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Friday, November 19, 2021

Novartis and Pharma AI

 Fairly broad look at how drugs can use AI:

Novartis empowers scientists with AI to speed the discovery and development of breakthrough medicines

By Bill Briggs

Here’s a cooking story unlike any you’ve heard before. That’s because the chefs are chemists, the ingredients are molecules, and the main course is a new medication designed to defeat illness.

At least, that’s Luca Finelli’s snackable description to explain in simple terms how scientists at Novartis are searching for breakthrough medicines powered by artificial intelligence (AI), part of a collaboration with Microsoft to get medicines to patients faster.

But that recipe hinges on the scientists’ ability to predict which blend of molecules can be transformed into medicines – a tedious process that traditionally takes decades and can cost billions.

“Creating the formulation to a drug is a bit like cooking,” says Finelli, vice president and head of insights, strategy and design at Novartis, a multinational pharmaceutical company headquartered in Basel, Switzerland.

“Typically, the formulation scientist needs to decide, ‘I will take this amount of this ingredient A and some amount of this ingredient B.’ They then try different combinations,” Finelli adds.

Each molecular combo must next be tested to gauge efficacy, stability, safety and more. Conducting those experiments can span years. And most promising drug candidates fail somewhere during that long journey.

But by leveraging the power of AI in collaboration with Microsoft, Novartis researchers may be able to shorten that process to weeks or even days.

How? Tools that use AI can sift quickly through stores of data and results from decades of laboratory experiments and suggest molecules with the desired characteristics that are optimized for the medicinal task at hand. Those drug leads might then be fast-tracked for additional testing and, if proven safe and effective, potentially be developed and manufactured as a remedy for illness. This AI-bolstered process could cut out years of trial-and-error experimenting with molecules that are less than ideal.

In fact, that functionality already has been “integrated into the decision-support system in front of our medicinal chemists,” says Shahram Ebadollahi, chief data and AI officer at Novartis.

The potential human impacts are vast, Ebadollahi says.

“If you look at every aspect of the pipeline – from early drug discovery and drug development to clinical trials and then on to manufacturing the drug at large scale – in 2020 alone, our medicines reached almost 800 million patients worldwide,” Ebadollahi says.

To accomplish this feat, Novartis scientists create molecules that never have been made, and these molecules will help develop new medicines to combat diseases for which there are no treatments, says Karin Briner, head of global discovery chemistry at Novartis Institutes of BioMedical Research.

The foundation for this work is the 2019 strategic partnership between Novartis and Microsoft to “reimagine medicine” by founding the Novartis AI Innovation Lab. The goal of that alliance is to help accelerate drug discovery for patients worldwide by augmenting scientists with cutting-edge technology platforms.

“Microsoft brings two things,” says Chris Bishop, lab director for Microsoft Research Europe.

“We bring our expertise in machine learning and our large-scale compute. Those don’t exist in the pharma world. And Microsoft can’t take this on (independently). We’re not a pharma company. So the partnership is absolutely crucial,” Bishop says. “That’s how the disruption will unfold. That collaboration is at the heart of this.”

Machine learning is a key part of AI, enabling computers to use algorithms to find patterns and trends within huge sets of data.

At Novartis, researchers can apply AI to comb through a trove of lab data from thousands of past drug-development experiments – findings buried in PDFs, Excel tables and written descriptions of the chemical properties of previously explored molecules.  .... " 

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