Structural analysis of factors related to FAM3C/ILEI dimerization and identification of inhibitor candidates targeting cancer treatment.

FAM3 is a superfamily of four cytokines that maintain a single globular structure ß -ß -α of three classes: FAM3A, B, C and D. FAM3C was the first member of this family related to cancer and is functionally characterized as an essential factor for the epithelial-mesenchymal transition (EMT), leading to late delays in tumor progression. Due to its crucial role in EMT and metastasis, FAM3C has been termed an interleukin-like EMT (ILEI) inducer. There are several studies on the part of FAM3C in the progression of cancer and other diseases. However, little is known about its cellular receptors and possible inhibitors. In this study, based on in silico approaches, we performed structural analyses of factors related to FAM3C/ILEI dimerization. We also identified four possible inhibitor candidates, expected to be exciting prototypes and could be submitted to future biological tests targeting cancer treatment.

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.

Molecular docking studies and 2D analyses of DPP-4 inhibitors as candidates in the treatment of diabetes.

Dipeptidyl peptidase-4 (DPP-4) is an important biological target related to the treatment of diabetes as DPP-4 inhibitors can lead to an increase in the insulin levels and a prolonged activity of glucagon-like peptide-1 (GLP-1) and gastric inhibitory polypeptide (GIP), being effective in glycemic control. Thus, this study analyses the main molecular interactions between DPP-4 and a series of bioactive ligands. The methodology used here employed molecular modeling methods, such as HQSAR (Hologram Quantitative Structure-Activity) analyses and molecular docking, with the aim of understanding the main structural features of the compound series that are essential for the biological activity. Analyses of the main interactions in the active site of DPP-4, in particular, the contribution of the hydroxyl coordination between Tyr547 and Ser630 by the water molecule, which is described in the literature as important for the coordinated interactions in the active site, were performed. Significant correlation coefficients of the best 2D model (r(2) = 0.942 and q(2) = 0.836) were obtained, indicating the predictive power of this model for untested compounds. Therefore, the final model constructed in this study, along with the information from the contribution maps, could be useful in the design of novel DPP-4 ligands with improved activity.