ADMET in silico modelling: towards prediction paradise?

Key Points There is an increasing need for good predictive tools of ADMET properties to serve two key aims — first, at the design stage of new compounds and compound libraries so as to reduce the risk of late-stage attrition, and second, to optimize the screening and testing by looking at only the most promising compounds. We want to predict properties that provide information about dose size and dose frequency, such as oral absorption, bioavailability, brain penetration, clearance (for exposure) and volume of distribution (for frequency). Two types of compuational approaches are used: molecular modelling and data modelling. Molecular modelling use quantum mechanical methods to assess the potential for interaction between the small molecules under consideration and proteins known to be involved in ADME processes, such as cytochrome P450s. For data modelling, quantitative structure–activity relationship (QSAR) approaches are typically applied. These use statistical tools to search for correlations between a given property and a set of molecular and structural descriptors of the molecules in question. Good predictive models for ADMET parameters depend crucially on selecting the right mathematical approach, the right molecular descriptors for the particular ADMET endpoint, and a sufficiently large set of experimental data relating to this endpoint for the validation of the model. This article describes recent advances in the prediction of physicochemical properties relevant to ADME (such as lipophilicity), ADME properties themselves (such as absorption), and toxicity issues (such as drug–drug interactions). In the next 10 years or so, the degree of automation in traditional drug metabolism departments will continue to increase and fully automated medium- and high-throughput in vitro assays will be used alongside in silico modelling and data interpretation. Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in