ADC Modeling and Design with Certara IQ™

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, we are joined by Kimiko McGirr, Certara’s modeling scientist who has contributed to developing Certara IQ’s library of antibody drug conjugate or ADC models.

ADCs are a trending modality with drug developers recently with more global acceptances seen. So, Kimiko, how are Certara’s ADC QSP models used, and how does it help researchers today address the well known challenges in developing ABCs?

Thanks, Jessica. So ADCs are a complex modality that require thorough optimizations of its components for success in the clinic. Sitara has two suites of models to help answer questions at different stages of the ADC development.

For drug design questions asked during the research and development stage, we typically recommend the ADC model pack, which is a set of models that generally describe the mechanistic reactions of the ADC, antibody, and payload in vitro and in vivo. These help models help assess the balance between drug properties, such as DAR antibody, affinity and, linker stability, for example. So from these results, researchers can gain valuable insights.

For example, they can better understand their in vitro results, explore what their molecule might look like in vivo, or make gono go decisions while selecting lead candidates.

For assisting with preclinical to clinical translation, we have developed a more in-depth QSP model, which will take a program’s preclinical data into consideration, as well as the, biology specific to an indication of interest.

The model will then predict expected pharmacological activity in the clinic and can further be refined once early clinical data becomes available. Altogether, both of these models can quantitatively guide decisions to take their program into the next step. To help overcome ADC challenges, such as a narrow therapeutic window, we’ve used these models to estimate off tissue, target binding, and drug exposure in tissues of concern, coupled with our efficacy modeling.

Thank you. So you talked to us about the various challenges of ABC development and how models particularly can support navigating these. So can we talk a little more about some of the features of the ABC models and how they are customizable to support optimizing ADC design and predicting therapeutic outcomes?

Sure. Yeah. So as I mentioned, we kind of have two different, types of ADC models. And so for the ADC model pack that we typically use for early feasibility assessment, that whole model pack was built upon our original, pack for antibodies. So all of the mechanisms included with those packs are available to use with ADCs.

I think one that would be particularly useful for example, could be the one that, investigates targeting multiple antigens, essentially modeling a bispecific ADC.

And there are other ones, such as, you know, competitive binding, that we can look into, with these model packs.

On the other hand, with our full QSP model, it’s even more modular in the sense that we can, use different mechanisms or relationships to develop a model specific to the data package, available for an ADC.

So examples of this modularity include investigating bystander effect, target heterogeneity, and the inclusion of a hematological toxicity model. These features allow us to account for different mechanisms of action to best represent the novel technologies developing in the ADC space.

Great. Thank you so much, Kimiko.

Yeah. And thank you for letting me speak to you about the a Certara’s ABC models. It’s been really fun.

With intuitive and scalable QSP model building, Certara IQ helps transform your drug development. To learn more about Certara IQ’s pre validated library of models, visit our website.