Dr. Rich Gliklich founded Outcome Sciences (now Quintiles Outcome) in 1998. The company was a spin-off of his lab at Harvard University and was acquired by Quintiles in 2011.
Where does Quintiles Outcome fit in the larger organization?
Quintiles Outcome is the post-approval and late-stage division of Quintiles. So our focus is on how products, therapies, devices, and procedures function in the real world with regards to safety, effectiveness, value, and quality-of-care.
After spending years and millions of dollars on research, what makes companies come to you for continued testing?
In the early phases of approval, getting to the regulator is something of a solitary focus, with a single stakeholder: the regulator. Once you get past approval, the other really critical stakeholders come more clearly into view. They are the payer, the physician, the patient, and in reality, a lot of people are starting to bring that view much earlier into development. It’s almost a two-part process, where a drug can be approved, but if it’s not reimbursed, then it has little value for the development company.
That’s really been the evolution in medicine over the last hundred years. There has been an increase in the complexity of the stakeholders and decision-makers who are looking over events and the growing spectrum of questions. Questions have moved from, “Does it work?” to “Does it work compared to…?”,“Is it better?” , “What’s the quality-of-life impact?” You also see more questions crossing boundaries from different stakeholders. The Food and Drug Administration, for example, is now concerned with effectiveness. Dr. Janet Woodcock [Director, Center for Drug Evaluation and Research] discussed this at the Quintiles Post-Approval Summit. They really want to understand the benefit harm relationship. Not just the harm aspect.
And what kind of challenges do these late-stage/post-approval studies pose that you don’t see in earlier-stage trials?
There are two areas where they differ substantively. One, is the types of patients, you really want a broad swath of patients who are older, sicker, have comorbidities, and are more ethnically diverse. And that’s not something you see in earlier phase trials.
And the second thing that you want is more research-naïve investigators. So you want doctors who are more reflective of Main Street clinicians. As soon as you get them, you spoil them in a way. You’re constantly seeking a standard of care in action that is closer to the experience that you see in real-world use. You don’t want just Mayo Clinic practitioners, you want the doctor down the street.
How large are these trials compared to early stage studies?
They tend to be larger, where a Phase 2 or 3 might be anywhere from hundreds to thousands of participants, these studies start in the thousands and might work up to the tens of thousands.
You discussed earlier how companies need to take effectiveness into account. In the Affordable Care Act, there are the beginnings of comparative-effectiveness research. What do you think that’s going to do for the post-approval marketplace?
Up until now, even with the discussion of comparative-effectiveness, most of the focus in the post-approval world has been on safety. Now, with Affordable Care, there’s going to be a much stronger emphasis on demonstrating effectiveness as step one. Comparative effectiveness is even harder; you need bigger studies and more controls. But that’s viewed in the legislation as a solution to the cost problem. The idea is that there’s some unknown characteristic in our healthcare system that would be improved by more knowledge about what works. And that this would avoid unnecessary costs that we’re currently spending. That’s a theory, but one that many people think is true. But that’s why there’s such a focus on effectiveness in general and comparative effectiveness research for making decisions between two products and trying to limit, if you will, the formulary that providers are using.
What constitutes a positive result for a post-approval trial?
This is where the nuance is really key. There’s more decision makers with different questions. There’s growth in new methods; we have the randomized trial, new methodologies in prospective observational research, new comparative effectiveness manual to guide how prospective observational research should be done. And now we have this new area of Big Data, looking at big databases. The trick is that it depends on the question; different methods are appropriate for different questions. So if you’re trying to make a decision on whether to approve a drug, that’s a major public health impact, so your standard of evidence may be stricter than if you’re on a formulary committee trying to decide if drug A or drug B will be Tier 1 or Tier 2.
The other issue is that none of these approaches, randomized trial, observational study, database analysis, alone is enough to give all of the stakeholders the information they need. If you look at Medicare, they have made several noncoverage determinations based on the fact that the randomized trial for a particular drug had good results, but didn’t include Medicare patients and therefore aren’t useful to making the decision.
In essence a positive trial is a statistical probability that the answer is more likely to be correct than not correct. But the method that you use and what you’re willing to accept as a possibility that even with a positive p-value that the study could be erroneous based on confounding and bias will differ based on what the decision is you’re trying to make and how timely that decision has to be.
For example, if you have a new procedure for bone marrow transplantation, you need to have a definitive study and you’re willing to wait for it because peoples’ lives are at stake. You’re probably going to have a large trial with a very strong probability of showing an effect or not.
Has technology changed the way you do these studies?
We’re really trying to leverage technology to make life easier for both the investigators and the patients. We’re doing a study in diabetes that is leveraging electronic health records. Patients are ID’d through their records and invited to participate. If they decide to participate, then that data is pulled from the electronic health records system. The patient interacts directly with the study through electronic patient reported outcomes over e-mail. That lowers the burden on both doctor and patient; they’re able to do this at home.
What do you see in the future of post-approval?
As I mentioned earlier, we have stakeholders with different needs. Both Dr. Woodcock and Dr. Stella Blackburn from the European Medicines Agency said that their regulatory agencies are very interested in effectiveness information. The FDA is looking at benefit and risk and in Europe they are launching post-approval efficacy studies to mirror post-approval safety studies. So we’re seeing a broadening interest in effectiveness and safety starting with regulators and it will expand from there.
More specifically, Quintiles Outcome is trying to be the most comprehensive provider of evidence including large, practical randomized trials; large prospective observational studies and registries; and the use of Big Data. Through these assets and capabilities we can help our clients decide on the best combination of approaches to get to the right answer in the most timely way.
Filed Under: Drug Discovery