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IVM’s Krishanrao Bhegde Institute of Pharmaceutical Education and Research
Drug repurposing, which identifies new therapeutic uses for existing drugs, offers a faster and more cost-effective alternative to traditional drug development by utilizing compounds with known safety profiles. A notable example is sildenafil, originally a cardiovascular drug, later repurposed for erectile dysfunction, pulmonary hypertension, and explored for anticancer potential due to its immunomodulatory and tumor-inhibiting effects. Despite its promise, repurposing faces challenges such as intellectual property limitations, low commercial incentives for off-patent drugs, and complex regulatory processes. Overcoming these barriers requires collaboration among academic, governmental, and non-profit sectors, policy reforms, and transparent data sharing to unlock the full potential of repurposed drugs in cancer and other major diseases
Drug repurposing serves as a practical and efficient alternative to the traditional drug discovery process. Unlike conventional approaches that involve lengthy, costly, and multi-stage development from discovery to post-marketing, repurposing focuses on identifying new therapeutic uses for existing drugs with established safety and pharmacokinetic profiles. This strategy reduces financial investment, shortens development timelines, and minimizes the likelihood of failure. Prominent examples include minoxidil, initially developed as an antihypertensive but later found effective for treating alopecia, and sildenafil, originally intended for angina but subsequently used for erectile dysfunction and pulmonary arterial hypertension. Since repurposed drugs already possess substantial preclinical and clinical data, many can advance directly to later clinical trial phases, such as Phase II. Ultimately, drug repurposing accelerates the translation of scientific research into clinical practice while maintaining the essential standards of safety and efficacy.
[1,2]
History:
Table:1[3]
|
Period |
Event |
|
1920 |
Concept of drug repurposing originated through serendipitous discoveries researchers noticed unexpected beneficial effects of existing drugs |
|
Mid–20th Century |
Increasing number of drugs observed to have secondary therapeutic uses beyond their original purpose. |
|
Late 20th Century |
Emergence of systematic experimental approaches (activity-based screening) to identify new drug uses. |
|
Early 2000s |
Development of computational and bioinformatics tools marked the beginning of in silico-based repositioning. |
|
2010 |
Integration of high-throughput screening, data mining, and molecular modeling made repurposing faster and data-driven. |
|
2015 |
Global market value of repurposed drugs estimated at $24.4 billion. |
|
2020 (Projection) |
Market projected to grow to $31.3 billion, showing rising industry and research interest. |
|
Present Era |
Around 30% of FDA-approved drugs are repurposed; computational and AI-assisted methods dominate research in this field. |
Approaches:
Drug Repurposing and Its Strategies Drug repurposing (or repositioning) involves identifying new medical uses for already approved drugs or drug candidates. This approach bypasses much of the risk, time, and cost involved in de novo drug development by leveraging compounds with established safety and pharmacokinetic data. Advances in fields such as computational biology, artificial intelligence, and big data have shifted repurposing from a reliance on chance to a systematic, evidence-oriented discipline.
This method revolves around broadening the use of an existing or investigational drug for new indications. It includes identifying drugs used off-label or revisiting discontinued agents for alternative purposes. Researchers examine a drug’s pharmacological profile, known actions, and adverse effects to predict alternate therapeutic roles. Sometimes, drugs set aside due to inadequate efficacy in one disease can be redirected to similar conditions that share biological mechanisms
This approach looks for drugs that may be applicable to diseases sharing underlying molecular or cellular traits. If two conditions display overlap in biological mechanisms, a treatment effective for one may help the other. This is particularly valuable in rare disorders, where therapeutics are scarce. For example, drugs that disrupt cell growth in cancer may be repositioned for dermatological diseases involving abnormal cell proliferation.
