OR WAIT 15 SECS
That was the collective vow sworn by Big Pharma last December following Pfizer's $1 billion–down–the–tubes withdrawal of the cholesterol compound they had touted as the most important drug of the decade. The question is, What's the right organizational construct to support innovation-or at least to stop Phase III failures?
That was the collective vow sworn by Big Pharma last December following Pfizer's $1 billion–down–the–tubes withdrawal of the cholesterol compound they had touted as the most important drug of the decade. The question is, What's the right organizational construct to support innovation—or at least to stop Phase III failures?
The large-cap pharmas are all carrying out interesting experiments in how they do R&D, hoping to save time and money at no cost to quality. With Januvia, its novel type 2 diabetes drug, Merck has shown that it can be done. With a new CEO, who believed in bringing all the voices in the company together as one, and the implementation of an integrated new R&D model, Merck got Januvia to market in record-breaking time. Now, a mere six years after it was born, the drug stands to earn $762 million in sales.
Gary Herman, executive director of Merck's Department of Experimental Medicine. Herman spearheaded early development of Januvia and is now bringing his ideas to additional therapeutic areas.
That achievement prompted us to start Pharm Exec's "R&D Innovation" series by interviewing Gary Herman, the executive director of the firm's year-old Department of Experimental Medicine. Herman spearheaded the Januvia early-development program—and is now charged with working his magic across Merck's many therapeutic areas.
Lesson number one: Change is never easy. And the process of getting better products out faster has as much to do with corporate culture as it does with science. Merck's storied scientists must now work in new ways—collaboratively, and with a ready acceptance of failure and ability to move on...quickly.
When did you first realize that the old way of doing R&D was no longer working and needed to be fixed?
It's been an evolution. Back in 2001, I started work on our dipeptidyl peptidase IV inhibitor—which became Januvia. We were significantly behind in this area of type 2 diabetes. We began to think about how we could do things much faster to determine whether our compounds were working. We used single-dose studies in patients with diabetes, giving them a glucose challenge. We then measured all kinds of biomarkers to figure out what the optimal dose would be. We were able to shave more than a year off of our development timeline. With Januvia, there was a company-wide recognition that we could really do things much faster.
How long did it take to get Januvia from lab to market?
The compound was born in early 2002. It was on the market at the end of 2006.
Four years—that's less than half of the drug-development process.
People worked very hard. It was prioritized. We recognized we could use experimental medicine to make a much faster determination about whether a compound was going to work. In fact, most drugs that go into man are not going to work—only about 8 percent end up working.
That was when I started to develop an interest. Lots of synergies were going on in the company. In 2004, 2005, there was a decision to have some of us working full-time trying to develop and integrate the discipline into how we do drug development.
Are there pivotal points in the discovery process where the biomarker approach is most useful?
The sweet spot is early in clinical development: After you've gone into human beings, you try to determine as early as possible whether the medication is likely to have a benefit. You're getting some confirmation of the effect on the biology that things are happening in a desirable way, but it's not proving this is doing everything it's going to do as a medication.
That's the first step. We see it as a guide to internal decision making. We can then pick out those drugs likely to work and focus our resources on them. By eliminating the drugs that don't seem to be working, we're going to increase the likelihood that we can get some innovative medications to patients.
Which therapeutic areas have seen the most progress in the application of this approach?
Diabetes is probably Merck's most mature biomarker state, because glucose is easy to measure. It's very accessible. The linkage between acute changes in glucose and long-term changes in glycemic control are very apparent. It's not hard to convince people if you see a drug that lowers glucose right away that it's probably got a good chance of working chronically. Some other areas are less mature.
Would you ideally like to apply this across the board?
Yes, to all therapeutic areas. Certainly some are more technologically challenging. But in many cases, it's more a cultural challenge: It's about learning to become comfortable at making decisions with a little bit less information than we've been accustomed to. Given the odds of success, we really have to look at a lot of different kinds of mechanisms. And very early on in the process, we have to try to pick out those that are likely to have benefit—rather than spending time on things that are not paying off.
