The burden of the disease is increasing yet there is no cure
It is estimated that 9.6% of men and 18% of women over the age of 60 worldwide experience the debilitating symptoms of osteoarthritis. As people tend to live longer, and the population of people over 60 is increasing, cases of the disease are on the rise. Direct and indirect costs of osteoarthritis in the EU are already substantial: in the UK alone, total costs for adaptive aids and devices, medicines, surgery and time off work are estimated to be equivalent to 1% of the gross national product per year. The burden will be greatest in developing countries, where life expectancy is increasing and access to arthroplasty and joint replacement is not readily available. Furthermore, although there are a wide range of devices and palliative medicines available that can relieve pain and improve quality of life, there are currently no pharmaceutical products that can halt or reverse the onset of osteoarthritis.
Despite the large and growing burden of this disease, many pharmaceutical companies have reduced or altogether abandoned drug development. One of the problems is that there are currently no reliable ways of measuring whether a specific treatment is working or not. This is partly because the mechanisms which lead to the disease in different subgroups of patients are poorly understood. Moreover, although the current mind-set for treatment in this field is moving towards personalised medicine, there are no accepted methods of classifying the patients according to diagnosis methodology, prognosis and treatment plan. It is clear that a better understanding of the disease is urgently needed.
Complex algorithms to hunt for disease subtypes
By bringing together a strong team of 25 partners from European clinical centres, basic research institutes, small and medium-sized enterprises (SMEs) and pharmaceutical companies, the APPROACH consortium will combine biomedical data of more than 10 000 patients and heathy people from 8 existing cohorts into a unified bioinformatics platform. With the help of complex algorithms, bioinformaticians will comb through this central database to identify subtypes or phenotypes of osteoarthritis. These subtypes will then be validated in a longitudinal clinical study, using existing and newly-developed biological markers. Ultimately, the identification of subtypes of this disease should lead to improved drug development and diagnostic tools, allowing osteoarthritis patients to receive better, more personalised treatment.
The project will focus on knee osteoarthritis – a very common form of osteoarthritis which is a major cause of impaired mobility (particularly in women) and therefore has a high clinical burden. This form of the disease is ideal for the APPROACH database because it has been extensively examined in large groups of patients and lots of standardised data, such as x-ray and MRI measurements, is available.
Valuable tools and outcomes
At the end of the project, APPROACH will provide valuable tools, methods and definitions that will be used to optimise future clinical trials for osteoarthritis, paving the way for personalised medicine. More specifically, the main outcomes of the project will be as follows:
- an integrated bioinformatics platform which functions as a repository for clinical data, biomarker data, images, as well as storage of bio-tissues from a broad spectrum of osteoarthritis patients;
- subsets of patients based on well-defined subtypes or phenotypes of the disease;
- new biological markers for osteoarthritis based on imaging, locomotion and biochemical methods;
- validation and optimisation of the next generation imaging methodologies, human motion analysis and biochemical methods to enable more efficient diagnosis and treatment of osteoarthritis patients.
A big step towards more personalised medicine for patients
By identifying different subtypes of osteoarthritis, APPROACH will make a big step forward in helping the pharmaceutical companies develop more effective treatments for the osteoarthritis patients. Patients will reap the greatest benefits as the identification of disease subtypes will lead to improved diagnostic tools and more effective, highly personalised treatments.