Transforming the Discovery of Targeted Protein Degraders: The Translational Power of Predictive PK/PD Modeling
Robin Haid, PhD, Bayer
Robin Haid is a third-year PhD student at Bayer’s Preclinical Modeling & Simulation subcluster, focusing on the PK/PD modeling of targeted protein degraders (TPDs). His aim is to integrate the experimental data that is routinely generated in drug discovery to arrive at a profound understanding that propels projects forward. Robin studied Pharmaceutical Sciences at ETH Zurich in Switzerland, where he graduated at the top of the 2023 class. His award-winning work on predictive PK/PD modelling of degraders was published in two articles and he has been featured as a speaker at several international conferences, most recently at PAGE in Rome.
March Webinar Recording
Targeted protein degraders (TPDs) are attracting considerable interest with the promise to address disease-related proteins not druggable with inhibitors due to lack of an amenable active site [1, 2]. Instead of altering the activity of their targets, degraders employ the cell’s own ubiquitin proteasome system to break them down completely. This mechanism of action (MOA) requires the drug to form a ternary complex with its target protein and an E3 ligase enzyme, which marks the target for removal [3]. The ensuing degradation has been observed to persist beyond the time frame of detectable TPD levels in vivo suggesting protein resynthesis is a slow process [4].
Despite their novel MOA, degrader PK/PD is still approached with a mindset deeply rooted in inhibitor drugs, which impedes the proper interpretation of experimental data [5]. Researchers have thus resorted to serendipity and testing unsustainably large numbers of compounds directly in vivo [2]. This trial-and-error based avenue is inefficient, time-consuming, and expensive, urging the clear need for predictive PK/PD modeling specifically tailored for TPDs to improve decision making.
Meanwhile, the modeling and simulation community has been fascinated with the unique MOA of TPDs, spawning ever more sophisticated and complex mathematical models [6–10]. While there are also a few publications pursuing a more fit-for-purpose approach, none of them feature practical use cases for how to support drug discovery [11–13].
Our comprehensive modeling framework, in contrast, emerged from the close collaboration with interdisciplinary project teams and addresses the questions raised there while relying exclusively on readily available data.