The Aminosteroid Derivative RM-133 Shows In Vitro and In Vivo Antitumor Activity in Human Ovarian and Pancreatic Cancers
Bibliographic record
Abstract
Ovarian and pancreatic cancers are two of the most aggressive and lethal cancers, whose management faces only limited therapeutic options. Typically, these tumors spread insidiously accompanied first with atypical symptoms, and usually shift to a drug resistance phenotype with the current pharmaceutical armamentarium. Thus, the development of new drugs acting via a different mechanism of action represents a clear priority. Herein, we are reporting for the first time that the aminosteroid derivative RM-133, developed in our laboratory, displays promising activity on two models of aggressive cancers, namely ovarian (OVCAR-3) and pancreatic (PANC-1) cancers. The IC50 value of RM-133 was 0.8 μM and 0.3 μM for OVCAR-3 and PANC-1 cell lines in culture, respectively. Based on pharmacokinetic studies on RM-133 using 11 different vehicles, we selected two main vehicles: aqueous 0.4% methylcellulose:ethanol (92:8) and sunflower oil:ethanol (92:8) for in vivo studies. Using subcutaneous injection of RM-133 with the methylcellulose-based vehicle, growth of PANC-1 tumors xenografted to nude mice was inhibited by 63%. Quite interestingly, RM-133 injected subcutaneously with the methylcellulose-based or sunflower-based vehicles reduced OVCAR-3 xenograft growth by 122% and 100%, respectively. After the end of RM-133 treatment using the methylcellulose-based vehicle, OVCAR-3 tumor growth inhibition was maintained for ≥ 1 week. RM-133 was also well tolerated in the whole animal, no apparent sign of toxicity having been detected in the xenograft studies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".