Summary
PROTACs (PROteolysis TArgeting Chimeras) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances in targeted protein degradation technology have been remarkable with several molecules moving into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step towards delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.
Highlights
Morphological profiling detects various PROTACs’ phenotypic signatures
Phenotypic signatures can be attributed to diverse biological responses
Chemical clustering from phenotypic signatures separates on drug selection
Trained in-silico machine learning models to predict PROTACs’ mitochondrial toxicity
Competing Interest Statement
All authors except of Maria-Anna Trapotsi and Andreas Bender are AstraZeneca employees