Mark Lambrecht

The cost of bringing a medicinal product through all phases of drug development to the clinic is upwards of $1.5 to 2.5 billion, with clinical trials making up a significant proportion of related R&D spend.

 It’s a complicated—and expensive—endeavor that pharma companies face. Submitting regulatory dossiers to global regulatory authorities, based on the outcomes of these clinical trials, requires a combination of harmonized data assets connected with 

 (RWD)—a powerful tool that can help drive clinical trial management and data-driven clinical decision-making.

Access to globally sourced and previously untapped, line level RWD and real-world evidence (RWE) is of increasing value when making regulatory submissions, meeting cost and speed business challenges head on. This, coupled with the clinical evidence about the usage and potential benefits or risks of a medical product derived from the analysis of RWD, is fundamental to drug development, programmatic risk reduction, commercial expansion prioritization, health care provision, and patient outcomes. But using RWE in clinical submissions is not without challenges.

BC Platforms and SAS formed a partnership

 in 2022 to support the use of RWE in regulatory submissions for medicinal products—delivering powerful analytic solutions and access to diverse global patient data, combined with repeatable, modular, and easy-to-use management capabilities that provide swift and controlled data access. By tapping into the power of data and analytics, drug developers can avoid major pitfalls encountered when using RWE to make regulatory submissions for medicinal products and improve regulatory submission success.

1. Develop a robust prespecified protocol and statistical analysis plan

A common mistake when seeking to leverage RWE to support or demonstrate a product’s safety and efficacy is failing to share a prespecified protocol and statistical analysis plan or SAP with the specified regulatory agency. Taking a proactive approach and aligning with FDA’s RWE framework recommendations can help guard against this risk. This includes ensuring that multiple analyses in electronic data sets are carried out quickly and inexpensively, making it possible to conduct numerous retrospective studies until the desired result is obtained.

Recent guidelines also recognize the need for a prespecified protocol and SAP as a potential risk to undermining study validity and recommend sponsors and requesting applicants provide draft versions of their proposed protocol and SAP for regulatory agency review and comment, prior to finalizing these documents and before conducting the study analyzes.

The key objective is ensuring cross-sponsor stakeholder visibility—visibility through project-specific dashboards, repeatable and audited analytics, and a clear, iterative package that sponsors can bring forward for pre-submission alignment with regulatory bodies.

One of the most common causes for regulatory submission failure in RWE studies is ‘data missingness.’ This can be due to misalignment with ‘real world’ patient visit cadences and resulting data, when compared to the predetermined cadences of clinical trial visits and data collection. The industry is also being challenged to incorporate and represent more diverse patient data across many therapeutic areas, including cancer, cardiovascular, and rare diseases.

To facilitate more complete data, stakeholders need direct access to complete, line level longitudinal electronic health records (EHRs), lab values, imaging, genomic, other ‘omic,’ and sample data, without compromising on security or legal privacy requirements. Aggregate level or claims data only do not suffice in telling the full patient journey and lack insight to physician notes and practice behaviors or reported outcomes.

, can address completeness through the ability to access entire patient journey retrospectively and prospectively, including access to the healthcare provider sites, and to patient recontact to generate genomic data, recruit to clinical trials, or collect biospecimens. Baseline characteristics are made more easily verifiable, and comparable patient cohorts from an existing trial can be easily lifted from populations across wider regions of interest if additional data, including more diverse data, is required.

Completeness of EHR data with additional enrichments like genomics, lab values, and imaging—which reflect high accuracy in the patient journey—have significant value in regulatory submission success and achieving comparability with active trial cohorts. This is especially noteworthy within rare and ultra-rare disease populations. Having a global reach and catchment from which to source patient counts, confirm the regional incidence of disease, as well as to engage providers, sites and patients saves critical time, reduces patient burden, and can also enhance cohort sizes.

In cases where cohort sizes are inherently small, look to recent FDA guidance regarding data linkage and the combining of data as possible solutions. Data governance and quality control procedures not only ensure completeness, but data provenance as well. As these methods can also introduce new methodological challenges—notably around data heterogeneity—turning to analytics can help demonstrate data quality, statistical confidence, bias omission, and complete audits and audit trails.

