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Expert Discusses How Data Informatics Is Driving Drug Development

Caroline Seymour
Published: Tuesday, Jan 29, 2019

Subha Madhavan, PhD

Subha Madhavan, PhD

Adaptive clinical trial designs are likely to become a staple of clinical research in the future, reducing both the time and cost of traditional trial development, explained Subha Madhavan, PhD, a chief data scientist at Georgetown University Medical Center.

“The single-drug, single-disease era is gone,” said Madhavan. “We as data scientists have to figure out how to utilize big data to better inform clinical studies that can help patients much more quickly.”

Reliant on real-world repositories of data, adaptive clinical trial designs are building on preexisting knowledge to further clinical development. I-SPY 2 (NCT01042379), led by Laura Esserman, MD, MBA, of the University of California, San Francisco (UCSF), is one such trial that is currently evaluating approximately 20 neoadjuvant treatments in patients with breast cancer. Unlike traditional models, adaptive trial designs enable patients to stop or switch to another arm of therapy, based on their response.

“We're still in early stages of applying novel techniques, such as machine learning and artificial intelligence to understand these data sets better,” explained Madhavan. “Once we do, we can improve how efficient our clinical trials are.”

In an interview with OncLive, Madhavan, who is also the director of the Innovation Center for Biomedical Informatics (ICBI) and an associate professor of oncology at Georgetown University Medical Center, discussed the role of data science and informatics in bringing novel clinical study designs to the field of oncology.

OncLive: What is the goal of the Innovation Center for Biomedical Informatics?

Madhaven: We worked on a collaborative team with Massachusetts Institute of Technology, Amgen, and UCSF, to think about innovative ways to design clinical studies. There are many challenges to designing clinical trials. We tend to design clinical studies from first principles, be it a single drug, indication, or phase. We flipped that [approach around] and asked whether we could combine these different models of clinical trials to make it more efficient and reduce the time and cost [of traditional clinical trials]. We recently published a paper titled Pipelines on that.

There are basket trials and there are umbrella trials. The basket trials test a certain drug across various tumor types. Umbrella trials test multiple drugs in a given tumor type across various gene mutations. A hypothesis-generating phase I study [is generally] a basket trial, which then identifies the populations that we can pass on to a phase II umbrella trial. Then, we can move on to a pragmatic phase III clinical trial. By assimilating this assembly of different study designs, we found that we could drastically reduce the amount of development time for clinical studies.

Does the I-SPY trial fall into this category?

Yes. I-SPY was designed and launched by Dr Laura Esserman at UCSF. It's an adaptive trial in which patients with breast cancer are randomized to different treatment arms as they come in, based on genetic testing and protein testing. The notion is that every patient who walks in the door benefits from our experience with prior patients on the study. Additionally, if patients develop comorbidities, relapse, or fail to respond to a certain [arm], we’re able to shut down the treatment arm or move them into a different treatment arm.

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