Breaking Down Barriers Between Clinical Trials and Clinical Care: Incorporating Real World Evidence into Regulatory Decision Making - 01/28/2019
- Speech by
Acting?Leadership RoleCommissioner of Food and Drugs - Food and Drug Administration
Remarks by Scott Gottlieb, M.D. as prepared for the Bipartisan Policy Center conference
Digital technologies are one of the most promising tools we have for making health care more efficient and more patient-focused.
New streams of real world data (RWD) gathered from electronic health records (EHRs), lab tests, wearable devices, insurance claims, and even social media can provide important evidence on product safety and effectiveness in settings or populations that may be very different than the information gleaned from registration trials used for approval.
This isn’t an indictment of the randomized controlled trial.
Far from it.
It’s a recognition that new approaches and new technologies can help expand the sources of evidence that we can use to make more reliable treatment decisions. And it’s a recognition that this evidence base can continue to build and improve throughout the therapeutic life of an FDA approved drug or medical device.
In many cases, these tools also help make prospective trials more efficient and more reflective of how care is delivered in the “real world”.
To take one example: Pragmatic and hybrid clinical trials, including decentralized trials that are conducted at the point of care – and that incorporate real world evidence (RWE) -- can help clinical trials become more agile and efficient by reducing administrative burdens on sponsors and those conducting trials, and can allow patients to receive treatments from community providers without compromising the quality of the trial or the integrity of the data that’s being collected.
RWE also has the potential to make America’s health care system more competitive and efficient as validated outcomes measures based on real world data are incorporated into value-based payment contracts.
This is all happening now across the healthcare industry.
Providers and other stakeholders are already exploring effective ways to leverage electronic tools to gather vast amounts of health-related data from EHRs and other sources. And they’re working on ways to use advanced analytics, including machine-learning algorithms, to transform data into evidence that can be used to help guide clinical decision-making or inform innovators during the development of medical products.
At FDA, we’re committed to advancing ways that data leveraged from these streams – typically called real-world data – is transformed into evidence using transparent data standards that can give all stakeholders confidence in the data’s provenance, so that more of this data can be used to generate the evidence that the agency needs to improve regulatory decision making. Advancing RWD into regulatory quality real world evidence is a key strategic priority for the FDA.
In December, we released our 2019 Strategic Framework that outlines how we’ll continue to advance these opportunities in the coming years. And how we plan to use these new tools and opportunities to advance our review of product effectiveness and post-market safety.
RWD and RWE are already being used extensively for postmarket monitoring of the safety of products during their use in real world settings. To give just a few examples, our use of RWD and RWE, derived from our Sentinel system, eliminated the need for postmarketing studies on nine potential safety issues involving five products, making our post-market evaluation of safety timelier and more effective.
And we’re just getting started incorporating these methods and tools into more routine elements of our review process.
Traditional postmarket studies typically require years to design and complete and cost millions of dollars. By encouraging the use of RWD and RWE, we may be able to provide patients and providers with important answers much sooner by potentially identifying a broader range of safety signals more quickly.
The use of Sentinel and similar tools will increasingly let us shift some studies and data collection to the point of care, making collection of data and development of actionable evidence more efficient.
To take advantage of these opportunities, the identification, evaluation, and addressing of postmarketing safety issues has now become a much more multidisciplinary venture within the agency. In addition, these endeavors increasingly involve closer interactions between the FDA, global regulatory authorities, and innovators.
Our work applying RWE to effectiveness decisions is also advancing. In the oncology setting, for example, we currently have new drug applications under review where RWD and RWE are helping to inform our ongoing evaluation as one component of the total complement of information on effectiveness that we’re evaluating.
This is especially relevant when it comes to the evaluation of treatments for uncommon conditions, such as very rare tumor types.
In appropriate cases, we’ve also accepted RWE to support the evaluation of efficacy in product approvals -- using data from registries, natural history studies and chart reviews -- to establish a comparison arm in single arm trials in oncology and rare diseases.
RWE captured in this way, throughout the totality of a product’s post-approval lifecycle, has been a significant aid in informing both the development of new products and changes to existing products. We’re also exploring ways to better harness patient-reported information. This includes our recently announced MyStudies mobile application.
There are additional advances we’re exploring under our new Framework. These involve the possible use of RWD and RWE to help support revisions to labeling about drug and biologic product effectiveness or safety. These changes could include adding or modifying an indication, such as a change in dose, dose regimen, or route of administration; adding a new population; or adding comparative effectiveness or safety information.
The Framework involves the establishment of demonstration projects and stakeholder engagement, as well as the use of internal processes to bring senior leadership input into the evaluation of RWE and promote shared learning and consistency in applying the Framework. We’re also working to develop new guidance documents to assist sponsors interested in developing and using RWE.
Our Framework for Real-World Evidence Program will apply a consistent strategy for harnessing these tools across our drug and biologic review programs. The Framework is aimed at leveraging information gathered from patients and the medical community to inform and shape the FDA’s decisions across our drug and biologic development efforts. This Framework will form a cornerstone of our efforts to advance the use of these tools. And in 2019 we’ll advance other new initiatives to better leverage RWD and RWE in our programs.
