Identification of inhibitors as drug candidates against Chagas disease.

Chagas disease, after more than a century after its discovery, is still a major public health problem. It is estimated that approximately 10 million people worldwide are infected with T. cruzi. However, the situation is more critical in Latin America and other regions where the disease is endemic. The largest number of cases occurs in Brazil, Argentina, and Mexico as more than 100 million people in these regions are located in areas with a high risk of contamination by the vector. The need for new therapeutic alternatives is urgent, as the available drugs have severe limitations such as low efficacy and high toxicity. From this scenario, in this work, we employed the virtual screening technique using cruzain and BDF2 as key biological targets for the survival of the parasite. Our objective was to identify potential inhibitors of T. cruzi trypomastigotes, which could be considered drug candidates against Chagas disease. For this, we employed different in silico methodologies and the obtained results were corroborated using in vitro biological assays. For the VS studies, a database containing synthetic compounds was simulated at the binding site of cruzain and BDF2. In addition, pharmacophoric models were constructed in the initial phases of VS, as well as other advanced analyses (molecular dynamics simulations, calculations of binding free energy, and ADME prediction) were carried out and the results allowed the selection of potential inhibitors of T. cruzi. Based on the obtained data, 32 different compounds commercially available were subjected to biological tests against the trypomastigote form of T. cruzi. As result, 11 of those compounds displayed significant activity against T. cruzi and can be considered potential candidates for the treatment of Chagas disease.

Topomer-CoMFA proposed as a tool to construct dual EGFR/HER-2 models.

Protein kinases (in this case, HER-2 and EGFR) are involved in cancer-related diseases. Some reports have shown unique CoMFA models using the sum of activities expressed as pIC50 (-log IC50), as the classical CoMFA technique would not be the best strategy to construct models for multitarget therapy considering that the molecular alignment will not be the same for different targets. An alternative for this problem is the use of Topomer-CoMFA, a variation of CoMFA, which does not require the alignment step in the generation of 3D models. In this study, we propose the combined use of the sum of activities and Topomer-CoMFA for the construction of a unique dual 3D model considering the inhibitory activities against EGFR and HER-2. For this, 88 compounds from the literature were divided into two groups: training (71) and test (17) sets. The biological activity of each compound, expressed as IC50 for EGFR and HER-2, was transformed into pIC50, summed, and used as the dependent variable in the Topomer-CoMFA analyses. The obtained model was considered statistically robust in the prediction of the dual activity of new compounds. Finally, based on the obtained model, we proposed structural modifications to some of the compounds used to improve the biological data. From the 3D model, we suggested new derivative compounds with improved biological activity for both targets. Therefore, the combination of the techniques proposed in this study proves to be a good strategy to construct better statistical models that can predict biological activities in multitarget systems.

Studies on the Dual Activity of EGFR and HER-2 Inhibitors Using Structure-Based Drug Design Techniques.

HER-2 and EGFR are biological targets related to the development of cancer and the discovery and/or development of a dual inhibitor could be a good strategy to design an effective drug candidate. In this study, analyses of the chemical properties of a group of substances having affinity for both HER-2 and EGFR were carried out with the aim of understanding the main factors involved in the interaction between these inhibitors and the biological targets. Comparative analysis of molecular interaction fields (CoMFA) and comparative molecular similarity index analysis (CoMSIA) techniques were applied on 63 compounds. From CoMFA analyses, we found for both HER-2 (r² calibration = 0.98 and q²cv = 0.83) and EGFR (r² calibration = 0.98 and q²cv = 0.73) good predictive models. Good models for CoMSIA technique have also been found for HER-2 (r² calibration = 0.92 and q²cv = 0.74) and EGFR (r² calibration = 0.97 and q²cv = 0.72). The constructed models could indicate some important characteristics for the inhibition of the biological targets. New compounds were proposed as candidates to inhibit both proteins. Therefore, this study may guide future projects for the development of new drug candidates for the treatment of breast cancer.