Authors
Shaon Basu, Jason Shu Lim Yu, Jessica Bosak, Sharan K Bagal, Claire Crafter, Charlene Fallan, Oliver Lemke, Anja Freiwald, Matthew E H White, Spyros I Vernardis, Christoph B Messner, Chrysiis Michaloglou, Kenneth Pryde, Antonio Ramos-Montoya, Sophie Regan, Monica Rodrigo-Brenni, Sascha Roth, Camilla Ruffilli, Michael Mülleder, Kevin Moreau, Markus Ralser
Published in
Nature chemical biology. Oct 09, 2026. Epub Oct 09, 2026.
Abstract
Heterobifunctional targeted degraders (HBDs) enable potent removal of protein targets but their clinical success can be hindered by de novo toxicity inherent to their bivalent chemistry. Here we introduce a drug discovery framework that exploits high-throughput proteomics to map and mitigate toxicity mechanisms of emerging drug modalities. Exposing androgen receptor (AR)-negative cells to a library of experimental AR-HBDs indicated for treatment-resistant prostate cancer linked widespread proteomic responses to hepatotoxicity of phthalimide degraders. Machine learning trained on proteomes mapped the primary toxicity mechanism to inhibition of electron transport chain complex I and identified safer analogs where a minor modification in the linker region mitigated off-target engagement. Proteome-optimized degraders displayed decreased hepatotoxicity, enhanced specificity and antitumor activity against treatment-resistant prostate cancer xenografts. Our findings establish a versatile framework for developing safer medicines and highlight the transformative potential of proteome-guided drug discovery.
PMID:
42855516
Bibliographic data and abstract were imported from PubMed on 10 Oct 2026.
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