Revolutionizing Clinical Trials Through Real-Time Data Capture and Analytics

January 25, 2018

This article discusses real-time data capture and analytics in clinical trials.

The pharma industry has a problem to solve, specifically that the trajectory of costs to bring new medicines to market is unsustainable, with a range of $3.7-12 billion per new drug.Clinical trials are particularly costly and time consuming, making up the longest and most expensive stage of drug development.2,Overall, this means that pharmaceutical companies must strictly prioritize their drug development portfolios, resulting in cancelled or delayed drug programs, that can also lead to promising new treatments being delayed or not developed at all.4

Further, the cost of clinical development is exacerbated by the current patchwork of dated clinical data systems with inadequate interoperability.The result for drug developers are problems such as: errors and delays in trial data, process inefficiencies, avoidable costs, regulatory audit findings, poor management insight, and delayed decision-making.

The adoption of today’s digital technologies offers a unique opportunity to revolutionize clinical trials with significant improvements in time, cost, and the quality of data collected through the introduction of real-time data capture. This article will explore this potential by asking some bold ‘What if?’ questions relating to patient safety, clinical development costs, and payment for outcomes (Figure 1).

 


 

Patient safety could be better than ever before

Has the sophistication of patient safety monitoring kept pace with the expectations of a risk averse society, or with the risks associated with potent new products that potentially have profound biologic outcomes? The pharmaceutical world cannot be seen in isolation, rather it should be considered in the setting of other industries that have risks to customer safety at their core. In the aviation industry, airlines and aircraft/engine manufacturers know the exact status of aircraft and their engines in real-time. This real-time monitoring contributes to low-cost air travel and passenger safety (see Box 1: Integrated Vehicle Health Management (IVHM).6

Is it acceptable to have patients taking part in clinical trials of potent, potentially harmful agents when safety data is delayed, is incomplete, and is not aggregated-meaning that it does not contribute to the ongoing monitoring of safety? Surely the industry should be able to claim that patients in clinical trials are the safest they have ever been and the technology used to achieve this is as sophisticated as that used in other industries? The question then must be how the industry can make this claim.

In addition to the ethical imperative to carefully oversee patient safety using the best available technology and knowledge, there are severe financial penalties if this is not adhered to. Sponsors cannot afford to waste resources on the futile completion of studies in which the side effects of an intervention negate the potential benefits, especially when the timely use of data would stop studies at an earlier stage.

There are already rare examples of good practice in this area. Today’s digital technologies can enable the collection of data directly from the patient in the clinic or at home (devices/wearable sensors/investigator entered); however, the key is sophisticated databasing technologies, aggregating, and providing analytical insights into the data. Our example is Cmed’s encapsia® technology, which enables real-time data capture and analytics to enable better patient safety in clinical trials.

 

What if 30% productivity gains could be achieved?

Pharmaceutical companies are investing in several technologies such as eSource (e.g. electronic informed consent, and direct data entry into tablet computers), and remote patient monitoring (e.g. wearable or home-based medical devices transmitting patient data securely) that will enable real-time data capture and analytics. These can detect common regulatory agency site audit inspection findings,7 such as problems with protocol compliance, source documents/CRF discrepancies, informed consent, and drug accountability. Significant costs can be avoided through reduction in CAPA (corrective action/preventive action) programs resulting from both external and internal audits. Real-time workflow data for study management will enable accurate trial status reporting, with benefits to drug supply reconciliation and wastage reduction, accurate and timely payments for investigational sites and CROs, and an overall improvement in study and program budget tracking.

The use of remote patient monitoring technologies has the potential for further cost reductions. For example, the typical Phase III protocol in 2012 had nearly 170 procedures performed on each patient across 11 visits.If multiple visits can be reduced, and technologies such as patient engagement apps introduced on the patient’s own smartphone (Bring Your Own Device-BYOD) it seems likely that the new digital technologies will lead to enhanced patient engagement, compliance, retention, and even initial recruitment. This combined with richer, more frequent sensor data sets could lead to reduced patient sample sizes and eventually new digital biomarkers and endpoints.9 Perhaps the largest cost savings will be through earlier and better decision-making, such as prompt trial termination or re-design decisions. Consequently, 30% productivity gains are achievable in clinical trials by embracing the technologies that will enable real-time data capture and analytics.

