Rapid quantitative methods for characterizing small molecules, peptides, proteins, or RNAs in a broad array of cellular assays would allow one to discover new biological activities associated with these molecules and also provide a more comprehensive profile of drug candidates early in the drug development process. Here we describe a robotic system, termed the automated compound profiler, capable of both propagating a large number of cell lines in parallel and assaying large collections of molecules simultaneously against a matrix of cellular assays in a highly reproducible manner. To illustrate its utility, we have characterized a set of 1,400 kinase inhibitors in a panel of 35 activated tyrosine-kinasedependent cellular assays in dose-response format in a single experiment. Analysis of the resulting multidimensional dataset revealed subclusters of both inhibitors and kinases with closely correlated activities. The approach also identified activities for the p38 inhibitor BIRB796 and the dual src͞abl inhibitor BMS-354825 and exposed the expected side activities for Glivec͞STI571, including cellular inhibition of c-kit and platelet-derived growth factor receptor. This methodology provides a powerful tool for unraveling the cellular biology and molecular pharmacology of both naturally occurring and synthetic chemical diversity.drug discovery ͉ high-throughput screening ͉ tyrosine kinase T he ability to simultaneously interrogate the activities of a library of molecules against a large panel of cellular assays would provide a rapid efficient means to begin to characterize and correlate the biological properties of both natural and synthetic chemical diversity. For example, libraries of noncoding RNAs, mutant growth factors, small molecule kinase inhibitors, or even existing drugs could be assayed for their potency and selectivity in pathway-based or receptor screens or toxicity and metabolic stability in diverse cell types to discover a new biological activity or optimize the pharmacological properties of a molecule (1-3). Although whole-cell systems represent an attractive milieu to characterize gene and small-molecule function, no robust and systematic method exists to correlate chemical structure and biological activity across a large number of molecules and cellular assays. To address this problem, we have developed an approach that affords rapid cost-effective broad-based cellular profiling in parallel against molecular libraries. An industrial-scale automated compound profiling (ACP) system has been designed, which consists of an automated tissue culture system for propagating cell lines, integrated with a system for automatically performing miniaturized cell-based assays in 384-or 1,536-well microplates. The ACP can rapidly test thousands of arrayed molecules, in replicates, in doseresponse format against hundreds of unique cellular assays in a single experiment.To demonstrate this capability, we focused on the problem of identifying selective small-molecule inhibitors of protein tyrosine kinases. Tyrosine ...

The standard activity threshold-based method (the "top X" approach), currently widely used in the high-throughput screening (HTS) data analysis, is ineffective at identifying good-quality hits. We have proposed a novel knowledge-based statistical approach, driven by the hidden structure-activity relationship (SAR) within a screening library, for primary hit selection. Application to an in-house ultrahigh-throughput screening (uHTS) campaign has demonstrated it can directly identify active scaffolds containing valuable SAR information with a greatly improved confirmation rate compared to the standard "top X" method (from 55% to 85%). This approach may help produce high-quality leads and expedite the hit-to-lead process in drug discovery.

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