It begins with a molecular target relevant to a new disease of interest. If established drugs modulate this target, they can be evaluated for repurposing. This method succeeds when disease mechanisms and target biology are well understood, allowing researchers to match treatments to other diseases through knowledge of shared
Modern repurposing increasingly relies on computational tools. Bioinformatics, machine learning, and data analytics allow rapid identification of drug–disease target links, utilizing data from genomics, proteomics, and clinical studies. These approaches ranging from genetic association and pathway mapping to molecular docking and retrospective data mining accelerate and reduce the costs of drug discovery. Deep learning models now predict drug–target affinities and prioritize candidates for further biological validation, while structure-based methods use simulations to forecast novel drug-target interactions.
Large genomic datasets are mined to find connections between specific genes and diseases. Once associated loci or pathways are pinpointed, researchers can look for drugs already modulating these targets, paving the way for personalized medicine and targeted repurposing based on patient genetics. Pathway Mapping Approach Researchers reconstruct disease-specific signaling or metabolic pathways using molecular data. By targeting critical nodes or regulators in these networks, existing drugs acting on these points can be re-evaluated for associated diseases, especially for complex illnesses with interconnected pathways.
This computational technique predicts how drugs physically bind to protein targets. Docking assesses binding affinity and orientation, allowing virtual screening of approved drugs against new disease targets. While it depends on reliable protein structures, docking remains a key cost-effective tool for prioritizing repurposing candidates. Retrospective Clinical Data Analysis by analyzing historical data from clinical trials or electronic health records, unexpected effects or benefits of known drugs can be identified. A classic example is the repurposing of sildenafil, initially aimed at angina, which was redeveloped for erectile dysfunction after positive effects were observed during trials. Signature Mapping and Phenotypic Screening Signature mapping compares the molecular fingerprints induced by drugs with those of diseases; drugs capable of reversing these signatures are considered promising candidates. Phenotypic screening, meanwhile, focuses on the observable effects created by drugs, enabling discovery even when mechanisms are not fully understood. These methods utilize genetic and pharmacological data to match drugs with new indications.
The most comprehensive strategy combines computational predictions with experimental validation. Hypotheses generated via in silico analysis are confirmed through laboratory and animal studies, ensuring both scientific robustness and clinical relevance. This integrative workflow is now a gold standard for reliable and efficient drug repurposing. [4,5]
Stages:
Fig:1[6]
Case studies:
Case No 1. Drug Repurposing of Metformin for Metformin for Cancer Mortality Reduction:
This case study explores the potential of repurposing metformin, a medication primarily used for type 2 diabetes, for reducing cancer mortality. Objective The main objective was to address the challenges and delays typically encountered in drug discovery by validating whether metformin could serve as an anticancer therapeutic agent. The study specifically aimed to determine metformin's association with reduced cancer mortality. Methods Researchers conducted a retrospective cohort study covering the years 1995 to 2021. The study analyzed electronic health records (EHRs) from Vanderbilt University Medical Center (VUMC) and Mayo Clinic. Data from 32,415 adult cancer patients were used, categorized into four groups: Diabetic patients using metformin Patients using other oral hypoglycemic Patients on insulin only Non-diabetic patients
RESULTS
Cancer patients using metformin had a 22% lower overall mortality compared to those using other oral hypoglycemic (Hazard Ratio, HR 0.78; 95% CI 0.69–0.88).Compared to patients using insulin only, metformin users showed 39% lower mortality (HR 0.61; 95% CI 0.53–0.70).The survival benefit also extended to non-diabetic patients, with metformin users showing 39% improved survival over non-diabetics (HR 0.77; 95% CI 0.71–0.85).These positive outcomes were replicated in at least one EHR for multiple cancer types, including breast, colorectal, lung, and prostate cancers .Conclusion Metformin use was strongly associated with reduced mortality related to cancer across various types and populations. The results support metformin's potential repurposing as an anticancer therapeutic agent.[7]
Case Study no.2: - Networking Medicine and GenAI: A Case Study On Drug Repurposing For Breast Cancer:
Study Objective:
Identify genetic disease signatures linked to druggable targets.
Match existing drugs to specific breast-cancer subgroups.
Methods:
Used UK Biobank data (11,088 cases + 22,176 controls; 547,197 SNPs). Applied Precision Life multi-omics platform for SNP-based network analysis. Discovered 174,000 disease signatures and 175 risk-associated genes.
