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How Can the Life Sciences Benefit from Using Simulated Data?
Posted on June 28th, 2019 by Christy J. Wilson in Pharma R&D
The life sciences, like many sectors, are putting a higher and higher premium on good data. In our digital world, access to reliable data is a necessity, whether it’s healthcare providers counting on data analytics to inform patient care or drug developers requiring insightful data to create effective new therapies.
But there are many obstacles surrounding access to and use of real-world data. It is often siloed, hard-to-find, hoarded by competitors and exists in numerous different formats. While there are now information solutions and platforms that address some of those challenges, privacy and security concerns are still problematic, particularly when it comes to sensitive health and medical information.
Given these complications, there is a growing interest in simulated or synthetic data, which TechCrunch describes as “computer-generated data that mimics real data.” Software algorithms can take in data samples (e.g. clinical trial data, medical records, etc) from the real world and generate simulated datasets that researchers can work with—without compromising patient confidentiality.
In a piece in The Atlantic titled “How Fake Data Could Protect Real People’s Privacy,” author Viviane Callier offers a case study on how university researchers using Census data sought to avoid privacy issues by modifying the dataset. “In their approach, the researchers feed the original Census data, which is kept confidential, into a complex statistical model that generates a simulated population that has the same general features as the original data,” writes Callier. “If you have a confidential dataset of 100 individuals’ ages and incomes, for example, a corresponding synthetic dataset composed of 100 imaginary individuals would have the same mean age and mean income as the original.”
A Vision of How Simulated Data Could Boost Development of Therapies
The implications for the life sciences, where the demand for patient data far outpaces the supply, is clear. In Virtusa’s white paper Unlock the Power of Simulated Data to Accelerate Research, author Santanu Sen considers the possibilities for rheumatoid arthritis (RA), an area where better therapies are much needed:
“If we can generate simulated data and simulate our study area (for example, the entire US population for RA), that will lead to novel insights and multiple research areas,” explains Sen. “This is where AI models/algorithms can help determine patient profile-specific therapeutic regimens, leverage evidence-based predictive model to help identify and mitigate side effects on therapies, optimize drug synergy in multi-drug regimens, and further perform drug-dose optimization to simultaneously realize maximal therapeutic efficacy and clinical safety.”
The Many Benefits of Simulated Data
The advantages that using simulated data provide to companies and researchers are significant and include:
* The ability to maintain people’s privacy
* Fewer concerns about regulations
* More cost-effective and efficient
* Flexible, customizable and easier to control
* Makes it possible to generate and work with much larger datasets than what might be found in the real world
* Provides a leg-up to start-ups that don’t already have access to large collections of expensive data like their established competitors
Brave New Synthetic World
These advantages could apply across sectors ranging from tech to the social sciences. But how will synthetic data apply specifically to life science organizations? According to Dane Stout’s white paper titled The New Synthetic, simulated data could be used to accelerate clinical studies, improve patient safety post-market, and incorporate care data earlier in the medical device design and development process.
“A promising example from clinical research may be to serve as a synthetic control arm for clinical trials,” adds Stout. “Rather than collecting data from patients who have been assigned to the control or standard-of-care arm, it’s possible to model those comparators using real-world data that has previously been collected.”
Simulated data represents a new horizon, and it is one that is still being tentatively explored. Real-world data is already complicated enough to deal with, so there is still much to learn about utilizing synthetic data in its place. But the immense advantages that it could provide for industries like pharma and biotech are so significant that it shouldn’t be long until we see its use more broadly adopted.
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Christy J. Wilson
Sr. Director, Pharma and Biotech Segment
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