The timeline to market a new drug or medical device, from the point of discovery to U.S. Food and Drug Administration approval, can stretch to a decade. By pooling its industry experience and technology, a new health research supergroup led by the Julia Lab within the MIT Computer Science and Artificial Intelligence Laboratory aims to significantly shorten the approval process for pharmaceutical and health care groups.
The team aims to leverage real-world evidence, observational data that are generated during routine clinical practice, and patient health care databases to augment label claims and/or support new drug applications with leading-edge software and algorithms and a depth of regulatory and clinical experience.
Calling themselves the Health Analytics Collective, the team includes MMS Holdings of Canton, Michigan, a data-focused contract research organization (CRO) to the pharmaceutical, biotechnology, and medical device industries; and the Center for Translational Medicine (CTM) at the University of Maryland School of Pharmacy, where the team will be based.
To cut down on the number of required clinical trials, the team compares effectiveness claims for similar drugs in development, examines available evidence from existing data, assesses available treatments, identifies treatment gaps, and evaluates patient risks. To process all of these data, the team relies on Julia, an MIT-incubated programming language designed to solve massive computational problems quickly and accurately, and the use of real-world evidence.
“The field of using real-world evidence in pharmaceuticals is new and the methods are still evolving,” says Alan Edelman, Julia co-founder and professor of applied mathematics. “We created this collective to forecast the future in health care and make critical decisions, giving data a longer life of more than just one use.”
Joga Gobburu, CTM’s director and a former U.S. Food and Drug Administration scientist, has spent years analyzing experiments and clinical trials to advise key drug development decisions. “[Real-world evidence] is new and challenging,” he says. “There is much research required to inform technical methodology and regulatory policy.”
The team members expect to create unique insights from data curation, data analytics, reporting, and regulatory submissions services for pharmaceutical companies, hospital and health care systems, and universities. By combining real-world evidence with their knowledge of areas lacking regulatory precedence, the team is available to guide a company’s business decisions with insights into an asset’s life-cycle management and due-diligence efforts in order to slash the development period of lifesaving drugs.
“Using real-world evidence for the purposes of regulatory drug approvals is an innovative approach that can be applied to support a wide variety of healthcare decisions,” says Uma Sharma, MMS’s chief scientific officer. “This group’s combined efforts will give sponsors of clinical trials the ability to bring safe, life-changing therapies to patients much more quickly.”
The team is riding a wave of big-data investments in the health care and pharmaceutical industries from hardware, software, and professional services, with revenues expected to grow at a compound annual growth rate of more than 15 percent annually, rising to $5.8 billion by the end of 2020.