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A rapid reversed-phase (RP) high-performance liquid chromatography method was developed and applied for simultaneous separation, and determination of flavonoids and phenolic acids in eight Plantago L. taxa (P. altissima L., P. argentea Chaix, P. coronopus L., P. holosteum Scop. ssp. depauperata Pilger, P. holosteum ssp. holosteum, P. holosteum ssp. scopulorum (Degen) Horvatić, P. lagopus L., and P. maritima L.) growing in Croatia. Chromatographic separation was carried out on Zorbax Eclipse XDB-C18 using gradient elution with a H2 O (pH 2.5, adjusted with CF3 COOH) and MeCN mixture at 30°. The contents of analyzed phenolic compounds (% of the dry weight of the leaves, dw) varied among examined species: rutin (max. 0.024%, P. argentea), hyperoside (max. 0.020%, P. lagopus), quercitrin (max. 0.013%, P. holosteum ssp. holosteum), quercetin (max. 0.028%, P. holosteum ssp. scopulorum), chlorogenic acid (max. 0.115%, P. lagopus), and caffeic acid (max. 0.046%, P. coronopus). Isoquercitrin was detected only in P. argentea (0.020%), while isochlorogenic acid content was below limit of quantification in all investigated species. Multivariate analyses (UPGMA and PCA) showed significant differences in contents of investigated polyphenolic compounds between different Plantago taxa. Accordingly, investigated substances might be employed as chemotaxonomic markers in the study of the complex genus Plantago.

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Capability of evolutionary neural network (ENN) based QSAR approach to direct the descriptor selection process towards stable descriptor subset (DS) composition characterized by acceptable generalization, as well as the influence of description stability on QSAR model interpretation have been examined. In order to analyze the DS stability and QSAR model generalization properties multiple random dataset partitions into training and test set were made. Acceptability criteria proposed by Golbraikh et al. [J. Comput.-Aided Mol. Des., 17 (2003) 241] have been chosen for selection of highly predictive QSAR models from a set of all models produced by ENN for each dataset splitting. All QSAR models that pass Golbraikh's filter generated by ENN for each dataset partition were collected. Two final DS forming principles were compared. Standard principle is based on selection of descriptors characterized by highest frequencies among all descriptors that appear in the pool [J. Chem. Inf. Comput. Sci., 43 (2003) 949]. Search across the model pool for DS that are stable against multiple dataset subsampling i.e. universal DS solutions is the basis of novel approach. Based on described principles benzodiazepine QSAR has been proposed and evaluated against results reported by others in terms of final DS composition and model predictive performance.

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