4. Ensure aligned RWE across different regions

The fourth most common failure in RWE study acceptance is the challenge of variability in physician practice and different standards-of-care across the globe regarding EHR data outcomes—a variability that can result in unclear outcome measures. It’s imperative for pharma companies to engage sites from all regions of the world and verify standard-of-care practices in regions of interest.

In the ‘provider questionnaire phase,’ regional feedback can help confirm standard-of-care practices that ensure well-defined diagnostic criteria within treatment pathways specific to their populations. This phase delivers an essential risk mitigation strategy for trial site identification and when developing data sourcing plans for RWE projects.

When incorporating underrepresented global populations within drug development, it is also critical to meet diversity and inclusion guidance that can underpin new market entries. Pharma companies must address the data needs and methodological issues, while in parallel aligning solutions that meet global regulatory guidance enforcements.

The bottom line? It is possible to overcome major areas of failure when using RWE in regulatory submissions. But the linchpin to that success is utilizing a vast network of provider sites to assemble the data within a GDPR+ technology platform and the analytics to enable access to the high-quality genomics and real-world data networks. Using a network of readily available line level patient data in analysis ready format, predefined statistical tools, visualizations, and advanced analytics can unlock previously untapped RWD assets from across the globe to accelerate insight generation for R&D and commercialization efforts. And those tangible results are accelerating drug development, healthcare provision, and clinical decision-making, which, ultimately, will positively impact patients and their families around the world.

, VP, strategic partnerships, BC Platforms; and 

, director of health and life Sciences, SAS

Subbiah, V (2023). The next generation of evidence-based medicine. 

https://doi.org/10.1038/s41591-022-02160-z

US Food and Drug Administration (2018). Framework for FDA’s Real-World Evidence Program. 

https://www.fda.gov/media/120060/download

US Food and Drug Administration (2021). Real-World Data: Assessing Electronic Health Records and Medical Claims Data To Support Regulatory Decision-Making for Drug and Biological Products, Draft Guidance for Industry. 

https://www.fda.gov/regulatory-information/search-fda-guidance-documents/real-world-data-assessing-electronic-health-records-and-medical-claims-data-support-regulatory

Articles

Utilizing a broad network of provider sites is key in overcoming failure and enabling access to high quality data.

Overcoming the Pitfalls of Using Real-World Evidence in Regulatory Submissions

	Today’s therapies are developed to be more personalized, precise, and real-time data-driven than ever, yet the clinical trials in which they are tested deliver results based on the average response from a given trial’s many participants. Further, new therapies are tested in clinical practice by physicians caring for patients that might never have been included in such trials. These patients are different from their experimental cohorts in myriad ways: they have different genetic backgrounds, may suffer from additional diseases and likely have a history of taking different medications.

	This implementation gap between clinical research and practice explains why promising new medicines often do not find uptake in clinical practice. To bridge this gap, clinical trials will make increasing use of mobile health technology and data that is specific to the individual patient. Indeed, having been digitized over the past decade, clinical trials are now bound to become more pragmatic, incorporating patient-specific data from IoT-enabled medical devices, digital health apps, and even digital biomarkers.

	The rising use of patient-specific data will lead to streamlined clinical trials. It will also narrow the implementation gap of new therapies by helping physicians, health tech companies, reimbursement agencies, and the public at large to understand the clinical, economic, societal, and scientific value of such therapies. The challenge of these new type of trials, however, goes beyond the expensive technology. It also lies in the complex processes needed to capture high-quality data and the required statistical design that amplifies process complexity. 

	Fortunately, mobile health technology is evolving quickly, becoming less expensive even as we engineer better, smarter, smaller sensors for measuring clinical parameters. Couple such advances with enormous leaps forward in statistics, AI, machine learning and real-time analysis, it’s easy to see how a growing number of trials in diverse therapeutic areas will benefit from this streamlined research model.

