Publications by authors named "Ruili Huang"

183 Publications

Identification of Compounds for Butyrylcholinesterase Inhibition.

SLAS Discov 2021 Jul 16:24725552211030897. Epub 2021 Jul 16.

Division for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA.

Butyrylcholinesterase (BChE) is a nonspecific cholinesterase enzyme that hydrolyzes choline-based esters. BChE plays a critical role in maintaining normal cholinergic function like acetylcholinesterase (AChE) through hydrolyzing acetylcholine (ACh). Selective BChE inhibition has been regarded as a viable therapeutic approach in Alzheimer's disease. As of now, a limited number of selective BChE inhibitors are available. To identify BChE inhibitors rapidly and efficiently, we have screened 8998 compounds from several annotated libraries against an enzyme-based BChE inhibition assay in a quantitative high-throughput screening (qHTS) format. From the primary screening, we identified a group of 125 compounds that were further confirmed to inhibit BChE activity, including previously reported BChE inhibitors (e.g., bambuterol and rivastigmine) and potential novel BChE inhibitors (e.g., pancuronium bromide and NNC 756), representing diverse structural classes. These BChE inhibitors were also tested for their selectivity by comparing their IC values in BChE and AChE inhibition assays. The binding modes of these compounds were further studied using molecular docking analyses to identify the differences between the interactions of these BChE inhibitors within the active sites of AChE and BChE. Our qHTS approach allowed us to establish a robust and reliable process to screen large compound collections for potential BChE inhibitors.
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http://dx.doi.org/10.1177/24725552211030897DOI Listing
July 2021

AI-based language models powering drug discovery and development.

Drug Discov Today 2021 Jun 30. Epub 2021 Jun 30.

National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA. Electronic address:

The discovery and development of new medicines is expensive, time-consuming, and often inefficient, with many failures along the way. Powered by artificial intelligence (AI), language models (LMs) have changed the landscape of natural language processing (NLP), offering possibilities to transform treatment development more effectively. Here, we summarize advances in AI-powered LMs and their potential to aid drug discovery and development. We highlight opportunities for AI-powered LMs in target identification, clinical design, regulatory decision-making, and pharmacovigilance. We specifically emphasize the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics. Finally, we set out the remaining challenges and propose possible solutions for improvement.
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http://dx.doi.org/10.1016/j.drudis.2021.06.009DOI Listing
June 2021

Development of Robust QSAR Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition.

Drug Metab Dispos 2021 Jun 28. Epub 2021 Jun 28.

NIH, United States

Cytochrome P450 (CYP) enzymes are responsible for the metabolism of >75% of marketed drugs, making it essential to identify the contributions of individual CYPs to the total clearance of a new candidate drug. Overreliance on one CYP for clearance levies a high risk of drug-drug interactions; and considering that several human CYP enzymes are polymorphic, it can also lead to highly variable pharmacokinetics in the clinic. Thus, it would be advantageous to understand the likelihood of new chemical entities to interact with the major CYP enzymes at an early stage in the drug discovery process. Typical screening assays using human liver microsomes do not provide sufficient information to distinguish the specific CYPs responsible for clearance. In this regard, we experimentally assessed the metabolic stability of ~5,000 compounds for the three most prominent xenobiotic metabolizing human CYPs, i.e., CYP2C9, CYP2D6 and CYP3A4, and used the datasets to develop quantitative structure-activity relationship models for the prediction of high clearance substrates for these enzymes. Screening library included the NCATS Pharmaceutical Collection, comprised of clinically approved low molecular weight compounds, and an annotated library consisting of drug-like compounds. To identify inhibitors, the library was screened against a luminescence-based CYP inhibition assay; and through cross-referencing hits from the two assays, we were able to distinguish substrates and inhibitors of these enzymes. The best substrate and inhibitor models (Balanced accuracies ~0.7), as well as the data used to develop these models, have been made publicly available (https://opendata.ncats.nih.gov/adme) to advance drug discovery across all research groups. In drug discovery and development, a drug candidate with an indiscriminate CYP metabolic profile is considered advantageous, since they provide less risk of potential issues with CYP polymorphisms and drug-drug interactions. In this study, we have developed robust substrate and inhibitor QSAR models for the three major xenobiotic metabolizing CYPs, i.e. CYP2C9, CYP2D6, and CYP3A4. The use of these models early in drug discovery will enable project teams to strategize or pivot when necessary, thereby accelerating drug discovery research.
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http://dx.doi.org/10.1124/dmd.120.000320DOI Listing
June 2021

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

Methods Mol Biol 2021 Jun 15. Epub 2021 Jun 15.

Stem Cell Translation Laboratory (SCTL), Division of Preclinical Innovation (DPI), National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.

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.
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http://dx.doi.org/10.1007/7651_2021_394DOI Listing
June 2021

A Universal and High-Throughput Proteomics Sample Preparation Platform.

Anal Chem 2021 06 10;93(24):8423-8431. Epub 2021 Jun 10.

National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States.

