Identification of potential inhibitors of casein kinase 2 alpha of Plasmodium falciparum with potent in vitro activity.

Drug resistance to commercially available antimalarials is a major obstacle in malaria control and elimination, creating the need to find new antiparasitic compounds with novel mechanisms of action. The success of kinase inhibitors for oncological treatments has paved the way for the exploitation of protein kinases as drug targets in various diseases, including malaria. Casein kinases are ubiquitous serine/threonine kinases involved in a wide range of cellular processes such as mitotic checkpoint signaling, DNA damage response, and circadian rhythm. In Plasmodium, it is suggested that these protein kinases are essential for both asexual and sexual blood-stage parasites, reinforcing their potential as targets for multi-stage antimalarials. To identify new putative PfCK2α inhibitors, we utilized an in silico chemogenomic strategy involving virtual screening with docking simulations and quantitative structure-activity relationship predictions. Our investigation resulted in the discovery of a new quinazoline molecule (542), which exhibited potent activity against asexual blood stages and a high selectivity index (>100). Subsequently, we conducted chemical-genetic interaction analysis on yeasts with mutations in casein kinases. Our chemical-genetic interaction results are consistent with the hypothesis that 542 inhibits yeast Cka1, which has a hinge region with high similarity to PfCK2α. This finding is in agreement with our in silico results suggesting that 542 inhibits PfCK2α via hinge region interaction.

Proteomic identification of metabolic changes in Paracoccidioides brasiliensis induced by a nitroheteroarylchalcone.

Aim: To access the metabolic changes caused by a chalcone derivative (LabMol-75) through a proteomic approach. Methods: Proteomic analysis was performed after 9 h of Paracoccidioides brasiliensis yeast (Pb18) cell incubation with the LabMol-75 at MIC. The proteomic findings were validated through in vitro and in silico assays. Results: Exposure to the compound led to the downregulation of proteins associated with glycolysis and gluconeogenesis, ß-oxidation, the citrate cycle and the electron transport chain. Conclusion: LabMol-75 caused an energetic imbalance in the fungus metabolism and deep oxidative stress. Additionally, the in silico molecular docking approach pointed to this molecule as a putative competitive inhibitor of DHPS.

Characterization of the RNA-dependent RNA polymerase from Chikungunya virus and discovery of a novel ligand as a potential drug candidate.

Chikungunya virus (CHIKV) is the causative agent of Chikungunya fever, an acute febrile and arthritogenic illness with no effective treatments available. The development of effective therapeutic strategies could be significantly accelerated with detailed knowledge of the molecular components behind CHIKV replication. However, drug discovery is hindered by our incomplete understanding of their main components. The RNA-dependent RNA-polymerase (nsP4-CHIKV) is considered the key enzyme of the CHIKV replication complex and a suitable target for antiviral therapy. Herein, the nsP4-CHIKV was extensively characterized through experimental and computational biophysical methods. In the search for new molecules against CHIKV, a compound designated LabMol-309 was identified as a strong ligand of the nsp4-CHIKV and mapped to bind to its active site. The antiviral activity of LabMol-309 was evaluated in cellular-based assays using a CHIKV replicon system and a reporter virus. In conclusion, this study highlights the biophysical features of nsP4-CHIKV and identifies a new compound as a promising antiviral agent against CHIKV infection.

Chalcones from Angelica keiskei (ashitaba) inhibit key Zika virus replication proteins.

Zika virus (ZIKV) is a dangerous human pathogen and no antiviral drugs have been approved to date. The chalcones are a group of small molecules that are found in a number of different plants, including Angelica keiskei Koidzumi, also known as ashitaba. To examine chalcone anti-ZIKV activity, three chalcones, 4-hydroxyderricin (4HD), xanthoangelol (XA), and xanthoangelol-E (XA-E), were purified from a methanol-ethyl acetate extract from A. keiskei. Molecular and ensemble docking predicted that these chalcones would establish multiple interactions with residues in the catalytic and allosteric sites of ZIKV NS2B-NS3 protease, and in the allosteric site of the NS5 RNA-dependent RNA-polymerase (RdRp). Machine learning models also predicted 4HD, XA and XA-E as potential anti-ZIKV inhibitors. Enzymatic and kinetic assays confirmed chalcone inhibition of the ZIKV NS2B-NS3 protease allosteric site with IC50s from 18 to 50 µM. Activity assays also revealed that XA, but not 4HD or XA-E, inhibited the allosteric site of the RdRp, with an IC50 of 6.9 µM. Finally, we tested these chalcones for their anti-viral activity in vitro with Vero cells. 4HD and XA-E displayed anti-ZIKV activity with EC50 values of 6.6 and 22.0 µM, respectively, while XA displayed relatively weak anti-ZIKV activity with whole cells. With their simple structures and relative ease of modification, the chalcones represent attractive candidates for hit-to-lead optimization in the search of new anti-ZIKV therapeutics.

