships [85]. Other notable resources include PubChem BioAssay, Repurpose DB, CLUE, and the ReFRAME database, which collectively provide over 20,000 compounds for repositioning research [86].

In India, the CSIR-Open Source Drug Discovery (OSDD) and NIPER-Ayurvedic Compound Database integrate chemical structures, bioactivity data, and traditional knowledge for computational exploration of repurposing opportunities [87]. These tools allow multi-target docking, network mapping, and prioritisation of candidate molecules for laboratory validation [88].

Machine-learning algorithms such as random forests, support vector machines, and deep neural networks have further enhanced predictive accuracy [89]. AI models can process heterogeneous datasets—chemical fingerprints, transcriptomics, proteomics, and adverse-event signals—to identify hidden therapeutic correlations [90]. Natural-language-processing engines are now being used to mine biomedical literature and electronic health records for potential repositioning leads [91]. The integration of such computational approaches drastically shortens hypothesis generation time, leading to faster bench-to-bedside translation [92].

Challenges and Limitations of Drug Repurposing

Despite its success, drug repurposing faces several scientific, regulatory, and economic hurdles [93].

1. Intellectual-Property and Patent Barriers

Repurposed drugs are often derived from molecules whose original patents have expired [94]. This limits financial incentives for pharmaceutical companies to invest in additional clinical development [95]. While the US FDA’s 505(b)(2) pathway allows limited exclusivity for new indications, India currently lacks such a provision, resulting in reduced industry participation [96].

2. Regulatory Ambiguity

Most regulatory authorities classify repurposed molecules as “new drugs,” requiring fresh clinical evidence for safety and efficacy [97]. The absence of harmonised global guidelines delays market authorisation [98]. India’s CDSCO follows Schedule Y, but experts suggest introducing a dedicated framework specific to repositioning [99].

3. Data Accessibility and Reproducibility

Repurposing depends heavily on the availability of high-quality, curated data [100]. Many pharmaceutical datasets remain proprietary, restricting AI model training [101]. Furthermore, inconsistencies between experimental protocols hinder reproducibility across laboratories [102]. Public–private data-sharing agreements are therefore essential to advance the field [103].

4. Clinical Validation and Translational Gaps

Computational predictions require rigorous pre-clinical and clinical validation before approval [104]. However, conducting new trials for old drugs often lacks commercial appeal [105]. Collaborative funding models involving governments and academic consortia are critical to overcome this limitation [106].

5. Ethical and Safety Considerations

Off-label use of repurposed drugs before formal approval raises ethical and safety concerns [107]. Pharmacovigilance networks must closely monitor adverse events to ensure patient safety [108]. Transparent reporting of trial results is necessary to maintain public trust [109].

Future Prospects and Global Opportunities

The future of drug repurposing lies in integrating AI-driven analytics, omics technologies, and real-world evidence into a unified translational framework [110].

1. AI and Multi-Omics Integration

Artificial intelligence can now predict drug behaviour across the molecular, cellular, and clinical levels [111]. Multi-omics integration—combining genomics, proteomics, metabolomics, and transcriptomics—allows personalised prediction of drug response [112]. Deep-learning algorithms trained on such data have identified novel therapeutic targets for cancer, neurodegenerative, and infectious diseases [113].

2. Global Collaboration Networks

International initiatives such as the Open Pandemics Consortium, Global Health Drug Discovery Institute, and WHO Collaborative Network on Drug Repositioning are standardising data-sharing frameworks [114]. These platforms enable rapid mobilisation of existing drugs against emerging global threats like COVID-19 and antimicrobial resistance [115]. Developing countries, including India, are increasingly contributing through low-cost screening models and open-source innovation [116].

3. Integration with Pharmacovigilance and Real-World Data

The convergence of repurposing with pharmacovigilance has created a feedback loop for continuous drug evaluation [117]. Adverse-event databases such as VigiBase and FAERS now assist in detecting both safety signals and novel therapeutic potentials [118]. Real-world data from electronic medical records help confirm efficacy in diverse patient populations [119].

4. Policy and Economic Reforms

Governments must incentivise repurposing by providing tax benefits, patent extensions, and accelerated review processes [120]. India’s upcoming Pharmaceutical Innovation Policy 2024 includes provisions for prioritising repositioned drugs for neglected and rare diseases [121]. Establishing a centralised Drug Repurposing Council could coordinate national efforts, ensuring ethical oversight and data transparency [122].

5. Education and Skill Development

Incorporating repurposing into pharmacy and biomedical curricula will create a skilled workforce proficient in bioinformatics, AI, and translational pharmacology [123]. Continuous professional development and interdisciplinary training will further strengthen India’s global presence in this domain [124].

CONCLUSION

Drug repurposing stands at the intersection of computational biology, clinical pharmacology, and public-health innovation [125]. By leveraging artificial intelligence, open data, and collaborative frameworks, the next decade will likely witness a surge of repositioned therapies for both common and neglected diseases [126]. With the right regulatory reforms and investment, India can transform drug repurposing from an academic exercise into a strategic pillar of its pharmaceutical innovation ecosystem [127].

Conclusion and Future Outlook

Drug repurposing has evolved from a serendipitous discovery process into a systematic, computationally driven branch of modern pharmacology [125]. Over the past three decades, this strategy has redefined the landscape of pharmaceutical innovation by offering faster, safer, and more economical routes to new therapies [126]. As global healthcare systems face rising costs and an increasing burden of chronic and emerging diseases, repurposing provides an efficient mechanism to extend the clinical value of existing molecules [1].

The success of drugs such as sildenafil, thalidomide, metformin, and remdesivir underscores the transformative potential of repositioning in addressing both acute and long-term medical needs [56]. These examples demonstrate that established safety and pharmacokinetic data can dramatically reduce the risks and costs associated with traditional de novo drug discovery [3]. Furthermore, advances in computational tools, bioinformatics databases, and artificial intelligence have made it possible to identify novel therapeutic indications within weeks, rather than years [83].

Globally, data-driven platforms such as DrugBank, Connectivity Map, and Open Targets are catalysing a shift toward precision repurposing [85]. Simultaneously, international collaborations—including the Open Pandemics Consortium and WHO ReFRAME Initiative—have accelerated the evaluation of existing drugs against global threats like COVID-19 and antimicrobial resistance [114]. These cooperative frameworks exemplify the value of open science and shared databases in addressing urgent health crises [115].

India’s Roadmap for Drug Repurposing

India has demonstrated strong potential in integrating repurposing within its pharmaceutical innovation ecosystem [16]. Government bodies such as CSIR, ICMR, and NIPER have established computational and experimental programmes that utilise both modern and traditional pharmacological knowledge [67]. The CSIR-Open Source Drug Discovery (OSDD) initiative has already yielded promising results for tuberculosis and COVID-19 [73].