Translating Data into Drugs: Artificial Intelligence in Repurposing Treatments for Complex Neurological Diseases

Abstract

Drug repurposing has become a viable substitute for conventional drug development, providing a quick and affordable way to find novel therapeutic applications for already-approved medications. This process has been further expedited by the incorporation of artificial intelligence (AI), which has made it possible to analyse transcriptome profiles, electronic health records, and huge biological databases. Simvastatin, Zileuton, and Efavirenz are the three case studies presented in this review that illustrate how AI enables medication repurposing for complex conditions like, neuropsychiatric, neurodegenerative disorders and cancer. The original mechanisms of action of the medications are discussed in each case, along with how drug repurposing has aided in tailoring the drug's activity to the specific ailment. The integration of AI into drug repurposing holds immense promise for accelerating the discovery of novel therapies, especially for complex and rare diseases. As computational models grow more sophisticated, they will be capable of uncovering deeper biological patterns, enabling precision medicine tailored to individual patient profiles. Advancements in multi-omics data integration, natural language processing, and generative AI could further refine drug-disease matching and mechanism prediction. However, several challenges remain. The quality and standardization of biomedical data, model interpretability, and validation of AI predictions in preclinical and clinical settings are significant hurdles. Additionally, ethical concerns regarding data privacy and the regulatory framework for AI-driven drug development must be addressed. Overcoming these challenges will be crucial for fully realizing the potential of AI in revolutionizing drug repurposing and clinical outcomes.