Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor

Yin, Cen; Yang, Xianhai; Wei, Mengbi; Liu, Huihui

Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor

2017

Yin, Cen | Yang, Xianhai | Wei, Mengbi | Liu, Huihui

Toxic chemicals entered into human body would undergo a series of metabolism, transport and excretion, and the key roles played in there processes were metabolizing enzymes, which was regulated by the pregnane X receptor (PXR). However, some chemicals in environment could activate or antagonize human pregnane X receptor, thereby leading to a disturbance of normal physiological systems. In this study, based on a larger number of 2724 structurally diverse chemicals, we developed qualitative classification models by the k-nearest neighbor method. Moreover, the logarithm of 20 and 50% effective concentrations (log EC ₂₀ and log EC ₅₀) was used to establish quantitative structure-activity relationship (QSAR) models. With the classification model, two descriptors were enough to establish acceptable models, with the sensitivity, specificity, and accuracy being larger than 0.7, highlighting a high classification performance of the models. With two QSAR models, the statistics parameters with the correlation coefficient (R ²) of 0.702–0.749 and the cross-validation and external validation coefficient (Q ²) of 0.643–0.712, this indicated that the models complied with the criteria proposed in previous studies, i.e., R ² > 0.6, Q ² > 0.5. The small root mean square error (RMSE) of 0.254–0.414 and the good consistency between observed and predicted values proved satisfactory goodness of fit, robustness, and predictive ability of the developed QSAR models. Additionally, the applicability domains were characterized by the Euclidean distance-based approach and Williams plot, and results indicated that the current models had a wide applicability domain, which especially included a few classes of environmental contaminant, those that were not included in the previous models.

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