Biomedical subjects
Publications and source records attributed to Stuart W Paine.
Starting with the weak agonist indomethacin, a series of potent, selective CRTh2 (DP(2)) antagonists have been discovered as potential treatments for asthma, allergic rhinitis and other inflammatory diseases.
We present herein a QSAR tool enabling an entirely in silico prediction of human and rat steady-state volume of distribution (Vss), to be made prior to chemical synthesis, preceding detailed allometric or mechanistic assessment of Vss. Three different statistical methodologies, Bayesian neural networks (BNN), classification and regression trees (CART), and partial least squares (PLS) were employed to model human (N=199) and rat (N=2086) data sets. The results in prediction of an independent test set show the human model has an r2 of 0.60 and an rms error in prediction of 0.48. The corresponding rat model has an r2 of 0.53 and an rms error in prediction of 0.37, indicating both models could be very useful in the early stages of the drug discovery process. This is the first reported entirely in silico approach to the prediction of rat and human steady-state volume of distribution.
This review of 61 references delineates contemporary computation quantitative structure activity relationship (QSAR) approaches that have been used to elucidate the molecular features that influence the binding and metabolism of a compound by the major phase 1 and phase 2 metabolising enzymes; Cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), respectively. Contemporary studies are applying 2D and 3D QSAR, pharmacophore approaches and nonlinear techniques (for example: recursive partitioning, neural networks and support vector machines) to model drug metabolism. Furthermore, this review highlights some of the challenges and opportunities for future research; the need to develop 'global' models for CYP and UGT metabolism and to extend QSAR for other important metabolising enzymes.
QSAR models for a diverse set of compounds for cytochrome P450 1A2 inhibition have been produced using 4 statistical approaches; partial least squares (PLS), multiple linear regression (MLR), classification and regression trees (CART), and bayesian neural networks (BNN). The models complement one another and have identified the following descriptors as important features for CYP1A2 inhibition; lipophilicity, aromaticity, charge, and the HOMO/LUMO energies. Furthermore all models are global and have been used to predict a diverse independent set of compounds. For the first time in the field of QSAR, the kappa index of agreement has comprehensively been used to assess the overall accuracy of the model's predictive power. The models are statistically significant and can be used as a rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries.