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 vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.
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We would like to thank Don Walker for helpful comments in the preparation of this manuscript.
Glossary of Terms Used in Medicinal Chemistry
The QSAR and Modelling Society
A structural or physicochemical property of a molecule or part of a molecule. Examples include log P, molecular mass and polar surface area.
A pharmacophore is the ensemble of steric and electronic features that are necessary to ensure the optimal supramolecular interactions with a specific biological target structure and to trigger (or to block) its biological response.
The building of a model using part of the data (that is, the training set), followed by validation of the model using the rest of the data (that is, the validation set). Finally, the model is tested using compounds (the test set) not used for training and validation.
- MULTIVARIATE ANALYSIS
A subset of statistical techniques that can deal with larger sets of molecular descriptors that is aimed at finding relationships or patterns in data sets. Examples include multiple linear regression (MLR) and partial least squares (PLS).
- NEURAL NETWORKS
Neural networks are computational models that are based on the principles of the functioning of the brain. They can be used to model nonlinear relationships between dependent (biological endpoint to be predicted) and independent (molecular and structural descriptors) variables. Examples include back-propagation and self-organising maps (SOM; also called Kohonen neural networks).
- RECURSIVE PARTITIONING OR DECISION TREES
A supervised learning method producing a tree-structured series of rules to predict a particular property using a set of molecular descriptors as input.
A technique that uses the three-dimensional molecular structures to derive a quantitative relationship between a biological property and properties derived from these three-dimensional structures, for example, related to their size and electrostatic fields.
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van de Waterbeemd, H., Gifford, E. ADMET in silico modelling: towards prediction paradise?. Nat Rev Drug Discov 2, 192–204 (2003). https://doi.org/10.1038/nrd1032
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