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.

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.

Glioblastoma (GBM) is a lethal brain cancer with a median survival time of approximately 15 months following treatment. Common in vitro GBM models for drug screening are adherent and do not recapitulate the features of human GBM in vivo. Here we report the genomic characterization of nine patient-derived, spheroid GBM cell lines that recapitulate human GBM characteristics in orthotopic xenograft models. Genomic sequencing revealed that the spheroid lines contain alterations in GBM driver genes such as PTEN, CDKN2A, and NF1. Two spheroid cell lines, JHH-136 and JHH-520, were utilized in a high-throughput drug screen for cell viability using a 1912-member compound library. Drug mechanisms that were cytotoxic in both cell lines were Hsp90 and proteasome inhibitors. JHH-136 was uniquely sensitive to topoisomerase 1 inhibitors, while JHH-520 was uniquely sensitive to Mek inhibitors. Drug combination screening revealed that PI3 kinase inhibitors combined with Mek or proteasome inhibitors were synergistic. However, animal studies to test these drug combinations in vivo revealed that Mek inhibition alone was superior to the combination treatments. These data show that these GBM spheroid lines are amenable to high-throughput drug screening and that this dataset may deliver promising therapeutic leads for future GBM preclinical studies.

Imaging and subsequent segmentation analysis in three-dimensional (3D) culture models are complicated by the light scattering that occurs when collecting fluorescent signal through multiple cell and extracellular matrix layers. For 3D cell culture models to be usable for drug discovery, effective and efficient imaging and analysis protocols need to be developed that enable high-throughput data acquisition and quantitative analysis of fluorescent signal. Here we report the first high-throughput protocol for optical clearing of spheroids, fluorescent high-content confocal imaging, 3D nuclear segmentation, and post-segmentation analysis. We demonstrate nuclear segmentation in multiple cell types, with accurate identification of fluorescently-labeled subpopulations, and develop a metric to assess the ability of clearing to improve nuclear segmentation deep within the tissue. Ultimately this analysis pipeline allows for previously unattainable segmentation throughput of 3D culture models due to increased sample clarity and optimized batch-processing analysis.

The Assay Guidance Manual (AGM) is an eBook of best-practices for the design, development, and implementation of robust assays for early drug discovery. Initiated by pharmaceutical company scientists, the manual provides guidance for designing a "testing funnel" of assays to identify genuine hits using high-throughput screening (HTS) and advancing them through pre-clinical development. Combined with a workshop/tutorial component, the overall goal of the AGM is to provide a valuable resource for training translational scientists. This article is protected by copyright. All rights reserved.

Two-dimensional monolayer cell proliferation assays for cancer drug discovery have made the implementation of large-scale screens feasible but only seem to reflect a simplified view that oncogenes or tumor suppressor genes are the genetic drivers of cancer cell proliferation. However, there is now increased evidence that the cellular and physiological context in which these oncogenic events occur play a key role in how they drive tumor growth in vivo and, therefore, in how tumors respond to drug treatments. In vitro 3D spheroid tumor models are being developed to better mimic the physiology of tumors in vivo, in an attempt to improve the predictability and efficiency of drug discovery for the treatment of cancer. Here we describe the establishment of a real-time 3D spheroid growth, 384-well screening assay. The cells used in this study constitutively expressed green fluorescent protein (GFP), which enabled the real-time monitoring of spheroid formation and the effect of chemotherapeutic agents on spheroid size at different time points of sphere growth and drug treatment. This real-time 3D spheroid assay platform represents a first step toward the replication in vitro of drug dosing regimens being investigated in vivo. We hope that further development of this assay platform will allow the investigation of drug dosing regimens, efficacy, and resistance before preclinical and clinical studies.

