Evidence map›Paper›PMID 39334891›Full record

ReviewBiomolecules2024

CYP3A-Mediated Carbon-Carbon Bond Cleavages in Drug Metabolism.

Junhui Zhou, Xuan Qin, Shenzhi Zhou, Kevin R MacKenzie, Feng Li

Abstract readReview
In one paragraph

Review in Biomolecules, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Junhui ZhouCenter for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.
Xuan QinCenter for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0003-3728-5911
Shenzhi ZhouCenter for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.
Kevin R MacKenzieCenter for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0001-9620-5868
Feng LiCenter for Drug Discovery, Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.ORCID 0000-0002-9680-4614

Funding

Mechanisms of Liver Toxicity of Anit-Depressant DuloxetineR01DK121970 · NIDDK · BAYLOR COLLEGE OF MEDICINE · PI LI, FENG · 2020 to 2023
$1.8M
NIH HHS 1R01DK121970-05A1
6 · The paper itself

Abstract

Cytochrome P450 enzymes (P450s) play a critical role in drug metabolism, with the CYP3A subfamily being responsible for the biotransformation of over 50% of marked drugs. While CYP3A enzymes are known for their extensive catalytic versatility, one intriguing and less understood function is the ability to mediate carbon-carbon (C-C) bond cleavage. These uncommon reactions can lead to unusual metabolites and potentially influence drug safety and efficacy. This review focuses on examining examples of C-C bond cleavage catalyzed by CYP3A, exploring the mechanisms, physiological significance, and implications for drug metabolism. Additionally, examples of CYP3A-mediated ring expansion via C-C bond cleavages are included in this review. This work will enhance our understanding of CYP3A-catalyzed C-C bond cleavages and their mechanisms by carefully examining and analyzing these case studies. It may also guide future research in drug metabolism and drug design, improving drug safety and efficacy in clinical practice.

Indexed as

CarbonCytochrome P-450 CYP3AAnimalsHumansPharmaceutical PreparationsCarbonCytochrome P-450 CYP3APharmaceutical Preparationscarbon–carbon bond cleavageCYP3Adrug metabolism

Identifiers

PMID39334891
PMCPMC11430781

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.