Cytotoxic Profiling of Annotated and Diverse Chemical Libraries Using Quantitative High-Throughput Screening

Abstract

Cell-based phenotypic screening is a commonly used approach to discover biological pathways, novel drug targets, chemical probes, and high-quality hit-to-lead molecules. Many hits identified from high-throughput screening campaigns are ruled out through a series of follow-up potency, selectivity/specificity, and cytotoxicity assays. Prioritization of molecules with little or no cytotoxicity for downstream evaluation can influence the future direction of projects, so cytotoxicity profiling of screening libraries at an early stage is essential for increasing the likelihood of candidate success. In this study, we assessed the cell-based cytotoxicity of nearly 10,000 compounds in the National Institutes of Health, National Center for Advancing Translational Sciences annotated libraries and more than 100,000 compounds in a diversity library against four normal cell lines (HEK 293, NIH 3T3, CRL-7250, and HaCat) and one cancer cell line (KB 3-1, a HeLa subline). This large-scale library profiling was analyzed for overall screening outcomes, hit rates, pan-activity, and selectivity. For the annotated library, we also examined the primary targets and mechanistic pathways regularly associated with cell death. To our knowledge, this is the first study to use high-throughput screening to profile a large screening collection (>100,000 compounds) for cytotoxicity in both normal and cancer cell lines. The results generated here constitute a valuable resource for the scientific community and provide insight into the extent of cytotoxic compounds in screening libraries, allowing for the identification and avoidance of compounds with cytotoxicity during high-throughput screening campaigns.

Keywords: cancer and cancer drugs; cell-based assays; cytotoxicity; profiling; ultra-high-throughput screening.

Figure 1.

a) Pie chart distribution of high-quality actives (orange), low-quality actives (yellow), and inactives (green) identified from the annotated compound library qHTS against the four normal cell lines for 48 hr incubation condition. High-quality actives: compounds in class −1.1, −1.2, −2.1, and −2.2, maximum response ≤ −50%, and EC₅₀ value ≤ 10 μM. Inactives: compounds in curve class 4; low-quality actives: compounds with shallow curves, single-point activity or inconclusive activity. b) Compound overlapping Venn diagram for HEK 293, HaCat, NIH 3T3, and CRL-7250 cell lines. Number of high-quality actives in each cell line and number of compounds overlapped were calculated.

Figure 2.

a) The comparison of AUC values of each compound in four normal cell lines and KB 3–1 human adenocarcinoma cells. In the heat map each row corresponds to a compound and each column to a cell line. Darker red color indicates more potent and efficacious compound. b) Pair-wise R² correlation matrix among five cell lines. Only high-quality actives were included in this analysis and the Pearson correlation was based on AUC values. c) Split violin plot showing the distribution of AUC values for all compounds screened and high-quality actives across four normal cell lines. The lines within each distribution area represent the 0.25, 0.5 and 0.75 quantiles.

Figure 3.

A treemap representation of the mechanisms of action (MOA) of all high-quality actives from the annotated library compounds screened. Box size represents the total number of compounds representing each MOA (bigger box size indicates more compounds present in the high-quality actives). Color represents the average AUC from the cytotoxicity screen in four normal cell lines (darker red indicates a lower AUC meaning a more potent and efficacious hits). 1 = Lineage specific differentiation; 2 = RNA polymerase; 3 = Immuno-suppressant.

Figure 4.

a) Enrichment analysis of active agents in each drug category. Enrichment ratio = the number of actives/the total number of drugs in each drug category. b) Dose-response curves for delanzomib, a representative proteasome inhibitor, in cytotoxicity screens. EC₅₀ HEK 293 = 148 nM, EC₅₀ NIH 3T3 = 82 nM, EC₅₀ CRL-7250 = 205 nM, EC₅₀ HaCat = 326 nM, EC₅₀ KB 3–1 = 186nM. c) A radar plot displaying Log EC₅₀ of all active proteasome inhibitors in four normal cell lines. d) Dose-response curves for TC-S7004, a potent and selective DYRK1A/B inhibitor, in cytotoxicity screens, including KB 3–1 cells. EC₅₀ KB 3–1 = 3.2 μM. e) Dose-response curves for LDN-212854, an ALK2 inhibitor, in cytotoxicity screens, including KB 3–1 cells. EC₅₀ HEK 293 = 16.4 μM, EC₅₀ KB 3–1 = 4.1 μM.

Figure 5.

a-b) Pie chart distribution of high-quality actives (orange), low-quality actives (yellow), and inactives (green) identified from the qHTS against diversity collection compound library in NIH 3T3 and HEK 293 cell line, respectively. c) Compound overlapping Venn diagram for NIH 3T3 and HEK 293 cell lines. Number of high-quality actives in each cell line was calculated and number of compounds overlapped were labeled. d) The comparison of AUC values of 588 cherry-picked compounds in HEK 293, NIH 3T3 and KB 3–1 cell lines. In the heat map each row corresponds to a compound and a column to a cell line. Darker red color indicates more potent and efficacious compound.

Figure 6.

a) Dose-response curves for NCGC00413522 in cytotoxicity screens, luciferase inhibition assay and CellTiter-Glo luciferase assay. EC₅₀ HEK 293 = 8.1 μM, EC₅₀ NIH 3T3 = 9.1 μM, EC₅₀ KB 3–1 = 8.1 μM. EC₅₀ Luciferase = 0.89 μM. EC₅₀ CTG Luciferase = 26.6 μM. b) Dose-response curves for NCGC00413607 in cytotoxicity screens, luciferase inhibition assay and CellTiter-Glo luciferase assay. EC₅₀ HEK 293 = 3.2 μM, EC₅₀ NIH 3T3 = 2.3 μM, EC₅₀ KB 3–1 = 2.3 μM. EC₅₀ Luciferase = 0.08 μM. EC₅₀ CTG Luciferase = 7.3 μM. c) Representative bright field images of HEK293 cells after 48 hr treatment.