Tom Mikkelsen, M.D., FRCPC is the President and Scientific Director of the Ontario Brain Institute (2015-present). He received his MD from the University of Calgary and completed clinical training in neurology at the Montreal Neurological Institute. Following this, he did post-doctoral training in tumour and molecular biology at the Ludwig Institute for Cancer Research in Montreal and then in La Jolla, California. Since 1992, he has led the brain tumour program at Henry Ford Hospital and was responsible for building the clinical trials program and laboratory of tumour biology. Together with other scientists, he helped assemble the Hermelin Brain Tumor Center, a leader in the understanding of the genetics of brain tumours and in the development of treatments for brain tumours. As Co-Director, he participated in the organization’s development on many levels spanning from face-to-face clinical care to clinical trials and translational and bench research.
Christa Studzinski, PhD, is a neuroscientist turned administrator. She obtained her PhD at the University of Toronto, where she studied Alzheimer’s disease and spun out a contract research organization for conducting the preclinical testing of cognitive enhancing drugs. Between 2007-2011, Christa completed postdoctoral fellowships at the University of Toronto and the University of Kentucky where she studied Parkinson’s Disease and Alzheimer’s Disease. Since 2011, she has worked at OBI to maximize the impact of neuroscience research by fostering partnerships between researchers, clinicians, industry partners, patients and their advocates.
Dr. Sibel Naska is the primary research programs contact for ONDRI at the Ontario Brain Institute (OBI). She obtained her PhD in neurobiology from Scuola Normale Superiore, Italy and completed post-doctoral training in neuroscience and stem cell biology at The Hospital for Sick Children, in Toronto. Her research focused on mechanisms involved in brain repair and connectivity as well as, stem cell therapy and high throughput drug discovery. She then held positions as research scientist and project manager of Canada-wide research projects focused on regenerative medicine and cancer research, prior to moving to her current role at OBI in 2015.
The following has been paraphrased from an interview with members of the Ontario Brain Institute on April 9th, 2018.
(Click here for the full audio version)
Where did the idea to form the Ontario Neurodegenerative Disease Research Initiative (ONDRI) come from?
One of the major themes that we push for at the Ontario Brain Institute is collaboration, so we decided to establish a pan-Ontario network of motivated researchers, clinicians, patient groups and commercial enterprises, to comprehensively tackle neurodegeneration. We wanted to get all the major groups in Ontario in this field to work together on a common platform, sharing data with open science principals, and using standardized methods that would allow us to share our findings internationally.
The second major theme is big data. The idea is to be relatively agnostic as to what data sets we should acquire because there are some interesting data driven strategies to sort out what the major targets are. One of the main targets of ONDRI is dementia and developing tools to enable us to look at dementia, not as a single disorder, but a broad spectrum of disorders that result in a similar set of symptoms we call dementia. We are trying to remove the name of the disease, and instead use a technique called deep phenotyping to characterize patients across multiple dimensions using a wide array of data points rather than just focusing on the clinical symptoms. Eventually our goal is to remove the label of the disease all-together when describing the population and use data to describe cohorts of patients, regardless of the disease or symptoms they come in with, towards the goal of identifying treatments that will be maximally effective.
Another good example is in Alzheimer’s disease (AD) where there have been many clinical trials that have failed because people with the disease are a mixed bag. In fact, there probably have been drugs that worked, but only in a small subset of the AD population. We are trying to identify those subsets using data techniques rather than the clinical, stamp collecting method that we have used in the past.
How have you been able to get everyone to be open about sharing their data?
To a certain extent it is the power of the purse, but we are also compelling partners by providing the infrastructure to facilitate this. Everyone knows that we can’t run trials in isolation, we need to do them in teams.
Also the data is patient data, so we are trying maximize the use of that data in a privacy compliant way. We have done a lot of work with hospitals and institutions to ensure that the proper security measures are in place to maximize privacy while also allowing researchers to access the data. This has also been driven by patients themselves demanding that their data be used, but in a way that is more likely to have an impact.
What role do you see patients playing in ONDRI to help speed along therapeutic advances?
One of our founding principles is that patient engagement into how research is done is critical. ONDRI has a patient advisory committee that ensures patient priorities are incorporated and that scientists get feedback directly from people with the lived experience and their caregivers.
There is also a matter of citizenship, if you live in a place like Canada with a single-payer health system you get your healthcare funded by the public. But, research also has to play in important part in that to ensure continuous improvement. The way costs are rising in healthcare, there is no question that we are going to have to become more efficient, which will come from the proper utilization of health data.
What outcomes have you seen so far from the program and what is expected in the near future?
One area has been in bringing the industry and technology sectors into healthcare. Given that healthcare these days is a data driven exercise, we have had some excellent engagement with artificial intelligence companies, including IBM Watson for drug discovery in Parkinson’s disease.
We have also been better able to inform diagnosis. Our model studying many neurodegenerative diseases together, and the integration of deep phenotyping, has enabled us to more accurately place trial participants into their correct cohorts. Having deep phenotyping embedded into the study means participants have to go through more assessments, but it does allow us to make more informed study and trial decisions. It allows us to identify patient groups that are more similar to one another which reduces the amount of variability in a trial, this improves trial efficiency and ensures better outcomes. In our current trials, we lose the significance of specific treatments because we have not been able to identify which group of patients the treatment would be best suited for.
How do you ensure that you have accurately identified a subset of patients?
We start with a broad population and then characterize the people into smaller and smaller sub categories until we get down to the ultimate subset, called N of 1, where each individual is treated as a unique case. The question is, what level of abstraction is actually informative or valuable? If we show a benefit of a drug in an N of 1 trial, that doesn’t tell us much about what would work in a healthcare system that has to deal with a large population. The goal is to reduce the groups to a uniform enough population where you can demonstrate impact in that sub-population. Right now we are still in the characterization phase, we don’t have enough data yet to be able to accurately characterize uniform subgroups.
What do you see our treatment of neurodegenerative diseases in Ontario looking like in 10-20 years?
There is no question that we are going to be able to create a more uniform series of diagnoses to ensure people get diagnosed at an earlier disease stage. Some of this may come from genomic data being collected from infants at birth to identify subgroups at risk. We are still in the discovery phase, but the pace of innovation has been accelerating. We are on the cusp of adopting routine genomic testing across the board to identify people at risk and better understand what environmental factors make some people progress more quickly than others. Our single-payer system allows us to collect a wide range of data: where people live, what environmental factors they are exposed to, their socio-economic background, etc. The successful integration of all that data, with the genomic data, is where the advances are going to come from.