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Histoplasmosis is a systemic form of endemic mycosis to the American continent and may be lethal to people living with HIV/AIDS. The drugs available for treating histoplasmosis are limited, costly, and highly toxic. New drug development is time-consuming and costly; hence, drug repositioning is an advantageous strategy for discovering new therapeutic options. This study was conducted to identify drugs that can be repositioned for treating histoplasmosis in immunocompromised patients. Homologous proteins among Histoplasma capsulatum strains were selected and used to search for homologous targets in the DrugBank and Therapeutic Target Database. Essential genes were selected using Saccharomyces cerevisiae as a model, and functional regions of the therapeutic targets were analyzed. The antifungal activity of the selected drugs was verified, and homology modeling and molecular docking were performed to verify the interactions between the drugs with low inhibitory concentration values and their corresponding targets. We selected 149 approved drugs with potential activity against histoplasmosis, among which eight were selected for evaluating their in vitro activity. For drugs with low minimum inhibitory concentration values, such as mebendazole, everolimus, butenafine, and bifonazole, molecular docking studies were performed. A chemogenomic framework revealed lanosterol 14-α-demethylase, squalene monooxygenase, serine/threonine-protein kinase mTOR, and the β-4B tubulin chain of H. capsulatum , respectively, as the protein targets of the drugs. Our strategy can be used to identify promising antifungal targets, and drugs with repositioning potential for treating H. capsulatum. [ABSTRACT FROM AUTHOR]

Glioblastoma multiforme (GBM) is the most devastating and widespread primary central nervous system tumor. Pharmacological treatment of this malignance is limited by the selective permeability of the blood-brain barrier (BBB) and relies on a single drug, temozolomide (TMZ), thus making the discovery of new compounds challenging and urgent. Therefore, aiming to discover new anti-glioma drugs, we developed robust machine learning models for predicting anti-glioma activity and BBB penetration ability of new compounds. Using these models, we prioritized 41 compounds from our in-house library of compounds, for further in vitro testing against three glioma cell lines and astrocytes. Subsequently, the most potent and selective compounds were resynthesized and tested in vivo using an orthotopic glioma model. This approach revealed two lead candidates, 4m and 4n , which efficiently decreased malignant glioma development in mice, probably by inhibiting thioredoxin reductase activity, as shown by our enzymological assays. Moreover, these two compounds did not promote body weight reduction, death of animals, or altered hematological and toxicological markers, making then good candidates for lead optimization as anti-glioma drug candidates. Image 1 • ML models were developed to predict of anti-glioma activity and BBB penetration. • New hits with antiproliferative activity were identified by virtual screening. • Three hits presented high potency and moderate cytotoxicity. • Compounds were able to inhibit TrxR enzyme. • Two lead compounds stopped the malignant glioma in vivo without promoting toxicity. [ABSTRACT FROM AUTHOR]

• We describe our prior efforts in open drug discovery for Ebola and Zika virus. • We summarize the current literature for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). • We detail computational repurposing efforts and results for SARS-CoV-2. • To be prepared for future outbreaks we argue we need novel broad-spectrum antivirals. • Limitations of these efforts include funding for experimental validation, and this lags behind the computational work. In the past decade we have seen two major Ebola virus outbreaks in Africa, the Zika virus in Brazil and the Americas and the current pandemic of coronavirus disease (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There is a strong sense of déjà vu because there are still no effective treatments. In the COVID-19 pandemic, despite being a new virus, there are already drugs suggested as active in in vitro assays that are being repurposed in clinical trials. Promising SARS-CoV-2 viral targets and computational approaches are described and discussed. Here, we propose, based on open antiviral drug discovery approaches for previous outbreaks, that there could still be gaps in our approach to drug discovery. [ABSTRACT FROM AUTHOR]

An early hit-to-lead optimization of a novel pyrazinylpiperazine series against L. infantum and L. braziliensis has been performed after an extensive SAR focusing on the benzoyl fragment of hit (4). Deletion of the meta -Cl of (4) led to the obtention of the para -hydroxyl derivative (12), on which the design of most monosubstituted derivatives of the SAR was based. Further optimization of the series, involving disubstituted benzoyl fragments and the hydroxyl substituent of (12), allowed the obtention of a total of 15 compounds with increased antileishmanial potency (IC 50 < 10 μM), nine of which displayed activity in the low micromolar range (IC 50 < 5 μM). This optimization ultimately identified the ortho , meta -dihydroxyl derivative (46) as an early lead for this series (IC 50 (L. infantum) = 2.8 μM, IC 50 (L. braziliensis) = 0.2 μM). Additional assessment of some selected compounds against other trypanosomatid parasites revealed that this series is selective towards Leishmania parasites, and in silico ADMET predictions revealed satisfactory profiles for these compounds, allowing further lead optimization of the pyrazinylpiperazine class against Leishmania. [Display omitted] • The pyrazinylpiperazine class is a relevant scaffold against Leishmania. • Hit-to-lead approach identified several optimized pyrazinylpiperazines. • Pyrazinylpiperazines delivered one early lead against Leishmania. • The most active compounds have interesting predicted ADMET properties. [ABSTRACT FROM AUTHOR]