去唾液酸糖蛋白受体
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药理学
药代动力学
小干扰RNA
药物开发
医学
药效学
基于生理学的药代动力学模型
计算生物学
药品
化学
生物
核糖核酸
体外
生物化学
肝细胞
基因
作者
Jae Yoon Jeon,Vivaswath S. Ayyar,Amitava Mitra
标识
DOI:10.1007/s11095-022-03333-8
摘要
The approval of four small interfering RNA (siRNA) products in the past few years has demonstrated unequivocally the therapeutic potential of this novel modality. Three such products (givosiran, lumasiran and inclisiran) are liver-targeted, using tris N-acetylgalactosamine (GalNAc)3 as the targeting ligand. Upon subcutaneous administration, GalNAc-conjugated siRNAs rapidly distribute into the liver via asialoglycoprotein receptor (ASGPR) mediated uptake in the hepatocytes, resulting in fast elimination from the systemic circulation. Patisiran, on the other hand, has been formulated in a lipid nanoparticle for optimal delivery to the liver. While several publications have described preclinical and clinical pharmacokinetic (PK) and pharmacodynamic (PD) results, including absorption, distribution, metabolism, and elimination (ADME) profiles in selected species as well as limited modeling efforts for siRNA therapeutics, there is no systematic review of the PK and PD models developed for these agents or work summarizing the utility and application(s) of such models in drug development and regulatory review. Here, we provide a mini-review of the current state of modeling efforts for siRNA therapeutics within the early preclinical, translational, and clinical stages of siRNA development. Diverse modeling methods including simple compartmental, mechanistic and systems PK/PD, physiologically-based PK (PBPK), population PK/PD, and dose-response-time models are introduced and reviewed. The utility of such models in development and regulatory review for siRNA therapeutics is also discussed with examples. Finally, the current knowledge gaps in mechanism of action of siRNA and resulting challenges in model development are summarized. The goal of this minireview is to trigger cross-functional discussion amongst all key stakeholders to generate key experimental datasets and align on current assumptions, model structures, and approaches to facilitate development and application of robust PK/PD models for siRNA therapeutics.
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