Chemical target and pathway toxicity mechanisms defined in primary human cell systems.

INTRODUCTION: The ability to predict the health effects resulting from drug or chemical exposure has been challenging due to the complexity of human biology. Approaches that detect and discriminate a broad range of mechanisms in testing formats that are predictive and yet cost-effective are needed. METHODS: Here, we evaluated the performance of BioMAP systems, primary human cell-based disease models, as a platform for characterization of chemical toxicity mechanisms. For this we tested a set of compounds with known or well-studied mechanisms in a panel of 8 BioMAP assays relevant to human respiratory, skin, immune and vascular exposure sites. RESULTS: We evaluated the ability to detect and distinguish compounds based on mechanisms of action, comparing the BioMAP activity profiles generated in a reduced sample number format to reference database profiles derived from multiple experiments. We also studied the role of BioMAP assay panel size and concentration effects, both of which were found to contribute to the ability to discriminate chemicals and mechanisms. DISCUSSION: Compounds with diverse mechanisms, including modulators of the NFkappaB pathway, microtubule function and mitochondrial activity, could be discriminated and classified into target and pathway mechanisms in both assay formats. Certain inhibitors of mitochondrial function, such as rotenone and sodium azide, but not others, were classified with inducers of endoplasmic reticulum stress, providing insight into the toxicity mechanisms of these agents. This method may have utility in classifying novel agents with unknown modes of action according to their effects on toxicity pathways.

An integrative biology approach for analysis of drug action in models of human vascular inflammation.

Unexpected drug activities discovered during clinical testing establish the need for better characterization of compounds in human disease-relevant conditions early in the discovery process. Here, we describe an approach to characterize drug function based on statistical analysis of protein expression datasets from multiple primary human cell-based models of inflammatory disease. This approach, termed Biologically Multiplexed Activity Profiling (BioMAP), provides rapid characterization of drug function, including mechanism of action, secondary or off-target activities, and insights into clinical phenomena. Using three model systems containing primary human endothelial cells and peripheral blood mononuclear cells in different environments relevant to vascular inflammation and immune activation, we show that BioMAP profiles detect and discriminate multiple functional drug classes, including glucocorticoids; TNF-alpha antagonists; and inhibitors of HMG-CoA reductase, calcineurin, IMPDH, PDE4, PI-3 kinase, hsp90, and p38 MAPK, among others. The ability of cholesterol lowering HMG-CoA reductase inhibitors (statins) to improve outcomes in rheumatic disease patients correlates with the activities of these compounds in our BioMAP assays. In addition, the activity profiles identified for the immunosuppressants mycophenolic acid, cyclosporin A, and FK-506 provide a potential explanation for a reduced incidence of posttransplant cardiovascular disease in patients receiving mycophenolic acid. BioMAP profiling can allow integration of meaningful human biology into drug development programs.