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This report is written by MaltSci based on the latest literature and research findings

What are the strategies for drug repurposing? ​

Abstract ​

Drug repurposing, also known as drug repositioning, has emerged as a pivotal strategy in modern pharmaceutical development, particularly in response to the growing demand for efficient and cost-effective therapeutic solutions. This approach entails identifying new therapeutic uses for existing drugs, thereby circumventing the lengthy and expensive processes typically associated with traditional drug discovery. The urgency for innovative therapeutic options has been accentuated by the high failure rates in clinical trials and the substantial financial investments required for developing new drugs from scratch. By leveraging the safety and efficacy data already established for existing compounds, drug repurposing presents a viable pathway to expedite the availability of treatments for various diseases. The significance of drug repurposing is underscored by its potential to address unmet medical needs, particularly in complex and multifactorial diseases such as cancer, neurodegenerative disorders, and infectious diseases. This report provides a comprehensive overview of the current strategies employed in drug repurposing, detailing their mechanisms and effectiveness. The report highlights various methodologies, including computational approaches, high-throughput screening, and network pharmacology, which enhance the identification and validation of repurposing candidates. Additionally, successful case studies, such as the repurposing of thalidomide for multiple myeloma and antiviral drugs for COVID-19, illustrate practical applications and outcomes. Despite its promise, drug repurposing faces challenges, including regulatory hurdles and the need for robust validation of new indications. The report concludes with an exploration of future perspectives and emerging technologies that may further enhance this field, emphasizing the significance of drug repurposing in contemporary medicine and its potential to transform therapeutic development.

Outline ​

This report will discuss the following questions.

  • 1 Introduction
  • 2 Overview of Drug Repurposing
    • 2.1 Definition and Importance
    • 2.2 Historical Context and Evolution
  • 3 Strategies for Drug Repurposing
    • 3.1 Computational Approaches
    • 3.2 High-Throughput Screening
    • 3.3 Network Pharmacology
  • 4 Case Studies of Successful Drug Repurposing
    • 4.1 Thalidomide for Multiple Myeloma
    • 4.2 Antiviral Drugs for COVID-19
    • 4.3 Other Notable Examples
  • 5 Challenges and Limitations
    • 5.1 Regulatory Hurdles
    • 5.2 Validation of New Indications
    • 5.3 Market and Commercialization Issues
  • 6 Future Perspectives
    • 6.1 Emerging Technologies in Drug Repurposing
    • 6.2 Potential for Personalized Medicine
  • 7 Conclusion

1 Introduction ​

Drug repurposing, also known as drug repositioning, has emerged as a pivotal strategy in modern pharmaceutical development, particularly in response to the growing demand for efficient and cost-effective therapeutic solutions. This approach entails identifying new therapeutic uses for existing drugs, thereby circumventing the lengthy and expensive processes typically associated with traditional drug discovery. The urgency for innovative therapeutic options has been accentuated by the high failure rates in clinical trials and the substantial financial investments required for developing new drugs from scratch. By leveraging the safety and efficacy data already established for existing compounds, drug repurposing presents a viable pathway to expedite the availability of treatments for various diseases [1][2].

The significance of drug repurposing is underscored by its potential to address unmet medical needs, particularly in complex and multifactorial diseases such as cancer, neurodegenerative disorders, and infectious diseases. For instance, the repurposing of thalidomide for multiple myeloma and antiviral drugs for COVID-19 treatment exemplifies how existing therapies can be effectively redirected to combat new health challenges [3]. This strategic approach not only minimizes the time and cost associated with drug development but also opens avenues for novel therapeutic applications that may have been overlooked in the original context of the drug's use [4].

Currently, the landscape of drug repurposing is evolving, with a plethora of methodologies being employed to enhance the identification and validation of repurposing candidates. These strategies encompass computational approaches, high-throughput screening, and network pharmacology, each contributing unique advantages to the repurposing process. Computational methods, including machine learning and artificial intelligence, have revolutionized the ability to analyze vast datasets and predict potential new uses for existing drugs [5]. High-throughput screening facilitates the rapid evaluation of drug candidates across various biological contexts, while network pharmacology allows for the exploration of drug interactions and multi-target effects [6].

Despite its promise, drug repurposing is not without challenges. Regulatory hurdles, the need for robust validation of new indications, and market considerations pose significant barriers to the successful implementation of repurposed therapies [7]. The complexities of navigating these obstacles require a thorough understanding of the pharmacological characteristics of the repurposed drugs, as well as the development of clear guidelines for evaluation and approval [2].