Major advances have been made to improve the sensitivity of mass analyzers, spectral quality, and speed of data processing enabling more comprehensive proteome discovery and quantitation. While focus has recently begun shifting toward robust proteomics sample preparation efforts, a high-throughput proteomics sample preparation is still lacking. We report the development of a highly automated universal 384-well plate sample preparation platform with high reproducibility and adaptability for extraction of proteins from cells within a culture plate. Digestion efficiency was excellent in comparison to a commercial digest peptide standard with minimal sample loss while improving sample preparation throughput by 20- to 40-fold (the entire process from plated cells to clean peptides is complete in ∼300 min). Analysis of six human cell types, including two primary cell samples, identified and quantified ∼4,000 proteins for each sample in a single high-performance liquid chromatography (HPLC)-tandem mass spectrometry injection with only 100-10K cells, thus demonstrating universality of the platform. The selected protein was further quantified using a developed HPLC-multiple reaction monitoring method for HeLa digests with two heavy labeled internal standard peptides spiked in. Excellent linearity was achieved across different cell numbers indicating a potential for target protein quantitation in clinical research.
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http://dx.doi.org/10.1021/acs.analchem.1c00265DOI Listing
June 2021

High-throughput screening assays for SARS-CoV-2 drug development: Current status and future directions.

Drug Discov Today 2021 May 25. Epub 2021 May 25.

Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD 20850, USA. Electronic address:

In response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, a panel of assays has been developed and applied to screen collections of approved and investigational drugs for anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) activity in a quantitative high-throughput screening (qHTS) format. In this review, we applied data-driven approaches to evaluate the ability of each assay to identify potential anti-SARS-CoV-2 leads. Multitarget assays were found to show advantages in terms of accuracy and efficiency over single-target assays, whereas target-specific assays were more suitable for investigating compound mechanisms of action. Moreover, strict filtering with counter screens might be more detrimental than beneficial in identifying true positives. Thus, developing novel HTS assays acting simultaneously against multiple targets in the SARS-CoV-2 life cycle will benefit anti-COVID-19 drug discovery.
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http://dx.doi.org/10.1016/j.drudis.2021.05.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8146264PMC
May 2021

Predictive Models to Identify Small Molecule Activators and Inhibitors of Opioid Receptors.

J Chem Inf Model 2021 Jun 28;61(6):2675-2685. Epub 2021 May 28.

Division of Pre-clinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States.

Opioid receptors (OPRs) are the main targets for the treatment of pain and related disorders. The opiate compounds that activate these receptors are effective analgesics but their use leads to adverse effects, and they often are highly addictive drugs of abuse. There is an urgent need for alternative chemicals that are analgesics and to reduce/avoid the unwanted effects in order to relieve the public health crisis of opioid addiction. Here, we aim to develop computational models to predict the OPR activity of small molecule compounds based on chemical structures and apply these models to identify novel OPR active compounds. We used four different machine learning algorithms to build models based on quantitative high throughput screening (qHTS) data sets of three OPRs in both agonist and antagonist modes. The best performing models were applied to virtually screen a large collection of compounds. The model predicted active compounds were experimentally validated using the same qHTS assays that generated the training data. Random forest was the best classifier with the highest performance metrics, and the mu OPR (OPRM)-agonist model achieved the best performance measured by AUC-ROC (0.88) and MCC (0.7) values. The model predicted actives resulted in hit rates ranging from 2.3% (delta OPR-agonist) to 15.8% (OPRM-agonist) after experimental confirmation. Compared to the original assay hit rate, all models enriched the hit rate by ≥2-fold. Our approach produced robust OPR prediction models that can be applied to prioritize compounds from large libraries for further experimental validation. The models identified several novel potent compounds as activators/inhibitors of OPRs that were confirmed experimentally. The potent hits were further investigated using molecular docking to find the interactions of the novel ligands in the active site of the corresponding OPR.
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http://dx.doi.org/10.1021/acs.jcim.1c00439DOI Listing
June 2021

Identification of hit compounds with anti-schistosomal activity on in vitro generated juvenile worms in cell-free medium.

PLoS Negl Trop Dis 2021 May 25;15(5):e0009432. Epub 2021 May 25.

Institute for Microbiology, Immunology and Hygiene, Technische Universität München, Munich, Germany.

Background: Anthelminthic treatment options against schistosomiasis are limited. The current treatment relies almost exclusively on a single drug, praziquantel (PZQ). As a consequence, the development of resistance to PZQ and limited activity of PZQ against earlier development stages are respectively a risk and a limitation to achieving the goals of the new WHO roadmap towards elimination. For the discovery of new chemical starting points, the in vitro drug screening on Schistosoma mansoni (S. mansoni) against newly transformed schistosomula (NTS) is still the most predominant approach. The use of only NTS in the initial screening limits sensitivity to potential new compounds which are predominantly active in later developmental stages. Using our recently described highly standardized, straightforward and reliable culture method that generates high rates of juvenile worms, we aimed to repurpose a subset of the National Center for Advancing Translational Sciences (NCATS) Pharmaceutical Collection (340 compounds) to identify new hits with an in vitro worm culture assay.

