Sung Kwang Lee

EaMEAD: Activation Energy Prediction of Cytochrome P450 Mediated Metabolism with Effective Atomic Descriptors

In an effort to improve drug design and predictions for pharmacokinetics (PK), an empirical model was developed to predict the activation energies (Ea) of cytochrome P450 (CYP450) mediated metabolism. The model, EaMEAD (Activation energy of Metabolism reactions with Effective Atomic Descriptors), predicts the Ea of four major metabolic reactions of the CYP450 enzyme: aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation. To build and validate the empirical model, the E(a) values of the substrates with diverse chemical structures (394 metabolic sites for aliphatic hydroxylation, 27 metabolic sites for N-dealkylation, 9 metabolic sites for O-dealkylation, and 85 metabolic sites for aromatic hydroxylation) were calculated by AM1 molecular orbital (MO). Empirical equations, Quantitative Structure Activity Relationship (QSAR) models, were derived using effective atomic charge, effective atomic polarizability, and bond dipole moments of the substrates as descriptors. EaMEAD is shown to accurately predict Ea with a correlation coefficient (R) of 0.94 and root-mean-square error (RMSE, unit is kcal/mol) of 0.70 for aliphatic hydroxylation, N-dealkylation, and O-dealkylation, and R of 0.83 and RMSE of 0.80 for aromatic hydroxylation, respectively. Physical origin and the role of the effective atomic descriptors of the models are presented in detail. With this model, the Ea of the metabolism can be rapidly predicted without any experimental parameters or time-consuming QM calculation. Regioselectivity prediction with our model is presented in the case of CYP3A4 metabolism. The reliability and ease of use of this model will greatly facilitate early stage PK predictions and rational drug design. Moreover, the model can be applied to develop the Ea prediction model of various types of chemical reactions.

Prediction of autoignition temperatures (AITs) for hydrocarbons and compounds containing heteroatoms by the quantitative structure–property relationship

Regression models that are useful for the explanation and prediction of autoignition temperatures of diverse compounds were provided by a quantitative structure–property relationships study (QSPR). Genetic functional approximation was used to find the best multiple linear regression within 72 molecular descriptors. After validation by correlation of the prediction set, nine descriptor models were evaluated in the best model. The nine descriptors were Ial, Ike, radius of gyration, 1χv, SC-2, the Balaban index JX, density, Kappa-3-AM and Jurs-FNSA-2, and information of structure features and their interactions was provide. The result of the best regression model showed that the square of the correlation coefficient (R2) for the autoignition temperature of the 157-member training set was 0.920, and the root mean square error (RMSE) was 25.876. The R2 of AIT for a 43-member prediction set was 0.910, and the RMSE was 28.968.