Statistics and Decision Making in High-Throughput Screening

Abstract

Screening is about making decisions on the modulating activity of one particular compound on a biological system. When a compound testing experiment is repeated under the same conditions or as close to the same conditions as possible, the observed results are never exactly the same, and there is an apparent random and uncontrolled source of variability in the system under study. Nevertheless, randomness is not haphazard. In this context, we can see statistics as the science of decision making under uncertainty. Thus, the usage of statistical tools in the analysis of screening experiments is the right approach to the interpretation of screening data, with the aim of making them meaningful and converting them into valuable information that supports sound decision making.In the HTS workflow, there are at least three key stages where key decisions have to be made based on experimental data: (1) assay development (i.e. how to assess whether our assay is good enough to be put into screening production for the identification of modulators of the target of interest), (2) HTS campaign process (i.e. monitoring that screening process is performing at the expected quality and assessing possible patterned signs of experimental response that may adversely bias and mislead hit identification) and (3) data analysis of primary HTS data (i.e. flagging which compounds are giving a positive response in the assay, namely hit identification).In this chapter we will focus on how some statistical tools can help to cope with these three aspects. Assessment of assay quality is reviewed in other chapters, so in Section 1 we will briefly make some further considerations. Section 2 will review statistical process control, Section 3 will cover methodologies for detecting and dealing with HTS patterns and Section 4 will describe approaches for statistically guided selection of hits in HTS.

Discovery of Novel Inhibitors of the Tautomerase Activity of Macrophage Migration Inhibitory Factor (MIF)

“…Additionally, in order to minimize the loss of valuable hits as a consequence of regional heterogeinity in plates due to plate handling, liquid handling, or reading effects, a pattern recognition and correction algorithm was applied. 26 The list of hits resulting from this new analysis was combined with the one generated before pattern correction to generate the final selection of hits for confirmation studies.…”

Digitoxin-Induced Cytotoxicity in Cancer Cells Is Mediated through Distinct Kinase and Interferon Signaling Networks

“…Data capture, storage, and analysis were done by a biostatistician. Quality and robustness of the synergistic screens was evaluated using biochemical checkpoints such as dynamic range, variability, and 'hit' rate, whose scores were incorporated in the Z factor, a well-established statistical parameter routinely used in high-throughput screening programs (12).…”

Theoretical and Experimental Relationships between Percent Inhibition and IC50 Data Observed in High-Throughput Screening

“…1,2 In a second step, the primary hits (i.e., the compounds passing a fixed threshold or having a larger than 3-6 standard deviation distance from the zero effect level) are selected for concentration-response curve (CRC) validation experiments. 2,3 The determination of an IC 50 ("potency") value is based on fitting the four-parameter logistic Hill equation 4,5 to the data, although more sophisticated models are sometimes used in detailed pharmacological investigations. 6 The basic assumption of this stepwise HTS design is a reasonable correlation between the percent inhibition and IC 50 values, as can be expected from the Hill equation for ideal inhibitors.…”