The discovery of chemotherapeutic agents for the treatment of cancer commonly uses cell proliferation assays in which cells grow as two-dimensional (2D) monolayers. Compounds identified using 2D monolayer assays often fail to advance during clinical development, most likely because these assays do not reproduce the cellular complexity of tumors and their microenvironment in vivo. The use of three-dimensional (3D) cellular systems have been explored as enabling more predictive in vitro tumor models for drug discovery. To date, small-scale screens have demonstrated that pharmacological responses tend to differ between 2D and 3D cancer cell growth models. However, the limited scope of screens using 3D models has not provided a clear delineation of the cellular pathways and processes that differentially regulate cell survival and death in the different in vitro tumor models. Here we sought to further understand the differences in pharmacological responses between cancer tumor cells grown in different conditions by profiling a large collection of 1912 chemotherapeutic agents. We compared pharmacological responses obtained from cells cultured in traditional 2D monolayer conditions with those responses obtained from cells forming spheres versus cells already in 3D spheres. The target annotation of the compound library screened enabled the identification of those key cellular pathways and processes that when modulated by drugs induced cell death in all growth conditions or selectively in the different cell growth models. In addition, we also show that many of the compounds targeting these key cellular functions can be combined to produce synergistic cytotoxic effects, which in many cases differ in the magnitude of their synergism depending on the cellular model and cell type. The results from this work provide a high-throughput screening framework to profile the responses of drugs both as single agents and in pairwise combinations in 3D sphere models of cancer cells.

In the fall of 2014, an international news agency reported that patients suffering from Ebola virus disease (EVD) in Liberia were treated successfully with lamivudine, an antiviral drug used to treat human immunodeficiency virus-1 and hepatitis B virus infections. According to the report, 13 out of 15 patients treated with lamivudine survived and were declared free from Ebola virus disease. In this study, the anti-Ebola virus (EBOV) activity of lamivudine and another antiretroviral, zidovudine, were evaluated in a diverse set of cell lines against two variants of wild-type EBOV. Variable assay parameters were assessed to include different multiplicities of infection, lengths of inoculation times, and durations of dosing. At a multiplicity of infection of 1, lamivudine and zidovudine had no effect on EBOV propagation in Vero E6, Hep G2, or HeLa cells, or in primary human monocyte-derived macrophages. At a multiplicity of infection of 0.1, zidovudine demonstrated limited anti-EBOV activity in Huh 7 cells. Under certain conditions, lamivudine had low anti-EBOV activity at the maximum concentration tested (320 μM). However, lamivudine never achieved greater than 30% viral inhibition, and the activity was not consistently reproducible. Combination of lamivudine and zidovudine showed no synergistic antiviral activity. Independently, a set of in vitro experiments testing lamivudine and zidovudine for antiviral activity against an Ebola-enhanced green fluorescent protein reporter virus was performed at the Centers for Disease Control and Prevention. No antiviral activity was observed for either compound. A study evaluating the efficacy of lamivudine in a guinea pig model of EVD found no survival benefit. This lack of benefit was observed despite plasma lamivudine concentrations in guinea pig of about 4 μg/ml obtained in a separately conducted pharmacokinetics study. These studies found no evidence to support the therapeutic use of lamivudine for the treatment of EVD.

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z', mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.

In response to the current global health emergency posed by the Zika virus (ZIKV) outbreak and its link to microcephaly and other neurological conditions, we performed a drug repurposing screen of ∼6,000 compounds that included approved drugs, clinical trial drug candidates and pharmacologically active compounds; we identified compounds that either inhibit ZIKV infection or suppress infection-induced caspase-3 activity in different neural cells. A pan-caspase inhibitor, emricasan, inhibited ZIKV-induced increases in caspase-3 activity and protected human cortical neural progenitors in both monolayer and three-dimensional organoid cultures. Ten structurally unrelated inhibitors of cyclin-dependent kinases inhibited ZIKV replication. Niclosamide, a category B anthelmintic drug approved by the US Food and Drug Administration, also inhibited ZIKV replication. Finally, combination treatments using one compound from each category (neuroprotective and antiviral) further increased protection of human neural progenitors and astrocytes from ZIKV-induced cell death. Our results demonstrate the efficacy of this screening strategy and identify lead compounds for anti-ZIKV drug development.