Learnings from bringing clinical trial management in-house, and how to modernize your approach to patient-first trials with advanced data science, real-world data and other patient intelligence
One of the biggest challenges for the pharmaceutical industry is the efficiency of the product cycle for creating new medicines. A great determining step in that process is the successful execution of clinical trials. Clearly, making sure that we’ve got the right patients and principal investigators is still a challenge.
Recently, Mike Ward, Head of Thought Leadership, Life Sciences and Healthcare at Clarivate spoke with Oriol Serra (at the time of recording Mr. Oriol Serra was Head of Site Intelligence & Site Selection at Pfizer) and Simon Andrews, Vice President of RWD Engagements and Innovations at Clarivate, on the topic.
Oriol, I think it would be useful if you could give us a brief description of what your role is, and also give us a definition of what clinical site optimization is and how important it is to the drug development productivity.
Glad to hear that this is a priority to start this discussion, and because it’s important to set the tone. I’m the Head of Site Intelligence and Site Selection, which is a functional service group that sits within Site Optimization and Global Product Development at Pfizer. What we do is very critical to how we plan and design clinical trials and how we execute them. Without sites, we cannot enroll patients.
For us, the main stream of work that we do is basically to create maximized opportunities for site placement for those clinical trials, which has become very important nowadays if you consider the environment, and the trial landscape. What we do is basically map those opportunities around the sites and understand which sites have the right level of expertise, as well as understand the KPIs for those studies for the successful completion of a clinical trial or a number of clinical trials.
We do that with very different considerations. We have to acknowledge the complexity of the clinical trial and find an appropriate investigator, have the expertise to enroll those patients, and then of course, obviously, also manage those patients for the conduct of the study. We also have to do it with the consideration of equitable and inclusive access to medicines these days, which is very, very critical as well.
Beyond that, we also have to use the intelligence we collect from those sites to optimize trial designs that ultimately basically enable the acceleration of breakthrough therapies for patients in need. As you can imagine, we touch many aspects of the drug development cycle with this component of site intelligence and therefore, it is very critical.
Simon, could you introduce yourself and explain to the audience your focus when it comes to clinical site optimization?
My role is responsible for engaging our clients, understanding the applications of our current real-world data repository, figuring out how to expand it beyond our traditional and legacy businesses. Our current classic use cases are very much immediately pre-launch, post-launch, a lot of commercial analytics, brand and marketing teams, but amidst a few major changes in the backdrop.
For example, in the United States, the FDA’s view on data is evolving, especially as it concerns upstream usage for clinical and regulatory use cases. Also, there’s way more data available now than there was five or six years ago.
My role is to be responsible for engaging clients for how to navigate all of that, and how to figure out where our data can play a role in new customer sets or new use cases.
Oriol, more often than not, the activity around clinical trial management was an activity that was previously outsourced by pharma and biotech companies to clinical research organizations more focused on that topic. Why has Pfizer decided to bring it back in-house?
That’s a great question, Mike, and I’m going to give you my opinion about this, which is my personal opinion and may or may not be aligned with Pfizer’s. This is my reading of the industry. When it comes to these types of services, the industry is cyclical. We go through models of outsourcing and in-sourcing. Regardless of who does the work, where we are today, it’s basically a pivotal moment that requires modernizing and changing the way we work. That’s acknowledged by the industry.
There’s a need for a more rapid response to develop breakthrough therapies. COVID-19 definitely brought that into the spotlight. As you may have seen articles coming out from thought leaders in the industry — those models we deployed for COVID-19 vaccine — they’re looking at ways to upscale them because, yes, there was a pandemic and yes, we need to provide rapid response, but there’s plenty of other situations with other patients that are in need of a breakthrough therapy that require that we do things differently. That research landscape as we understand it, which is currently saturated as well, requires that we think differently.
There’s a large number of patients out there who have never been offered a clinical trial. There’s a lot of opportunity there to basically bring access and awareness to these therapies that are in research for those patients for a clinical trial. We saw the need to think differently when it comes about site selection and set placement to map those opportunities with considerations of the environment of the site’s overall considerations like decentralized clinical trials.
For us, it was more of a strategic decision to build those capabilities ahead of when the requirements would be put in place. Certainly, that helped us address some of the challenges that we’ve been facing over the last almost two years, head on. I think that was a strategic decision and we placed this internally and it helped us provide a rapid response to the needs of the business. As I mentioned, I believe that we are seeing that modernization of the way we do site intelligence and site selection in other areas of the industry.
Could you elaborate on modernization? What are the key elements of that modernization?
Site selection and site optimization was a service that was very operational in nature for many, many years. If you think about it, we have to start analyzing site placement opportunities differently with considerations of the environment. That means we need larger volume of data to be ingested for our models—our predictive and prescriptive models—before you can move forward with adoption of data science capabilities, data engineering capabilities.
I think we moved away, and Simon would probably agree. You barely hear any more about “big data;” you just hear “data.” That big data thing we were discussing eight years ago, it’s just data. There’s just a large volume of data we have to map and connect and inform our decisions. That’s why I require modernizing the type of service that site selection teams were providing in the past. It’s not just transactional, it’s not just operational. It’s intelligent and it requires a different set of capabilities. That’s when I require modernizing.
If you think about the requirement by decentralized trials or decentralized trial operations, that only augments that need. I think that modernizing this space is very much in alignment of what the industry requires today and will certainly augment in terms of requirements on the months and years to come.
Which patients are you listening to and how does that influence your own activities?
I’m part of a team called ‘site optimization,’ and site optimization has different services. One of them is site intelligence, but we have a patient recruitment team that works hand on hand—we lock arms and we have another team that focuses on that trial diversity. All of these activities we do, they’re orchestrated—they happen in parallel, but they’re orchestrated. We talk to investigators and we map the opportunities of placing a site using real world evidence that has the identified patient data, so we can model the requirements for the type of patient we’re going to need, and then we see the opportunities of the site.
We talk to investigators to optimize designs, but in parallel, the patient recruitment team is talking to patients to collect their insights about if the study, as we’re thinking, could be done differently. Sometimes we sit down and we think the site’s going to play out this way, but then a patient tells you, for example, “this is a blister package you’re giving me for the trial, and I have atopic artrosis, I cannot open it.”
These are the area within situations where we collected feedback from patients that provide glaring evidence. There are others, many others: flexibility on the visits, the type of journey we are asking the patient for in the clinical trial, the burden that it creates to their day to day because they have to work.
This information is certainly considered in the design process of a clinical trial alongside with site intelligence. Then again, we engage with communities that basically promote access to clinical trials to underserved communities. That takes consideration into where we place sites and how we map the opportunities to place some states versus others. It’s an orchestra of activities that certainly do consider the patient needs and requirements as one of the pivotal components of successful planning of a clinical trial.
The above is a partial transcript from this episode. Full episode questions discussed include:
- Bringing clinical trial management in-house and ROI impact
- Using advanced data science, real world data, patient intelligence and other key elements to modernize clinical trials
- How real patient needs and situations should influence the clinical trial’s approach
- Key clinical site optimization challenges and how to tackle them
- What pharmaceutical and biotech companies should consider if they’re going down the same path
Click here to listen to the full episode.
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