ArticleACS omega2023
Predictive Modeling of HMG-CoA Reductase Inhibitory Activity and Design of New HMG-CoA Reductase Inhibitors.
Article in ACS omega, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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.
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Who cites it
3 citing papers in PubMed.
- Robust machine learning and ensemble learning approach to predict variation in experimental data for multiple measurements and anomalies.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- Transformer-Decoder GPT Models for Generating Virtual Screening Libraries of HMG-Coenzyme A Reductase Inhibitors: Effects of Temperature, Prompt Length, and Transfer-Learning Strategies.Journal of chemical information and modeling · 2024Article
- QSAR Regression Models for Predicting HMG-CoA Reductase Inhibition.Pharmaceuticals (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As blood cholesterol increases, it accumulates in the intima of blood vessels, elevating the risk of atherosclerosis and coronary artery disease. Drugs that inhibit enzymes essential for cholesterol synthesis are effective in improving blood cholesterol levels. Statins are used to treat hypercholesterolemia as they inhibit 3-hydroxyl-3-methylglutaryl coenzyme A (HMG-CoA) reductase (HMGR), the rate-limiting enzyme in cholesterol synthesis. Statins are known to exert their effects by translocating to the liver, where they are taken up by the organic anion transporting polypeptide 1B1 (OATP1B1). Therefore, we hypothesized that a compound with high HMGR inhibitory activity and high affinity for OATP1B1 would be an excellent new therapeutic agent for hypercholesterolemia with increased liver selectivity and fewer side effects. In this study, we developed two models for predicting HMGR inhibitory activity and OATP1B1 affinity to propose the chemical structure of a new therapeutic agent for hypercholesterolemia with both high inhibitory activity and high liver selectivity. HMGR inhibitory activity and OATP1B1 affinity prediction models were constructed with high prediction accuracy for the test data:
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Registered trials
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