CLM296: a highly selective inhibitor targeting ALDH1A3-driven tumor growth and metastasis in breast cancer
Bibliographic record
Abstract
ABSTRACT Aldehyde dehydrogenase 1A3 (ALDH1A3) increases tumor growth, metastasis, and chemoresistance in many solid tumors, including triple-negative breast cancer (TNBC), glioblastoma, melanoma, lung, and colon cancers, yet no clinically approved inhibitors exist. Here, we present CLM296, a novel and highly selective ALDH1A3 inhibitor designed to address this unmet need. CLM296 exhibits potent inhibition of ALDH1A3 activity in TNBC cells (half-maximal inhibitory concentration = 2 nM) with no off-target effects on the highly homologous ALDH1A1 isoform. RNA sequencing confirmed its specificity, demonstrating selective suppression of ALDH1A3-regulated gene expression only, and a lack of effect in control cells that have minimal ALDH1A3 expression. Transwell assays showed that CLM296 reduced the increased invasion of cells induced by ALDH1A3. Once daily dosing of 4mg/kg CLM296 in mice specifically reduced ALDH1A3-mediated gene expression in tumors and impeded ALDH1A3-driven tumor growth and lung metastasis in TNBC xenografts. There was no observed toxicity in the mice as evidenced by stable mouse body weights and no significant changes in blood creatinine and ALT levels. Pharmacokinetic studies of CLM296 revealed broad tissue distribution, including tumor, lung, liver, and brain. With oral administration the terminal elimination half-life of CLM296 exceeded 12 hours, resulting in sustained ALDH1A3-inhibiting concentrations beyond 24 hours. Together, these findings establish CLM296 as a potential first-in-class ALDH1A3 inhibitor with high selectivity for ALDH1A3, favorable pharmacokinetics, and a positive preclinical safety profile. CLM296 represents a promising therapeutic candidate to complement standard-of-care treatments in ALDH1A3+ cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".