This report aims to provide a comprehensive overview of the current strategies employed in drug repurposing, detailing their mechanisms and effectiveness. It will first define the concept of drug repurposing and explore its historical context and evolution, highlighting the milestones that have shaped its development. Subsequently, the report will delve into the various strategies utilized in drug repurposing, including computational approaches, high-throughput screening, and network pharmacology. Following this, case studies of successful drug repurposing efforts will be examined, illustrating practical applications and outcomes. The discussion will then address the challenges and limitations inherent in the drug repurposing process, culminating in an exploration of future perspectives and emerging technologies that may further enhance this field. By synthesizing these elements, this report aims to illuminate the significance of drug repurposing in contemporary medicine and its potential to transform therapeutic development.

2 Overview of Drug Repurposing ​

2.1 Definition and Importance ​

Drug repurposing, also known as drug repositioning, refers to the process of identifying new therapeutic uses for existing drugs that have already been approved for other indications. This approach has gained prominence due to its potential to significantly reduce the time, cost, and risk associated with traditional drug development. The strategies employed in drug repurposing can be categorized into several key methodologies, each leveraging existing knowledge and infrastructure to expedite the identification of new therapeutic applications.

One primary strategy involves serendipitous observations, where unexpected effects of drugs lead to the discovery of new indications. This often occurs when clinicians or researchers notice beneficial outcomes from a drug that was originally developed for a different disease. Another method is the systematic screening of existing drugs, which involves directed efforts to identify new uses for compounds that have failed in previous trials or were initially developed for different therapeutic areas [2].

Additionally, in silico approaches play a crucial role in drug repurposing. These techniques utilize computational methods to analyze large datasets, including genomic information, to identify potential drug candidates. For instance, transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking have been effectively implemented to reposition drugs for various diseases, including breast cancer [1]. The integration of artificial intelligence (AI) and machine learning (ML) further enhances these efforts by providing systematic identification of drug repurposing leads based on extensive data resources [5].

Network-based strategies also contribute significantly to drug repurposing. These strategies focus on evaluating drug interactions within a molecular environment, allowing for the identification of multi-target hits that can be effective for new indications [6]. The application of such methodologies is supported by the growing availability of biobanks and databases that link genomic data to clinical outcomes, facilitating the discovery of novel therapeutic uses [8].

Moreover, a thorough understanding of the pharmacological characteristics of repurposed drugs is essential. This includes evaluating the drug formulation for new indications, assessing its performance in representative biological assays, and ensuring robust clinical trial methodologies that provide conclusive evidence of efficacy [2]. Addressing regulatory concerns, such as the lack of clear guidelines for evaluation and market exclusivity, is also crucial for the successful application of drug repurposing strategies [2].

In summary, drug repurposing encompasses a variety of strategies, including serendipitous discovery, systematic screening, in silico methodologies, and network-based evaluations. These approaches collectively aim to leverage existing knowledge and infrastructure to identify new therapeutic applications for approved drugs, ultimately facilitating faster and more cost-effective drug development processes.

2.2 Historical Context and Evolution ​

Drug repurposing, the strategy of identifying new therapeutic uses for existing drugs, has evolved significantly over the years, leveraging various methodologies to enhance its effectiveness and efficiency. The historical context of drug repurposing highlights its emergence as a pragmatic alternative to traditional drug development, which is often characterized by high costs and lengthy timelines.

Several strategies have been identified for successful drug repurposing. These include:

  1. Serendipitous Observation: Many drug repurposing opportunities arise from unexpected effects observed during clinical use of a drug for its original indication. This method relies on clinical experiences and anecdotal evidence to discover new therapeutic applications.

  2. Systematic Screening: This approach involves directed efforts to evaluate existing drugs for new indications through systematic methodologies. Techniques such as high-throughput screening (HTS) of drug libraries can identify candidates with potential efficacy in different disease contexts [9].

  3. In Silico Approaches: The integration of bioinformatics and computational techniques has transformed drug repurposing. These methods include:

    • Transcriptomic Signature Matching: This technique matches the gene expression profiles of diseases with those affected by drugs, identifying potential repurposable candidates [1].
    • Network-Based Strategies: These involve analyzing drug interactions and their effects on multiple biological targets, allowing for a more comprehensive understanding of a drug's potential in new therapeutic areas [6].
    • Molecular Docking and Simulations: Computational models can predict how existing drugs might interact with new targets, expediting the identification of candidates [8].

  4. Data Integration: The use of large biobanks that connect genomic data with electronic health records facilitates the identification of repurposing opportunities by correlating drug effects with genetic profiles [8].

  5. Biological Assays: Rigorous evaluation of drug candidates in representative biological assays with translational potential is crucial. This includes preclinical studies that can predict clinical outcomes [2].

