Personalised arthritis medicine saves lots of money
The company DNAlytics, the BioWin “Rheumagène” project, and the “RheumaKit” diagnostic kit are all contributing to this approach.
Detecting arthritis early on is one of the key challenges facing rheumatology. However, some patients present with undifferentiated arthritis, meaning that it is not possible to establish a diagnosis from the initial assessment. By analyzing our genes and other factors (such as clinical and biological factors and medical history), personalized medicine allows us to tell the difference between patients who will benefit from a particular treatment and from those who won’t. This might result in tremendous savings in the healthcare budget. The company DNAlytics, the BioWin “Rheumagène” project, and the “RheumaKit” diagnostic kit are all contributing to this approach.
Diagnosing arthritis
The aim of the Rheumagène project (2010– 2013) was to develop a reliable early differential diagnosis test to detect rheumatoid arthritis and distinguish it from different types of undifferentiated arthritis. By the end of the Rheumagène project, the partners involved had identified differential expression profiling for multiple types of undifferentiated arthritis (rheumatoid and seronegative arthritis, as well as degenerative osteoarthritis) and validated a mathematical model that calculates the most likely diagnosis.
In 2014, DNAlytics, a spin-off of the Université Catholique de Louvain (UCL), put the results of this research into practice by developing the RheumaKit. This kit makes it possible to carry out a fast, reliable molecular diagnosis. The results are analyzed on the RheumaKit.com platform. These data help doctors to prescribe a suitable treatment and prevents the illness from becoming worse. The results found by the tool are over 90% reliable.
Can we pay for the treatment?
The DNAlytics team and its clinical partners have increasingly developed the functionalities of the RheumaKit to address an important problem—providing medical practitioners with guidance on choosing a suitable treatment for patients suffering from rheumatoid arthritis. To fully grasp the importance of the RheumaKit in this field, it should be noted that the biggest challenge posed by rheumatoid arthritis is finding the right treatment. Each patient is prescribed a treatment that, while relatively inexpensive, does not work in 4 out of 10 cases on average. They then move on to a biological treatment. There are around ten such biological treatments, and all are extremely expensive, costing around €15,000 a year for the rest of the patient’s life. A proportion of the treatments available in this indication (anti-TNFs) represent the costliest item in the budget of the Belgian National Institute for Health and Disability Insurance (INAMI/RIZIV) in terms of medication.
What is worrying is that almost all of these drugs work in different ways and are on average only 60% effective. There is currently no way of telling which treatment should be given to which patient. In practice, this means that rheumatologists have to prescribe one on a trial basis and then wait between three and six months to see whether it has been effective. If the treatment does not work, then they have to prescribe a second alternative and so on until they find one that is effective. In many cases, the patient’s condition deteriorates, ultimately resulting in costs that come from their inability to work, physiotherapy, and treatments that are prescribed for nothing.
RheumaKit makes it possible to carry out a fast, reliable molecular diagnosis
To put it plainly, nearly 40% of the money spent on the costliest item in the INAMI/RIZIV’s medication budget is badly used! By way of comparison, this is more than the budget restrictions recently imposed on healthcare in Belgium. There is a real need to provide doctors with tools that will help them make a more informed choice about which treatment should be prescribed to avoid “going in blind.”
The economic scale of this problem could also be more clearly determined by analyzing the data collected by the INAMI/RIZIV or the Belgian Agence InterMutualiste (a national agency that collects and analyses data from health insurance providers). Strikingly, to date these data have remained inaccessible to the diagnostic sector, despite the fact that it would clearly be in the interests of the owners of the data, to allow controlled access to them.
No longer “going in blind”
DNAlytics is taking several actions to move in this direction. First, it launched a free mobile application (available on iOS and Android) that allows patients to calculate their disease activity scores and share these with their rheumatologist (on RheumaKit.com). Its aim is to rapidly identify whether a treatment that has been prescribed is working.Second, with the support of the European Commission’s H2020 program and the Walloon government, DNAlytics gradually increased the molecular data that RheumaKit can provide to rheumatologists. The diagnosis for each patient is made in relation to “gene families,” i.e., metabolic pathways that work together for specific biological functions, such as inflammation, bone deformation, and the immune system. Some of these families are directly targeted by existing treatments, such as anti-CD20 therapy, anti-TNFs, and anti-IL6 drugs.
However, some of these families are more active than others. Being able to visualize—for each patient—the specific level of activity of these different metabolic pathways helps to inform doctors’ thinking when choosing a treatment. Without this, they have no choice but to make the decision “blind.” Clinical trials are currently in progress with the aim of predicting patients’ actual responses to some of these treatments in a more proactive way before they have even started a treatment. These trials involve clinical centers in Flanders, Brussels, and Wallonia, as well as in France and Spain.
DNAlytics is a spin-off of the Université Catholique de Louvain (UCL) and specializes in data analysis, mathematical modeling, and machine learning. Since its foundation in 2012, this Louvain-la-Neuve-based company has been offering data mining consultancy services to pharmaceutical and biotechnology companies and university laboratories. It also develops predictive models that assist medical practitioners in making decisions, whether in relation to diagnosis, prognosis, or choosing a suitable treat
