Computational approaches to predict drug metabolism

Abstract

Background: Metabolism is one of the key parameters to be investigated and optimized in drug discovery projects. Metabolically unstable compounds or potential inhibitors of important enzymes should be detected as early as possible. As more compounds are synthesized than can be investigated experimentally, powerful computational methods are needed. Objective: We give an overview of state-of-the-art in-silico methods to predict experimental metabolic endpoints with a focus on the applicability in pharmaceutical industry. A macroscopic as well as a microscopic view of the metabolic fate and the interaction with metabolizing enzymes are given. Methods: Ligand-, protein- and rule-based approaches are presented. Conclusion: Although considerable progress has been made, the results of the calculations still need careful inspection. The domain of applicability of the models as well as methodological limitations have to be taken into account.

Keywords::

• ADME
• descriptor
• in-silico
• metabolism
• prediction
• protein–ligand interaction
• QSAR

Disclosure statement

The authors state no conflicts of interests and have received no payment in preparation of this manuscript.

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