Predictive QSAR modeling study on berberine derivatives with hypolipidemic activity

Abstract

Berberine (BBR) isolated from a Chinese herb, is identified as a new cholesterol-lowering small molecule, and hundreds of berberine derivatives have been obtained for optimization of their hypolipidemic activities in recent years. However, so far there is no available quantitative structure-activity relationship (QSAR) model used for the development of novel BBR analogs with hypolipidemic activities, mainly due to lack of lipid-lowering molecular mechanisms and target identification of BBR. In this paper, the tactics using ligand efficiency indices instead of pIC₅₀ as the activity could be adopted for the development of BBR QSAR models. A series of 59 BBR derivatives with hypolipidemic activities have been studied and split randomly into three sets of training and test sets. Statistical quality of most building models shows obviously robust. Best calculated model that employ LLE indice as the activity (Model 6) has the following statistical parameters: for training set R² = 0.984, Q² = 0.981, RMSE = 0.1160, and for test set R² = 0.989, RMSE = 0.0067. This model would be used for the development of novel BBR analogues with lipid-lowering activities as a hit discovery tool.

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59 berberine (BBR) derivatives with hypolipidemic activity were split into three training and test sets. Four ligand efficiency indices (BEI, LLE, SEI, and LELP) instead of the negative logarithm of ten of half maximal inhibitory concentration (pIC₅₀) as the activity could be used for the development of BBR quantitative structure-activity relationship (QSAR) models. The predictive potency of most built models were robust.

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