Understanding the SARS-CoV-2 virus' pathways of infection, virus-host-protein interactions, and mechanisms of virus-induced cytopathic effects will greatly aid in the discovery and design of new therapeutics to treat COVID-19. Chloroquine and hydroxychloroquine, extensively explored as clinical agents for COVID-19, have multiple cellular effects including alkalizing lysosomes and blocking autophagy as well as exhibiting dose-limiting toxicities in patients. Therefore, we evaluated additional lysosomotropic compounds to identify an alternative lysosome-based drug repurposing opportunity. We found that six of these compounds blocked the cytopathic effect of SARS-CoV-2 in Vero E6 cells with half-maximal effective concentration (EC50) values ranging from 2.0 to 13 μM and selectivity indices (SIs; SI = CC50/EC50) ranging from 1.5- to >10-fold. The compounds (1) blocked lysosome functioning and autophagy, (2) prevented pseudotyped particle entry, (3) increased lysosomal pH, and (4) reduced (ROC-325) viral titers in the EpiAirway 3D tissue model. Consistent with these findings, the siRNA knockdown of ATP6V0D1 blocked the HCoV-NL63 cytopathic effect in LLC-MK2 cells. Moreover, an analysis of SARS-CoV-2 infected Vero E6 cell lysate revealed significant dysregulation of autophagy and lysosomal function, suggesting a contribution of the lysosome to the life cycle of SARS-CoV-2. Our findings suggest the lysosome as a potential host cell target to combat SARS-CoV-2 infections and inhibitors of lysosomal function could become an important component of drug combination therapies aimed at improving treatment and outcomes for COVID-19.

The first-line treatments for uncomplicated Plasmodium falciparum malaria are artemisinin-based combination therapies (ACTs), consisting of an artemisinin derivative combined with a longer acting partner drug. However, the spread of P. falciparum with decreased susceptibility to artemisinin and partner drugs presents a significant challenge to malaria control efforts. To stem the spread of drug resistant parasites, novel chemotherapeutic strategies are being evaluated, including the implementation of triple artemisinin-based combination therapies (TACTs). Currently, there is limited knowledge on the pharmacodynamic and pharmacogenetic interactions of proposed TACT drug combinations. To evaluate these interactions, we established an in vitro high-throughput process for measuring the drug concentration-response to three distinct antimalarial drugs present in a TACT. Sixteen different TACT combinations were screened against 15 parasite lines from Cambodia, with a focus on parasites with differential susceptibilities to piperaquine and artemisinins. Analysis revealed drug-drug interactions unique to specific genetic backgrounds, including antagonism between piperaquine and pyronaridine associated with gene amplification of plasmepsin II/III, two aspartic proteases that localize to the parasite digestive vacuole. From this initial study, we identified parasite genotypes with decreased susceptibility to specific TACTs, as well as potential TACTs that display antagonism in a genotype-dependent manner. Our assay and analysis platform can be further leveraged to inform drug implementation decisions and evaluate next-generation TACTs.

Antiviral drug development for COVID-19 is occurring at an unprecedented pace, yet there are still limited therapeutic options for treating this disease. We hypothesized that combining drugs with independent mechanisms of action could result in synergy against SARS-CoV-2, thus generating better antiviral efficacy. Using in silico approaches, we prioritized 73 combinations of 32 drugs with potential activity against SARS-CoV-2 and then tested them in vitro. Sixteen synergistic and eight antagonistic combinations were identified; among 16 synergistic cases, combinations of the FDA-approved drug nitazoxanide with remdesivir, amodiaquine, or umifenovir were most notable, all exhibiting significant synergy against SARS-CoV-2 in a cell model. However, the combination of remdesivir and lysosomotropic drugs, such as hydroxychloroquine, demonstrated strong antagonism. Overall, these results highlight the utility of drug repurposing and preclinical testing of drug combinations for discovering potential therapies to treat 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-à-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.

The mechanisms leading to organ level toxicities are poorly understood. In this study, we applied an integrated approach to deduce the molecular targets and biological pathways involved in chemically induced toxicity for eight common human organ level toxicity end points (carcinogenicity, cardiotoxicity, developmental toxicity, hepatotoxicity, nephrotoxicity, neurotoxicity, reproductive toxicity, and skin toxicity). Integrated analysis of in vitro assay data, molecular targets and pathway annotations from the literature, and toxicity-molecular target associations derived from text mining, combined with machine learning techniques, were used to generate molecular targets for each of the organ level toxicity end points. A total of 1516 toxicity-related genes were identified and subsequently analyzed for biological pathway coverage, resulting in 206 significant pathways (p-value <0.05), ranging from 3 (e.g., developmental toxicity) to 101 (e.g., skin toxicity) for each toxicity end point. This study presents a systematic and comprehensive analysis of molecular targets and pathways related to various in vivo toxicity end points. These molecular targets and pathways could aid in understanding the biological mechanisms of toxicity and serve as a guide for the design of suitable in vitro assays for more efficient toxicity testing. In addition, these results are complementary to the existing adverse outcome pathway (AOP) framework and can be used to aid in the development of novel AOPs. Our results provide abundant testable hypotheses for further experimental validation.

