Pharmacokinetic Modeling Overview

Principles

Definition

Pharmacokinetic modeling is a quantitative analytical approach employing mathematical models to describe, predict, and simulate the time course of drug disposition within biological systems. This research field investigates the processes of drug absorption, distribution, metabolism, and excretion (ADME), integrating physiological and physicochemical parameters to inform drug development, optimize dosing strategies, and enhance understanding of drug behavior in vivo.

Ontological type

Core Methods

Model Parameters

Clinical Applications

Key developments

Key Figures

Foundational and Population PK era

Rowland and Tozer helped establish modern clinical pharmacokinetics in the 1980s with formal compartmental models and PK/PD linkages that guide dosing decisions. Ron Sheiner and colleagues pioneered the population approach, promoting nonlinear mixed-effects estimation and model-based decision making in pharmacokinetics. Holford advanced covariate modeling and PK/PD integration, enabling systematic accounting for interindividual variability in population analyses. Beal and collaborators contributed to NLME methods and the practical NONMEM software that standardized population PK analyses in research and clinical practice.

Mechanistic PBPK Integration era

Mats O. Karlsson [1] is a leading figure in pharmacometrics active at Karolinska Institutet [3] and Universidad de Navarra [4] during the Mechanistic PBPK Integration era. His key contributions include the Xpose—an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM [7], which helped streamline population PK/PD analyses and broaden access to mechanistic modeling in this era. Malcolm Rowland [2] is associated with University of Manchester [5] and National Cheng Kung University [6] during this era. His influential 2006 work Physiologically based pharmacokinetic modelling 2: Predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions [8] advanced mechanistic tissue distribution predictions and cross‑species extrapolation central to mechanistic PBPK.

Regulatory Data-Driven PBPK era

Rowland and Tozer established the physiologically based pharmacokinetic PBPK framework that became the backbone of regulator-ready modeling, linking organ physiology to whole-body drug kinetics and enabling mechanistic dosing concepts. Mager advanced in vitro–in vivo extrapolation and parameterization from in vitro data to refine organ-clearance estimates for regulatory submissions. Jamei and colleagues expanded population PBPK, introducing variability and data-fusion approaches that support risk assessment and scenario analyses in decision-making. Regulators and scholars such as Woodcock and EMA expert panels promoted standardized workflows, validated platforms, and transparent data-driven PBPK pipelines that integrate machine learning and organ-on-a-chip inputs for regulator-ready submissions.