Interview: Biomarker/Endpoint Guidance and why they advance science
Colin Miller is Co-Founder and CEO at The Bracken Group, a life science consultancy providing high-level expert support in imaging, regulatory, due-diligence, and more. A scientist by education, Colin has written more than 70 peer-reviewed scientific and medical publications, published 3 books, and holds 3 patents.
Tell us a bit about your background
Most of my career has been focused at the intersect of medical imaging in clinical trials, though my other areas of expertise include clinical development strategy, osteoarthritis, radiopharmaceuticals, and nuclear medicine. I previously served as Senior Vice President of Medical Affairs at Bioclinica (now called Clario) and also worked at Syntex Pharmaceuticals (now part of Roche) and Procter & Gamble Pharmaceuticals. Much of this experience coalesces in The Bracken Group: we’re a life science and biopharmaceutical partnership with an analytics and marketing division.
What are biomarkers? What are surrogate endpoints?
Simply put, a biomarker is something we can measure or quantify in the body. Specific examples of biomarkers include everything from x-ray imaging to genetic tests, to blood and blood pressure readings. For purposes of clinical data, biomarkers are used to evaluate how the body responds to a treatment for a certain condition as a quantitative assessment, not so much for measuring if that person feels better or worse.
A surrogate endpoint is a biomarker used in clinical trials to provide a quantitative endpoint. It’s essentially a substitute for how a patient is functioning. Surrogate endpoints tell us if a treatment is working or not, but they aren’t always true measurements of how well a treatment works since that can be more subjective—e.g. “How do I feel today?”
Ideally, though not always, a surrogate endpoint tells us if a treatment is effective earlier that we would have known by just using clinical signs and symptoms. This is important within clinical trials because if it is in fact working, that can lead to earlier approval of new drugs to help treat fairly serious diseases.
How do they help measure or advance clinical findings?
Since biomarkers help quantify the effects of treatment in the body, they can be used to predict how the body is responding, or not responding, or even if it’s being made more susceptible to disease. For clinical trials, a surrogate endpoint must be effective on the same biochemical or functional pathway to which the treatment intervention is applied. These endpoints provide essential insights, both early on and throughout the course of disease or treatment, as to whether or not a new therapeutic treatment is doing its job.
Can you provide some examples where you’ve seen biomarkers used in a specific disease category or diagnostic area?
ECG is a good example of a biomarker and surrogate endpoint; another common one is cholesterol measurement. High cholesterol is a very good marker of increased risk of heart attack and stroke, and yet most people do not notice their cholesterol level until there is a medical emergency or heart condition. The development of statin drugs ensured that knowledge of our cholesterol levels became standard clinical practice as there was now something to treat it.
What benefits and challenges are associated with imaging in a DCT?
About half of all trials require medical imaging in some manner. Even something as ubiquitous as osteoarthritis (OA) or rheumatoid arthritis (RA) require radiographs (X-rays) and MRI for the endpoints to develop disease-modifying drugs and not just analgesics. Consequently, the benefit to having medical imaging in DCT is the significantly increased number of trials and therapeutics that can be entertained in a DCT model. It opens up a world of possibility.
As for challenges, trials involving medical imaging are generally more expensive than those without imaging, so the cost benefit approach must be carefully evaluated. However, one of the major drivers of going to DCT is the challenge of patient recruitment and the time for development. Once this is taken fully into consideration—and the lost ROI on the lost time of the product on the market, etc.—the cost of correctly managing the imaging is actually quite minimal.
Of course, the downside to not managing the imaging correctly is the risk of missing an endpoint due to incorrect evaluation.
How do you collect wet and dry biomarkers in a DCT?
So-called “wet biomarkers” are measurements from serum, urine and other body fluids. “Dry biomarkers” are measurements from things like medical imaging, but ECG is truly a biomarker and the QT complex is a safety assessment.
There are going to be a couple of approaches to the extensive use of imaging in clinical trials. One approach is that there will be more in-home and portable systems available. For example, ultrasound units are now becoming the size of a smartphone, portable X-ray equipment is becoming compact, and there are now companies offering in-home radiographs.
For years, we have seen the use of mobile scanners built into the back of an RV or semi-trailer. While not inexpensive, the cost of patient recruitment and the need for medical imaging conducted at the patients’ convenience will tip the balance on the ROI if evaluated correctly.
How are DCTs accommodating for advancements in imaging techniques?
One of my specific areas of clinical expertise is medical imaging and, generally speaking, medical imaging has not been an endpoint in decentralized clinical trials. The current clinical trial business model is for sponsors to utilize imaging core labs, which collect and centralize the images in an approach that is not aligned with the requirements for a DCT, but for good historical reason: it’s taken several years for DCT to “come of age.” The next steps are to include some more complex requirements of collecting standardized imaging in these types of trials.
At The Bracken Group we’ve been considering these issues for some time; several years ago we designed a such a process for a client for the collection of bone mineral density using dual energy X-ray absorptiometry (DXA) in osteoporosis. The trial did not go ahead, but the framework and concepts were established, and we are collaborating with a software company to bring this approach to DCT. I am also aware of one imaging CRO that is also evaluating the change in approach.
So, while medical imaging in clinical trials has not been a key aspect of DCTs, this will have to change. As we are seeing advancements in the management of clinical trials, so we are with medical imaging—there is an explosion of trials using artificial intelligence (AI) in medical imaging and this is migrating into the clinical trial arena. I believe we will start to see a confluence of events occurring where DCT meets imaging and AI to really change the way we approach the development of new chemical and molecular entities.
Where do you see the benefit to the patient and the investigative site with DCTs, and how will this benefit drug development as a whole?
The benefit to the patient is the ability to participate in the latest development of new therapies without having to visit the clinical or hospital. With superior systems in place, patients have access to their data, including medical images. This latter aspect had not been available until companies such as FujiFilm Healthcare developed a cloud-based software program in which patients and physicians can have access at the patient and group level. Another benefit is allowing the images to managed for the more traditional independent clinical read.
How do you see DCTs, and specifically types of biomarker/endpoints, evolving in the near and extended future?
DCTs provide the ability to recruit more participants and, more importantly, to have them in a real-world setting. The challenge is that the data is not as clean as might be anticipated in a more traditional clinical trial setting.
However, an interesting new development is the use of blockchain technology with companies such as Bioveras. This is starting to appear in clinical trials as a means of helping to ensure the data is verified and non-fungible.
And leveraging AI in this environment will allow the evaluation of new biomarkers, which could provide valuable new insights into the course of disease and ultimately help lead to new drugs and devices.