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Why the pandemic is forcing oncology clinical trials to evolve

By Brian Buntz | April 2, 2021

Pandemic oncology clinical trials

Photo by Markus Spiske from Pexels

COVID-19 has complicated oncology trials and care, slowing down the success seen against cancer.

While the cancer death rate fell 29% between 1991 and 2017, according to the American Cancer Society, the coronavirus pandemic has threatened to interfere with oncology drug development trials.

COVID-19’s disproportionate impact on oncology

“Cancer patients have been disproportionately negatively affected by COVID-19,” said Jeff Elton, CEO of ConcertAI, an AI company specializing in oncology. Immunocompromised patients with hematological malignancies such as multiple myeloma, in particular, face outsized risks from COVID-19 infection.

To reduce the risk for patients, many oncology clinics in 2019 limited capacity while making significant treatment changes. “It wasn’t unusual to see people queuing through tents in the parking lot and being very segregated in different zones as they were going through treatment,” Elton said.

Such an environment made it difficult to run traditional clinical trials in the most disruptive pandemic phase in 2020. “The classic execution models of doing clinical research were very difficult to deploy” Elton said.

And yet, medical oncologists and biomedical research organizations are aware of the potentially life-saving option that clinical trials offer cancer patients. There’s a value to knowing whether experimental therapies are on track to winning regulatory approval and finding broader use.

Cancer trials adapt

To maintain clinical trial progress, clinical trials have adapted in the past year in several ways. First, sponsors have simplified study designs that reduce the burden to patients and clinical and medical oncology sites.

“You saw biopharma beginning to look to move things that had historically been largely at only the academic medical centers into high-capability community settings,” Elton said.

Such changes in venue have opened up access to clinical studies in the past year. “If we make clinical studies more like the standard of care, then it’s a lot easier to move them into community settings,” Elton said.

The pandemic has also played a role in driving interest in decentralized trial designs.

Assisting in this are machine learning and other AI tools for patient identification and scrutinizing study eligibility. Such tools are “now becoming almost de facto standard,” Elton said. They can optimize clinical trial design, analyzing, for instance, electronic medical record data for prospective clinical trials. AI tools can also help formulate inclusion and exclusion criteria for clinical trials. AI-based tools can also “minimize the number of patients required to get to a certain level of statistical power,” Elton said.

They can also boost patient recruitment by ensuring that a clinical trial is as close to the standard care treatment as possible. In that way, “the provider is going to decide what’s in the best interest of the patients, and patients are going to decide what’s in their own best interest,” Elton said.

While there has been a buzz around AI in healthcare and clinical trials in the past, COVID-19 is catalyzing change. “All of a sudden, you see the merits of alternative approaches,” Elton said. “I’m not aware of a single sponsor that would plan on returning back and just restoring all the old ways of working,” he added.

Healthcare providers committed to biomedical research are in a similar position. “In fact, if anything, providers are seeing the integration of localized AI and ML technologies to augment their research capacity as being super important,” Elton said. “We’re seeing this from the sponsors, too.”

 


Filed Under: clinical trials, Drug Discovery, Drug Discovery and Development, Infectious Disease
Tagged With: American Cancer Society, clinical trials, oncology
 

About The Author

Brian Buntz

As the pharma and biotech editor at WTWH Media, Brian has almost two decades of experience in B2B media, with a focus on healthcare and technology. While he has long maintained a keen interest in AI, more recently Brian has made making data analysis a central focus, and is exploring tools ranging from NLP and clustering to predictive analytics.

Throughout his 18-year tenure, Brian has covered an array of life science topics, including clinical trials, medical devices, and drug discovery and development. Prior to WTWH, he held the title of content director at Informa, where he focused on topics such as connected devices, cybersecurity, AI and Industry 4.0. A dedicated decade at UBM saw Brian providing in-depth coverage of the medical device sector. Engage with Brian on LinkedIn or drop him an email at bbuntz@wtwhmedia.com.

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