21st Century Studies with 21st Century Tools – Randomized Clinical Trials, Big and Real World Data

12th August 2015 (Last Updated July 16th, 2018 10:34)

Nigel Hughes, Director, Integrative Healthcare Informatics, Janssen R&D, divulges his perspective on the evolution of big and real world data into the 21st Century

21st Century Studies with 21st Century Tools – Randomized Clinical Trials, Big and Real World Data

The first controlled clinical trial of the modern era was performed by James Lind in 1747 whilst investigating scurvy in English sailors, but it was the first randomized curative trial, of streptomycin, performed in England in 1946, almost 70 years ago by the Medical Research Council that established the foundation for modern medicine. The pharmaceutical industry has been honing this approach ever since, but in recent times we have seen more scrutiny as to the real world relevance of the randomized clinical trial (RCT).1

Over those 70 years there has been increasing evolution of ethical and regulatory oversight, with an emphasis on pharmacovigilance and safety to ensure the rights of patients and avoid doing harm (in part prompted by the Thalidomide disaster in 1957-1961, and others), incorporating the Nuremberg Code in 1947, the Universal Declaration of Human Rights in 1948, and the Helsinki Declaration in 1964. Numerous regional and local laws also enshrined ethical practice in the conduct of RCTs and studies, with the seminal publication of Good Clinical Practice by the International Conference on Harmonization in 1996.

Clearly, truncating considerable evolution in the conduct of RCTs in almost three quarters of a century cannot do justice to the progress made, and the considerable work of diverse individuals and organizations during the 20th century. Now, in the 21st century we are likely to see unprecedented transformations in how we conduct RCTs and allied studies within markets that not only want proof of efficacy and safety for regulatory requirements, but real world effectiveness of therapies, devices and diagnostics.

RCTs - Why are they no longer sufficient?

There is no assertion that RCTs are going to be replaced as a gold standard, not at least for regulatory filings and approvals, but certainly for other purposes, they may actually be a brass standard. RCTs are of course the evaluation of the efficacy and safety of an investigational compound or intervention within a controlled environment, as per a prior designed protocol, within a highly selected patient cohort as per inclusion and exclusion criteria.

As such, though this has proven to be a mainstay of efficacy and safety evaluation for marketing authorization, it does not reflect the actual use of the compound or intervention within real world clinical practice. When a compound enters the commercial market, it can often be initially used in the more advanced and sickest patients, with worse efficacy versus clinical trials. Even prior to authorization, as a whole, active compounds have seen efficacy versus placebo plummet over the decades, likely for multifactorial reasons, from biology to the regulatory process.2,3

Regulatory approval used to be viewed as the final hurdle for any pharmaceutical company, but further steps to assure access and reimbursement have increased the focus on real world effectiveness and value for money from payers and governments. More representative data, reflecting outcomes in a population more reflective of the patients being seen by clinicians in everyday practice, as well as the detection of known or new, additional safety signals, is now a priority for the pharmaceutical industry.

There are only two types of data to worry about - data you own, and data you don't

In the 20th century data created and owned by the pharmaceutical and allied industries, such as from RCTs and sponsored observational studies, was sufficient, but as noted previously, real world data is increasingly required, and this is most often data not created or owned by the industry, but by clinicians and patients themselves.

Demographic data, diagnosis, natural history, treatment history and outcomes are increasingly being liberated from paper records and incorporated into digital records internationally. Though there are still challenges with transitioning data from machine readable, to human readable, actionable information, the opportunity to evaluate longitudinal patient data is unprecedented. The ability to generate insights from real world patients will only be improved as we see linked data from the overall patient experience with illness, not just the outcome of a fifteen minute consultation and diagnostic evaluation with a physician.

The impending explosion of patient generated data, not limited to patient reported outcomes (PROs), but also from wearables and digital biomarkers, as well as insights from social and/or patient networks, is already demonstrating a completely different profiling of what is to be a patient. Furthermore, changes in the management of patient consent, and how health data is stored, governed and accessed will have significant implications for both clinical practice and health research. We are the crest of a wave that is not just being facilitated, but driven by new technologies.

How the pharmaceutical and allied industries can collaborate and work with data providers/custodians will be a defining action in how future health markets work. No company can work alone, and purchasing data from commercial providers, such as payer-level data, is insufficient to answering all lines of enquiry for R&D or commercial purposes. Sadly, not all data grows on trees.

So, how can real world and/or big data help?

There are many scenarios where data obtained from the real world setting, i.e. data not derived from the artificial environment of a clinical trial, but from clinical practice, as well as data that is characterized as "big", so typically of significant variety, rapid velocity and high volume, with veracity ("the four V's"), can build upon RCTs. The following three are likely "big ticket items" for consideration:

  • Translational research, requiring well-characterized patients with potential bio-banked samples, or an opportunity for recall
  • in silico modeling of study protocols, inclusion and exclusion criteria evaluation and subject identification to lessen the risk of study amendments or recruitment bottlenecks
  • Phase IV studies, inclusive of Post Authorization Safety Studies (PASS) and/or Post Authorization Efficacy Studies (PAES), and pharmacovigilance surveillance derived from clinical practice data

Recent research suggested study amendments cost on average $0.5 million each, with significant and costly delays in patient identification, recruitment and retention being commonplace.4 The ability to evaluate the feasibility of a study protocol, inclusive of inclusion/exclusion criteria could allow for more efficient and less expensive studies. Subject identification and targeted recruitment, based on real world data interrogation cannot be achieved from payer-level or administrative data sources, but needs to be linked to electronic health records, under the right ethical, legal and governance conditions.

Moreover, the development of clinical research networks incorporating real world data, such as the EU Innovative Medicines Initiatives (IMI), Electronic Health Records for Clinical Research (EHR4CR) or European Medical Information Framework (EMIF), point to a future where "putting in the digital pipes" between hospitals willing to allow health data access will allow for the wider identification, access, evaluation and (re)use of health data for R&D that supports and augments RCTs.

One small step for a researcher, one giant leap for R&D

What is being briefly outlined here is a challenge to the culture and practice of the pharmaceutical industry that defined the second half of the 20th century with respect to medical evidence for pharmacological agents. Now, in the 21st century, markets are economically challenged to unprecedented levels, but also more sophisticated and discerning about paying for outcomes and quality. As such we need 21st century, real world data driven tools and solutions, and not 20th century ones that do not fully meet the needs of our consumers, customers and payers today and tomorrow.

References:

1. Bhatt A; Evolution of Clinical Research: A History Before and Beyond James Lind; Perspect Clin Res 2010; 1(1): 6-10; www.ncbi.nlm.nih.gov/pmc/articles/PMC3149409/; accessed 27/07/2015
2. Eichler HG, et al; Bridging the efficacy-effectiveness gap: a regulator's perspective of addressing variability in drug response; Nature Reviews Drug Discovery 2011; 10: 495-506; www.nature.com/nrd/journal/v10/n7/full/nrd3501.html; accessed 27/07/2015
3. Begley S; New drugs trail many old ones in effectiveness against disease; Reuters, Monday June 3rd 2013; www.reuters.com/article/2013/06/03/health-drugs-effectiveness-idUSL2N0EC1E720130603; accessed 27/07/2015
4. Sertkaya A, et al; Examination of Clinical Trial Costs and Barriers for Drug Development; Eastern Research Group, July 25th 2013