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使用多尺度机理性药理动力学-毒理动力学 (PK-TK) 模型对编码IgG的mRNA的床边模型:用Anti-Claudin 18.2进行的案例研究
Devam A Desai1, Rodrigo Cristofoletti1
1Department of Pharmaceutics, College of Pharmacy, University of Florida, Gainesville, Florida, USA.
Clinical and translational science
|September 22, 2025
概括
一个新的多尺度药理动力学-毒理动力学模型预测mRNA编码的抗体行为在体内. 这一框架提高了mRNA疗法的翻译可预测性,有助于临床试验的剂量选择.
科学领域:
- 药理学和毒理学 药理学和毒理学
- 生物技术是生物技术.
- 免疫学 免疫学 免疫学
背景情况:
- 在体内表达mRNA编码抗体是一种有前途的治疗策略.
- 将临床前发现转化为mRNA疗法的临床应用,在传递,吸收,翻译和结合方面面临挑战.
研究的目的:
- 为mRNA编码抗体疗法开发一个多尺度机理性药理动力学-毒理动力学 (PK-TK) 模型.
- 从临床前模型到人类预测,描述和预测编码抗克劳丁18.2IgG的mRNA治疗药物的体内行为.
主要方法:
- 集成的关键过程:脂质纳米粒子 (LNP) 传递,通过LDLR进行内细胞分裂,内体逃脱,mRNA转化,IgG分布,标结合和细胞因子升高.
- 从小鼠,大鼠和非人类灵长类动物 (NHPs) 的体外和体内数据中获取.
- 采用全米缩放,并考虑了人类转化LDLR表达的物种间差异.
主要成果:
- 该模型成功地重复了小鼠的mRNA,表达的IgG和细胞因子/化学因子水平.
- 敏感性分析发现了关键的翻译瓶.
- 人类预测为首次在人体试验中选择0.01 mg/kg的起始剂量提供了信息.
结论:
- 多尺度PK-TK模型通过突出纳米粒子处理和mRNA转化中的特定物种差异,为剂量选择提供了合理的基础.
- 这一框架提高了基于mRNA的蛋白质疗法的翻译可预测性,简化了临床开发.
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