Real-world evidence and clinical research diversity

pharmafile | March 31, 2021 | Feature | Research and Development rwe 

Clinical trials are immensely valuable in establishing the safety and effectiveness of a drug in carefully selected participants, but what we can learn from them about real-world populations is often limited. In this feature, Darcy Jimenez explores how real-world evidence can improve the representation of those often excluded from clinical research.

Clinical trials are, by their very nature, exclusive. In order to optimise the cohort of trial participants and obtain precise data on the safety and efficacy of a drug, researchers must employ strict eligibility criteria.

The result of this criteria, however, is a trial process that notoriously underrepresents certain groups. A JAMA Network Open study published this month is one of the most recent to find that racial and ethnic diversity is lacking in clinical trials. Analysis of 230 US-based clinical vaccine trials revealed that white individuals were overrepresented almost 78% of the time, while Black and Hispanic or Latin people were represented only 10.6% and 11.6% of the time, respectively. Represented the least in the trials were Asian individuals (5.7%) and Native American or Alaska Native individuals (0.4%).

This disparity in medical research can have significant consequences when it comes to establishing the effects of medicines in different population groups. A 2014 study in Clinical Pharmacology & Therapeutics found a variation in responses across ethnic groups in almost 20% of drugs approved between 2008 and 2013. Additionally, research by Shen et al in 2007, for example, revealed that people of colour with atrial fibrillation, a condition affecting the speed and rhythm of the heart, were at greater risk of warfarin-related intracranial haemorrhage than their white counterparts.

If certain groups of people aren’t represented in clinical research, it’s harder to establish which treatments are or are not suitable for them. Diversity in clinical research is essential to ensuring drugs are safe and effective for real-world populations, and is therefore a public health issue.

Dominik Ruettinger, Global Head of Early Clinical Development in Oncology at Roche, explains how the current clinical trials process can introduce bias into drugs research.      

“We know only about 5% of oncology patients go onto clinical trials – there you have your first bias,” Ruettinger tells Pharmafocus. “I think it’s at least 60% to 70% of trials that look at better-performing patients, so we are selecting patients that are doing better, and that does not represent the real-world population.

“In a community hospital the population is much more heterogeneous; they suffer from comorbidities, they’ve had various previous therapies, they have other concomitant medications,” he adds. “And that’s something we are not learning from our clinical trials.”

Welcome to the real world

Real-world evidence (RWE) can provide a means of filling in the gaps left by disparate representation in clinical trials. RWE is generated from real-world data – that is, data derived from the real world rather than a clinical trial environment. Sources of real-world data include electronic health records, product and disease registries, medical claims and billing data, and patient-generated data. RWE provides researchers, companies, and regulators with valuable information that isn’t available from clinical studies, and can give insights into how medical conditions are characterised and treatments are responded to in demographics that otherwise aren’t properly represented in medical research.

“We know there are significant differences, but we need to understand those first to be able to address them, so that’s where we use real-world evidence a lot,” Ruettinger says. He cites the bone marrow cancer multiple myeloma as an example of a disease that has been found to present differently across racial groups.

“We know in African Americans it seems to be a very different disease; we have an earlier age of onset, we know that these patients have a better prognosis,” he continues. “At the same time, we know that the relative benefit is less in this population. So that doesn’t go together very well, that needs attention.”

Ruettinger outlines the ways RWE can help to bridge the gap between clinical trials and effectively treating real-world populations.

“One is very operational: if you intend to include more African Americans in a multiple myeloma trial, you can use real-world data to understand where these patients are,” he says. “We’ve used real-world data to select our clinical trial sites, so we can see where a certain population of patients live, and then we select the clinical trial site that’s right in the middle of that area.

“We can get increased, optimised access for these patients to that clinical trial by simply having it closer to their homes.”

Ruettinger continues: “But then also, we can complement data out of clinical trials by looking at real world data.

“In underrepresented or underserved populations, we can try to understand what the inequality really is, then make sure we specifically define the target patient population and have those in our clinical trials.”

Similarly, David Webb, Christison Professor of Therapeutics and Clinical Pharmacology at the University of Edinburgh, tells Pharmafocus that while RWE is not a substitute for clinical trials, it can serve as a useful supplement.

