High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Preclinical Evaluation of the FGFR-Family Inhibitor Futibatinib for Pediatric Rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is the most common pediatric soft tissue sarcoma. Despite decades of clinical trials, the overall survival rate for patients with relapsed and metastatic disease remains below 30%, underscoring the need for novel treatments. FGFR4, a receptor tyrosine kinase that is overexpressed in RMS and mutationally activated in 10% of cases, is a promising target for treatment. Here, we show that futibatinib, an irreversible pan-FGFR inhibitor, inhibits the growth of RMS cell lines in vitro by inhibiting phosphorylation of FGFR4 and its downstream targets. Moreover, we provide evidence that the combination of futibatinib with currently used chemotherapies such as irinotecan and vincristine has a synergistic effect against RMS in vitro. However, in RMS xenograft models, futibatinib monotherapy and combination treatment have limited efficacy in delaying tumor growth and prolonging survival. Moreover, limited efficacy is only observed in a PAX3-FOXO1 fusion-negative (FN) RMS cell line with mutationally activated FGFR4, whereas little or no efficacy is observed in PAX3-FOXO1 fusion-positive (FP) RMS cell lines with FGFR4 overexpression. Alternative treatment modalities such as combining futibatinib with other kinase inhibitors or targeting FGFR4 with CAR T cells or antibody-drug conjugate may be more effective than the approaches tested in this study.

Drug combinations identified by high-throughput screening promote cell cycle transition and upregulate Smad pathways in myeloma.

Drug resistance and disease progression are common in multiple myeloma (MM) patients, underscoring the need for new therapeutic combinations. A high-throughput drug screen in 47 MM cell lines and in silico Huber robust regression analysis of drug responses revealed 43 potentially synergistic combinations. We hypothesized that effective combinations would reduce MYC expression and enhance p16 activity. Six combinations cooperatively reduced MYC protein, frequently over-expressed in MM and also cooperatively increased p16 expression, frequently downregulated in MM. Synergistic reductions in viability were observed with top combinations in proteasome inhibitor-resistant and sensitive MM cell lines, while sparing fibroblasts. Three combinations significantly prolonged survival in a transplantable Ras-driven allograft model of advanced MM closely recapitulating high-risk/refractory myeloma in humans and reduced viability of ex vivo treated patient cells. Common genetic pathways similarly downregulated by these combinations promoted cell cycle transition, whereas pathways most upregulated were involved in TGFß/SMAD signaling. These preclinical data identify potentially useful drug combinations for evaluation in drug-resistant MM and reveal potential mechanisms of combined drug sensitivity.

Quantitative Bioactivity Signatures of Dietary Supplements and Natural Products.

Dietary supplements and natural products are often marketed as safe and effective alternatives to conventional drugs, but their safety and efficacy are not well regulated. To address the lack of scientific data in these areas, we assembled a collection of Dietary Supplements and Natural Products (DSNP), as well as Traditional Chinese Medicinal (TCM) plant extracts. These collections were then profiled in a series of in vitro high-throughput screening assays, including a liver cytochrome p450 enzyme panel, CAR/PXR signaling pathways, and P-glycoprotein (P-gp) transporter assay activities. 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 (the NCATS Pharmaceutical Collection or NPC). Many of the approved drugs have well-annotated mechanisms of action (MOAs), 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 NPC to predict the MOAs of the DSNP/TCM substances. Our results suggest that many of these substances may have significant bioactivity and potential toxicity, and they provide a starting point for further research on their clinical relevance.

CellMinerCDB: NCATS Is a Web-Based Portal Integrating Public Cancer Cell Line Databases for Pharmacogenomic Explorations.

