qHTS Assays Research Articles

A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening Assays

A main goal of the U.S. Tox21 program is to profile a 10K-compound library for activity against a panel of stress-related and nuclear receptor signaling pathway assays using a quantitative high-throughput screening (qHTS) approach. However, assay artifacts, including nonreproducible signals and assay interference (e.g., autofluorescence), complicate compound activity interpretation. To address these issues, we have developed a data analysis pipeline that includes an updated signal noise–filtering/curation protocol and an assay interference flagging system. To better characterize various types of signals, we adopted a weighted version of the area under the curve (wAUC) to quantify the amount of activity across the tested concentration range in combination with the assay-dependent point-of-departure (POD) concentration. Based on the 32 Tox21 qHTS assays analyzed, we demonstrate that signal profiling using wAUC affords the best reproducibility (Pearson’s r = 0.91) in comparison with the POD (0.82) only or the AC50 (i.e., half-maximal activity concentration, 0.81). Among the activity artifacts characterized, cytotoxicity is the major confounding factor; on average, about 8% of Tox21 compounds are affected, whereas autofluorescence affects less than 0.5%. To facilitate data evaluation, we implemented two graphical user interface applications, allowing users to rapidly evaluate the in vitro activity of Tox21 compounds.

Quantitative high-throughput identification of drugs as modulators of human constitutive androstane receptor.

The constitutive androstane receptor (CAR, NR1I3) plays a key role in governing the transcription of numerous hepatic genes that involve xenobiotic metabolism/clearance, energy homeostasis, and cell proliferation. Thus, identification of novel human CAR (hCAR) modulators may not only enhance early prediction of drug-drug interactions but also offer potentially novel therapeutics for diseases such as metabolic disorders and cancer. In this study, we have generated a double stable cell line expressing both hCAR and a CYP2B6-driven luciferase reporter for quantitative high-throughput screening (qHTS) of hCAR modulators. Approximately 2800 compounds from the NIH Chemical Genomics Center Pharmaceutical Collection were screened employing both the activation and deactivation modes of the qHTS. Activators (115) and deactivators (152) of hCAR were identified from the primary qHTS, among which 10 agonists and 10 antagonists were further validated in the physiologically relevant human primary hepatocytes for compound-mediated hCAR nuclear translocation and target gene expression. Collectively, our results reveal that hCAR modulators can be efficiently identified through this newly established qHTS assay. Profiling drug collections for hCAR activity would facilitate the prediction of metabolism-based drug-drug interactions, and may lead to the identification of potential novel therapeutics.

Chemogenomic profiling of endogenous PARK2 expression using a genome-edited coincidence reporter.

Parkin, an E3 ubiquitin ligase, is a central mediator of mitochondrial quality control and is linked to familial forms of Parkinson's disease (PD). Removal of dysfunctional mitochondria from the cell by Parkin is thought to be neuroprotective, and pharmacologically increasing Parkin levels may be a novel therapeutic approach. We used genome-editing to integrate a coincidence reporter into the PARK2 gene locus of a neuroblastoma-derived cell line and developed a quantitative high-throughput screening (qHTS) assay capable of accurately detecting subtle compound-mediated increases in endogenous PARK2 expression. Interrogation of a chemogenomic library revealed diverse chemical classes that up-regulate the PARK2 transcript, including epigenetic agents, drugs controlling cholesterol biosynthesis, and JNK inhibitors. Use of the coincidence reporter eliminated wasted time pursuing reporter-biased false positives accounting for ∼2/3 of the actives and, coupled with titration-based screening, greatly improves the efficiency of compound selection. This approach represents a strategy to revitalize reporter-gene assays for drug discovery.

Profiling of the Tox21 10K compound library for agonists and antagonists of the estrogen receptor alpha signaling pathway.

The U.S. Tox21 program has screened a library of approximately 10,000 (10K) environmental chemicals and drugs in three independent runs for estrogen receptor alpha (ERα) agonist and antagonist activity using two types of ER reporter gene cell lines, one with an endogenous full length ERα (ER-luc; BG1 cell line) and the other with a transfected partial receptor consisting of the ligand binding domain (ER-bla; ERα β-lactamase cell line), in a quantitative high-throughput screening (qHTS) format. The ability of the two assays to correctly identify ERα agonists and antagonists was evaluated using a set of 39 reference compounds with known ERα activity. Although both assays demonstrated adequate (i.e. >80%) predictivity, the ER-luc assay was more sensitive and the ER-bla assay more specific. The qHTS assay results were compared with results from previously published ERα binding assay data and showed >80% consistency. Actives identified from both the ER-bla and ER-luc assays were analyzed for structure-activity relationships (SARs) revealing known and potentially novel ERα active structure classes. The results demonstrate the feasibility of qHTS to identify environmental chemicals with the potential to interact with the ERα signaling pathway and the two different assay formats improve the confidence in correctly identifying these chemicals.

