Computational Predictions of Drug Side Effects
A recent study by Xie et al published this month in PLOS Computational Biology highlights a computational approach to identify potential side effects of pharmaceuticals. If successful, this could allow identification of potential adverse events before the drugs are tested in human clinical trials. This is critical to the drug development life cycle, as unexpected side effects account for a third of all drug failures during the development process.
At the heart of this approach, specific drug molecules which are designed to bind to targeted proteins (in order to achieve a therapeutic outcome) are screened against the RCSB Protein Data Bank (PDB). The screening attempts to determine if there are other non-targeted proteins to which these molecules may also inadvertently bind and from which side effects could occur. On a technical level, this integrated approach to studying protein–ligand interactions on a structural proteome-wide scale combines protein functional site similarity search, small molecule screening, and protein–ligand binding affinity profile analysis.
The focus of this study was Select Estrogen Receptor Modulators (SERMs), a class of drug that includes tamoxifen, which are widely used to treat and prevent breast cancer and other diseases.
In the study, they have identified a possible molecular mechanism for the previously observed side effect of SERMs that involves the inhibition of the Sacroplasmic Reticulum Ca2+ ion channel ATPase protein (SERCA) transmembrane domain. The prediction provides molecular insight which lead to reducing the adverse effect of SERMs and is supported by clinical and in vitro observations.
This strategy is now being applied to discover off-targets for other commercially available pharmaceuticals. If successful, it could lead ultimately be included in a drug discovery pipeline in an effort to optimize drug leads and reduce unwanted side effects.
