Testing thousands of chemicals to identify potential androgen receptor (AR) agonists or antagonists would cost millions of dollars and take decades to complete using current validated methods. High-throughput in vitro screening (HTS) and computational toxicology approaches can more rapidly and inexpensively identify potential androgen-active chemicals. We integrated 11 HTS ToxCast/Tox21 in vitro assays into a computational network model to distinguish true AR pathway activity from technology-specific assay interference. The in vitro HTS assays probed perturbations of the AR pathway at multiple points (receptor binding, coregulator recruitment, gene transcription, and protein production) and multiple cell types. Confirmatory in vitro antagonist assay data and cytotoxicity information were used as additional flags for potential nonspecific activity. Validating such alternative testing strategies requires high-quality reference data. We compiled 158 putative androgen-active and -inactive chemicals from a combination of international test method validation efforts and semiautomated systematic literature reviews. Detailed in vitro assay information and results were compiled into a single database using a standardized ontology. Reference chemical concentrations that activated or inhibited AR pathway activity were identified to establish a range of potencies with reproducible reference chemical results. Comparison with existing Tier 1 AR binding data from the U.S. EPA Endocrine Disruptor Screening Program revealed that the model identified binders at relevant test concentrations (<100 μM) and was more sensitive to antagonist activity. The AR pathway model based on the ToxCast/Tox21 assays had balanced accuracies of 95.2% for agonist (n = 29) and 97.5% for antagonist (n = 28) reference chemicals. Out of 1855 chemicals screened in the AR pathway model, 220 chemicals demonstrated AR agonist or antagonist activity and an additional 174 chemicals were predicted to have potential weak AR pathway activity.

BACKGROUND: All biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data. RESULTS: A large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms. CONCLUSIONS: We demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.

Human neuronal cells differentiated from induced pluripotent cells have emerged as a new model system for the study of disease pathophysiology and evaluation of drug efficacy. Differentiated neuronal cells are more similar in genetics and biological content to human brain cells than other animal disease models. However, culture of neuronal cells in assay plates requires a labor-intensive procedure of plate precoating, hampering its applications in high-throughput screening (HTS). We developed a simplified method with one-step seeding of neural stem cells in assay plates by supplementing the medium with a recombinant human vitronectin (VTN), thus avoiding plate precoating. Robust results were obtained from cell viability, calcium response, and neurite outgrowth assays using this new method. Our data demonstrate that this approach greatly simplifies high-throughput assays using neuronal cells differentiated from human stem cells for translational research.

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.

Impairing the division of cancer cells with genotoxic small molecules has been a primary goal to develop chemotherapeutic agents. However, DNA mismatch repair (MMR)-deficient cancer cells are resistant to most conventional chemotherapeutic agents. Here we have identified baicalein as a small molecule that selectively kills MutSα-deficient cancer cells. Baicalein binds preferentially to mismatched DNA and induces a DNA damage response in a MMR-dependent manner. In MutSα-proficient cells, baicalein binds to MutSα to dissociate CHK2 from MutSα leading to S-phase arrest and cell survival. In contrast, continued replication in the presence of baicalein in MutSα-deficient cells results in a high number of DNA double-strand breaks and ultimately leads to apoptosis. Consistently, baicalein specifically shrinks MutSα-deficient xenograft tumors and inhibits the growth of AOM-DSS-induced colon tumors in colon-specific MSH2 knockout mice. Collectively, baicalein offers the potential of an improved treatment option for patients with tumors with a DNA MMR deficiency. Cancer Res; 76(14); 1-9. ©2016 AACR.

Chemical toxicity can arise from disruption of specific biomolecular functions or through more generalized cell stress and cytotoxicity-mediated processes. Here, responses of 1060 chemicals including pharmaceuticals, natural products, pesticidals, consumer, and industrial chemicals across a battery of 815 in vitro assay endpoints from 7 high-throughput assay technology platforms were analyzed in order to distinguish between these types of activities. Both cell-based and cell-free assays showed a rapid increase in the frequency of responses at concentrations where cell stress/cytotoxicity responses were observed in cell-based assays. Chemicals that were positive on at least 2 viability/cytotoxicity assays within the concentration range tested (typically up to 100 μM) activated a median of 12% of assay endpoints whereas those that were not cytotoxic in this concentration range activated 1.3% of the assays endpoints. The results suggest that activity can be broadly divided into: (1) specific biomolecular interactions against one or more targets (eg, receptors or enzymes) at concentrations below which overt cytotoxicity-associated activity is observed; and (2) activity associated with cell stress or cytotoxicity, which may result from triggering specific cell stress pathways, chemical reactivity, physico-chemical disruption of proteins or membranes, or broad low-affinity non-covalent interactions. Chemicals showing a greater number of specific biomolecular interactions are generally designed to be bioactive (pharmaceuticals or pesticidal active ingredients), whereas intentional food-use chemicals tended to show the fewest specific interactions. The analyses presented here provide context for use of these data in ongoing studies to predict in vivo toxicity from chemicals lacking extensive hazard assessment.

