Design of in vitro test assays and prediction of animal model suitability
New compounds succesfull in the preclinical stage often fail in the clinical phase due to the lack of efficacy in humans. One of the reasons on the incompatibility between real patients and animal models are structural differences between binding sites in human and animal proteins. Homology modeling can predict these incompatibilities, providing a tool for constructing the models of targets from various species. The most appropriate animal model can be chosen based on the interspecies comparison of the binding pockets.
Homology model-based prediction of drug metabolism and toxicity
One of the important issues in drug discovery is the occurrence of undesired pharmacological effects and the weak metabolic stability of the drug molecule. There is a number of identified proteins that are responsible for those effects, for some of them no experimental structural information is available. This is where protein structure prediction can be used as a tool: obtaining high resolution models of helps design the molecules with low affinities to 'non-target' proteins.
Good example of the common side-effect related protein, that must be modeled due to the lack of experimental structures is a potassium channel: human ether-a-go-go-related gene (hERG). Inhibition of this protein by the small molecule is responsible for a cardiovascular toxicity (QT-prolongation) and already caused several withdrawals from the market.
Homology modeling is also useful in studying the metabolic fate of NCE's. Cytochrome P450 (CYP450), the group of proteins responsible for the metabolism of xenobiotics. The group consists of several subtypes, however only for few of them X-ray structures are available. Homology models, combined with pharmacophoric models and quantum chemical calculations were shown successful in predicting the preferred sites of metabolism within small molecules1. Homology models can also be used in studying the inhibition by small molecules, which is can help avoiding harmful drug-drug interactions2.
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