The Future of Clinical Trials – Redux

Doug Stewart, Clinical Professionals Director of Training Academy and Hosted Employment, has updated his popular clinical trials article from last year about the future of clinical trials and new technology and methods that will help boost progress in drug development and patient safety. 

In 2015, Professor Allen Frances of Duke University published an article sharing his fears, concerns and indeed, open scorn for the current methodologies of pharmaceutical clinical trials.

“It’s been many years since I have trusted anything I read in a medical or psychiatric journal. There is an enterprise wide positive bias; findings never seem to replicate; benefits are hyped; harms are hidden.”[i]

Allen goes on to apportion blame for this state of affairs:

“Drug companies bear most of the blame — the research they sponsor is shoddy and market driven. Scientists are also to blame when they torture data so much it will confess to anything. Medical journals are to blame when they publish positive findings from lousy studies and reject negative results from well done studies. And journalists are to blame when they uncritically accept phony claims.”[ii]

For anyone who is professionally involved in clinical research, these criticisms were deeply concerning, and were shared by many others.[iii] It was clear that radical changes were needed.

“The only responsible courses of action are to improve designs and measures, standardize implementation, change sponsors, achieve complete transparency, report harms as thoroughly as benefits, and eliminate hype. With all the limitations, there is simply no substitute for randomized placebo controlled studies — we must improve them because we can’t do without them.”[iv]

The criticism of the pharmaceutical industry was accurate, but it appears that the industry was aware of these issues as well – in 2012, Transcelerate was formed as a collaboration “across the global biopharmaceutical research and development community to identify, prioritize, design and facilitate implementation of solutions designed to drive the efficient, effective and high quality delivery of new medicines.”[v]

Their initiatives are many and varied, but are certainly addressing the potential solutions proposed by Allen. In summary, they propose transparency of data, data sharing, far more patient involvement in trial design and agreed standard protocol templates, allowing far more clarity and reproducibility of trials.

The International Council on Harmonisation appears to share many of the same aspirations. It has adapted its guidelines to include radical changes to the design, implementation and monitoring of clinical trials.[vi]

Industry and ICH appear to be working collaboratively to improve the patient experience, the quality of the data, access to metadata and overall transparency of results. The principles are in place, the goals are clear – how will this all be implemented?

Patient Centric trials

“Patients want transparency. Help us to understand what’s working and what isn’t. Share results. Give us hope for a cure or a better medicine. Treat patients taking part in a clinical trial as investors; they are investing their bodies in your trial so keep them informed about that investment. They also want to be thanked afterwards. These are basic things”.[vii]

There is a strong movement towards including patient groups and representatives in the trial team, from design through to sharing of results. Patients want their study to be integrated into their care. They want the culture of the sponsor and the site to be centred around them. Many companies, both CROs and sponsors, are paying more than lip service to this, with departments being devoted to patient involvement. Given that recruitment of patients to trials is one of the biggest challenges in clinical research, this represents a welcome move. More importantly though, this enables patients to ask the questions that they want answered, not simply those of the marketing department.

Leading on from this, any developments that make trials more available to patients with work commitments or those who live long distances from sites are desirable. Patient profiling, identifying the individual needs of the person taking part can help to reduce issues with recruitment, broaden the patient demographic and improve retention on studies.

Wearable Technology

The internet and wearable technologies (such as health trackers and smart watches) mean that data can be collected much more easily, as well as over longer periods. For example, rather than a single twelve lead ECG being taken at the site on a specified day, ECG data can be captured over the course of the whole study with minimal inconvenience. Patient surveys and interviews can be captured via the internet, enabling scheduling of events to suit the patient. These initiatives are already happening, but in a relatively restricted fashion. The advantages are undeniable, and are already leading to further innovations, and the concept of the Siteless Clinical Trial.

