Integrating Genomic Data with AI Algorithms to Optimize Personalized Drug Therapy: A Pilot Study

Abstract

Personalized medicine has become more prominent in the course of the last few years to improve treatment methods by taking into account patients’ genetic makeup. Combining the genomic information into powerful new AI platforms in drug therapies opens up the way of reducing drug toxicity while enhancing the prospects for drug efficacy. This pilot study aims to determine the possibilities of using AI to analyze genomics data to help improve the approachability and effectiveness of drug therapies, which has been a major challenge given the lacunae in precision in the treatment strategies used. This pilot study is intended to enroll 50 patients with diverse chronic diseases. Targeted gene-specific sequencing was performed to obtain polymorphic loci on drug metabolism and treatment efficacy. AI tools such as machine learning models are used to help find patterns and relationships between genomic data and treatment results and risks. These were then compared to clinical outcomes in order to determine the viability of the AI-integrated method for recommending drug regimens. This study shows that the incorporation of genomic data in conjunction with AI greatly improves the accuracy of individualized pharmacotherapy. The AI-generated suggestions matched well with the enhanced patient experience to show the potential of this concept in the real world. It employs a broader clinically ascertained population and is warranted to replicate these findings, supporting the benefits of using genomic-informed AI applications for drug therapy to drive further development of personalized medicine.