  6. Clinical Trial Methodologies: Successful repurposing often hinges on robust clinical trial designs, including biomarker-driven approaches to provide conclusive evidence of a drug's efficacy in its new indication [2].

  7. Leveraging Existing Knowledge: The repurposing strategy benefits from established pharmacological data, safety profiles, and structural insights of existing drugs, which can significantly reduce the time and cost associated with drug development [10].

The historical context of drug repurposing underscores its increasing relevance in contemporary biomedical research, particularly as it relates to addressing unmet medical needs efficiently. The evolution of this strategy reflects a growing recognition of the potential to repurpose existing drugs to combat complex diseases, including various cancers and chronic conditions, by optimizing known compounds rather than developing new entities from scratch [11]. This approach not only expedites the process but also enhances the likelihood of success due to the prior safety and efficacy data available for existing drugs.

3 Strategies for Drug Repurposing ​

3.1 Computational Approaches ​

Drug repurposing, which involves identifying new applications for existing drugs, is increasingly recognized as a cost-effective and efficient strategy in pharmaceutical development. Various computational approaches have been developed to facilitate this process, leveraging the vast amounts of biomedical data available today. The strategies for drug repurposing can be broadly categorized into three main types: disease-centric, target-centric, and drug-centric approaches.

  1. Disease-Centric Approaches: This strategy focuses on understanding the underlying mechanisms of diseases to identify potential therapeutic applications for existing drugs. By analyzing disease pathways and molecular interactions, researchers can pinpoint drugs that may have effects on diseases other than their original indications. For instance, network-based models are employed to explore the relationships between diseases and existing treatments, allowing for the identification of novel drug-disease relationships that could be beneficial for new therapies [12].

  2. Target-Centric Approaches: In this method, the focus is on specific biological targets, such as proteins or genes, that are implicated in various diseases. By examining the interaction of existing drugs with these targets, researchers can discover new therapeutic uses. Structure-based approaches, which evaluate how small chemical compounds bind to macromolecular targets, are particularly relevant here. These methods enable the identification of existing drugs that can interact with new targets, potentially leading to new applications [12].

  3. Drug-Centric Approaches: This strategy involves analyzing the chemical properties and mechanisms of action of existing drugs to find new therapeutic indications. Computational tools are employed to evaluate the profiles of approved drugs and predict their efficacy against different diseases. Machine learning algorithms and artificial intelligence (AI) methods are increasingly used to enhance the predictive capabilities of these analyses, enabling the identification of off-target effects that may reveal new therapeutic potentials [13].

In addition to these primary strategies, several computational methods have emerged to support drug repurposing efforts. These include:

  • Network-Based Approaches: These approaches utilize complex biological networks to explore drug interactions and disease associations. They can be divided into clustering and propagation methods, which help in identifying functional relationships between proteins associated with diseases [12].

  • Artificial Intelligence and Machine Learning: AI and machine learning tools are becoming essential in drug repurposing by allowing for the analysis of large datasets and the identification of patterns that may not be immediately apparent. These methods can process diverse data types, including genomic, proteomic, and clinical data, to facilitate the discovery of new drug applications [5].

  • Knowledge Graphs: The use of knowledge graphs represents a novel approach to drug repurposing, providing a structured representation of the relationships between drugs, targets, and diseases. This method allows for intuitive exploration of biomedical knowledge and enhances the predictive capabilities of drug-disease associations [14].

Overall, the integration of these computational approaches not only accelerates the drug repurposing process but also enhances the ability to identify new therapeutic uses for existing drugs, thereby potentially reducing the time and cost associated with traditional drug development pathways [13][15].

3.2 High-Throughput Screening ​

Drug repurposing, the strategy of identifying new therapeutic uses for existing drugs, employs various methodologies to facilitate the transition from laboratory findings to clinical applications. High-throughput screening (HTS) is one of the prominent strategies utilized in this domain, enabling researchers to rapidly evaluate a large number of compounds for potential new indications.

HTS involves the automated testing of thousands of compounds against specific biological targets or disease models. This approach allows for the identification of candidates that may have unexpected effects or efficacy against conditions other than those for which they were originally developed. The method is particularly beneficial in drug repurposing as it leverages existing safety data on these compounds, thus significantly reducing the time and cost associated with the drug development process.

In addition to HTS, several other strategies for drug repurposing have been identified. These include:

  1. Serendipitous Observations: Some drugs are found to have beneficial effects in conditions that were not initially anticipated. This can occur during clinical use or in post-marketing studies, leading to further investigation of these effects.

  2. Systematic Screening: This approach involves the targeted evaluation of existing drugs for new indications based on biological mechanisms, pharmacological profiles, or through computational methods. It includes the analysis of failed investigational drugs to explore new therapeutic applications.

