Supporting the development and optimization of mRNA-encoded therapeutics through a multiscale Physiologically Based Pharmacokinetic (PBPK) platform
Elio Campanile (1,2), Elisa Pettinà (3), Giada Fiandaca (3,4), Lorena Leonardelli (2), Lorenzo Dasti (3), Stefano Giampiccolo (2), Luca Marchetti (2,3)
(1) Department of Mathematics, University of Trento, Italy. (2) Fondazione the Microsoft Research – University of Trento Centre for Computational and Systems Biology, Rovereto, Italy. (3) Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Italy. (4) Current affiliation : Macbes team, Inria Center at Université Côte d'Azur, France.
Introduction: In recent years, antibody-based therapeutics have shown promise, particularly in cancer treatment. However, the high cost of laboratory experiments, complex production processes, and short half-life pose significant challenges. In contrast, in vitro-transcribed (IVT) mRNA formulations offer a cost-effective alternative, enabling endogenous production of therapeutic proteins within the patient's cells. By utilizing the patient's own cellular machinery, this approach not only ensures the production of proteins tailored to the patient's specific needs but also provides a protective environment that reduces the likelihood of protein degradation.
Objectives: This work aims to develop a flexible, general-purpose, multiscale Physiologically Based Pharmacokinetic (PBPK) platform to accurately predict the PK time-series of therapeutic protein concentrations following the administration of IVT mRNA formulations. Through rigorous validation studies based on literature data on various therapeutic proteins in pre-clinical animal models, we aim to establish a reliable tool to facilitate the development of mRNA-encoded therapeutics.
Methods: A whole-body PBPK multiscale model implemented using MATLAB2023b SimBiology was extended to simulate recombinant and mRNA-encoded protein trafficking. An entirely new model layer describing mRNA trafficking, uptake, translation, and clearance was added to the two-pore PBPK model developed by Sepp et al. [1]. The model was also extended to support various administration routes, including intravenous, intradermal, and intramuscular injections. Drug-dependent key parameters, including clearance uptake, FcRn affinity, and mRNA translation, were identified to facilitate the adaptation of the model for fitting and validation across a broad spectrum of mRNA-based therapeutics. Furthermore, an identifiability analysis utilizing GENSSI [2] was conducted on all mRNA-related model parameters, resulting in their local structural identifiability.
Results: Our model could fit with good accuracy publicly available time-series PK data of mRNA-encoded proteins of various sizes. Specifically, we leveraged the studies by Huang et al. [2], focusing on the B7H3xCD3 BiTE, and by Wu et al. [3], centered on Pembrolizumab, to calibrate and validate our model. Both papers focus on cancer treatment and provide PK time-series for recombinant and mRNA-encoded protein therapy, both used to calibrate and test the model. Despite the considered proteins differ in size and presence/absence of the FC receptor, our platform was successfully calibrated and effectively validated in both cases, proving its adaptability and versatility to support dose-finding applications for a range of mRNA-based therapeutics.
Conclusions: Our multiscale PBPK model preserves the foundational properties of the original PBPK model by Sepp et al. [1] while adding a layer to account for mRNA trafficking, uptake, translation, and clearance. This enhancement enables our model to offer a comprehensive framework for predicting mRNA-encoded drug concentration profiles in specific organs, especially at the site of action, with enhanced accuracy. Moreover, the model can be easily adapted to incorporate novel layers and interactions with cell populations, e.g., BiTE binding to T-cells. While validation was restricted to preclinical animal models due to limitations in data availability, our preliminary tests indicate that our model could serve as a first step to assist, in silico, dose and schedule translation across different animal species and to humans.
Moving forward, acquiring new data will be pivotal for advancing mRNA-based therapies. Our work lays the foundation for further exploration and refinement of these treatment strategies, serving as virtual laboratories for optimizing dose and schedule-finding applications.
References:
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[2] Chis O, Banga JR, Balsa-Canto E. GenSSI: a software toolbox for structural identifiability analysis of biological models. Bioinformatics. 2011 Sep 15;27(18):2610-1. doi: 10.1093/bioinformatics/btr431. Epub 2011 Jul 22. PMID: 21784792; PMCID: PMC3167050.
[3] Huang C, Duan X, Wang J, Tian Q, Ren Y, Chen K, Zhang Z, Li Y, Feng Y, Zhong K, Wang Y, Zhou L, Guo G, Song X, Tong A. Lipid Nanoparticle Delivery System for mRNA Encoding B7H3-redirected Bispecific Antibody Displays Potent Antitumor Effects on Malignant Tumors. Adv Sci (Weinh). 2023 Jan;10(3):e2205532. DOI: 10.1002/advs.202205532
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