The gold‐standard approach for modeling pharmacokinetic mediated drug–drug
interactions is the use of physiologically‐based pharmacokinetic modeling and population …

Combination therapy or concomitant drug administration can be associated with
pharmacokinetic drug–drug interactions, increasing the risk of adverse drug events and …

Both machine learning and physiologically-based pharmacokinetic models are becoming
essential components of the drug development process. Integrating the predictive …

Physiologically-based pharmacokinetics (PBPK) modeling is a robust tool that supports drug
development and the pharmaceutical industry and regulatory authorities. Implementation of …

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are
complex and often unfavorable. To predict clinical PK in early drug discovery, we built …

Although computational predictions of pharmacokinetics (PK) are desirable at the drug
design stage, existing approaches are often limited by prediction accuracy and human …

A crucial feature of physiologically based pharmacokinetic (PBPK) modeling is the ability to
separate compound-dependent properties from population-dependent properties, enabling …

Early prediction of human pharmacokinetics (PK) and drug–drug interactions (DDI) in drug
discovery and development allows for more informed decision making. Physiologically …

Interactions between co-administered medicines can reduce efficacy or lead to adverse
effects. Understanding and managing such interactions is essential in bringing safe and …

Modeling and simulation of drug disposition has emerged as an important tool in drug
development, clinical study design and regulatory review, and the number of physiologically …