Leveraging Real-World Data for Regulatory Success

Real-World Data (RWD) can provide invaluable insight during the research and development of a new medicine, with the potential to provide comprehensive insights into the health and treatment of patients in their daily lives. The growing number of publicly available real-world data sources including electronic health records, mobile apps, claims data bases, health services etc. can help to fill gaps in our knowledge about how medicines and treatments perform in diverse, real-world scenarios.

Whilst randomised clinical trials (RCTs) continue to be the gold standard for evaluating treatment safety and efficacy to meet regulatory approval, they do not reflect the impact of a treatment in an everyday setting. This is because they are conducted under optimised conditions using small, well-defined patient populations and short-term endpoints.

By capturing data from a wider range of patient populations and healthcare environments, RWD can add to the bigger picture of the value of a treatment by allowing longer-term evaluation of treatment outcomes and safety in a real-life setting. In addition, RWD can provide valuable insights into the gaps in the market in terms of unmet medical needs.

Global regulators are recognising that the rapid evolution of RWD sources and the information they provide is a valuable and expanding resource for informing and improving the efficiency of development decision-making. This is reflected in the growing body of regulatory guidance issued in recent years covering data quality, best practice and methods to support the wider use of RWD both in regulatory submissions and post-authorisation.

Real-World Data in Regulatory Submissions

While RWD is commonly used post-authorisation and for the evaluation of the market opportunity, in some instances it can provide important information to supplement data obtained in clinical trials. For example, it can be used in:

1. External control arms – when RCTs are impacted by small populations, such as in rare disease indications or due to difficulties with recruitment or ethical issues, suitable integration of data from external sources such as RWD can ease study population issues, making potentially stalled studies feasible.
2. Clinical trial design – assessing the feasibility of a clinical trial and providing additional context around the assumptions for endpoints and treatment effects. This is particularly useful in situations where there is limited clinical trial data.
3. Supporting regulatory submissions – by bridging the gap between the tight constraints of the evidence generated within an RCT and the realities of the clinic, RWD can help inform regulatory decision-making and so add value to a regulatory submission particularly where information is scarce or otherwise difficult to obtain.

In these instances, suitable use of RWD can help accelerate development by increasing efficiency while reducing delays and costs. Regulatory submissions incorporating real-world data can demonstrate the long-term effectiveness and safety of treatments, which may not be fully captured in a shorter duration clinical trial, whilst also showing how it works across diverse populations. These two factors allow for a much more comprehensive understanding of the impact of a treatment.

Post-Market Surveillance

Some authorisation decisions are conditional, requiring further follow up post-approval. Monitoring the safety and efficacy of a drug after it has been approved for public use is an ongoing process that helps ensure that the benefits of a treatment outweigh any potential risk. Utilising real-world data for post-market surveillance has a wide range of benefits, from monitoring adverse events, evaluating longer-term outcomes, and assessing treatment patterns and adherence.

1. Monitoring adverse events – By using electronic health records, insurance claims, patient registries, and patient-reported outcomes via health apps, real-world data can accurately track adverse events at scale. Since RWD encompasses a diverse patient population, it can help identify rare or unexpected side effects that may not become apparent in the more traditional environment of clinical trials.
2. Evaluating long-term outcomes – Unlike clinical trials, which typically have shorter follow-up periods, real-world data can track patients over a much longer timescale, providing a more comprehensive understanding of a drug’s long-term impact.
3. Assessing treatment patterns and adherence – Analysis of RWD can uncover how different patient groups use the medication, including off-label uses, which can help inform updates to treatment guidelines and recommendations. By examining patterns of medication adherence and persistence, RWD can help to identify factors that affect patient compliance and treatment success.

Real World Evidence: Simulating patient level outcome data to inform clinical trial design based on real-world, clinical trial and literature data. The Challenge: GSK were designing a phase III programme for a novel treatment for anaemia associated with chronic kidney disease in dialysis patients. Both the novel treatment and recombinant human erythropoietin (current standard of care) modulate the Haemoglobin (Hb) levels. However, there were some complexities to consider when designing the study: Low and/or very high levels of Hb are both associated with an increased risk of major adverse cardiac events (MACE) which was the primary outcome. Baseline risk factors (older age, diabetes, prior myocardial infarction (MI), heart failure (HF), stroke) are also known to increase risk of MACE. The complex relationship of varying risks over time in different risk groups at baseline may lead to uncertainty for accurate outcome prediction. The Approach: Study simulation software KerusCloud was used to simulate realistic patient level outcome data to inform clinical trial design under various different scenarios. The Results: Simulations using KerusCloud identified that: Studies enriched with high-risk patients resulted in smaller sample size or duration. The analysis approach had minimal impact on study power when Hb levels were well controlled. The Impact: KerusCloud successfully utilised RWD to inform simulations that helped optimize design and analysis options for a study where there were multiple sources of uncertainty. The simulations confirmed the validity of assumptions for the study under different conditions for the design and analysis options and treatment effects. Viable alternative study approaches were identified e.g., enriched study design which reduced study duration and sample size by as much as 30%, saving time, costs and patient burden.

The current digital and technological revolution is providing a growing range of alternative sources of RWD that offers us a window into how  medicines are used in the real world. Liberating insights more effectively from these  has the power to  transform the development of new drugs s and the delivery of healthcare by giving drug developers a wider view of both the needs they are addressing and the benefits of the medicines they are making.

While integrating and leveraging RWD effectively can still present challenges, early engagement with biostatisticians and data scientists helps to overcome these. This can include identifying relevant data sources, ensuring the data quality, mitigating for missing data, and aligning the data so that its use meets the robust requirements of regulators.  Biostatisticians can also support the use of RWD to evaluate the commercial opportunity and clinical need as well as inform the feasibility of the development plan, clinical trial design, positioning and market access of an investigational therapeutic.

With access to these capabilities, drug developers are well-placed to harness the massive potential of RWD to target and improve the efficiency of developing new drugs, increasing regulatory success and so the speed at which they can deliver medicines to patients with unmet clinical need.

Partner with Exploristics’ to transform your trial development through our industry-leading clinical study design platform, KerusCloud, and our team of seasoned biostatistics experts.

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