  3. Network-Based Strategies: This method evaluates drug combinations and interactions within a molecular environment, assessing multi-target hits that can provide insights into the broader applicability of existing drugs in various disease contexts.

  4. Data Integration and Bioinformatics: The utilization of large biobanks and databases that integrate genomic data with clinical outcomes has emerged as a powerful tool in drug repurposing. Techniques such as Mendelian randomization and multi-omic studies are employed to identify repositioning opportunities by correlating genetic data with drug responses.

  5. Artificial Intelligence and Machine Learning: These advanced computational techniques facilitate the identification of potential drug repurposing candidates by analyzing vast datasets, enhancing the efficiency of the screening process, and predicting off-target effects that may be therapeutically relevant.

  6. Clinical Evaluation: Rigorous clinical trials are essential for validating the efficacy of repurposed drugs in new indications. This includes biomarker-driven approaches that provide conclusive evidence of a drug's effectiveness for its new use.

Overall, the integration of these strategies allows for a more efficient and targeted approach to drug repurposing, addressing the challenges associated with traditional drug development pathways and enabling quicker access to new therapeutic options for patients [1][2][8].

3.3 Network Pharmacology ​

Drug repurposing, a strategy aimed at identifying new therapeutic uses for existing drugs, has gained significant traction due to its potential to reduce the cost and time associated with traditional drug discovery. Various methodologies have emerged within this field, particularly emphasizing network pharmacology, which utilizes complex biological networks to enhance the identification of repurposable drugs.

One prominent strategy is the network-based approach, which interprets human diseases as local disturbances within the human interactome network. This method leverages the interconnectedness of disease genes, allowing researchers to assess the proximity of drug targets to these genes. For instance, Fiscon et al. (2022) described how drugs exert their effects by acting on targets that are closely associated with disease-related genes, thus facilitating the identification of off-label drugs that may be effective against specific diseases[16].

In network pharmacology, the evaluation of drug interactions within a molecular environment is crucial. Kowshik et al. (2024) emphasized the importance of network-based strategies, which focus on the evaluation of drug combinations and multi-target hits, enabling a more comprehensive analysis of drug interactions[6]. This approach is particularly beneficial for complex diseases, where single-target therapies may be insufficient.

Moreover, computational methods, such as machine learning and deep learning, are increasingly integrated into drug repurposing strategies. For example, Lee et al. (2022) highlighted that network-based methodologies are often combined with machine learning techniques to analyze vast amounts of biomedical data, thereby enhancing the identification of potential drug candidates[17]. These data-driven approaches facilitate the integration of diverse biological and clinical data, improving the accuracy of predictions regarding drug-disease associations.

Another aspect of network pharmacology is the utilization of genomic data. Wang et al. (2024) reviewed methodologies that leverage genomic information linked to electronic health records to identify repurposing opportunities, employing strategies such as Mendelian randomization and multi-omic analyses[8]. This genomic integration allows for a more targeted approach in discovering new applications for existing drugs, aligning with the poly-pharmacology paradigm where drugs can interact with multiple targets.

Furthermore, the implementation of graph neural networks in drug repurposing exemplifies the innovative methodologies being explored. Park and Cho (2025) introduced a network-based computational framework, DRAW+, which utilizes attention mechanisms and noise filtering to enhance drug repositioning predictions, demonstrating superior performance compared to traditional methods[18].

In summary, drug repurposing strategies, particularly those grounded in network pharmacology, leverage the intricate relationships within biological networks, genomic data, and advanced computational techniques to identify new therapeutic uses for existing drugs. This multifaceted approach not only accelerates the drug discovery process but also addresses the challenges posed by complex diseases.

4 Case Studies of Successful Drug Repurposing ​

4.1 Thalidomide for Multiple Myeloma ​

Drug repurposing, also known as drug repositioning, is a strategy that involves exploring existing medications for new therapeutic uses beyond their original indications. This approach can significantly reduce the time and financial costs associated with drug development, making it particularly advantageous in the treatment of diseases like multiple myeloma (MM), where new drug development is often lengthy and expensive.

Thalidomide serves as a prominent case study in the successful repurposing of a drug for the treatment of multiple myeloma. Initially withdrawn from the market due to its severe teratogenic effects, thalidomide was later recognized for its anti-angiogenic and immunomodulatory properties, leading to its reintroduction in clinical settings for various conditions, including MM. Thalidomide has been used effectively since 1997 in treating both newly diagnosed and relapsed/refractory MM patients, demonstrating increased response rates when used in combination with dexamethasone and other agents [19][20].

