Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Pharmacology

Background:

• Berberine (BBR) is a small molecule from Chinese herbs with cholesterol-lowering properties.
• Hundreds of BBR derivatives have been synthesized to enhance hypolipidemic activities.
• Existing quantitative structure-activity relationship (QSAR) models for BBR are limited due to unknown lipid-lowering mechanisms.

Purpose of the Study:

• To develop a robust QSAR model for predicting hypolipidemic activities of BBR derivatives.
• To establish a reliable tool for the discovery of novel BBR analogues with improved lipid-lowering effects.
• To explore the use of ligand efficiency indices in BBR QSAR modeling.

Main Methods:

• A dataset of 59 BBR derivatives with known hypolipidemic activities was compiled.
• Ligand efficiency indices were used as the activity descriptor instead of pIC50.
• The dataset was randomly divided into training and test sets for model validation.
• QSAR models were built and statistically evaluated for robustness.

Main Results:

• The best QSAR model (Model 6) demonstrated excellent statistical performance.
• For the training set, R² = 0.984, Q² = 0.981, and RMSE = 0.1160.
• For the test set, R² = 0.989 and RMSE = 0.0067, indicating high predictive power.
• The developed model showed robust statistical quality.

Conclusions:

• The developed QSAR model using ligand efficiency indices is a reliable tool for hit discovery.
• This model can guide the design and synthesis of novel berberine analogues with enhanced lipid-lowering activities.
• The study provides a new approach for developing BBR QSAR models in the absence of identified molecular targets.