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ORIGINAL RESEARCH article
Front. Pharmacol.
Sec. Translational Pharmacology
Volume 15 - 2024 |
doi: 10.3389/fphar.2024.1454785
A quantitative systems pharmacology (QSP) platform for preclinical to clinical translation of in-vivo CRISPR-Cas therapy
Provisionally accepted- University of Florida, Gainesville, United States
In-vivo CRISPR Cas genome editing is a complex therapy involving lipid nanoparticle (LNP), messenger RNA (mRNA), and single guide RNA (sgRNA). This novel modality requires prior modeling to predict dose-exposure-response relationships due to limited information on sgRNA and mRNA biodistribution. This work presents a QSP model to characterize, predict, and translate the Pharmacokinetics/Pharmacodynamics (PK/PD) of CRISPR therapies from preclinical species (mouse, non-human primate (NHP)) to humans using two case studies: transthyretin amyloidosis and LDL-cholesterol reduction. PK/PD data were sourced from literature.The QSP model incorporates mechanisms post-IV injection: 1) LNP binding to opsonins in liver vasculature; 2) Phagocytosis into the Mononuclear Phagocytotic System (MPS);3) LNP internalization via endocytosis and LDL receptor-mediated endocytosis in the liver; 4) Cellular internalization and transgene product release; 5) mRNA and sgRNA disposition via exocytosis and clathrin-mediated endocytosis; 6) Renal elimination of LNP and sgRNA; 7) Exonuclease degradation of sgRNA and mRNA; 8) mRNA translation into Cas9 and RNP complex formation for gene editing.Monte-Carlo simulations for 1000 subjects showed the model could recapitulate LNP pharmacokinetics across species. The rate of internalization in interstitial layer were 0.039 1/h in NHP and 0.007 1/h in humans. The rate of exocytosis were 6.84 1/h in mouse, 2690 1/h in NHP, and 775 1/h in humans. Pharmacodynamics were modeled using an indirect response model, estimating first-order degradation rate (0.493 1/d) and TTR reduction parameters in NHP.
Keywords: Quantitative Systems Pharmacology (QSP), Gene Therapy, liver disease (Ld), Rare disease (RD), Translational modelling
Received: 25 Jun 2024; Accepted: 30 Aug 2024.
Copyright: © 2024 Desai, Schmidt and Cristofoletti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Devam A. Desai, University of Florida, Gainesville, United States
Rodrigo Cristofoletti, University of Florida, Gainesville, United States
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