Igniting drug discovery: pushing CETSA^®-MS to scale

For the development of effective and safe drugs, a clear understanding of the direct drug-protein interactions and the mode of action in a physiologically relevant biological system is of highest relevance. A high number of drug candidates fail throughout all stages of drug development due to lack of efficiency or undesired side effects potentially caused by off-target binding. These failures lead to an immense loss of resources and time, which ultimately translates into a prolonged suffering time for patients from their diseases. Consequently, understanding of the molecular interactions of biologically active small molecules at very early stages is crucial for early de-risking, compound prioritization and data-driven allocation of research resources to the most promising drug development projects.

Over the past years, chemical proteomics approaches have increasingly gained popularity within early drug discovery for unbiased target and off-target identification, target engagement assays as well as to facilitate compound mode of action deconvolution. Among others, the CEllular Thermal Shift Assay (CETSA®) technology is emerging as a key technology for drug discovery programs. Multiple studies have shown that CETSA® coupled to mass spectrometry (MS) is a powerful tool that allows for label-free target deconvolution in complex samples such as cell lysates, intact cells and tissues. However, although multiplexing approaches were reported over the past years, CETSA®-MS workflows remain laborious. Additionally accounting for the required MS measurement time, the throughput of this technology is still very limited.

Here, we will provide an overview about the compressed CETSA® MS platform that we established at Bayer AG. The improvements we implemented led to a largely automated workflow that enables proteome-wide target engagement studies at higher throughput. Here, we showcase how this platform is used for flexible and fast evaluation of compound-protein interactions at scale, and how we use it to support ongoing drug discovery projects with drug-candidate prioritization and early de-risking of drug-development programs.