What's an example of researchers learning to be a little more comfortable with having a little less information?
Scientists are wired to try to prove things. Particularly at Merck, we pride ourselves on our scientific rigor. Doing absolutely perfect experiments means looking at it from every angle, collecting as much data as you can. To do that requires a substantial effort and substantial resources. The key is, What's enough information? And what's going to give us reasonable certainty that something is—or, more likely, is not—working?
At Merck, we talk a lot about the 80/20 rule. It's about confidence that we're on the right path. There's a small chance we could be wrong, but we don't have to be perfect in that initial decision. If something is working, we can then start to be much more rigorous about getting the proof. It allows us to prioritize which agents we are going to full-court press on.
So is the goal to introduce a lot more compounds into Phase I than in the past, and then, in Phase II, the bar gets raised much higher?
You try to look at as many mechanisms as you can that appear to be working in animals and through all of your preclinical validation. Almost everything Merck ever takes into man looks great on paper—yet at the end, we're left with only an 8 percent success rate. By eliminating the compounds that don't appear to have benefit very early using biological or imaging or even molecular profiling endpoints, the probability that something is going to work in Phase II will be higher.
What kind of mind-set change does the researcher have to go through, and how do you reorganize the team to incentivize that?
People need to have a portfolio mind-set. We need to be asking, What is our aggregate success going to be if we look at a lot of things in a lot of therapeutic areas and take out those that look like losers as fast as possible? This is opposed to thinking, I'm only working on this one project, and I have to keep this project alive no matter what. Ultimately, people begin to see that it's the best thing in aggregate that matters. They might be personally disappointed if an individual program they're working on has to stop, but the overall mission for Merck is to get medicines to patients.
We're trying to create rewards for getting to proof-of-concept in the franchise or therapeutic area, and not just have a molecule go into man. Peter Kim, our president of research, has talked about rewards for failing fast—for making a no-go as early as possible based on biomarkers. We're also trying to share with people how expensive it is to delay making those early decisions. The understanding is, We can do a lot more without spending a lot more.
How far along are you are in the overall process of implementing this across your entire R&D?
We're off to a great start. We launched in 2006. We now have 40 individuals in the group, including physician scientists for every therapeutic area, and we have managed to become very well integrated into the fabric of the organization. We've recruited individuals who are really passionate about translational science and about collaboration—the two pillars of this approach. And we have had some successes in terms of clinical studies that will give Merck the opportunity to make decisions earlier. We still have to see whether those will play out in development.
You've had some success with diabetes, your test case. There must be a lot of activity there now.
Diabetes is an important focus for Merck. The use of experimental medicine has been very well integrated into our thinking. And there are other areas, mostly internal-based medicine, where the markers and tools are fairly proximal. They are there, and it's just a matter of using them. The cultural challenge is having scientists who were used to working for years and years on a program, now having to be able, maybe after a single dose, to say, "This doesn't appear to be working; let's stop. Let's work on something else."
And on the other side, in late-stage development, people are trained to think about using traditional clinical endpoints—symptom scores; global quality of life; or surrogates accepted by the FDA, like blood pressure or hemoglobin A—and now we will make decisions using a biomarker. A pharmacological challenge requires some getting used to when it doesn't feel as certain as those other approaches. But we're getting there.
With scientists you're really challenging a deeply held value in the tradition of proof. Yet you're asking them to trust—or trust and validate.
Validation is a very high bar. That means you've proven that your early measure predicts outcomes. Very few things are truly validated. We use the term qualify. And that means we've got evidence that gives us confidence and a reasonable early read on what's happening with the biology.
We also are asking, Why do you think this is going to work in the first place? What is your hypothesis based on what you know about the biology and what you saw in your preclinical work? Let's corroborate the hypothesis in humans. Then let's plan to turn this into a drug. People gravitate a little more to that, because that corresponds to their instincts to do good science. We get traction there.
