Hanspeter Gubler

Compound Set Enrichment: A Novel Approach to Analysis of Primary HTS Data

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.

Comparison of Multivariate Data Analysis Strategies for High-Content Screening

High-content screening (HCS) is increasingly used in biomedical research generating multivariate, single-cell data sets. Before scoring a treatment, the complex data sets are processed (e.g., normalized, reduced to a lower dimensionality) to help extract valuable information. However, there has been no published comparison of the performance of these methods. This study comparatively evaluates unbiased approaches to reduce dimensionality as well as to summarize cell populations. To evaluate these different data-processing strategies, the prediction accuracies and the Z′ factors of control compounds of a HCS cell cycle data set were monitored. As expected, dimension reduction led to a lower degree of discrimination between control samples. A high degree of classification accuracy was achieved when the cell population was summarized on well level using percentile values. As a conclusion, the generic data analysis pipeline described here enables a systematic review of alternative strategies to analyze multiparametric results from biological systems.

Theoretical and experimental relationships between percent inhibition and IC50 data observed in high-throughput screening.

The four-parameter logistic Hill equation models the theoretical relationship between inhibitor concentration and response and is used to derive IC50 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 IC50 values for compounds above a threshold. In screening practice, however, a “good” correlation between percent inhibition values and IC50 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 IC50 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.

Comparison of Variability and Sensitivity between Nuclear Translocation and Luciferase Reporter Gene Assays

High-content screening (HCS), a technology based on subcellular imaging by automated microscopy and sophisticated image analysis, has emerged as an important platform in small-molecule screening for early drug discovery. To validate a subcellular imaging assay for primary screening campaigns, an HCS assay was compared with a non—image-based readout in terms of variability and sensitivity. A study was performed monitoring the accumulation of the forkhead transcription factor of the O subfamily (FOXO3a) coupled with green fluorescent protein in the nucleus of human osteosarcoma (U-2 OS) cells. In addition, the transcription of a luciferase gene coupled with a FOXO3a-responsive promoter was monitored. This report demonstrates that both assay formats show good reproducibility in primary and concentration response screening despite differences in statistical assay quality. In primary screening, the correlation of compound activity between the 2 assays was low, in contrast to the good correlation of the IC50 values of confirmed compounds. Furthermore, the high-content imaging assay showed a mean shift of 2.63-fold in IC50 values compared with the reporter gene assay. No chemical scaffold was specifically found with 1 of the technologies only, however these results validate the HCS technology against established assays for screening of new molecular entities. (Journal of Biomolecular Screening 2009:59-65)

High-Throughput Screening Data Analysis

An overview over the role and past evolution of High Throughput Screening (HTS) in early drug discovery is given and the different screening phases which are sequentially executed to progressively filter out the samples with undesired activities and properties and identify the ones of interest are outlined. The goal of a complete HTS campaign is to identify a validated set of chemical probes from a larger library of small molecules, antibodies, siRNA, etc. which lead to a desired specific modulating effect on a biological target or pathway. The main focus of this chapter is on the description and illustration of practical assay and screening data quality assurance steps and on the diverse statistical data analysis aspects which need to be considered in every screening campaign to ensure best possible data quality and best quality of extracted information in the hit selection process. The most important data processing steps in this respect are the elimination of systematic response errors (pattern detection, categorization and correction), the detailed analysis of the assay response distribution (mixture distribution modeling) in order to limit the number of false negatives and false discoveries (false discovery rate and p-value analysis), as well as selecting appropriate models and efficient estimation methods for concentration-response curve analysis.