	Clinical trials are by necessity run in a very sequential, risk-averse, rigorous, and statistically controlled manner. Producing evidence in a statistically robust and scientifically validated manner ensures that the medicines and therapies brought to market are both safe and efficacious. 

	By transitioning from paper-based clinical trials to digital systems in recent years, pharmaceutical sponsors have been able to better plan and conduct trials, recruit patients and analyze results more rapidly, and improve standardization. Pharmaceutical companies and CROs have deployed this digital layer in support of the standard clinical trial, but many have not changed the traditional, phased process. 

	Now digitally-enhanced clinical trials are being overhauled in favor of more profound streamlining that incorporates into the development phase real-world patient information from a clinical setting (as opposed to the experimental trial setting). This next wave of clinical trial design will combine the best of both worlds: the gold standard randomized controlled trials (RCT) 

the ability to adapt the design based on real world data. It will require under-used trial design methodologies such as N-of-1 trials and adaptive design techniques to ensure analysis results are robust and statistically reliable.

Real world data used in the context of clinical trials

 (RWD) is all data that is not captured as part of the RCT but in the practice of clinical care. Beyond health records, RWD can include both information derived from insurance claims or patient registry retrospectively and/or capture new data prospectively during clinical development or after the therapy has been approved.

	Real world evidence (RWE) is clinical evidence derived from analyzing RWD. In complement to RCTs, such analyses can reveal potential short- or long-term risks and benefits of drugs or therapies. For example, the STRIDE-PD study found an unexpected incidence of prostate cancer in the group taking Parkinson’s medication entacapone, compared to the control group. At the FDA’s request, the drug’s manufacturer conducted an 

 using Finnish patient registries and ultimately concluded that patients taking entacapone were at no additional risk of prostate cancer. 

	Global regulatory agencies such as the FDA and the EMA are setting the tone. Both recently issued policy texts indicating a readiness to accept RWD to better assess the safety, effectiveness and, in some cases, efficacy of new therapeutics. The 

 for a real world evidence (RWE) program allows the use of RWE for decisions of effectiveness but only in limited conditions. The 

 that RWE can be used for complementing RCT decisions in carefully defined settings.

 is an example of the potential of crowdsourced historical clinical trial data. The cancer research initiative allows researchers to submit, collect, and analyze such data for the benefit of research in its online analytics environment. Used in this way, historical trial data can guide better trial development and act as comparator arms.

	RWD is not a magic formula that solves all the information gaps of clinical trials, of course. But when collected to gather more meaningful and observational data about the trial recruits, it can help advance understanding of a therapy. Digital technology such as wearables or adherence-measuring techniques might tell us whether the patient is dosed correctly, for example, or perhaps provide more confidence about optimal dose or shed light on possible adverse events and interactions with concomitant medications.

	Clinical trials are optimized to capture very specific information about treated patients with the goal of demonstrating the efficacy of a therapy under optimal study conditions. 

, or effectiveness trials, expand to include information from interventions and interactions with many healthcare professionals even outside the context of the experiment trial setting. They also involve patients that would otherwise be excluded from a RCT, but they still include prospectively enrolled subjects that are randomized for the treatment under investigation. 

	Pragmatic trials require an information system that links up electronic health records (EHRs) across primary care physicians, pharmacies, and hospitals. The world’s first large scale pragmatic trial and well published example is the 

	Pragmatic trials are gaining in popularity for a couple of reasons:

	· Firstly, the generated data helps ensure that the trials are relevant and include the right patients and that the evidence is gathered not just about clinical aspects but also patient outcomes, economic relevance and impact on quality of life parameters. 

	· Secondly, the RWE captured during a pragmatic trial contains a treasure grove of insights about the subjects, which can aid in closing the implementation gap. 

	In contrast, RWE such as that derived from claims and EHR data is known to be useful for epidemiology and in addressing health economics questions but often falls short of advancing clinical information about individual patients because of lack of EHR information for those same patients. For some disease areas, claims databases might be more useful than other sources of patient data, because they contain more information captured by primary care physicians sought after pragmatic trials, too.

