Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic approach that is used to predict absorption, distribution, metabolism, and elimination of drugs by combining population-specific physiological and anatomical characteristics with drug-specific data. PBPK modeling assists in predicting drug response in special populations such as pediatric, geriatric, and pregnant populations, where it is difficult to conduct clinical studies. The most common applications of PBPK approaches are predicting drug-drug interactions, prediction of pediatric drug doses, and prediction of drug response in chronic disease patients (e.g., chronic kidney disease, chronic heart failure, and chronic liver disease).

There is wide interindividual variability in drug exposures between healthy and disease populations. There is a need for dose optimization in chronic disease patients based on disease severity as the pharmacokinetics of the administered drugs change subsequently. There are many ethical and legal constraints in conducting clinical trials for dose optimization in chronic disease patients. Moreover, The Food and Drug Administration (FDA) of America requires and accepts pharmacokinetic modeling and simulation data for registering new drug applications (NDA). Up to 2019, 45% of the NDAs to the FDA contained applications of PBPK. The application of PBPK models can reduce the number of clinical trials needed in dose-finding studies and it has the potential to make dose-finding clinical trials confirmatory rather than exploratory. PBPK models can be used to predict the effects of different pathophysiological changes (e.g., changes in blood flow, protein binding, intrinsic clearance, etc.) in chronic conditions. After evaluation with the reported clinical data, the developed PBPK models can be used to suggest model-informed drug dosing in chronic conditions based on total and unbound drug exposures.

The aim of the Special Issue is to collate original research and review articles describing advances in this field.

Potential topics include but are not limited to the following:

• Mechanistically explaining the differences in the pharmacokinetics of high and low clearance drugs between the healthy and disease (chronic liver disease, chronic heart failure, and chronic kidney diseases) populations
• Optimizing administered drug doses based on unbound and total systemic drug concentrations in chronic disease patients for dose individualization
• Suggesting model-informed drug dosing in special populations such as pediatric, geriatric, and pregnant populations
• Predicting metabolic drug-drug interactions in healthy and disease populations for suggesting model-informed precision dosing