Translational strategies to implement personalized medicine: rheumatoid arthritis examples

Advances in the molecular definition of disease, biomarker technologies and informatics have brought us to the threshold of a new way to individualize treatment for patients - personalized medicine. However, while the clinical translation of drug metabolism and cancer-related genomics data has resulted in accepted individualized treatment paradigms, this has not occurred as frequently or efficiently for patients with common chronic diseases such as rheumatoid arthritis. This gap between the rapidly increasing amount of disease-related genomic information and its clinical translation can be addressed through the creation and testing of personalized medicine treatment hypotheses using the same strategies that translational medicine scientists utilize to achieve proof-of-concept for drugs with novel targets. This is illustrated with three testable personalized medicine hypotheses for rheumatoid arthritis where known genetic markers in patients can potentially be used to select the most appropriate treatments and dose. Incentives resulting from changes in government and regulatory agency policies, investments in sample and data repositories, acceptance of new economic models by pharmaceutical companies and third party payers as well as more training, research support and academic opportunities for translational medicine scientists are all needed to speed up the implementation of personalized medicine for patients with rheumatoid arthritis and other common chronic diseases.