Beyond Profiling: Using ADMET Models to Guide Decisions

Abstract

ADMET Models, whether in silico or in vitro, are commonly used to 'profile' molecules, to identify potential liabilities or filter out molecules expected to have undesirable properties. While useful, this is the most basic application of such models. Here, we will show how models may be used to go 'beyond profiling' to guide key decisions in drug discovery. For example, selection of chemical series to focus resources with confidence or design of improved molecules targeting structural modifications to improve key properties. To prioritise molecules and chemical series, the success criteria for properties and their relative importance to a project's objective must be defined. Data from models (experimental or predicted) may then be used to assess each molecule's balance of properties against those requirements. However, to make decisions with confidence, the uncertainties in all of the data must also be considered. In silico models encode information regarding the relationship between molecular structure and properties. This is used to predict the property value of a novel molecule. However, further interpretation can yield information on the contributions of different groups in a molecule to the property and the sensitivity of the property to structural changes. Visualising this information can guide the redesign process. In this article, we describe methods to achieve these goals and drive drug-discovery decisions and illustrate the results with practical examples.

Evaluation of Three State-of-the-Art Metabolite Prediction Software Packages (Meteor, MetaSite, and StarDrop) through Independent and Synergistic Use

“…The overall prediction pattern of these software tools yields more precise, albeit less sensitive, predictions compared with Meteor. Therefore, in the elucidation of a compound's metabolites in early lead optimization and soft spot identification, StarDrop (Segall et al, 2009;Earnshaw, 2010) (http://www.optibrium.com/downloads) and MetaSite (Ahlström et al, 2007;Boyer et al, 2009;Trunzer et al, 2009;Myatt et al, 2010;Wu et al, 2010) are much more appropriate tools.…”

Structure-based design and biochemical evaluation of sulfanilamide derivatives as hepatitis B virus capsid assembly inhibitors

“…Also, de novo receptor-ligand binding simulation and chemical evolution simulation could be used to find possible candidate molecules which bind to specific site 10 . Screening process as ADMET prediction or conformational change simulation could be applied to predict the traits of molecules such as toxicity, metabolism and inhibition strength 11,12 . The aim of this research is to find a candidate molecule which effectively inhibits assembly of the HBV capsid with low toxicity.…”

“…In the ADME prediction step, we estimated aqueous solubility, blood-brain barrier penetration, hepatotoxicity, plasma protein binding and intestinal absorption. Mutagenicity, carcinogenicity, LD50 and chronic LOAEL were examined in toxicity prediction steps 11,12 . By estimating properties of each ligand, we screened some molecules which have undesirable traits such as low solubility or high toxicity.…”

A fragment-based approach for the in silico prediction of blood-brain barrier permeation

“…For the past decades, problems with absorption, distribution, metabolism and excretion (ADME) have been one of the major reasons for the failure of attractive compounds in advanced stages of drug development. [4][5][6] Traditionally, in vivo and in vitro models are employed in the pharmaceutical industry for the evaluation of pharmacokinetic parameters. However, animal models and cell-based assays are typically time consuming and expensive, and thus not applicable to the early screening of large libraries of compounds.…”