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Background: The impact of schistosomiasis, which affects over 230 million people, emphasizes the urgency of developing new antischistosomal drugs. Artificial intelligence is vital in accelerating the drug discovery process. Methodology & results: We developed classification and regression machine learning models to predict the schistosomicidal activity of compounds not experimentally tested. The prioritized compounds were tested on schistosomula and adult stages of Schistosoma mansoni. Four compounds demonstrated significant activity against schistosomula, with 50% effective concentration values ranging from 9.8 to 32.5 µM, while exhibiting no toxicity in animal and human cell lines. Conclusion: These findings represent a significant step forward in the discovery of antischistosomal drugs. Further optimization of these active compounds can pave the way for their progression into preclinical studies.

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Pesticides are used to control and combat insects and pests in the agricultural sector, households, and public health programs. The frequent and disorderly use of these pesticides may lead to variety of undesired effects. Therefore, natural products have many advantages over to synthetic compounds to be used as insecticides. The goal of this study was to find natural products with insecticidal potential against Musca domestica and Mythimna separata. To achieve this goal, we developed predictive QSAR models using MuDRA, PLS, and RF approaches and performed virtual screening of 117 natural products. As a result of QSAR modeling, we formulated the recommendations regarding physico-chemical characteristics for promising compounds active against Musca domestica and Mythimna separata. Homology models were successfully built for both species and molecular docking of QSAR hits vs known insecticides allowed us to prioritize twenty-two compounds against Musca domestica and six against Mythimna separata. Our results suggest that pimarane diterpenes, abietanes diterpenes, dimeric diterpenes and scopadulane diterpenes obtained from aerial parts of species of the genus Calceolaria (Calceolariaceae: Scrophulariaceae) can be considered as potential insecticidal.

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Chagas disease is a neglected tropical disease (NTD) caused by the protozoan parasite Trypanosoma cruzi and is primarily transmitted to humans by the feces of infected Triatominae insects during their blood meal. The disease affects 6-8 million people, mostly in Latin America countries, and kills more people in the region each year than any other parasite-born disease, including malaria. Moreover, patient numbers are currently increasing in non-endemic, developed countries, such as Australia, Japan, Canada, and the United States. The treatment is limited to one drug, benznidazole, which is only effective in the acute phase of the disease and is very toxic. Thus, there is an urgent need to develop new, safer, and effective drugs against the chronic phase of Chagas disease. Using a QSAR-based virtual screening followed by in vitro experimental evaluation, we report herein the identification of novel potent and selective hits against T. cruzi intracellular stage. We developed and validated binary QSAR models for prediction of anti-trypanosomal activity and cytotoxicity against mammalian cells using the best practices for QSAR modeling. These models were then used for virtual screening of a commercial database, leading to the identification of 39 virtual hits. Further in vitro assays showed that seven compounds were potent against intracellular T. cruzi at submicromolar concentrations (EC50 < 1 µM) and were very selective (SI > 30). Furthermore, other six compounds were also inside the hit criteria for Chagas disease, which presented activity at low micromolar concentrations (EC50 < 10 µM) against intracellular T. cruzi and were also selective (SI > 15). Moreover, we performed a multi-parameter analysis for the comparison of tested compounds regarding their balance between potency, selectivity, and predicted ADMET properties. In the next studies, the most promising compounds will be submitted to additional in vitro and in vivo assays in acute model of Chagas disease, and can be further optimized for the development of new promising drug candidates against this important yet neglected disease.

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Calcium signaling has an essential role in fundamental processes of Plasmodium life cycle, including migration, cell invasion and parasite development. Two important players in calcium homeostasis, the Histidine Triad (HIT) protein that is implicated in calcium signaling in mammalian cells and calmodulin, which is a classic calcium sensor in eukaryotes are present in Plasmodium falciparum, however theirs function is unknown in the parasite. Here, we investigated the involvement of the P. falciparum Histidine Triad protein (PfHint-1) and calmodulin (PfCaM) in calcium signaling and intracellular proteolysis. For this, we targeted PfHint-1 with a hemagglutinin tail and overexpressed both proteins. We observed that PfHint-1 is expressed throughout the erythrocytic stages and partially colocalizes to the endoplasmic reticulum. Parasites overexpressing PfHint-1 displayed lower ER Ca2+ content and a higher [Ca2+]cyt rise in the parasite cytosol upon Ca2+ addition to the extracellular medium after depletion of ER calcium store. PfCaM-overexpressing parasites exhibit a higher [Ca2+]cyt rise after challenge with the calmodulin inhibitor, calmidazolium. The calcium-dependent proteolytic activity in PfCaM- and PfHint-1-overexpressing parasites was increased and correlated to alterations in calcium homeostasis. Taken together, our results indicate the participation of these proteins in P. falciparum fundamental cellular processes and highlights promising targets for the development of antimalarial drugs.

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Abstract: Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Resumen: El desarrollo de un nuevo fármaco es un proceso complejo y arriesgado que requiere una enorme cantidad de tiempo y dinero. Se estima que el proceso estándar para producir un nuevo fármaco, desde su descubrimiento hasta que acaba en el mercado, puede tardar hasta 15 años y tener un costo de mil millones de dólares (USD). Por fortuna, este escenario ha cambiado recientemente con la llegada de nuevas tecnologías y metodologías. Entre ellas, los métodos computacionales se han convertido en un componente determinante en muchos programas de descubrimiento de fármacos. En un esfuerzo por incrementar las posibilidades de encontrar nuevas moléculas con potencial farmacológico, se utilizan técnicas como el cribado virtual de quimiotecas construidas con base en ligandos o estructuras para la identificación de hits y hasta para la optimización de compuestos líder. En lo que respecta al diseño y descubrimiento de nuevos candidatos a fármacos contra el cáncer, estos enfoques tienen, a la fecha, un impacto importante y aportan nuevas posibilidades terapéuticas. En este artículo se revisa el concepto del diseño racional de moléculas con potencial farmacológico, ilustrando los principios clave con algunos de los ejemplos más representativos y exitosos de moléculas identificadas mediante estas aproximaciones. Se incluyen casos desarrollados en el campo del diseño de fármacos contra el cáncer con la finalidad de mostrar cómo, con la ayuda del diseño asistido por computadora, se pueden generar nuevos fármacos que den esperanza a millones de pacientes.

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Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

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RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.