Connect with us

Tech

How AI simplifies data management for drug discovery

Published

on

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.

Tech

The hunter-gatherer groups at the heart of a microbiome gold rush

Published

on

The hunter-gatherer groups at the heart of a microbiome gold rush


The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania. 

Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.

Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.

A changing way of life

The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.

Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.

One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.

Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.

The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.

Continue Reading

Tech

The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

Published

on

The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.

Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost. 

But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.

—Jessica Hamzelou

Eric Schmidt has a 6-point plan for fighting election misinformation

—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures

The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.

Continue Reading

Tech

Navigating a shifting customer-engagement landscape with generative AI

Published

on

Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

Continue Reading

Copyright © 2021 Seminole Press.