Dona Flor and her two husbands: Discovery of novel HDAC6/AKT2 inhibitors for myeloid cancer treatment.

Unlabelled: Hematological cancer treatment with hybrid kinase/HDAC inhibitors is a novel strategy to overcome the challenge of acquired resistance to drugs. We collected IC datasets from the ChEMBL database for 13 cancer cell lines (72 h cytotoxicity, measured by MTT), known inhibitors for 38 kinases, and 10 HDACs isoforms, that we identified by target fishing and literature review. The data was subjected to rigorous biological and chemical curation leaving the final datasets ranging from 76 to 8173 compounds depending on the target.

Modeling interactions between Heparan sulfate and proteins based on the Heparan sulfate microarray analysis.

Heparan sulfate (HS), a sulfated polysaccharide abundant in the extracellular matrix, plays pivotal roles in various physiological and pathological processes by interacting with proteins. Investigating the binding selectivity of HS oligosaccharides to target proteins is essential, but the exhaustive inclusion of all possible oligosaccharides in microarray experiments is impractical. To address this challenge, we present a hybrid pipeline that integrates microarray and in silico techniques to design oligosaccharides with desired protein affinity.

Small molecule antiviral compound collection (SMACC): A comprehensive, highly curated database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses cost thousands if not millions of human lives and trillions of dollars. We have identified, collected, curated, and integrated all chemogenomics data from ChEMBL for 13 emerging viruses that hold the greatest potential threat to global human health. By identifying and solving several challenges related to data annotation accuracy, we developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic and target-based assays for these viruses that we dubbed SMACC (Small Molecule Antiviral Compound Collection).

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules.

Diseases caused by new viruses costs thousands if not millions of human lives and trillions of dollars in damage to the global economy. Despite the rapid development of vaccines for SARS-CoV-2, the lack of small molecule antiviral drugs that work against multiple viral families (broad-spectrum antivirals; BSAs) has left the entire world’s human population vulnerable to the infection between the beginning of the outbreak and the widespread availability of vaccines. Developing BSAs is an attractive, yet challenging, approach that could prevent the next, inevitable, viral outbreak from becoming a global catastrophe.

Conserved coronavirus proteins as targets of broad-spectrum antivirals.

Coronaviruses are a class of single-stranded, positive-sense RNA viruses that have caused three major outbreaks over the past two decades: Middle East respiratory syndrome-related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). All outbreaks have been associated with significant morbidity and mortality. In this study, we have identified and explored conserved binding sites in the key coronavirus proteins for the development of broad-spectrum direct acting anti-coronaviral compounds and validated the significance of this conservation for drug discovery with existing experimental data.

QSAR Modeling of SARS-CoV M Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2.

The main protease (M) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) M of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database.

Learning from history: do not flatten the curve of antiviral research!

Here, we explore the dynamics of the response of the scientific community to several epidemics, including Coronavirus Disease 2019 (COVID-19), as assessed by the numbers of clinical trials, publications, and level of research funding over time. All six prior epidemics studied [bird flu, severe acute respiratory syndrome (SARS), swine flu, Middle East Respiratory Syndrome (MERS), Ebola, and Zika] were characterized by an initial spike of research response that flattened shortly thereafter. Unfortunately, no antiviral medications have been discovered to date as treatments for any of these diseases.

Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2.

The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinical trials.

Discovery of new potent hits against intracellular Trypanosoma cruzi by QSAR-based virtual screening.

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.

QSAR-Based Virtual Screening: Advances and Applications in Drug Discovery.

Virtual screening (VS) has emerged in drug discovery as a powerful computational approach to screen large libraries of small molecules for new hits with desired properties that can then be tested experimentally. Similar to other computational approaches, VS intention is not to replace or assays, but to speed up the discovery process, to reduce the number of candidates to be tested experimentally, and to rationalize their choice. Moreover, VS has become very popular in pharmaceutical companies and academic organizations due to its time-, cost-, resources-, and labor-saving.

Efficacy of sertraline against : an in vitro and in silico study.

Background: Drug repurposing has been an interesting and cost-effective approach, especially for neglected diseases, such as Chagas disease.

Methods: In this work, we studied the activity of the antidepressant drug sertraline against trypomastigotes and intracellular amastigotes of the Y and Tulahuen strains, and investigated its action mode using cell biology and in silico approaches.

Results: Sertraline demonstrated in vitro efficacy against intracellular amastigotes of both strains inside different host cells, including cardiomyocytes, with IC values between 1 to 10 μM, and activity against bloodstream trypomastigotes, with IC of 14 μM.

Computer-aided identification of novel anti-paracoccidioidomycosis compounds.

Aim: The shape-based virtual screening was used for the identification of new compounds anti-paracoccidioidomycosis (PCM).

Materials & Methods: The study was performed according to the following steps: collection and curation of a dataset of quinolinyl N-oxide chalcones with anti-PCM activity, development and validation of shape-based models, application of the best model for virtual screening, and experimental validation.

Results & Conclusion: Among 31 computational hits, eight compounds showed potent antifungal activity and low cytotoxicity for mammalian cells.

QSAR-Driven Design and Discovery of Novel Compounds With Antiplasmodial and Transmission Blocking Activities.

Malaria is a life-threatening infectious disease caused by parasites of the genus , affecting more than 200 million people worldwide every year and leading to about a half million deaths. Malaria parasites of humans have evolved resistance to all current antimalarial drugs, urging for the discovery of new effective compounds. Given that the inhibition of deoxyuridine triphosphatase of (dUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target.

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.

Computer-aided discovery of two novel chalcone-like compounds active and selective against Leishmania infantum.

Leishmaniasis are infectious diseases caused by parasites of genus Leishmania that affect affects 12 million people in 98 countries mainly in Africa, Asia, and Latin America. Effective treatments for this disease are urgently needed. In this study, we present a computer-aided approach to investigate a set of 32 recently synthesized chalcone and chalcone-like compounds to act as antileishmanial agents.

Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening.

Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents.

QSAR-Driven Discovery of Novel Chemical Scaffolds Active against Schistosoma mansoni.

Schistosomiasis is a neglected tropical disease that affects millions of people worldwide. Thioredoxin glutathione reductase of Schistosoma mansoni (SmTGR) is a validated drug target that plays a crucial role in the redox homeostasis of the parasite. We report the discovery of new chemical scaffolds against S.

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.

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.