Methodology/principal Findings: Cercariae were mechanically transformed into skin-stage (SkS) schistosomula and continuously cultured for 3-6 weeks to the liver stage (LiS). A commercial source of serum was identified, and decrease of NTS/well along with optimal drug testing conditions was established to test compounds on early and late LiS worms. The library was screened in 96-well format assays using praziquantel (PZQ) as a positive control. Primary screening allowed a 5.9% hit rate and generated two confirmed hits on adult worms; a prophylactic antianginal agent and an antihistaminic drug.

Conclusion: With this standardized and reliable in vitro assay, important S. mansoni developmental stages up to LiS worms can be generated and cultured over an extended period. When exposed to a subset of the NCATS Pharmaceutical Collection, 3 compounds yielded a defined anti-schistosomal phenotype on juvenile worms. Translation of activity on perfused adult S. mansoni worms was achieved only for perhexiline (a prophylactic antianginal agent) and astemizole (an antihistaminic drug).
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http://dx.doi.org/10.1371/journal.pntd.0009432DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8191877PMC
May 2021

Retro Drug Design: From Target Properties to Molecular Structures.

bioRxiv 2021 May 12. Epub 2021 May 12.

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.
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http://dx.doi.org/10.1101/2021.05.11.442656DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8132216PMC
May 2021

Unraveling Gene Fusions for Drug Repositioning in High-Risk Neuroblastoma.

Front Pharmacol 2021 23;12:608778. Epub 2021 Apr 23.

Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.

High-risk neuroblastoma (NB) remains a significant therapeutic challenge facing current pediatric oncology patients. Structural variants such as gene fusions have shown an initial promise in enhancing mechanistic understanding of NB and improving survival rates. In this study, we performed a comprehensive investigation on the translational ability of gene fusions for patient stratification and treatment development for high-risk NB patients. Specifically, three state-of-the-art gene fusion detection algorithms, including ChimeraScan, SOAPfuse, and TopHat-Fusion, were employed to identify the fusion transcripts in a RNA-seq data set of 498 neuroblastoma patients. Then, the 176 high-risk patients were further stratified into four different subgroups based on gene fusion profiles. Furthermore, Kaplan-Meier survival analysis was performed, and differentially expressed genes (DEGs) for the redefined high-risk group were extracted and functionally analyzed. Finally, repositioning candidates were enriched in each patient subgroup with drug transcriptomic profiles from the LINCS L1000 Connectivity Map. We found the number of identified gene fusions was increased from clinical the low-risk stage to the high-risk stage. Although the technical concordance of fusion detection algorithms was suboptimal, they have a similar biological relevance concerning perturbed pathways and regulated DEGs. The gene fusion profiles could be utilized to redefine high-risk patient subgroups with significant onset age of NB, which yielded the improved survival curves (Log-rank value ≤ 0.05). Out of 48 enriched repositioning candidates, 45 (93.8%) have antitumor potency, and 24 (50%) were confirmed with either on-going clinical trials or literature reports. The gene fusion profiles have a discrimination power for redefining patient subgroups in high-risk NB and facilitate precision medicine-based drug repositioning implementation.
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http://dx.doi.org/10.3389/fphar.2021.608778DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8105087PMC
April 2021

Profiling the Tox21 Chemical Collection for Acetylcholinesterase Inhibition.

Environ Health Perspect 2021 Apr 12;129(4):47008. Epub 2021 Apr 12.

Division for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA.

Background: Inhibition of acetylcholinesterase (AChE), a biomarker of organophosphorous and carbamate exposure in environmental and occupational human health, has been commonly used to identify potential safety liabilities. So far, many environmental chemicals, including drug candidates, food additives, and industrial chemicals, have not been thoroughly evaluated for their inhibitory effects on AChE activity. AChE inhibitors can have therapeutic applications (e.g., tacrine and donepezil) or neurotoxic consequences (e.g., insecticides and nerve agents).

Objectives: The objective of the current study was to identify environmental chemicals that inhibit AChE activity using and models.

Methods: To identify AChE inhibitors rapidly and efficiently, we have screened the Toxicology in the 21st Century (Tox21) 10K compound library in a quantitative high-throughput screening (qHTS) platform by using the homogenous cell-based AChE inhibition assay and enzyme-based AChE inhibition assays (with or without microsomes). AChE inhibitors identified from the primary screening were further tested in monolayer or spheroid formed by SH-SY5Y and neural stem cell models. The inhibition and binding modes of these identified compounds were studied with time-dependent enzyme-based AChE inhibition assay and molecular docking, respectively.

Results: A group of known AChE inhibitors, such as donepezil, ambenonium dichloride, and tacrine hydrochloride, as well as many previously unreported AChE inhibitors, such as chelerythrine chloride and cilostazol, were identified in this study. Many of these compounds, such as pyrazophos, phosalone, and triazophos, needed metabolic activation. This study identified both reversible (e.g., donepezil and tacrine) and irreversible inhibitors (e.g., chlorpyrifos and bromophos-ethyl). Molecular docking analyses were performed to explain the relative inhibitory potency of selected compounds.

Conclusions: Our tiered qHTS approach allowed us to generate a robust and reliable data set to evaluate large sets of environmental compounds for their AChE inhibitory activity. https://doi.org/10.1289/EHP6993.
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http://dx.doi.org/10.1289/EHP6993DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041433PMC
April 2021

Mining of high throughput screening database reveals AP-1 and autophagy pathways as potential targets for COVID-19 therapeutics.

