Identification of inhibitors of bacterial transcription/translation machinery utilizing a miniaturized 1536-well format screen

This report presents the miniaturization of a HTS screen to identify inhibitors of prokaryotic transcription-translation in a 1536-well format. The in vitro assay design utilized the bacterial expression machinery to drive expression of a firefly luciferase reporter gene, which was read as an endpoint luminesence measurement. This multicomponent system permits identification of inhibitors at different steps in this pathway. Successful miniaturization required integration of homogeneous assay formats, robust liquid-handling workstations, and second-generation imaging systems. Comparison of data from a triplicate 1536-well screen of a subset of a target library that had been previously validated and followed up for hit confirmation in a 384-well plate format confirmed that triplicate screening yields data of higher confidence and quality, eliminates the time-consuming and potentially error-prone step of cherry-picking, and reduces the number of false positives and negatives. The substantial savings of reagents and reduction of the numbers of plates to process obtained in a 1536-well format as compared to a 384-well format allowed a full triplicate evaluation of the entire library of 183,000 compounds at lower cost and in less time. The triplicate-screen statistics are consistent with a highly reliable data set with a coefficient of variation of 14.8% and Z' and Z values of 0.57 and 0.25, respectively. This screen resulted in the identification of 1,149 hits (0.63% hit rate), representing a compound population at 2.5 standard deviations from the mean cutoff. Furthermore, the data demonstrate good agreement between IC(50) values derived for this assay in a 1536-well format and 384-well format.

Confirmation of primary active substances from high throughput screening of chemical and biological populations: a statistical approach and practical considerations

Many biologically important substances are discovered through screening of relevant chemical or biological libraries. The ability to find the active substances ("hits") from any random collection is largely determined by the quality of the assay and screening conditions. When a large population is screened for a specific characteristic, each member of that population is usually tested only once. Errors in the measurements require additional follow-up tests to confirm that each hit from the primary screen is truly active. In this report, we present a statistical model system that predicts the reliability of hits from a primary test as affected by the error in the assay and the choice of the hit threshold (hit limit). The hit confirmation rate, as well as false positive (representing substances that initially fall above the hit limit but whose true activity are below the hit limit) and false negative (representing substances that initially fall below the hit limit but whose true activity are in fact greater than the hit limit) rates have been analyzed with this model by computational simulation. This model can also be used in screen validation and post-screening data analysis. The statistical analysis presented here has broad implications and is applicable to screening of any large population for any specific characteristic. Obvious applications include drug discovery, gene chip analysis, population biology, directed molecular evolution, biological panning, and combinatorial material sciences.