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Venglustat is a known allosteric inhibitor for ceramide glycosyltransferase, investigated in diseases caused by lysosomal dysfunction. Here, we identified venglustat as a potent inhibitor (IC

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

Dietary Supplements and Natural Products have minor oversight of their safety and efficacy. We assembled a collection of Dietary Supplements and Natural Products (DSNP) as well as Traditional Chinese Medicinal (TCM) Plant extracts, which were screened against an in vitro panel of assays, including a liver cytochrome p450 enzyme panel, CAR/PXR signaling pathways, and P-gp transporter assays, to assess their activity. This pipeline facilitated the interrogation of Natural Product-Drug Interaction (NaPDI) through prominent metabolizing pathways. In addition, we compared the activity profiles of the DSNP/TCM substances with those of an approved drug collection. Many of the approved drugs have well-annotated mechanisms of action (MOA) while the MOAs for most of the DSNP and TCM samples remain unknown. Based on the premise that compounds with similar activity profiles tend to share similar targets or MOA, we clustered the library activity profiles to identify overlap with the NCATS Pharmaceutical Collection to predict the MOAs of the DSNP/TCM substances. Overall, we highlight four significant bioactivity profiles (measured by p-values) as examples of this prediction. These results can be used as a starting point for further exploration on the toxicity potential and clinical relevance of these substances.

Cancer is one of the most serious health problems that usually require heavy medical treatment. It is important to ensure that no additional burden is placed on patients due to the modes of administration and/or poor quality of pharmaceuticals. In this regard, understanding, quantifying, and improving the photostability (resistance to UV light or sunlight) of drugs is among the important elements that can improve the patient's quality of life. In this work, the photochemical properties of a wide range of furanone analogues of combretastatin A-4 and their antiproliferative activity against A-431 epidermoid carcinoma cells were studied in a search for compounds with improved photostability and antiproliferative activity. It was found that the incorporation of an arylidene moiety led to a significant improvement in photostability, while the antiproliferative activity strongly depends on the nature of the aryl residue in the arylidene moiety. The high photostability of arylidenes was achieved due to the delocalization of the central double bond of the 1,3,5-hexatriene system, which limited the 6π-electrocyclization. The best results in terms of antiproliferative activity were obtained for thiophene arylidene (IC

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. To explore whether historical medicinal chemistry efforts suggest the possibility of discovering novel BSAs, we (i) identified, collected, curated, and integrated all chemical bioactivity data available in ChEMBL for molecules tested in respective assays for 13 emerging viruses that, based on published literature, hold the greatest potential threat to global human health; (ii) identified and solved the challenges related to data annotation accuracy including assay description ambiguity, missing cell or target information, and incorrect BioAssay Ontology (BAO) annotations; (iii) developed a highly curated and thoroughly annotated database of compounds tested in both phenotypic (21,392 entries) and target-based (11,123 entries) assays for these viruses; and (iv) identified a subset of compounds showing BSA activity. For the latter task, we eliminated inconclusive and annotated duplicative entries by checking the concordance between multiple assay results and identified eight compounds active against 3-4 viruses from the phenotypic data, 16 compounds active against two viruses from the target-based data, and 35 compounds active in at least one phenotypic and one target-based assay. The pilot version of our SMACC (Small Molecule Antiviral Compound Collection) database contains over 32,500 entries for 13 viruses. Our analysis indicates that previous research yielded very small number of BSA compounds. We posit that focused and coordinated efforts strategically targeting the discovery of such agents must be established and maintained going forward. The SMACC database publicly available at https://smacc.mml.unc.edu may serve as a reference for virologists and medicinal chemists working on the development of novel BSA agents in preparation for future viral outbreaks.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted researchers to pivot their efforts to finding antiviral compounds and vaccines. In this study, we focused on the human host cell transmembrane protease serine 2 (TMPRSS2), which plays an important role in the viral life cycle by cleaving the spike protein to initiate membrane fusion. TMPRSS2 is an attractive target and has received attention for the development of drugs against SARS and Middle East respiratory syndrome. Starting with comparative structural modeling and a binding model analysis, we developed an efficient pharmacophore-based approach and applied a large-scale in silico database screening for small-molecule inhibitors against TMPRSS2. The hits were evaluated in the TMPRSS2 biochemical assay and the SARS-CoV-2 pseudotyped particle entry assay. A number of novel inhibitors were identified, providing starting points for the further development of drug candidates for the treatment of coronavirus disease 2019.

