Harnessing the Power of AI in Pharmacokinetics and Pharmacodynamics: A Comprehensive Review

Abstract

Personalized medicine, medication discovery, and development might all benefit greatly from AI’s incorporation into pharmacokinetics and pharmacodynamics. Target identification, therapeutic effectiveness prediction, drug design optimization, obstacles, and future possibilities are all explored in this survey of AI applications in these areas. An overview of pharmacokinetics and pharmacodynamics is presented first, stressing the significance of knowing how drugs are absorbed, distributed, metabolized, and excreted and the correlation between drug concentration and pharmacological effect. The article then looks into the function of AI in target identification, exploring how machine learning algorithms and data integration may be used to discover new drug targets and enhance the design of existing ones. Classification and regression methods are also investigated for their potential use in the prediction of therapeutic efficacy using AI. Patient data, molecular interaction data, and clinical response data are just a few examples of the types of data that may be used to fuel the creation of predictive models that might assist in dosage and efficacy optimization. Metrics and procedures for validating these models are addressed to evaluate their efficacy. Additionally, de novo drug design, virtual screening, and structure-based drug design are all discussed in relation to the use of AI in optimizing drug development. The paper provides examples of how AI has been applied successfully in different settings, demonstrating its potential to hasten the drug discovery process and enhance treatment outcomes. We examine data availability, interpretability, and ethical implications as challenges and limits of AI in pharmacokinetics and pharmacodynamics. To guarantee these technologies’ proper and ethical use, we also discuss the regulatory elements and rules for applying AI in drug research. Possibilities and prospects for the use of AI in pharmacokinetics and pharmacodynamics are discussed as a conclusion to the review. It stresses the significance of regulatory standards and clinical translation, as well as the incorporation of multiomics data, deep learning methods, real-time monitoring, explainable artificial intelligence, collaborative networks, and more.