Multitask learning-driven identification of novel antitrypanosomal compounds.

Background: Chagas disease and human African trypanosomiasis cause substantial death and morbidity, particularly in low- and middle-income countries, making the need for novel drugs urgent. Methodology & results: Therefore, an explainable multitask pipeline to profile the activity of compounds against three trypanosomes (Trypanosoma brucei brucei, Trypanosoma brucei rhodesiense and Trypanosoma cruzi) were created. These models successfully discovered four new experimental hits (LC-3, LC-4, LC-6 and LC-15). Among them, LC-6 showed promising results, with IC50 values ranging 0.01-0.072 µM and selectivity indices >10,000. Conclusion: These results demonstrate that the multitask protocol offers predictivity and interpretability in the virtual screening of new antitrypanosomal compounds and has the potential to improve hit rates in Chagas and human African trypanosomiasis projects.

In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases.

Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safe and effective treatments. In such context, drug repositioning, which allows finding novel indications for approved drugs whose pharmacokinetic and safety profiles are already known, emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent of the typical drug discovery process that involves the systematic screening of chemical compounds against drug targets in high-throughput screening (HTS) efforts, for the identification of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics attempts to identify all potential ligands for all possible targets and diseases. In this review, we summarize current methodological development efforts in drug repositioning that use state-of-the-art computational ligand- and structure-based chemogenomics approaches. Furthermore, we highlighted the recent progress in computational drug repositioning for some NTDs, based on curation and modeling of genomic, biological, and chemical data. Additionally, we also present in-house and other successful examples and suggest possible solutions to existing pitfalls.

Computational drug discovery for the Zika virus

Few Zika virus (ZIKV) outbreaks had been reported since its first detection in 1947, until the recent epidemics occurred in South America (2014/2015) and expeditiously became a global public health emergency. This arbovirus reached 0.5-1.3 million cases of ZIKV infection in Brazil in 2015 and rapidly spread in new geographic areas such as the Americas. Despite the mild symptoms of the Zika fever, the major concern is related to the related severe neurological disorders, especially microcephaly in newborns. Advances in ZIKV drug discovery have been made recently and constitute promising approaches to ZIKV treatment. In this review, we summarize current computational drug discovery efforts and their applicability to discovery of anti-ZIKV drugs. Lastly, we present successful examples of the use of computational approaches to ZIKV drug discovery.

Evaluation of cytotoxic effect of the combination of a pyridinyl carboxamide derivative and oxaliplatin on NCI-H1299 human non-small cell lung carcinoma cells.

Even with all improvements in both diagnostic and therapeutic techniques, lung cancer remains as the most lethal and prevalent cancer in the world. Therefore, new therapeutic drugs and new strategies of drug combination are necessary to provide treatments that are more efficient. Currently, standard therapy regimen for lung cancer includes platinum drugs, such as cisplatin, oxaliplatin, and carboplatin. Besides of the better toxicity profile of oxaliplatin when compared with cisplatin, peripheral neuropathy remains as a limitation of oxaliplatin dose. This study presents LabMol-12, a new pyridinyl carboxamide derivative with antileishmanial and antichagasic activity, as a new hit for lung cancer treatment, which induces apoptosis dependent of caspases in NCI-H1299 lung cancer cells both in monolayer and 3D culture. Moreover, LabMol-12 allows a reduction of oxaliplatin dose when they are combined, thereby, it is a relevant strategy for reducing the side effects of oxaliplatin with the same response. Molecular modeling studies corroborated the biological findings and suggested that the combined therapy can provide a better therapeutically profile effects against NSCLC. All these findings support the fact that the combination of oxaliplatin and LabMol-12 is a promising drug combination for lung cancer.

3D-QSAR approaches in drug design: perspectives to generate reliable CoMFA models.

Drug discovery is mostly guided by innovative and knowledge by the application of experimental and computational approaches. Quantitative structure-activity relationships (QSAR) have a critical task in the discovery and optimization of lead compounds, thereby contributing to the development of new chemical entities. 3D-QSAR methods use the information of the tridimensional molecular structure of ligands and can be applied to elucidate the relationships between 3D molecular interactions and their measured biological property, therefore, providing a rational approach for the development of new potential compounds. The purpose of this review is to provide a perspective of the utility of 3DQSAR approaches in drug design, focusing on progress, challenges and future orientations. The essential steps involved to generate reliable and predictive CoMFA models are discussed. Moreover, we present an example of application of a CoMFA study to derive 3D-QSAR models for a series of oxadiazoles inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR).

Advances in methods for predicting phase I metabolism of polyphenols.

Flavonoids are natural polyphenols that can be found in many vegetables, citric fruits and dietary supplements and are widely consumed worldwide in the human diet. Over the past 30 years, studies have demonstrated that these compounds present significant biological activities, and their antioxidant properties may be responsible for the prevention of many diseases such as neurodegeneration, atherosclerosis, tumor generation, and microbial infections. Moreover, studies have shown that flavonoids may be substrates of cytochrome P450 enzymes and undergo bioactivation to metabolites that inhibit tumor cell growth. Therefore, it is important to understand the CYP450-mediated metabolic profiles of polyphenolic compounds during drug discovery and development processes. This review highlights ligand-based and structure-based methods to predict the Phase I metabolism of polyphenols. Moreover, an integrated in silico approach for the prediction of Phase I metabolism of the flavonoids quercetin, rutin, naringenin and naringin, which provided useful information about the most likely metabolites of these flavonoids and their interactions with amino acid residues of CYP2C9, is described.