Innovation:
Combined Generative AI, text-mining, and network-based methods for real-world biomedical data analysis. Accelerated discovery of drug repurposing opportunities.
Conclusion:
Integrating genomic data with AI and network medicine reduces R&D costs, supports personalized therapy, and improves outcomes.
Provides a scalable model for precision drug repurposing in complex diseases such as breast cancer.[8]
Case No. 3: - Some Other Case Study
Artificial Intelligence (AI) has significantly accelerated drug repurposing by uncovering novel therapeutic applications for existing drugs through data-driven insights. The following examples highlight successful and emerging AI-based case studies in drug repurposing:
1. Baricitinib (Rheumatoid Arthritis → COVID-19):
Originally approved for rheumatoid arthritis, Baricitinib was identified as a potential COVID-19 treatment through AI-driven deep learning and knowledge graph analysis. These tools revealed that Baricitinib inhibits the enzymes AAK1 and GAK, which play crucial roles in viral entry and inflammation. Clinical data confirmed reduced viral replication and faster patient recovery, leading to its FDA Emergency Use Authorization in 2020.
2. Ketamine (Anesthetic / Antidepressant → Cocaine Use Disorder):
AI models utilizing machine learning and electronic health record (EHR) mining predicted that ketamine’s modulation of NMDA receptors could help treat cocaine use disorder (CUD). Clinical trials supported this prediction, demonstrating ketamine’s ability to reduce cocaine craving and relapse rates. The therapy is currently in ongoing clinical trials for validation.
3. Efavirenz (HIV Antiretroviral → Parkinson’s Disease):
Using deep learning and network pharmacology, researchers identified Efavirenz, an HIV non-nucleoside reverse transcriptase inhibitor (NNRTI), as a potential treatment for Parkinson’s disease. AI predicted its activation of the enzyme CYP46A1, leading to reduced α-synuclein aggregation and enhanced neuroprotection. Preclinical studies supported these findings, and the drug has progressed to Phase 2 clinical studies.
4. Cataract Prevention in Diabetes (Drug Screening):
Machine learning applied to large clinical datasets identified several existing drugs that might prevent diabetic cataracts. Among these, angiotensin receptor blockers (ARBs) and certain anti-inflammatory agents were found to significantly lower the risk of cataract extraction in diabetic patients. These insights have undergone retrospective validation, supporting their potential for clinical repurposing.
5. Vandetanib + Everolimus (Cancer → Diffuse Intrinsic Pontine Glioma):
AI-driven genomic profiling and drug repositioning algorithms predicted that combining Vandetanib (a thyroid cancer drug) with Everolimus (an mTOR inhibitor) could effectively treat diffuse intrinsic pontine glioma (DIPG), a rare pediatric brain tumor. The combination enhanced drug delivery across the blood-brain barrier and showed tumor reduction in preclinical models. This approach has advanced to early-phase clinical trials.
6. DREAM-RD Project (Fragile X Syndrome):
The DREAM-RD project utilized machine learning, deep learning, and transcriptomic analysis to identify potential therapies for Fragile X Syndrome (FXS). AI algorithms reversed disease-specific gene expression signatures, and early clinical trials have demonstrated improvements in cognition and behavioral outcomes, marking a promising step toward precision therapy for neurodevelopmental disorders. This compilation underscores how AI technologies—ranging from deep learning to knowledge graphs are transforming drug repurposing by enabling faster hypothesis generation, improved prediction accuracy, and more efficient clinical translation.[9]
Case Study No.4: - Case Study of Minoxidil
Minoxidil, initially developed in the 1970s as an oral vasodilator for severe refractory hypertension, works by opening ATP-sensitive potassium channels, reducing peripheral resistance and lowering blood pressure. Due to serious side effects, oral minoxidil is now reserved for resistant hypertension unresponsive to other drugs.