A critical overview of computational approaches employed for COVID-19 drug discovery.

COVID-19 has resulted in huge numbers of infections and deaths worldwide and brought the most severe disruptions to societies and economies since the Great Depression. Massive experimental and computational research effort to understand and characterize the disease and rapidly develop diagnostics, vaccines, and drugs has emerged in response to this devastating pandemic and more than 130 000 COVID-19-related research papers have been published in peer-reviewed journals or deposited in preprint servers. Much of the research effort has focused on the discovery of novel drug candidates or repurposing of existing drugs against COVID-19, and many such projects have been either exclusively computational or computer-aided experimental studies. Herein, we provide an expert overview of the key computational methods and their applications for the discovery of COVID-19 small-molecule therapeutics that have been reported in the research literature. We further outline that, after the first year the COVID-19 pandemic, it appears that drug repurposing has not produced rapid and global solutions. However, several known drugs have been used in the clinic to cure COVID-19 patients, and a few repurposed drugs continue to be considered in clinical trials, along with several novel clinical candidates. We posit that truly impactful computational tools must deliver actionable, experimentally testable hypotheses enabling the discovery of novel drugs and drug combinations, and that open science and rapid sharing of research results are critical to accelerate the development of novel, much needed therapeutics for COVID-19.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence.

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

CATMoS: Collaborative Acute Toxicity Modeling Suite.

BACKGROUND: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals. OBJECTIVES: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 (LD50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [LD50 (LD50≤50mg/kg)], and nontoxic chemicals (LD50>2,000mg/kg). METHODS: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches. RESULTS: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results. DISCUSSION: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Artificial Intelligence Applied to the Rapid Identification of New Antimalarial Candidates with Dual-Stage Activity.

Increasing reports of multidrug-resistant malaria parasites urge the discovery of new effective drugs with different chemical scaffolds. Protein kinases play a key role in many cellular processes such as signal transduction and cell division, making them interesting targets in many diseases. Protein kinase 7 (PK7) is an orphan kinase from the Plasmodium genus, essential for the sporogonic cycle of these parasites. Here, we applied a robust and integrative artificial intelligence-assisted virtual-screening (VS) approach using shape-based and machine learning models to identify new potential PK7 inhibitors with in vitro antiplasmodial activity. Eight virtual hits were experimentally evaluated, and compound LabMol-167 inhibited ookinete conversion of Plasmodium berghei and blood stages of Plasmodium falciparum at nanomolar concentrations with low cytotoxicity in mammalian cells. As PK7 does not have an essential role in the Plasmodium blood stage and our virtual screening strategy aimed for both PK7 and blood-stage inhibition, we conducted an in silico target fishing approach and propose that this compound might also inhibit P. falciparum PK5, acting as a possible dual-target inhibitor. Finally, docking studies of LabMol-167 with P. falciparum PK7 and PK5 proteins highlighted key interactions for further hit-to lead optimization.

CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast™ metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast™/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of ∼875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment. https://doi.org/10.1289/EHP5580.

Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs.

Many drug candidates fail therapeutic development because of poor aqueous solubility. We have conceived a computer-aided strategy to enable polymeric micelle-based delivery of poorly soluble drugs. We built models predicting both drug loading efficiency (LE) and loading capacity (LC) using novel descriptors of drug-polymer complexes. These models were employed for virtual screening of drug libraries, and eight drugs predicted to have either high LE and high LC or low LE and low LC were selected. Three putative positives, as well as three putative negative hits, were confirmed experimentally (implying 75% prediction accuracy). Fortuitously, simvastatin, a putative negative hit, was found to have the desired micelle solubility. Podophyllotoxin and simvastatin (LE of 95% and 87% and LC of 43% and 41%, respectively) were among the top five polymeric micelle-soluble compounds ever studied experimentally. The success of the strategy described herein suggests its broad utility for designing drug delivery systems.

