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A proteome-wide screen helped researchers trace toxicity in some experimental prostate-cancer degraders to inhibition of mitochondrial electron transport chain complex I. The team then made small linker changes to selected compounds; in preclinical models, the optimized versions showed less hepatotoxicity while preserving androgen-receptor (AR) degradation and antitumor activity. The findings demonstrate a drug-discovery approach, not a safer treatment established for people.
How proteomics can reveal degrader toxicity
Heterobifunctional degraders, often called PROTACs, are designed with two binding parts: one attaches to a target protein and the other recruits an E3 ligase. This can bring the target into the ubiquitin-proteasome system, which marks proteins for degradation. But a degrader may also affect other proteins or cellular processes, creating effects that are difficult to anticipate from its intended target alone.
Basu, Yu, Bosak and colleagues used high-throughput data-independent acquisition (DIA) proteomics to measure protein-level changes across cells exposed to compounds. They combined those profiles with machine learning to identify patterns associated with drug effects and infer potential toxicity mechanisms. Their work first used FDA-approved compounds to establish the framework, then applied it to experimental AR-targeting degraders.
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What the degrader screen measured
The team screened 204 structurally unique AR-targeting degraders, including designs that recruited either cereblon (CRBN) or von Hippel–Lindau (VHL). The campaign generated 2,113 samples and reported 4,043 proteins measured per sample. The authors used a timsTOF HT instrument in dia-PASEF acquisition mode.
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The screen used HepG2 cells, which lack detectable AR expression. Because the compounds were designed to degrade AR, profiling them in an AR-negative cell model helped reveal cellular responses not dependent on the intended target. This is useful for exposing off-target effects, but it is not a complete model of how a drug behaves in an organism.
What toxicity mechanism did the researchers identify?
Some CRBN-recruiting AR degrader designs produced broad proteomic changes linked to mitochondrial effects. Proteome-trained machine-learning models mapped the primary toxicity mechanism to inhibition of electron transport chain complex I. That result gave the researchers a mechanistic lead: rather than treating toxicity as an unexplained outcome, they could use protein-response patterns to guide compound redesign.
In the separate set of FDA-approved compounds used to establish the workflow, the article reports that 47 compounds—60% of that tested set—produced below-mean proteomic perturbation, while clotrimazole produced the largest proteomic change. Those figures describe the study’s approved-compound set, not a general benchmark for drug toxicity.
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In selected compounds, the researchers made minor linker modifications and found analogs with reduced off-target engagement and hepatotoxicity in the models tested. The optimized compounds retained AR degradation and prostate-cancer selectivity in the reported experiments. The finding suggests that linker design can influence a degrader’s broader cellular effects, though it does not establish that linker changes will reduce toxicity for other compounds or targets.
Did the optimized degrader work against prostate cancer?
The authors tested one optimized analog, compound 3, in a castration-resistant prostate cancer xenograft model. It inhibited tumor growth in the C4-2 model and, under the study’s reported conditions, inhibited growth better than enzalutamide. These are results from preclinical models; they do not show that compound 3 is effective or safer in people.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the study does—and does not—establish
The work supports using proteome-wide measurements to identify potential toxicity mechanisms early in degrader discovery and to inform optimization. The evidence presented is preclinical. It does not establish human safety, dosing, clinical efficacy, regulatory approval, or how well the approach will perform across other degrader targets and tissues. HepG2 cells lacking the intended AR target are helpful for examining off-target responses, but cannot alone predict a drug’s full effects in patients.
Source: Basu S, Yu JSL, Bosak J, et al., “Proteome-guided drug discovery maps and mitigates therapeutic degrader toxicity,” Nature Chemical Biology, published October 9, 2026. https://doi.org/10.1038/s41589-026-02347-2.
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