How AI simplifies data management for drug discovery


Calithera conducts registered clinical trials of 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 treatments. The company needs to gather details about hundreds of patients. While some of its trials are in the early stages and involve only a small number of patients, others cover more than 100 research centers around the world.

“In the world of life sciences, one of the biggest challenges we have is the huge amount of data we generate, more than any other business,” says Behruz Najafi, Kalitera’s lead information technology strategist. (Najafi is also the chief information and technology officer of the medical technology company Innovio.) Kalitera must store and manage data, making sure it is available when needed, even after years. It must also meet specific FDA requirements on how data is generated, stored and used.

Even something as simple as updating a file server has to follow a strictly defined FDA protocol with a few testing and review steps. Najafi says that all this struggle with compliance data could add 30% to 40% to a company’s overhead costs like its, both in direct costs and in working hours. These are resources that could otherwise be directed to additional research or other value-added activities.

Calithera has circumvented most of these additional costs and greatly improved its ability to track its data by placing it in what Najafi calls a secure “storage container,” a protected area for regulated content, part of a larger cloud document management application. artificial intelligence. AI never sleeps, never misses and can learn to distinguish hundreds of different types of documents and data forms.

Here’s how it works: Clinical data or patient data is placed into a system and scanned by an AI that recognizes certain features related to accuracy, completeness, compliance, and other aspects of the data. The AI ​​may indicate if the test result is missing, or if the patient has not provided the required diary entry. He knows who is allowed access to certain types of data and what it is and what is forbidden to do with it. It can detect ransomware attacks and avoid them. And it can automatically document it all to meet the FDA or any other regulatory body.

“This approach relieves us of the burden of compliance,” Najafi says. Once data from its many research sites appears on the platform, Kalitera knows that artificial intelligence will make sure it is safe, complete and compliant, and indicates any problems.

Managing drug discovery data according to research needs and regulatory requirements can be, as Najafi observes, difficult and expensive. The life sciences industry may borrow data management techniques and platforms developed for other industries, but they need to be modified to manage security and verification levels, as well as detailed audit trails, which are a way of life for drug developers. AI can streamline these tasks by improving the security, consistency, and authenticity of data – freeing up overhead for pharmaceutical companies and research organizations to apply their core mission.

Sophisticated data management environment

Compliance with regulations helps ensure that new medications and devices are safe and work as intended. It also protects the privacy and personal information of thousands of patients involved in clinical trials and market research. Regardless of their size – huge global conglomerates or miniature startups trying to bring a single product to market – drug developers should follow the same standard practice to document, verify, verify and protect every piece of information related to a clinical trial.

When researchers conduct a double-blind study — the gold standard for confirming drug efficacy — they must store anonymous patient information. But they should easily strip the anonymization of the data later, making them identifiable so that patients in the control group can receive the test drug, and so the company can track — sometimes for years — how the product operates in the real world.

The burden of data management is complicated by new and medium-sized biological companies, says Ramin Farasat, director of strategy and products at Egnyte, a Silicon Valley software company that creates and maintains an artificial intelligence data management platform used by Calithera and several hundred others. research companies.

“This approach relieves us of the burden of compliance,” Najafi says. Once data from his many research sites hit the platform, Kalitera knows that artificial intelligence will make sure it is safe, complete and compliant, and indicates any problems.

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This content was prepared by Insights, MIT Technology Review’s own content group. This is not written by the MIT Technology Review.



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