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Injecting real-world data into every day clinical trials – RWE and RWD

Published on 29/03/22 at 02:15pm

The past two years have seen unprecedented pressure on healthcare systems, its workers, and clinical research as a whole. COVID-19 has presented the need for rapid clinical trials to support the effort to address not only the original strain of the virus, but also emerging mutations as they have arisen. Outside of the pandemic, healthcare has had to continue working at the same pace, and standard – leading to more digitalised trial settings. Where trials fall short, however, is in a lack of access, representation, and diversity, and data from the real world may be the key to pick up the slack.

RWD, such as that provided by EHRs, or data from mobile devices, is a by-product of everyday patient care, and can be used to produce deep analyses, which might later inform clinical trials. RWD can be used as a verification tool for data presented in clinical trials, with a more heterogenous patient population, and potentially broader geographical spread. Factors such as location can significantly affect trial results through, due to varied culture, lifestyle, access to healthcare, poverty, homelessness, and more.

RWD can be utilised to identify unmet healthcare needs, underserved patient groups, and areas for further study – treatments can then be modified, or designed to target specific patient groups. We spoke to ICON’s Katheen Mandziuk, who highlighted this application of RWD: “researchers can buttress trial results with data supporting what is actually occurring in the real world outside of the clinical trial. With advanced Artificial Intelligence (AI) and machine learning (ML) technologies, we can view trends and insights in the data that may not be apparent at the smaller site level. These valuable trends may be applied in the ongoing clinical trial to help investigators and study level staff adapt to trends in the patient population being studied. If all patients with a particular condition of a particular gender or age group have a common comorbidity or serious adverse event with a given therapy, the RWD may serve as a signal to watch for in certain populations.”

Randomised controlled trials (RCTs) also have an extensive list of inclusion and exclusion criteria, critical for that phase of the development of the drug. Clinical trials may therefore leave out certain patient populations out through screening processes: this impacts patients with morbidities, and comorbidities, such as renal disease, heart failure, and more. As the presence of these morbidities is higher in the older population, they are often left out of the clinical trial process. However, these patients typically make up a higher percentage of patients being treated in the real world.

 

Beyond the borders of comorbidities 

 

Clinical trials exclude participants along other lines, and these participants often make up a significant portion of the overall patient population. Male breast cancer patients are typically underrepresented, or excluded, from breast cancer clinical trials. An estimated 2,710 men in the US will be diagnosed with breast cancer this year, and 530 men diagnosed will die from the disease in the US in 2022. Of these, Black men have the highest incidence rates of breast cancer – 2.7 out of every 100,000 men – however, this demographic is typically underrepresented in clinical trial settings.1

RWE is particularly useful to gain approval for treatments aimed at patient populations not represented in clinical trials. In 2019, Pfizer’s drug Ibrance was approved for men with HR+, HER2- metastatic breast cancer. Pfizer’s supplemental regulatory submission for male breast cancer primarily included RWE in order to make the case that Ibrance could be used to treat men. Expanding the scope of evidence generation means that healthcare professionals can better serve specific patient groups, communities, and individuals with multiple comorbidities.

We asked Kathleen Mandziuk how RWD can be used to inform trial recruitment. She shared: “The utilisation of RWD can help direct study design and patient recruitment efforts to ensure that the clinical trial population is representative of the real-world patient population and detect enrolment bias in near-real time. When the patients recruited for a particular study are in alignment with the patient population, the clinical trial will yield better trial results that are aligned with the patient population.”

We also spoke to Heather Moses, Country Medical Director, Novartis Oncology UK, who highlighted where RWD can fill in the blanks left by RCTs: “Real world data and the evidence it supplies can validate and extend the information gathered by RCTs. Unlike an RCT, real world evidence gathering doesn’t start and end at a particular time, so data collected over a long period of time can be analysed and acted upon; representing how real populations behave, as a snapshot and longitudinally.”

RWD can therefore provide game-changing care for those living with rare diseases. Stephen Pyke, Executive Vice President, Clinical Data and Digital Services, Parexel, shared with Pharmafocus: “There are settings where RCTs are simply not possible or ethical to undertake. For example, in rare and very serious diseases, there is a desire to ensure all patients have the best chance of a positive outcome. In these trials, the use of an ‘external control arm’, or RWE as a comparator, allows researchers to bring in external evidence on standard-of-care outcomes to contrast with the trial data on active treatment. RCTs are good at answering the question, ‘Can it work?’ Instead, when we emulate as closely as possible to the standard of care and management, we can answer a different question, ‘Does it work in practice?’”

 

Underserved and underrepresented: Healthcare under pressure

 

A 2021 JAMA Open Network study uncovered the extent of clinical trial underrepresentation in the US: “In this cross-sectional study of 230 US-based clinical trials with 219,555 participants, Black or African American, American Indian or Alaska Native, Hispanic or Latino, and older adults were underrepresented and women were overrepresented compared with the US population.”2 The study found that Black and African American individuals were represented in just 10.6% of clinical trials. Meanwhile, 48.5% of trials reporting race or ethnicity did not include Indigenous American participants, and 60.4% of the trials did not include Hawaiian participants, or Pacific Islander participants.

