Workshop: Pharmacokinetic (PBPK) modelling

The role for physiologically-based pharmacokinetic (PBPK) modelling in predicting toxicity
OpenTox Euro 2014 workshop: Pharmacokinetic (PBPK) modelling

Mohammed Atari


Cyprotex Discovery Limited


Mohammed Atari, Prakash Patel, Simon Thomas

Tue, 23. Sep. 2014


Physiologically based pharmacokinetic (PBPK) modelling provides a powerful means of integrating ADME and physicochemical data to predict in vivo pharmacokinetics in humans and pre-clinical animals. Predictions of pharmacokinetics (PK) from ADME data can enhance the ability to select compounds that are most likely to have appropriate PK in vivo.

The determination of physicochemical and ADME properties during early drug discovery ('early ADME data') enables PK prediction to be performed at any stage from lead identification onwards. PK prediction thus serves to integrate the data from various ADME/physicochemical screens – whether in vitro or in silico – greatly increasing their value over and above that of the raw data alone. In particular the role of sensitivity analysis – in which the effect of uncertainty in an input property on the value of an output (predicted) property is quantified – is a powerful tool for informing, and helping to direct – chemistry during lead optimisation.

In this workshop, the focus will be on understanding the fundamentals of PBPK modelling, the use of appropriate ADME and physicochemical data as inputs, and the utilisation of results during early drug discovery. For case study investigation of various aspects of PK prediction, participants will have access to Cloe® PK software. This is a powerful, yet intuitive, web-based program using a PBPK model for PK prediction. Its simple input data and comprehensive reporting make it suitable, not only for ADME/PK scientists, but also for toxicologists, medicinal chemists and biologists.

In addition to the prediction of PK properties, the use of univariate and multivariate sensitivity analyses as an aid to directing chemistry optimisation will be investigated. Such models have the potential to fulfil several roles in novel compound discovery, including: identification of compounds that are likely to have unacceptable in vivo toxicity; ranking compounds on expected toxicity; optimising the design of pharmaceutical dosing regimes to minimise side effects, whilst maintaining desired therapeutic efficacy.