Artificial Intelligence and Machine Learning in Pharmacokinetics and Pharmacodynamic Studies

Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools in pharmacokinetics (PK) and pharmacodynamics (PD) studies, revolutionizing drug development and personalized medicine [1]. AI and ML algorithms analyse massive datasets, integrate complicated variables, and uncover patterns and correlations that traditional approaches lack [2]. In PK studies, AI and ML models can predict drug absorption, distribution, metabolism, and excretion parameters, enabling more accurate dosage recommendations and individualized treatment plans [3]. Moreover, in PD studies, these techniques can elucidate intricate relationship between drug concentration and pharmacological effect, aiding in dose optimization, response prediction, and the identification of potential biomarkers [4, 5]. AI and ML have the potential to accelerate the drug discovery process, optimize therapeutic regimen, and contribute to precision medicine by providing valuable insight into the complex interaction between drugs, diverse patient pool and diseases [6–8]. There are several software tools and programming libraries available that can be used for applying AI and ML techniques to PK and PD studies of nanoparticles. Some commonly used AI/ML tools are mentioned in Table 6.1.