your speakers for our 2 events

Biography

• Studied Chemistry at Oxford University, followed by several years in the fledgling computer software industry as a scientific programmer and consultant.
• Ph.D. training and postdoctoral research was conducted in the fields of Metabolic Control Analysis, and computer simulation of the behaviour of biochemical pathways: fields that are today part of the discipline known as Systems Biology.
• This was followed by a year as a lecturer in Biochemistry at Brunel University.
• One year as research scientist at Procter&Gamble, Germany
• Joined Cyprotex in 1999, having responsibility for the development of predictive in silico methods in the company, predominantly in the area of physiologically based pharmacokinetic modelling for the prediction of in vivo pharmacokinetics in humans and pre-clinical species.

Abstract

Physiologically based pharmacokinetic/pharmacodynamic modelling for lead optimisation of CNS-active therapeutic

With approximately 90% of compounds that enter clinical trials failing to become approved drugs, the onus is on drug discovery projects to deliver better-optimised candidates to pre-clinical development. Clinical failures can arise for a number of reasons, but lack of clinical efficacy accounts for a significant proportion.

One promising means of improving the estimate of likely clinical efficacy is the use of physiologically-based pharmacokinetic/pharmacodynamic (PBPK/PD) modelling. This technique builds on the method of PBPK modelling for the prediction of in vivo pharmacokinetics from in vitro ADME data, by additionally predicting pharmacodynamic effects from in vitro activity data. Thus, lead optimisation can take place by optimising the predicted human in vivo activity, which simultaneously takes pharmacokinetics and binding to the target into account. Receptor- and enzyme-mediated screening are particularly amenable to this approach, because of the relative ease of incorporating binding data into the appropriate models.

In this presentation, the fundamentals of PBPK/PD modelling will be briefly reviewed and some examples described. Recent work on PBPK/PD modelling of CNS-active agents will be discussed. This is both a necessary and highly challenging area, because drug concentrations in the relevant biophase – such as the brain interstitial fluid – can be markedly different from concentrations in the blood, as a result of the ability of the blood-brain barrier to restrict the egress of many xenobiotics from the brain capillaries. The intra-brain concentrations can be predicted by the PBPK modelling, whilst incorporation of receptor binding data will predict the agonism/antagonism of the receptor that will achieved in vivo.