AI Improving Dose Decisions and Patient Outcomes in Oncology- with Shefali Kakar of Novartis

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Shefali Kakar, Global Head of PK Sciences and Oncology at Novartis.
Shefali joins Emerge's Matthew DeMello to discuss how deeper data analysis and AI-driven modeling are giving leaders earlier clarity on dose decisions, safety signals and patient variability. She highlights how these approaches reduce avoidable sub-studies and strengthen the evidence base behind high stakes development calls. Just a quick note for our audience the views expressed by Shefali Kakar on today's program do not reflect that of Novartis or its leadership. According to Nielsen, 91% of podcast listening happens alone, indicating deep, undistracted attention, ideal for complex B2B messaging. To learn how leading brands and AI startups connect with enterprise AI buyer audiences at scale, download our media kit at image.com/adone. That's emeoj.com/adone.
Now, the conversation with Shefali.

**Matthew DeMello** (1:32)
Shefali, welcome back to the program. It's great having you.

**Shefali Kakar** (1:35)
Thank you, Matthew, for having me here again.

**Matthew DeMello** (1:37)
Absolutely. Last time, we spoke a lot about the clinical trials process, a very instrumental to make the understatement of the year part of the drug development space where we're seeing all kinds of AI being deployed. Right now, we're going right to the beginning of the process in terms of how life sciences organizations can make the best investments possible and decrease the most risk in terms of where they're investing in these drugs. Just as I was saying towards the end of the last episode where we had you on, we're seeing efficiencies that create all kinds of trade-offs. At the beginning of the process, at the end of the process, different kinds of technology being deployed. I was making the example towards the end of the last program about how we're seeing such step-level changes in protein engineering. We're able to make so many more specific drugs for so many more rarer conditions than we did before, and that needs to be a consideration.
On the other side of the drug development process and clinical trials, we need to have stronger patient segmentation. You were telling us in response to that, that's been controversial in the past. One of the many ways we're seeing data change these processes is that's no longer the case anymore. But just in terms of, really right from the beginning, where are we going to, where are life sciences folks going to put their chips on the table, just in terms of what drugs, based on this new deluge of data from all parts of the process, to get a greater clarity on what's really going to make it to market 10 to 15 years later, which is still the numbers that we're citing. Everybody that comes back on the show for all of the promise of these technologies to bring down that cost from the proverbial one and a half billion to two and a half billion price tag to develop a new drug in the 10 to 15 years it takes to market.
Those costs and that time spend is still very much in place. But how we at least make those bets on what is going to get through these processes is a lot different now. How are smarter, more multifactorial models reshaping early drug development? If we can kind of take that much larger view from the beginning of the process.

**Shefali Kakar** (3:49)
I think if you remove the word AI and really think of like really what are the different ways we are utilizing the mass data, right? And there's so many different ways to look at it.
Really as early as when we start to think about the chemicals themselves. Every carbon to hydrogen to nitrogen change can actually be modeled. How is it going to translate into your safety? It's going to translate into your pharmacokinetics. How is it going to change maybe your activity? And these relationships are now no longer being looked at just from one program level but across industry program level. There are companies that have emerged across that actually just focus on doing this kind of analysis for you. And small biotechs could utilize this and large pharma companies could utilize it. Some of them also do the analysis and then make the right molecule for you. So I think it's a whole new world that has emerged on before even trying to come up with a molecule, almost doing this in silico drug discovery in finding this. The same is happening on the biologics run where we're really dealing with protein chemistry and saying certain parts of the protein can actually be again assumed that this is going to really help us with certain safety parameters or really making sure that this drug lasts in the body for almost a month because when we're trying to go to the market with something that needs to be injected to you once a month, it has a higher probability of success on the market than something that you have to inject every single day. So we're always trying to look for ways to really ensure what are the different aspects of the molecule that you can design in and then be able to say, I want to, it's almost like creating your own little drug. I want it to last longer. I want it to be a little bit stronger on working on this particular protein versus not hitting this three other proteins that cause a problem. You also are compared to what's on the market already and then trying to be better than what's already on the market.