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Improving the Design and Analysis of High-Throughput Screening Technology Comparison Experiments Using Statistical Modeling
1 Non-Clinical Statistics Group, Pfizer Development Operations, Sandwich, UK.
* To whom correspondence should be addressed. E-mail: neil.benson{at}pfizer.com.
Contemporary small-molecule drug discovery frequently involves the screening of large compound files as a core activity. Subsequently cost, speed, and safety become critical issues. In order to meet this need, numerous technologies have been developed to allow mix and measure approaches, facilitate miniaturization, and to increase speed and to minimize the use of potentially hazardous reagents such as radioactive materials. However, despite the on-paper advantages of these new technologies, risks can remain undefined. For example, the question of whether the novel method will facilitate identification of active chemical series in a way that is comparable with conventional methods arises. In order to address this question, we have taken the approach of carrying out experiments to directly compare the output of high-throughput screens using a given novel approach and a traditional method. The concordance between the screening methods can then be determined via comparison of the numbers and structures of the active molecules identified. This article describes the approach taken in our laboratory to minimize variability in such experiments and shows data that exemplifies the general result of lower than expected concordance. Statistical modeling was subsequently used to facilitate this interpretation. The model used Key Words: high-throughput screening, concordance, statistical modeling, technology
First published on October 18, 2005, doi:10.1177/1087057105280779 This article has been cited by other articles:
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-distribution function to generate a real-activity frequency relationship with added normal random error and occasional outliers to represent assay variability. Hence, the effect of assay parameters such as the threshold, the number of real actives, and the number of outliers and the standard deviation could readily be explored. The model was found to describe the data reasonably and moreover was found to be of great utility when it came to planning further optimal experiments. A key conclusion from the model was that concordance between screening methods could appear poor even when one approach is compared with itself. This occurs simply because the result is a function of assay threshold, standard deviation and the true compound % activity. In response to this finding we have adopted alternative experimental designs that more reliably measure the concordance between screening methods.