Antitrypanosomal Activity of Acetogenins Isolated from the Seeds of Porcelia macrocarpa Is Associated with Alterations in Both Plasma Membrane Electric Potential and Mitochondrial Membrane Potential.

As part of a drug discovery program aimed at the identification of anti- Trypanosoma cruzi metabolites from Brazilian flora, four acetogenins (1-4) were isolated from the seeds of Porcelia macrocarpa and were identified by NMR spectroscopy and HRESIMS. The new compounds 1 and 2 displayed activity against the trypomastigote (IC50 = 0.4 and 3.6 µM) and amastigote (IC50 = 23.0 and 27.7 µM) forms. The structurally related known compound 3 showed less potency to the amastigotes, with an IC50 value of 58 µM, while the known compound 4 was inactive. To evaluate the potential mechanisms for parasite death, parameters were evaluated by fluorometric assays: (i) plasma membrane permeability, (ii) plasma membrane electric potential (ΔΨp), (iii) reactive oxygen species production, and (iv) mitochondrial membrane potential (ΔΨm). The results obtained indicated that compounds 1 and 2 depolarize plasma membranes, affecting ΔΨp and ΔΨm and contributing to the observed cellular damage and disturbing the bioenergetic system. In silico studies of pharmacokinetics and toxicity (ADMET) properties predicted that all compounds were nonmutagenic, noncarcinogenic, nongenotoxic, and weak hERG blockers. Additionally, none of the isolated acetogenins 1-4 were predicted as pan-assay interference compounds.

Unveiling the Kinomes of Leishmania infantum and L. braziliensis Empowers the Discovery of New Kinase Targets and Antileishmanial Compounds.

Leishmaniasis is a neglected tropical disease caused by parasites of the genus Leishmania (NTD) endemic in 98 countries. Although some drugs are available, current treatments deal with issues such as toxicity, low efficacy, and emergence of resistance. Therefore, there is an urgent need to identify new targets for the development of new antileishmanial drugs. Protein kinases (PKs), which play an essential role in many biological processes, have become potential drug targets for many parasitic diseases. A refined bioinformatics pipeline was applied in order to define and compare the kinomes of L. infantum and L. braziliensis, species that cause cutaneous and visceral manifestations of leishmaniasis in the Americas, the latter being potentially fatal if untreated. Respectively, 224 and 221 PKs were identified in L. infantum and L. braziliensis overall. Almost all unclassified eukaryotic PKs were assigned to six of nine major kinase groups and, consequently, most have been classified into family and subfamily. Furthermore, revealing the kinomes for both Leishmania species allowed for the prioritization of potential drug targets that could be explored for discovering new drugs against leishmaniasis. Finally, we used a drug repurposing approach and prioritized seven approved drugs and investigational compounds to be experimentally tested against Leishmania. Trametinib and NMS-1286937 inhibited the growth of L. infantum and L. braziliensis promastigotes and amastigotes and therefore might be good candidates for the drug repurposing pipeline.

Butenolides from Nectandra oppositifolia (Lauraceae) displayed anti-Trypanosoma cruzi activity via deregulation of mitochondria.