In 1987, a topical formulation was introduced for androgenic alopecia (male and female pattern hair loss). It promotes hair growth by shortening the telogen phase and prolonging the anagen phase through conversion to its active metabolite, minoxidil sulfate, and by enhancing scalp microcirculation and VEGF expression.
Indications:
Topical: FDA-approved for androgenic alopecia; off-label for alopecia areata, chemotherapy-induced alopecia, scarring alopecia, and hypotrichosis.
Oral: Approved for resistant hypertension; low-dose oral minoxidil (<5 mg/day) used off-label for alopecia.
Pharmacokinetics:
Orally well absorbed (95%), metabolized by sulfotransferase, excreted renally, with a 3–4 hr. half-life but effects lasting up to 72 hrs.
Dosage:
Topical: 2%–5% solution/foam, applied 1–2 times daily.
Oral: 0.25–2.5 mg daily (alopecia off-label); 5–100 mg/day (hypertension).
Adverse Effects:
Topical: Itching, irritation, dermatitis, telogen effluvium, hypertrichosis.
Oral: Hypotension, tachycardia, edema, pericardial effusion, gynecomastia.
Contraindications:
Pregnancy, breastfeeding, scalp infections, hypersensitivity, and patients <18 years.
Monitoring: Regular assessment of blood pressure, heart rate, scalp condition, renal function, and cardiac status is essential.
Toxicity:
Accidental ingestion may cause hypotension and tachycardia; treated with fluids, vasopressors, and activated charcoal if needed.
minoxidil is a repurposed drug that shifted from an antihypertensive agent to a widely used hair growth stimulant, demonstrating the value of drug repurposing in modern therapeutics.[10]
Barriers
1.Insufficient Efficacy or Lack of Superiority
The leading cause of abandonment is when a drug fails to show adequate effectiveness for its intended indication or does not perform better than existing treatments.
Examples: Agouron’s TyMITAQ™ and Pfizer’s Capravirine were discontinued after showing limited improvement over standard therapies.
2. Strategic and Business Considerations
Decisions are often driven by commercial factors such as profitability and return on investment (ROI).
Drugs with smaller markets or limited revenue potential are frequently deprioritized, even if they are clinically promising.
3. Limited Market Potential
Products targeting small patient groups or offering low financial returns are less likely to be developed further.
Example: Vaccines like Prevnar 13® generate far less annual revenue making them less attractive for companies.
4. Misalignment with Corporate Disease Focus
Compounds that fall outside a company’s primary therapeutic areas are often sold or discontinued.
Example: AstraZeneca transferred its schizophrenia drug to Millendo Therapeutics for possible use in PCOS, which was outside AstraZeneca’s research priorities.
5. Effects of Mergers and Acquisitions
Corporate restructuring and mergers often lead to portfolio reductions and the termination of overlapping programs.
Example: After Pfizer’s merger with Wyeth, several projects—including Imagabalin—were discontinued to align with new company priorities.
6. Managerial Misjudgments
Decision-makers sometimes underestimate a drug’s potential (known as “Type II errors”).
These false-negative assessments can prematurely end development for drugs that might have succeeded with the right strategy or target.
Example: Dalcetrapib by Roche was abandoned after disappointing trial outcomes, which negatively influenced similar research efforts.
7. Flaws in Research Design
Trials may fail because of inappropriate choices in dosage, patient selection, or study endpoints. Such design flaws can create misleading results, making a potentially useful drug appear ineffective.
Examples: Nelivaptan was tested in unsuitable populations, and Aducanumab was initially dropped but later revived after dose adjustments.
8. Complexity of Disease Mechanisms
Incomplete understanding of biological pathways, especially in diseases like Alzheimer’s, psychiatric disorders, and cardiovascular diseases, hinders development.
This knowledge gap leads to repeated trial failures—over 200 Alzheimer’s drug candidates have failed for such reasons.
. High Placebo Response Rates
Conditions such as depression and irritable bowel syndrome often show high placebo effects (up to 40–50%), making it difficult to demonstrate a drug’s real benefit.
10. Regulatory and Data Requirements
Some promising drugs are shelved because regulators demand more extensive data or longer trials, increasing cost and time burdens.
