How biologists of the future could displace some data scientists in drug development

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A decade ago, data scientist seemed like the sexiest job of the 21st century, to paraphrase an influential Harvard Business Review article.

In the pharmaceutical industry, data science certainly continues to have tremendous potential, but in years to come, data-savvy biologists could have as least as much of an impact on drug development as data scientists, according to David Harel, co-founder and president of Cytoreason, which has developed a computational disease model for drug developers.

“We call this the biologist of the future,” Harel said, referring to biologists with significant data science training received either in academia or on the job.

The consulting firm Gartner has espoused a similar idea, which it terms a citizen data science to refer to workers outside of statistics and analytics who create data science models based on predictive or prescrip…

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Why Big Pharma is partnering with startups as it becomes more data-driven

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Big Pharma’s ability to innovate has grown in recent years, and the industry’s increasing reliance on data could help it sustain momentum in the future.

While McKinsey notes that the industry has been relatively slow in adopting technologies such as AI and automation, the industry is growing more tech-savvy.

“The acceptance of data is picking up across the industry,” said David Harel, co-founder and president of CytoReason, an Israeli startup working with six of the top ten pharma companies.

At the same time, pharmaceutical research is headed in a much more collaborative direction where partnership and licensing are common, said Ed White, chief analyst and VP of IP and innovation research at Clarivate.

The pharma industry has traditionally had a large amount of data to tap in drug discovery and development, but the volume of data in the fi…

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When can computer models replace animal trials?

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The pandemic has forced a rethink of clinical research, but the pharma industry continues to rely on animal testing. While pundits have observed that computer modeling and techniques such as microdosing can reduce animal testing, animal testing continues to be integral in preclinical studies. 

But computer models are now sufficiently accurate to predict the response of many drugs, said David Harel, CEO of CytoReason (Tel Aviv, Israel). “We are getting to the point that computer models of certain diseases can generate better predictions than animal models,” he said. 

But there are caveats. It could take longer to move from animal-based safety testing, which often involve rodents. Such animal trials tend to be limited in size. “They’re not a big burden. And they’re not super expensive,” Harel said. And regulators frequently consider animal data when evaluating drug safety. But …

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