In Silico Modeling Of ADME

ADME (absorption, distribution, metabolism and excretion) properties of lead candidates with respect to the ADME processes have become the key factors in early phase drug development & discovery. In silico ADME models will help yield compounds with good target affinity, reasonable drug-like properties and ensure greater likelihood of acceptable ADME properties. P-glycoprotein (P-gp) and cytochrome P450 3A4 (P450 3A4) enzyme have become the two main ADME targets because of their special physiological functions in drug ADME process. In this work, using various computational methods, several in silico models for P-gp and CYP3A4 have been explored.I. Using artificial neural networks (ANN), we have built a model for classification of P-gp substrates and inhibitors, obtaining an average accuracy of prediction of 82.3%. Meanwhile, a virtual screening model has also been well developed for flavonoid derivatives, a sort of most promising P-gp lead inhibitors. II. We propose a time-dependent metabolic control theory to the analysis and quantitative modeling of CYP450 catalytic pathway. This implementation leads to the findings that the P450 cycle is robust. The results also show that the two electrons transfers to the cycle have different impacts on the dynamics of the system. III. Applying the electro-topological state theory and pharmacophore modeling, we have studied the chemical mechanism for steroid selective 6-beta oxidation by P450 3A4 enzyme. IV. An in silico based on Bayesian-regularized neural network has been proposed to model Km values of P450 3A4. V. A dynamic model using differential equations to analyze the interplay between P450 3A4 and P-gp has been developed for the model molecule verapamil. The results might indicate that there is no complementary function of CYP3A4 and P-glycoprotein in enterocytes to reduce the bioavailability of orally administered drugs.