Major advances have been made in the field of precision medicine for treating cancer. However, many open questions remain that need to be answered to realize the goal of matching every patient with cancer to the most efficacious therapy. To facilitate these efforts, we have developed CellMinerCDB: National Center for Advancing Translational Sciences (NCATS; https://discover.nci.nih.gov/rsconnect/cellminercdb_ncats/), which makes available activity information for 2,675 drugs and compounds, including multiple nononcology drugs and 1,866 drugs and compounds unique to the NCATS. CellMinerCDB: NCATS comprises 183 cancer cell lines, with 72 unique to NCATS, including some from previously understudied tissues of origin. Multiple forms of data from different institutes are integrated, including single and combination drug activity, DNA copy number, methylation and mutation, transcriptome, protein levels, histone acetylation and methylation, metabolites, CRISPR, and miscellaneous signatures. Curation of cell lines and drug names enables cross-database (CDB) analyses. Comparison of the datasets is made possible by the overlap between cell lines and drugs across databases. Multiple univariate and multivariate analysis tools are built-in, including linear regression and LASSO. Examples have been presented here for the clinical topoisomerase I (TOP1) inhibitors topotecan and irinotecan/SN-38. This web application provides both substantial new data and significant pharmacogenomic integration, allowing exploration of interrelationships. SIGNIFICANCE: CellMinerCDB: NCATS provides activity information for 2,675 drugs in 183 cancer cell lines and analysis tools to facilitate pharmacogenomic research and to identify determinants of response.

Suite of TMPRSS2 Assays for Screening Drug Repurposing Candidates as Potential Treatments of COVID-19.

SARS-CoV-2 is the causative viral pathogen driving the COVID-19 pandemic that prompted an immediate global response to the development of vaccines and antiviral therapeutics. For antiviral therapeutics, drug repurposing allows for rapid movement of the existing clinical candidates and therapies into human clinical trials to be tested as COVID-19 therapies. One effective antiviral treatment strategy used early in symptom onset is to prevent viral entry. SARS-CoV-2 enters ACE2-expressing cells when the receptor-binding domain of the spike protein on the surface of SARS-CoV-2 binds to ACE2 followed by cleavage at two cut sites by TMPRSS2. Therefore, a molecule capable of inhibiting the protease activity of TMPRSS2 could be a valuable antiviral therapy. Initially, we used a fluorogenic high-throughput screening assay for the biochemical screening of 6030 compounds in NCATS annotated libraries. Then, we developed an orthogonal biochemical assay that uses mass spectrometry detection of product formation to ensure that hits from the primary screen are not assay artifacts from the fluorescent detection of product formation. Finally, we assessed the hits from the biochemical screening in a cell-based SARS-CoV-2 pseudotyped particle entry assay. Of the six molecules advanced for further studies, two are approved drugs in Japan (camostat and nafamostat), two have entered clinical trials (PCI-27483 and otamixaban), while the other two molecules are peptidomimetic inhibitors of TMPRSS2 taken from the literature that have not advanced into clinical trials (compounds 92 and 114). This work demonstrates a suite of assays for the discovery and development of new inhibitors of TMPRSS2.

Retro Drug Design: From Target Properties to Molecular Structures.

To deliver more therapeutics to more patients more quickly and economically is the ultimate goal of pharmaceutical researchers. The advent and rapid development of artificial intelligence (AI), in combination with other powerful computational methods in drug discovery, makes this goal more practical than ever before. Here, we describe a new strategy, retro drug design, or RDD, to create novel small-molecule drugs from scratch to meet multiple predefined requirements, including biological activity against a drug target and optimal range of physicochemical and ADMET properties. The molecular structure was represented by an atom typing based molecular descriptor system, optATP, which was further transformed to the space of loading vectors from principal component analysis. Traditional predictive models were trained over experimental data for the target properties using optATP and shallow machine learning methods. The Monte Carlo sampling algorithm was then utilized to find the solutions in the space of loading vectors that have the target properties. Finally, a deep learning model was employed to decode molecular structures from the solutions. To test the feasibility of the algorithm, we challenged RDD to generate novel kinase inhibitors from random numbers with five different ADMET properties optimized at the same time. The best Tanimoto similarity score between the generated valid structures and the available 4,314 kinase inhibitors was < 0.50, indicating a high extent of novelty of the generated compounds. From the 3,040 structures that met all six target properties, 20 were selected for synthesis and experimental measurement of inhibition activity over 97 representative kinases and the ADMET properties. Fifteen and eight compounds were determined to be hits or strong hits, respectively. Five of the six strong kinase inhibitors have excellent experimental ADMET properties. The results presented in this paper illustrate that RDD has the potential to significantly improve the current drug discovery process.