As therapeutic interventions become more complex, the costs of clinical trials have ratcheted up again. Genetic modification treatments, such as CAR-T therapies, are a good example where the application of newer technologies and approaches can have financial as well as medical and scientific benefits.

Importantly, the benefits of such investments in clinical trial infrastructure go further by laying the foundations for ‘beyond the pill’ applications that will enable payment for outcomes.

Remote patient sensing technologies combined with patient engagement apps on patients’ own smartphones during clinical trial phases will not only expedite the clinical trials process, but will aggregate unique patient behavioral data sets and insights that will facilitate algorithm and services development to maximize effective drug use and resulting patient outcomes.

It could be argued that the industry’s current struggle to ‘bolt-on’ digital services to new medicines10 is largely due to a lack of earlier deployments of these technologies in development and research (i.e. investigating digital biomarkers/endpoints). This is exacerbated by multiple fragmented approaches across complex organizational structures constantly managing short-term budgetary tensions. A ‘digital continuum’ is required that will facilitate the continuity and alignment needed for scaled adoption of these digital advancements and the means for outcomes-based payment.

 

Challenging the current Phase III model

Research from Yale School of Medicine11 found that nearly one-third of FDA approved drugs from 2001 through 2010 had major safety issues years after they were widely available to patients, highlighting inadequacies in traditional approval processes. The idea that Phase III trials could be replaced post-Phase II by technology enabled ‘in-life’ testing utilizing ‘live’, conditional, adaptive, or rolling licenses was suggested some time ago.12 Indeed, partial implementations have been seen within the FDA accelerated approval (AA) and EU conditional marketing authorization (CMA) initiatives. However, this vision has been challenged by the lack of available infrastructure and high costs to implement the necessary data capture, analytics, and reporting technologies.

Although cost will always remain a concern, the current ubiquitous nature of the internet, smartphones, and the decreasing cost of medical grade sensor technologies make this vision a possibility. Indeed, the common underlying infrastructure needed for real-time data capture and analysis for both randomized clinical trials and real-world evidence could enable convergence of data sources to make ‘live’ or conditional licenses a reality. For pharma companies, this could dramatically change the productivity and economics of their business. For patients, this could mean gaining access to life-saving new medicines years earlier while improving safety monitoring.

 

What is the industry waiting for?

There is a lot of talk about patient-centricity, but if pharma is truly patient-centric why are technologies not being implemented faster? Multiple barriers to adoption are evident including different global legal requirements, data privacy, and regulatory frameworks. High up-front costs, complex technology integrations, fear of project delays, rapid technology cycles, vast data volumes, and the necessary organizational changes are all important challenges in introducing these technologies. In addition, behavioral barriers in patients, caregivers, healthcare professionals, and within pharma personnel will all be significant in adoption.

Although the journey will be difficult, these barriers can be overcome through greater focus, investment, and collaboration between pharma and regulators, investigators, patient groups, and technology companies. The regulators are more receptive to the benefits of technology than many perceive-for example, the FDA called for input on new clinical trial technologies13 in 2015 and announced the formation of a new FDA Digital Health unit14 in 2017.

However, it is not enough to just invest in custom indication-specific services with little transferability across the trial portfolio. A new, modern clinical trial platform approach can change the industry, purposely designed for interoperability rather than continual patchwork of dated systems derived from historical silos. Investment in this foundational clinical infrastructure will enable scaled digital solutions across therapeutic areas. An ecosystem of best-of-breed suppliers is needed with system interoperability and pre-competitive collaboration amongst pharma on key technology platforms, perhaps facilitated by cross-industry groups such as TransCelerate Biopharma,15 the Pistoia Alliance,16 or the Clinical Trials Transformation Initiative.17

If truly patient-centric, then pharma must accelerate the scaled adoption of digital technologies that enable real-time data capture and analysis. After all, the industry wants to make patients safer than ever before in clinical trials and beyond.

