The variation is because IBD’s causes and drivers are so complex. There are some genetic elements at play, but these are not enough to explain the majority of IBD cases.
So, what’s the secret to seeing the full picture of IBD? It may come down to collecting more puzzle pieces: researchers believe that there is a range of factors that are important for disease development, including environmental, genetic and immune factors, so it is important to look at them all together.
One recent exciting line of research has focused on the role of the gut microbiome – that is, the vast ecosystem of over 100 trillion microorganisms that live inside our guts – and its influence on human metabolism, immune responses and the nervous system. These microorganisms could hold important clues to the development and potential treatment of IBD.
The complexity of the condition makes IBD management very difficult. It is estimated that about 80% of people with IBD do not experience long-term remission.1
Doctors do have some treatments at their disposal, but have no way of knowing in advance how well they will work in each patient.
If we could predict the potential course and severity of disease, then it would alter the way we treat patients: we could do so in a personalised manner, rather than over or undertreating them through a ‘trial and error’ approach.
This is where
“Our goal is to help people to be on the right therapy at the right time,” says Amanda. “The ideal situation is knowing when the right moment is to intervene and the right treatment that each person will respond to. PHC is about having a personalised care plan and using the tools we have in the most appropriate way for each patient.”
So, how far along are we in achieving that goal? While personalised healthcare has mostly been focused on finding predictive biomarkers for specific therapies – that is, specific molecules or genes that signpost an increased or decreased chance of treatment success in those who have them – the potential for personalised healthcare in IBD goes much further than this. By collecting all the various pieces of the puzzle from a variety of sources, we can piece them together and see the bigger picture.
Across clinical trials, we collect huge amounts of information – genetic information, tissue and stool samples, colonoscopy images, as well as clinical data – to assess how the disease behaves before and during therapy. We collect this information from people with IBD across all ages, genders and ethnicities. We also collect patient-reported outcomes and other sources of real-world data to truly understand how the disease is behaving in different people, and we look at promising research such as the microbiome.
All of this information is examined using advanced analytics – machine learning and artificial intelligence – to help us identify patterns and better understand what is driving the disease in specific ‘clusters’ of patients with IBD. From this we can come up with ways to deliver better care for them.
At Roche, we are looking at finding patterns in disease to help us identify who would benefit from accelerated therapy or more aggressive therapy. We are also looking at indicators to predict key symptoms such as flares, which would help doctors to escalate therapy before events worsen. Importantly, it’s about applying these patterns to each patient to ensure they are treated as optimally as possible throughout the entire journey of their disease.
IBD is an area of real unmet need where there is the potential to make a significant difference to patients’ lives.
“Every week in my day-to-day life I hear about someone else who is diagnosed or whose children have been diagnosed with IBD,” says KT Park, Global Head of Gastroenterology & Hepatology, PD Clinical Science. “I believe that we can make a real difference to these patients. We are really excited and inspired about what we can achieve and we are fully committed to doing so.”
1) Sandborn W. The Present and Future of Inflammatory Bowel Disease Treatment. Gastroenterol Hepatol (N Y). 2016 Jul; 12(7): 438–441.
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