Associate Director, PKPD Modeler GSK Waltham, Massachusetts, United States
Background and
Objectives: Proteolysis targeting chimeras (PROTACs) are drug molecules that degrade specific proteins of interest by leveraging a cell’s ubiquitin proteasome system. PROTACS simultaneously bind to the target protein of interest and an E3 ligase forming a ternary complex [1]. The protein of interest is polyubiquitinated by a selected E3 ligase triggering its degradation by the proteasome [1]. PROTAC technology is growing rapidly with a number of molecules in clinical trials [1,2], however translational pharmacokinetic/pharmacodynamic (PKPD) models that are useful in a drug discovery setting are still emerging [3].
A few mechanistic models describing the mechanism of ternary complex formation and ubiquitin-proteasome system (UPS) mediated protein degradation have been published so far [4,5,6]. Although complex, these publications establish a mechanistic PK/PD modeling framework that can be utilized to optimize key properties of potent and selective heterobifunctional protein degraders. The main limitation of these approaches is that they require a large number of parameters that can be challenging to measure. Further, the models are mostly applied to sparse in vitro or in vivo experimental data and do not showcase the in vitro to in vivo translation of these model systems.
Methods: Here we present a simple semi-mechanistic translational PKPD modeling framework to explore the impact of key compound properties on in vivo protein degradation. In this framework, PROTAC PK is described with a flexible one or two compartmental model. PK is linked to a turnover PD model incorporating an additional saturable clearance route to describe protein degradation. The model is solely parameterized with basic PK and in vitro data available at the early stages of a PROTAC discovery project. A similar semi-mechanistic PKPD model for PROTACS has been published recently [7]. However, the difference between the presented model and the recent publication is that the current model has the flexibility to be parameterized primarily with in vitro data rather than fitting to in vivo protein degradation.
Results and
Conclusions: The main utility of this PROTAC PKPD modeling tool is to 1) assess the critical parameters to be optimized during the lead optimization phase 2) rank ordering of molecules to be tested in vivo by prospective predictions of in vivo protein degradation responses 3) early human dose prediction of candidate molecules. We illustrate the applicability of this modeling framework based on three early discovery project examples.
Citations: 1. Garber, Ken. "The PROTAC gold rush." Nat. Biotechnol 40, no. 1 (2022): 12-16. 2. Liu, Zi, Mingxing Hu, Yu Yang, Chenghao Du, Haoxuan Zhou, Chengyali Liu, Yuanwei Chen et al. "An overview of PROTACs: a promising drug discovery paradigm." Molecular biomedicine 3, no. 1 (2022): 46. 3. Watt, Gillian F., Paul Scott-Stevens, and Lu Gaohua. "Targeted protein degradation in vivo with Proteolysis Targeting Chimeras: Current status and future considerations." Drug Discovery Today: Technologies 31 (2019): 69-80. 4. Bartlett, Derek W., and Adam M. Gilbert. "A kinetic proofreading model for bispecific protein degraders." Journal of pharmacokinetics and pharmacodynamics 48 (2021): 149-163. 5. Guzzetti, Sofia, and Pablo Morentin Gutierrez. "An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi-or fully-mechanistic models and exact steady state solutions." Journal of Pharmacokinetics and Pharmacodynamics 50, no. 5 (2023): 327-349. 6. Haid, Robin Thomas Ulrich, and Andreas Reichel. "A mechanistic pharmacodynamic modeling framework for the assessment and optimization of proteolysis targeting chimeras (PROTACs)." Pharmaceutics 15, no. 1 (2023): 195. 7. Wang, Angelia F., and Vivaswath S. Ayyar. "Pharmacodynamic Models of Indirect Effects and Irreversible Inactivation with Turnover: Applicability to Mechanism-Based Modeling of Gene Silencing and Targeted Protein Degradation." Journal of Pharmaceutical Sciences 113, no. 1 (2024): 191-201.
