Getting the right drug to the right patient

Long-term outcome in patients with rheumatoid arthritis (RA) is highly dependent upon aggressive pharmacological control of inflammation early in the disease course.  Despite the importance of selecting the optimal medication soon after disease onset, there is no clinical or biomarker predictor of drug treatment response. A genomic biomarker would be particularly useful for drugs that block the inflammatory cytokine TNF-alpha (TNF), as these drugs are first-line biological disease modifying anti-rheumatic drugs DMARDs, yet induce remission in only ~30% of patients. We hypothesize that the complete spectrum of genetic variation (from common to rare) influences response to anti-TNF therapy in a highly polygenic manner.  To test this hypothesis, we are conducting a multi-center GWAS (1) to find single alleles to influence response to therapy; (2) to build polygenic models that predict response to therapy; and (3) to develop new statistical methods that integrate genomic data with GWAS to find true signal within the noise.  We are also performing targeted sequencing of genes from the TNF signaling pathways in RA patients treated with anti-TNF therapy.

We have a number of other projects in various stages of development, many of which are funded by the Pharmacogenomics Research Network (PGRN): expression and cellular profiling in subsets of immune cells in RA patients treated with immunsuppressive therapy.

See a blog entry (here) on why we chose the term "precision medicine" over a related concept, "personalized medicine".