The modern world is home to a host of real-world data sources, from electronic health records and health surveys to smartphone apps and medical insurance claims. They offer invaluable insights on patient populations or particular disease areas that can inform the drug development process, from its early stages to post-authorization. Suitably harnessed, it offers a broader overview and understanding of outstanding clinical needs and commercial opportunities in drug development, augmenting the insights from randomized controlled trials (RCTs).
Real-world data is not just useful to keep track of an individual treatment’s progress in an everyday setting. Data gathered in this way can add significant value during the research and development of new medicines. It can be used to explore new clinical questions, identify new uses for medicines, fill in knowledge gaps on the use of medicines in real-world settings or evaluate unstudied factors influencing a patient’s outcome to improve patient care. However, developers face cost, time and capability hurdles that prevent them from fully exploiting the opportunities offered by real-world data. Without the means to access and explore such resources, developers will continue to be limited to a restricted view of a treatment’s potential impact.
Complementing RCTs
There is currently a barrier between the structured RCT research setting and the everyday medical practice setting. Both settings provide valuable data on a drug’s safety and efficacy over different time periods and among different patient groups, yet only the former is clearly standardized and defined.
RCTs have long been the well-established point of reference to evaluate new treatments. Whilst they are effective at satisfying regulators and ensuring that a drug ticks the right boxes to make it to market, the results do not necessarily translate into performance in the real world once it is authorized and being prescribed to patients. Authorization doesn’t negate the need to continue to research a treatment in the longer-term. Indeed, the value of post-authorization studies is now fully recognized by regulators.
Post-Authorization Studies
Post-authorization information has been captured in Phase IV studies for some time and its necessity in optimizing the safe use of marketed medicines is already recognized by the European Medicines Agency’s (EMA) 2015 directive, which obliges developers to perform Phase IV studies according to the Agency’s current scientific advice framework. The primary purpose of Phase IV clinical studies is to monitor effectiveness in the general population, whilst collecting information about any adverse events associated with widespread use. However, whilst such information is commonly monitored post-authorization at ‘Phase IV’, arguably this does not yet go far enough to fully harness real-world data and obtain a comprehensive clinical picture.
Building on the understanding that the breadth of data generated post marketing can impact on the utility of the medicine and the development of new medicines, new practices are being employed by healthcare providers to produce real-world data of higher quality. For example, Learning Healthcare Systems are being developed by care providers to capture and feedback many forms of clinical knowledge to inform multiple stakeholders in healthcare development and delivery, including the pharmaceutical industry. Development of these types of networks is crucial to creating a joined-up approach to harnessing post-marketing information and offer currently largely untapped value to clinical innovators.
Pharmaceutical companies, regulators and medical insurers also now recognize the inherent value of real-world data to deliver insight and are beginning to make progress in how they use it. They are moving from an acute focus on safety and commercial considerations towards using data across the end-to-end product lifecycle to support regulatory decisions, advance disease understanding and clinical guidelines, and support outcome-based reimbursement decisions.
The Challenges of Analyzing Real-World Data
Yet, as the regulatory and commercial environment becomes more favorable to incorporating real-world data, there are still many issues around its delivery. RCTs have set protocols and established methods of data collection, whereas real-world data is collected in a more ad hoc fashion often with great variability in how, when and what is recorded. This disparity creates difficulties when trying to obtain reliable information and currently limits its utility.
Data sources need to be suitably aligned to enable meaningful information to be extracted. Whilst the increasing use of electronic data collection (EDC) will undoubtedly improve the quality of data, there remains the need for all stakeholders to understand what simple steps we can already take to facilitate the analysis of real-world data and so drive better healthcare decision-making. These include:
Preparation and cleaning of data - evaluation of any potential bias and the use of statistical modelling approaches to handle the structure and sources of variability in the data. Without these quality control measures there is a substantial risk of drawing false conclusions and misinterpreting the data.
Understanding data missingness – undergo an analysis of the realworld data to understand if gaps in the data exist and determine steps, such as insertion of synthetic data, that can be taken to improve the interpretability of the information.
Common data standards and analysis methods that incorporate these best practices are gradually emerging as the drive to realise realworld data’s potential grows. These now feed into and are supported by evolving guidelines for the use of real-world data by regulators such as the FDA.
Summary
There is a clear need to understand the effectiveness of new treatments in real-life settings over longer time periods and ensure that this information is fed back into the next generation of clinical research. Finding effective ways to harness real-world data from the plethora of sources available will transform the development of new clinical interventions and complement the traditional evidence from RCTs to provide a holistic view of new medicines and the diseases they target. With this in mind, the life sciences sector is now highly focused on striving to exploit this growing resource for the benefit of all of the stakeholders involved in the development and use of medicines.