Our Framework also calls on us to develop new guidance on considerations for designing clinical trials that include pragmatic design elements as one tool for generating evidence of effectiveness for regulatory decisions. And we’ll also evaluate the potential role of observational studies in contributing to the body of evidence for demonstrating drug and biologic product efficacy. The goal is to develop a path for ensuring that RWE solutions can play a more integral role in drug development and regulatory life cycle at the FDA.
Today, I’m announcing four additional activities that’ll help FDA and stakeholders advance these opportunities for the benefit of patients.
First, to support the seamless integration of digital technologies in clinical trials, this year we plan to convene a stakeholder meeting to help develop a framework on how digital systems can be used to enhance the efficient oversight of clinical trials. These technologies present important opportunities to streamline drug trials and improve data site integrity by remotely monitoring data trends, accrual, and integrity over the course of a trial. To take one example, traditional on-site monitoring of each clinical site to evaluate study conduct and perform 100% source data verification is highly resource intensive. It accounts for up to a third of the total clinical trial cost. But traditional on-site monitoring doesn’t guarantee data quality. Findings must still be evaluated to determine their true impact, and whether additional actions are needed.
By working collaboratively with the clinical trial community and patient groups, we can develop scientific and technical standards for incorporating new technologies into clinical trials to make them more agile and accessible to patients and regulators. This includes through the use of early quality checks to ensure that we have the reliable data we need to confidently assess patient safety and product efficacy.
To take one example, remote- and risk-based monitoring can help to provide better regulatory oversight. These approaches may lower development costs, and enable more trial sites to answer important scientific and clinical questions as a way to improve patient care.
Second, we also see great potential in using digital technologies to bring clinical trials to the patient, rather than always requiring the patient to travel to the investigator. This is an FDA priority.
We believe that more accessible clinical trials can facilitate participation by more diverse patient populations within diverse community settings where patient care is delivered, and in the process can generate information that’s more representative of the real world and may help providers and patients make more informed treatment decisions.
This approach, called decentralized clinical trials, can help move prospective collection of data from the real-world -- including randomization -- outside of the brick and mortar boundaries of traditional clinical research facilities, tapping into not only EHRs but additional digital health tools like wearable devices.
To support the development and adoption of decentralized trials, the FDA established a formal working group on decentralized trials, and we’re working towards writing a guidance document to outline our approach. We’ll have much more to say about this in the next month.
Third, we’re also exploring how reviewers can have more insight into how labeling changes inform provider prescribing decisions and patient outcomes. The FDA’s Information Exchange and Data Transformation -- or INFORMED -- is using RWD to examine the impact of a recent FDA labeling change for two approved products from weight-based dosing to flat-dosing of immune checkpoint inhibitors. This project is focused on how community practices are adopting the flat dose after the labeling change, and factors that may affect adoption.
As the volume, velocity, and variety of real world data reaching the agency increases, we have an opportunity to use new software-based machine learning algorithms – like natural language processing or deep learning – to help develop regulatory science tools like surrogate endpoints or digital biomarkers that can be used to guide more efficient development programs. Machine learning and Artificial Intelligence skills are highly sought after, and it can be difficult for the agency to compete with the lucrative pay packages available in the private sector.
But the FDA has many staff with deep quantitative expertise. And we want to help develop and retain the agency’s human capital in these fields. Among other steps, in 2019 INFORMED is going to be working with the medical product centers to develop an FDA curriculum on Machine Learning and Artificial Intelligence in partnership with external academic partners.
The aim of this program is to improve the ability of FDA reviewers and managers to evaluate products that incorporate advanced algorithms and facilitate the FDA’s capacity to develop novel regulatory science tools harnessing these approaches. The agency will also pilot a competitive fellowship program under INFORMED in Artificial Intelligence and Machine Learning that allows post-doctoral fellows from leading academic centers to join the FDA for two-year fellowships to develop high-impact AI-based regulatory science tools by working closely with mentors from the agency’s medical product centers.
These are just a few of our ongoing efforts in RWE and RWD.
The FDA’s Oncology Center of Excellence (OCE) is also working with Friends of Cancer Research, the National Cancer Institute, and other stakeholders to harmonize reference standards for assessing tumor mutational burden (TMB), -- as determined by multiple proprietary assays -- to help identify cancer patients who are more likely to respond to immunotherapy.
Harmonizing the measurement of tumor mutational burden across commercial assays used in routine oncology care can help reduce treatment variability and improve the utility of TMB as a potential biomarker for enriching clinical trials testing immunotherapies.
OCE is also working on a project exploring whether it’s possible to use real world endpoints, like time to treatment discontinuation (TTD) as a potential real-world endpoint for pragmatic randomized clinical trials for FDA approved therapies in the postmarket setting. Through Project: Switch, OCE is investigating whether well-matched contemporaneous synthetic control arms based on prior clinical trials can be used to make inferences regarding the effect of a new drug, or whether a synthetic control could be used to compare data to active control arms in ongoing randomized controlled trials in rare tumor types where the standard of care remained stagnant, and the prognosis is especially poor.
By engaging with multiple stakeholders through collaborative forums and working closely with our agency partners at the Centers for Medicare and Medicaid Services and the National Institutes of Health, the agency can help promote more transparent standards for curating data, interoperability, and RWE generation that can help ensure that every American patient – no matter where they live, - benefits from the full potential of these technologies to make our healthcare system safer, smarter, and move patient focused.