In Silico Driven Prediction of MAPK14 Off-Targets Reveals Unrelated Proteins with High Accuracy

Abstract

During the discovery and development of new drugs, candidates with undesired and potentially harmful side-effects can arise at all stages, which poses significant scientific and economic risks. Most of such phenotypic side-effects can be attributed to binding of the drug candidate to unintended proteins, so-called off-targets. The early identification of potential off-targets is therefore of utmost importance to mitigate any downstream risks. We showcase how the combination of knowledge-based in silico off-target screening and state-of-the-art biophysics can be applied to rapidly identify off-targets for a MAPK14 inhibitor. Out of 13 predicted off-targets, six proteins were confirmed to interact with the inhibitor in vitro, which translates to an exceptional hit rate of 46%. For two proteins, affinities in the lower micromolar range were obtained: The kinase IRE1 and the Hematopoietic Prostaglandin D Synthase, which is entirely unrelated to MAPK14 and is involved in different cell-regulatory processes. The whole off-target identification/validation pipeline can be completed as fast as within two months, excluding delivery times of proteins. These results emphasize how computational off-target screening in combination with MicroScale Thermophoresis can effectively reduce downstream development risks in a very competitive time frame and at low cost.