Edward Ainscow - Articles and news items

Addressing kinetic applications in High Content Screening

Issue 5 2008, Past issues / 29 September 2008 /

Traditional drug discovery screening assays tend to employ simplistic endpoint assays that often monitor the activity of a single target. While these approaches are amenable to high-throughput screening they provide limited information on how candidate drugs influence complex biological systems that exist in vivo. Such limitations are a contributing factor to high attrition rates of drugs as a consequence of poor efficacy in clinical trials.

Statistical techniques for handling high content screening data

Issue 5 2007, Past issues / 21 September 2007 / Edward Ainscow, Research Scientist, AstraZeneca

One of the chief incentives for the use of high content screening (HCS) approaches is the data rich return one gets from an individual assay. However, conventional methods for hit selection and activity determination are not well suited to handling multi-parametric data. Tools borrowed from the genomics area have been applied to HCS data, but there are important differences between the two data types that are driving the development of novel statistical approaches for HCS data analysis. This article will describe the use of techniques such as principal component analysis, classification trees, neural networks and random forests, as well as recently published approaches for the identification and classification of compound profiles resulting from HCS assays.


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