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How AI simplifies data management for drug discovery

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How AI simplifies data management for drug discovery


Calithera is running registered clinical trials on its products to study their safety, whether they’re 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 its trials are in early stages and involve only a small number of patients, others span more than 100 research centers across the globe.

“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 chief information and technology officer for health-care tech company Innovio.) Calithera must store and manage the data while making sure it’s readily available when needed, even years from now. It also must comply with specific FDA requirements on how the data is generated, stored, and used.

Even something seemingly as simple as upgrading a file server must follow a strictly defined FDA protocol with multiple testing and review steps. Najafi says all this compliance-related data wrangling can add 30% to 40% to the overhead of a company like his, in both direct cost and hours of staff time. These are resources that could otherwise be put toward more research or other value-added activities.

Calithera has sidestepped much of that additional cost and vastly improved its ability to track its data by putting it in what Najafi calls a secure “storage container,” a protected area for regulated content, part of a larger cloud document management application, largely driven by artificial intelligence. AI never sleeps, never gets bored, and can learn to distinguish among hundreds of different types of documents and forms of data.

Here’s how it works: clinical or patient data is put into the system and scanned by AI, which recognizes specific features that pertain to accuracy, completeness, compliance with regulations, and other aspects of the data. AI can flag when there’s a missing test result, or when a patient hasn’t submitted a required diary entry. It knows who’s allowed to access certain types of data and what they are and are not allowed to do with it. It can detect ransomware attacks and head them off. And it can automatically document all that to the satisfaction of the FDA or any other regulatory body.

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

Managing drug discovery data to comply with the needs of research and the requirements of regulators can be, as Najafi observes, onerous and expensive. The life-sciences industry can borrow data management techniques and platforms developed for other industries, but they must be modified to handle the levels of security and validation, and the detailed audit trails, that are a way of life for drug developers. AI can streamline these tasks, improving the security, consistency, and validity of data—freeing up overhead for drug companies and research organizations to apply to 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 who participate in clinical trials and post-market research. No matter their size—enormous global conglomerates or tiny startups trying to get a single product to market—drug developers must adhere to the same standard practices to document, audit, validate, and protect every shred of information connected with a clinical trial.

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

The data management burden falls hard on emerging and midsize biosciences companies, says Ramin Farassat, chief strategy and product officer at Egnyte, a Silicon Valley software company that makes and supports the AI-enabled data management platform used by Calithera and several hundred other life-sciences companies.

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

Download the full report.

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

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Yann LeCun has a bold new vision for the future of AI

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The Download: Yann LeCun’s AI vision, and smart cities’ unfulfilled promises


Melanie Mitchell, an AI researcher at the Santa Fe Institute, is also excited to see a whole new approach. “We really haven’t seen this coming out of the deep-learning community so much,” she says. She also agrees with LeCun that large language models cannot be the whole story. “They lack memory and internal models of the world that are actually really important,” she says.

Natasha Jaques, a researcher at Google Brain, thinks that language models should still play a role, however. It’s odd for language to be entirely missing from LeCun’s proposals, she says: “We know that large language models are super effective and bake in a bunch of human knowledge.”

Jaques, who works on ways to get AIs to share information and abilities with each other, points out that humans don’t have to have direct experience of something to learn about it. We can change our behavior simply by being told something, such as not to touch a hot pan. “How do I update this world model that Yann is proposing if I don’t have language?” she asks.

There’s another issue, too. If they were to work, LeCun’s ideas would create a powerful technology that could be as transformative as the internet. And yet his proposal doesn’t discuss how his model’s behavior and motivations would be controlled, or who would control them. This is a weird omission, says Abhishek Gupta, the founder of the Montreal AI Ethics Institute and a responsible-AI expert at Boston Consulting Group. 

“We should think more about what it takes for AI to function well in a society, and that requires thinking about ethical behavior, amongst other things,” says Gupta. 

Yet Jaques notes that LeCun’s proposals are still very much ideas rather than practical applications. Mitchell says the same: “There’s certainly little risk of this becoming a human-level intelligence anytime soon.”