Macroautophagy is a major cellular degradation pathway for long-lived proteins and cellular organelles to maintain cellular homeostasis. Reduced autophagy has been implicated in neurodegenerative diseases, metabolic syndrome, and tumorigenesis. In contrast, increased autophagy has been shown to protect against tissue injury and aging. Here we employed a cell-based quantitative high-throughput image screening (qHTS) for autophagy modulators using mouse embryonic fibroblasts (MEFs) that are stably expressing GFP-LC3. The library of pharmacologically active compounds (LOPAC) was used to screen for the autophagy modulators in compounds alone or in combination with the lysosome inhibitor chloroquine (CQ). The GFP-LC3 puncta were then quantified to measure autophagic flux. The primary screening revealed 173 compounds with efficacy more than 40%. These compounds were cherry-picked and re-tested at multiple different concentrations using the same assay. A number of novel autophagy inducers, inhibitors, and modulators with dual-effects on autophagy were identified from the cherry-pick screening. Interestingly, we found a group of compounds that induce autophagy are related to dopamine receptors and are commonly used as clinical psychiatric drugs. Among them, indatraline hydrochloride (IND), a dopamine inhibitor, and chlorpromazine hydrochloride (CPZ) and fluphenazine dihydrochloride (FPZ), two dopamine receptor antagonists, were further evaluated. We found that FPZ-induced autophagy through mTOR inhibition but IND and CPZ induced autophagy in an mTOR-independent manner. Our data suggest that image-based autophagic flux qHTS can efficiently identify autophagy inducers and inhibitors.

Filoviruses are highly infectious, and no FDA-approved drug therapy for filovirus infection is available. Most work to find a treatment has involved only a few strains of Ebola virus and testing of relatively small drug libraries or compounds that have shown efficacy against other virus types. Here we report the findings of a high-throughput screening of 319,855 small molecules from the Molecular Libraries Small Molecule Repository library for their activities against Marburg virus and Ebola virus. Nine of the most potent, novel compounds that blocked infection by both viruses were analyzed in detail for their mechanisms of action. The compounds inhibited known key steps in the Ebola virus infection mechanism by blocking either cell surface attachment, macropinocytosis-mediated uptake, or endosomal trafficking. To date, very few specific inhibitors of macropinocytosis have been reported. The 2 novel macropinocytosis inhibitors are more potent inhibitors of Ebola virus infection and less toxic than ethylisopropylamiloride, one commonly accepted macropinocytosis inhibitor. Each compound blocked infection of primary human macrophages, indicating their potential to be developed as new antifiloviral therapies.

Treatment of the symptomatic asexual stage of Plasmodium falciparum relies almost exclusively on artemisinin (ART) combination therapies (ACTs) in endemic regions. ACTs combine ART or its derivative with a long-acting partner drug to maximize efficacy during the typical three-day regimen. Both laboratory and clinical studies have previously demonstrated that the common drug resistance determinants P. falciparum chloroquine resistance transporter (PfCRT) and multidrug resistance transporter (PfMDR1) can modulate the susceptibility to many current antimalarial drugs and chemical compounds. Here we investigated the parasite responses to dihydroartemisinin (DHA) and various Ca(2+) and Na(+) channel blockers and showed positively correlated responses between DHA and several channel blockers, suggesting potential shared transport pathways or mode of action. Additionally, we demonstrated that PfCRT and PfMDR1 could also significantly modulate the pharmacodynamic interactions of the compounds and that the interactions were influenced by the parasite genetic backgrounds. These results provide important information for better understanding of drug resistance and for assessing the overall impact of drug resistance markers on parasite response to ACTs.

BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. CITATION: Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.