	Pragmatic trials are more expensive as they involve digital health apps, integrated health systems (such as linking pharmacy prescriptions with clinical records) and better data and analytics platforms. 

Mobile apps and mobile health technology 

	Digitally-enhanced clinical trials include additional sources of high quality or regulatory-grade information such as data derived from patient wearables or patient-reported outcomes. Using these digital health initiatives will greatly enrich the data available to help determine the optimal dosing scheme of a new drug, for example. 

	Patient wearables such as those used in diabetes or cardiovascular trials are creating an abundance of data points, but finding meaning in this data is not easy. For wearables data to be useful in a RCT, it needs to be meaningful both clinically and scientifically. And, importantly, the apps or wearables must be approved by the regulators. The 

 around “software as a medical device,” and the European new Medical Device Regulation offers a 

 to allow algorithms and apps to be approved as clinical-grade devices. 

	The ability to capture data from patients in real time or near real time will not merely digitally enhance RCTs. It will change them profoundly.

Scientific advances require new trial designs

	At the same time as RCT design and the use of RWE are being investigated, 

 using gene therapy, gene transfer, or immunotherapy. The enormous biological complexity and tremendous amounts of data generated by such trials require new trial designs and the use of mobile health technology. Further, the related analytical systems must be able to rapidly test hypotheses, find biomarkers and deal with genomics, and monitor trial results as they become available.

The quantified self-impact of AI, engineering and technology on trials

	The ability to capture clinical and adherence information in near real-time from patients using mobile health technology and apps is exploding. Deep learning AI technology that can generate biomarkers that have relevance as endpoints for an intervention’s safety or efficacy is under development. 

	Multi-sponsor environments can accommodate pragmatic and personalized trials across different trials as the complexity and expense of these trials will be shared across sponsors. It is likely that these systems will not be the classic EHR systems that are used today. 

New statistical designs for future clinical trials

	In 2019, more statistical designs will be used that can accommodate streamlined clinical trials. Going from a classically three-phased trial approach to one longer trial that constantly integrates latest insights from the patients, so called cohort expansion trials, is what the FDA has issued in a 

	Interestingly, the industry association Biotechnology Innovation Organization (BIO) and the Drug Information Association’s Adaptive Designs Scientific Working Group submitted multiple comments to the guidance. The latter group wrote, “While the draft guidance recognizes the desirability of adaptive, learn-as-you-go, seamless designs operating between trial participants, it ignores the real possibility of adaptively individualizing dose within participants on an N-of-1 basis,” as 

reported by the Regulatory Affairs Professionals Society

	Basket trials test the effect of one drug on a single mutation in a variety of tumor types at the same time. Umbrella trials have many different treatment arms in a single trial, but all tested for one disease. Different treatment baskets group patients that share certain genetic anomalies in their tumors. These studies also have the potential to greatly increase the number of patients who are eligible to receive certain drugs relative to other trials designs. (

https://www.asco.org/research-progress/clinical-trials/clinical-trial-resources/clinical-trial-design-and-methodology

	Adaptive trials aim to evolve the treatment given to patients in the trial based on the results during the trial. N-of-1 trials take the personalized treatment even further by considering the treatment of the individual as one experiment by itself. Physicians and patients are blinded for the therapy given, and appropriate controls, such as periods of standard care followed by wash-out periods, are given. They can be properly statistically designed and controlled. The ability to personalize treatment using omics assays, the available of inexpensive and regulatory-grade devices and wearables, and the right regulatory environment will increase the interest in these types of trials. 

	Advances in scientific and clinical practice, RWE, the ability to monitor patients with regulatory-grade devices, and the digital health ecosystem will lead to more complex trials that ultimately lead to better therapies. The first step is to embrace the use of real world data with randomized controlled trials to deliver the find the right therapy, for the right patient, at the right time.  

, Director of the Health and Life Sciences Global Practice at SAS

Mark Lambrecht

Articles

Overcoming the Pitfalls of Using Real-World Evidence in Regulatory Submissions

The Impact of AI, Precision Medicine, Mobile Health Technology on Streamlined Clinical Trials