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Smart Devices, Big Data, and the Value of Long-Term Research

Posted on August 29th, 2016 by in Pharma R&D

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In a new feature in The Atlantic, author Jessa Gamble makes the case that Modern Medicine Is Too Reliant on Short-Term Studies, too often preferring the instant gratification that comes from a short study, only to find those results soon challenged by yet another short-term study.

“The longitudinal study has great power because it can compare later values with the same person’s baseline state,” she argues. “What is average for the general population may represent an alarming shift for any given person.”

Gamble points out, however, that long-term observation only works if it can be sufficiently unobtrusive. “The moment monitoring becomes burdensome for subjects, the results may not be representative of normal life—and subjects may start disappearing from a study.” And that’s why now is the perfect time for modern medicine to embrace long-term studies on a large scale – because we have the technology to make the process almost seamless with people’s lives.

Wearable Experiments

Over the last few years, wearable technology has become increasingly accepted, and devices like the Fitbit (which tracks data such as heart rate, quality of sleep, and number of steps walked) are especially popular. In fact, Forrester Research has found that a full 20% of online adults use a wearable device. With such high adoption rates of health monitoring devices, which are typically worn unobtrusively like a watch, it’s easy to see how long-term studies could be conducted with little effort or bother on the part of the participants.

Some such studies are already in motion. As Virginia Lau of Medical Marketing & Media recently reported, “The Michael J. Fox Foundation for Parkinson’s Research teamed up with specialty drugmaker Cynapsus Therapeutics and Intel to incorporate wearable technology in a Phase-III clinical trial of APL-130277, an experimental drug being tested to treat ‘off’ episodes, which are periods of time when the symptoms of the disease return even though the patient is still on a therapy.” Lau explains that participants in the study use a wearable device and a smartphone app that sends the information gathered to a cloud platform, where researchers grab the data to glean insights about the disease and potential drug reactions that can impact movement and perspiration.

While studies like this which utilize wearable monitoring devices can benefit from the large and detailed quantities of data that they are able to easily collect, it’s a double-edged sword. After all, at some point that data has to be analyzed. That’s the challenge that Big Data is now presenting to multiple industries, as it is fraught with both the possibilities of great knowledge and the prospect of endlessly time-consuming analysis.

The Role of Smart Devices in R&D

A few years ago, right around the time the Fitbit came onto the market in fact, McKinsey was making the case for the incredible value of Big Data in the report How Big Data Can Revolutionize Pharmaceutical R&D, inviting us to imagine a world where potential patients for clinical trials are identified through an expanding array of sources, where predictive modeling of biological processes and drugs is far more sophisticated, where data is captured electronically and flows easily between functions and organizations, and where trials are monitored in real time.

“Pharmaceutical companies can deploy smart devices to gather large quantities of real-world data not previously available to scientists,” they wrote. “Remote monitoring of patients through sensors and devices represents an immense opportunity. This kind of data could be used to facilitate R&D, analyze drug efficacy, enhance future drug sales, and create new economic models that combine the provision of drugs and services.”

Fortunately, the technology is rapidly catching up to the vision, both in its ability to collect data with wearable devices and to analyze that information with increasingly sophisticated data mining tools. And for pharmaceutical companies, this is not only promising on the R&D side, but also where pharmacovigilance is concerned.

Data and Drug Safety

In an article for Drug Discovery & Development on pharmacovigilance, Data2Life CEO Itzik Lichtenfeld opined that monitoring and reporting cycles can now be measured in days rather than years due to signal detection technologies and the proliferation of secondary data sources like clinical data and electronic health records, claims files, existing medical literature, regulatory reports, and social media.

“A lot of work is required to make these data sets into something useful or meaningful for PV. Even more effort is required as pharmas seek to combine and cross-reference real-world data with other types of data (case reports, claims data, existing research studies),” writes Lichtenfeld.

“Today, the industry finds itself in the very early stages of a long-term journey toward a more data-driven and analytic-enabled approach to PV, post-marketing surveillance and drug safety management as a whole,” he says, noting that on that journey, new algorithms, natural language processing, and flexible technology platforms all must have a role in dealing with this deluge of data so that it can be helpful to pharmacovigilance teams.

Seeing the Big Picture with Big Data

“The word ‘monitoring’ is rarely sexy,” admits Jessa Gamble, but she also insists that it is how you build a solid case around evolving conditions. “To understand how people change, one must watch them changing.”

By focusing less on the short-term satisfaction of results from quick, publishable experiments, and taking the time to monitor patients through long-term studies, the pharmaceutical industry can improve everything from discovery to safety. With the tools for collecting and analyzing the data becoming more effective and easy-to-use, now is the time to go long.

Read more on ScienceDirect, Elsevier’s leading information solution for pharma researchers to more effectively and efficiently search authoritative, full-text scientific, technical and health content.


All opinions shared in this post are the author’s own.

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