Characterization of <scp>TR</scp> ‐107, a novel chemical activator of the human mitochondrial protease <scp>ClpP</scp>
Bibliographic record
Abstract
Abstract We recently described the identification of a new class of small‐molecule activators of the mitochondrial protease ClpP. These compounds synthesized by Madera Therapeutics showed increased potency of cancer growth inhibition over the related compound ONC201. In this study, we describe chemical optimization and characterization of the next generation of highly potent and selective small‐molecule ClpP activators (TR compounds) and demonstrate their efficacy against breast cancer models in vitro and in vivo. We selected one compound (TR‐107) with excellent potency, specificity, and drug‐like properties for further evaluation. TR‐107 showed ClpP‐dependent growth inhibition in the low nanomolar range that was equipotent to paclitaxel in triple‐negative breast cancer (TNBC) cell models. TR‐107 also reduced specific mitochondrial proteins, including OXPHOS and TCA cycle components, in a time‐, dose‐, and ClpP‐dependent manner. Seahorse XF analysis and glucose deprivation experiments confirmed the inactivation of OXPHOS and increased dependence on glycolysis following TR‐107 exposure. The pharmacokinetic properties of TR‐107 were compared with other known ClpP activators including ONC201 and ONC212. TR‐107 displayed excellent exposure and serum t 1/2 after oral administration. Using human TNBC MDA‐MB‐231 xenografts, the antitumor response to TR‐107 was investigated. Oral administration of TR‐107 resulted in a reduction in tumor volume and extension of survival in the treated compared with vehicle control mice. ClpP activation in vivo was validated by immunoblotting for TFAM and other mitochondrial proteins. In summary, we describe the identification of highly potent new ClpP agonists with improved efficacy against TNBC, through targeted inactivation of OXPHOS and disruption of mitochondrial metabolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".