| Summary: | 碩士 === 國立東華大學 === 化學系 === 98 === P-glycoprotein (P-gp) is an ATP-dependent membrane transporter that plays a pivotal role in eliminating xenobiotics by active extrusion of xenobiotics from the cell. Multidrug resistance (MDR) is highly associated with the overexpression of P-glycoprotein (P-gp) by cells, resulting in increased efflux of chemotherapeutical agents and reduction of intracellular drug accumulation. It is of clinical importance to develop P-gp inhibitors that can reverse MDR in the process of drug discovery and development. An in silico model was derived to predict the inhibition of P-gp using the newly invented pharmacophore ensemble/support vector machine (PhE/SVM) scheme based on the data compiled from the literature. The predictions by the PhE/SVM model are in good agreement with the experimental observations for those molecules in the training set (n = 31, r2 = 0.89, q2 = 0.86, RMSE=0.40, s = 0.28), the test set (n = 88, r2 = 0.87, RMSE = 0.39, s = 0.25) and the outlier set (n = 10, r2 = 0.96, RMSE = 0.10, s = 0.05). The generated PhE/SVM model also showed high accuracy when subjected to those validation criteria generally adopted to gauge the predictivity of a theoretical model. Thus, it can be asserted that this PhE/SVM model is an accurate, fast and robust model and can be employed to predict P-gp inhibitor binding affinity to facilitate drug discovery and drug development by designing drug candidates with better metabolism profile.
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