Design, Synthesis, and Evaluation of Antineoplastic Activity of Novel Carbocyclic Nucleosides

Aliuska M Helguera; J E Rodríguez-Borges; Olga Caamaño; Xerardo García-Mera; Maykel Pérez González; M Natália D S Cordeiro

doi:10.1002/minf.200900033

Design, Synthesis, and Evaluation of Antineoplastic Activity of Novel Carbocyclic Nucleosides

Mol Inform. 2010 Mar 15;29(3):213-31. doi: 10.1002/minf.200900033. Epub 2010 Mar 5.

Authors

Aliuska M Helguera^{1

2

3}, J E Rodríguez-Borges⁴, Olga Caamaño⁵, Xerardo García-Mera⁶, Maykel Pérez González², M Natália D S Cordeiro⁷

Affiliations

¹ REQUIMTE, Chemistry Department, University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal fax: +351 220402659.
² CBQ, Department of Chemistry, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
³ Department of Chemistry, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba.
⁴ CIQ, Chemistry Department, University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal.
⁵ Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15706 Santiago de Compostela, Spain fax: +34 981594912.
⁶ Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, 15706 Santiago de Compostela, Spain fax: +34 981594912. xerardo.garcia@usc.es.
⁷ REQUIMTE, Chemistry Department, University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal fax: +351 220402659. ncordeir@fc.up.pt.

PMID: 27462765
DOI: 10.1002/minf.200900033

Abstract

Cancer is the leading cause of death among men and women under age 85. Every year, millions of individuals are diagnosed with cancer. But finding new drugs is a complex, expensive, and very time-consuming task. Over the past decade, the cancer research community has begun to address the in silico modeling approaches, such as Quantitative Structure-Activity Relationships (QSAR), as an important alternative tool for targeting potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Early efforts with 2D classification models built from part of this dataset were very encouraging. Here we report a further detailed evaluation of classification models to flag potential anticancer activities derived from a variety of 3D molecular representations. A quantitative 3D-model model that discriminates anticancer compounds from the inactive ones was attained, which allowed the correct classification of 82 % of compounds in such a large and diverse dataset, with only 5 % of false inactives and 11 % of false actives. The model developed here was then used to select and design a new series of nucleosides, by classifying beforehand them as active/inactive anticancer compounds. From the compounds so designed, 22 were synthesized and evaluated for their inhibitory effects on the proliferation of murine leukemia cells (L1210/0), of which 86 % were well-classified as active or inactive, and only two were false actives, corroborating the good predictive ability of the present discriminant model. The results of this study thus provide a valuable tool for the design of novel potent anticancer nucleoside analogues.

Keywords: 3D-DRAGON descriptors; Antitumor agents; Leukemia; Nucleosides; QSAR.