How AI Simplifies Data Management for Drug Discovery


Calithera is conducting registered clinical trials on its products to study their safety, whether they are effective in patients with specific gene mutations, and how well they work in combination with other therapies. The company must collect detailed data on hundreds of patients. While some of his trials are in the early stages and involve only a small number of patients, others span more than 100 research centers around the world.

“In the life sciences world, one of the biggest challenges we have is the sheer amount of data we generate, more than any other business,” says Behrooz Najafi, chief information technology strategist at Calithera. (Najafi is also the director of technology and information for the healthcare technology company Innovio.) Calithera must store and manage data while ensuring that it is available when it is needed, even within a few years. You must also comply with specific FDA requirements for how data is generated, stored, and used.

Even something seemingly as simple as updating a file server must follow a strictly defined FDA protocol with multiple testing and review steps. Najafi says that all of this dispute over compliance-related data can add 30-40% to the overhead of a company like yours, both in direct costs and hours of staff time. These are resources that could otherwise go to further research or other value-added activities.

Calithera has circumvented much of that additional cost and vastly improved its ability to track your data by placing it in what Najafi calls a secure “storage container,” a protected area for regulated content, part of a document management application on the Web. largest cloud, powered largely by artificial intelligence. AI never sleeps, never gets bored, and can learn to distinguish between hundreds of different types of documents and forms of data.

Here’s how it works: clinical or patient data is placed into the system and scanned by AI, which recognizes specific characteristics pertaining to accuracy, integrity, regulatory compliance, and other aspects of the data. The AI ​​can flag when a test result is missing or when a patient has not submitted a mandatory journal entry. You know who has permission to access certain types of data and what they are and what they cannot do with them. It can detect and prevent ransomware attacks. And it can automatically document all of that to the satisfaction of the FDA or any other regulatory body.

“This approach takes the burden of compliance off us,” Najafi says. Once the data from its many research sites is on the platform, Calithera knows that the AI ​​will make sure it is safe, complete and compliant with all regulations, and will flag any issues.

Managing drug discovery data to meet research needs and regulatory requirements can be, as Najafi observes, burdensome and expensive. The life sciences industry can borrow data management techniques and platforms developed for other industries, but they need to be modified to handle the levels of security and validation, and detailed audit trails, which are a way of life for developers. medicines. AI can simplify these tasks, improving data security, consistency and validity, freeing up overhead for pharmaceutical companies and research organizations to apply their core mission.

An intricate 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 the thousands of patients participating in clinical trials and post-marketing research. Regardless of their size – huge global conglomerates or small startups trying to bring a single product to market – drug developers must adhere to the same standard practices to document, audit, validate, and protect every bit of information related to a clinical trial. .

When researchers conduct a double-blind study, the gold standard for proving a drug’s efficacy, they must keep patient information anonymous. But they must easily anonymize the data later, making it identifiable, so that patients in the control group can receive the test drug and so that the company can track, sometimes for years, how the product performs in real-world use. .

The burden of data management falls heavily on emerging and midsize bioscience companies, says Ramin Farassat, director of strategy and products at Egnyte, a Silicon Valley software company that manufactures and supports the data management platform enabled by AI used by Calithera and several hundred other companies. science companies.

“This approach takes the burden of compliance off us,” Najafi says. Once the data from its many research sites is on the platform, Calithera knows that the AI ​​will make sure it is secure, complete, and compliant with all regulations, and will flag any issues.

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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by the editorial staff of MIT Technology Review.


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