Selectivity of imidazole–dioxolane compounds for<i>in vitro</i>inhibition of microsomal haem oxygenase isoforms
Bibliographic record
Abstract
Haem oxygenases (HO) are involved in the catalytic breakdown of haem to generate carbon monoxide (CO), iron and biliverdin. It is widely accepted that products of haem catabolism are involved in biological signaling in many physiological processes. Conclusions to most studies in this field have gained support from the judicious use of synthetic metalloporphyrins such as chromium mesoporphyrin (CrMP) to selectively inhibit HO. However, metalloporphyrins have also been found to inhibit other haem-dependent enzymes, such as nitric oxide synthase (NOS), cytochromes P-450 (CYPs) and soluble guanylyl cyclase (sGC), induce the expression of HO-1 or exhibit varied toxic effects. To obviate some of these problems, we have been examining non-porphyrin HO inhibitors and the present study describes imidazole-dioxolane compounds with high selectivity for inhibition of HO-1 (rat spleen microsomes) compared to HO-2 (rat brain microsomes) in vitro. (2R,4R)-2-[2-(4-chlorophenyl)ethyl]-2-[(1H-imidazol-1-yl)methyl]-4-methyl-1,3-dioxolane hydrochloride) was identified as the most selective inhibitor with a concentration of 0.6 microM inhibiting HO-1(inducible) by 50% compared with 394 microM for HO-2 (constitutive). These compounds were found to have no effects on the catalytic activities of rat brain NOS and lung sGC, but were potent inhibitors of microsomal CYP2E1 and CYP3A1/3A2 activities. In conclusion, we have identified imidazole-dioxolanes that are able to inhibit microsomal HO in vitro with high selectivity for HO-1 compared to HO-2, and little or no effect on the activities of neuronal NOS and sGC. These molecules could be used to facilitate studies on the elucidation of physiological roles of HO/CO in biological systems.
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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".