Most of all, that pill is the sum of thousands and thousands of individual decisions that have guided its journey from the moment a scientist conceived the idea, through unimaginable layers of tests, trials, protocols and approvals, to the moment you popped it out of its blister pack. 

Now the company that made that pill, Novartis, wants to use AI to enhance every stage of the journey. Same rigorous science, same uncompromising focus on quality, just better decision-making. And faster.

New arts

The name Novartis was coined in 1996 when the Swiss multinational pharmaceuticals company took on its current form. The name came from the Latin novae artes, meaning ‘new skills’. It’s an apt name for a company dedicated to producing truly innovative medicines, and it’s taken on an extra dimension now as Novartis looks to develop even newer skills in AI.

Overseeing the clinical development process is Shreeram Aradhye, Novartis’s President, Development and Chief Medical Officer. A trained physician, he is absolutely the sort of doctor you would want treating you if you were sick, bubbling with good humour and enthusiasm for his subject. ‘I’m excited to have this conversation,’ he says, when asked to discuss how AI is being embedded in the development of new medicines. ‘And what I’m most excited about is not the technical part of AI, it’s the human part of the engagement with this technology.’

Shreeram describes the company’s purpose as ‘turning molecules into medicines and getting them to patients.’ It’s a neat little summary for a process that is one of the most complex on the planet. From the point that a promising compound is identified, it takes on average a decade to get it to market. Development can cost hundreds of millions of dollars, sometimes even billions. And all that money only buys you a one-in-ten chance of success: 90% of drugs that enter development fail.

Transformative innovation

One of Novartis’s key corporate tenets is the idea of ‘transformative innovation’. The company doesn’t aim for small, incremental improvements; they focus on dramatic advances that can significantly improve patients’ quality of life by curing, treating, or even preventing diseases in genuinely novel ways. 

But how do you transform a system as complicated as drug development? Complex systems are, by their nature, hard to change. It requires delicate judgement to decide how much to bite off at once. You can go big, try to imagine the future and leap towards it in one gigantic bound - but the track record of this kind of big bang approach is, to put it mildly, mixed. Monolithic technology programmes almost never work inside a complex system.

Small changes - the ‘easy wins’ and ‘low-hanging fruit’ - have a higher chance of success, at least locally where they’re deployed. But they probably won’t make a measurable difference to the overall performance of the organisation. A lot of companies’ AI investments fall into this category, discrete initiatives with no mechanism to join them up, that only reinforce siloed decision-making.

Shreeram’s having none of that. ‘We replaced the word “divisions” with “continuum”,’ he says. ‘Commercial organisations all have to work together along this continuum, and all of them can be AI-enabled to be more efficient at the individual level, and at a team level.’

The best way to use AI to change a complex system, as Shreeram recognised, is to break down that continuum into modular components that are valuable in their own terms, but which connect together into something much more transformative. And the key challenge here is to decide what form those individual components or increments should take. Particularly with AI, it’s easy to be misled by the outputs that the models create. AI specialises in forecasts, clusters and classifications of data. It summarises documents and generates new ones. All these capabilities are impressive but none of them, in their raw form, are high-value business outputs.

But can you prove it?

‘A medicine,’ says Shreeram, with his knack for capturing complex processes in quotable one-liners, ‘is simply a molecule plus evidence.’ Making sure that evidence gets gathered through a series of ever-larger and more ambitious trials is one of his core responsibilities. But as these trials can last months or even years, there’s plenty of time for things to go awry. 

‘A medical trial isn’t like launching a rocket and letting it go,’ Shreeram says. ‘It’s the equivalent of a ship that has to navigate across the complexity of the ocean.’ And there’s not much room for error. A molecule might be effective, but if the trial isn’t designed and managed well then the data might not show it. So Novartis turned to AI to support the decisions that help them chart a course through that complexity.

‘We spent a year systematically looking at how AI can enable our clinical trials,’ says Shreeram. It starts with the scientific protocols that underpin any trial. These are a sequence of decisions that shape everything that follows. What primary and secondary goals should we set for the study? What type of experimental design should we use, and how should we collect the data? What should be the eligibility criteria for participants? What dosage, frequency and duration of treatment should we specify? How often should we bring patients in for assessment?

Shreeram describes a tool Novartis developed called Protocol AI. ‘It was our effort to say: “Can we enable the person who writes the protocol? Can they be augmented by an AI tool that gives them access to knowledge and information about comparable trials that have been designed for the same medical condition? Can they learn from the previous performance of similar trials to understand the impact of different design features, and what their future implications might be?”’

Innovate differently

Each of these is a compelling use-case, and they’re already starting to show results. 

Trials that used AI in the design process have been more likely to finish ahead of schedule. Sites that AI identifies as being suitable for a particular trial have strong potential to recruit patients faster than ones that weren’t selected by the AI, and produce the diversity of patient population that the experimental protocol demands. Those sites also get approved faster. ‘What used to take weeks now… doesn’t,’ Shreeram marvels.

Incrementally, each of these is making a difference. But to fully achieve the kind of transformative innovation that Novartis aspires to, the company is working with Faculty to tie it all together with an Intelligent Decision System called Frontier. This system uses a ‘computational twin’, a sophisticated digital simulation that connects data sources, operational processes and machine learning models together in an interactive, virtual replica of the clinical trial process.

In this controlled environment, Novartis can experiment with different ways of linking up its various AI initiatives to make sure they are all working in synergy to deliver the greatest benefits. This means systematically deconstructing the decision-making process to see where the key decision points are, what data will be required to inform them, and where future AI investments will have most impact in enabling those decisions.

Ultimately, it will let Novartis connect decisions across different functions (such as clinical, operational, supply, strategy, regulatory) and at different levels (for example by patient, site, cohort, medical condition, program, or the whole portfolio of trials). It will provide a scalable framework that allows future investments in data and AI to connect seamlessly to what’s already in place.

And in all this, Shreeram never loses sight of the bottom line. ‘I'm not focusing on efficiency. I'm talking about value, and I always say the value is not just the amount of money saved. It's value to patients, too, so that they can benefit from earlier treatment and faster access to new medicines.’

Novartis have already started accelerating that vital outcome with AI-enabled processes. Connected together in the service of human decision-makers, they will transform it.