Correction to: Human Pluripotent Stem Cells for High-Throughput Drug Screening and Characterization of Small Molecules.

This chapter was inadvertently published with an error, the ninth point under 'Materials' section must read.

A Suite of TMPRSS2 Assays for Screening Drug Repurposing Candidates as Potential Treatments of COVID-19.

SARS-CoV-2 is the causative viral pathogen driving the COVID-19 pandemic that prompted an immediate global response to the development of vaccines and antiviral therapeutics. For antiviral therapeutics, drug repurposing allowed for rapid movement of existing clinical candidates and therapies into human clinical trials to be tested as COVID-19 therapies. One effective antiviral treatment strategy used early in symptom onset is to prevent viral entry. SARS-CoV-2 enters ACE2-expressing cells when the receptor-binding domain of the spike protein on the surface of SARS-CoV-2 binds to ACE2 followed by cleavage at two cut sites on the spike protein. TMPRSS2 has a protease domain capable of cleaving the two cut sites; therefore, a molecule capable of inhibiting the protease activity of TMPRSS2 could be a valuable antiviral therapy. Initially, we used a fluorogenic high-throughput screening assay for the biochemical screening of 6030 compounds in NCATS annotated libraries. Then, we developed an orthogonal biochemical assay that uses mass spectrometry detection of product formation to ensure that hits from the primary screen are not assay artifacts from the fluorescent detection of product formation. Finally, we assessed the hits from the biochemical screening in a cell-based SARS-CoV-2 pseudotyped particle entry assay. Of the six molecules advanced for further studies, two are approved drugs in Japan (camostat and nafamostat), two have entered clinical trials (PCI-27483 and otamixaban), while the other two molecules are peptidomimetic inhibitors of TMPRSS2 taken from the literature that have not advanced into clinical trials (compounds 92 and 114). This work demonstrates a suite of assays for the discovery and development of new inhibitors of TMPRSS2.

Using in vitro ADME data for lead compound selection: An emphasis on PAMPA pH 5 permeability and oral bioavailability.

Membrane permeability plays an important role in oral drug absorption. Caco-2 and Madin-Darby Canine Kidney (MDCK) cell culture systems have been widely used for assessing intestinal permeability. Since most drugs are absorbed passively, Parallel Artificial Membrane Permeability Assay (PAMPA) has gained popularity as a low-cost and high-throughput method in early drug discovery when compared to high-cost, labor intensive cell-based assays. At the National Center for Advancing Translational Sciences (NCATS), PAMPA pH 5 is employed as one of the Tier I absorption, distribution, metabolism, and elimination (ADME) assays. In this study, we have developed a quantitative structure activity relationship (QSAR) model using our ∼6500 compound PAMPA pH 5 permeability dataset. Along with ensemble decision tree-based methods such as Random Forest and eXtreme Gradient Boosting, we employed deep neural network and a graph convolutional neural network to model PAMPA pH 5 permeability. The classification models trained on a balanced training set provided accuracies ranging from 71% to 78% on the external set. Of the four classifiers, the graph convolutional neural network that directly operates on molecular graphs offered the best classification performance. Additionally, an ∼85% correlation was obtained between PAMPA pH 5 permeability and in vivo oral bioavailability in mice and rats. These results suggest that data from this assay (experimental or predicted) can be used to rank-order compounds for preclinical in vivo testing with a high degree of confidence, reducing cost and attrition as well as accelerating the drug discovery process. Additionally, experimental data for 486 compounds (PubChem AID: 1645871) and the best models have been made publicly available (https://opendata.ncats.nih.gov/adme/).

Human Pluripotent Stem Cells for High-Throughput Drug Screening and Characterization of Small Molecules.

