PBPK modelling - Articles and news items

Figure 1: Schematic representation of body compartments in physiologically based pharmacokinetic modelling. Abbreviations: Qorgan: Blood flow to organ

Physiologicaly based pharmacokinetic modelling of transporters in drug discovery and development

Issue 4 2012, Toxicology / 3 September 2012 / Pradeep Sharma and Katherine Fenner, Global DMPK, AstraZeneca R&D

Physiologically based pharmacokinetic (PBPK) models describe the different compartments (tissues) in the body linked via arterial and venous blood flow (Figure 1). The volume of each tissue and blood flows are available from literature data1-5 and PBPK models have been developed for many species including rat, mouse, dog, pig and human2,6,7. PBPK models can be applied to many aspects of the drug develop ment continuum, from drug discovery8 and into development including use in regulatory responses9.

PBPK modelling is becoming a tool of choice in the pharmaceutical industry for the prediction of pharmacokinetic parameters, drugdrug interactions (DDI) and tissue distribution from in vitro data. PBPK modelling was able to become a mainstream tool in the pharma – ceutical industry with advances in in vitro metabolism techniques along with the ability to predict tissue distribution parameters or Kp values for a number of classes of compounds10-13. These models usually assume that the liver and kidney are the only organs where elimination occurs and that blood flow to these organs limits the excretion rate. Recently, with advances in in vitro techniques to study transporter proteins, the input of these data in PBPK models is becoming more commonplace.

Man on a phone analysing data and charts on computer screen

Integrating preclinical data into early clinical development

Contract Research, Issue 4 2012 / 3 September 2012 / Vikash Sinha, Clinical Pharmacology Leader, Janssen Research and Development

One of the important goals in preclinical and early clinical drug development is to reduce attrition rates and to improve our ability to pick winners and drop potential loser drug candidates. By being able to efficiently translate preclinical data and observations into possible clinical outcomes, one can make the drug development process more cost-effective. Identifying preclinical models – in silico, in vitro, in vivo – or assays that can best predict clinical observations is not trivial. It requires understanding of preclinical-to-clinical correlations and the success of translational science may vary depending on the therapeutic area where one is working. For example, anti-infectives or cancer therapeutic areas have validated biomarkers which can be useful in selecting the right drug candidate in early drug development…


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