The main goal of high-throughput screening (HTS) is to identify active chemical series rather than just individual active compounds. In light of this goal, a new method (called compound set enrichment) to identify active chemical series from primary screening data is proposed. The method employs the scaffold tree compound classification in conjunction with the Kolmogorov-Smirnov statistic to assess the overall activity of a compound scaffold. The application of this method to seven PubChem data sets (containing between 9389 and 263679 molecules) is presented, and the ability of this method to identify compound classes with only weakly active compounds (potentially latent hits) is demonstrated. The analysis presented here shows how methods based on an activity cutoff can distort activity information, leading to the incorrect activity assignment of compound series. These results suggest that this method might have utility in the rational selection of active classes of compounds (and not just individual active compounds) for followup and validation.

Methods that monitor the quality of a biological assay (i.e., its ability to discriminate between positive and negative controls) are essential for the development of robust assays. In screening, the most commonly used parameter for monitoring assay quality is the Z' factor, which is based on 1 selected readout. However, biological assays are able to monitor multiple readouts. For example, novel multiparametric screening technologies such as high-content screening provide information-rich data sets with multiple readouts on a compound's effect. Still, assay quality is commonly assessed by the Z' factor based on a single selected readout. This report suggests an extension of the Z' factor, which integrates multiple readouts for assay quality assessment. Using linear projections, multiple readouts are condensed to a single parameter, based on which the assay quality is monitored. The authors illustrate and evaluate this approach using simulated data and real-world data from a highcontent screen. The suggested approach is applicable during assay development, to optimize the image analysis, as well as during screening to monitor assay robustness. Furthermore, data sets from high-content imaging assays and other state-of-the-art multiparametric screening technologies, such as flow cytometry or transcript analysis, could be analyzed.

The four-parameter logistic Hill equation models the theoretical relationship between inhibitor concentration and response and is used to derive IC 50 values as a measure of compound potency. This relationship is the basis for screening strategies that first measure percent inhibition at a single, uniform concentration and then determine IC 50 values for compounds above a threshold. In screening practice, however, a "good" correlation between percent inhibition values and IC 50 values is not always observed, and in the literature, there seems confusion about what correlation even to expect. We examined the relationship between percent inhibition data and IC 50 data in HDAC4 and ENPP2 high-throughput screening (HTS) data sets and compared our findings with a series of numerical simulations that allowed the investigation of the influence of parameters representing different types of uncertainties: variability in the screening concentration (related to solution library and compound characteristics, liquid handling), variations in Hill model parameters (related to interaction of compounds with target, type of assay), and influences of assay data quality parameters (related to assay and experimental design, liquid handling). In the different sensitivity analyses, we found that the typical variations of the actual compound concentrations in existing screening libraries generate the largest contributions to imperfect correlations. Excess variability in the ENPP2 assay above the values of the simulation model can be explained by compound aggregation artifacts.

Time-resolved (TR) fluorescence resonance energy transfer (FRET) is a widely accepted technology for high throughput screening (HTS), being able to detect and quantify the interactions of specific biomolecules in a homogeneous format. TR-FRET has several advantages for HTS applications that reduce assay artifacts such as compound interference. However, in some cases artifacts due to compound autofluorescence, color quenching, or signal stability are still observed. This report presents strategies addressing these issues by several means. One recommendation is the recording and visualization of differences in the donor/acceptor fluorescence, which allows the identification of compound artifacts. Another suggestion is to adjust the time delay, between excitation and recording of the fluorescence, in order to reduce compound interference. Furthermore, configuring the assay to allow the TR-FRET measurement to be taken at different time points, creating a reaction time course, allows background correction for each sample. Finally, the optimization of the FRET pair, to ensure assay signal stability under screening conditions, can improve the assay quality. This report presents examples of how these simple steps can be applied to enhance the quality of TR-FRET screening campaigns.

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