Design, synthesis, and biological evaluation of novel antiestrogens
Bibliographic record
Abstract
Breast cancer is the most common form of cancer in women in the Western world and the second most fatal after lung cancer. Estrogens play a crucial role in normal breast development, but also contribute to mammary tumorigenesis. Estrogen signaling is mediated by intracellular estrogen receptors (ERα and ERβ), with ERα being a common target in breast cancer therapeutics. Two classes of competitive estrogen inhibitors have been investigated and proved to be useful for breast cancer treatment: full antiestrogens (FAE's) and selective estrogen receptor modulators (SERM's). A novel platform, FORECASTER, was developed in the Moitessier laboratory with the aim of integrating computational, medicinal and combinatorial chemistry. The successful validation of FORECASTER as a computer-aided drug design platform is described. In our quest for potent SERMs, this platform was used to build virtual combinatorial libraries and to filter and extract a highly diverse library from the NCI database. The virtual screening of a diverse library seeded with known active compounds followed by a search for analogs yielded a high enrichment factor. Moreover, the virtual screening of a designed virtual combinatorial library that included known actives resulted in a highly discriminative process. The laboratory of Dr. Sylvie Mader at University of Montreal showed that only FAE's induce a strong and fast SUMOylation of ERα in a variety of cell lines, strongly suggesting that SUMOylation is a general property related to full antiestrogenicity. A collaboration between the Gleason and Mader laboratories resulted in an SAR study of ERα ligands with different side chain lengths that revealed a specific side chain length of 15 to 19 atoms was necessary to differentiate SERM's from FAE's through the induction of SUMOylation. Histone deacetylase inhibitors (HDACi's) have emerged as a new and growing class of anticancer agents. Recent findings have shown that co-treatment of SERM's or FAE's with HDACi's can significantly decrease the growth of breast cancer cells. Inspired by the success of the vitamin D/HDACi hybrids developed within our group, and taking advantage of structural features of both kinds of molecules, we developed a new family of antiestrogen/HDACi hybrids. A first generation set of hybrids were designed by replacing the tertiary amide of FAE ICI-164,384 with three different zinc binding groups: hydroxamic acid, o-aminoanilide and N-butylhydroxamate. Both the hydroxamic acid and the o-aminoanilide hybrids displayed both antiestrogenic and HDAC inhibitory activities whereas N-butylhydroxamate hybrid displayed antiestrogenic activity but a poor HDAC inhibition profile. All hybrids inhibited MCF-7 cell proliferation and validated the proof of concept regarding the formation of this class of hybrids. However, none of the hybrids showed an improved profile compared to known antiestrogens or HDACi's. A second generation set of hybrids exploiting the same zinc binding groups with SERM- and FAE-like side chains were developed in an effort to balance the activity of our hybrids. Hydroxamic acids showed a consistent inhibition of HDAC's while displaying poor antiestrogenic activity in most cases. Ortho-amino anilides showed modest HDAC inhibition activity and only aliphatic-based o-amino-anilides behaved as antiestrogens. N-Butyl-hydroxamates showed a reduced potency to inhibit HDAC's compared to their hydroxamic acid counterparts but retained antiestrogenic behaviour. Overall, our second-generation hybrids did not show equal or improved HDAC inhibition and antiestrogenic activities relative to known antiestrogens and HDACi's.
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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 | 0.000 | 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) | 0.016 | 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".