A quantitative systems pharmacology (QSP) platform for preclinical to clinical translation of in-vivo CRISPR-Cas therapy

内化 内吞作用 亚基因组mRNA 体内 基因组编辑 细胞生物学 药理学 化学 清脆的 生物 受体 生物化学 遗传学 基因
作者
Devam A Desai,Stephan Schmidt,Rodrigo Cristofoletti
出处
期刊:Frontiers in Pharmacology [Frontiers Media]
卷期号:15: 1454785-1454785 被引量:10
标识
DOI:10.3389/fphar.2024.1454785
摘要

Background: 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. Methods: 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 were performed for 1000 subjects and showed a reduction in serum TTR. Results: The rate of internalization in interstitial layer was 0.039 1/h in NHP and 0.007 1/h in humans. The rate of exocytosis was 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. Discussion: The QSP model effectively characterized biodistribution and dose-exposure relationships, aiding the development of these novel therapies. The utility of platform QSP model can be paramount in facilitating the discovery and development of these novel agents.
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