Sci Rep 2021 03 24;11(1):6725. Epub 2021 Mar 24.

Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), DPI/NCATS, 9800 Medical Center Drive, Rockville, MD, 20850, USA.

The recent global pandemic of the Coronavirus disease 2019 (COVID-19) caused by the new coronavirus SARS-CoV-2 presents an urgent need for the development of new therapeutic candidates. Many efforts have been devoted to screening existing drug libraries with the hope to repurpose approved drugs as potential treatments for COVID-19. However, the antiviral mechanisms of action of the drugs found active in these phenotypic screens remain largely unknown. In an effort to deconvolute the viral targets in pursuit of more effective anti-COVID-19 drug development, we mined our in-house database of approved drug screens against 994 assays and compared their activity profiles with the drug activity profile in a cytopathic effect (CPE) assay of SARS-CoV-2. We found that the autophagy and AP-1 signaling pathway activity profiles are significantly correlated with the anti-SARS-CoV-2 activity profile. In addition, a class of neurology/psychiatry drugs was found to be significantly enriched with anti-SARS-CoV-2 activity. Taken together, these results provide new insights into SARS-CoV-2 infection and potential targets for COVID-19 therapeutics, which can be further validated by in vivo animal studies and human clinical trials.
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http://dx.doi.org/10.1038/s41598-021-86110-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990955PMC
March 2021

Drug Repurposing Screen for Compounds Inhibiting the Cytopathic Effect of SARS-CoV-2.

Front Pharmacol 2020 25;11:592737. Epub 2021 Jan 25.

National Center for Advancing Translational Sciences, Rockville, MD, United States.

Drug repurposing is a rapid approach to identify therapeutics for the treatment of emerging infectious diseases such as COVID-19. To address the urgent need for treatment options, we carried out a quantitative high-throughput screen using a SARS-CoV-2 cytopathic assay with a compound collection of 8,810 approved and investigational drugs, mechanism-based bioactive compounds, and natural products. Three hundred and nineteen compounds with anti-SARS-CoV-2 activities were identified and confirmed, including 91 approved drugs and 49 investigational drugs. The anti-SARS-CoV-2 activities of 230 of these confirmed compounds, of which 38 are approved drugs, have not been previously reported. Chlorprothixene, methotrimeprazine, and piperacetazine were the three most potent FDA-approved drugs with anti-SARS-CoV-2 activities. These three compounds have not been previously reported to have anti-SARS-CoV-2 activities, although their antiviral activities against SARS-CoV and Ebola virus have been reported. These results demonstrate that this comprehensive data set is a useful resource for drug repurposing efforts, including design of new drug combinations for clinical trials for SARS-CoV-2.
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http://dx.doi.org/10.3389/fphar.2020.592737DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7942396PMC
January 2021

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

Bioorg Med Chem Lett 2021 05 6;40:127906. Epub 2021 Mar 6.

National Center for Advancing Translational Sciences, National Institutes of Health, 9800 Medical Center Drive, Bethesda, MD 20892-3370, USA. Electronic address:

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'-NO substituent can be replaced with a 4'-CN or 4'-CF 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.
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http://dx.doi.org/10.1016/j.bmcl.2021.127906DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7936759PMC
May 2021

Biological activity-based modeling identifies antiviral leads against SARS-CoV-2.

Nat Biotechnol 2021 06 23;39(6):747-753. Epub 2021 Feb 23.

Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, USA.

Computational approaches for drug discovery, such as quantitative structure-activity relationship, rely on structural similarities of small molecules to infer biological activity but are often limited to identifying new drug candidates in the chemical spaces close to known ligands. Here we report a biological activity-based modeling (BABM) approach, in which compound activity profiles established across multiple assays are used as signatures to predict compound activity in other assays or against a new target. This approach was validated by identifying candidate antivirals for Zika and Ebola viruses based on high-throughput screening data. BABM models were then applied to predict 311 compounds with potential activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Of the predicted compounds, 32% had antiviral activity in a cell culture live virus assay, the most potent compounds showing a half-maximal inhibitory concentration in the nanomolar range. Most of the confirmed anti-SARS-CoV-2 compounds were found to be viral entry inhibitors and/or autophagy modulators. The confirmed compounds have the potential to be further developed into anti-SARS-CoV-2 therapies.
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http://dx.doi.org/10.1038/s41587-021-00839-1DOI Listing
June 2021

An Integrated Systems Biology Approach Identifies the Proteasome as A Critical Host Machinery for ZIKV and DENV Replication.

Genomics Proteomics Bioinformatics 2021 Feb 18. Epub 2021 Feb 18.

