Canvass: A Crowd-Sourced, Natural-Product Screening Library for Exploring Biological Space

Natural products and their derivatives continue to be wellsprings of nascent therapeutic potential. However, many laboratories have limited resources for biological evaluation, leaving their previously isolated or synthesized compounds largely or completely untested. To address this issue, the Canvass library of natural products was assembled, in collaboration with academic and industry researchers, for quantitative high-throughput screening (qHTS) across a diverse set of cell-based and biochemical assays. Characterization of the library in terms of physicochemical properties, structural diversity, and similarity to compounds in publicly available libraries indicates that the Canvass library contains many structural elements in common with approved drugs. The assay data generated were analyzed using a variety of quality control metrics, and the resultant assay profiles were explored using statistical methods, such as clustering and compound promiscuity analyses. Individual compounds were then sorted by structural class and activity profiles. Differential behavior based on these classifications, as well as noteworthy activities, are outlined herein. One such highlight is the activity of (−)-2(S)-cathafoline, which was found to stabilize calcium levels in the endoplasmic reticulum. The workflow described here illustrates a pilot effort to broadly survey the biological potential of natural products by utilizing the power of automation and high-throughput screening

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

Quantitative Chemotherapeutic Profiling of Gynecologic Cancer Cell Lines Using Approved Drugs and Bioactive Compounds

Heterogeneous response to chemotherapy is a major issue for the treatment of cancer. For most gynecologic cancers including ovarian, cervical, and placental, the list of available small molecule therapies is relatively small compared to options for other cancers. While overall cancer mortality rates have decreased in the United States as early diagnoses and cancer therapies have become more effective, ovarian cancer still has low survival rates due to the lack of effective treatment options, drug resistance, and late diagnosis. To understand chemotherapeutic diversity in gynecologic cancers, we have screened 7914 approved drugs and bioactive compounds in 11 gynecologic cancer cell lines to profile their chemotherapeutic sensitivity. We identified two HDAC inhibitors, mocetinostat and entinostat, as pan-gynecologic cancer suppressors with IC50 values within an order of magnitude of their human plasma concentrations. In addition, many active compounds identified, including the non-anticancer drugs and other compounds, diversely inhibited the growth of three gynecologic cancer cell groups and individual cancer cell lines. These newly identified compounds are valuable for further studies of new therapeutics development, synergistic drug combinations, and new target identification for gynecologic cancers. The results also provide a rationale for the personalized chemotherapeutic testing of anticancer drugs in treatment of gynecologic cancer

Profiling the Tox21 Library Compounds for Cytochrome P450 Inhibitory Activity

Poster presentated to SOT Conference 2024: A New Approach Method (NAM) to Screen for the Impact of Endogenous Stress on Chemical Toxicity Search for CCTE records in EPA’s Science Inventory by typing in the title at this link.https://cfpub.epa.gov/si/si_public_search_results.cfm?advSearch=true&showCriteria=2&keyword=CCTE&TIMSType=&TIMSSubTypeID=&epaNumber=&ombCat=Any&dateBeginPublishedPresented=07/01/2017&dateEndPublishedPresented=&dateBeginUpdated=&dateEndUpdated=&DEID=&personName=&personID=&role=Any&journalName=&journalID=&publisherName=&publisherID=&sortBy=pubDate&count=25</p

The Toxmatrix: Chemo-Genomic Profiling Identifies Interactions That Reveal Mechanisms of Toxicity

