Search

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD50 (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD50: 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD50 = 1.2399 × 0JA +2.6911 × 1JA – 0.4445 × JB (R2 = 0.9860), where 0JA is zero order connectivity index, 1JA is the first order connectivity index and JB = 0JA × 1JA is the cross factor. The model was shown to have a good forecasting ability.

Diterpenoid alkaloids are extracted from plants. These compounds have broad biological activities, including effects on the cardiovascular system, anti-inflammatory and analgesic actions, and anti-tumor activity. The anti-inflammatory activity was determined by carrageenan-induced rat paw edema and experimental trauma in rats. The number of studies focused on the determination, quantitation and pharmacological properties of these alkaloids has increased dramatically during the past few years. In this work we built a dataset composed of 15 diterpenoid alkaloid compounds with diverse structures, of which 11 compounds were included in the training set and the remaining compounds were included in the test set. The quantitative chemistry parameters of the 15 diterpenoid alkaloids compound were calculated using the HyperChem software, and the quantitative structure–activity relationship (QSAR) of these diterpenoid alkaloid compounds were assessed in an anti-inflammation model based on half maximal effective concentration (EC50) measurements obtained from rat paw edema data. The QSAR prediction model is as follows: log ( E C 50 ) = − 0.0260 × SAA + 0.0086 × SAG + 0.0011 × VOL − 0.0641 × HE − 0.2628 × LogP − 0.5594 × REF − 0.2211 × POL − 0.1964 × MASS + 0.088 × BE + 0.1398 × HF (R2 = 0.981, Q2 = 0.92). The validated consensus EC50 for the QSAR model, developed from the rat paw edema anti-inflammation model used in this study, indicate that this model was capable of effective prediction and can be used as a reliable computational predictor of diterpenoid alkaloid activity.