Department of Pharmacology & Molecular Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA. Electronic address:

The zika virus (ZIKV) and dengue virus (DENV) flaviviruses exhibit similar replicative processes but have distinct clinical outcomes. A systematic understanding of virus-host protein-protein interaction networks can reveal cellular pathways critical to viral replication and disease pathogenesis. Here we employed three independent systems biology approaches toward this goal. First, protein array analysis of direct interactions between individual ZIKV/DENV viral proteins and 20,240 human proteins revealed multiple conserved cellular pathways and protein complexes, including proteasome complexes. Second, an RNAi screen of 10,415 druggable genes identified the host proteins required for ZIKV infection and uncovered that proteasome proteins were crucial in this process. Third, high-throughput screening of 6016 bioactive compounds for ZIKV inhibition yielded 134 effective compounds, including six proteasome inhibitors that suppress both ZIKV and DENV replication. Integrative analyses of these orthogonal datasets pinpoint proteasomes as critical host machinery for ZIKV/DENV replication. Our study provides multi-omics datasets for further studies of flavivirus-host interactions, disease pathogenesis, and new drug targets.
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http://dx.doi.org/10.1016/j.gpb.2020.06.016DOI Listing
February 2021

Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets.

Chem Res Toxicol 2021 Feb 29;34(2):541-549. Epub 2021 Jan 29.

Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, FDA, 3900 NCTR Road, Jefferson, Arkansas 72079, United States.

Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model's performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.
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http://dx.doi.org/10.1021/acs.chemrestox.0c00373DOI Listing
February 2021

Evaluation of chemical compounds that inhibit neurite outgrowth using GFP-labeled iPSC-derived human neurons.

Neurotoxicology 2021 03 27;83:137-145. Epub 2021 Jan 27.

Division for Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA. Electronic address:

Due to the increasing number of drugs and untested environmental compounds introduced into commercial use, there is recognition for a need to develop reliable and efficient screening methods to identify compounds that may adversely impact the nervous system. One process that has been implicated in neurodevelopment is neurite outgrowth; the disruption of which can result in adverse outcomes that persist later in life. Here, we developed a green fluorescent protein (GFP) labeled neurite outgrowth assay in a high-content, high-throughput format using induced pluripotent stem cell (iPSC) derived human spinal motor neurons and cortical glutamatergic neurons. The assay was optimized for use in a 1536-well plate format. Then, we used this assay to screen a set of 84 unique compounds that have previously been screened in other neurite outgrowth assays. This library consists of known developmental neurotoxicants, environmental compounds with unknown toxicity, and negative controls. Neurons were cultured for 40 h and then treated with compounds at 11 concentrations ranging from 1.56 nM to 92 μM for 24 and 48 h. Effects of compounds on neurite outgrowth were evaluated by quantifying total neurite length, number of segments, and maximum neurite length per cell. Among the 84 tested compounds, neurite outgrowth in cortical neurons and motor neurons were selectively inhibited by 36 and 31 compounds, respectively. Colchicine, rotenone, and methyl mercuric (II) chloride inhibited neurite outgrowth in both cortical and motor neurons. It is interesting to note that some compounds like parathion and bisphenol AF had inhibitory effects on neurite outgrowth specifically in the cortical neurons, while other compounds, such as 2,2',4,4'-tetrabromodiphenyl ether and caffeine, inhibited neurite outgrowth in motor neurons. The data gathered from these studies show that GFP-labeled iPSC-derived human neurons are a promising tool for identifying and prioritizing compounds with developmental neurotoxicity potential for further hazard characterization.
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http://dx.doi.org/10.1016/j.neuro.2021.01.003DOI Listing
March 2021

Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury.

Chem Res Toxicol 2021 Feb 21;34(2):495-506. Epub 2020 Dec 21.

Department of Computer Science, University of Texas at Arlington, Arlington, Texas 76013, United States.

Drug-induced liver injury (DILI) is a crucial factor in determining the qualification of potential drugs. However, the DILI property is excessively difficult to obtain due to the complex testing process. Consequently, an screening in the early stage of drug discovery would help to reduce the total development cost by filtering those drug candidates with a high risk to cause DILI. To serve the screening goal, we apply several computational techniques to predict the DILI property, including traditional machine learning methods and graph-based deep learning techniques. While deep learning models require large training data to tune huge model parameters, the DILI data set only contains a few hundred annotated molecules. To alleviate the data scarcity problem, we propose a property augmentation strategy to include massive training data with other property information. Extensive experiments demonstrate that our proposed method significantly outperforms all existing baselines on the DILI data set by obtaining a 81.4% accuracy using cross-validation with random splitting, 78.7% using leave-one-out cross-validation, and 76.5% using cross-validation with scaffold splitting.
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http://dx.doi.org/10.1021/acs.chemrestox.0c00322DOI Listing
February 2021

Characterization of human pregnane X receptor activators identified from a screening of the Tox21 compound library.

Biochem Pharmacol 2021 02 14;184:114368. Epub 2020 Dec 14.

