In the world of commercial research and science, there’s probably no undertaking more daunting – or more expensive – than the process of bringing a new medicine to market.

For a new compound to make it from initial discovery through development, testing and clinical trials to finally earn regulatory approval can take a decade or more.

Nine out of 10 promising drug candidates fail somewhere along the way. As a result, on average, it costs life sciences companies $2.6 billion to introduce a single new prescription drug.

This is much more than just a challenge for life sciences companies. Streamlining drug development is an urgent issue for human health more broadly. From uncovering new ways to treat age-old sicknesses like malaria that still kills hundreds of thousands of people every year, to finding new cancer treatments, or developing new vaccines to prevent highly-contagious diseases from turning into global pandemics, the impact in terms of lives saved worldwide would be enormous if we could make inventing new medicines faster.

This is why Novartis and Microsoft are collaborating to explore how to take advantage of advanced Microsoft AI technology combined with Novartis’ deep life sciences expertise to find new ways to address the challenges underlying every phase of drug development – including research, clinical trials, manufacturing, operations and finance.

 

In a recent interview, Novartis CEO Vas Narasimhan spoke about the potential for this alliance to unlock the power of AI to help Novartis accelerate research into new treatments for many of the thousands of diseases for which there is, as yet, no known cure.

In the biotech industry, there have been amazing scientific advances in recent years that have the potential to revolutionize the discovery of new, life-saving drugs. Because many of these advances are based on the ability to analyze huge amounts of data in new ways, developing new drugs has become as much an AI and data science problem as it is a biology and chemistry problem. This means companies like Novartis need to become data science companies to an extent never seen before. Central to our work together is a focus on empowering Novartis associates at each step of drug development to use AI to unlock the insights hidden in vast amounts of data, even if they aren’t data scientists. That’s because while the exponential increase in digital health information in recent years offers new opportunities to improve human health, making sense of all the data is a huge challenge.

The issue isn’t just a problem of the overwhelming volume. Much of the information exists in the form of unstructured data, such as research lab notes, medical journal articles, and clinical trial results, all of which is typically stored in disconnected systems. This makes bringing all that data together extremely difficult. Our two companies have a dream.

We want all Novartis associates – even those without special expertise in data science – to be able to use Microsoft AI solutions every day, to analyze large amounts of information and discover new correlations and patterns critical to finding new medicines. The goal of this strategic collaboration is to make this dream a reality.

This offers the potential to empower everyone from researchers exploring the potential of new compounds and scientists figuring out dosage levels, to clinical trial experts measuring results, operations managers seeking to improve supply chains more efficiently, and even business teams looking to make more effective decisions.

And as associates work on new problems and develop new AI models, they will continually build on each other’s work, creating a virtuous cycle of exploration and discovery. The result? Pervasive intelligence that spans the company and reaches across the entire drug discovery process, improving Novartis’ ability to find answers to some of the world’s most pressing health challenges.

As part of our work with Novartis, data scientists from Microsoft Research and research teams from Novartis will also work together to investigate how AI can help unlock transformational new approaches in three specific areas. The first is about personalized treatment for macular degeneration – a leading cause of irreversible blindness. The second will involve exploring ways to use AI to make manufacturing new gene and cell therapies more efficient, with an initial focus on acute lymphoblastic leukemia. And the third area will focus on using AI to shorten the time required to design new medicines, using pioneering neural networks developed by Microsoft to automatically generate, screen and select promising molecules. As our work together moves forward, we expect that the scope of our joint research will grow.