 

Andrew Griffiths is chief scientific officer at Cmed Clinical Services; James P Angus is commercial director at IVHM Centre, Cranfield University, UK; and Andy N Brown is director at Drug Development Consulting Ltd., UK.

 

 

 

References

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2. PAREXEL. Parexel Biopharmaceutical R&D Statistical Sourcebook 2016/2017. PAREXEL International Corporation; 2016.

3. EFPIA. The Pharmaceutical Industry in Figures. Key data 2016. 2016.

4. PhRMA. 2016 profile. Biopharmaceutical research industry. Phrma. http://www.phrma.org/report/industry-profile-2016. Published 2016. Accessed July 15, 2017.

5. Getz K. Veeva Announces EDC and eSource to Transform Clinical Data Management | Veeva. https://www.veeva.com/resources/veeva-announces-edc-and-esource-to-transform-clinical-data-management/. Published 2016. Accessed July 14, 2017.

6. Jennions IK. The developing field of integrated vehicle health management. Journal of Aerospace Sciences and Technologies. 2013;65(1A):69-78

7. Shetty Y, Saiyed A. Analysis of warning letters issued by the US Food and Drug Administration to clinical investigators, institutional review boards and sponsors: a retrospective study. Journal of Medical Ethics. 2014;0:1-6.

8. Getz K, Campo R. Drug Development Study Designs have Reached the Danger Zone. Expert Review of Clinical Pharmacology. 2013;6(6):589-591.

9. CTTI. Novel Endpoints. Clinical Trials Transformation Initiative. https://www.ctti-clinicaltrials.org/projects/novel-endpoints. Published May 27, 2016. Accessed July 6, 2017.

10. Neville S. Digital disrupters take big pharma “beyond the pill.” Financial Times. https://www.ft.com/content/d7a60642-0361-11e7-ace0-1ce02ef0def9. Published April 24, 2017. Accessed July 6, 2017.

11. Downing NS, Shah ND, Aminawung JA, et al. Postmarket Safety Events Among Novel Therapeutics Approved by the US Food and Drug Administration Between 2001 and 2010. JAMA. 2017;317(18):1854-1863. doi:10.1001/jama.2017.5150.

12. Arlington S, Barnett S, Hughes S, Palo J. Pharma 2010: The threshold of innovation. 2002.

13. FDA. Using Technologies and Innovative Methods To Conduct Food and Drug Administration-Regulated Clinical Investigations of Investigational Drugs; Establishment of a Public Docket. Federal Register. https://www.federalregister.gov/documents/2015/10/29/2015-27581/using-technologies-and-innovative-methods-to-conduct-food-and-drug-administration-regulated-clinical. Published October 29, 2015. Accessed July 15, 2017.

14. Brennan Z. FDA to Create Digital Health Unit | RAPS. http://www.raps.org/Regulatory-Focus/News/2017/05/04/27484/FDA-to-Create-Digital-Health-Unit/. Published 2017. Accessed July 15, 2017.

15. Kellar E, Bornstein SM, Caban A, et al. Optimizing the Use of Electronic Data Sources in Clinical Trials: The Landscape, Part 1. Drug Inf J. 2016;50(6):682-696. doi:10.1177/2168479016670689.

16. Pistoia Alliance. Wearables/IoT - Lifescience’s and data standards: transmission protocol, metadata. https://ip3.pistoiaalliance.org/subdomain/main/end/node/1588. Published 2015. Accessed July 7, 2017.

17. CTTI. Mobile Clinical Trials (MCT). Clinical Trials Transformation Initiative. https://www.ctti-clinicaltrials.org/programs/mobile-clinical-trials. Published May 22, 2016. Accessed July 7, 2017.