Metabolic Stability for Drug Discovery and Development

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Abstract
Metabolic stability refers to the susceptibility of compounds to biotransformation in the context of selecting and/or designing drugs with favourable pharmacokinetic properties. Metabolic stability results are usually reported as measures of intrinsic clearance, from which secondary pharmacokinetic parameters such as bioavailability and half-life can be calculated when other data on volume of distribution and fraction absorbed are available. Since these parameters are very important in defining the pharmacological and toxicological profile of drugs as well as patient compliance, the pharmaceutical industry has a particular interest in optimising for metabolic stability during the drug discovery and development process. In the early phases of drug discovery, new chemical entities cannot be administered to humans; hence, predictions of these properties have to be made from in vivo animal, in vitro cellular/subcellular and computational systems. The utility of these systems to define the metabolic stability of compounds that is predictive of the human situation will be reviewed here. The timing of performing the studies in the discovery process and the impact of recent advances in research on drug absorption, distribution, metabolism and excretion (ADME) will be evaluated with respect to the scope and depth of metabolic stability issues. Quantitative prediction of in vivo clearance from in vitro metabolism data has, for many compounds, been shown to be poor in retrospective studies. One explanation for this may be that there are components used in the equations for scaling that are missing or uncertain and should be an area of more research. For example, as a result of increased biochemical understanding of drug metabolism, old assumptions (e.g. that the liver is the principal site of first-pass metabolism) need revision and new knowledge (e.g. the relationship between transporters and drug metabolising enzymes) needs to be incorporated into in vitro-in vivo correlation models. With ADME parameters increasingly being determined on automated platforms, instead of using results from high throughput screening (HTS) campaigns as simple go/no-go filters, the time saved and the many compounds analysed using the robots should be invested in careful processing of the data. A logical step would be to investigate the potential to construct computational models to understand the factors governing metabolic stability. A rational approach to the use of HTS assays should aim to screen for many properties (e.g. physicochemical parameters, absorption, metabolism, protein binding, pharmacokinetics in animals and pharmacology) in an integrated manner rather than screen against one property on many compounds, since it is likely that the final drug will represent a global average of these properties.