1,4-Dihydropyridine Scaffold in Medicinal Chemistry, The Story So Far And Perspectives (Part 2): Action in Other Targets and Antitargets
Bibliographic record
Abstract
1,4-Dihydropyridines were introduced in the last century for the treatment of coronary diseases. Then medicinal chemists decorated the 1,4-DHP nucleus, the most studied scaffold among L-type calcium channel blockers, achieving diverse activities at several receptors, channels and enzymes. We already described (Ioan et al. Curr. Med. Chem. 2011, 18, 4901-4922) the effects of 1,4-DHPs at ion channels and G-protein coupled receptors. In this paper we continue the analysis of the wide range of biological effects exerted by compounds belonging to this chemical class. In particular, focus is given to the ability of 1,4-DHPs to revert multi drug resistance that, after over 20 years of research, continues to be of great interest. We also describe activities on other targets and the action of 1,4-DHPs against several diseases. Finally, we report and review the interaction of 1,4-DHPs with the hERG channel, transporters and phase I metabolizing enzymes. This work is a starting point for further exploration of the 1,4-DHP core activities on targets, off-targets and antitargets. Keywords: 1, 4-Dihydropyridine, alzheimer disease, anti-atherosclerotic action, antitargets, calcium antagonists, cytochromes P450, glucorticoid hormone receptors, HIV-1 protease inhibitors, MDR, oxidative stress prevention, PAF antagonists, P-gp, TRH, tuberculosis
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".