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Record W4366215565 · doi:10.3390/molecules28083499

Description of Chemical Synthesis, Nuclear Magnetic Resonance Characterization and Biological Activity of Estrane-Based Inhibitors/Activators of Steroidogenesis

2023· review· en· W4366215565 on OpenAlexaff
Donald Poirier

Bibliographic record

VenueMolecules · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAldose Reductase and Taurine
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSteroidHormoneAromataseChemistryIn vivoEnzymeSteroid sulfataseBiochemistrySteroid hormonePharmacologyBiologyInternal medicineCancerMedicineBreast cancer

Abstract

fetched live from OpenAlex

Steroid hormones play a crucial role in several aspects of human life, and steroidogenesis is the process by which hormones are produced from cholesterol using several enzymes that work in concert to obtain the appropriate levels of each hormone at the right time. Unfortunately, many diseases, such as cancer, endometriosis, and osteoporosis as examples, are caused by an increase in the production of certain hormones. For these diseases, the use of an inhibitor to block the activity of an enzyme and, in doing so, the production of a key hormone is a proven therapeutic strategy whose development continues. This account-type article focuses on seven inhibitors (compounds 1–7) and an activator (compound 8) of six enzymes involved in steroidogenesis, namely steroid sulfatase, aldo-keto reductase 1C3, types 1, 2, 3, and 12 of the 17β-hydroxysteroid dehydrogenases. For these steroid derivatives, three topics will be addressed: (1) Their chemical synthesis from the same starting material, estrone, (2) their structural characterization using nuclear magnetic resonance, and (3) their in vitro or in vivo biological activities. These bioactive molecules constitute potential therapeutic or mechanistic tools that could be used to better understand the role of certain hormones in steroidogenesis.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.355
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.260
Teacher spread0.223 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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