LeCun would agree. His aim is to sow the seeds of a new approach in the hope that others build on it. “This is something that is going to take a lot of effort from a lot of people,” he says. “I’m putting this out there because I think ultimately this is the way to go.” If nothing else, he wants to convince people that large language models and reinforcement learning are not the only ways forward. 

“I hate to see people wasting their time,” he says.

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The Download: Yann LeCun’s AI vision, and smart cities’ unfulfilled promises

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The Download: Yann LeCun’s AI vision, and smart cities’ unfulfilled promises


“We’re addicted to being on Facebook.”

—Jordi Berbera, who runs a pizza stand in Mexico City, tells Rest of World why he has turned to selling his wares through the social network instead of through more conventional food delivery apps.

The big story

“Am I going crazy or am I being stalked?” Inside the disturbing online world of gangstalking

August 2020

Jenny’s story is not linear, the way that we like stories to be. She was born in Baltimore in 1975 and had a happy, healthy childhood—her younger brother Danny fondly recalls the treasure hunts she would orchestrate. In her late teens, she developed anorexia and depression and was hospitalized for a month. Despite her struggles, she graduated high school and was accepted into a prestigious liberal arts college.

There, things went downhill again. Among other issues, chronic fatigue led her to drop out. When she was 25 she flipped that car on Florida’s Sunshine Skyway Bridge in an apparent suicide attempt. At 30, after experiencing delusions that she was pregnant, she was diagnosed with schizophrenia. She was hospitalized for half a year and began treatment, regularly receiving shots of an antipsychotic drug. “It was like having my older sister back again,” Danny says.

On July 17, 2017, Jenny jumped from the tenth floor of a parking garage at Tampa International Airport. After her death, her family searched her hotel room and her apartment, but the 42-year-old didn’t leave a note. “We wanted to find a reason for why she did this,” Danny says. And so, a week after his sister’s death, Danny—a certified ethical hacker—decided to look for answers on Jenny’s computer. He found she had subscribed to hundreds of gangstalking groups across Facebook, Twitter, and Reddit; online communities where self-described “targeted individuals” say they are being monitored, harassed, and stalked 24/7 by governments and other organizations—and the internet legitimizes them. Read the full story.

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The US Supreme Court has overturned Roe v. Wade. What does that mean?

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The US Supreme Court has overturned Roe v. Wade. What does that mean?


Access to legal abortion is now subject to state laws, allowing each state to decide whether to ban, restrict or allow abortion. Some parts of the country are much stricter than others—Arkansas, Oklahoma and Kentucky are among the 13 states with trigger laws that immediately made abortion illegal in the aftermath of the ruling. In total, around half of states are likely to either ban or limit access to the procedure, with many of them refusing to make exceptions, even in pregnancies involving rape, incest and fetuses with genetic abnormalities. Many specialized abortion clinics may be forced to close their doors in the next few days and weeks.

While overturning Roe v Wade will not spell an end to abortion in the US, it’s likely to lower its rates, and force those seeking them to obtain them using different methods. People living in states that ban or heavily restrict abortions may consider travelling to other areas that will continue to allow them, although crossing state lines can be time-consuming and prohibitively expensive for many people facing financial hardship.

The likelihood that anti-abortion activists will use surveillance and data collection to track and identify people seeking abortions is also higher following the decision. This information could be used to criminalize them, making it particularly dangerous for those leaving home to cross state lines.

Vigilante volunteers already stake out abortion clinics in states including Mississippi, Florida and North Carolina, filming people’s arrival on cameras and recording details about them and their cars. While they deny the data is used to harass or contact people seeking abortions, experts are concerned that footage filmed of clients arriving and leaving clinics could be exploited to target and harm them, particularly if law enforcement agencies or private groups were to use facial recognition to identify them.

Another option is to order so-called abortion pills to discreetly end a pregnancy at home. The pills, which are safe and widely prescribed by doctors, are significantly less expensive than surgical procedures, and already account for the majority of abortions in the US.

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