Meindert Danhof, PharmD, PhD, Leiden University, Leiden Academic Center for Drug Research, Division of Pharmacology, Einsteinweg 55, P.O. Box 9502, 2300 RA Leiden, the Netherlands


A major challenge in drug discovery and development is the prediction, in a strictly quantitative manner, of drug effects in man on the basis information from in vitro bioassays and/or in vivo animal studies. This impels developing tools with much improved properties for extrapolation and prediction, such as mechanism-based PKPD modelling and simulation.

Mechanism-based PKPD models are based on principles from systems pharmacology and contain specific expressions to characterize processes on the causal path between plasma concentration and response. This includes a) the target distribution, b) the target interaction/activation and c) transduction and the homeostatic control mechanisms, which may be operative [1,2]. The utilisation of these models relies on novel biomarkers characterising specific processes on the causal path in a quantitative manner [3]. An essential feature of mechanism-based PKPD models is the strict distinction between “drug-specific” and “biological system-specific” pharmacodynamic parameters to describe in vivo drug effects. The next development in mechanism-based PKPD modeling has been the introduction of the concept of disease systems analysis, to characterize drug effects on disease processes and disease progression. Disease systems analysis aims at the distinction between drug effects on the “disease status” versus the “disease process” enabling the prediction of long-term treatment effect [4].

We have successfully developed mechanism-based PKPD models for drugs acting at various targets including A1 Adenosine, μ Opioid, 5-HT1A Serotonin and GABAAreceptors. Our findings show that in general a drug’s in vivo intrinsic efficacy can be accurately predicted on the basis of in vitro bioassays. Prediction of the in vivo potency on the other hand appears to be more difficult, presumably as result of complexities at the level of the target site distribution. Our results also show that equilibrium concentrationeffect relationships can be readily scaled from pre-clinical animal models to humans. The utility of this approach has recently been demonstrated for (semi-)synthetic opioids where a mechanism-based PK-PD model has been developed which can predict the clinical analgesic and respiratory depressant effects on the basis of preclinical in vitro and in vivo data [5]. In contrast, the scaling of transduction and homeostatic feedback mechanisms appears to be more complex. An example of the latter is our work on the allometric scaling of different biomarkers for 5-HT1A receptor agonists from preclinical in vitro and in vivo models to man [6].
The first application of disease systems analysis has been in the field of type 2 diabetes mellitus, where it has been shown that drug effects on the deterioration of beta cell function and insulin sensitivity can be quantified by analyzing a cascade of biomarker responses [7]. Due to their inherent complexity however, disease systems models may become unidentifiable. Recently it has been shown that redefinition of disease systems in terms of dimensionless variables may yield models with a reduced number of parameters, that are identifiable and in which the functional behavior is maintained [8]. These models are currently applied in the analysis of treatment effects in osteoporosis. It is concluded that mechanism-based PKPD models provide a unique scientific basis for the prediction of efficacy and safety of novel drugs in humans.


1. Danhof M, DeJongh J, DeLange ECM, Della Pasqua OE, Ploeger BA and Voskuyl RA (2007) Mechanism-based pharmacokinetic-pharmacodynamic modeling: biophase distribution, receptor theory and dynamical systems analysis. Ann. Rev. Pharmacol. Toxicol. 47, 357-400.

2. Danhof M, DeLange ECM, Della Pasqua OE, Ploeger BA, and Voskuyl RA (2008): Mechanism-based pharmacokinetic-pharmacodynamic (PKPD) modeling in translational drug research. Trends Pharmacol Sci. 29(4): 186-191.

3. Danhof M, Alvan G, Dahl SG, Kuhlmann J, and Paintaud G (2005): Mechanismbased Pharmacokinetic-Pharmacodynamic Modelling – A new classification of biomarkers. Pharm. Res. 22: 1432-1437

4. Post TM, Freijer JI, De Jongh J, and Danhof M (2005): Disease System Analysis: basic Disease Progression Models in Degenerative Disease. Pharm. Res. 22: 1038-1049

5. Yassen A, Olofsen E, Kan J, Dahan A, and Danhof M. (2007): Animal-tohuman extrapolation of the pharmacokinetic and pharmacodynamic properties of buprenorphine. Clin. Pharmacokinet. 46:433-447.

6. Zuideveld KP, Van der Graaf PH, Peletier LA, and Danhof M (2007): Allometric scaling of pharmacodynamic responses: application to 5-HT1A receptor mediated responses from rat to man. Pharm. Res.24:2031-2039 (2007)

7. De Winter W, De Jongh J, Post T, Ploeger B, Urquhart R, Moules I, Eckland D, and Danhof M (2006): A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying Type 2 Diabetes Mellitus. J Pharmacokinet Pharmacodyn. 33(3):313-343

8. Schmidt S, Post TM, Peletier LA, Boroujerdi MA and Danhof M (2011) Coping with Time Scales in Disease Systems Analysis: Application to Bone Remodeling (2011). J Pharmacokinet Pharmacodyn. October 26, E-published ahead of print.