Frequent hitter analysis

For frequent hitter analysis, we defined a frequent hitter score that depends on the number of screens in which a compound participated and on the number of screens where this compound was a hit We aimed at identilying a simple, empirical score that allows us to rank compounds with respect to their promiscuity, also in cases where compounds where tested in a different number of assays. A biological assay system is modeled as a biased coin that yields hit or non-hit with certain probabilities and the various assays to which a compound is subjected as a sequence of independent coin flips. Thus, we use a binomial distribution function to estimate the relative probabiUty of identifying a compound as a hit n times in k independent assays by chance. The probabiUties for the events hit and non-hit were estimated empirically from a set of assays. 

As part of the secondary pharmacology assessment, we performed a frequent hitter analysis looking at the binomial survival function score (BSF) of each compound. Each compound was checked for how many times it was tested in an HTS and how many times it had been rated active or inactive. The BSF score of a compound is the log value of the chance that this compound is not a frequent hitter. So, a BSF score of —2 means there is a 1% (10 ) chance that this compound is no frequent hitter. 

Figure 15.7 shows an analysis of the selectivity of2094 recent hits in high-throughput screens at Roche. Of these, almost half were specific for the screen in which they were identified as a hit - there were no other screens where these compounds had been titrated in a dose-response assay. However, 8% of the compounds had been tested in dose-response assays for more than five projects. These compounds are frequent hitters, and the most commonly occurring substructures are shown in Figure 15.8. Several of these compounds either are electrophilic, or can give electrophilic species on oxidation. The nitrobenzoxadiazoles are known fluorescent compounds. The oxindoles...

We rely mainly on workflows built in KNIME workflows to analyze this data. The per-compound data are also available through our project data marts as described and shown in Section 13.2.5. In the following, we describe two application examples. The first one is a compound-based analysis using primary assay data to identify frequent hitters. The second example is an analysis based on the dose- esponse data of a complete hit set. 

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