Statins, a group of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibitors, are one of the most common drugs on the market, with over 100 million people receiving statin treatment. Primarily known for their cholesterol lowering benefits, statins have been protecting people at high risk from cardiovascular disease for the past 40 years. Whilst statins are generally safe and well tolerated, statin-associated muscle symptoms (SAMS) affect 5-30% of statin patients, and accounts for as much as 60% of statin discontinuation. Unfortunately, there is currently a lack of understanding around how statins are contributing to SAMS in patients. Furthermore, it is unclear whether SAMS occurs through on-target effects, for instance HMGCR-cholesterol related effects, or through currently unexplored off-target effects of secondary gene targets such as HDAC2 and ITGAL. Understanding the effects of statin targets would influence how patients are prescribed statins, as well as how future cholesterol lowering drugs are developed.
Here we utilise the Broad Institute’s CLUE Connectivity Map (CMap), an online and publicly available database of human cell-specific perturbation-driven gene expression signatures. The premise of this compendium is that two compounds with similar perturbation signatures should have similar mechanisms of action (MoA). Following this hypothesis, we have utilised CMap to try to understand the MoA of SAMS. We have identified compounds highly connected to statins (tau score >95) that share similar adverse muscle effects, with one in particular that we have taken forward for further investigation. We then performed functional enrichment and protein-protein interaction network analysis on genes commonly perturbed by statins and our compound of interest. Summary-based Mendelian Randomisation analysis is used to infer whether SAMS is occurring through on- or off-target effects. Preliminary results indicate that this in-silico pipeline shows promise in identifying alternative mechanisms of action that are driving drug effects.