Using a pharmacophore representation concept to elucidate molecular similarity of dopamine antagonists.

The pharmacophoric concept plays an important role in ligand-based drug design methods to describe the similarity and diversity of molecules, and could also be exploited as a molecular representation scheme. A three-point pharmacophore method was used as a molecular representation perception. This procedure was implemented for dopamine antagonists of the D(2) receptor subtype. The molecular structures of the antagonists included in this analysis were categorized into two structurally distinct classes. Using structural superposition with internal energy minimization, two pharmacophore models were deduced. Based on these two models other D(2) antagonists that fulfil them were derived and studied. This procedure aided the identification of the common 3D patterns present in diverse molecules that act at the same biological target and the extraction of a common molecular framework for the two structural classes. The pharmacophoric information was found to be suitable for guiding superposition of structurally diverse molecules, using a more biologically meaningful selection of the targeting points.

Microscopical examination of the localisation patterns of two novel rhodamine derivatives in normal and neoplastic colonic mucosa.

Tissue characterisation by fluorescence imaging, using exogenous fluorophores, is a promising method for cancer detection. Histochemical alterations in the composition of mucins, when neoplastic transformations occur, could be exploited to derive more selective fluoroprobes indicative of early malignant transformation. The aim of this work was to develop and examine tumour selective fluoroprobes for colon cancer diagnosis, as well as to determine the morphological components where selective dye accumulation has occurred. Two novel fluoroprobes: rhodamine B-L-leucine amide and rhodamine B-phenylboronic acid were synthesised and examined together with Mayer's mucicarmine, alexa 350-wheat germ agglutinin (WGA) and tetramethyl rhodamine-concanavalin A (ConA). Fluorescence microscopy studies were performed with deparaffinised human colon sections, using an epifluorescence microscope equipped with a colour CCD camera. The intense accumulation of the novel fluoroprobes was localised in the amorphous material in the lumen of neoplastic crypts. To gain insight into the localisation patterns, mucicarmine, alexa 350-WGA and tetramethyl rhodamine-ConA were used. Alexa 350-WGA reacted primarily with mucin secreted in the malignant crypt lumen suggesting that this material is rich in sialic acid and N-acetylglucosaminyl residues. These derivatives clearly and consistently distinguished non-neoplastic from neoplastic human colon tissue sections. The intense accumulation at the altered mucins indicates that they could be used as fluoroprobes of biochemical alterations for carcinoma detection.

Texture analysis of fluorescence microscopic images of colonic tissue sections.

The aim of this study was to assess the potential of texture analysis for the characterization of fluorescence images from colonic tissue sections stained with a novel and selective fluoroprobe, Rhodamine B-phenylboronic acid. Fluorescence microscopy images of colonic healthy mucosa (n = 35) and adenocarcinomas (n = 35) were digitally captured and subjected to image texture analysis. Textural features derived from the grey level co-occurrence matrix were calculated. A modified version of the multiple discriminant analysis criterion was used to choose an appropriate subset of features. A minimum Mahalanobis distance, linear discriminant classifier and a simple evaluation 'score' method were used to classify image feature data into the two categories. A subset of four textural features was selected and used for the description and classification of each image field. They were found appropriate to correctly classify 95% of the images into the two classes, using two different classifiers. These features contained information about local homogeneity and grey level linear dependencies of the image. This study demonstrated that texture analysis techniques could provide valuable diagnostic decision support in a complex domain such as colorectal tissue.