The mechanisms by which thalidomide exerts its anti-myeloma effects are multifaceted. It has been shown to inhibit tumor necrosis factor-alpha (TNF-α), which plays a role in the proliferation of myeloma cells. Additionally, thalidomide affects the tumor microenvironment, potentially by inhibiting angiogenesis, which is crucial for tumor growth and survival [21].

Clinical trials have highlighted thalidomide's efficacy, with combination therapies yielding response rates greater than 80% in patients with newly diagnosed and relapsed MM [22]. Furthermore, ongoing research continues to refine thalidomide's applications, including the development of thalidomide-based small molecule degraders aimed at enhancing its therapeutic efficacy and targeting specific molecular pathways associated with MM [19].

In summary, the repurposing of thalidomide for multiple myeloma exemplifies a successful strategy in drug repurposing, demonstrating how existing medications can be leveraged to provide effective treatments for challenging diseases. The insights gained from thalidomide's clinical use and its mechanisms of action have not only improved patient outcomes but have also paved the way for further research into similar therapeutic strategies in the management of MM and potentially other malignancies [23].

4.2 Antiviral Drugs for COVID-19 ​

Drug repurposing has emerged as a crucial strategy in addressing the urgent need for effective treatments during the COVID-19 pandemic. This approach leverages existing drugs, which have established safety profiles, to find new therapeutic applications for diseases like COVID-19. Several strategies and case studies illustrate the effectiveness of drug repurposing in the context of antiviral treatments for COVID-19.

One prominent strategy is the combination of existing drugs that target different pathways associated with the disease. For instance, a network-based approach has been proposed that combines anti-inflammatory agents, such as melatonin, with antiviral agents like toremifene. This strategy aims to mitigate the aberrant inflammatory responses while simultaneously reducing viral replication, thus addressing both the viral infection and the associated inflammatory response seen in COVID-19 patients (Cheng et al., 2020) [24].

Another significant strategy involves the repurposing of antiviral drugs that were initially developed for other viral infections. For example, remdesivir, originally designed for the treatment of Ebola virus, has been repurposed and authorized for emergency use against COVID-19. However, the clinical benefits of remdesivir have been questioned, as no significant improvements in mortality or clinical outcomes have been consistently reported (Gatti & De Ponti, 2021) [25]. This highlights the importance of ongoing clinical assessments to evaluate the efficacy of repurposed drugs.

Computational methods also play a vital role in drug repurposing. These methods utilize algorithms to analyze existing drug databases and predict potential new uses for approved medications. For instance, studies have shown that computational approaches can identify drugs that interact with key viral targets, such as the RNA-dependent RNA polymerase of SARS-CoV-2, which is crucial for viral replication (Mohamed et al., 2021) [26]. This method allows for a rapid assessment of drug candidates that may not have been previously considered for COVID-19 treatment.

Moreover, the integration of artificial intelligence (AI) and machine learning into drug repurposing efforts has been suggested to enhance the identification of potential therapeutic candidates. By optimizing data analysis and predictive modeling, researchers can improve the success rate of finding effective treatments (Lee & Chen, 2021) [27].

The importance of addressing the pharmacokinetics and pharmacodynamics of repurposed drugs cannot be overstated. For example, drugs like hydroxychloroquine and tocilizumab have shown varying degrees of efficacy against SARS-CoV-2 in clinical settings, underscoring the necessity of evaluating the safety and effectiveness of these agents (Mule et al., 2022) [28].

In conclusion, drug repurposing for COVID-19 involves a multifaceted approach that includes the combination of existing therapies, computational modeling, and AI-driven strategies. The case studies of antiviral drugs highlight both the potential and challenges of this approach, emphasizing the need for continued research and clinical trials to validate the efficacy of repurposed treatments.

4.3 Other Notable Examples ​

Drug repurposing, also known as drug repositioning, is a strategic approach aimed at identifying new therapeutic uses for existing drugs that have already been approved for other indications. This method has gained traction due to its potential to reduce the time, cost, and risk associated with traditional drug development. Several strategies and methodologies have emerged to facilitate drug repurposing, along with notable case studies that exemplify its success.

One prominent strategy for drug repurposing is the systematic observation of unexpected effects of drugs, which may lead to new therapeutic applications. This serendipitous discovery has been foundational in identifying potential new uses for existing medications. Additionally, there are structured approaches that involve screening previously failed investigational drugs to identify new indications, as well as systematic methodologies that integrate biological and clinical data to identify repurposing opportunities (Mittal & Mittal, 2021) [2].

The utilization of genomic data has also transformed drug repurposing methodologies. Over the past decade, advancements in biobanks that link genomic information with electronic health records have enabled researchers to explore drug-repositioning opportunities more effectively. Common strategies include Mendelian randomization and multi-omic-based studies, which leverage large datasets to identify potential drug candidates for new indications (Wang et al., 2024) [8].

