Analysis of screening data

Screening data present a special case that, due to its inherent characteristics, is not well served by traditional statistical approaches [11,12,19,20]. 

Why First consider which factors influence the power of a statistical test. Gad [11] established the basic factors that influence the statistical performance of any bioassay in terms of its sensitivity and error rates. Recently, Healy [21] presented a review of the factors that influence the power of a study (the ability to detect a dose-related effect when it actually exists). In brief, the power of a study depends on seven aspects of study design  

There are several ways to increase power—each with a consequence. 

Increase the sample size Design test to detect larger differences Use a more powerful significance test Increase the significance level Use one-tailed decision rule Greater resources required Less useful conclusions Stronger assumptions required Higher statistical false-p ositive rate Blind to effects in the op posite direction 

Timely and constant incorporation of knowledge of test system characteristics and performance will reduce background variability and allow sharper focus on the actual variable of interest. There are, however, a variety of nontraditional approaches to the analysis of screening data. 

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The quality of data was such that even differences in activity between identical compounds supplied by different vendors sufficient to cause them to be misclassified using less rigorous assays could be noted. For example, resveratrol would be identified as active at 2.3 i.M in one sample but inactive in the other, when a 50% threshold is used. The authors suggest that qHTS shifts the analysis of screening data from a statistical to a pharmacological... 

In general, the first step in virtual screening is the filtering by the application of Lipinski s Rule of Five [20]. Lipinski s work was based on the results of profiling the calculated physical property data in a set of 2245 compounds chosen from the World Drug Index. Polymers, peptides, quaternary ammonium, and phosphates were removed from this data set. Statistical analysis of this data set showed that approximately 90% of the remaining compounds had ... 

More recent publications on sulfosuccinates have confirmed the minimal or close to zero skin and eye irritation caused by these products. In a general screening of product safety evaluation methods the authors [16] rejected the sulfosuccinate from further consideration in the statistical analysis of experimental data (variance analysis) because the product had not shown any irritation in the Duhring-Chamber test. The sulfosuccinate (based on fatty alcohol ethoxy late) was tested in a screening with 14 other surfactants, namely, alkyl sulfates, sulfonates, ether sulfates, and a protein fatty acid condensation product. 

Jacobsson, M., Liden, P., Stjernschantz, E., Bostrom, H., Norinder, U. Improving Structure-based Virtual Screening by Multivariate Analysis of Scoring Data./. Med. Chem. 2003, 46, 5781-5789. 

Using SELDI technology, a-defensin isoforms were found to be elevated in serum from colon cancer patients and in protein extracts from CRC [59]. This result was confirmed by expression analysis of microarray data obtained from 283 tumors and normal tissues followed by serum analysis of colon cancer patients and controls by ELISA. This study yielded a diagnostic sensitivity of 70%i and specificity of 83% for a-defensin in colon cancer [60]. Although these figures appear too low for developing a screening test, this... 

M Jacobsson, P Liden, E Stjemschantz, H Bostrom, U Norinder (2003) Improving Structure-Based Virtual Screening by Multivariate Analysis of Scoring Data, J Med Chem 46(26) 5781—5789... 

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