New Testosterone Derivatives as Semi-Synthetic Anticancer Agents Against Prostate Cancer: Synthesis and Preliminary Biological Evaluation
Bibliographic record
Abstract
Prostate cancer (PC) is a major health issue in the world. Treatments of localized PC are quite efficient and usually involve surgery, radiotherapy and/or hormonal therapy. Metastatic PC is however rarely curable to this day. Treatments of metastatic PC involve radiotherapy, chemotherapy and hormonal treatment such as orchiectomy, antiandrogens and luteinizing hormone-releasing hormone agonists. The suppression of tumor growth by hormonal treatment is efficient but overtime resistance still occurs and the disease progresses. Thus, more urgently than ever there is a need for discovery of new treatment options for castration-resistant PC (CRPC). Hence, we designed and tested a series of amide derivatives located at position 7α of testosterone as prospective "natural" or "semisynthetic" anticancer agents against CRPC with the goal of discovering therapeutic alternatives for the disease. This manuscript describes an efficient path towards the target molecules that are made in only 6 or 7 chemical steps from testosterone in good overall yields. This strategy can be used to make several compounds of interest that present higher biological activity than the classic antiandrogen; cyproterone acetate (3). The best testosterone-7α-amide was the N-2-pyridylethylamide (25) which was as active as the antiandrogen cyproterone acetate (3) on androgen-dependent LNCaP cells and 2.7 times more active on androgen-independent PC3 prostate cancer cells. The results obtained show the synthetic feasibility and the potential for future development of this unique class of semi-synthetic anticancer agents that offer the premise of new treatment modalities for patients afflicted with CRPC.
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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) | 0.002 | 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".