Clinical Researcher—October 2024 (Volume 38, Issue 5)
GOOD MANAGEMENT PRACTICE
Edited by Gary W. Cramer, Managing Editor for ACRP
Andrew MacGarvey, CEO and Founder of Coronado Research, which recently launched in the United Kingdom as a consultancy-led, professional services organization operating in the clinical development arena, has been involved in the clinical research enterprise for more 25 years. In his early career, he worked for a variety of contract research organizations in statistical programming and clinical data management. His interest in technology later took him to a software company when the evolution of electronic data capture was still very new, but for the last 16 years he has focused on data analytics and growing businesses with activities in the EMEA region, the United States, and Asia.
ACRP: What is the current climate like for startup companies servicing the clinical research enterprise?
MacGarvey: The current climate for the right startups is positive. Sponsor organizations are adopting new technologies and taking them in-house to gain control of, and derive extra value from, their data. As they navigate this transition, there is a place for speciality companies—first, to provide support around change management, and then ongoing execution capability as their customers embed the new solutions and processes into their development model. One key attribute these companies need is agility; the landscape is changing quickly, and startups are able to engage with the challenges without needing to evolve existing capabilities to meet the current models.
ACRP: What was your inspiration for the direction of Coronado Research, and what previous experiences gave you the foundation for its creation?
MacGarvey: I have spent my whole career in data services; it has become more and more apparent to me that sponsors could optimize the clinical development process by better leveraging the vast amount of data and metadata associated with clinical trials and beyond. I had seen use cases going live and making massive differences to my customers, and I started to think that if we could join the dots across the various stakeholders in development, there would be a real benefit for patients.
The data are critical assets; we now have the technology to access those data and the tools to bring the insights they hold to those charged with developing treatments and, crucially, those responsible for getting those treatments into markets worldwide. The true inspiration came from a chance meeting with a father who has set up a charitable foundation to search for a cure for his daughter, who has a rare disease. He has raised millions of dollars to help advance the science and now faces raising tens of millions more to run the clinical trials. When he told me his story and his race against time, I told him I would do whatever I could to help him. Those patients out there who, like his daughter, are waiting for treatments and cures need all of us to work tirelessly to accelerate the drug development process.
ACRP: In a press release about Coronado’s activities, you had stated that the “blockbuster model isn’t working.” Why do you believe this is the case?
MacGarvey: This is linked very much to my previous answer. The current drug development process is well-worn, and we should recognize that it has delivered some amazing breakthroughs and dramatically increased safety for trial subjects. The problem is we developed the process in a world of blockbuster drugs, meaning widely recognized treatments with huge global markets.
However, science has moved very quickly; we are now in the world of increasingly personalized medicine and, in the case of rare diseases, very small patient populations. The “market” for new treatments is smaller (and in some cases very much smaller) than it was for much more common conditions. With the costs of drug development still increasing year-on-year and programs taking longer to complete, the shrinking market results in prohibitively expensive treatments—treatments which are approved but not taken up by payers, and worse, potential treatments sitting on the laboratory bench because the economics don’t work when coupled with the risk of failure.
These are all known problems; we now need to innovate to overcome them.
ACRP: Market changes can influence the success or failure of a new drug. How can we better forecast to ensure that not too much time or resources are invested into an investigational product that might fail? Let alone one that fails in the trial stage?
MacGarvey: We need to connect the dots from the regulatory stage for an investigational product through to commercialization of the approved product. As I have discussed, the challenge with people in our sector is that they all agree that we are still working in siloes in a big way. My hope is that data will be the “golden thread” that helps break through the barriers and bring teams together.
Now, we tend to focus solely on approval—especially in terms of what we need to do to get the submission accepted. When we have a successful submission, we then say, “OK, how are we going to get this into the market?” There are many examples of treatments coming in front of committees and being refused on cost grounds alone. We need to step back and work out how to get these treatments to the patients.
ACRP: What would you say are the biggest challenges facing the biopharmaceutical industry? And how can we better maximize the use of data and technology in clinical trials?
MacGarvey; There is a major challenge facing our industry, and it is here now. While we have a workable system for moving drugs from the laboratory to the patient, many of my peers agree it is becoming less fit for purpose. That is partially because of the move to more personalized or stratified medicine. But there is also the issue of ever-increasing volumes of data. We have not reached the inflexion point where we routinely use those data to accelerate the trial process; in fact, one might argue they are slowing the process down as we, as a profession, get to grips with artificial intelligence/machine learning. We must use the latest techniques to mine the data, and my ambition is to help drive change around the use of data to accelerate the development process and ultimately get treatments to patients faster.
–ACRP–