How can RWD be used to pick up this slack? With COVID-19 exposing and entrenching health inequalities across the globe, RWD can help to uncover and combat these disparities. RWD can identify gaps in the healthcare system, and areas of unmet need, and inform what further research or trial processes ought to take place, among what patient populations.

 

Real data, randomised trials, and pandemic limitations

 

Heather Moses highlighted the change in data collection brought about by the pandemic: “The COVID-19 pandemic enabled a paradigm shift in how real world data is collected, used and valued by healthcare providers, policy-makers, patients and society as a whole.

To provide a very specific example, the DATA-CAN research hub used real-time weekly data on cancer care to determine the adverse effect of the pandemic on cancer services in the UK. Data showed that there were dramatic reductions in the demand for, and supply of, cancer services during the pandemic which may contribute to substantial excess mortality among people with cancer. Data has shown that there have been nearly 50,000 fewer cancer diagnoses across the UK during the pandemic and UK policymakers have since announced a 10-year Cancer Plan for England – a direct response to real world data. Cancer care is a key priority for the NHS, so we are trailblazing innovation to deliver a better, longer life to cancer patients in the UK through our innovative new medicines and novel ways of delivering care.”

COVID-19 had a significant impact on the way trials could be conducted. “When existing study sites shut down to treat urgent COVID-19 cases at hospitals, clinics and large integrated delivery networks (IDNs), it was very difficult to resume the usual course of business of clinical research,” Kathleen Mandziuk shared.

“Therefore, the clinical research world had to immediately pivot to decentralized clinical trials and using remote patient monitoring technologies and home health visits/telehealth services. Many of these solutions and technologies use RWD to benchmark what happens in the general patient population. For example, through EMR records, we can notate the reference ranges in lab tests, standard of care in prescribing patterns, treatments and clinical practice. When we have these reference ranges in RWD, we can better compare normal population-based outcomes vs outliers that we may see or expect to see in the controlled clinical research study settings.”

Arnaub Chatterjee, Senior Vice President at Acorn AI at Medidata, a Dassault Systèmes company, told Pharmafocus that “expediency has been crucial in the global fight against the virus, but shortened timelines have meant that the level of long-term data that we have been able to collect has not been as extensive as with a typical trial and typical timelines. Long-term safety and monitoring in the real world will therefore be critical in better understanding COVID-19, patient response to the virus and ultimately in improving drug performance.”

 

RWD going global

 

RWD is being used to address healthcare inequality all over the globe. In September 2021, BC Platforms (BCP) entered into a collaboration with the African Institute of Everyone Genome (AiEG), a South African genomic company, focused on building the largest integrated clinical and genomics data biobank in Africa. As part of the collaboration, BCP’s platform enables RWD research, involving over 10 million consenting patient genomes, from all 54 countries in the continent, to be collected over a period of 10-15 years. The main goal of this research is to enable drug development and clinical research for patients in Sub-Saharan Africa.

At the time the collaboration was announced, there had been a poverty of investment in African genome research, with only 1% of total funding directed towards genomics research and clinical studies in Africa. In contrast with up to one million genomes globally, only around 5,000 to 10,000 whole African genomes have been studied. What draws this statistic into starker terms of global healthcare inequality is the fact that 25% of the world’s population is expected to be based on the African continent by 2050.

The importance of understanding the full genetic diversity of patients in Sub-Saharan Africa is critical for drug development: novel target identification, validation, and early insights into adverse events. The genetic diversity represented by patients from the African continent is the greatest in the world.

When the collaboration was announced, Joe Mojapelo, CEO of AiEG, commented: “Through the RWE that will be produced by future studies in collaboration with BCP, leveraging their integrated cloud-based platform alongside our region-leading database, we are keen to contribute to a new world of research equity. This means promoting access to African genomic data, and one day eradicating unnecessary side effects for the people of South Africa, the African continent, and eventually the global African diaspora.”

 

Real world, dream clouds, and data drawbacks

 

Data collection is limited in real-world settings in comparison to that of clinical trials: trials are designed to collect data and therefore permission has been given to analyse personal data on how age, sex, race and other factors affect symptoms, drug responses, and medication efficacy. However, data protection, and ethical and legal concerns complicate the issue of population-wide studies based on data presented from, say, EHRs.

With each of their limitations and benefits, RCTs and RWD work best, perhaps, in tandem: Arnaub Chatterjee commented: “Having a bridge between the trial and real-world evidence means an advancement in long-term monitoring and safety surveillance for patients. Real world data may mimic some patterns of controlled trials, but not all. Figuring out what is unique to the controlled trial and what is unique to the real-world data will happen in the context of the overlap in the trial setting and that could help to provide some longitudinal framing to help regulators make better decisions.”

 

References

1. Visit: www.cancer.net/cancer-types/breast-cancer-men/statistics 

2. JAMA study: Assessment of the Inclusion of Racial/Ethnic Minority, Female, and Older Individuals in Vaccine Clinical Trial

 

Ana Ovey

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