Development and Application of Physiologically Based Pharmacokinetic-Modeling Tools to Support Drug Discovery

Abstract

Physiologically based pharmacokinetic (PBPK) modeling integrates physicochemical (PC) and in vitro pharmacokinetic (PK) data using a mechanistic framework of principal ADME (absorption, distribution, metabolism, and excretion) processes into a physiologically based whole-body model. Absorption, distribution, and clearance are modeled by combining compound-specific PC and PK properties with physiological processes. Thereby, isolated in vitro data can be upgraded by means of predicting full concentration-time profiles prior to animal experiments. The integrative process of PBPK modeling leads to a better understanding of the specific ADME processes driving the PK behavior in vivo, and has the power to rationally select experiments for a more focussed PK project support. This article presents a generic disposition model based on tissue-composition-based distribution and directly scaled hepatic clearance. This model can be used in drug discovery to identify the critical PK issues of compound classes and to rationally guide the optimization path of the compounds toward a viable development candidate. Starting with a generic PBPK model, which is empirically based on the most common PK processes, the model will be gradually tailored to the specifics of drug candidates as more and more experimental data become available. This will lead to a growing understanding of the 'drug in the making', allowing a range of predictions to be made for various purposes and conditions. The stage is set for a wide penetration of PK modeling and simulations to form an intrinsic part of a project starting from lead discovery, to lead optimization and candidate selection, to preclinical profiling and clinical trials.

A physiologically based in silico kinetic model predicting plasma cholesterol concentrations in humans

“…Model predictions for TC deviated on average less than 40% from experimentally observed values, which is relatively good compared with the current state of the art for PBK models of exogenous substances (26)(27)(28)45 ). This is all the more remarkable because the model was obtained via a relatively straightforward translational adaptation of our previously developed mouse model.…”

“…The average relative deviations between model predictions and experimental data were 36%, 49%, and 43% for TC, HDL-C, and non-HDL-C, respectively. This is considered successful within the present state-of-the-art of PBK modeling, where quantitative predictions may generally be correct within one order of magnitude (25)(26)(27)(28). These model predictions are generally within the experimental error margin given the small patient groups sizes (generally n < 20).…”

The virtual laboratory approach to pharmacokinetics: design principles and concepts

“…A great deal of in vitro data on physicochemical properties and specific ADME processes is already available at early stages of the drug discovery process. These data related to new drug candidates can be used in physiologically-based pharmacokinetic (PBPK) models to predict, analyze and optimize the pharmacokinetics of the compounds [5,6]. For instance, a combination of only five compound-specific parameters (fraction unbound in plasma, blood plasma ratio, intrinsic clearance, pKa and octanol water partition coefficient) and known species-related physiological parameters are required for the first PK estimates of an i.v.…”

The usage of a three-compartment model to investigate the metabolic differences between hepatic reductase null and wild-type mice

“…All three models are based on the representation of the drug absorption using a series of blocks or compartments. The "Whole Body" models have been of great interest since this approach gives a better idea of where the metabolism takes place if all systems are taken into account (Lüpfert and Reichel, 2005). As such compartments are assigned to the lungs, heart, liver, kidney etc.…”