Inside Certara IQ™: Building the Future of QSP with Josh Apgar & Chris Bouton

Introducing Certara IQ, an AI enabled modeling platform for scaling and democratizing QSP modeling. Certara IQ offers a range of model libraries licensed separately, spanning numerous pharmacologies and therapeutic areas.

In this video, I want to welcome Chris Bouton, chief technology officer and the head of AI at Certara, and Josh Apgar, VP and head of QSP software at Certara. Both Chris and John Josh have come together to develop Certara’s AI powered QSP modeling software, Certara IQ. Let’s start by laying the foundation for our discussion.

So I want to start by asking you guys about biosimulation and QSP modeling and how it’s impacting the industry as it is today.

So, you know, biosimulation has been an emerging technology really over the last thirty years. And by analogy, you know, other areas, have their own kind of engineering science that goes along with them that’s helped them become more and more efficient as they go. And biosimulation is really that science for the pharma industry.

And so today we’re gonna talk about QSP modeling, but that sits in a larger set of biosimulation that, really has been impacting, you know, all parts of the pharma pipeline.

Yeah. If you think about it, you know, complex biological systems are, complicated machines. In many other areas where engineering applies, we’ve learned how to simulate complex machines.

Biological systems are one of those areas where we haven’t really fully, figured out how to do so yet. But biosimulation is is really the the process by which we understand how to computationally model a highly complex system like a biological system.

And AI is going to help in that in in that ability.

Thank you for that overview. Now let’s dive a little bit deeper into how CTR IQ is creating real world impact for teams in the field. How is this platform a game changer for drug developers, developers, especially when it comes to modeling foreign decisions, making them faster across all phases of r and d?

You know, if you think of the farmer development, you know, life cycle as this long series of decisions, each one of which needs to be informed by data, Qsp and AI really come together. One, to make it a lot easier to bring that data together into a form that’s understandable. And then once that’s, you know, understood is then use it to drive a decision. So we’re really seeing its use starting all the way early, in, you know, even at the time of target nomination, understanding, you know, which target should be gone you should go after, you know, what are the optimal drug properties for those targets, you know, through the clinical candidate selection, you know, and where the so the biggest impacts have really been in the transition to clinic, where we’re taking all of the data that’s been gathered through all the programs up to that point and really using it to set up an efficient and successful clinical campaign.

So things like first in human dose projection, understanding therapeutic index early, you know, driving recommended phase two dose. And so it’s really it’s one of those things where there’s not one point in the pipeline where it’s having its impact. It’s really streamlining the whole process end to end in a way that just makes programs run more efficiently. And so we’ve seen from, you know, from companies that have used it that they’re saving, you know, on the order of five million dollars per program was an estimate from Pfizer, that was published recently.

You know, and so if you sort of think about that at the scale of the whole portfolio, this really could have an impact that changes the industry.

And also what we’re realizing is that, this is a perfect time to attempt to add AI capabilities to the kinds of QSP modeling that has that have happened, over the last decade or so. The reason for that is that LLMs add a whole new dimension to how rapidly we can build models, how we can assess and validate and verify models, and then also how we can utilize models, in a wide range of contexts. So really it’s, it’s an extraordinary time to think about how to apply AI and LLMs in particular in the context of QSP modeling.

Like, I think if we’ve been talking about this five years ago, the discussion would a lot be about, you know, when will these two technologies really be ready from time to time? When will LLMs be at the point where they’re gonna drive, you know, routine business processes? When will QSB be validated to the point where you can use it to drive, you know, big decisions? And I think for both technology, those have arrived, right? And it’s sort of really the time to start synthesizing those together, into a platform. And so it’s sort of no longer about whether these technologies are valuable, but it’s really about how to put them together in a way that scales to the size of a whole portfolio.

So this is a great transition because my next question was going to be about timing. Why do you feel like now is the time for teams to adopt this platform as the new standard in QSP modeling?

Yeah. So I think in the past, the challenges have been getting this to work at all. You know, I think that’s where a lot of the focus has been. There’s a lot of sort of individual science effort on each project.

I think now the science has really matured to the point where we know that this works. We know it delivers high value. We’ve seen many, many examples of that. And so now is the time where investments in technology will really pay off because it allows you to then take these ideas and spread them across the whole portfolio.

So you’re not just impacting a couple of decisions here and there, but you’re able to change the kind of fundamental process by which drugs are discovered.

And I think that it’s also important to note that there are so many different ways that you wanna analyze and work with these models that, instead of having a whole grab bag of different types of tools that aren’t connected together, that aren’t easily usable together, By building a platform that allows for the concerted analysis and and development of models, you can put all of those tools related to those models in one place.

Standardize the kind of data, required, across all those tools, and really enable users to, build, use, apply models, in many different projects, in many different contexts, all all in the same environment.

And and that that way of of bringing a platform to the market where people can do that is something that we think that, we Certara and and and only Certara can do.

And and that’s why we think that it’s a really a game changer, for the market to, be be bringing iQ to market.

So I think, you know, Certara, we we’re in this sort of really interesting position where in addition to being technology developers, we also have the largest QSP consulting group, in industry. So we use these tools on a on a daily basis. So they really have matured over the years through our own use. And we’re really seeing the impact of this integration in our own practice. But it’s very clear that to scale to the size of the whole industry, this is gonna be around producing tools that we can share with the industry at large, to really have that kind of scale and that impact.

Very interesting. Thank you. Finally, I’d like to talk about how staying ahead of the curve in technology impacts long term success for companies. So what makes Certara IQ a future proof investment for pharma and biotech teams that are aiming to stay competitive in model informed drug development?

So, you know, I think one is, you know, Certara IQ is sort of part of a broader MIDD portfolio strategy that Sitara has been working on. And so you’ll see coming from us, you know, increasing numbers of things that kind of plug into this overall approach. But even just looking at QSP itself is the platform is really designed to be extensible. You can build new models that represent new pharmacologies.

You can build new calculations and new capabilities into it. So it’s really sort of a foundation by which you can build, you know, new pipelines and new analysis. And so that allows it to grow as your portfolio grows and changes. So you’re not gonna be, when you move into the next modality, all of a sudden not able to use these tools that the they’re able to grow and follow. And we really use that successfully as, you know, the, as the industry has sort of moved, so it’s more heavily biotherapeutics focused, more multi specifics, more, advanced modalities that QSP is able to follow that along, and kind of keep pace with that level of innovation.

And and we also know that AI technologies are gonna be very important for QSP modeling going forward. And we also know that AI technologies are changing and advancing so rapidly that we really have to future proof for, the new things that, are coming down the pike on on literally a weekly basis nowadays.

And so that means that by building a platform that is extensible, that utilizes standards, for the kinds of data that we’re working with, that allows for interoperation of the different tools in the platform, We we help to future proof for, any new kind of AI technology that might also be applicable to QSP modeling, in in the future, whether that’s next week or next month or next year. Right? And it’s it’s probably all of the above. And so, so by, by starting now and, and thinking about how to extend this platform, now we enable, broader, utilization of these technologies going forward.

Thank you for having us. Obviously, it’s a very exciting time for us, and we’re looking forward to sharing this technology with the industry.

Thank you so much. And, it’s been great to speak with you, and we’re really excited about where iQ is going.

With intuitive and scalable QSP model building, Certara IQ helps transform your drug development. To learn more, visit our website.