Drug discovery information integration: virtual humans for pharmacokinetics

The ‘virtual human’ refers to simulation models based on explicit mathematical models of mammalian physiology. When applied to pharmacokinetics (PK), the virtual human embodies models that can be parameterised for different species, different individuals and populations of individuals, and that allow simulation of PK from measured and/or predicted in vitro properties. The models are independent of the properties of any specific drug, and can be used for the prediction of drug behaviour in specific human individuals and in pre-clinical species. The hope is that these models will allow the prediction of PK throughout the drug discovery and development process, enabling effective targeting of synthetic chemistry efforts to compounds with the required therapeutic effect, careful evaluation of multiple candidates for development, assessment of the potential for drug-drug interactions, evaluation of multiple formulations, efficient clinical trial design and other benefits, including reduction of animal usage in drug discovery. However, we believe that the biggest impact on drug discovery productivity would accrue from achieving two clear objectives: an estimate of human in vivo activity on every compound synthesised and a reliable prioritisation, based on predictions of human in vivo activity, for the next round of synthesis. It is realistic to believe that the benefits of delivering these objectives could, through shortening the time to decision and increasing the chance of success, lead to a five- to ten-fold increase in discovery output. It is also now possible to demonstrate that a combination of virtual human simulation software and industrial-scale absorption, distribution, metabolism and elimination (ADME) testing can deliver both of these objectives.