Computational approaches to predict drug metabolism.
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.


CONCLUSIONS
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.
Czodrowski
Paul
P
Department of Lead Discovery - Computational Chemistry, Boehringer Ingelheim Pharma GmbH & Co. KG, 88397 Biberach, Germany.
Kriegl
Jan M
JM
Scheuerer
Stefan
S
Fox
Thomas
T
D001711:Biotransformation; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D004790:Enzyme Induction; D004798:Enzymes / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000378:metabolism*; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship
D004798:Enzymes; D004364:Pharmaceutical Preparations