Drug targeting CYP2E1 for the treatment of early-stage alcoholic steatohepatitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alcoholic steatohepatitis (ASH)-the inflammation of fatty liver-is caused by chronic alcohol consumption and represents one of the leading chronic liver diseases in Western Countries. ASH can lead to organ dysfunction or progress to hepatocellular carcinoma (HCC). Long-term alcohol abstinence reduces this probability and is the prerequisite for liver transplantation-the only effective therapy option at present. Elevated enzymatic activity of cytochrome P450 2E1 (CYP2E1) is known to be critically responsible for the development of ASH due to excessively high levels of reactive oxygen species (ROS) during metabolization of ethanol. Up to now, no rational drug discovery process was successfully initiated to target CYP2E1 for the treatment of ASH. METHODS: In this study, we applied a rational drug design concept to develop drug candidates (NCE) including preclinical studies. RESULTS: A new class of drug candidates was generated successfully. Two of the most promising small compounds named 12-Imidazolyl-1-dodecanol (abbr.: I-ol) and 1-Imidazolyldodecane (abbr.: I-an) were selected at the end of this process of drug discovery and developability. These new ω-imidazolyl-alkyl derivatives act as strong chimeric CYP2E1 inhibitors at a nanomolar range. They restore redox balance, reduce inflammation process as well as the fat content in the liver and rescue the physiological liver architecture of rats consuming continuously a high amount of alcohol. CONCLUSIONS: Due to its oral application and therapeutic superiority over an off-label use of the hepatoprotector ursodeoxycholic acid (UDCA), this new class of inhibitors marks the first rational, pharmaceutical concept in long-term treatment of ASH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".