The Distribution of Standard Deviations Applied to High Throughput Screening

Abstract

High throughput screening (HTS) assesses compound libraries for “activity” using target assays. A subset of HTS data contains a large number of sample measurements replicated a small number of times providing an opportunity to introduce the distribution of standard deviations (DSD). Applying the DSD to some HTS data sets revealed signs of bias in some of the data and discovered a sub-population of compounds exhibiting high variability which may be difficult to screen. In the data examined, 21% of 1189 such compounds were pan-assay interference compounds. This proportion reached 57% for the most closely related compounds within the sub-population. Using the DSD, large HTS data sets can be modelled in many cases as two distributions: a large group of nearly normally distributed “inactive” compounds and a residual distribution of “active” compounds. The latter were not normally distributed, overlapped inactive distributions – on both sides –, and were larger than typically assumed. As such, a large number of compounds are being misclassified as “inactive” or are invisible to current methods which could become the next generation of drugs. Although applied here to HTS, it is applicable to data sets with a large number of samples measured a small number of times.

Statistical models for identifying frequent hitters in high throughput screening

“…Practitioners of HTS have learned to recognize a wide range of chemical and physical processes leading to apparent activity in HTS assays 13 . The science of active compound detection and corresponding statistical practice developed early 14,15 with many subsequent refinements [16][17][18][19] . The underpinning instrumental technologies of HTS have been influential for increasing the scale achievable in routine laboratory work and these technologies are widely deployed in the form of plate readers, lab robotics, and compound libraries accessible to researchers 20 .…”

“…The public investment and consequential data sharing provide a record containing many large primary HTS data sets. A previous investigation into a small number of these discovered an identifiable set of active compounds having excess variance many of which contained PAIN motifs 18 . This work generated an interest in understanding statistical behavior in HTS at scale.…”

An influenza A hemagglutinin small-molecule fusion inhibitor identified by a new high-throughput fluorescence polarization screen

“…2 A ). The average Z′ for the HTS was 0.81 with an overall hit rate of 0.01% where molecules with FP < 3× and the CV were considered hits ( 28 , 29 ) ( Fig. 2 A ).…”

Major Improvements in Robustness and Efficiency during the Screening of Novel Enzyme Effectors by the 3-Point Kinetics Assay

“…The 3-point method of kinetic analysis answers the current demand for new breakthroughs in the discovery of inhibitors and activators for targets of interest in pharmaceutical research. 33–35 Its first principles are those of the LM recently proposed by us as a new tool to detect enzymatic assay interferences, 22 with the main difference that LM curves are now used to detect “interferences” caused by candidate enzyme effectors. Using the screening for Atx3 77Q modulators as a practical example, major improvements in robustness are achieved through judicious elimination of outliers, by adopting advanced normalization methods, and by increasing the assay resilience to systematic and random variability.…”