Accelerating Drug Discovery Through Repurposing

The pharmaceutical industry grapples with the persistent challenge of high attrition rates and escalating costs inherent in drug development. The journey from bench to bedside often spans over a decade, consuming billions of dollars in research and development (R&D) expenditure, with a significant probability of failure at each phase. This necessitates exploring alternative strategies to expedite drug discovery and optimize resource allocation. Drug repurposing, identifying new therapeutic applications for existing drugs, emerges as a compelling solution. This approach capitalizes on prior investments in R&D, mitigates risk by leveraging established safety and pharmacokinetic profiles, and accelerates the delivery of treatments to patients.

In Silico Drug Repurposing

Advances in bioinformatics and systems biology have fueled the rise of repurposing of in silico drugs. This strategy harnesses the power of algorithms and machine learning to analyze vast datasets, encompassing drug-target interactions, omics data, and network pharmacology, to unveil hidden therapeutic potential and expedite the development of new treatments.

Predictive Modeling of Drug-Target Interactions

Sophisticated algorithms predict potential drug-target interactions by analyzing molecular structures, binding affinities, and pharmacodynamic properties. This approach leverages quantitative structure-activity relationship models and pharmacophore mapping to identify possible new interactions. The Connectivity Map (CMap) project, initiated by the Broad Institute, exemplifies this approach. CMap utilizes gene expression profiles to connect drugs, genes, and diseases, enabling researchers to identify potential repurposing candidates based on their transcriptional signatures. By comparing the gene expression profiles of different drugs, researchers can identify compounds that exhibit similar effects on cellular processes, suggesting that they may be efficacious against the same diseases.

Integrating Omics Data for Pathway Analysis

Combining genomic, proteomic, and transcriptomic data with network analysis can reveal disease pathways and identify drugs that modulate those pathways. This approach leverages the growing body of knowledge on the molecular underpinnings of disease to identify potential drug targets and predict the effects of drugs on those targets. For example, researchers utilized gene expression data to determine the anti-ulcer drug cimetidine as a possible treatment for colorectal cancer. They discovered that cimetidine inhibits gene expression in tumor growth and metastasis, suggesting that it could effectively slow disease progression. 

Experimental Validation - From Bench to Bedside

While in silico approaches offer valuable insights, experimental validation remains crucial for confirming computational predictions and exploring new drug applications. Key techniques include high-throughput screening (HTS), cell-based assays, and animal models. These methods provide direct evidence of drug activity and safety, allowing researchers to assess the potential of repurposed drugs in a controlled environment.

High-Throughput Screening (HTS)

HTS enables the automated screening of large libraries of existing drugs against specific disease models to identify compounds with therapeutic potential. This approach allows researchers to rapidly test thousands of drugs against a particular disease target, identifying those that exhibit promising activity. The National Institutes of Health (NIH) Clinical Collection, a library of FDA-approved drugs, is widely used for HTS in drug repurposing initiatives. This collection provides a readily available source of drugs with known safety profiles, allowing researchers to focus on identifying their therapeutic potential for new indications.

Cell-Based Assays

In vitro, drug efficacy, and toxicity testing in cell cultures provide valuable preclinical data. This approach allows researchers to assess the effects of drugs on specific cell types, providing insights into their mechanisms of action and potential for adverse events. Using cell lines representing different tissues and organs, researchers can better understand how drugs will behave in the human body.

Animal Models

In vivo studies in animal models assess drug activity, pharmacokinetics, and safety in a living organism. This approach provides a more comprehensive assessment of drug efficacy and safety, considering the complex interactions between different organ systems. Animal models also allow researchers to study drugs’ absorption, distribution, metabolism, and excretion (ADME), providing insights into their bioavailability and potential for drug-drug interactions.

Observational Studies - Leveraging Real-World Data

Real-world data and clinical observations can reveal existing drugs’ unexpected benefits or side effects, prompting further investigation for repurposing. This approach leverages the vast amount of data generated in clinical practice, providing valuable insights into drugs’ real-world effectiveness and safety.

Pharmacovigilance

Systematic monitoring of drug safety data through pharmacovigilance programs can identify adverse events or unexpected therapeutic benefits. This involves collecting and analyzing data on adverse drug reactions, providing valuable insights into the safety profile of drugs and potential off-target effects. The discovery of sildenafil (Viagra) for erectile dysfunction, for example, stemmed from its initial development as a treatment for angina. The drug's unexpected side effects during clinical trials led to its repurposing. This highlights the importance of pharmacovigilance in identifying potential new uses for existing drugs.

