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Riding the wave: The FDA and real world evidence

Published on 18/01/18 at 10:23am

Following on from this week's earlier piece on real world evidence, Jacqueline Corrigan-Curay, Director of CDER's Office of Medical Policy at the FDA, tells Pharmafocus how the US regulator has and continues to embrace the technology.

In what ways is the FDA already employing real world data? How has it affected your ongoing processes?

FDA has a long history of using real world data (RWD) to monitor and evaluate the safety of medical products after they are licensed. In 2008, FDA expanded these efforts when it launched the Sentinel Initiative which was intended to develop a national electronic system for medical product safety surveillance. As of September 2017, the Sentinel System has more than 223 million members within a network of 17 data partners and many more collaborating institutions. The data in the Sentinel system is largely claims and pharmacy data.

The current Sentinel System includes the Active Risk Identification and Analysis (ARIA) system. This system complements existing FDA surveillance capabilities that track adverse events of FDA-regulated drug products. In addition, the Sentinel Initiative created focused surveillance efforts around vaccine safety using the Postmarket Rapid Immunization Safety Monitoring (PRISM) system, and it supports regulatory review of blood and blood products with its Blood Surveillance Continuous Active surveillance Network (BloodSCAN).

FDA may begin planning for a post-marketing study in the Sentinel System even before a drug is approved based on the identification of a serious safety concern. If such a concern prompts the need for post-approval monitoring, FDA assesses whether the Sentinel System’s ARIA system can meet the specific safety monitoring need. If it can, the agency begins planning for a future ARIA study. If ARIA is not sufficient to meet the safety monitoring need, then the agency can require a sponsor to conduct a post-marketing safety study. In this way, Sentinel’s ARIA system is an integral and strategic part of the regulatory planning process to monitor serious safety concerns identified in the review of new drug applications.

In addition, FDA also may use the Sentinel System to examine safety questions identified after a drug is approved. For example, the Sentinel System has evaluated the risk of stroke after antipsychotics, the risk of seizures after ranolazine, and the risk of venous thromboembolism after extended or continuous cycle oral contraceptives.

The Sentinel Initiative also contains FDA-Catalyst, which leverages the Sentinel infrastructure and supplements it with data from interventions or interactions with health plan members and/or providers. FDA is currently supporting a randomised trial that uses data that are part of the Sentinel System to test whether a patient and provider educational intervention can increase anticoagulant use for individuals who, according to the data within the Sentinel System, have atrial fibrillation and are at increased risk of stroke. Not only is this a critical public health question, but it also provides proof of concept for conducting interventional effectiveness trials using the Sentinel Infrastructure.

In addition to using RWD in safety monitoring and assessment, RWD and real world evidence (RWE) has also been used in approval decisions. This is particularly relevant in rare diseases and in oncology drugs. In these settings, single arm trials may be conducted when randomised, controlled trials cannot be. Historical clinical data can serve as a control or reference population providing information that helps to define the untreated course of disease, and allows a comparison to the results from the single arm treatment. This use of RWD has provided support for many oncology and rare disease drugs over the past years.

What shortcomings exist in the regulatory system that could be addressed by RWE?

RWD, which are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources, can come from electronic health records (EHRs), claims and billing activities, product and disease registries, patient-related activities in out-patient or in-home use settings, and health-monitoring devices. For safety, FDA recognised that spontaneous reporting of adverse events does not yield a complete safety picture, and being able to access data on millions of individuals using a drug allows for a more informed assessment of safety risks.

Regarding evidence of efficacy, one of the challenges with traditional clinical trials, on which most product approvals are based, is the considerable investment and time it takes to complete these trials. Using RWD may help streamline and improve the efficiency of clinical trials. For example, RWD may be used by sponsors to generate data on disease outcomes and/or prognostic indicators that may lead to more efficient trials. RWD can also inform how many patients will be eligible for the trial and where those patients are located. During a trial, data from EHRs and claims can be used to populate electronic case report forms with information on the patient, including medical history and medication use, or to capture data on certain outcomes of interest, without requiring study investigators to obtain these data.

Integrating clinical research into health care systems may not only facilitate use of RWD in clinical trials but may also lead to clinical trials with more diverse populations, as barriers to participation may be lower when the research is conducted at the point of care. Analysis of RWD may also provide new information on safety and inform clinical practice.

The ability to generate RWE, however, is dependent on the availability and quality of RWD. The ability to use these data to generate evidence will depend on several factors, including whether the data can be readily accessed, whether they contain the information needed to answer the relevant clinical/regulatory question, and whether they are complete and accurate. In addition, the data in a single system may not be comprehensive and the ability to link data systems may be necessary for effective generation of RWE.

Though you already have a range of RWE-supported systems in place, how do you plan to ensure they are able to meet rising demands and expectations?

