Modeling & Simulation Expert Bayer AG / Vividion Inc. San Diego, California, United States
Disclosure(s):
Robin T.U Haid, PhD: No relevant disclosure to display
Objectives: 1) Establish and validate a quantitative systems pharmacology (QSP) model for targeted protein degraders (TPDs) to better understand their mechansim of action (MOA). 2) Leverage these insights to identify potential use cases for model-informed drug discovery and development in the preclinical setting. 3) Apply the developed modeling framework to in-house TPD projects to assess its practical utility and impact on decision making.
Methods: Our QSP framework builds on an earlier model describing ternary complex formation in biochemical assays [1] and assumes binding/unbinding to be in rapid equilibrium. Its parameters can be classified as either compound-specific (three binding affinities) or system specific (expression levels of E3 ligase as well as target protein baseline half-life and rate of induced target degradation by the UPS) with all of them being observable in orthogonal assays. Model lumping affords a separate set of four intuitive parameters (baseline protein half-life as well as degradation potency, extent of maximal degradation and concentration of maximal degradation) to describe protein degradation over time, both in vitro as well as in vivo. Going beyond degradation, the model directly links protein levels to downstream pharmacodynamic effects, while also accounting for potential target inhibition by the degrader drug.
Results: To validate the full QSP model, protein degradation was accurately predicted for a set of nine different compounds in three different cell lines [2]. The lumped mechanistic model was then used to predict in vivo protein degradation from in vitro data for twelve compounds belonging to three different projects. Predictions were then also extended to a downstream readout of efficacy, again using in vitro data as input. These modeling activities revealed useful insights about the TPD mechanism of action, such as identifying AUC as the main PK/PD driver. However, infrequent dosing regimens (e.g., monthly) were found to require sustained release formulations, as protein resynthesis takes place on the time frame of days rather than months [3]. Finally, modeling reveals fundamental differences between conventional inhibitors and degraders, demonstrating that the simple concept of functional potency does not apply to the latter.
Conclusions: Our findings demonstrate that the full QSP model can be used to 1) guide medicinal chemistry during compound optimization by identifying target values for the degrader’s binding affinities and 2) translate drug effects across different cell types and species by leveraging data about baseline protein half-life and E3 ligase expression levels. The lumped mechanistic model, in turn, allows to identify suitable compounds for animal studies and informs experimental design with regards to selection of 1) dose schedules, 2) measurement time points and 3) number of animals. The proposed predictive modeling approach thus promises to transform the discovery of degrader drugs by leveraging in vitro data to a greater extent than ever before. Going forward, this framework furthermore lays out a rational path for clinical translation, helping with getting patients the right dose of the right drug.
Citations: [1] Han, B. A Suite of Mathematical Solutions to Describe Ternary Complex Formation and their Application to Targeted Protein Degradation by Heterobifunctional Ligands. J. Biol. Chem. 2020, 295, 15280–15291. [2] Haid, R.T.U.; Reichel A. A Mechanistic Pharmacodynamic Modeling Framework for the Assessment and Optimization of Proteolysis Targeting Chimeras (PROTACs). Pharmaceutics 2023, 15, 195. [3] Harling, J.D.; Scott-Stevens, P.; Gaohua, L. Developing Pharmacokinetic/Pharmacodynamic Relationships with PROTACs. In: Protein Degradation with New Chemical Modalities: Successful Strategies in Drug Discovery and Chemical Biology; Weinmann, H., Crews, C., Eds.; Royal Society of Chemistry: London, UK, 2021; p. 79.
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