Screening campaigns

A large screening campaign on Enterococcus faecium FabD provided the two hits A000326719 (5) and A000124358 (6). Carboxypyrrole 5 shows an IC50 of 55 (iM, while 4-hydroxyquinoline 6 is reported as a low... 

By trying to address all of these issues in parallel, our aim is to increase the probability that any particular screening campaign will be successful. The factors that influence the outcome of each roll of the HTS dice are multifactorial, and altering the odds in our favor for each of these factors is the purpose of much of the methods-development activities currently carried out in most research-based pharmaceutical companies. Only by understanding the odds of different strategies and components can one build an approach that balances long and short odds. 

The interpretation of the large amounts of data generated in a screening campaign cannot be performed manually. Customized software has been designed to automatically evaluate the data and search for the compounds that non-covalently bind to the protein [21, 22]. For flow injection ESI-MS data of GPC spin column eluates of ten component mixtures, the following steps were taken to automatically interpret the data. The raw data were combined, smoothed, background... 

Figure 12.2 Arginine methyltransferase inhibitors from random and virtual screening campaigns (IC50 values in brackets if no enzyme is mentioned, against hPRMTI).

During a screening campaign some plates and even entire batches can fail due to various reasons. To control the quality of plates, during a screen, we use the Z-primes and the Multivariate Z-primes nodes we have developed. Plates failing to fulfill a minimal criterion such as a Z-prime factor above 0.3 are eliminated from further analysis and scheduled for rescreening. A further useful node to verify the quality of plates is the CV node that calculates coefficients of variance for selected parameters. 

Lastly, it is important to visualize plates to obtain a graphical overview of the screen. To this end we have developed a Plate Viewer to create heatmaps and an R template in KNIME to generate scatter plots of screening campaigns. These tools allow to visualize row and column artifacts and to compare the performance of various plate batches during a screening campaign. 

A successful application of DOE to a high throughput screening campaign remains to be reported in the literature. 

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