EXPLAINABLE MULTILINGUAL AGENT-TO-AGENT SYSTEM FOR DRUG REPOSITIONING IN ONCOLOGY: INTEGRATING LITERATURE, CLINICAL TRIALS, AND EHRS
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Keywords
Drug Repositioning, Clinical Trials, Patient EHR, AI in Healthcare, Multimodal Agents, Gabapentin, Aberrant Crypt Foci.
Abstract
Another trend that is fast gaining popularity as an alternative to traditional drug discovery is drug repositioning, which involves identifying novel therapeutic applications of current therapies. This work proposes an in silico, multi-agent drug repositioning method that incorporates literature mining, clinical trial design analysis, and patient electronic health record (EHR) profiling. A set of three specialized agents, including LiteratureAgent, Trial Protocol Agent, and Patient EHRAgent, is integrated in the system to evaluate the medical compatibility between available drugs and target diseases using Python and real-world datasets. The model is tested under Gabapentin (DB00996) as a potential Aberrant Crypt Foci (MESH:D Appeas/D058739) treatment. Analysis results indicate a match score of 0.78 and an eligibility score of 0.43, and thus an overall recommendation score of 0.61. The explainable, modular nature of the system illustrates the practical viability of artificial intelligence to aid regulatory decision-making and trial selection during drug development.
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