Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries

Abstract

High-throughput screening (HTS) of chemical compounds to identify modulators of molecular targets is a mainstay of pharmaceutical development. Increasingly, HTS is being used to identify chemical probes of gene, pathway, and cell functions, with the ultimate goal of comprehensively delineating relationships between chemical structures and biological activities. Achieving this goal will require methodologies that efficiently generate pharmacological data from the primary screen and reliably profile the range of biological activities associated with large chemical libraries. Traditional HTS, which tests compounds at a single concentration, is not suited to this task, because HTS is burdened by frequent false positives and false negatives and requires extensive follow-up testing. We have developed a paradigm, quantitative HTS (qHTS), tested with the enzyme pyruvate kinase, to generate concentration-response curves for >60,000 compounds in a single experiment. We show that this method is precise, refractory to variations in sample preparation, and identifies compounds with a wide range of activities. Concentration-response curves were classified to rapidly identify pyruvate kinase activators and inhibitors with a variety of potencies and efficacies and elucidate structure-activity relationships directly from the primary screen. Comparison of qHTS with traditional single-concentration HTS revealed a high prevalence of false negatives in the single-point screen. This study demonstrates the feasibility of qHTS for accurately profiling every compound in large chemical libraries (>10(5) compounds). qHTS produces rich data sets that can be immediately mined for reliable biological activities, thereby providing a platform for chemical genomics and accelerating the identification of leads for drug discovery.

Fig. 1.

Reproducibility of PK qHTS. Interscreen data from triplicate qHTS runs of the Prestwick collection. (a) Data for 104 compounds fitting concentration–response curves with inhibitory (blue) or stimulatory (red) activity are shown. Lines connect the data points for each compound titration and replicate. (b) Data for 1,016 compounds did not fit to a concentration–response curve. (c) Representative correlation plot of compounds with AC₅₀ <60 μM identified from runs 1 and 2 (r² = 0.98; n = 58; median MSR = 1.1). For runs 1 vs. 3 and 2 vs. 3, r² = 0.99 and 0.98, respectively.

Fig. 2.

qHTS of PK. (a) A 3D scatter plot of qHTS data lacking (blue) or showing (red) concentration–response relationships were obtained for all 60,793 samples. (b) All 368 intraplate titration curves for the control activator R5P (red) and the control inhibitor luteolin (blue) are shown. Lines connect the data for each titration. (c) Correlation plot of duplicate actives with AC₅₀ <60 μM (r² = 0.81; n = 22; median MSR = 4). (d) Titration of independent resveratrol samples derived from the screen.

Fig. 3.

Classes of titration curves obtained from the qHTS. Lines connecting titration data corresponding to inhibitory and stimulatory compounds are shown. (a) Classes 1a (blue) and 1b (teal) inhibitors display full and partial activity, respectively. (b) Classes 1a (blue) and 1b (teal) activators. (c) Incomplete curves for inhibitors and activators having AC₅₀ values within and beyond the tested titration range are Classes 2a (blue) and 2b (teal), respectively. (d) Incomplete inhibitory (blue) and stimulatory (red) curves that show weak activity and poor fits are Class 3. Curve classes are defined further in Table 1.

Fig. 4.

Pharmacological profile of library activity. (a) Number and percentage of activators and inhibitors in each curve class. (Inset) Distribution of activators (light gray), inhibitors (dark gray), and inactives identified from the qHTS. (b) AC₅₀ distribution of activators and inhibitors in each curve class. Regions 1, 2, and 3 indicate concentration ranges of absent, observed, and extrapolated potencies, respectively. The shaded area indicates the concentration range tested. Arrow a indicates the enzyme concentration (10 nM) at which the lowest AC₅₀ can be observed, whereas arrow b is the no observable effect level (NOEL). To represent the range and average activity of a series, a normal distribution fit was calculated by using Origin software based on the maximum, minimum, and mean of the activity data.

Fig. 5.

Analysis of single-concentration screening data at 11 μM. A scatter plot of the 11 μM data, with the right half colored by the curve class as follows: Class 1a (red), Class 1b (green), Class 2a (dark blue), Class 2b (light blue), Class 3 (orange), and Class 4 inactives (gray). The thresholds for three and six SD are indicated. Analysis based exclusively on a three and six SD activity selection threshold (i.e., without the aid of curve classification, left side of the figure) results in confirmed positives, false positives, and false negatives of 1,431 (98%), 30 (2%), and 845 (40%), 534 (99%), 5 (1%), and 1,602 (75%), respectively.

Fig. 6.

Pharmacological profile of four analog series. (a–d) Potency and curve class distribution of four representative scaffolds. Curves corresponding to Class 1a (red), Class 1b (green), Class 2 (blue), Class 3 (orange), and inactive (gray) are depicted in the bar charts. Curve Classes 1–3 (white) and Class 4 (light gray) are indicated in the pie charts. (c) Additional inactives containing a quinazoline core but lacking a phenyl substituent at position R1 (dark gray) are shown in the pie chart. Normal distribution was calculated as described in Fig. 4.