Multiple Sclerosis Discovery -- Episode 22 with Dr. Paul Matthews

[intro music]   Hello, and welcome to Episode Twenty-two of Multiple Sclerosis Discovery, the podcast of the MS Discovery Forum. I’m your host, Dan Keller.   This week’s podcast features an interview with Dr. Paul Matthews about the Optimize project in the United Kingdom. But to begin, here’s a brief summary of some of the latest developments on the MS Discovery Forum at msdiscovery.org.   Some good news came from the pharmaceutical company Genzyme. On November 14th at 9 pm Eastern time, the FDA approved the drug alemtuzumab – trade name Lemtrada – for relapsing forms of MS. The FDA previously rejected the drug in 2013 due to concerns about study design and side effects. There is still some concern over safety, though, so the company is releasing it to only a limited number of patients. The prescription will also come with a host of other drugs to protect against harmful side effects. Researchers aren’t quite sure how the drug works, but it appears to target monocytes, T cells, and B cells.   Researchers announced a new mouse model for fatigue at the 2014 Society for Neuroscience meeting in Washington, D.C. The model works by enhancing expression of the pro-inflammatory cytokine, interleukin-1-beta. The model caused mice to reduce physical activity, without showing other signs of illness such as fever or anhedonia. Middle-aged and aged female mice were most affected by the treatment, whereas young mice showed no difference in signs of fatigue. The model gives credence to the idea that fatigue is not produced from dysfunction in the arousal system, but rather is a result of inflammation. The researchers said that they hoped the model will help illuminate the neurobiology of fatigue, the most common and debilitating symptom of MS.   If you would like to keep up with all things MSDF, please consider subscribing to our weekly newsletter. We keep our newsletter up-to-date with all of our news stories, blogs, and items from our professional and research resource sections. We’re also on Twitter; follow us at msdforum. And on Facebook, you can like us at facebook.com/ms discovery forum.   [transition music]   Now to the interview. Professor Paul Matthews is at Imperial College London in brain sciences. Last week he talked with MSDF about imaging in MS. This week we’re discussing his involvement in a UK-based project intended to optimize and personalize MS treatment.   Interviewer – Dan Keller Welcome, Professor Matthews. You’re participating in the Optimize project in the UK. Can you tell me about that?   Interviewee – Paul Matthews Well, thanks, Dan. Optimize has been an exciting journey and we’re still at the early stages, but let me tell you a little bit about it. Over three years ago, a number of us got together to discuss what the barriers to development of stratified or personalized medicine for multiple sclerosis was. We all recognized what the potential could be if we could really figure out how to target medicines to responders, we would have a way of most appropriately staging the introduction of different medicines across patient populations, not exposing people who didn’t need them to drugs of higher risk and insuring that those who did need them got them early. This is a particular problem in the United Kingdom where there is a much more formal process for progressing from first-line DMTs to more powerful agents. And, in fact, there’s also – dare I say it – I mean, a frank therapeutic nihilism and a surprisingly small number of MS patients receive treatment because of the perceived lack of benefit to many of these first-line therapies.   Now how to change this. I think what we realized is that we need to have much more granular data on the characteristics of patients being treated and how they fared after their treatment over the long-term. The data provided within the usual clinical context is not only limited, but it ends up being rather patchy over time. In order to enable that, we needed tools that would both collect data and incentivize collection of complete data of high quality. Now a note about this. We all know how to do this within the context of clinical trials, but it’s hugely expensive; it’s expensive because there are multiple people always involved to crosscheck that the data is completely acquired in each paper, and secondarily, there are audit procedures in place in retrospect to insure that this is being done. This really isn’t feasible in routine clinical practice.   A colleague of mine, Rory Collins, who has specialized in setting up very large-scale clinical trials in areas like China and India, has shown how very simple electronic tools can help both insure that data is acquired completely and that there is an electronic audit trail to follow-up on data that isn’t. What they showed is that by creating simple electronic questionnaires that wouldn’t let the questionnaire be closed unless data of an appropriate type was entered in the field, and then automatically interrogating the data for quality from center to center and following up where there were potential lapses, one could begin to incentivize acquisition of the right data and actually make it flow faster.   So how could we make this happen within the MS space in the UK? Well, what we realized is that the toolkits were all there. The EU IMI program already has funded my colleague, Yike Guo, who’s head of the Imperial College Data Science Institute, to create a tool built around a platform called eTRIKS. This is a data management environment that allows links to apps or iPads or any other peripheral electronic tool for very powerful distributed data capture. We then, in gathering together a number of stakeholder meetings which involved people with MS, the MS societies, a number of industry representatives, and what I’m really pleased to say is leads from fully 18 of the major MS centers across the United Kingdom pretty much ringing the country, together created the vision of building such an electronic tool, distributing the types of input devices across the different centers, and beginning to create a database that could be held centrally or in a distributed fashion using all the new tricks of modern IT.   