In terms of notable case studies, various drugs have successfully transitioned from their original indications to new therapeutic applications. For instance, the repurposing of certain non-cancer drugs for cancer treatment has emerged as a powerful alternative strategy in oncology. The review by Pantziarka et al. (2021) highlights how existing medicines developed for other disease areas can be effectively utilized to develop novel cancer treatments (Pantziarka et al., 2021) [29]. Another example is the successful use of existing drugs to address treatment gaps in complex diseases, including neurodegenerative disorders and infectious diseases (Hussain et al., 2025) [3].

Moreover, computational approaches, such as artificial intelligence and machine learning, have been increasingly employed to identify repurposing leads from existing drugs. These methods utilize big data resources to systematically analyze drug interactions and predict new therapeutic applications, significantly accelerating the drug repurposing process (Tanoli et al., 2021) [5].

The challenges associated with drug repurposing include regulatory hurdles, intellectual property concerns, and the need for robust clinical trial methodologies to validate the efficacy of repurposed drugs. Overcoming these challenges is essential for the successful adoption of repurposed therapies in clinical practice (Kowshik et al., 2024) [6].

In summary, drug repurposing encompasses a variety of strategies, including serendipitous discovery, systematic screening, and genomic data integration. Notable case studies illustrate the successful application of this approach across various therapeutic areas, supported by advancements in computational methods. As the field continues to evolve, addressing the inherent challenges will be crucial for maximizing the potential of repurposed drugs in clinical settings.

5 Challenges and Limitations ​

5.1 Regulatory Hurdles ​

Drug repurposing, the strategy of identifying new therapeutic uses for existing drugs, presents a range of challenges and limitations, particularly in the context of regulatory hurdles. Despite its potential advantages, such as reduced development costs and expedited timelines, several regulatory barriers impede the effective implementation of drug repurposing initiatives.

One significant challenge is the lack of sufficient private incentives for investment in off-patent medicines. This issue is exacerbated by regulatory barriers that arise when prescribing drugs by their active ingredient, which can limit the ability to market repurposed drugs effectively. Moreover, the predominance of academic and non-profit sponsors, who often lack the regulatory objectives necessary for successful drug authorization, further complicates the situation (Garcia-Diaz et al. 2025) [30].

Additionally, the regulatory framework itself can pose obstacles. For instance, there is often insufficient regulatory expertise and awareness of the unique challenges associated with drug repurposing. Common barriers include inadequate downstream drug development, limited financial incentives, and poor collaboration among stakeholders (Spin et al. 2024) [31].

Health Technology Assessment (HTA) methods have been suggested as a means to address some of these challenges. By incorporating HTA methods early in the drug repurposing process, stakeholders can evaluate the value proposition of repurposed drugs, which can inform further research and facilitate the regulatory process (Abu-Zahra et al. 2024) [32].

Furthermore, legal and intellectual property issues present significant hurdles in drug repurposing. For instance, many repurposed drugs may not offer adequate patent protection, which diminishes the financial incentives for pharmaceutical companies to pursue these opportunities. This is compounded by the complex nature of negotiating intellectual property agreements in multi-partner collaborations (Pushpakom et al. 2019) [33].

In summary, while drug repurposing holds great promise for addressing unmet medical needs, particularly in the context of complex diseases, the path to successful implementation is fraught with regulatory and legal challenges. Overcoming these barriers requires a concerted effort from various stakeholders, including regulators, researchers, and policymakers, to create a more conducive environment for drug repurposing initiatives.

5.2 Validation of New Indications ​

Drug repurposing is a strategy that involves identifying new therapeutic uses for existing drugs, and it has gained traction as a promising approach to accelerate the drug development process. Various strategies have been employed in drug repurposing, each with its own set of challenges and limitations, particularly concerning the validation of new indications.

The strategies for drug repurposing can be categorized into several key methodologies. One prominent approach is serendipitous observation, where unexpected therapeutic effects of a drug are discovered during its use for an original indication. Additionally, systematic methodologies have been developed to identify, screen, and develop existing drug molecules for new off-label indications. These systematic approaches often involve leveraging large datasets, molecular docking, and network analysis to identify potential candidates for repurposing [2][34].

However, despite the potential benefits, drug repurposing faces significant challenges. One major limitation is the high attrition rate associated with repurposing attempts, which can stem from insufficient understanding of the drug's pharmacological characteristics, formulation issues, and the complexities involved in evaluating efficacy in biological assays [2]. Regulatory concerns also pose significant hurdles, particularly the lack of clear guidelines for the evaluation of repurposed drugs and the market exclusivity challenges that can discourage investment [2][33].

