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.

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.

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.

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.

Chalcone Derivatives: Promising Starting Points for Drug Design.

Medicinal chemists continue to be fascinated by chalcone derivatives because of their simple chemistry, ease of hydrogen atom manipulation, straightforward synthesis, and a variety of promising biological activities. However, chalcones have still not garnered deserved attention, especially considering their high potential as chemical sources for designing and developing new effective drugs. In this review, we summarize current methodological developments towards the design and synthesis of new chalcone derivatives and state-of-the-art medicinal chemistry strategies (bioisosterism, molecular hybridization, and pro-drug design). We also highlight the applicability of computer-assisted drug design approaches to chalcones and address how this may contribute to optimizing research outputs and lead to more successful and cost-effective drug discovery endeavors. Lastly, we present successful examples of the use of chalcones and suggest possible solutions to existing limitations.

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. mansoni using a combi-QSAR approach followed by virtual screening of a commercial database and confirmation of top ranking compounds by in vitro experimental evaluation with automated imaging of schistosomula and adult worms. We constructed 2D and 3D quantitative structure-activity relationship (QSAR) models using a series of oxadiazoles-2-oxides reported in the literature as SmTGR inhibitors and combined the best models in a consensus QSAR model. This model was used for a virtual screening of Hit2Lead set of ChemBridge database and allowed the identification of ten new potential SmTGR inhibitors. Further experimental testing on both shistosomula and adult worms showed that 4-nitro-3,5-bis(1-nitro-1H-pyrazol-4-yl)-1H-pyrazole (LabMol-17) and 3-nitro-4-{[(4-nitro-1,2,5-oxadiazol-3-yl)oxy]methyl}-1,2,5-oxadiazole (LabMol-19), two compounds representing new chemical scaffolds, have high activity in both systems. These compounds will be the subjects for additional testing and, if necessary, modification to serve as new schistosomicidal agents.

Analogues of Marine Guanidine Alkaloids Are in Vitro Effective against Trypanosoma cruzi and Selectively Eliminate Leishmania (L.) infantum Intracellular Amastigotes.

Synthetic analogues of marine sponge guanidine alkaloids showed in vitro antiparasitic activity against Leishmania (L.) infantum and Trypanosoma cruzi. Guanidines 10 and 11 presented the highest selectivity index when tested against Leishmania. The antiparasitic activity of 10 and 11 was investigated in host cells and in parasites. Both compounds induced depolarization of mitochondrial membrane potential, upregulation of reactive oxygen species levels, and increased plasma membrane permeability in Leishmania parasites. Immunomodulatory assays suggested an NO-independent effect of guanidines 10 and 11 on macrophages. The same compounds also promoted anti-inflammatory activity in L. (L.) infantum-infected macrophages cocultived with splenocytes, reducing the production of cytokines MCP-1 and IFN-γ. Guanidines 10 and 11 affect the bioenergetic metabolism of Leishmania, with selective elimination of parasites via a host-independent mechanism.

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization.

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R(2)=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q(2)ext=0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds.

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71-88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation.

Natural products as leads in schistosome drug discovery.

Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ), the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs) as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS) strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.

Discovery of new potential hits of Plasmodium falciparum enoyl-ACP reductase through ligand- and structure-based drug design approaches.

We here report the discovery of novel Plasmodium falciparum enoyl-ACP reductase (PfENR) inhibitors as new antimalarial hits through ligand- and structure-based drug design approaches. We performed 2D and 3D QSAR studies on a set of rhodanine analogues using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Statistical and satisfactory results were obtained for the best HQSAR (r(2) of 0.968 and qLOO(2) of 0.751), CoMFA (r(2) of 0.955 and qLOO(2) of 0.806) and CoMSIA (r(2) of 0.965 and qLOO(2) of 0.659) models. The information gathered from the QSAR models guided us to design new PfENR inhibitors. Three new hits were predicted with potency in the submicromolar range and presented drug-like properties.

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.

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.

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.

Structure-based prediction and biosynthesis of the major mammalian metabolite of the cardioactive prototype LASSBio-294.

A new bioactive compound of the N-acylhydrazone class, LASSBio-294, was shown to produce a cardioinotropic effect and vasodilation. In this study, we report the structure-based drug metabolism prediction, biosynthesis and identification of the major mammalian metabolite of LASSBio-294.

Design of new dopamine D2 receptor ligands: biosynthesis and pharmacological evaluation of the hydroxylated metabolite of LASSBio-581.

LASSBio-581 is a N-phenylpiperazine derivative designed for the treatment of schizophrenia. In this study, four strains of filamentous fungi were screened for their capabilities to biotransform LASSBio-581. Cunninghamella echinulata ATCC 9244 was chosen to scale up the biosynthesis of the p-hydroxylated metabolite of LASSBio-581. The chemical structure of the metabolite was confirmed by NMR, LC-MS and X-ray crystallography. Binding studies performed on brain homogenate indicated that the p-hydroxylated metabolite can be considered more selective for dopamine receptors than LASSBio-581, and, therefore, can be used to design new selective dopamine inhibitors.

4D-QSAR: perspectives in drug design.

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. The quantitative structure-activity relationship (QSAR) formalisms are among the most important strategies that can be applied for the successful design new molecules. This review provides a comprehensive review on the evolution and current status of 4D-QSAR, highlighting present challenges and new opportunities in drug design.