Drug Repurposing and Bioinformatics: Using Bioinformatics to Speed Up Drug Discovery Beyond Primary Indications

Finding new therapeutic applications for already-approved medications, or “drug repurposing,” has drawn a lot of attention because it is quicker and less expensive than creating new medications from the ground up. Because it can predict a variety of drug properties, including pharmacokinetics, toxicity, off-target interactions, and molecular mechanisms, bioinformatics is essential to this process. Potential new uses for current medications can be found with the aid of bioinformatics tools and databases that integrate genomic, proteomic, and cheminformatic data. Bringing a new drug to market can take more than ten years due to the time-consuming and costly nature of traditional drug discovery. By using well-known drugs, drug repurposing, on the other hand, speeds up development. Bioinformatics makes this possible by predicting drug mechanisms, interactions, and possible side effects through the analysis of large biological datasets.

Bioinformatics Approaches in Drug Repurposing

1. Identifying Off-Target Interactions

Many drugs don’t just interact with their main intended receptor—they also affect other biological targets. Bioinformatics tools like Swiss Target Prediction (Daina et al., 2019) and the Similarity Ensemble Approach (SEA) (Keiser et al., 2007) help identify these off-target interactions by analyzing chemical structures and binding affinities. This insight makes it easier to discover new therapeutic applications for existing medications.

2. Network-Based Drug Repurposing

Protein-protein interaction (PPI) networks are used in network pharmacology to find other pathways that a drug may affect. These interactions are analysed by programs such as Cytoscape (Shannon et al., 2003) and STRING (Szklarczyk et al., 2021), which offer new disease contexts in which a medication may be therapeutically potential.

3. Transcriptomics and Gene Expression Analysis

Drug repurposing involves studying how medications influence gene expression. The Connectivity Map (CMap) (Lamb et al., 2006) helps researchers link drug-induced gene expression patterns to different disease states, revealing potential new uses for existing drugs beyond their original purpose.

4. Virtual Screening and Molecular Docking

Tools like PyRx  helps in virtual screening for drug discovery and molecular docking. It analyses binding affinity and molecular interactions, it combines AutoDock (Trott & Olson, 2010) and AutoDock Vina (J. Eberhardt et al.,2021)to forecast how a drug will bind to its target. This facilitates the evaluation of possible effectiveness in novel disease contexts.

5. Pharmacokinetics and Toxicity Prediction

Candidates for repurposing need to have their safety and bioavailability assessed. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are predicted by tools such as ADMET Predictor (Cheng et al., 2012) and pkCSM (Pires et al., 2015), guaranteeing the safety and efficacy of repurposed medications.

6. Machine Learning and AI in Drug Repurposing

New drug uses can be quickly predicted by artificial intelligence (AI) models that have been trained on chemical, biological, and clinical data. Deep learning is integrated by platforms such as DeepChem (Ramsundar et al., 2019) to uncover hidden connections between medications and illnesses, speeding up the repurposing process.

Bioinformatics can detect off-target interactions, predict efficacy using network pharmacology, and evaluate safety using pharmacokinetic modelling, it drastically cuts down on the time and expense needed for drug repurposing. The field is being further revolutionised by the integration of AI and big data analytics, which presents a promising method for swiftly discovering novel treatments for rare and emerging diseases.

References

• Cheng, F., Li, W., Zhou, Y., et al. (2012). AdmetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. Journal of Chemical Information and Modeling, 52(11), 3099-3105.
• Daina, A., Michielin, O., & Zoete, V. (2019). SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Research, 47(W1), W357-W364.
• J. Eberhardt, D. Santos-Martins, A. F. Tillack, and S. Forli. (2021). AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. Journal of Chemical Information and Modeling.
• Keiser, M. J., Setola, V., Irwin, J. J., et al. (2007). Predicting new molecular targets for known drugs. Nature, 462(7270), 175-181.
• Lamb, J., Crawford, E. D., Peck, D., et al. (2006). The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science, 313(5795), 1929-1935.
• Pires, D. E., Blundell, T. L., & Ascher, D. B. (2015). pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. Journal of Medicinal Chemistry, 58(9), 4066-4072.
• Ramsundar, B., Feinberg, E. N., Walters, W. P., & Pande, V. (2019). Machine learning for molecular and materials science. Annual Review of Materials Research, 49, 71-103.
• Shannon, P., Markiel, A., Ozier, O., et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498-2504.
• Szklarczyk, D., Gable, A. L., Nastou, K. C., et al. (2021). The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Research, 49(D1), D605-D612.

Trott, O., & Olson, A. J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461