Design and Synthesis of Potent in Vitro and in Vivo Anticancer Agents Based on 1-(3′,4′,5′-Trimethoxyphenyl)-2-Aryl-1H-Imidazole

A novel series of tubulin polymerization inhibitors, based on the 1-(3',4',5'-trimethoxyphenyl)-2-aryl-1H-imidazole scaffold and designed as cis-restricted combretastatin A-4 analogues, was synthesized with the goal of evaluating the effects of various patterns of substitution on the phenyl at the 2-position of the imidazole ring on biological activity. A chloro and ethoxy group at the meta- and para-positions, respectively, produced the most active compound in the series (4o), with IC50 values of 0.4-3.8 nM against a panel of seven cancer cell lines. Except in HL-60 cells, 4o had greater antiproliferative than CA-4, indicating that the 3'-chloro-4'-ethoxyphenyl moiety was a good surrogate for the CA-4 B-ring. Experiments carried out in a mouse syngenic model demonstrated high antitumor activity of 4o, which significantly reduced the tumor mass at a dose thirty times lower than that required for CA-4P, which was used as a reference compound. Altogether, our findings suggest that 4o is a promising anticancer drug candidate that warrants further preclinical evaluation

Classifying web documents in a hierarchy of categories: a comprehensive study

Most of the research on text categorization has focused on classifying text documents into a set of categories with no structural relationships among them (flat classification). However, in many information repositories documents are organized in a hierarchy of categories to support a thematic search by browsing topics of interests. The consideration of the hierarchical relationship among categories opens several additional issues in the development of methods for automated document classification. Questions concern the representation of documents, the learning process, the classification process and the evaluation criteria of experimental results. They are systematically investigated in this paper, whose main contribution is a general hierarchical text categorization framework where the hierarchy of categories is involved in all phases of automated document classification, namely feature selection, learning and classification of a new document. An automated threshold determination method for classification scores is embedded in the proposed framework. It can be applied to any classifier that returns a degree of membership of a document to a category. In this work three learning methods are considered for the constructio