Prediction of pharmacological classification by means of chromatographic parameters processed by principal component analysis

Classification of drugs in absorption classes using the classification and regression trees (CART) methodology

“…Different in vitro techniques, like Caco-2 membrane permeability, Parallel Artificial Membrane Permeability Assay (PAMPA) and animal tissue based methods, have been discussed by several authors [1,2] but these techniques are not very valuable in the high-throughput screening of diverse candidate drug molecules. Next to these techniques based on artificial membranes, isolated gut-segments or cultivated intestinal cells, HPLC-methods were developed, using either classical reversed-phase conditions [3], special stationary phases or special mobile phases [1,2,4,5]. A method with a special stationary phase is immobilized artificial membrane (IAM)-chromatography [1,2], which uses a stationary phase containing fosfatidylcholine groups, to mimic the lipophilic environment of the celmembranes [4].…”

Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure–retention relationship studies

“…The composition of the methanol/aqueous buffer mobile phase ranged from 75:25 to 0:100 (v/v). To compare retentions, they were extrapolated to 0% organic modifier [16] and the log k w values thus obtained are used.…”

“…The chromatographic data used were obtained from Nasal et al [16] and consisted of the logarithms of the extrapolated retention factors (log k w ) for 83 basic drugs. The data concern the retention in buffer/methanol mixtures on a Unisphere PBD, a polybutadiene-coated alumina, column at pH 11.7 using isocratic elution.…”

Comprehensive evaluation of antioxidant activity: A chemometric approach using principal component analysis

“…This is because of its capacity to reduce the dimensionality of the original dataset by retaining the maximum variability of a large number of variables (in terms of the variance-covariance structure) by few underlying factors (principal components -PCs) without losing the important information. Upon application of PCA, the number of variables in a data set is reduced by finding linear combinations of those variables which explain most of the data variability [39]. In the PCA application, there are several ways to preprocess a data set.…”