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Life science companies are continuously looking for ways to advance clinical research while simultaneously improving the understanding of drugs they are developing. One of the biggest issues for researchers is the high failure rate of new drugs during clinical development. The stakes are high in a global pharma market that is expected to exceed $1.2 trillion by 2018.

The average cost of bringing a drug from development to FDA approval is over $2.5 billion, according to a recent study by The Tufts Center for the Study of Drug Development. This figure includes costs for the drugs that don’t make it through to the approval phase, and the Tufts Center notes that higher drug failure rates contribute significantly to increases in R&D costs.

But there’s a big opportunity here: If life science companies can get enough insight early in development, they can create a more efficient drug development process and prioritize resources for the most promising therapies. Big data analytics and new clinical technology — such as mobile health solutions and wearable devices — promise to significantly change how trials are conducted and increase the value of the data and insights that come out of these trials.

Advancements in computing power and predictive analytics tools enable us to process vast amounts of information and develop insights in mere seconds. Technology’s role is to bring together disparate data sources so the industry can share data and use advanced analytics to make better decisions — all with the goal of getting effective drugs to market faster.

For example, 23andMe, the Silicon Valley maker of personalized genetic tests, has hired Genentech R&D executive Richard Scheller to lead a new therapeutics group that will use the company’s archived genetic data to find correlations and patterns across different disease states in an effort to develop its own therapies.

And other industry partners are already working together to figure out how to best deploy wearable devices to patients and link the data from these devices to traditional clinical data to measure changes in patient behavior and use the information for regulatory-acceptable decision-making. Recently, Medidata (my company) and Garmin worked together to incorporate activity trackers into clinical trials. Garmin’s activity tracker measured steps taken, distance, calories burned, and hours slept to capture patient data during clinical trials 24/7, without the clinic visits. And companies like Vital Connect have received FDA clearance to use their biosensors to capture clinical-grade biometrics.

A patient’s health is traditionally measured in the clinic, but the life science industry is approaching an age where it can connect to a new class of behavioral data that has never before been accessible. Both of the above examples have led to increased patient engagement during clinical trials and ultimately bigger and smarter data, all of which are crucial to discovering groundbreaking treatments.

When you ask a patient how they feel, you get subjective responses. Subjective data is useful in science, but objective data is always better. The life science industry can now gather new kinds of objective data through mobile devices and activity trackers. This provides a real-world, real-time measure of patient physiology and how a drug affects quality of life — an increasingly important measure for pharmaceutical companies, regulators, and insurance companies.

This is evident in something as simple as the six-minute walk test, which has been used for years in clinical trials involving cardiovascular, respiratory, and central nervous system diseases as a valid proxy for disease severity. There is nothing mathematically or scientifically wrong with the test, but with new technology we can capture a more comprehensive measurement of patient health. Instead of putting patients in front of a doctor for a six-minute snapshot of their ability to walk, patients can now wear a device that continuously measures their activity and provides a complete picture of movement without visiting the doctor’s office. Patients don’t have to disrupt their day, and physicians and researchers can be armed with much richer, more nuanced data than a six-minute test.

Apple’s recent unveiling of ResearchKit that will use iOS apps for medical research signals an interest across industries in the value of patient-direct data.

Mobile devices and big data analytics can also significantly diminish the burden on patients. Wearable devices can reduce the number of times patients need to go to a clinic and can provide a better, fuller picture of physiological data needed to measure a drug’s impact, minimizing needless testing on patients. GlaxoSmithKline, for example, spoke at the South by Southwest conference last month about its interest in the use of biosensors for clinical trials to improve data quality. Implementing these biosensors would not only lead to increased data but would also reduce interruptions in a patient’s day through remote monitoring. Technology could therefore improve the patient experience in clinical trials at large.

Ultimately, if life science companies can show not only that their drug is effective in treating a condition but that it also dramatically improves a patient’s quality of life, it can help regulators make better decisions about which drugs to accelerate to market and can help differentiate a drug from other “me too” compounds.

But we’ll only see this change if there is buy-in from all key stakeholders. When regulators are more comfortable with new approaches to clinical development, pharma companies will be more likely to use new technology and big data analytics in their studies. In parallel, regulators will show more willingness to accept these approaches when pharma shows a commitment to introducing standardized approaches backed by strong scientific evidence.

Mike Capone is Chief Operating Officer at Medidata, where he plays a central role in product and solution development, professional services, go-to-market and day-to-day operations. Before Medidata, Mike spent 25 years with ADP — one of the world’s largest B2B software providers — where he held positions in product development, information technology, and operations.

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