11. Economic and Logistical Challenges in Repurposing
Reassessing shelved drugs is often viewed as financially impractical.
Companies prefer focusing resources on developing new patented drugs instead of reviving older compounds.
Patent and Exclusivity Challenges
12.Intellectual property protection for repurposed drugs is often weak.
While new formulation or orphan drug exclusivity options exist, they rarely provide sufficient commercial protection or profit assurance.
13. Need for Differentiation
Repurposed drugs must clearly differ from existing generic versions — often through changes in formulation, dosage, delivery route, or combination therapy — to justify market entry and pricing.
14. Regulatory and Clinical Complexities
Repurposing projects must navigate multiple regulatory frameworks and ensure clinical trial feasibility early on.
Compliance with CMC (Chemistry, Manufacturing, and Controls) and regulatory pathway clarity are key challenges.
15. Timing and Strategic Planning
Rapid decision-making after identifying a new indication is crucial. Delays in strategic planning can result in loss of exclusivity or missed market opportunities.
16. Market Uncertainty
Repurposing initiatives often face unclear reimbursement models, pricing strategies, and market demand forecasts, making investors hesitant. [11,12]
Strategies to Overcome Barriers:
Use of existing approval frameworks – Apply regulatory pathways like the EU Orphan Drug Designation or US “Specialty Drug” status to gain temporary market exclusivity and financial returns.
Develop new formulations, dosages, or derivatives to create patentable variations and justify investment.
Article 48: Allows non-profit entities to submit evidence for new therapeutic indications addressing unmet needs; if approved, MAHs must update product labels.
Article 84: Grants 4 years of data exclusivity for repurposed drugs ≥25 years old with new clinical benefit evidence.
US 505(b)(2) Pathway – Enables sponsors (including non-manufacturers) to use prior safety/efficacy data for new indications and even “labelling-only” extensions applicable to all generic versions.
EMA STAMP Pilot Programmed – Provides scientific and regulatory advice to support academics/NGOs (“Champions”) in generating evidence meeting regulatory standards.
Public funding and investment – Governments should expand funding beyond early research to confirmatory clinical trials.
Public–private partnerships (PPPs) Promote collaborations between academia, industry, and government to share costs and expertise.
Social Impact Bonds Link public or private investment returns to measurable social/health outcomes in repurposing projects.
Interventional PharmacoEconomics (IVPE) Encourage health-economic models that justify investment in repurposing based on system-wide cost savings.
Government leadership Governments to play a central role throughout the drug’s lifecycle, ensuring continuity from discovery to market access.
Research prioritization Focus public funding and fast-track review on rare diseases and unmet medical needs.
Regulatory capacity building – Provide training and resources so academic researchers and NGOs understand marketing authorization and post-approval obligations.
Access to data from failed trials – Facilitate open data sharing to accelerate identification of viable repurposing opportunities.
Framework for “Champions” – Support non-profit entities to act as intermediaries between evidence generation and commercial MAH engagement.
Early establishment of pharmacokinetic-pharmacodynamic (PKPD) links is crucial to select appropriate dosages, administration routes, and formulations for new indications. Leveraging modeling and simulation can help optimize dose selection and reduce the risk of costly failures in later clinical stages.
Developing research hypotheses should be grounded in comprehensive biological, pharmacological, and clinical evidence. Incorporating insights from genetics, clinical practice, and real-world studies ensures that candidate selection is robust, while also encouraging exploration beyond already well-studied diseases. Combining Computational and Experimental Tools
Artificial intelligence, bioinformatics, and network pharmacology enable more efficient identification of new drug-disease relationships. These predictions should then be validated experimentally. Methodologies may focus on finding new targets for existing drugs, new diseases that match known drug mechanisms, or new uses for established targets.
Adopting a comprehensive, team-based approach such as the UCL Therapeutic Innovation Network provides step-by-step guidance from project inception to regulatory approval. This model brings together experts from scientific, clinical, and business domains, and helps address intellectual property and regulatory barriers by linking researchers with industry, funders, and patient groups.