Computational methods to predict molecular properties regarding safety and toxicology represent alternative approaches to expedite drug development, screen environmental chemicals, and thus significantly reduce associated time and costs. There is a strong need and interest in the development of computational methods that yield reliable predictions of toxicity, and many approaches, including the recently introduced deep neural networks, have been leveraged towards this goal. Herein, we report on the collection, curation, and integration of data from the public data sets that were the source of the ChemIDplus database for systemic acute toxicity. These efforts generated the largest publicly available such data set comprising > 80,000 compounds measured against a total of 59 acute systemic toxicity end points. This data was used for developing multiple single- and multitask models utilizing random forest, deep neural networks, convolutional, and graph convolutional neural network approaches. For the first time, we also reported the consensus models based on different multitask approaches. To the best of our knowledge, prediction models for 36 of the 59 end points have never been published before. Furthermore, our results demonstrated a significantly better performance of the consensus model obtained from three multitask learning approaches that particularly predicted the 29 smaller tasks (less than 300 compounds) better than other models developed in the study. The curated data set and the developed models have been made publicly available at https://github.com/ncats/ld50-multitask, https://predictor.ncats.io/, and https://cactus.nci.nih.gov/download/acute-toxicity-db (data set only) to support regulatory and research applications.

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., We cover diverse methodologies, computational approaches, and case studies illustrating the ongoing efforts to develop viable drug candidates for treatment of COVID-19.

The rise of novel artificial intelligence (AI) methods necessitates their benchmarking against classical machine learning for a typical drug-discovery project. Inhibition of the potassium ion channel, whose alpha subunit is encoded by the human ether-a-go-go-related gene (hERG), leads to a prolonged QT interval of the cardiac action potential and is a significant safety pharmacology target for the development of new medicines. Several computational approaches have been employed to develop prediction models for the assessment of hERG liabilities of small molecules including recent work using deep learning methods. Here, we perform a comprehensive comparison of hERG effect prediction models based on classical approaches (random forests and gradient boosting) and modern AI methods [deep neural networks (DNNs) and recurrent neural networks (RNNs)]. The training set (∼9000 compounds) was compiled by integrating the hERG bioactivity data from the ChEMBL database with experimental data generated from an in-house, high-throughput thallium flux assay. We utilized different molecular descriptors including the latent descriptors, which are real-value continuous vectors derived from chemical autoencoders trained on a large chemical space (>1.5 million compounds). The models were prospectively validated on ∼840 in-house compounds screened in the same thallium flux assay. The best results were obtained with the XGBoost method and RDKit descriptors. The comparison of models based only on latent descriptors revealed that the DNNs performed significantly better than the classical methods. The RNNs that operate on SMILES provided the highest model sensitivity. The best models were merged into a consensus model that offered superior performance compared to reference models from academic and commercial domains. Furthermore, we shed light on the potential of AI methods to exploit the big data in chemistry and generate novel chemical representations useful in predictive modeling and tailoring a new chemical space.

Methyltransferases (MTases) play diverse roles in cellular processes. Aberrant methylation levels have been implicated in many diseases, indicating the need for the identification and development of small molecule inhibitors for each MTase. Specific inhibitors can serve as probes to investigate the function and validate therapeutic potential for the respective MTase. High-throughput screening (HTS) is a powerful method to identify initial hits for further optimization. Here, we report the development of a fluorescence-based MTase assay and compare this format with the recently developed MTase-Glo™ luminescence assay for the application in HTS. Using protein N-terminal methyltransferase 1 (NTMT1) as a model system, we miniaturized to 1,536-well quantitative HTS format. Through a pilot screen of 1,428 pharmacologically active compounds and subsequent validation, we discovered that MTase-Glo™ produced lower false positive rates than the fluorescence-based MTase assay. Nevertheless, both assays displayed robust performance along with low reagent requirements and can potentially be employed as general HTS formats for the discovery of inhibitors for any MTase.