Siteless Clinical Trials

The concept is that the more the patient can be treated, communicated with and observed remotely, the less need for the patient to disrupt their normal routine there is. As well as convenience for the individual, this would also mean that anomalies caused by changes to routine, and the artificial nature of the site visit would be minimised. As recently as last month, one company had raised over $38 million to advance these technologies, believing that as well as improving the experience of the patient, capturing more raw data and eventually lowering costs, these technologies will also enable minority groups to be far better represented.[viii]

Risk Based Trial Design and Monitoring

These technologies, along with older ones such as eCRFs for data capture and digital uploads form monitoring systems directly into the trial databases mean that far greater amounts of data can be captured and analysed – which in turn calls for a very different approach to monitoring the studies, and indeed to their design. Risk based trial design and monitoring is a requirement of ICH GCP from June this year in all new studies. This effectively ends the routine monitoring visit, replacing it with centralised monitoring of trial data and algorithmic applications being used to present these data to the study team, effectively in real time, to give indications of the main areas for the remote monitor to concentrate on. Not without a certain amount of controversy (there are concerns that the site/CRA relationship will be affected negatively), there is little doubt that this approach will improve both patient safety and data quality.

All of which, in turn, leads to larger, cleaner data sets.

Big Data

The pharmaceutical industry has been relatively slow to catch up with the “big data” revolution. Much of this delay can be put down to jealously guarded intellectual property concerns of a legal and financial nature. However, calls for the publishing of all study reports have grown, and the requirements of regulatory agencies have enforced them.  This is not the same as publishing individual data sets, or indeed collective ones, but it does appear to have had the effect of encouraging cooperation with the data themselves. In fact, some of the blue chip pharmaceutical companies have been actively setting standards for data sharing since 2013, but it is only now that the real efforts look like bearing fruit. Given that the volume of data captured in trials is going from mega and giga bytes to tera bytes, it is logical to share anonymised data captured for similar interventions.

Volume and complexity of data does, of course, lead to the logical challenge of how that data can be analysed in a timely and useful manner. After all, although there are many more needles being captured, they are being done so in bigger and more numerous haystacks.

Artificial Intelligence (AI) and Machine Learning

These exercises in collaboration are predicted to radically alter the nature and timelines of regulatory submissions for new medicines and devices. By the use of AI and Machine Learning “… (Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”, Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves) results and conclusions could be drawn from less artificial, contrived study designs and more from real world evidence.”[ix] Thus creating a virtuous circle of reinforcement – the use of ever advancing technology to capture and analyse ever more data from more and better designed studies in a more “real world” environment.

Again though, we face the same issue that we started with – the essential conservatism of the pharmaceutical industry, and the adoption of the necessary technologies and changes in trial design and data capture that have already been discussed. The cautious optimist though, will note that the revisions to ICH E6 acknowledge the usefulness of technology in study planning and data capture more than ever before.

In summary, collaboration, cooperation, transparency and the patient are providing the direction of travel for clinical researchers. There will, no doubt, be surprises on the way, and IP lawyers may be shifting in their chairs uncomfortably, but the author feels a lot more comfortable than he did when he read the thoughts of Professor Frances!

[i] http://www.huffingtonpost.com/allen-frances/the-crisis-of-confidence-_b_6432236.html

[ii] http://www.huffingtonpost.com/allen-frances/the-crisis-of-confidence-_b_6432236.html

[iii] http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124 http://www.bbc.co.uk/news/health-25576520

[iv] http://www.huffingtonpost.com/allen-frances/the-crisis-of-confidence-_b_6432236.html ibid.

[v] http://www.transceleratebiopharmainc.com/about/

[vi] http://www.ich.org/fileadmin/Public_Web_Site/ICH_Products/Guidelines/Efficacy/E6/E6_R2__Step_4.pdf

[vii] http://www.pharmatimes.com/magazine/2016/may_2016/patient-centricity_ghost_in_the_machine

[viii] https://pharmaphorum.com/news/science-37-gets-funding-develop-site-less-clinical-trials/

[ix] https://www.forbes.com/sites/bernardmarr/2016/12/06/what-is-the-difference-between-artificial-intelligence-and-machine-learning/#4331b6822742

 

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