Novel Cell-Based Hepatitis C Virus Infection Assay for Quantitative High-Throughput Screening of Anti-Hepatitis C Virus Compounds

Therapy for hepatitis C virus (HCV) infection has advanced with the recent approval of direct-acting antivirals in combination with peginterferon and ribavirin. New antivirals with novel targets are still needed to further improve the treatment of hepatitis C. Previously reported screening methods for HCV inhibitors either are limited to a virus-specific function or apply a screening method at a single dose, which usually leads to high false-positive or -negative rates. We developed a quantitative high-throughput screening (qHTS) assay platform with a cell-based HCV infection system. This highly sensitive assay can be miniaturized to a 1,536-well format for screening of large chemical libraries. All candidates are screened over a 7-concentration dose range to give EC50s (compound concentrations at 50% efficacy) and dose-response curves. Using this assay format, we screened a library of pharmacologically active compounds (LOPAC). Based on the profile of dose-dependent curves of HCV inhibition and cytotoxicity, 22 compounds with adequate curves and EC50s of <10 μM were selected for validation. In two additional independent assays, 17 of them demonstrated specific inhibition of HCV infection. Ten potential candidates with efficacies of >70% and CC50s (compound concentrations at 50% cytotoxicity) of <30 μM from these validated hits were characterized for their target stages in the HCV replication cycle. In this screen, we identified both known and novel hits with diverse structural and functional features targeting various stages of the HCV replication cycle. The pilot screen demonstrates that this assay system is highly robust and effective in identifying novel HCV inhibitors and that it can be readily applied to large-scale screening of small-molecule libraries.

Robust Analysis of High Throughput Screening (HTS) Assay Data

Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity). Since thousands of compounds are simultaneously evaluated in a qHTS assay, it is not practically feasible for an investigator to perform residual analysis to determine the variance structure before performing statistical inferences on each compound. Since it is well known that the variance structure plays an important role in the analysis of linear and nonlinear regression models, it is therefore important to have practically useful and easy to interpret methodology that is robust to the variance structure. Furthermore, given the number of chemicals that are investigated in the qHTS assay, outliers and influential observations are not uncommon. In this article, we describe preliminary test estimation (PTE)-based methodology that is robust to the variance structure as well as any potential outliers and influential observations. Performance of the proposed methodology is evaluated in terms of false discovery rate (FDR) and power using a simulation study mimicking a real qHTS data. Of the two methods currently in use, our simulations studies suggest that one is extremely conservative with very small power in comparison to the proposed PTE-based method whereas the other method is very liberal. In contrast, the proposed PTE-based methodology achieves a better control of FDR while maintaining good power. The proposed methodology is illustrated using a dataset obtained from the National Toxicology Program (NTP). Additional information, simulation results, data, and computer code are available online as supplementary materials.

A Three-Stage Algorithm to Make Toxicologically Relevant Activity Calls from Quantitative High Throughput Screening Data

Background: The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations than at a single concentration. In vitro quantitative high throughput screening (qHTS) assays are multiple-concentration experiments with an important role in the National Toxicology Program’s (NTP) efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a more predictive science based on broad inclusion of biological observations.Objective: We developed a systematic approach to classify substances from large-scale concentration–​response data into statistically supported, toxicologically relevant activity categories.Methods: The first stage of the approach finds active substances with robust concentration–response profiles within the tested concentration range. The second stage finds substances with activity at the lowest tested concentration not captured in the first stage. The third and final stage separates statistically significant (but not robustly statistically significant) profiles from responses that lack statistically compelling support (i.e., “inactives”). The performance of the proposed algorithm was evaluated with simulated qHTS data sets.Results: The proposed approach performed well for 14-point-concentration–response curves with typical levels of residual error (σ ≤ 25%) or when maximal response (|RMAX|) was > 25% of the positive control response. The approach also worked well in most cases for smaller sample sizes when |RMAX| ≥ 50%, even with as few as four data points.Conclusions: The three-stage classification algorithm performed better than one-stage classification approaches based on overall F-tests, t-tests, or linear regression.

Exploiting Domain Knowledge for Improved Quantitative High-Throughput Screening Curve Fitting

Least-squares fitting of the Hill equation to quantitative high-throughput screening (qHTS) assays results in frequent unsatisfactory fits. We learn and exploit prior knowledge to improve the Hill fitting in a nonlinear regression method called domain knowledge fitter (DK-fitter). This paper formulates and solves DK-fitter for 44 public qHTS data sets. This new Hill parameter estimation technique is validated using three unbiased approaches, including a novel method that involves generating simulated samples. This paper fosters the extraction of higher quality information from screens for improved potency evaluation.

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

BackgroundQuantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center.ObjectivesOur goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points.MethodsWe obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data.ResultsBoth the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models.ConclusionsConcentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points.

A Miniaturized Glucocorticoid Receptor Translocation Assay Using Enzymatic Fragment Complementation Evaluated with qHTS

Nuclear translocation is an important step in glucocorticoid receptor (GR) signaling and assays that measure this process allow the identification of nuclear receptor ligands independent of subsequent functional effects. To facilitate the identification of GR-translocation agonists, an enzyme fragment complementation (EFC) cell-based assay was scaled to a 1536-well plate format to evaluate 9,920 compounds using a quantitative high throughput screening (qHTS) strategy where compounds are assayed at multiple concentrations. In contrast to conventional assays of nuclear translocation the qHTS assay described here was enabled on a standard luminescence microplate reader precluding the requirement for imaging methods. The assay uses beta-galactosidase alpha complementation to indirectly detect GR-translocation in CHO-K1 cells. 1536-well assay miniaturization included the elimination of a media aspiration step, and the optimized assay displayed a Z' of 0.55. qHTS yielded EC(50) values for all 9,920 compounds and allowed us to retrospectively examine the dataset as a single concentration-based screen to estimate the number of false positives and negatives at typical activity thresholds. For example, at a 9 microM screening concentration, the assay showed an accuracy that is comparable to typical cell-based assays as judged by the occurrence of false positives that we determined to be 1.3% or 0.3%, for a 3sigma or 6sigma threshold, respectively. This corresponds to a confirmation rate of approximately 30% or approximately 50%, respectively. The assay was consistent with glucocorticoid pharmacology as scaffolds with close similarity to dexamethasone were identified as active, while, for example, steroids that act as ligands to other nuclear receptors such as the estrogen receptor were found to be inactive.