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Editorial [Hot Topic: Predicting Drug Metabolism In Silico (Guest Editors: Drs. Michael Sorich and Paul Smith)]

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In silico (computational) simulation of chemical-biological interactions underpins efforts to remedy the escalating average cost and timeframe required to develop a marketable pharmaceutical. Chemicals with poor drug metabolism properties in humans are unfavourable for medicinal use, typically presenting problems with bioavailability, half life, inter-individual variability, and drug-drug interactions. As a result, it is increasingly seen as prudent to screen chemicals for these properties as early as possible in the drug discovery and development process. In silico methods are generally orders of magnitude faster and less expensive than the best in vitro methods, enabling screening of drug metabolism properties on much larger numbers of chemicals and much earlier in the drug discovery pipeline. Existing in silico screens span a range of different metabolic properties for an array of different phase I and phase II enzymes. The Cytochrome P450 (CYP) family of phase I enzymes plays the greatest role in drug metabolism in humans and consequently the majority of research has focused on this enzyme. Nevertheless, Phase 2 enzymes are increasingly recognized as important and research in this area is growing. One of the most common goals of in silico screens is the prediction of the ability of a chemical to inhibit a drug metabolizing enzyme. Other important metabolic properties studied include regioselectivity of metabolism, the ability of a chemical to be metabolised, metabolic stability, and induction of drug metabolising enzymes. A variety of in silico methodologies have been applied to predict these properties, including two- and threedimensional quantitative structure activity relationships (2D- and 3D-QSAR), pharmacophore modelling, quantum chemistry, protein modelling, and docking simulations. The major current challenges of in silico screens include validation, accuracy and interpretability.

In this issue of Current Topics in Medicinal Chemistry, titled Predicting drug metabolism in silico, the current major issues, directions, techniques, and applications of this area are reviewed in detail. Although each review has a different focus, there is sufficient overlap to appreciate the variety of perspectives existing in the field today.

Chohan, Paine and Waters begin the issue with an in-depth review of the contemporary 2D QSAR, 3D QSAR, and pharmacophore approaches that have been applied to gain insight into the molecular features influencing binding and metabolism by the major human phase 1 and phase 2 drug metabolising enzymes

Fox and Kriegl follow on by reviewing a variety of machine learning techniques that commonly underlie recent global QSAR studies on drug metabolism properties. The application and recent progress of these methods for the prediction of drug metabolism properties are considered in detail. This leads into a more specific review by Yap, Xue, Li and Chen on the use of support vector machines (SVM) - one of the most popular and powerful machine learning methods of the moment - for the prediction of CYP substrates and inhibitors. The discussion of SVM methodology, performance, difficulties and future prospects are a must-read for any researcher considering using machine learning methods for the prediction of drug metabolism.

1568 Current Topics in Medicinal Chemistry, 2006, Vol. 6, No. 15 Editorial Arimoto details the key findings of recent pharmacophore, QSAR and structure-based modeling undertaken to understand and predict metabolic properties of the major human CYP isoforms; CYP1A2, 2A6, 2C9, 2D6 and 3A4. Subsequently, Marechal and Sutcliffe review the structure-based modeling of human CYPs, focusing particularly on CYP2D6. The recent crystallization of a number of mammalian and human CYPs has been a significant step forward in using structure-based methods to understand the active site of CYPs and predict chemical binding and metabolism.

The use of in silico methods for the prediction of drug metabolism induction is reviewed in detail by Schuster, Steindl and Langer. Such models are complementary to those described in other reviews in this issue. In this review there is a focus on the modelling methods used, their applicability, limitations and recent applications.

Westwood, Kawamura, Fullan, Russell and Sim conclude the issue with a review on ligand- and structure-based modelling of N-acetyltransferases for the prediction of chemical binding and metabolism. N-acetyltransferase is an important phase 2 drug metabolising enzyme family and the variety and depth of research described herein indicates the potential for future work on the in silico prediction of drug metabolism by phase 2 enzymes..
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Document Type: Research Article

Affiliations: Sansom Institute School of Pharmacy and Medical Sciences University of South Australia Adelaide, SA 5000 Australia.

Publication date: 01 August 2006

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