Vitamin D Analogs- Drug Design Based on Proteins Involved in Vitamin D Signal Transduction
Bibliographic record
Abstract
Vitamin D analogs have proven to be very valuable tools for the treatment of calcium-related diseases and certain hyperproliferative conditions such as renal osteodystrophy, psoriasis and cancer. In general, vitamin D analogs exploit the enzymic and receptor machinery of the 1alpha,25-dihydroxyvitamin D(3) (1alpha,25(OH)(2)D(3)) signal transduction pathway. Key proteins in this cascade include the vitamin D receptor (VDR), the vitamin D-binding protein (DBP) and three cytochrome P450s (CYP27A, CYP27B and CYP24) which effect the synthesis and breakdown of the natural hormone, 1alpha,25(OH)(2)D(3). Analogs have been designed which reduce or enhance the importance of each of these proteins in the signal transduction pathway. Vitamin D prodrugs require one or more steps of activation and overcome congenital or acquired blocks in the 1alpha-hydroxylation step. By far the biggest class of vitamin D analogs are the VDR agonists which directly mimic 1alpha,25(OH)(2)D(3) and trigger protein conformational changes in the receptor which lead to changes in the transcriptional machinery at vitamin D-responsive genes. Other emerging classes of molecules include the VDR antagonists and CYP24 inhibitors which target different events in the cascade. This review assesses the relative importance of each of the proteins of the vitamin D cascade, evaluates the success of these modifications in tailoring drugs in all classes for selected disease states and contemplates future directions for the field.
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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) | 0.001 | 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) | 0.002 | 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".