Posted by Andrew Burrows on 01 September 2017
The future of clinical trials - INDUSTRY VOICES
Artificial Intelligence, mHealth, Internet of Things (IoMT), big data, social media; there's no doubt that technology is changing clinical trials. However, everyone has different ideas about how best to use these technologies and where they are taking the trials of the future.
Additionally, with innovative trial designs, increasingly patient-centric approaches, biomarkers and personalised medicine, clinical trials could look very different in 5, 10 or 20 years.
We asked dozens of experts from every area of clinical trials one simple question: what is the future of clinical trials? Discover their answers below.
Patricia Salber, MD (@docweighsin), CEO, The Doctor Weighs In
‘One barrier to clinical trials, particularly for new drugs that hold the promise of substantially better clinical outcomes compared with the standard of care, is that patients and their doctors may not be willing to take a chance that they will be randomized to the control group.
Substituting big data analytics for a control group could conceivably allow the efficacy of the drug to be tested on all willing to participate, but instead of being compared to a concurrent control group, they could be compared to a control group culled from an enormous data base. In fact, for some clinical questions, it is possible that the trial could be conducted entirely within the database.
Although there may always be a need for actual patient participation in some studies, moving many of them into the world of big data analytics could reduce both the time and cost of bringing a therapy to market compared with traditional clinical trials. It would also allow for a wider variety of patients to be included making the results more applicable to real life compared to trials where exclusion criteria often limit the findings to a much more homogeneous population.’
Dr Steven Anderson, Chief Scientific Officer, Covance
‘Basic and applied sciences are providing a better understanding of disease biology and the relevant biomarkers (diagnostic, prognostic, therapy response etc) associated with specific diseases or subcategories of that disease. With that improved understanding, comes the increased incorporation of biomarkers in the design and execution of clinical trials.
Biomarkers are incorporated in a variety of ways including exploratory endpoints, patient stratification and selection for study arms, all the way through the use as a companion diagnostic for an approved therapy. Biomarker driven trials are now common across all therapeutic areas and are important features of breakthrough therapy designations and the pathway for accelerated approval. Genomic and proteomic approaches to biomarker discovery and validation will support the continuing trend of the important use of biomarkers in the success of drug development.’
John Mack (@pharmaguy), Editor & Publisher, Pharma Marketing News
‘I just have one word: mobile.’
Isabelle Naëije, Assoc. Global Trial Director, GDO Trial Management Oncology, Novartis Pharma AG
‘The era of personalized medicine has started. Every disease is fragmented in several sub-diseases. For example, we don’t look for a treatment for breast cancer anymore. We want to know what is the appropriate treatment for every individual patient suffering from breast cancer. The way clinical trials are designed and conducted has to change accordingly. We need to tackle clinical development of every compound as it was a compound for an orphan disease indication.’
Michelle Petersen (@shelleypetersen), Founder, Healthinnovations
‘When it comes to the evolution of clinical trials, the good news is that there is still room for a vast amount of improvement; the even better news is that the technology to enable improvement is already in existence. To simplify, the desired result of any clinical trial is reproducibility at every stage, therefore, every stage must be taken into account. The starting point is Artificial Intelligence (AI) which will be veined throughout every phase, improving accessibility, remote dosage systems, adherence and drug design.
We begin at the crucial and neglected drug discovery phase, which when honed, will allow other phases to smoothly bleed into one another. Here evolved AI will improve the drug design algorithm, massive mathematical extrapolations which predict efficacy and safety. In the future evolved AI will predict interactions and safety to the epigenetic level for even the most basic compound with a negligible margin-of-error; this will lead to highly desirable virtual trials, predicting and providing patient data for the outcome of each phase.’
Dr Nick van Terheyden (@drnic1), The Incrementalist, incrementalhealthcare.com
‘Clinical trials must move from the traditional method and systems to a more rapid and responsive model that speeds up the process of scientific validation and utilization of new treatments.
We live in the most measured age in history where all data is being captured. This offers a new window into the workings of our world and the human body and this requires a shift in scientific thinking. We will need new ways of understanding this data as correlation supersedes causation. No more discarding or selective use of data but rather full and open publishing, democratizing the access to the data and allowing everyone the opportunity to mine the data for insights.
This approach is not without risk as we develop models of understanding that cannot be explained by current science that will prove difficult to test. The models will be dependent on inclusion of all the data and missing elements or comprehension of the underlying data will skew the output in unexpected ways and will demand rigorous scientific vigilance.
While correlation is not causation the data will offer early insights without specific theories that will need testing and validation continually. No longer will proven theories remain standing forever but will become part of the scientific lexicon that will be incrementally improved over time. Science will be challenged and tested as we continue to expand our knowledge and understanding exponentially.’
Bruce Hellman, CEO/Co-Founder, uMotif
‘Patients today are frustrated. They are frustrated that new treatments are taking too long to come to market; that they don't have the opportunity to easily participate in studies; and that their everyday experience isn't being used by researchers to develop new insights for their benefit.
Having captured over 64 million data points from almost 20,000 participants in observational studies across the world, we've heard first hand from patients that they want to take part in research using their own devices. Patients want to contribute their data, share their experiences and regularly report their outcomes - to help make a difference for themselves, and also for patients in the future.
To make this a reality, trials need to be designed with and around patients, helping them have a great trial experience. This 'patient-centric' approach will not only improve trials from the patient's perspective, but will also deliver huge benefits for sponsors: cheaper studies, quicker recruitment, lower dropout rates, richer datasets and patients who are highly engaged - even, where appropriate, beyond the trial.’
Maneesh Juneja (@ManeeshJuneja), Digital Health Futurist
‘The future of clinical trials begins with designing every process with the patient as the partner. This is easier said than done. Whilst advancing technologies such as AI & IoT will make this easier, fundamentally it requires transforming the culture of your organisation to be inclusive in everything you do. This has to be a vision that every member of your organisation believes in, not just senior leadership.
Additionally, I believe improvements to clinical trials could be made even faster if we harnessed the power of cross sector collaboration. We have to be open to new ideas from anyone anywhere on Earth.’
Dan Sfera, BS, MBA (@TheRealDanSfera), The Clinical Trials Guru, DSCS CRO
‘The future of clinical trials is much like the future of any other industry. It will be run more efficiently by automation and Artificial Intelligence.
We will be able to fail faster and therefore succeed quicker due to big data and analytics. We will connect and communicate to our customers (patients) better via social media. We will be able to reach out and educate thousands of key opinion leaders and influencers (mostly research naive physicians) via content marketing to explore the opportunities that clinical trials can provide not only to their patients, but to their existing private practices.
As a matter of fact, when I think of it, this is not only the future of clinical research, it is also what we are starting to see materialize in the present day and age.’