QSAR Models Based on Lipophilicity for Quinoxaline 1,4-di-N-Oxides as Antitubercular Candidates

DUCHOWICZ, PABLO R.; ESTHER, VICENTE; MERCADER, ANDREW G.; PÉREZ-SILANES, SILVIA; ALDANA, IGNACIO; FERNÁNDEZ, FRANCISCO M.; CASTRO, EDUARDO A.; MONGE, ANTONIO

Vienna, Austria

Simposio; XXth International Symposium on Medicinal Chemistry; 2008

European Federation for Medicinal Chemistry

Tuberculosis (TB), an infection of Mycobacterium tuberculosis, still remains the leading cause of worldwide death among infectious diseases. The statistics indicate that 1.6 million people throughout the world die from Tuberculosis, and there were an estimated 8.8 million new cases in 2005. The current frontline therapy for tuberculosis consists of administering three or more different drugs during an extended period of time. Consequently, problems due to multidrug-resistant TB arise and it is necessary to develop new therapeutic agents in order to treat drug resistant forms of the disease. As a result of the anti-tuberculosis research project, our group published several studies in which the synthesis and biological evaluation of a large amount of quinoxalines have been described [1-3], thus leading to several compounds that showed important antitubercular activity in vitro. This study resorted to the Quantitative Structure-Activity Relationships (QSAR) formalism [4] for predicting the anti-TB activity on a set including more than 300 quinoxaline derivatives compiled from recent publications. For this purpose, we analyzed the effect of lipophilicity values on the activity of compounds through QSAR models, expressing the lipophilicity contribution with the octanol/water partition coefficient (logKow) as calculated via different modelling techniques reported in the specialized literature [5]. It is known that the different estimation methods of logKow would not yield to the same numerical value for characterizing this quantity for a specific organic chemical, and thus this study discovered the most suitable method. The best linear regression models established with our variable subset selection algorithm [6] were predictive. The application of the QSAR equations developed now enables the proposal of new candidate structures that still do not have experimentally assigned biological data. References: [1] Zarranz, B.; Jaso, A.; Aldana, I.; Monge, A., Synthesis and antimycobacterial activity of new quinoxaline-2-carboxamide 1,4-di-N-oxide derivatives. Bioorg. Med. Chem. 2003, 11, 2149-2156. [2] Jaso, A.; Zarranz, B.; Aldana, I.; Monge, A., Synthesis of new 2-acetyl and 2-benzoyl quinoxaline 1,4-di-N-oxide derivatives as anti-Mycobacterium tuberculosis agents. Eur. J. Med. Chem. 2003, 38, 791-800. [3] Jaso, A.; Zarranz, B.; Aldana, I.; Monge, A., Synthesis of new quinoxaline-2-carboxylate 1,4-dioxide derivatives as anti-Mycobacterium tuberculosis agents. J. Med. Chem. 2005, 48, 2019-2025. [4] Hansch, C.; Leo, A., Exploring QSAR. Fundamentals and Applications in Chemistry and Biology. American Chemical Society: Washington, D. C., 1995. [5] Virtual Computational Chemistry Laboratory, http://www.vcclab.org/lab/alogps [6] Duchowicz, P. R.; Mercader, A. G.; Fernández, F. M.; Castro, E. A., Prediction of Aqueous Toxicity for Heterogeneous Phenol Derivatives by QSAR. Chemom. Intell. Lab. Syst. 2008, 90, 97-107.