The first thing is acquiring the data, the second is doing useful with it. The second thing that’s rather neat about the eTRIKS platform is that we have shown how it can be built to allow different levels of access, so that there can be access by high-level users who get to see the whole dataset, but also by specialized users who might want to see only a part of it – like a doctor interrogating it for his or her patient – or, importantly, a person with MS interrogating it to see how the data that they have entered stands relative to that that’s entered across the country by all patients; it allows people with MS to begin to gauge how they’re doing relative to others with their disease.   Now, I think the latter point is worth building on, because I think all of us have been hugely impressed by the power of sites like Patients Like Me to engage people with the disease in the dialog about their disease and make them full participants in capturing data information. With this kind of distributed data platform where doctors and people with MS can enter data whenever and wherever they are to a central database which can organize it and allow it then to be interrogated as needed, means that we can begin to think about asking patients to enter data on the fly from home. Why is this important? Well, this actually completely transforms the way in which we understand the disease, it really gives us a much deeper sense of the patient experience. Rather than sampling a patient once every month or once every six months, we can actually capture how they’re doing through a day. And if we add to this some extra sensor technologies – say, for example, about movement – we can literally do this from moment to moment.   So the vision thus is that if we can use these modern IT tools to capture data from distributed sources – from doctors using iPads, from patients using apps, from sensors that people with MS wear – we can capture data in a central resource that can be distributed to those for the purposes that they need it in near real-time, and in turn provide a common environment for its analysis. I think it’s exciting. Now we’re at the early stages, the basic tools have been designed, we’re starting to build the sensor technology. And our genuine hope is with the completion of the first set of agreements with one of the companies who’s been the first to really take a plunge with us, we’re going to be able to create a beta form of the tool in 6-9 months.   Now before closing, I do want to add one thing. This is an exciting vision but the notion of building a database is hardly a new one and many people have had it. There is something that’s special about this vision and it’s the thing that I’m most proud of that’s come together from all of these stakeholders. It’s the vision of creating a database that will be an open database; open to all researchers once it’s built, not held privately by those who built it. And I think this is what could become a game-changer. Moreover, we see that the tools that we’re building in order to create this – the IT tools, the distributed apps, and so on – are tools that the community should own and should be able to improve on. So our intention is fully, as this program develops, to release a software for open-access use as well as the data. Our hope is that even if this doesn’t provide the solution of the future, it will begin to incentivize this kind of practice where we all share this important data to work together to find solutions to this disease.   MSDF Besides collecting MS-specific data, will it also look at general health and comorbidities to see how that affects outcomes?   Dr. Matthews No. That’s a really good question. Like so many doctors now, we’re very much focused on the progressive forms of the disease. Our belief is that comorbidities make major contributions to this, and that by influencing these comorbidities we may have the biggest short-term impact on our patients’ lives. So one of the advantages of a big data capture tool is that we can capture data on all of the other disorders that afflict people with MS, as well, and begin with, again, greater granularity because of contributions from people with as well as their doctors to look at this in ways that wonderful databases like NARCOMS haven’t been able to do. This is an important task for the future and one that we really want to grasp. We’re hoping with further funding to be able to link this to bioresources, as well, and the ability to access a patient’s fluid samples for Omics analyses certainly can add greatly to this.   MSDF Very good, I appreciate it.   Dr. Matthews You’re welcome, Dan, it’s been good speaking to you.   [transition music]   Thank you for listening to Episode Twenty-two of Multiple Sclerosis Discovery. This podcast was produced by the MS Discovery Forum, MSDF, the premier source of independent news and information on MS research. MSDF’s executive editor is Robert Finn. Msdiscovery.org is part of the non-profit Accelerated Cure Project for Multiple Sclerosis. Robert McBurney is our President and CEO, and Hollie Schmidt is vice president of scientific operations.   Msdiscovery.org aims to focus attention on what is known and not yet known about the causes of MS and related conditions, their pathological mechanisms, and potential ways to intervene. By communicating this information in a way that builds bridges among different disciplines, we hope to open new routes toward significant clinical advances.   We’re interested in your opinions. Please join the discussion on one of our online forums or send comments, criticisms, and suggestions to [email protected].    [outro music]