Normally, when you hear about innovation in R&D, the focus is on the “R” and not the “D.” There’s a tendency to assume that lab-based scientists do the really creative research, whereas clinical development is a fairly straightforward, check-the-boxes activity.
There’s a kernel of truth in this otherwise unfair assumption. Just as the basic design of a car hasn’t changed much from the cars our grandparents drove — gas and brake pedals, four tires, and a steering wheel — clinical trials still have time-honored features like randomization, control arms and blinding. What is changing, though, is that we’ve begun to augment well-established methods with innovations that promise to make drug development faster, less expensive and more successful.
One big element in this transformation is broader use of adaptive study designs. In conventional clinical trials, the study protocol is carved in stone at the outset, and you execute that design with no deviations from start to finish. In adaptive trials, you can build flexibility into the protocol, which allows you to change key parameters of the study in response to incoming data.
Adaptive designs are typically employed in Phase II trials, where results from a small group of patients are used to decide if a much larger Phase III study is warranted. “Adaptive” doesn’t mean you can make any change that seems advantageous—the options need to be specified in advance. But if the early data conform to pre-established criteria, you can alter the size or duration of the study, drop or add doses to ensure more patients receive the optimal dose, or bring in more of the types of patients who seem to be responding well to the test drug.
Adaptive modifications don’t change the properties of the drug molecule being tested, but they improve the odds of testing it in the right patients, at the right dose, for the right duration to show its risks and benefits. Greater clarity can reduce the risk of false-positive or false-negative results. A false positive — thinking a drug works when it doesn’t — can lead a company to invest heavily in a large study that will ultimately fail to deliver what patients need. A false negative can cast doubt on the value of a therapy and terminate development of an investigational drug with real benefits for patients Ultimately, adaptive designs can allow us to more readily achieve a personalized medicine, in which patients most likely to benefit from a drug are the ones to receive it.
The basic idea of adaptive clinical trial designs isn’t new — clinical scientists from academia and industry have been studying this concept for two decades. But for much of that time, these methods were seen as too unproven and risky. Several years ago, Amgen decided there was more risk in not adopting these new approaches, which have the potential to deliver more successful studies more quickly and with lower development costs.
In conventional trials, this trio of priorities — cost, speed, and likelihood of success — involve trade-offs that make it hard to pursue all three goals simultaneously. For example, to boost the likelihood of success, you normally need to collect more data from more patients, which translates into added time and expense. But implemented correctly, adaptive designs can potentially avoid these tradeoffs and facilitate smaller and faster trials that reveal a drug’s true potential with more precision.
Two developments are accelerating the trend toward adaptive designs. First, the FDA is encouraging clinical innovation and partnering with companies that are willing to try new approaches. For example, Amgen has an investigational therapy for lupus, efavaleukin alfa (formerly AMG 592), which is participating in the FDA’s Complex Innovative Trial Design (CID) Pilot Program. We plan to use an adaptive design to work to zero in on the optimal dose and to ensure more patients receive this optimal dose to increase the likelihood that the right dose is selected for future studies.
A related development has been greater access to real-world data and advances in computational methods that use these data to simulate clinical trials. Simulations can’t predict how a drug will perform in an actual clinical study, but they can show how different study designs are expected to perform under different scenarios.
In designing any trial, you need to make assumptions about a whole range of variables — the effect size of the drug you are testing; how long it takes for this effect to emerge; the response rate to the placebo or comparator drugs, etc. For our study in lupus patients, we ran millions of simulations, plugging in different values for these variables and others, with the goal of finding the design options most likely to yield reliable results.
Innovative designs extend to trials where the drug itself is the element that is open to modification. The COVID-19 pandemic has underscored the importance of speed in drug development, and adaptive platform trials provide a way to rapidly test multiple potential therapies using a single protocol.
As part of the COVID R&D Alliance, Amgen is partnering with Takeda and UCB in the COMMUNITY study, which will initially test three potential treatments for patients hospitalized with COVID-19. The design is efficient because it uses same entry criteria for all agents being evaluated, and it employs a common control arm and a common futility bar to evaluate for efficacy. Test drugs can be dropped from the trial if they show lack of efficacy, and new agents can be introduced quickly to take advantage of the established protocol.
Amgen is using a similar concept to investigate sotorasib, a potential new targeted therapy for patients with non-small cell lung cancer who carry a mutated gene known as KRAS G12C. Many of these patients have failed to respond to standard therapies, so there is an urgent need to evaluate other potential treatment options rapidly. To accelerate testing of sotorasib in combination with other cancer therapies, we established a 10-arm master protocol with a highly flexible design. The goal is to detect any positive efficacy signals as early as possible, so that the most promising combinations can be quickly identified and expanded into larger studies.
Clinical innovation isn’t only a smarter way to do drug development, it is better for patients as well. The sooner we can determine whether an investigational therapy or specific dose works or not, the sooner we can either advance that therapy or stop testing it in patients. Speed and clarity are important in research, but even more important to patients searching for the right treatment for their disease.