The validation of new indications for repurposed drugs is a critical phase in the drug repurposing process. Successful validation typically requires robust clinical trial methodologies, including the use of biomarker-driven approaches to provide conclusive evidence of efficacy in the new indication [2]. Furthermore, it is essential to conduct evaluations in representative biological assays that have translational potential, ensuring that findings from preclinical studies can be effectively translated to clinical settings [2].

Despite these challenges, advancements in methodologies, including the integration of real-world clinical data and pharmacoepidemiological approaches, have shown promise in enhancing the validation process. These approaches leverage observational data from large patient populations to establish the efficacy of candidate drugs before they enter formal clinical trials [34]. However, it is crucial to acknowledge that these methods are also subject to limitations and potential biases, necessitating careful design and execution of validation studies [34].

In summary, while drug repurposing offers a strategic avenue for discovering new therapeutic applications for existing drugs, the complexities associated with validation, regulatory frameworks, and the need for comprehensive evaluation methods present significant challenges that must be addressed to realize its full potential in clinical practice.

5.3 Market and Commercialization Issues ​

Drug repurposing, which involves identifying new therapeutic uses for existing drugs, presents a promising avenue for addressing various medical conditions, particularly in oncology and other areas. However, it is accompanied by several challenges and limitations, particularly in terms of market and commercialization issues.

One of the primary challenges in drug repurposing is the financial disincentive for commercial entities to invest in the repurposing of off-patent drugs. As noted, "off-patent medicines do not provide enough private incentives for investment," leading to a situation where the market may lack interest in pursuing marketing authorization for repurposed indications, even when trials demonstrate efficacy (Garcia-Diaz et al. 2025) [30]. This lack of commercial motivation is exacerbated by the regulatory burden associated with gaining approval for new indications, which can be significant, especially for drugs that are already available on the market.

Additionally, the repurposing of generic drugs faces specific hurdles, such as obtaining research funding. The commercial parties involved often find the financial return on investment inadequate to justify the costs associated with clinical trials, which are necessary for demonstrating the safety and efficacy of the repurposed drug in its new indication (van der Pol et al. 2023) [35]. Consequently, the likelihood of achieving successful marketing authorization for repurposed drugs remains low, particularly for those that are off-patent.

Regulatory challenges also play a critical role in the commercialization of repurposed drugs. For instance, the existing regulatory frameworks may not adequately accommodate the unique aspects of drug repurposing, leading to prolonged approval processes and uncertainty regarding the pathway to market (Breckenridge & Jacob 2019) [36]. These barriers can deter both academic and industry stakeholders from pursuing repurposing opportunities.

Furthermore, there is a notable gap in the understanding of the market dynamics and reimbursement strategies related to repurposed drugs. Many repurposed drugs may not achieve a favorable market position due to competition with existing therapies, leading to difficulties in establishing a viable business case (Malla et al. 2024) [37]. This is particularly true for diseases that are less common or for which there are already established treatments, as the economic incentives for drug companies to invest in repurposing may be minimal.

In summary, while drug repurposing holds significant potential for accelerating the development of new therapeutic options, particularly in oncology, the associated market and commercialization challenges—including financial disincentives, regulatory hurdles, and inadequate understanding of market dynamics—pose substantial barriers to its success. Addressing these challenges requires innovative policy solutions, increased public funding, and improved collaboration between stakeholders in the healthcare ecosystem to ensure that repurposed drugs can effectively reach patients in need.

6 Future Perspectives ​

6.1 Emerging Technologies in Drug Repurposing ​

Drug repurposing, also known as drug repositioning, is a strategic approach aimed at identifying new therapeutic uses for existing approved or investigational drugs. This methodology has gained traction due to its potential to expedite the drug development process, reduce costs, and mitigate the risks associated with traditional drug discovery. Various strategies and emerging technologies have been developed to enhance the effectiveness of drug repurposing efforts.

One prominent strategy involves the utilization of computational approaches. These approaches leverage machine learning and algorithms to model disease and drug interactions, facilitating the identification of potential repurposing candidates. For instance, Wang et al. (2024) highlight the expansion of large biobanks that integrate genomic data with electronic health records, enabling the identification of drug-repositioning opportunities through methodologies such as Mendelian randomization and network-based studies[8]. The integration of these diverse data sources allows for a more targeted identification of repurposable drugs, streamlining the discovery process.

In addition to computational methods, experimental approaches play a crucial role in drug repurposing. These methods involve traditional wet-lab experiments to validate computational predictions and explore drug interactions within a biological context. Kowshik et al. (2024) emphasize the importance of network-based strategies that evaluate drug combinations in a molecular environment, allowing for a more comprehensive analysis of drug interactions[6].