“Randomised, controlled trials remain the gold standard in medicine. They are generally essential for proving the efficacy of new medicines and new therapies of various sorts,” Webb says. “And the reason why we continue to use them 50 years on, even though we have access to a wide range of data sources, is that the randomisation process tends to markedly reduce biases, and also confounding that can occur when you don’t randomise patients.

“However, it is the case that in general, randomised controlled trials select patients carefully, and they’ll often select them to avoid patients with renal disease, kidney impairment, significant liver disease, heart failure,” he says. “And, of course, if you look at the population as a whole, and the fact that we often are treating older people, they have a number of other morbidities and conditions that may have been eliminated in the study of a new treatment, and so we don’t know how effective they’re going to be in those groups of people.”

Webb explains that RWE can be used to confirm or challenge the findings made in clinical studies, and assess the suitability of a treatment in real-world populations.

“You can learn a lot by real-world study in broader populations. In that situation, you would want to see the same benefit you saw in the clinical trials in the broader population,” he says. “If you didn’t find that, that would be quite concerning.”

“If you find the same order of magnitude benefit, then what you’re looking for is any differences in the wider group that might help you understand who might benefit most – but more importantly, who might be put at risk by a new treatment.”

A study published last year in Clinical and Translational Science used RWE to establish a difference in allopurinol-related severe cutaneous adverse reactions (SCAR) across certain East Asian populations. SCAR incidence rates were found to be highest in Taiwan, followed by South Korea, and lowest in Japan – highlighting the value of RWE in identifying population differences in reactions to drugs. The analysis of these real-world populations also identified chronic kidney disease, female gender, and old age as common risk factors.

As well as researchers, drug regulators’ recognition of the value of RWE in improving representation has become clear in recent years.

In 2018, the FDA published a framework for its RWE programme, intended to evaluate RWE’s potential to support the approval of a new indication for a drug already approved, or to help support or satisfy drug post-approval study requirements. In the framework, the agency’s Center for Drug Evaluation and Research Director, Janet Woodcock, said: “FDA will work with its stakeholders to understand how RWE can best be used to increase the efficiency of clinical research, and answer questions that may not have been answered in the trials that led to the drug approval, for example how a drug works in populations that weren’t studied prior to approval.”

The EMA’s framework for RWE acknowledges that while cultures, lifestyles, healthcare systems, and more vary widely across the European Union, all EMA-approved medicines are authorised centrally with the same product information. The framework highlights how RWE can, among another things, help to address unmet medical needs, assess off-label use options for drugs, and establish the relevance of clinical data against clinical use.

In the UK, the Clinical Practice Research Datalink (CPRD) is real-world research service jointly funded by the National Institute for Health Research and the UK’s drugs regulator MHRA. The CPRD collects anonymised patient data, encompassing 60 million patients, from a network of GP practices across the UK. The data collected by the service provides a longitudinal, representative UK population health dataset, and has resulted in over 2,700 peer-reviewed publications investigating drug safety, use of medicines, effectiveness of health policy, health care delivery, and disease risk factors.

Janet Valentine, Director of the CPRD, told Pharmafocus: “CPRD’s innovative data-enabled clinical trials services are delivered in partnership with GP practices based in the community. This approach offers the opportunity to reach out to patients in socioeconomic and ethnic groups and geographical areas that are usually underrepresented in clinical trials.”

For Ruettinger, the adoption of RWE by pharmaceutical companies isn’t just essential, but inevitable – and he feels ‘very positive’ about what this means for racial and ethnic diversity in medical research.

“We’re generating much more data, with many more devices available, much more technology available – and we got much better at analysing the data,” Ruettinger says.

“There’s this old saying: you can only solve a problem if you acknowledge there is one. So real world evidence helps us a lot in understanding what the problem is, and understanding the magnitude of it,” he continues. “A lot of times we have a sense it’s there, but it’s really important to approach this in a data-driven way.

“If you are able to prove there’s a difference in how male and female patients respond to certain medical intervention, then you can select or design a specific trial for it. If we have knowledge of ethnic differences in the response to treatment, we can then specifically target certain ethnicities.”

Professor Webb is also optimistic: “We are in a fantastic era where real-world studies are getting easier to do, cheaper to do, and our methodologies are becoming more robust,” he says. “I think this is an era in which such studies are really important.”

While RWE is in some ways a sticking plaster – clinical trials must strive to better represent real-world populations – it can prove hugely valuable in identifying the different ways people experience medical conditions and react to drugs, and therefore help to provide individuals with the safest and most effective treatment for them.


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