Electronic Health Records (EHRs)

Analysis of large EHR datasets can uncover patterns of drug use and identify potential new indications. This approach leverages the growing availability of electronic health records to identify trends in drug prescribing and patient outcomes. By applying data mining techniques and statistical analysis to these data, researchers can identify potential new uses for existing drugs and generate hypotheses for further investigation. For example, researchers at Harvard Medical School explored the association between the use of anti-inflammatory medications and the risk of developing Parkinson's disease. They analyzed data from about 140,000 patients and found that regular use of non-steroidal anti-inflammatory drugs, particularly ibuprofen, was associated with a reduced risk of Parkinson's disease. This finding suggests a potential neuroprotective effect of ibuprofen and supports further investigation into its possible repurposing for Parkinson's disease prevention. This example highlights the potential of EHR data mining to uncover hidden therapeutic benefits of existing drugs and drive drug repurposing efforts.

Success Stories in Drug Repurposing

The history of medicine is filled with examples of successful drug repurposing. Thalidomide, aspirin, hydroxychloroquine, and raloxifene are just a few examples that highlight the diverse range of therapeutic applications that can be discovered through drug repurposing, ranging from cancer to cardiovascular disease to autoimmune disorders.

Thalidomide: Initially marketed as a sedative in the late 1950s, thalidomide was withdrawn from the market due to its severe teratogenic effects. However, in the 1960s, it was discovered that thalidomide had potent anti-inflammatory and immunomodulatory properties. This led to its repurposing for leprosy treatment. Further research revealed its efficacy in multiple myeloma, a cancer of plasma cells. Today, thalidomide is a valuable treatment option for these conditions, demonstrating the potential for even drugs with a troubled past to be repurposed for new therapeutic uses.

Aspirin: One of the world’s oldest and most widely used drugs, aspirin was originally derived from willow bark and used for its analgesic and antipyretic properties. In the 20th century, its anti-inflammatory effects were discovered, leading to its use in treating rheumatoid arthritis and other inflammatory conditions. Further research revealed its ability to inhibit platelet aggregation, reducing the risk of blood clots. Today, aspirin is widely prescribed for its cardioprotective effects, preventing heart attacks and strokes in individuals at risk.

Hydroxychloroquine: Originally developed as an antimalarial drug during World War II, hydroxychloroquine has been repurposed to treat autoimmune diseases such as rheumatoid arthritis and lupus. Its immunomodulatory effects help control the overactive immune response that drives these conditions. While its use in COVID-19 remains controversial, its established efficacy in autoimmune diseases highlights the potential for repurposing antimalarial drugs for other therapeutic applications.

Raloxifene: Developed to prevent and treat osteoporosis in postmenopausal women, raloxifene was later found to have additional benefits in reducing the risk of breast cancer. This discovery stemmed from its selective estrogen receptor modulator (SERM) properties, which enable it to act as an estrogen agonist in some tissues and an antagonist in others. This example highlights the potential for drugs developed for one indication to have unexpected benefits in other areas, underscoring the importance of exploring the full therapeutic potential of existing drugs.

How DrugBank Can Help

At DrugBank, we are proud to play a critical role in advancing drug repurposing. As a comprehensive online database providing detailed information on drugs and drug targets, DrugBank empowers researchers with the knowledge and tools they need to unlock the hidden potential of existing medications.

Our database serves as a centralized repository of drug information, offering researchers detailed insights into drug properties, mechanisms of action, and known targets. This wealth of information enables scientists to identify drugs that have the potential to interact with new targets or modulate disease pathways, opening up new avenues for therapeutic exploration.

DrugBank data is not only valuable for manual exploration but also serves as a powerful engine for computational drug repurposing. Our structured data can be seamlessly integrated with other datasets and utilized in computational models to predict drug-target interactions and identify repurposing opportunities. This allows researchers to leverage the power of artificial intelligence, machine learning, and network analysis to accelerate the identification of potential new uses for existing drugs.

We invite researchers, pharmaceutical companies, and academic institutions to join us to unlock the full potential of drug repurposing. Together, we can accelerate the development of new and innovative treatments, transforming the landscape of healthcare and improving the lives of patients around the globe.

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