Moreover, the advent of next-generation technologies, particularly in the fields of genomics and network biology, has significantly transformed drug repurposing methodologies. Nabirotchkin et al. (2020) discuss how combining human genetics data with network biology approaches can enhance the identification of repurposable drugs, thereby increasing the speed and efficiency of drug discovery[38]. This next-generation approach allows for the identification of single or combination therapies that could address both common and rare diseases.

The application of informatics in drug repurposing has also been noteworthy. Zhou et al. (2023) describe how advancements in informatics techniques related to genomics, systems biology, and biophysics have accelerated the repurposing of drugs, particularly in oncology[1]. These informatics-driven methodologies facilitate the identification of potential therapeutic uses for existing drugs by analyzing large datasets and integrating various biological and clinical information.

Despite the promising landscape of drug repurposing, several challenges remain. The integration of diverse data types can be technically challenging, and biases or incomplete understanding of drug interactions can hinder the identification of novel therapeutic applications. Wang et al. (2024) note that these technical challenges must be addressed to realize the full potential of drug repurposing[8].

In summary, the strategies for drug repurposing encompass a combination of computational, experimental, and informatics approaches. The integration of genomic data, network biology, and machine learning is paving the way for more efficient and targeted drug discovery processes. However, ongoing efforts to overcome technical challenges and improve the understanding of drug interactions will be essential for the successful implementation of these strategies in clinical practice.

6.2 Potential for Personalized Medicine ​

Drug repurposing, the strategy of identifying new therapeutic uses for existing drugs, has gained significant traction in recent years due to its potential to streamline drug development processes and reduce costs. Various strategies have emerged to facilitate drug repurposing, particularly in the context of personalized medicine.

One prominent approach is the integration of human genetics data and network biology, which enhances the identification of drug candidates for both rare and common diseases. This next-generation drug repurposing utilizes genome-wide association studies (GWAS) to correlate genetic data with drug effects, thus accelerating the drug discovery process while minimizing costs (Nabirotchkin et al., 2020) [38]. The incorporation of machine learning methods further refines this approach, enabling more precise matching of drugs to their potential new indications based on genetic and molecular profiles.

Computational techniques are also pivotal in drug repurposing strategies. The use of in silico methods allows researchers to evaluate the interactions between drugs and their targets in a molecular environment, facilitating the identification of multi-target drugs that may be effective against various diseases (Kowshik et al., 2024) [6]. This network-based strategy emphasizes the analysis of drug interactions, providing a comprehensive understanding of how existing medications can be utilized for new therapeutic purposes.

Additionally, advancements in medicinal chemistry and experimental strategies have been developed to enhance the re-engineering of drugs for new indications. These innovations aim to tailor existing medications to better fit their new therapeutic contexts, potentially increasing their efficacy and safety profiles (Abdelsayed et al., 2022) [39].

The challenges associated with drug repurposing, including regulatory hurdles and the need for clinical validation, can be mitigated by leveraging existing safety data and pharmacokinetic profiles of approved drugs. This advantage allows for a more expedited pathway to clinical application compared to traditional drug development, which is often fraught with high costs and long timelines (Nossier et al., 2025) [40].

Moreover, the field of oncology has particularly benefited from drug repurposing strategies, where existing drugs are explored for their efficacy against various cancers. The urgency of addressing unmet medical needs in cancer treatment has spurred interest in repurposing efforts, leading to the identification of new uses for previously approved therapies (Pantziarka et al., 2021) [29].

In summary, the strategies for drug repurposing are diverse and increasingly sophisticated, encompassing computational approaches, genetic insights, and innovative medicinal chemistry techniques. These methodologies not only promise to enhance the efficiency of drug discovery but also hold significant potential for the advancement of personalized medicine, allowing for tailored therapeutic strategies that align with individual patient profiles.

7 Conclusion ​

In summary, drug repurposing represents a transformative strategy in modern medicine, effectively addressing the urgent need for innovative therapies while mitigating the high costs and lengthy timelines typically associated with traditional drug development. The comprehensive exploration of methodologies, including serendipitous discovery, systematic screening, computational approaches, and network pharmacology, underscores the versatility and potential of repurposing existing drugs. Notable case studies, such as the repurposing of thalidomide for multiple myeloma and antiviral drugs for COVID-19, exemplify the successful application of this strategy in clinical settings. However, challenges remain, including regulatory hurdles, market dynamics, and the need for robust validation of new indications. Future research directions should focus on enhancing computational techniques, integrating genomic data, and fostering collaborations between academia and industry to streamline the drug repurposing process. By overcoming existing barriers, drug repurposing can continue to evolve as a vital approach in the quest for effective treatments for complex diseases, ultimately improving patient outcomes and transforming therapeutic development.

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