Special issue: Pharmacology in The Netherlands Mechanism-based pharmacokinetic- pharmacodynamic (PK-PD) modeling in translational drug research Meindert Danhof 1 , Elizabeth C.M. de Lange 1 , Oscar E. Della Pasqua 1 , Bart A. Ploeger 2 and Rob A. Voskuyl 1 1 Leiden University, Leiden-Amsterdam Center for Drug Research, Division of Pharmacology, Einsteinweg 55, P.O. Box 9503, 2300 RA Leiden, The Netherlands 2 LAP&P Consultants BV, Archimedesweg 31, 2333 CM Leiden, The Netherlands The use of pharmacokinetic-pharmacodynamic (PK-PD) modeling in translational drug research is a promising approach that provides better understanding of drug efficacy and safety. It is applied to predict efficacy and safety in humans using in vitro bioassay and/or in vivo animal data. Current research in PK-PD modeling focuses on the development of mechanism-based models with improved extrapolation and prediction properties. A key element in mechanism-based PK-PD modeling is the explicit distinction between parameters for describing (i) drug-specific properties and (ii) biological system- specific properties. Mechanism-based PK-PD models contain specific expressions for the characterization of processes on the causal path between drug exposure and drug response. The different terms represent: target-site distribution, target binding and activation and transduc- tion. Ultimately, mechanism-based PK-PD models will also characterize the interaction of the drug effect with disease processes and disease progression. In this review, the principles of mechanism-based PK-PD modeling are described and illustrated by recent applications. Introduction – PK-PD modeling in translational drug research Modern drug discovery and development is associated with high attrition rates exceeding 90%, which are mainly caused by lack of efficacy and/or unexpected safety con- cerns [1]. This raises the question of how to improve the prediction of drug efficacy and safety in humans on the basis of information from in vitro bioassays and/or in vivo animal studies. In recent years, pharmacokinetic-pharmacodynamic (PK-PD) modeling has become a key success factor in modern drug discovery and development. Currently, it provides the basis for optimization of the dosing regimen and, if necessary, the controlled delivery of new drugs in phase-2 clinical trials. In addition, it is increasingly used for optimization of the design of phase-3 clinical trials by clinical trial simulation. Most recently, PK-PD modeling has also been applied in translational drug research, particularly in interfacing drug discovery and early drug development. Within this context, PK-PD modeling lends itself to (i) drug candidate selection, (ii) lead optimization and (iii) the optimization of early proof-of-concept trials using information from pre-clinical studies [2]. Applications of PK-PD modeling in translational drug research commonly rely on the prediction, in a strictly quantitative manner, of the PK-PD properties of drugs in humans using prior information from pre-clinical in vitro and in vivo studies. For this purpose, PK-PD modeling has developed from an empirical and descriptive approach into a mechanistic science recognizing the (patho)-physiological mechanisms underlying PK-PD relationships. Furthermore, it is well established that mechanism-based PK-PD models have much improved extrapolation and prediction proper- ties compared to earlier, empirical PK-PD models [2,3]. Translational pharmacology aims to predict features of drug exposure and drug response Mechanism-based PK-PD modeling aims at the prediction of exposure–response relationships in humans, including their inherent intra- and inter-individual variability. Exposure is expressed as the time course of the drug concentration in plasma, whereas response is expressed as the time course of the drug-effect intensity. Mechanism-based PK-PD models contain specific expressions to quantitatively characterize processes on the causal path between plasma concentration and effect, such as (i) drug target-site distribution, (ii) drug target binding and activation and (iii) transduction mechanisms (including the homeostatic feedback mechanisms that might modulate the response) [3]. Ultimately, mechan- ism-based PK-PD models can also characterize the inter- action of drug effect with disease processes [4]. A key element of mechanism-based PK-PD modeling is the explicit distinction between drug-specific and biological system-specific parameters. Drug-specific parameters describe the interaction between the drug and the bio- logical system in terms of target affinity and target acti- vation, whereas system-specific parameters describe the functioning of the biological system. The distinction be- tween drug-specific and biological system-specific parameters is crucial for predicting drug effects in humans Review Corresponding author: Danhof, M. (m.danhof@lacdr.leidenuniv.nl). 186 0165-6147/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.tips.2008.01.007 Available online 18 March 2008