The hallmark pathological features of Alzheimer's disease (AD) brains are senile plaques, comprising β-amyloid (Aβ) peptides, and neuronal inclusions formed from tau protein. These plaques form 10–20 years before AD symptom onset, whereas robust tau pathology is more closely associated with symptoms and correlates with cognitive status. This temporal sequence of AD pathology development, coupled with repeated clinical failures of Aβ-directed drugs, suggests that molecules that reduce tau inclusions have therapeutic potential. Few tau-directed drugs are presently in clinical testing, in part because of the difficulty in identifying molecules that reduce tau inclusions. We describe here two cell-based assays of tau inclusion formation that we employed to screen for compounds that inhibit tau pathology: a HEK293 cell-based tau overexpression assay, and a primary rat cortical neuron assay with physiological tau expression. Screening a collection of ∼3500 pharmaceutical compounds with the HEK293 cell tau aggregation assay, we obtained only a low number of hit compounds. Moreover, these compounds generally failed to inhibit tau inclusion formation in the cortical neuron assay. We then screened the Prestwick library of mostly approved drugs in the cortical neuron assay, leading to the identification of a greater number of tau inclusion inhibitors. These included four dopamine D2 receptor antagonists, with D2 receptors having previously been suggested to regulate tau inclusions in a Caenorhabditis elegans model. These results suggest that neurons, the cells most affected by tau pathology in AD, are very suitable for screening for tau inclusion inhibitors.

Histone deacetylases (HDACs) are epigenetic modulators linked to diseases including cancer and neurodegeneration. Given their therapeutic potential, highly sensitive biochemical and cell-based profiling technologies have been developed to discover small-molecule HDAC inhibitors. Ultimately, the therapeutic action of these inhibitors is dependent on a physical engagement with their intended targets in cellular and tissue environments. Confirming target engagement in the cellular environment is particularly relevant for HDACs since they function as part of cell type-specific multiprotein complexes. Here we implemented two recently developed high-throughput target engagement technologies, NanoBRET and SplitLuc CETSA, to profile 349 compounds in the Epigenetic-Focused collection for HDAC1 binding. We found that the two HDAC1 target engagement assays correlated well with each other and with orthogonal activity-based assays, in particular those carried out in cellular environments rather than with isolated HDAC proteins. The assays detected a majority of the previously described HDAC1 inhibitors in the collection and, importantly, triaged HDAC inhibitors known to target other HDACs.

A novel protocol for the synthesis of functionalized phenanthrenes through photocyclization of diarylethenes (DAE) under UV irradiation is proposed. The reaction proceeds through 6π-electrocyclization with the formation of a cyclic (closed) intermediate that undergoes a rearrangement affording unsymmetrical phenanthrenes in good yields. However, in contrast to benzene derivatives, the photocyclization of naphthalene diarylethenes proceeds more slowly, which is confirmed by DFT calculations. The transformation was performed on a 1 mmol scale. The scalability showed that the diarylethenes bearing oxazole, thiazole, pyrazole and imidazole as aryl moieties are more prone to photorearrangement and can be used in preparative organic synthesis.

The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for an acute treatment for the disease. We investigate whether compounds that bind the human ACE2 protein can interrupt SARS-CoV-2 replication without damaging ACE2’s natural enzymatic function. Initial compounds were screened for binding to ACE2 but little interruption of ACE2 enzymatic activity. This set of compounds was extended by application of quantitative structure-activity analysis, which resulted in 512 virtual hits for further confirmatory screening. A subsequent SARS-CoV-2 replication assay revealed that five of these compounds inhibit SARS-CoV-2 replication in human cells. Further effort is required to completely determine the antiviral mechanism of these compounds, but they serve as a strong starting point for both development of acute treatments for COVID-19 and research into the mechanism of infection.Abstract FigureTOC Graphic: Overall study design.

