Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis.

Background: Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.

Methods: We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab.

Early patient stratification and predictive biomarkers in drug discovery and development: a case study of ulcerative colitis anti-TNF therapy.

The current drug discovery paradigm is long, costly, and prone to failure. For projects in early development, lack of efficacy in Phase II is a major contributor to the overall failure rate. Efficacy failures often occur from one of two major reasons: either the investigational agent did not achieve the required pharmacology or the mechanism targeted by the investigational agent did not significantly contribute to the disease in the tested patient population.

A computable cellular stress network model for non-diseased pulmonary and cardiovascular tissue.

Background: Humans and other organisms are equipped with a set of responses that can prevent damage from exposure to a multitude of endogenous and environmental stressors. If these stress responses are overwhelmed, this can result in pathogenesis of diseases, which is reflected by an increased development of, e.g.

Construction of a computable cell proliferation network focused on non-diseased lung cells.

Background: Critical to advancing the systems-level evaluation of complex biological processes is the development of comprehensive networks and computational methods to apply to the analysis of systems biology data (transcriptomics, proteomics/phosphoproteomics, metabolomics, etc.). Ideally, these networks will be specifically designed to capture the normal, non-diseased biology of the tissue or cell types under investigation, and can be used with experimentally generated systems biology data to assess the biological impact of perturbations like xenobiotics and other cellular stresses.