Proteome-wide measurements helped researchers identify a toxicity mechanism in certain experimental androgen-receptor (AR) degraders and guide the design of modified compounds with less toxicity in preclinical models. The study, published in Nature Chemical Biology on October 9, 2026, does not establish that the optimized compounds are safe or effective in people.
What the study set out to find
Heterobifunctional degraders, commonly called PROTACs, join a molecule that binds a target protein to one that recruits an E3 ligase. This can direct the target protein toward degradation through the ubiquitin-proteasome system. Because a degrader can also affect proteins and processes beyond its intended target, the researchers asked whether broad protein measurements could help reveal unwanted effects early in drug design.
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Basu, Yu, Bosak and colleagues first developed their high-throughput proteomics workflow using FDA-approved compounds, then applied it to experimental AR-targeting degraders. The approach combines data-independent acquisition (DIA) proteomics with machine learning to map how cells respond to compounds and infer possible toxicity mechanisms. The study in Nature Chemical Biology was published October 9, 2026.
How the degrader screen worked
The team screened 204 structurally unique AR-targeting degraders, including designs that recruited either cereblon (CRBN) or von Hippel–Lindau (VHL). The campaign comprised 2,113 samples and reported a depth of 4,043 proteins per sample. Proteins were measured using a timsTOF HT instrument in dia-PASEF acquisition mode. These are study-specific figures, not general performance benchmarks for proteomics platforms. Basu et al., 2026.
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For the degrader screen, the researchers used HepG2 cells, which lack detectable AR expression. Since the intended target was absent, changes in these cells could help expose effects that did not depend on degrading AR. This design is useful for flagging off-target responses, but it cannot by itself predict how a compound will behave across tissues or in a whole organism.
What proteomics revealed about toxicity
Certain CRBN-recruiting AR degraders caused broad proteomic changes associated with mitochondrial effects. Models trained on the proteomic data linked the main toxicity signature to inhibition of electron transport chain complex I, a component of mitochondrial energy production. The finding gives the researchers a possible mechanism to investigate rather than a clinical diagnosis or proof that every compound recruiting CRBN will have the same effect.
In the separate set of approved compounds used to establish the workflow, 47 compounds—reported as 60% of that tested set—produced below-mean proteomic perturbation, while clotrimazole induced the largest proteomic change. Those results describe that particular comparison set; they are not a universal ranking of drug toxicity. Basu et al., 2026.
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The team tested selected analogs with minor linker modifications. In the experimental models reported, some modified compounds showed reduced off-target engagement and hepatotoxicity while retaining AR degradation and activity against prostate-cancer models. This is a design lesson from the tested compounds: a relatively small structural change can alter a degrader’s unwanted effects without necessarily removing its intended activity. It does not show that linker changes will reliably make other degraders safer.
What happened in the prostate-cancer model
One optimized analog, compound 3, inhibited tumor growth in a castration-resistant prostate-cancer xenograft model. In the C4-2 model and under the study’s reported conditions, the authors described it as more effective at inhibiting tumor growth than enzalutamide. That is a preclinical, model-specific comparison; it does not establish superiority in patients. Basu et al., 2026.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the findings do—and do not—show
- What they show: High-throughput proteomics and machine learning can help map cellular responses to experimental degraders, identify a toxicity-associated mechanism, and inform compound optimization in the models studied.
- What they do not show: The work does not establish human safety, a safe dose, clinical efficacy, regulatory approval, or that the approach will work for every degrader target or tissue.
- Why the cell model matters: AR-negative HepG2 cells make it easier to focus on effects outside the intended target, but they cannot capture all the biology that may shape a drug’s effects in patients.
The study therefore supports proteome-guided toxicity assessment as a preclinical discovery strategy. Whether the optimized compounds or the broader workflow translate into clinical benefit remains unestablished.
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