The global health emergency posed by the outbreak of Zika virus (ZIKV), an arthropod-borne flavivirus causing severe neonatal neurological conditions, has subsided, but there continues to be transmission of ZIKV in endemic regions. As such, there is still a medical need for discovering and developing therapeutical interventions against ZIKV. To identify small-molecule compounds that inhibit ZIKV disease and transmission, we screened multiple small-molecule collections, mostly derived from natural products, for their ability to inhibit wild-type ZIKV. As a primary high-throughput screen, we used a viral cytopathic effect (CPE) inhibition assay conducted in Vero cells that was optimized and miniaturized to a 1536-well format. Suitably active compounds identified from the primary screen were tested in a panel of orthogonal assays using recombinant Zika viruses, including a ZIKV Renilla luciferase reporter assay and a ZIKV mCherry reporter system. Compounds that were active in the wild-type ZIKV inhibition and ZIKV reporter assays were further evaluated for their inhibitory effects against other flaviviruses. Lastly, we demonstrated that wild-type ZIKV is able to infect a 3D-bioprinted outer-blood-retina barrier tissue model and disrupt its barrier function, as measured by electrical resistance. One of the identified compounds (3-Acetyl-13-deoxyphomenone, NCGC00380955) was able to prevent the pathological effects of the viral infection on this clinically relevant ZIKV infection model.

The estrogen receptor α (ERα) is a target of intense pharmacological intervention and toxicological biomonitoring. Current methods to directly quantify cellular levels of ERα involve antibody-based assays, which are labor-intensive and of limited throughput. In this study, we generated a post-translational reporter cell line, referred to as MCF7-ERα-HiBiT, by fusing a small pro-luminescent nanoluciferase (NLuc) tag (HiBiT) to the C-terminus of endogenous ERα in MCF7 cells. The tag allows the luminescent detection and quantification of endogenous ERα protein by addition of the complementary NLuc enzyme fragment. This MCF7-ERα-HiBiT cell line was optimized for quantitative high-throughput screening (qHTS) to identify compounds that reduce ERα levels. In addition, the same cell line was optimized for a qHTS cellular thermal shift assay to identify compounds that bind and thermally stabilize ERα. Here, we interrogated the MCF7-ERα-HiBiT assay against the NCATS Pharmacological Collection (NPC) of 2,678 approved drugs and identified compounds that potently reduce and thermally stabilize ERα. Our novel post-translational reporter cell line provides a unique opportunity for profiling large pharmacological and toxicological compound libraries for their effect on ERα levels as well as for assessing direct compound binding to the receptor, thus facilitating mechanistic studies by which compounds exert their biological effects on ERα.

Current high-throughput screening assay optimization is often a manual and time-consuming process, even when utilizing design-of-experiment approaches. A cross-platform, Cloud-based Bayesian optimization-based algorithm was developed as part of the National Center for Advancing Translational Sciences (NCATS) ASPIRE (A Specialized Platform for Innovative Research Exploration) Initiative to accelerate preclinical drug discovery. A cell-free assay for papain enzymatic activity was used as proof of concept for biological assay development and system operationalization. Compared with a brute-force approach that sequentially tested all 294 assay conditions to find the global optimum, the Bayesian optimization algorithm could find suitable conditions for optimal assay performance by testing 21 assay conditions on average, with up to 20 conditions being tested simultaneously, as confirmed by repeated simulation. The algorithm could achieve a sevenfold reduction in costs for lab supplies and high-throughput experimentation runtime, all while being controlled from a remote site through a secure connection. Based on this proof of concept, this technology is expected to be applied to more complex biological assays and automated chemistry reaction screening at NCATS, and should be transferable to other institutions.

The skeletal photorearrangement including 6π-electrocyclization induced by UV light of ortho-halogen-substituted diarylethenes has been studied. It has been found that the reaction pathways leading to bi- or tricyclic frameworks depend on the kind of halogen substituent and solvent. Photocyclization with halogen abstraction leads to bicyclic fused aromatics, while the tricyclic frameworks are formed due to the tandem 6π-electrocyclization/sigmatropic shift reaction. THF is preferred as the solvent in the former process and chloroform in the latter reaction. It was found for the first time that, owing to the ability of this series of diarylethenes to undergo skeletal photorearrangement with the release of the bromide cation, they can be used both as brominating agents and as Lewis acids for catalyzing electrophilic reactions.

Nucleic acid nanoparticles, or NANPs, are rationally designed to communicate with the human immune system and can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide NANPs’ design to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, our research team has developed a computational model based on the transformer architecture able to predict the immune activities of NANPs via construction of so-called artificial immune cell, or AI-cell. The AI-cell will aid addressing in timely manner the current critical public health challenges related to overdose and safety criteria of nucleic acid therapies and promote the development of novel biomedical tools.

