Hypothesis testing in high-throughput screening for drug discovery

Following the success of small-molecule high-throughput screening (HTS) in drug discovery, other large-scale screening techniques are currently revolutionizing the biological sciences. Powerful new statistical tools have been developed to analyze the vast amounts of data in DNA chip studies, but have not yet found their way into compound screening. In HTS, characterization of single-point hit lists is often done only in retrospect after the results of confirmation experiments are available. However, for prioritization, for optimal use of resources, for quality control, and for comparison of screens it would be extremely valuable to predict the rates of false positives and false negatives directly from the primary screening results. Making full use of the available information about compounds and controls contained in HTS results and replicated pilot runs, the Z score and from it the p value can be estimated for each measurement. Based on this consideration, we have applied the concept of p-value distribution analysis (PVDA), which was originally developed for gene expression studies, to HTS data. PVDA allowed prediction of all relevant error rates as well as the rate of true inactives, and excellent agreement with confirmation experiments was found.