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Calithera is conducting registered clinical trials on its products to examine their safety, whether they are effective in patients with specific gene mutations, and how well they work in conjunction with other treatments. The company needs to collect detailed data on hundreds of patients. Some trials are in the early stages and involve only a small number of patients, while others span more than 100 research centers around the world.
“In the life sciences world, one of the biggest challenges we have is the enormous amount of data we generate, more than any other business,” says Behrooz Najafi, Calithera’s lead information technology strategist. (Najafi is also head of information and technology for healthcare technology company Innovio.) Calithera must store and manage data, ensuring it’s ready when needed, even years from now. It must also comply with certain FDA requirements for how data is created, stored, and used.
Even something that seems as simple as upgrading a file server must follow a strictly defined FDA protocol with multiple testing and review steps. Najafi says all this data debate about compliance can add 30 to 40% to the overheads of a company like his, in both direct cost and hours of operation. These are resources that could otherwise be allocated to further research or other value-added activities.
Calithera avoided most of this additional cost and greatly improved its ability to monitor its data, largely driven by what Najafi calls a secure “storage container”, a protected space for edited content, part of a larger cloud document management application. artificial intelligence. AI never sleeps, never gets bored, and can learn to distinguish hundreds of different document and data formats.
Here’s how it works: clinical or patient data is placed in the system and scanned by AI that recognizes certain characteristics regarding accuracy, completeness, regulatory compliance, and other aspects of the data. AI can flag when there is an incomplete test result or when a patient does not submit a required diary entry. It knows who is allowed access to certain types of data and what to do with them. It can detect ransomware attacks and stop them. And it can automatically document all this to the satisfaction of the FDA or any other regulatory body.
“This approach takes the burden of compliance off us,” says Najafi. When data from many research sites is added to the platform, Calithera knows that it will make sure the AI is safe, complete and compliant with all regulations and flag any situation. problems.
As Najafi observes, managing drug discovery data to comply with research requirements and regulatory requirements can be cumbersome and expensive. The life sciences industry can borrow data management techniques and platforms developed for other industries, but they must be modified to address the levels of security and verification and detailed audit trails that are a way of life for pharmaceutical developers. AI can streamline these tasks, improve the security, consistency, and validity of data, and free up the overhead for pharmaceutical companies and research organizations to apply to their core tasks.
A complex data management environment
Regulatory compliance helps ensure that new drugs and devices are safe and work as intended. It also protects the privacy and personal information of thousands of patients participating in clinical trials and post-market research. Regardless of their size (either massive global conglomerates or small startups trying to launch a single product), drug developers must adhere to the same standard practices for documenting, auditing, validating and protecting any information associated with a clinical trial.
When researchers conduct a double-blind study, which is the gold standard for proving the effectiveness of a drug, patients are required to keep their information anonymous. But they need to make the data easily anonymized and identifiable later on, so that patients in the control group can receive the test drug, and so the company can track how the product performs in real-world use—sometimes for years.
Ramin Farassat, chief strategy and product officer at Silicon Valley software company Egnyte, which makes and supports the AI-powered data management platform used by Calithera and hundreds of other lives, says the burden of data management falls on emerging and midsize bioscience companies. science companies.
“This approach takes the burden of compliance off us,” says Najafi. Once data from many research sites enters the platform, Calithera knows that AI will make sure it is safe, complete, and complies with all regulations and flag any issues.
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This content is produced by Insights, the exclusive content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.
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