When Zika virus emerged as a public health emergency there were no drugs or vaccines approved for its prevention or treatment. We used a high-throughput screen for Zika virus protease inhibitors to identify several inhibitors of Zika virus infection. We expressed the NS2B-NS3 Zika virus protease and conducted a biochemical screen for small-molecule inhibitors. A quantitative structure-activity relationship model was employed to virtually screen ∼138,000 compounds, which increased the identification of active compounds, while decreasing screening time and resources. Candidate inhibitors were validated in several viral infection assays. Small molecules with favorable clinical profiles, especially the five-lipoxygenase-activating protein inhibitor, MK-591, inhibited the Zika virus protease and infection in neural stem cells. Members of the tetracycline family of antibiotics were more potent inhibitors of Zika virus infection than the protease, suggesting they may have multiple mechanisms of action. The most potent tetracycline, methacycline, reduced the amount of Zika virus present in the brain and the severity of Zika virus-induced motor deficits in an immunocompetent mouse model. As Food and Drug Administration-approved drugs, the tetracyclines could be quickly translated to the clinic. The compounds identified through our screening paradigm have the potential to be used as prophylactics for patients traveling to endemic regions or for the treatment of the neurological complications of Zika virus infection.

Since 2009, the Tox21 project has screened ∼8500 chemicals in more than 70 high-throughput assays, generating upward of 100 million data points, with all data publicly available through partner websites at the United States Environmental Protection Agency (EPA), National Center for Advancing Translational Sciences (NCATS), and National Toxicology Program (NTP). Underpinning this public effort is the largest compound library ever constructed specifically for improving understanding of the chemical basis of toxicity across research and regulatory domains. Each Tox21 federal partner brought specialized resources and capabilities to the partnership, including three approximately equal-sized compound libraries. All Tox21 data generated to date have resulted from a confluence of ideas, technologies, and expertise used to design, screen, and analyze the Tox21 10K library. The different programmatic objectives of the partners led to three distinct, overlapping compound libraries that, when combined, not only covered a diversity of chemical structures, use-categories, and properties but also incorporated many types of compound replicates. The history of development of the Tox21 "10K" chemical library and data workflows implemented to ensure quality chemical annotations and allow for various reproducibility assessments are described. Cheminformatics profiling demonstrates how the three partner libraries complement one another to expand the reach of each individual library, as reflected in coverage of regulatory lists, predicted toxicity end points, and physicochemical properties. ToxPrint chemotypes (CTs) and enrichment approaches further demonstrate how the combined partner libraries amplify structure-activity patterns that would otherwise not be detected. Finally, CT enrichments are used to probe global patterns of activity in combined ToxCast and Tox21 activity data sets relative to test-set size and chemical versus biological end point diversity, illustrating the power of CT approaches to discern patterns in chemical-activity data sets. These results support a central premise of the Tox21 program: A collaborative merging of programmatically distinct compound libraries would yield greater rewards than could be achieved separately.

SARS-CoV-2 is the viral pathogen causing the COVID19 global pandemic. Consequently, much research has gone into the development of preclinical assays for the discovery of new or repurposing of FDA-approved therapies. Preventing viral entry into a host cell would be an effective antiviral strategy. One mechanism for SARS-CoV-2 entry occurs when the spike protein on the surface of SARS-CoV-2 binds to an ACE2 receptor followed by cleavage at two cut sites ("priming") that causes a conformational change allowing for viral and host membrane fusion. TMPRSS2 has an extracellular protease domain capable of cleaving the spike protein to initiate membrane fusion. A validated inhibitor of TMPRSS2 protease activity would be a valuable tool for studying the impact TMPRSS2 has in viral entry and potentially be an effective antiviral therapeutic. To enable inhibitor discovery and profiling of FDA-approved therapeutics, we describe an assay for the biochemical screening of recombinant TMPRSS2 suitable for high throughput application. We demonstrate effectiveness to quantify inhibition down to subnanomolar concentrations by assessing the inhibition of camostat, nafamostat, and gabexate, clinically approved agents in Japan. Also, we profiled a camostat metabolite, FOY-251, and bromhexine hydrochloride, an FDA-approved mucolytic cough suppressant. The rank order potency for the compounds tested are nafamostat (IC50 = 0.27 nM), camostat (IC50 = 6.2 nM), FOY-251 (IC50 = 33.3 nM), and gabexate (IC50 = 130 nM). Bromhexine hydrochloride showed no inhibition of TMPRSS2. Further profiling of camostat, nafamostat, and gabexate against a panel of recombinant proteases provides insight into selectivity and potency.

Lactate dehydrogenase (LDH) catalyzes the conversion of pyruvate to lactate, with concomitant oxidation of reduced nicotinamide adenine dinucleotide as the final step in the glycolytic pathway. Glycolysis plays an important role in the metabolic plasticity of cancer cells and has long been recognized as a potential therapeutic target. Thus, potent, selective inhibitors of LDH represent an attractive therapeutic approach. However, to date, pharmacological agents have failed to achieve significant target engagement in vivo, possibly because the protein is present in cells at very high concentrations. We report herein a lead optimization campaign focused on a pyrazole-based series of compounds, using structure-based design concepts, coupled with optimization of cellular potency, in vitro drug-target residence times, and in vivo PK properties, to identify first-in-class inhibitors that demonstrate LDH inhibition in vivo. The lead compounds, named NCATS-SM1440 (43) and NCATS-SM1441 (52), possess desirable attributes for further studying the effect of in vivo LDH inhibition.

With the recent spread of severe acute respiratory syndrome coronavirus (SARS-CoV-2)_ infecting >16 million people worldwide as of 28 July 2020, causing >650 000 deaths, there is a desperate need for therapeutic agents and vaccines. Building on knowledge of previous outbreaks of SARS-CoV-1 and Middle East respiratory syndrome (MERS), the development of therapeutic antibodies and vaccines against coronavirus disease 2019 (COVID-19) is taking place at an unprecedented speed. Current efforts towards the development of neutralizing antibodies against COVID-19 are summarized. We also highlight the importance of a fruitful antibody development pipeline to combat the potential escape plans of SARS-CoV-2, including somatic mutations and antibody-dependent enhancement (ADE).