Biomedical subjects

Frederick J King

Publications and source records attributed to Frederick J King.

3 recordsLinked to original sources

PubMed2006

An efficient rapid system for profiling the cellular activities of molecular libraries.

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-kinase-dependent 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 src/abl inhibitor BMS-354825 and exposed the expected side activities for Glivec/STI571, 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.

Animals↗

PubMed2003

Genome-scale functional profiling of the mammalian AP-1 signaling pathway.

Large-scale functional genomics approaches are fundamental to the characterization of mammalian transcriptomes annotated by genome sequencing projects. Although current high-throughput strategies systematically survey either transcriptional or biochemical networks, analogous genome-scale investigations that analyze gene function in mammalian cells have yet to be fully realized. Through transient overexpression analysis, we describe the parallel interrogation of approximately 20,000 sequence annotated genes in cancer-related signaling pathways. For experimental validation of these genome data, we apply an integrative strategy to characterize previously unreported effectors of activator protein-1 (AP-1) mediated growth and mitogenic response pathways. These studies identify the ADP-ribosylation factor GTPase-activating protein Centaurin alpha1 and a Tudor domain-containing hypothetical protein as putative AP-1 regulatory oncogenes. These results provide insight into the composition of the AP-1 signaling machinery and validate this approach as a tractable platform for genome-wide functional analysis.

Animals↗

PubMedDate not supplied

Learning from the data: mining of large high-throughput screening databases.

High-throughput screening (HTS) campaigns in pharmaceutical companies have accumulated a large amount of data for several million compounds over a couple of hundred assays. Despite the general awareness that rich information is hidden inside the vast amount of data, little has been reported for a systematic data mining method that can reliably extract relevant knowledge of interest for chemists and biologists. We developed a data mining approach based on an algorithm called ontology-based pattern identification (OPI) and applied it to our in-house HTS database. We identified nearly 1500 scaffold families with statistically significant structure-HTS activity profile relationships. Among them, dozens of scaffolds were characterized as leading to artifactual results stemming from the screening technology employed, such as assay format and/or readout. Four types of compound scaffolds can be characterized based on this data mining effort: tumor cytotoxic, general toxic, potential reporter gene assay artifact, and target family specific. The OPI-based data mining approach can reliably identify compounds that are not only structurally similar but also share statistically significant biological activity profiles. Statistical tests such as Kruskal-Wallis test and analysis of variance (ANOVA) can then be applied to the discovered scaffolds for effective assignment of relevant biological information. The scaffolds identified by our HTS data mining efforts are an invaluable resource for designing SAR-robust diversity libraries, generating in silico biological annotations of compounds on a scaffold basis, and providing novel target family specific scaffolds for focused compound library design.

Algorithms↗