Quantitative structure/activity relationship modelling of pharmacokinetic properties using genetic algorithm-combined partial least squares method.

Quantitative structure/activity relationship (QSAR) approaches have widely been applied to gain deeper understandings of the relationships between ADME parameters and molecular structure and properties. QSAR models for predicting ADME properties are required to cover structurally diverse compounds. In the present investigation, we describe application of genetic algorithm-combined partial least squares (GA-PLS) method to QSAR modelling of various ADME properties. By selecting an appropriate set of molecular descriptors automatically by the use of genetic algorithm, many ADME properties could be well-explained by simple molecular descriptors derived from 2-dimensional chemical structure.

Two- and three-dimensional QSAR of carrier-mediated transport of beta-lactam antibiotics in Caco-2 cells.

In this study, we investigated whether such a topological descriptor-based approach is suitable for predicting the carrier-mediated transport of 20 beta-lactam antibiotics that are substrates of peptide transporters. To select the molecular descriptors that can effectively predict a targeted property in QSAR analysis, the genetic algorithm-combined partial least squares approach was used. The feasibility of the two-dimensional (2D)-QSAR approach was compared with that of comparative molecular field analysis (CoMFA). The logarithm of the uptake values of 20 beta-lactam antibiotics in Caco-2 cells obtained from the literature ranged from -1.15 to 1.09 (nmol/cm2/2 h). When preliminary leave-one-out cross-validated partial least squares analyses implemented in the SYBYL/CoMFA program were conducted, the r2pred was 0.759 and the standard error of prediction (s) was 0.373. However, the 2D-QSAR approach based on Molconn-Z descriptors gave a better predictability (r2pred = 0.923, s = 0.211), where 14 descriptors were selected and the optimal number of principal components was 4. Considering that the 2D-topological descriptors are less computationally intensive and practically completely automated, the simple 2D-QSAR model is also of great importance in drug discovery settings.

QSAR analysis of the inhibition of recombinant CYP 3A4 activity by structurally diverse compounds using a genetic algorithm-combined partial least squares method.

PURPOSE: To develop a quantitative structure/activity relationship (QSAR) model for predicting drug-CYP 3A4 interactions. METHOD: The inhibitory effect of 53 structurally diverse drugs on the metabolism of 7-benzyloxy-4-trifluoromethyl coumarin (BFC) by recombinant CYP 3A4 was evaluated using a rapid microtiter plate assay. For each drug, a total of 220 two-dimensional topological indices were calculated using Molconn-Z software. Using a genetic algorithm-based partial least squares (GA-PLS) method, the desired descriptors were automatically selected to maximize the predictability of the IC50 values. RESULTS: The IC50 values of the drugs tested ranged from 9 nM to 2 mM. Based on the GA-PLS method, five principal components derived from 20 Molconn-Z descriptors were found to be effective for QSAR modeling. Interestingly, these descriptors suggested that the molecular size would be an important factor in determining drug-CYP 3A4 interactions. In the leave-one-out prediction, the rpred and the standard error of prediction (s) were 0.754 and 0.787, respectively. Even in an external validation, the predictions were in good agreement with experimental values (rpred = 0.744, s = 0.769, n = 9). CONCLUSIONS: The proposed model, in which two-dimensional topological descriptors were used as molecular descriptors, was able to predict drug-CYP 3A4 interactions with reasonable accuracy.

Effect of heat on characteristics of chitosan film coated on theophylline tablets.

The effect of heat on the characteristics of chitosan film coated on theophylline tablets was studied. Chitosan of high viscosity grade with molecular weight in the range of 800,000-1,000,000, 80-85% degree of deacetylation was used as a film former by dissolving in 1% v/v acetic acid solution. The coated tablets had been cured at 40, 60, and 100 degrees C for 6, 12, and 24 hr. The morphology of the film at the edge and surface of coated tablets was investigated using scanning electron microscopy. Film cracking was increased and clearly observed in the coated tablets cured at 100 degrees C for 24 hr. As a result, more water could be absorbed into the tablets, followed by faster disintegration and faster drug release. The evidence of partial conversion of chitosonium acetate to chitin in the 13C nuclear magnetic resonance (NMR) spectra of chitosan films cured at 40, 60, and 100 degrees C was observed, but it had no effect on drug release behavior. Theophylline tablets coated with chitosan films gave sustained release behavior in various media, i.e., distilled water, 0.1 N hydrochloric acid, pH 4.5 acetate buffer, and pH 6.8 phosphate buffer. In addition, the film coating temperature at 55-60 degrees C and curing process at 40 and 60 degrees C had no effect on the drug release from theophylline tablets coated with chitosan polymer. Finally, it might be concluded that both the physical and chemical properties of chitosan films were affected by heat.

Quantitative structure/property relationship analysis of Caco-2 permeability using a genetic algorithm-based partial least squares method.

Caco-2 cell monolayers are widely used systems for predicting human intestinal absorption. This study was carried out to develop a quantitative structure-property relationship (QSPR) model of Caco-2 permeability using a novel genetic algorithm-based partial least squares (GA-PLS) method. The Caco-2 permeability data for 73 compounds were taken from the literature. Molconn-Z descriptors of these compounds were calculated as molecular descriptors, and the optimal subset of the descriptors was explored by GA-PLS analysis. A fitness function considering both goodness-of-fit to the training data and predictability of the testing data was adopted throughout the genetic algorithm-driven optimization procedure. The final PLS model consisting of 24 descriptors gave a correlation coefficient (r) of 0.886 for the entire dataset and a predictive correlation coefficient (r(pred)) of 0.825 that was evaluated by a leave-some-out cross-validation procedure. Thus, the GA-PLS analysis proved to be a reasonable QSPR modeling approach for predicting Caco-2 permeability.