Collaboration identifies tool to predict response to rheumatoid arthritis therapy

Published: 29-Mar-2018

Study published in Journal of Translational Medicine demonstrates predictive power of EpiSwitch to identify inadequate response to methotrexate in early rheumatoid arthritis

The study was a collaboration between Oxford BioDynamics, Pfizer and the Institute of Infection, Immunity and Inflammation at the University of Glasgow.

Providing patients with the correct therapy during the early stages of rheumatoid arthritis is essential for preventing long term disability.

Oxford BioDynamics, a biotechnology company focused on the discovery and development of epigenetic biomarkers based on regulatory genome architecture, for use within the pharmaceutical and biotechnology industry, has published its collaborative work in the Journal of Translational Medicine, titled: “Chromosome conformation signatures define predictive markers of inadequate response to methotrexate in early rheumatoid arthritis”.

Professor Carl Goodyear, the Institute of Infection, Immunity and Inflammation at the University of Glasgow, said: “The study we have undertaken provides proof of concept, and demonstrates the feasibility of using EpiSwitch to predict who will or will not respond to a given therapy in rheumatoid arthritis."

"This ability to determine whether or not a patient will respond to their chosen medicine may have far-reaching socio-economic implications, which would not be restricted to just healthcare costs. The sooner we achieve control of disease activity in patients, the more likely we are to decrease the risk of disability and therefore enhance the future quality of life of these patients.”

The study investigated whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from people with early rheumatoid arthritis obtained before methotrexate (MTX) treatment could assist in identifying the likelihood of a response to first line disease-modifying anti-rheumatic drugs (DMARDs).

Dr Alexandre Akoulitchev, Chief Scientific Officer of Oxford BioDynamics, commented: “There is a pressing need in rheumatoid arthritis to identify patients who will not respond to first line disease-modifying anti-rheumatic drugs. These initial results demonstrate that EpiSwitch can identify, with a high degree of specificity, those patients that will not respond to MTX."

"This has been one of the main challenges in rheumatoid arthritis management for more than two decades. Our results provide a proof of principle that stratification of response to MTX is possible and offers the potential to provide alternative treatments for non-responders to MTX earlier in the course of the disease to improve clinical outcomes.”

The study successfully showed that a CCS found in the blood samples obtained in early rheumatoid arthritis could identify patients that will not respond adequately to MTX with a high degree of accuracy.

MTX is the anchor drug for the treatment of people with new onset rheumatoid arthritis. However, a significant proportion of patients treated with MTX do not respond to therapy. It can take at least 6 months to determine if a patient will not exhibit a sufficient response to MTX; this would then lead to a change in treatment.

It is generally believed that substantial damage can occur as a result of inflammation during the early stages of rheumatoid arthritis and damage correlates very well with future disability, and loss of function. Thus, choosing the correct therapy early in the disease is very important.

Dr Claudio Carini, a member of Oxford BioDynamics’s Scientific Advisory Board, said: “Despite advances in medicine, not all patients respond favourably to drugs. A proportion of patients under therapy don't benefit from their treatment, or experience adverse reactions to the medication. The identification of a predictive signature in rheumatoid arthritis creates unique opportunities in the management of the disease, helping to identify patients that are more likely to respond to a given therapy thus reducing unwanted drug side effects."

"We believe the ability to detect predictive signatures using EpiSwitch allows the identification of responders and non-responders prior to large Phase III clinical trials."

"This can have a profound effect on the size and cost of clinical trials by eliminating non-responders and drastically reducing the number of subjects required to demonstrate effect. Our method to stratify patients may ultimately affect clinical practice not only in rheumatoid arthritis but in a wide variety of diseases, including cancer.”

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