Category: Data Analysis and Informatics
Drug repositioning (or drug repurposing) involves finding new therapeutic indications for approved, discontinued or experimental drugs. This strategy has considerable advantages over the search for de novo drugs since the cost and time required for the approval of an innovative medication are greatly reduced. It is a particularly useful strategy to search new treatments for neglected diseases such as Chagas disease, an infectious disease caused by Trypanosoma cruzi that affects more than 6 millons people in Latin America.
In this work, we have developed in silico models capable of recognizing inhibitors of N-myristoyl transferase (NMT), that catalyzes the transfer of myristate from myristoyl coenzyme A to the amino terminal glycine residue of the protein target. This enzyme has been genetically and biochemically validated as a molecular target for T. cruzi, although scarcely exploited to the moment for the search of drug candidates against Chagas disease.
Through an extensive literature search we have compiled a database comprising compounds previously tested against trypanosomatid NMT (including NMT from species of the Trypanosoma genre). Using this database, we have inferred and validated 1000 computational linear classifiers capable of discriminating between “inhibitors” and “non-inhibitors” of these molecular targets. The models were generated by machine learning methods using the R environment. The best individual models were combined by different ensemble learning approaches. The best ensembles were applied in the virtual screening of Drug Bank 4.0 and Sweetlead databases, which compile drugs already approved by international regulatory agencies.
After submitting the resulting hits to applicability domain assessment and using accessibility as additional selection criterion, 3 drugs were purchased for in vitro evaluation: Dicyclomine (for the treatment of irritable bowel syndrome), quinestrol (used in hormone replacement therapy) and danazol (for the treatment of endometriosis and fibrocystic breast disease). These 3 drugs showed a strong trypanocidal activity on T. cruzi epimastigotes.
These results reflect the ability of computer guided drug repositioning to identify novel trypanocidal compounds with a relatively small investment of time and resources.
Lucas Alberca– PhD Student, Laboratory of Bioactive Compounds Research and Development (LIDeB) - Faculty of Exact Sciences - National University of La Plata, Quilmes, Buenos Aires, Argentina
Laboratory of Bioactive Compounds Research and Development (LIDeB) - Faculty of Exact Sciences - National University of La Plata
Quilmes, Buenos Aires, Argentina
Lucas Nicolás Alberca
Laboratory of Bioactive compounds Research and Development (LIDeB)
National University of La Plata
I am Lucas Nicolás Alberca, from Argentina. I obtained a bachelor in Biotechnology and Molecular Biology in the National University of La Plata. In 2014, I obtained a scholarship from the Argentinean National Council of Scientific and Technical Research (CONICET) and I started my PhD studies at the Laboratory of Bioactive Compounds Research and Development (LIDeB). My PhD thesis focuses on the generation of computational models for the search of new trypanocidal drugs for the treatment of Chagas disease. My experience and research interests are computational tools and the analysis of large databases to develop in silico models capable of identifying compounds with biological activity.