National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, United States. Electronic address:

The pregnane X receptor (PXR; NR1I2) is an important nuclear receptor whose main function is to regulate enzymes within drug metabolism. The main drug metabolizing enzyme regulated by PXR, cytochrome P450 (CYP) 3A4, accounts for the metabolism of nearly 50% of all marketed drugs. Recently, PXR has also been identified as playing a role in energy homeostasis, immune response, and cancer. Due to its interaction with these important roles, alongside its drug-drug interaction function, it is imperative to identify compounds which can modulate PXR. In this study, we screened the Tox21 10,000 compound collection to identify hPXR agonists using a stable hPXR-Luc HepG2 cell line. A pharmacological study in the presence of a PXR antagonist was performed to confirm the activity of the chosen potential hPXR agonists in the same cells. Finally, metabolically competent cell lines - HepaRG and HepaRG-PXR-Knockout (KO) - were used to further confirm the potential PXR activators. We identified a group of structural clusters and singleton compounds which included potentially novel hPXR agonists. Of the 21 selected compounds, 11 potential PXR activators significantly induced CYP3A4 mRNA expression in HepaRG cells. All of these compounds lost their induction when treating HepaRG-PXR-KO cells, confirming their PXR activation. Etomidoline presented as a potentially selective agonist of PXR. In conclusion, the current study has identified 11 compounds as potentially novel or not well-characterized PXR activators. These compounds should further be studied for their potential effects on drug metabolism and drug-drug interactions due to the immense implications of being a PXR agonist.
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http://dx.doi.org/10.1016/j.bcp.2020.114368DOI Listing
February 2021

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity.

Chem Res Toxicol 2021 Feb 29;34(2):412-421. Epub 2020 Nov 29.

Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, Maryland 20850, United States.

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 (-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.
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http://dx.doi.org/10.1021/acs.chemrestox.0c00305DOI Listing
February 2021

The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.

Chem Res Toxicol 2021 Feb 3;34(2):189-216. Epub 2020 Nov 3.

Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, United States.

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.
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http://dx.doi.org/10.1021/acs.chemrestox.0c00264DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887805PMC
February 2021

Methylene blue is a potent and broad-spectrum inhibitor against Zika virus and .

Emerg Microbes Infect 2020 Dec;9(1):2404-2416

Wadsworth Center, New York State Department of Health, Albany, NY, USA.

Many flaviviruses including the Dengue virus (DENV), Zika virus (ZIKV), West Nile virus, Yellow Fever virus, and Japanese encephalitis virus are significant human pathogens, unfortunately without any specific therapy. Here, we demonstrate that methylene blue, an FDA-approved drug, is a broad-spectrum and potent antiviral against Zika virus and Dengue virus both and . We found that methylene blue can considerably inhibit the interactions between viral protease NS3 and its NS2B co-factor, inhibit viral protease activity, inhibit viral growth, protect 3D mini-brain organoids from ZIKV infection, and reduce viremia in a mouse model. Mechanistic studies confirmed that methylene blue works in both entry and post entry steps, reduces virus production in replicon cells and inhibited production of processed NS3 protein. Overall, we have shown that methylene blue is a potent antiviral for management of flavivirus infections, particularly for Zika virus. As an FDA-approved drug, methylene blue is well-tolerated for human use. Therefore, methylene blue represents a promising and easily developed therapy for management of infections by ZIKV and other flaviviruses.
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http://dx.doi.org/10.1080/22221751.2020.1838954DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7646565PMC
December 2020

XIST lost induces ovarian cancer stem cells to acquire taxol resistance via a KMT2C-dependent way.

Cancer Cell Int 2020 4;20:436. Epub 2020 Sep 4.

Department of Gynaecology and Obstetrics, The First People's Hospital of Shangqiu, Henan, No. 292 Kaixuan South Road, 476100 Shangqiu, Henan People's Republic of China.

Background/aims: The expression levels of long non-coding RNA XIST are significantly associated with paclitaxel (Pac) sensitivity in ovarian cancer, but the mechanism of action remains unclear. Therefore, this experimental design was based on lncRNA XIST analysis to regulate the effect of XIST on the tumor stem cell and paclitaxel sensitivity in ovarian cancer.

Methods: Sphere assay and fluorescence activated cell sorting (FACS) were used to determine the expression levels of XIST and sensitivity to paclitaxel treatment. The effect of the proliferation was detected by MTT assay. Target gene prediction and screening, luciferase reporter assays were used to validate downstream target genes for lncRNA XIS and KMT2C. The expression of KMT2C was detected by RT-qPCR and Western blotting. RT-qPCR was used to detect the expression of cancer stem cell-associated genes SOX2, OCT4 and Nanog. The tumor changes in mice were detected by in vivo experiments in nude mice.

Results: There was an inverse correlation between the expression of XIST and cancer stem cell (CD44 + /CD24-) population. XIST promoted methylation of histone H3 methylation at lysine 4 by enhancing the stability of lysine (K)-specific methyltransferase 2C (KMT2C) mRNA. XIST acted on the stability of KMT2C mRNA by directly targeting miR-93-5p. Overexpression of miR-93-5p can reverse the XIST overexpression-induced KMT2C decrease and sphere number increase. Overexpression of KMT2C inhibited XIST silencing-induced proliferation of cancer stem cells, and KMT2C was able to mediate paclitaxel resistance induced by XIST in ovarian cancer. The study found that XIST can affect the expression of KMT2C in the ovarian cancer via targeting miR-93-5p.

