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Improving Early Drug Discovery through ADME Modelling

An Overview

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Abstract

Drug development is an intrinsically risky business. Like a high stakes poker game the entry costs are high and the probability of winning is low. Indeed, only a tiny percentage of lead compounds ever reach US FDA approval. At any point during the drug development process a prospective drug lead may be terminated owing to lack of efficacy, adverse effects, excessive toxicity, poor absorption or poor clearance. Unfortunately, the more promising a drug lead appears to be, the more costly it is to terminate its development. Typically, the cost of killing a drug grows exponentially as a drug lead moves further down the development pipeline. As a result there is considerable interest in developing either experimental or computational methods that can identify potentially problematic drug leads at the earliest stages in their development. One promising route is through the prediction or modelling of ADME (absorption, distribution, metabolism and excretion). ADME data, whether experimentally measured or computationally predicted, provide key insights into how a drug will ultimately be treated or accepted by the body. So while a drug lead may exhibit phenomenal efficacy in vitro, poor ADME results will almost invariably terminate its development. This review focuses on the use of ADME modelling to reduce late-stage attrition in drug discovery programmes. It also highlights what tools exist today for visualising and predicting ADME data, what tools need to be developed, and the importance of integrating ADME data to aid in compound selection during the earliest phases of drug discovery. In particular, it highlights what tools exist today for visualising and predicting ADME data including: (1) ADME parameter predictors; (2) metabolic fate predictors; (3) metabolic stability predictors; (4) cytochrome P450 substrate predictors; and (5) physiology-based pharmacokinetic (PBPK) modelling software. It also discusses what kinds of tools need to be developed, and the importance of integrating ADME data to aid in compound selection during the earliest phases of drug discovery.

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Acknowledgements

The author acknowledges Genome Canada, Genome Alberta and the National Institute for Nanotechnology (NRC) for their financial support in the preparation of this review. The author has no conflicts of interest that are directly relevant to the content of this review.

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  1. Departments of Biological Science and Computing Science, University of Alberta, Edmonton, Alberta, Canada

    David S. Wishart

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Wishart, D.S. Improving Early Drug Discovery through ADME Modelling. Drugs R D 8, 349–362 (2007). https://doi.org/10.2165/00126839-200708060-00003

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