Physiologically Based Pharmacokinetic (PBPK) modeling in the age of AI.
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A physiologically based pharmacokinetic (PBPK) model is a set of quantitative hypotheses regarding descriptions of absorption, distribution, metabolism, and excretion (ADME) that are supported by scientific evidence and biochemical and physiological assumptions. PBPK models can provide a means for estimating internal dose metrics from applied doses of xenobiotics or exposures to environmental chemicals and are typically expressed as systems of ordinary differential equations (ODE). The traditional approach for building PBPK models is labor-intensive, time-consuming, and expensive. Each model must first undergo a tedious quality assurance review before use to ensure biological plausibility and correct implementation. This traditional approach for developing PBPK models cannot keep up with the increasing demand in chemical risk assessment and the needs of the pharmaceutical industry. Recent developments in Machine Learning (ML)/Artificial Intelligence (AI) have opened new avenues for integration in PBPK modeling to enhance efficiency and accuracy. Methods such as ODE training and neural ODEs can take advantage of substantial data sets accumulated for PBPK-related ADME. Preliminary neural ODEs also have the advantage of handling time-course observations at irregular intervals where classic neural network models would fail. Ultimately, these methods can potentially provide a new way to approach the parameterization of the complex models used in PBPK modeling. We can see several first steps in this direction that hold great promise for advancements in personalized medicine, data-driven parameterization, optimization of the clinical trial design, and real-time adaptive modeling. The future of PBPK modeling lies in the seamless integration of advanced modeling techniques and ML/AI technologies. As such, we will discuss some achievements and prospective directions of ML/AI implementations into ADME/PBPK research in the area of chemical risk assessment.