Conclusion: XIST promoted the sensitivity of ovarian cancer stem cells to paclitaxel in a KMT2C-dependent manner.
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http://dx.doi.org/10.1186/s12935-020-01500-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7487955PMC
September 2020

Drug Repurposing Screen for Compounds Inhibiting the Cytopathic Effect of SARS-CoV-2.

bioRxiv 2020 Aug 18. Epub 2020 Aug 18.

National Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD, 20850.

Drug repurposing is a rapid approach to identifying therapeutics for the treatment of emerging infectious diseases such as COVID-19. To address the urgent need for treatment options, we carried out a quantitative high-throughput screen using a SARS-CoV-2 cytopathic assay with a compound collection of 8,810 approved and investigational drugs, mechanism-based bioactive compounds, and natural products. Three hundred and nineteen compounds with anti-SARS-CoV-2 activities were identified and confirmed, including 91 approved drug and 49 investigational drugs. Among these confirmed compounds, the anti-SARS-CoV-2 activities of 230 compounds, including 38 approved drugs, have not been previously reported. Chlorprothixene, methotrimeprazine, and piperacetazine were the three most potent FDA approved drugs with anti-SARS-CoV-2 activities. These three compounds have not been previously reported to have anti-SARS-CoV-2 activities, although their antiviral activities against SARS-CoV and Ebola virus have been reported. These results demonstrate that this comprehensive data set of drug repurposing screen for SARS-CoV-2 is useful for drug repurposing efforts including design of new drug combinations for clinical trials.
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http://dx.doi.org/10.1101/2020.08.18.255877DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7444282PMC
August 2020

Massive-scale biological activity-based modeling identifies novel antiviral leads against SARS-CoV-2.

bioRxiv 2020 Jul 27. Epub 2020 Jul 27.

The recent global pandemic caused by the new coronavirus SARS-CoV-2 presents an urgent need for new therapeutic candidates. While the importance of traditional approaches such as QSAR in such efforts in unquestionable, these models fundamentally rely on structural similarity to infer biological activity and are thus prone to becoming trapped in the very nearby chemical spaces of already known ligands. For novel and unprecedented threats such as COVID-19 much faster and efficient paradigms must be devised to accelerate the identification of new chemical classes for rapid drug development. Here we report the development of a new biological activity-based modeling (BABM) approach that builds on the hypothesis that compounds with similar activity patterns tend to share similar targets or mechanisms of action. In BABM, compound activity profiles established on massive scale across multiple assays are used as signatures to predict compound activity in a new assay or against a new target. We first trained and validated this approach by identifying new antiviral lead candidates for Zika and Ebola based on data from ~0.5 million compounds screened against ~2,000 assays. BABM models were then applied to predict ~300 compounds not previously reported to have activity for SARS-CoV-2, which were then tested in a live virus assay with high (>30%) hit rates. The most potent compounds showed antiviral activities in the nanomolar range. These potent confirmed compounds have the potential to be further developed in novel chemical space into new anti-SARS-CoV-2 therapies. These results demonstrate unprecedented ability using BABM to predict novel structures as chemical leads significantly beyond traditional methods, and its application in rapid drug discovery response in a global public health crisis.
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http://dx.doi.org/10.1101/2020.07.27.223578DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7402069PMC
July 2020

Drug Repositioning for Noonan and LEOPARD Syndromes by Integrating Transcriptomics With a Structure-Based Approach.

Front Pharmacol 2020 26;11:927. Epub 2020 Jun 26.

National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.

Noonan and LEOPARD syndromes (NS and LS) belong to a group of related disorders called RASopathies characterized by abnormalities of multiple organs and systems including hypertrophic cardiomyopathy and dysmorphic facial features. There are no approved drugs for these two rare diseases, but it is known that a missense mutation in PTPN11 genes is associated with approximately 50% and 70% of NS and LS cases, respectively. In this study, we implemented a hybrid computational drug repositioning framework by integrating transcriptomic and structure-based approaches to explore potential treatment options for NS and LS. Specifically, disease signatures were derived from the transcriptomic profiles of human induced pluripotent stem cells (iPSCs) from NS and LS patients and reverse correlated to drug transcriptomic signatures from CMap and L1000 projects on the basis that if disease and drug transcriptomic signatures are reversely correlated, the drug has the potential to treat that disease. The compounds that were ranked top based on their transcriptomic profiles were docked to mutated and wild-type 3D structures of PTPN11 by an adjusted Induced Fit Docking (IFD) protocol. In addition, we prioritized repositioned candidates for NS and LS by a consensus ranking strategy. Network analysis and phenotypic anchoring of the transcriptomic data could discriminate the two diseases at the molecular level. Furthermore, the adjusted IFD protocol was able to recapitulate the binding specificity of potential drug candidates to mutated 3D structures, revealing the relevant amino acids. Importantly, a list of potential drug candidates for repositioning was identified including 61 for NS and 43 for LS and was further verified from literature reports and on-going clinical trials. Altogether, this hybrid computational drug repositioning approach has highlighted a number of drug candidates for NS and LS and could be applied to identifying drug candidates for other diseases as well.
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http://dx.doi.org/10.3389/fphar.2020.00927DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333460PMC
June 2020

The E3 ubiquitin ligase MARCH1 regulates antimalaria immunity through interferon signaling and T cell activation.

