Elucidating mechanisms of toxicity using phenotypic data from primary human cell systems--a chemical biology approach for thrombosis-related side effects.

Here we describe a chemical biology approach for elucidating potential toxicity mechanisms for thrombosis-related side effects. This work takes advantage of a large chemical biology data set comprising the effects of known, well-characterized reference agents on the cell surface levels of tissue factor (TF) in a primary human endothelial cell-based model of vascular inflammation, the BioMAP® 3C system. In previous work with the Environmental Protection Agency (EPA) for the ToxCast™ program, aryl hydrocarbon receptor (AhR) agonists and estrogen receptor (ER) antagonists were found to share an usual activity, that of increasing TF levels in this system.

Characterization of Novel PI3Kδ Inhibitors as Potential Therapeutics for SLE and Lupus Nephritis in Pre-Clinical Studies.

SLE is a complex autoimmune inflammatory disease characterized by pathogenic autoantibody production as a consequence of uncontrolled T-B cell activity and immune-complex deposition in various organs, including kidney, leading to tissue damage and function loss. There is a high unmet need for better treatment options other than corticosteroids and immunosuppressants. Phosphoinositol-3 kinase δ (PI3Kδ) is a promising target in this respect as it is essential in mediating B- and T-cell function in mouse and human.

Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms.

Addressing the safety aspects of drugs and environmental chemicals has historically been undertaken through animal testing. However, the quantity of chemicals in need of assessment and the challenges of species extrapolation require the development of alternative approaches. Our approach, the US Environmental Protection Agency's ToxCast program, utilizes a large suite of in vitro and model organism assays to interrogate important chemical libraries and computationally analyze bioactivity profiles.

Building predictive models for mechanism-of-action classification from phenotypic assay data sets.

Compound mechanism-of-action information can be critical for drug development decisions but is often challenging for phenotypic drug discovery programs. One concern is that compounds selected by phenotypic screening will have a previously known but undesirable target mechanism. Here we describe a useful method for assigning mechanism class to compounds and bioactive agents using an 84-feature signature from a panel of primary human cell systems (BioMAP systems).