I think, too, that when people become aware of the consequences of not making these early decisions on our mission, they start to have the courage to change their thinking.
Since you're putting experimental compounds into human bodies in a different way, are there different safety concerns in this model?
The requirements for safety and monitoring in this model are no different from the old ones. We are complementing traditional approaches by getting additional information early so that we can get some insight about the biology. It still goes through all the rigorous toxicology that any other small molecule or biologic would go through. We still do monitoring in patients very, very carefully in the early stages. And if we see a positive outcome through an experimental endpoint, we still then do all the traditional approaches. So we're not lowering the safety bar. We're getting additional information early.
Does this process change the kind of information you're providing FDA for drug approval?
At the moment, it is not intended to alter the approval process. We are first trying to impact our internal decision making. The data packages provided for a drug that's being submitted will be the same. If anything, there will be more understanding about the biology and the mechanism of action. Someday, this may provide us a way to create additional surrogate measures that can streamline the process, but it is not a near-term goal here.
Given all the work going on at Merck in the R&D division, is there a parallel wholesale innovation taking place in other areas of the company?
Our CEO, Dick Clark, talks about "one Merck" thinking. The initiatives that included use of experimental medicine go from end to end—from how we pick targets all the way to patent expiration. So we're breaking down the silos. There's a lot of communication and collaboration.
Describe what made the development of Januvia so fast.
It typically takes about 10 years from target identification to filing. With Januvia, there were a number of elements that allowed us to cut that time in half. One of them was that we had the right people who worked seamlessly across boundaries. Discovery and Clinical and Chemistry worked together as one Merck.
That's how innovation occurs: You bring diverse, talented people together and put the problems you are trying to solve on the table. We had real alignment; we had our strategy worked out ahead of time. We knew what we would do if we saw a positive outcome, what we would do if we got a gray-zone outcome, and what we would do if it didn't work. And everybody was all ready to go.
We relied very heavily on that early single-dose experiment in type 2 diabetes. And that saved us more than a year because we went from Phase I directly into large dose-range finding trials in patients. And, in fact, the dose that we predicted from that early Phase I study was, in fact, the clinical dose. We went into man in 2002. We were in Phase IIb in the early part of 2003, which is very, very fast.
So it requires some luck, the right people, and an experimental-medicine mentality. It also helped that our Phase III program had very good alignment between the clinical and the commercial sides to make decisions about what was essential to be part of that package. And you make choices. We didn't do every single study that could be done. We did the essential studies. We focused on safety and tolerability and efficacy in the most critical setting.
I guess the devil's advocate argument against this whole experimental-medicine approach is the Viagra story: a compound for diabetes that wasn't looking so hot but was in Phase III anyway, and then that surprising side effect turned up, so to speak. Do you have to address this argument?
You know, you can keep hoping for the winning lottery ticket, but we're scientists, and we have to have think about our research from a portfolio-management perspective. There's always going to be feelings like "Wouldn't it be great if..." or "We might miss something unless...." That's just part of the scientific process. But if each scientist thinks in aggregate and in terms of meeting our mission, we're still going to be much better off.
When will we be able to see evidence of this model's success by looking in your pipeline?
This is going to take a little while given the cycle time. We're developing metrics internally so that we have leading indicators: Are we making more decisions early? Are we stepping up and showing the courage to make the early decisions? Our initial goal is making more early decisions. But, ultimately, our goal is getting more drugs to patients.
And if you say one isn't working based on certain information and that you're going to take it off the board, then there are always other candidates, right?
Absolutely. There are lots of interesting, compelling targets—many more targets than drugs—so you have to find the one that has the greatest potential to help people. And each one always look good on paper.
It's strikes me that "failing faster" is like the concept of early detection with disease. As a patient, you're constantly being told to check out a symptom as soon as possible. Presumably, then, some of the same resistances that make early detection difficult make your approach difficult.
I tell scientists: Look, when the molecule is born, its fate is determined. It's up to us to find out what the truth is, but in the end we can't change its fate.