Significance COVID-19 has caused more than 2.5 million deaths worldwide. It is imperative that we develop therapies that can mitigate the effect of the disease. While searching for individual drugs for this purpose has been met with difficulties, synergistic drug combinations offer a promising alternative. However, the lack of high-quality training data pertaining to drug combinations makes it challenging to use existing machine learning methods for effective novel combination prediction tasks. Our proposed approach addresses this challenge by leveraging additional readily available data, such as drug−target interactions, thus enabling an effective in silico search for synergistic combinations against SARS-CoV-2., Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug−target interaction and drug−drug synergy. The model consists of two parts: a drug−target interaction module and a target−disease association module. This design enables the model to utilize drug−target interaction data and single-agent antiviral activity data, in addition to available drug−drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical−chemical combination data exists.

The effect of electron and proton acceptors on the photocyclization of diarylethenes has been studied. Without any additives, the deprotonation reaction is predominant, although other processes, including the sigmatropic shift, are not excluded. A deuterium exchange experiment has shown that a strong base (DABCO) facilitates the deprotonation reaction, thereby limiting the sigmatropic shift. In the presence of an oxidizing agent or additional sources of radicals (O2, I2, TEMPO), the processes of deprotonation and rearrangement (H-shift) are practically not observed, and the reaction proceeds along a radical pathway with the formation of phenanthrene or its heterocyclic analogue.

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytopathic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (∼16% of predicted hits) active compounds (efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further investigation resulted in the identification of allosteric binders to host receptor angiotensin-converting enzyme 2; these compounds were then shown to inhibit the entry of pseudoparticles bearing spike protein of wild-type SARS-CoV-2, as well as South African B.1.351 and UK B.1.1.7 variants.

We have studied the photoreaction of 1,2-diarylethenes under aerobic conditions in the presence of various amines to prevent side processes promoted by singlet oxygen. It has been found that the most amines quite effectively deactivate processes associated with singlet oxygen, but primary and secondary amines unlike tertiary ones, react with substrates resulting in various side products. Among the studied amines, the most effective additive for preventing side processes, including those associated with singlet oxygen is imidazole, which is practically not consumed in photoreaction. It was shown that imidazole can also prevents the photodegradation of organic photochromes in solutions. The results obtained can be used in various branches of science, technology and medicine to improve the photostability of photosensitive organics (dyes).

Syntheses under shock in nitrogen bubbled samples of the water – formamide – bicarbonate – sodium hydroxide system at pH 8.63, 9.46 and 10.44 were performed in the stainless steel preservation capsules. The maximum temperature and pressure in the capsules reached 545 K and 12.5 GPa respectively. Using the LC-MS-MS analysis, the 21 synthesis products have been identified, including amines and polyamines, carboxamide, acetamide and urea derivatives, compounds containing aniline, pyrrolidine, pyrrole, imidazole, as well as alcohol groups. It was found that the Fischer-Tropsch-type syntheses with catalysis on the surface of the stainless steel of the conservation capsule associated with the adsorbed hydrogen cyanide reactions and transamidation processes play the main role in the shock syntheses. Formation reactions of all the above-mentioned compounds have been suggested. It was proposed that hydrogen cyanide, ammonia, isocyanic acid, aminonitrile, aminoacetonitrile, as well as adsorbed species H(a), CH(a), CH2(a), CHOH(a), NH2(a) and H2CNH(a) are especially important for the formation of the products. A reduction reaction of adsorbed bicarbonate with hydrogen to formaldehyde has been first postulated. In the studied system also classical reactions take place – Wohler’s synthesis of urea and Butlerov’s synthesis of methenamine. It was suggest that material of meteorites may be an effective catalyst in the Fischer-Tropsch-type syntheses at falling of the iron-nickel meteorites in the water – formamide regions on the early Earth. It was concluded that life could have originated due to the impact of meteorites on alkaline water-formamide lakes located near volcanoes on the early Earth.

