Development of AI-Based Drug Formulations for Personalized Medicine

DOI: https://doi.org/10.63345/ijrmp.org.v8.i1.1

Sushmita Jain

Independent Researcher

Agra, India

Abstract

Personalized medicine represents a paradigm shift in healthcare by tailoring treatment strategies to individual patient characteristics. In recent years, artificial intelligence (AI) has emerged as a powerful tool to enhance drug formulation development, offering the potential to predict formulation behavior, optimize drug release profiles, and improve clinical outcomes. This manuscript explores the development of AI‐based drug formulations for personalized medicine. It examines current methodologies, reviews literature up to 2019, and presents a comprehensive methodology that integrates machine learning algorithms with high‐throughput screening and pharmacokinetic modeling. A survey of stakeholders—including clinicians, pharmaceutical scientists, and data scientists—was conducted to evaluate the feasibility and acceptance of AI-driven personalized formulations. Statistical analysis, including regression models and variance analysis, supports the hypothesis that AI can significantly reduce development times and improve formulation accuracy. The results indicate that AI models can be successfully trained on existing drug databases to predict optimal formulation parameters for individual patients. Overall, this study provides insights into the integration of AI into pharmaceutical development, emphasizing the need for interdisciplinary collaboration to overcome challenges related to data standardization and model interpretability. Future work should focus on expanding datasets, refining algorithms, and integrating real-time patient data for adaptive formulation adjustments. These developments could pave the way for a new era in personalized healthcare, where drug formulations are not only optimized for efficacy but also minimize adverse effects based on individual genetic and metabolic profiles.

Keywords

AI; drug formulation; personalized medicine; machine learning; pharmacokinetics; high-throughput screening; clinical outcomes.

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