BACKGROUND: From a previous screening of Brazilian biodiversity for antitrypanosomal activity, the n-hexane extract from twigs of Nectandra oppositifolia (Lauraceae) demonstrated in vitro activity against Trypanosoma cruzi. PURPOSE: To perform the isolation and chemical characterization of bioactive compounds from n-hexane extract from twigs of N. oppositifolia and evaluate their therapeutical potential as well as to elucidate their mechanism of action against T. cruzi. METHODS/STUDY DESIGN: Bioactivity-guided fractionation of the n-hexane extract from twigs of N. oppositifolia afforded three related butenolides: isolinderanolide D (1), isolinderanolide E (2) and secosubamolide A (3). These compounds were evaluated in vitro against T. cruzi (trypomastigote and amastigote forms) and against NCTC (L929) cells for mammalian cytotoxicity. Additionally, phenotypic analyzes of compounds-treated parasites were performed: alterations in the plasma membrane permeability, plasma membrane electric potential (ΔΨp), mitochondrial membrane potential (ΔΨm) and induction of ROS. RESULTS: Compounds 1-3 were effective against T. cruzi, with IC50 values of 12.9, 29.9 and 12.5 µM for trypomastigotes and 25.3, 10.1 and 12.3 µM for intracellular amastigotes. Furthermore, it was observed alteration in the mitochondrial membrane potential (ΔΨm) of parasites treated with butenolides 1-3. These compounds caused no alteration to the parasite plasma membrane, and the deregulation of the mitochondria might be an early event to cell death. In addition, in silico studies showed that all butenolides were predicted to be non-mutagenic, non-carcinogenic, non hERG blockers, with acceptable human intestinal absorption, low inhibitory promiscuity with the main five CYP isoforms, and with high metabolic stability. Otherwise, tested butenolides showed unfavorable blood-brain barrier penetration (BBB+). CONCLUSION: Our results demonstrated the anti-T. cruzi effects of compounds 1-3 isolated from N. oppositifolia and indicated that the lethal effect of these compounds in trypomastigotes of T. cruzi could be associated to the alteration in the mitochondrial membrane potential (ΔΨm).

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. The checkerboard assay showed that most promising hit (compound 3) displayed additive effects with the antifungal cotrimoxazole and amphotericin B. Therefore, the shape-based virtual screening allowed us to discover promising compounds in prospective hit-to-lead optimization studies for tackling PCM.

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 Plasmodium, 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 Plasmodium falciparum (PfdUTPase) induces wrong insertions in plasmodial DNA and consequently leading the parasite to death, this enzyme is considered an attractive antimalarial drug target. Using a combi-QSAR (quantitative structure-activity relationship) approach followed by virtual screening and in vitro experimental evaluation, we report herein the discovery of novel chemical scaffolds with in vitro potency against asexual blood stages of both P. falciparum multidrug-resistant and sensitive strains and against sporogonic development of P. berghei. We developed 2D- and 3D-QSAR models using a series of nucleosides reported in the literature as PfdUTPase inhibitors. The best models were combined in a consensus approach and used for virtual screening of the ChemBridge database, leading to the identification of five new virtual PfdUTPase inhibitors. Further in vitro testing on P. falciparum multidrug-resistant (W2) and sensitive (3D7) parasites showed that compounds LabMol-144 and LabMol-146 demonstrated fair activity against both strains and presented good selectivity versus mammalian cells. In addition, LabMol-144 showed good in vitro inhibition of P. berghei ookinete formation, demonstrating that hit-to-lead optimization based on this compound may also lead to new antimalarials with transmission blocking activity.

Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides?

Computational models have earned broad acceptance for assessing chemical toxicity during early stages of drug discovery or environmental safety assessment. The majority of publicly available QSAR toxicity models have been developed for datasets including mostly drugs or drug-like compounds. We have evaluated and compared chemical spaces occupied by cosmetics, drugs, and pesticides, and explored whether current computational models of toxicity endpoints can be universally applied to all these chemicals. Our analysis of the chemical space overlap and applicability domain (AD) of models built previously for twenty different toxicity endpoints showed that most of these models afforded high coverage (>90%) for all three classes of compounds analyzed herein. Only T. pyriformis models demonstrated lower coverage for drugs and pesticides (38% and 54%, respectively). These results show that, for the most part, historical QSAR models built with data available for different toxicity endpoints can be used for toxicity assessment of novel chemicals irrespective of the intended commercial use; however, the AD restriction is necessary to assure the expected prediction accuracy. Local models may need to be developed to capture chemicals that appear as outliers with respect to global models.

In Vitro, In Silico, and In Vivo Analyses of Novel Aromatic Amidines against Trypanosoma cruzi.

Five bis-arylimidamides were assayed as anti-Trypanosoma cruzi agents by in vitro, in silico, and in vivo approaches. None were considered to be pan-assay interference compounds. They had a favorable pharmacokinetic landscape and were active against trypomastigotes and intracellular forms, and in combination with benznidazole, they gave no interaction. The most selective agent (28SMB032) tested in vivo led to a 40% reduction in parasitemia (0.1 mg/kg of body weight/5 days intraperitoneally) but without mortality protection. In silico target fishing suggested DNA as the main target, but ultrastructural data did not match.