FDA is always seeking to optimise the Sentinel System so that it can meet the needs of the agency and public. For example, the Sentinel System is incorporating inpatient electronic medical record data through the addition of Hospital Corporation of America (HCA) as a new data partner. This will greatly complement the existing large claims database in the Sentinel Distributed Database.

FDA is engaged in several projects to explore what RWD can tell us about the safety and effectiveness of products, and to further expand our ability to use RWE for safety assessment. For example, as part of a big data analytics initiative at the FDA called Information Exchange and Data Transformation (INFORMED), the Oncology Center for Excellence is collaborating with Flatiron Health to examine how RWD can be used to gain insights into the safety and effectiveness of new cancer therapies.

In addition, in June 2017, FDA announced a partnership with CancerLinQ, the American Society of Clinical Oncology’s big data initiative. FDA and CancerLinQ will be using real world, aggregate, de-identified patient care data from oncology practices to understand a variety of issues related to the appropriate use of newly approved therapies. The initial focus will be on immunotherapy agents approved for melanoma.

FDA also has been exploring the use of RWE in the assessment of Influenza and Herpes Zoster vaccine effectiveness, having produced three publications on the topic. Several additional projects to explore the use of RWE in the assessment of vaccine effectiveness and on the use of such assessments to inform regulatory decision making are ongoing.

FDA is also supporting projects that will facilitate research across data networks. For example, FDA is leading an effort that includes NIH’s National Center for Advancing Translational Sciences, National Cancer Institute, National Library of Medicine, and the HHS Office of the National Coordinator for Health Information Technology to develop a general framework by harmonising several Common Data Models used by these systems. Common Data Models are ways to standardise and organise data.

FDA is collaborating closely with the University of California in San Francisco (UCSF), Carol Franc Buck Breast Care Center on developing the electronic source (eSource)/One Source programme is a standardised system to capture patient information and data electronically in EHRs, a detailed digital version of a patient’s medical history. After verification, various users, such as investigators conducting clinical trials, can share and access the data.

What obstacles do you think have impeded the integration of RWD into regulatory decision making? How is the FDA working to overcome these?

FDA has been using RWD for post-marketing safety evaluation for more than 20 years and began its work on the Sentinel Initiative close to 10 years ago and has realised the potential of RWD in carrying out its regulatory mission.

The application of RWD and RWE has been expanding over the past years, focused on areas such as use patterns of drugs, comparative effectiveness, and information relevant to payors. However, there are challenges in using RWD/RWE for drug approvals. These challenges include data completeness, accuracy, and traceability, limitations of linkages between clinical practice and research data collection methods, and limitations inherent in using observational studies to draw efficacy conclusions, are being addressed by FDA and many other organisations through pilot projects and extensive research. FDA is working with many other groups to develop approaches that may allow us to increase our ability to incorporate RWD/RWE in regulatory decision-making. In particular, FDA is working on policies regarding the use of RWE to support new indications and other labelling changes for an approved drug.

The Center for Drug Evaluation and Research (CDER) has also considered using RWD as the basis for historical controls, some of which are based on the use of the drug in practice that have been use for many years in the approval of drugs to treat rare diseases.

There are also challenges in linking data sources necessary to capture the full spectrum of clinical care while ensuring the privacy of patient’s data. Not all data elements needed for evidence generation may be available in a single RWD source. For instance, mortality data are often not completely captured in health care data and their availability from other sources may not be timely. Likewise, measures of depression and the other elements of most other disease scales used in regulatory trials may not be captured in clinical records and RWD would need to be supplemented if those endpoints are used.

FDA continues to collaborate with industry, academia, CROs, and other government entities and stakeholders through public meetings and workshops to identify the key issues that will need to be addressed to realise the potential of RWE.

Do you think common standards need to be clarified on the use of RWE before it can be widely adopted in the regulatory system?

The benefits of harnessing these data will depend on being able to integrate systems and have the data elements be consistent across systems. The use of common standards would enhance the flow of data from clinical to research databases, which would increase ability to use such data for regulatory purposes

What other benefits could wider use of RWE bring to drug regulation? What challenges do you think wider adoption presents?

There may be additional benefits to using RWE in drug development, both in developing and conducting clinical trials. By using the appropriate data sources and study designs, RWE may be able to provide additional evidence regarding the safety and effectiveness of a medication after it is on the market. However, to achieve this goal, it will be necessary to obtain access to high quality data with appropriate controls to protect patient privacy. In addition, as we move toward more patient-focused drug development, it may be necessary to access data sources other than those generated through the health care system which largely reflect a provider’s assessment of the patient’s experience, for example mobile technologies might be used to capture patient reported outcomes if such data are not captured in available sources of RWD. 

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