Human pluripotent stem cells (hPSCs), such as induced pluripotent stem cells (iPSCs), hold great promise for drug discovery, toxicology studies, and regenerative medicine. Here, we describe standardized protocols and experimental procedures that combine automated cell culture for scalable production of hPSCs with quantitative high-throughput screening (qHTS) in miniaturized 384-well plates. As a proof of principle, we established dose-response assessments and determined optimal concentrations of 12 small molecule compounds that are commonly used in the stem cell field. Multi-parametric analysis of readouts from diverse assays including cell viability, mitochondrial membrane potential, plasma membrane integrity, and ATP production was used to distinguish normal biological responses from cellular stress induced by small molecule treatment. Collectively, the establishment of integrated workflows for cell manufacturing, qHTS, high-content imaging, and data analysis provides an end-to-end platform for industrial-scale projects and should leverage the drug discovery process using hPSC-derived cell types.

Discovery of Small Molecule Entry Inhibitors Targeting the Fusion Peptide of SARS-CoV-2 Spike Protein.

SARS-CoV-2 entry into host cells relies on the spike (S) protein binding to the human ACE2 receptor. In this study, we investigated the structural dynamics of the viral S protein at the fusion peptide (FP) domain and small molecule binding for therapeutics development. Following comparative modeling analysis and docking studies of our previously identified fusion inhibitor chlorcyclizine, we performed a pharmacophore-based virtual screen and identified two novel chemotypes of entry inhibitors targeting the FP. The compounds were evaluated in the pseudoparticle viral entry assay and SARS-CoV-2 cytopathic effect assay and showed single-digital micromole inhibition against SARS-CoV-2 as well as SARS-CoV-1 and MERS. The characterization of the FP binding site of SARS-CoV-2 S protein provides a promising target for the structure-based development of small molecule entry inhibitors as drug candidates for the treatment of COVID-19.

Discovery of TMPRSS2 Inhibitors from Virtual Screening as a Potential Treatment of COVID-19.

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.

Retro Drug Design: From Target Properties to Molecular Structures.

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate µ opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

Application of niclosamide and analogs as small molecule inhibitors of Zika virus and SARS-CoV-2 infection.

Zika virus has emerged as a potential threat to human health globally. A previous drug repurposing screen identified the approved anthelminthic drug niclosamide as a small molecule inhibitor of Zika virus infection. However, as antihelminthic drugs are generally designed to have low absorption when dosed orally, the very limited bioavailability of niclosamide will likely hinder its potential direct repurposing as an antiviral medication. Here, we conducted SAR studies focusing on the anilide and salicylic acid regions of niclosamide to improve physicochemical properties such as microsomal metabolic stability, permeability and solubility. We found that the 5-bromo substitution in the salicylic acid region retains potency while providing better drug-like properties. Other modifications in the anilide region with 2'-OMe and 2'-H substitutions were also advantageous. We found that the 4'-NO2 substituent can be replaced with a 4'-CN or 4'-CF3 substituents. Together, these modifications provide a basis for optimizing the structure of niclosamide to improve systemic exposure for application of niclosamide analogs as drug lead candidates for treating Zika and other viral infections. Indeed, key analogs were also able to rescue cells from the cytopathic effect of SARS-CoV-2 infection, indicating relevance for therapeutic strategies targeting the COVID-19 pandemic.

Discovery of TMPRSS2 inhibitors from virtual screening.

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.

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2.

Antiviral drug development for coronavirus disease 2019 (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 US Food and Drug Administration (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 SARS-CoV-2 Cytopathic Effect Is Blocked by Lysosome Alkalizing Small Molecules.

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 Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.

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.

Identifying SARS-CoV-2 Entry Inhibitors through Drug Repurposing Screens of SARS-S and MERS-S Pseudotyped Particles.

While vaccine development will hopefully quell the global pandemic of COVID-19 caused by SARS-CoV-2, small molecule drugs that can effectively control SARS-CoV-2 infection are urgently needed. Here, inhibitors of spike (S) mediated cell entry were identified in a high throughput screen of an approved drugs library with SARS-S and MERS-S pseudotyped particle entry assays. We discovered six compounds (cepharanthine, abemaciclib, osimertinib, trimipramine, colforsin, and ingenol) to be broad spectrum inhibitors for spike-mediated entry. This work could contribute to the development of effective treatments against the initial stage of viral infection and provide mechanistic information that might aid the design of new drug combinations for clinical trials for COVID-19 patients.