Leishmaniases are neglected diseases, caused by intracellular protozoan parasites of the Leishmania (L.) genus. Although the principal host cells of the parasites are macrophages, neutrophils are the first cells rapidly recruited to the site of parasites inoculation, where they play an important role in the early recognition and elimination of the parasites. The nature of early interactions between neutrophils and Leishmania could influence the outcome of infection. Herein we aimed to evaluate whether different Leishmania strains, responsible for distinct clinical manifestations, could influence ex vivo functional activity of neutrophils. Human polymorphonuclear leukocytes were isolated from 14 healthy volunteers and the ex vivo infection of these cells was done with two L. infantum and one L. major strains. Infection parameters were determined and neutrophils activation was assessed by oxidative burst, degranulation, DNA release and apoptosis; cytokine production was measured by a multiplex flow cytometry analysis. Intracellular amastigotes were rescued to determine Leishmania strains survival. The results showed that L. infantum and L. major promastigotes similarly infected the neutrophils. Oxidative burst, neutrophil elastase, myeloperoxidase activity and apoptosis were significantly increased in infected neutrophils but with no differences between strains. The L. infantum-infected neutrophils induced more DNA release than those infected by L. major. Furthermore, Leishmania strains induced high amounts of IL-8 and stimulated the production of IL-1β, TNF-α, and TGF-β by human neutrophils. We observed that only one strain promoted IL-6 release by these neutrophils. The production of TNF-α was also differently induced by the parasites strains. All these results demonstrate that L. infantum and L. major strains were able to induce globally a similar ex vivo activation and apoptosis of neutrophils; however, they differentially triggered cytokines release from these cells. In addition, rescue of intracellular parasites indicated different survival rates further emphasizing on the influence of parasite strains within a species on the fate of infection.

Effective drugs against SARS-CoV-2 are urgently needed to treat severe cases of infection and for prophylactic use. The main viral protease (nsp5 or 3CLpro) represents an attractive and possibly broad-spectrum target for drug development as it is essential to the virus life cycle and highly conserved among betacoronaviruses. Sensitive and efficient high-throughput screening methods are key for drug discovery. Here we report the development of a gain-of-signal, highly sensitive cell-based luciferase assay to monitor SARS-CoV-2 nsp5 activity and show that it is suitable for the screening of compounds in a 384-well format. A benefit of miniaturisation and automation is that screening can be performed in parallel on a wild-type and a catalytically inactive nsp5, which improves the selectivity of the assay. We performed molecular docking-based screening on a set of 14,468 compounds from an in-house chemical database, selected 359 candidate nsp5 inhibitors and tested them experimentally. We identified two molecules which show anti-nsp5 activity, both in our cell-based assay and in vitro on purified nsp5 protein, and inhibit SARS-CoV-2 replication in A549-ACE2 cells with EC₅₀ values in the 4–8 μM range. The here described high-throughput-compatible assay will allow the screening of large-scale compound libraries for SARS-CoV-2 nsp5 inhibitors. Moreover, we provide evidence that this assay can be adapted to other coronaviruses and viruses which rely on a viral protease.

Identifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands.

Leishmaniases are neglected parasitic diseases in spite of the major burden they inflict on public health. The identification of novel drugs and targets constitutes a research priority. For that purpose we used Leishmania infantum initiation factor 4A (LieIF), an essential translation initiation factor that belongs to the DEAD-box proteins family, as a potential drug target. We modeled its structure and identified two potential binding sites. A virtual screening of a diverse chemical library was performed for both sites. The results were analyzed with an in-house version of the Self-Organizing Maps algorithm combined with multiple filters, which led to the selection of 305 molecules. Effects of these molecules on the ATPase activity of LieIF permitted the identification of a promising hit (208) having a half maximal inhibitory concentration (IC₅₀) of 150 ± 15 μM for 1 μM of protein. Ten chemical analogues of compound 208 were identified and two additional inhibitors were selected (20 and 48). These compounds inhibited the mammalian eIF4I with IC₅₀ values within the same range. All three hits affected the viability of the extra-cellular form of L. infantum parasites with IC₅₀ values at low micromolar concentrations. These molecules showed non-significant toxicity toward THP-1 macrophages. Furthermore, their anti-leishmanial activity was validated with experimental assays on L. infantum intramacrophage amastigotes showing IC₅₀ values lower than 4.2 μM. Selected compounds exhibited selectivity indexes between 19 to 38, which reflects their potential as promising anti-Leishmania molecules. Leishmaniases constitute a group of neglected parasitic diseases that inflict major burden on public health. Novel drugs and targets need to be identified since current therapies have adverse side effects. Herein, we focused on Leishmania infantum translation initiation factor 4A (LieIF), as a potential drug target. LieIF, a pivotal enzyme in the translation machinery, is also implicated in host-pathogen interactions. We modeled its 3D structure and identified two pockets, which were used in virtual screenings of a chemical compound library. Therefore, we selected and purchased 305 compounds. We established a reliable ATPase screening assay to test the molecules against the enzymatic activity of LieIF and its mammalian homologue. A promising hit was retained and further characterized. It inhibited both proteins but showed different kinetic properties. It was used as a basis to identify similar analogues and two additional inhibitors were identified. All three hits reduced the viability of the extracellular promastigote form of the parasite, but they had no significant cytotoxic effects on host cells. They also affected the viability of the intracellular amastigote form and reduced the macrophage infection. This selectivity is very promising and indicates that these inhibitors would constitute an avenue to develop strategies to fight leishmaniases.

Drug discovery and repurposing against COVID-19 is a highly relevant topic with huge efforts dedicated to delivering novel therapeutics targeting SARS-CoV-2. In this context, computer-aided drug discovery is of interest in orienting the early high throughput screenings and in optimizing the hit identification rate. We herein propose a pipeline for Ligand-Based Drug Discovery (LBDD) against SARS-CoV-2. Through an extensive search of the literature and multiple steps of filtering, we integrated information on 2,610 molecules having a validated effect against SARS-CoV and/or SARS-CoV-2. The chemical structures of these molecules were encoded through multiple systems to be readily useful as input to conventional machine learning (ML) algorithms or deep learning (DL) architectures. We assessed the performances of seven ML algorithms and four DL algorithms in achieving molecule classification into two classes: active and inactive. The Random Forests (RF), Graph Convolutional Network (GCN), and Directed Acyclic Graph (DAG) models achieved the best performances. These models were further optimized through hyperparameter tuning and achieved ROC-AUC scores through cross-validation of 85, 83, and 79% for RF, GCN, and DAG models, respectively. An external validation step on the FDA-approved drugs collection revealed a superior potential of DL algorithms to achieve drug repurposing against SARS-CoV-2 based on the dataset herein presented. Namely, GCN and DAG achieved more than 50% of the true positive rate assessed on the confirmed hits of a PubChem bioassay.