Drug Repurposing in Psoriasis: Unlocking Therapeutic Insights Through Computational Methods

Psoriasis is an autoimmune inflammatory skin disease of chronic nature that heavily compromises the quality of life in patients, whose pathogenesis is based on the multifactorial interplay of genetic and environmental elements that are responsible for the varied clinical presentations and comorbidities. The conventional translation of research results into newly approved medicines continues to be a time-consuming and expensive process, which has seen Drug Repurposing as a revolutionary approach in contemporary drug discovery. By using already tested drugs on humans, Drug Repurposing saves time and investment needed to discover new therapeutic uses. Advances in computational tools like molecular docking, network pharmacology, and machine learning have further expedited this process, enabling the integration of biomedical information from chemical structures to clinical outcomes. Network strategies probe for biological interactions to uncover new therapeutic opportunity, whereas artificial intelligence and machine learning utilize sophisticated algorithms to uncover drug–disease relationships and predict efficacy across various datasets. Additionally, hybrid approaches that integrate omics information with conventional computational methods offer a richer paradigm for candidate prioritization and validation. In the case of psoriasis, where chronic unmet therapeutic needs persist, drug repurposing offers a promising alternative to traditional discovery pipelines, with the benefits of known safety profiles, accelerated clinical translation, and regulatory tractability.