A chemical genomics “Toxmatrix” method was developed to elucidate mechanisms of cytotoxicity using neuronal models. Quantitative high-throughput screening (qHTS) was applied to systematically screen each toxicant against a panel of 70 modulators, drugs or chemicals that act on a known target, to identify interactions that either protect or sensitize cells to each toxicant. Thirty-two toxicants were tested at 10 concentrations for cytotoxicity to SH-SY5Y human neuroblastoma cells, with results fitted to the Hill equation to determine an IC₅₀ for each toxicant. Thirty-three toxicant:modulator interactions were identified in SH-SY5Y cells for 14 toxicants, as modulators that shifted toxicant IC₅₀ values lower or higher. The target of each modulator that sensitizes cells or protects cells from a toxicant suggests a mode of toxicant action or cellular adaptation. In secondary screening, we tested modulator-toxicant pairs identified from the SH-SY5Y primary screening for interactions in three differentiated neuronal human cell lines: dSH-SY5Y, conditionally immortalized dopaminergic neurons (LUHMES), and neural stem cells. Twenty toxicant-modulator pairs showed pronounced interactions in one or several differentiated cell models. Additional testing confirmed that several modulators acted through their primary targets. For example, several chelators protected differentiated LUHMES neurons from four toxicants by chelation of divalent cations and buthionine sulphoximine sensitized cells to 6-hydroxydopamine and 4-(methylamino)­phenol hemisulfate by blocking glutathione synthesis. Such modulators that interact with multiple neurotoxicants suggest these may be vulnerable toxicity pathways in neurons. Thus, the Toxmatrix method is a systematic high-throughput approach that can identify mechanisms of toxicity and cellular adaptation

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Treatment regimens, especially in cancer, often include more than one medicine in order to achieve durable outcomes. Identifying the optimal combination of treatments has historically been done through clinical trial and error. And for many conditions, such as pancreatic cancer, an optimal treatment protocol has remained elusive, and the best available treatment combinations provide only modest benefit. Recent developments have led to the application of both experimental screening approaches and in silico modeling methods to identify synergistic drug combinations and expand the therapeutic options for multiple diseases. Here we conduct a study to compare different predictive approaches for identifying new treatment combinations for pancreatic cancer using cell line growth as an initial proxy for clinical utility. NCATS performed screening involving 496 pairwise combinations of 32 antineoplastic drugs, tested against the PANC-1 human pancreatic carcinoma cell line in duplicates using a 10 × 10 matrix format. This dataset served as the basis for generating and training advanced AI/ML models focused on pancreatic cancer. Next, three independent groups (NCATS, UNC and MIT), though in a collaborative manner, utilized three different workflows with AL/ML approaches to discover new perspective drug combinations against pancreatic cancer among over 1.5 million drug combinations. As a result of this collaboration, 88 proposed combinations were tested in a cell-based assay; 53 of them were synergistic (hit rate ~60%). While all machine learning approaches demonstrate advances in the direction of predicting synergistic drug combinations, graph convolutional networks resulted in the best performance with a hit rate ~83%, and Random Forest delivered the highest precision of 65%. Interestingly, all utilized AL/ML methods among the three groups proposed different drug combinations with a small overlap of only two combos from 90. This study demonstrates the potential of a collaborative modeling approach for prioritizing drug combinations in large-scale screening campaigns, particularly when focusing on maximizing the efficacy of drugs known to exhibit synergy

Standardization of ELISA protocols for serosurveys of the SARS-CoV-2 pandemic using clinical and at-home blood sampling

Understanding the infection parameters and host responses against SARS-CoV-2 require data from large cohorts using standardized methods. Here, the authors optimize a serum ELISA protocol that has minimal cross-reactivity and flexible sample collection workflow in an attempt to standardize data generation and help inform on COVID-19 pandemic and immunity

Functional Genomic Screening Reveals Splicing of the EWS-FLI1 Fusion Transcript as a Vulnerability in Ewing Sarcoma

Ewing sarcoma cells depend on the EWS-FLI1 fusion transcription factor for cell survival. Using an assay of EWS-FLI1 activity and genome-wide RNAi screening, we have identified proteins required for the processing of the EWS-FLI1 pre-mRNA. We show that Ewing sarcoma cells harboring a genomic breakpoint that retains exon 8 of EWSR1 require the RNA-binding protein HNRNPH1 to express in-frame EWS-FLI1. We also demonstrate the sensitivity of EWS-FLI1 fusion transcripts to the loss of function of the U2 snRNP component, SF3B1. Disrupted splicing of the EWS-FLI1 transcript alters EWS-FLI1 protein expression and EWS-FLI1-driven expression. Our results show that the processing of the EWS-FLI1 fusion RNA is a potentially targetable vulnerability in Ewing sarcoma cells