THE COVID-19 CATALYST: A CRITICAL REVIEW OF ACCELERATED DRUG REPURPOSING STRATEGIES, LESSONS LEARNED, AND FUTURE DIRECTIONS
DOI:
https://doi.org/10.22159/ijap.2026v18i3.57254Keywords:
Algorithmic prediction, Drug repurposing, COVID-19, Omics-guided research, In silico approach, Molecular docking, Accelerated clinical developmentAbstract
Objective: To examine the significant effects of the COVID-19 pandemic on medication repurposing as a swift alternative to conventional drug development, highlighting algorithmic, omics-based, and clinical translation methodologies.
Methods: The COVID-19 pandemic is discussed starting with core concepts, encompassing definitions, classification systems, regulatory perspectives, and notable historical developments. For the reverse matching of drugs to illness profiles, we looked at both advanced computational tools and omics-based methods. The paper discussed flexible clinical trial approaches and how the rules were altered during the epidemic.
Results: Drug-candidate identification was shortened from years to months thanks to AI-assisted virtual screening, which enabled the rapid examination of thousands of approved compounds. About forty promising repurposed medications started clinical trials during the first year of the pandemic. Reliable efficacy results were rapidly produced by adaptive, data-driven trials, such as RECOVERY and ACTT. Successful medications like remdesivir, dexamethasone, and baricitinib showed definite benefits in recovery and mortality despite failures like hydroxychloroquine and lopinavir/ritonavir.Toxicity prediction and target matching were further enhanced by integrated chemical and biological databases, enabling quicker and more precise go/no-go determinations.
Conclusion: AI techniques, omics data, and adaptive clinisupervision, significantly reducing drug-development timescales from years to months without compromising regulatory oversight, as demonstrated by the COVID-19 pandemic. In addition to addressing obstacles such as off-target effects, regulatory limitations, intellectual property concerns, and limited model generalizability, the study emphasizes that future advancements will depend on the increased use of real-world evidence, precision-medicine-based repurposing, and open research collaboration.
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