Development of a whole body physiologically based model to characterise the pharmacokinetics of benzodiazepines. 1: Estimation of rat tissue-plasma partition ratios.

Three methods for estimation of the equilibrium tissue-to-plasma partition ratios (Kp values) in the presence of tissue concentration time data have been investigated. These are the area method, the open loop (tissue specific) method and the whole body model(closed loop) method, each with different model assumptions. Additionally, multiple imputations, a technique for dealing with deficiencies in data sets (i.e., missing tissues) is used. The estimated Kp values by the three methods have been compared and the limitations and advantages of each approach drawn. The area method, which is essentially model free, gives only a crude estimate of Kp without making any statement of its uncertainty; whereas both the open and closed loop methods provide an estimate of this. The closed loop method, where the most assumptions are made, is the approach that gives the best overall estimates of Kp, which was confirmed by comparing the predicted concentration-time profiles with experimental data. Although the estimates from the closed loop method, as well as the other two methods, are conditioned on the data, they are the most reliable for both propagating parameter variability and uncertainty through a whole body physiologically based model, as well as for extrapolation to human. A series of benzodiazepines, namely alprazolam, chlordiazepoxide, clobazam, diazepam, flunitrazepam, midazolam and triazolam in rat is used as a case study in the current investigation.

Comparative assessment of methods to develop QSARs for the prediction of the toxicity of phenols to Tetrahymena pyriformis.

Quantitative structure-activity relationships (QSARs) for the toxicity of 200 phenols to the ciliated protozoan Tetrahymena pyriformis, and the validation of the QSARs using a test set of a further 50 compounds, are reported. The phenols are structurally heterogeneous and represent a variety of mechanisms of toxic action including polar narcosis, weak acid respiratory uncoupling, electrophilicity, and those compounds capable of being metabolised or oxidised to quinones. For each compound, a total of 108 physico-chemical descriptors have been calculated. A variety of methods were utilised to develop QSARs and are compared. The response-surface, or two parameter, approach was found to be successful, but only following the removal of compounds known to form quinones. Stepwise regression produced a seven parameter QSAR with good statistical fit, but was less interpretable and transparent than the response-surface. Partial least squares produced a good model for phenolic toxicity following supervised selection of parameters, this, however, was the least transparent of all approaches attempted. In all approaches, a large number of outliers were observed, typically these were compounds capable of being metabolised to quinones. The strengths and weaknesses of each of the approaches to predict the toxicity of the validation (test) set of phenols to T. pyriformis are discussed.

Structure-based classification of antibacterial activity.

The aim of this study was to develop a simple quantitative structure-activity relationship (QSAR) for the classification and prediction of antibacterial activity, so as to enable in silico screening. To this end a database of 661 compounds, classified according to whether they had antibacterial activity, and for which a total of 167 physicochemical and structural descriptors were calculated, was analyzed. To identify descriptors that allowed separation of the two classes (i.e. those compounds with and without antibacterial activity), analysis of variance was utilized and models were developed using linear discriminant and binary logistic regression analyses. Model predictivity was assessed and validated by the random removal of 30% of the compounds to form a test set, for which predictions were made from the model. The results of the analyses indicated that six descriptors, accounting for hydrophobicity and inter- and intramolecular hydrogen bonding, provided excellent separation of the data. Logistic regression analysis was shown to model the data slightly more accurately than discriminant analysis.