As more biotech companies use RWE to support approvals, broader real-world outcomes for their products will emerge.
While randomised clinical trials (RCTs) remain the standard for evidence generation, real-world evidence (RWE) serves as a disrupter, bringing greater efficiency to the development life cycle and novel ways to improve regulatory decision-making - ultimately maximising revenue.
At the same time, the evidence produced by collecting, standardising and analysing real world data (RWD), which then informs RWE, has a purpose across an array of research applications. In the European Union and United States markets, it is being used in myriad ways, from continuous monitoring and drug utilisation studies to routine safety monitoring and filling evidentiary gaps.
With a variety of RWD sources available - such as electronic health records (EHRs) - and innovative services to gather, process and analyse them, RWE sourced from RWD is rapidly becoming the latest frontier for companies to build a competitive advantage through faster and more efficient access to this type of information and insights. The recent uptick in the use of RWE can be attributed to the following:
- Greater availability of and access to RWD
- Ability for it to influence decision-making across the product life cycle
- Continued focus on the development of guidelines for its standardised use
- Growing payer interest in using it to support a product’s value story
Due to the dynamic nature of the biotech market, new complex research needs will rise as more data become available and technology brings innovative ways to process and use collected data. These complexities that exist across the business spectrum, whether it’s scientific, operational, regulatory or market access, need to be accounted for in the development of a successful research and commercialisation plan.
It’s within this context, that biotechs will need to develop a comprehensive RWE strategy to maximise revenue in biotech trials.
Already, there are several instances from therapeutic areas - including oncology and immunotherapy, to medical devices and diagnostics - where the US Food & Drug Administration (FDA) has accepted RWE instead of data from a control arm through a traditional RCT.
The FDA has been receptive to examining historical, real-world cohorts of patients treated under the standard of care for safety and efficacy data, particularly where a control arm is not possible. As more success is achieved in these cases, the FDA will most likely continue accepting RWE as an asset in regulatory decision-making. For example, in 2018, the FDA unveiled its own MyStudies app for the collection of RWD, to continue to drive the use of RWE moving forward. This app enables developers to advance new ways to access and use data collected directly from patients, with the necessary controls in place to ensure patient privacy.
Technology advancements such as artificial intelligence, machine learning and natural language processing, can increase RWE output, decrease time to actionable insights, and take advantage of the vast data sources available.
Also, insights gained from existing data, or as a by-product of clinical care, can improve clinical trial efficiency by (1) streamlining clinical trial recruitment, (2) supporting regulatory filings, (3) expanding patient access, and (4) being used for drug safety surveillance. With the right technology, infrastructure and support, RWD can be collected, stored, analysed and reported, bringing the benefits of RWE to clinical research and commercialisation decision-making.
While RWE can be useful on its own, it can also serve as a tool to inform and supplement traditional clinical trials in protocol feasibility assessment, site/physician identification, patient identification, and historical controls.
Using RWE in this manner provides - in some cases - the only feasible option, in addition to delivering cost and speed benefits versus a prospective controlled study. Enhancing feasibility and patient recruitment through RWD analyses, as well as using readily accessible RWD for emerging research applications, are just the beginning of where RWE and clinical research can merge for more effective and efficient delivery of research objectives. Innovative ways of converging the two, through RCTs conducted within a real-world setting, also known as pragmatic trials, will continue to advance.
For instance, RWD can cut time and costs in enrolment processes, which can consume nearly 40 percent of a clinical trial budget. Through a protocol feasibility assessment, researchers can use RWD to estimate the size of an available patient population based on the defined inclusion/exclusion criteria. That criterion can be applied against de-identified patient data from providers’ EHR systems to determine eligible patients by provider, allowing sponsors to focus on sites that serve a population aligned with the protocol. Software can be programmed at those sites to alert physicians to suitable patients within their practices, giving providers the opportunity to easily consent and enrol prospective participants.
To capitalise on computing advancements, biotech companies should adopt an RWE technology platform that incorporates smart data processing, analysis and outcomes. When this platform is integrated into an overall comprehensive RWE strategy, new digital disruption innovations can enhance efficiency in drug development, improve patient enrolment and retention, drive new business and maximise revenue.
Deploying a RWE strategy
By identifying what evidence is needed to support regulators’ and payers’ decision-making, biotechs can develop an evidence generation plan across a product’s life cycle to leverage outputs and identify any existing data gaps. As more biotech companies use RWE to support approvals and monitor real-world performance of their products, broader real-world outcomes for their products will emerge. As a result, biotechs will accumulate an abundance of data that will need to be strategically synthesised and analysed.
For successful RWE deployment and enhanced revenue, biotechs should:
- Develop a plan early in the product lifecycle and revisit it at key milestones
- Build an integrated, cross-functional team with the necessary expertise to ensure RWD is applied at all appropriate steps
- Identify expected evidence gaps in advance, and fill them with a combination of primary or secondary RWD-derived evidence
A strategic plan requires flexibility and focus on high priority research objectives and platform scalability and architecture. Cloud-based systems that can combine disparate RWD assets into a single repository and provide analytics that can support teams working across the product lifecycle can serve as a centralised environment for all of the biotech’s RWD assets, breaking down any potential ‘data silos.’ This way, each audience across the enterprise has the capacity to run analysis on the right data sets that are specific to their purpose. As more RWE is generated within the biotech industry, its insights will have a great impact on informing stakeholders - whether its manufacturers, regulators, clinicians, patients, or payers. Having a strategic plan in place will be necessary to maximise revenue.