It is essential to clarify regulatory requirements and development viability from the start, taking into account safety, ethics, clinical need, and market landscape. Evaluating these factors early helps ensure a viable path to approval, particularly where competition or alternative therapies already exist. Learning from Successes and Failures
Semaglutide advanced as an obesity treatment by methodically building on both preclinical and clinical research. In contrast, the attempted repurposing of hydroxychloroquine for COVID-19 failed due to insufficient translation from lab to clinical settings. Thalidomide, though initially problematic, ultimately succeeded in new indications through careful management of risks and a deeper understanding of its mechanisms
Encouraging shared databases, nurturing public–private partnerships, and creating multidisciplinary institutional support mechanisms help drive progress in drug repurposing, facilitating a seamless transition from laboratory discoveries to real-world clinical applications. [13,14]
Future Scope:
1.Expansion of Computational and AI Approaches
Future research will increasingly rely on advanced computational tools such as network pharmacology, machine learning, artificial intelligence (AI), and multi-omics integration to systematically identify new therapeutic applications for existing drugs.
2.Utilization of Real-World Data
The growing availability of real-world datasets—including electronic health records, patient registries, and insurance claims—offers valuable opportunities to uncover unforeseen drug–disease associations and validate repurposing hypotheses.
3. Mechanism-Driven Repurposing
There is a shift toward mechanism-based repurposing, where drugs are matched to molecular targets, pathways, and specific disease subtypes rather than discovered by chance. This approach aligns closely with the goals of precision medicine.
4.Collaborative and Open-Access Frameworks
Strengthening collaboration among academia, industry, and regulatory agencies will be crucial. Establishing shared databases, compound libraries, and data-sharing platforms will make repurposing a mainstream, sustainable strategy.
5.Addressing Regulatory and IP Challenges
The future of drug repurposing depends on reforming regulatory and intellectual property policies to encourage innovation. New business models, patent frameworks, and public–private partnerships can make repurposing commercially viable.
6.Focus on Neglected and Complex Diseases
Repurposing holds immense potential for rare, neglected, and complex diseases including cancers, neurodegenerative disorders, and autoimmune conditions—where conventional drug discovery remains high-risk and underfunded.
7.Multi-Drug Combinations and Polypharmacology
Future strategies will explore drug combinations and synergy studies, expanding beyond the traditional single-drug approach. This can optimize efficacy and minimize resistance in multifactorial diseases.
8.Improved Pre-Clinical and Clinical Validation
Establishing robust validation pipelines will be essential to transition computational findings into clinical applications. Standardized pre-clinical models and adaptive trial designs can accelerate translation to patient care. [15,16 ,17,18]
CONCLUSION
Drug repurposing represents an efficient and innovative approach to modern drug discovery by identifying new therapeutic uses for existing compounds. It reduces development time, cost, and risk by utilizing drugs with established safety profiles. Over the years, the strategy has evolved from accidental observations to data-driven processes supported by computational modeling, artificial intelligence, and real-world evidence. Successful examples such as sildenafil, metformin, and minoxidil highlight its clinical and economic value.
However, the field still faces challenges, including weak intellectual property protection, limited commercial incentives, and complex regulatory pathways. Addressing these barriers through supportive policies, public–private collaboration, and transparent data sharing is essential to ensure sustainability.
In the future, integrating multi-omics data, AI-driven analytics, and collaborative frameworks will enable more precise and efficient repurposing efforts. With continued innovation and cooperation among academia, industry, and regulators, drug repurposing has the potential to transform healthcare by expanding treatment options and improving patient outcomes globally.
REFERENCES
Smita Kirtikar, Shubham Waghmare, Rasika Giri, Dr. S. Arote, Repositioning Approved Drugs for New Therapeutic Indications: Strategies, Advances, and Challenges, Int. J. of Pharm. Sci., 2026, Vol 4, Issue 5, 6333-6344, https://doi.org/10.5281/zenodo.20354784
10.5281/zenodo.20354784