Proc Natl Acad Sci U S A 2020 07 30;117(28):16567-16578. Epub 2020 Jun 30.

Malaria Functional Genomics Section, Laboratory of Malaria and Vector Research, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, MD 20892-8132;

Malaria infection induces complex and diverse immune responses. To elucidate the mechanisms underlying host-parasite interaction, we performed a genetic screen during early (24 h) infection in mice and identified a large number of interacting host and parasite genes/loci after transspecies expression quantitative trait locus (Ts-eQTL) analysis. We next investigated a host E3 ubiquitin ligase gene () that was clustered with interferon (IFN)-stimulated genes (ISGs) based on the similarity of the genome-wide pattern of logarithm of the odds (LOD) scores (GPLS). inhibits MAVS/STING/TRIF-induced type I IFN (IFN-I) signaling in vitro and in vivo. However, in malaria-infected hosts, deficiency of reduces IFN-I production by activating inhibitors such as SOCS1, USP18, and TRIM24 and by altering immune cell populations. deficiency increases CD86DC (dendritic cell) populations and levels of IFN-γ and interleukin 10 (IL-10) at day 4 post infection, leading to improved host survival. T cell depletion reduces IFN-γ level and reverse the protective effects of deficiency, which can also be achieved by antibody neutralization of IFN-γ. This study reveals functions of MARCH1 (membrane-associated ring-CH-type finger 1) in innate immune responses and provides potential avenues for activating antimalaria immunity and enhancing vaccine efficacy.
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http://dx.doi.org/10.1073/pnas.2004332117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7368286PMC
July 2020

Two-Dimensional Cellular and Three-Dimensional Bio-Printed Skin Models to Screen Topical-Use Compounds for Irritation Potential.

Front Bioeng Biotechnol 2020 21;8:109. Epub 2020 Feb 21.

Division of Pre-Clinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, United States.

Assessing skin irritation potential is critical for the safety evaluation of topical drugs and other consumer products such as cosmetics. The use of advanced cellular models, as an alternative to replace animal testing in the safety evaluation for both consumer products and ingredients, is already mandated by law in the European Union (EU) and other countries. However, there has not yet been a large-scale comparison of the effects of topical-use compounds in different cellular skin models. This study assesses the irritation potential of topical-use compounds in different cellular models of the skin that are compatible with high throughput screening (HTS) platforms. A set of 451 topical-use compounds were first tested for cytotoxic effects using two-dimensional (2D) monolayer models of primary neonatal keratinocytes and immortalized human keratinocytes. Forty-six toxic compounds identified from the initial screen with the monolayer culture systems were further tested for skin irritation potential on reconstructed human epidermis (RhE) and full thickness skin (FTS) three-dimensional (3D) tissue model constructs. Skin irritation potential of the compounds was assessed by measuring tissue viability, epithelial electrical resistance (TEER), and secretion of cytokines interleukin 1 alpha (IL-1α) and interleukin 18 (IL-18). Among known irritants, high concentrations of methyl violet and methylrosaniline decreased viability, lowered TEER, and increased IL-1α secretion in both RhE and FTS models, consistent with irritant properties. However, at low concentrations, these two compounds increased IL-18 secretion without affecting levels of secreted IL-1α, and did not reduce tissue viability and TEER, in either RhE or FTS models. This result suggests that at low concentrations, methyl violet and methylrosaniline have an allergic potential without causing irritation. Using both HTS-compatible 2D cellular and 3D tissue skin models, together with irritation relevant activity endpoints, we obtained data to help assess the irritation effects of topical-use compounds and identify potential dermal hazards.
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http://dx.doi.org/10.3389/fbioe.2020.00109DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7046801PMC
February 2020

High-Throughput Screening to Predict Chemical-Assay Interference.

Sci Rep 2020 03 4;10(1):3986. Epub 2020 Mar 4.

NIH/NIEHS/DIR/BCBB, RTP, NC, United States.

The U.S. federal consortium on toxicology in the 21 century (Tox21) produces quantitative, high-throughput screening (HTS) data on thousands of chemicals across a wide range of assays covering critical biological targets and cellular pathways. Many of these assays, and those used in other in vitro screening programs, rely on luciferase and fluorescence-based readouts that can be susceptible to signal interference by certain chemical structures resulting in false positive outcomes. Included in the Tox21 portfolio are assays specifically designed to measure interference in the form of luciferase inhibition and autofluorescence via multiple wavelengths (red, blue, and green) and under various conditions (cell-free and cell-based, two cell types). Out of 8,305 chemicals tested in the Tox21 interference assays, percent actives ranged from 0.5% (red autofluorescence) to 9.9% (luciferase inhibition). Self-organizing maps and hierarchical clustering were used to relate chemical structural clusters to interference activity profiles. Multiple machine learning algorithms were applied to predict assay interference based on molecular descriptors and chemical properties. The best performing predictive models (accuracies of ~80%) have been included in a web-based tool called InterPred that will allow users to predict the likelihood of assay interference for any new chemical structure and thus increase confidence in HTS data by decreasing false positive testing results.
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http://dx.doi.org/10.1038/s41598-020-60747-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055224PMC
March 2020