The National Center for Advancing Translational Sciences (NCATS) has been actively generating SARS-CoV-2 high-throughput screening data and disseminates it through the OpenData Portal (https://opendata.ncats.nih.gov/covid19/). Here, we provide a hybrid approach that utilizes NCATS screening data from the SARS-CoV-2 cytophatic effect reduction assay to build predictive models, using both machine learning and pharmacophore-based modeling. Optimized models were used to perform two iterative rounds of virtual screening to predict small molecules active against SARS-CoV-2. Experimental testing with live virus provided 100 (~16% of predicted hits) active compounds (Efficacy > 30%, IC50 ≤ 15 μM). Systematic clustering analysis of active compounds revealed three promising chemotypes which have not been previously identified as inhibitors of SARS-CoV-2 infection. Further analysis identified allosteric binders to host receptor angiotensin-converting enzyme 2, which were able to inhibit the entry of pseudoparticles bearing spike protein of wild type SARS-CoV-2 as well as South African B.1.351 and UK B.1.1.7 variants.

Endoplasmic reticulum (ER) dysregulation is associated with pathologies including neurodegenerative, muscular, and diabetic conditions. Depletion of ER calcium can lead to the loss of resident proteins in a process termed exodosis. To identify compounds that attenuate the redistribution of ER proteins under pathological conditions, we performed a quantitative high-throughput screen using the Gaussia luciferase (GLuc)-secreted ER calcium modulated protein (SERCaMP) assay, which monitors secretion of ER-resident proteins triggered by calcium depletion. We identify several clinically used drugs, including bromocriptine, and further characterize them using assays to measure effects on ER calcium, ER stress, and ER exodosis. Bromocriptine elicits protective effects in cell-based models of exodosis as well as in vivo models of stroke and diabetes. Bromocriptine analogs with reduced dopamine receptor activity retain similar efficacy in stabilizing the ER proteome, indicating a non-canonical mechanism of action. This study describes a strategic approach to identify small-molecule drugs capable of improving ER proteostasis in human disease conditions.

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.

[Image: see text] Activation-induced deaminase (AID) not only mutates DNA within the immunoglobulin loci to generate antibody diversity, but it also promotes development of B cell lymphomas. To tame this mutagen, we performed a quantitative high-throughput screen of over 90 000 compounds to see if AID activity could be mitigated. The enzymatic activity was assessed in biochemical assays to detect cytosine deamination and in cellular assays to measure class switch recombination. Three compounds showed promise via inhibition of switching in a transformed B cell line and in murine splenic B cells. These compounds have similar chemical structures, which suggests a shared mechanism of action. Importantly, the inhibitors blocked AID, but not a related cytosine DNA deaminase, APOBEC3B. We further determined that AID was continually expressed for several days after B cell activation to induce switching. This first report of small molecules that inhibit AID can be used to gain regulatory control over base editors.

COVID-19 is undoubtedly the most impactful viral disease of the current century, afflicting millions worldwide. As yet, there is not an approved vaccine, as well as limited options from existing drugs for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro . Overall, we identified 16 synergistic and 8 antagonistic combinations, 4 of which were both synergistic and antagonistic in a dose-dependent manner. Among the 16 synergistic cases, combinations of nitazoxanide with three other compounds (remdesivir, amodiaquine and umifenovir) were the most notable, all exhibiting significant synergy against SARS-CoV-2. The combination of nitazoxanide, an FDA-approved drug, and remdesivir, FDA emergency use authorization for the treatment of COVID-19, demonstrate a strong synergistic interaction. Notably, the combination of remdesivir and hydroxychloroquine demonstrated strong antagonism. Overall, our results emphasize the importance of both drug repurposing and preclinical testing of drug combinations for potential therapeutic use against SARS-CoV-2 infections.

The SARS-CoV-2 pandemic has prompted researchers to pivot their efforts to finding antiviral compounds and vaccines. In this study, we focused on the human host cell transmembrane protease serine 2 (TMPRSS2), which plays an important role in the viral life cycle by cleaving the spike protein to initiate membrane fusion. TMPRSS2 is an attractive target and has received attention for the development of drugs against SARS and MERS. Starting with comparative structural modeling and binding model analysis, we developed an efficient pharmacophore-based approach and applied a large-scale in silico database screening for small molecule inhibitors against TMPRSS2. The hits were evaluated in the TMPRSS2 biochemical assay and the SARS-CoV-2 pseudotyped particle (PP) entry assay. A number of novel inhibitors were identified, providing starting points for further development of drug candidates for the treatment of COVID-19.