Zhou Jie, Zhang Qing, Chen Yi, Xue Cun, Li Yue-Ting, Huang Yong, Zheng Lin, Huang Jing, Chen Si-Ying, Gong Zi-Peng
Provincial Key Laboratory of Pharmaceutics in Guizhou Province, State Key Laboratory of Functions and Applications of Medicinal Plants, Engineering Research Center for the Development and Application of Ethnic Medicine and Traditional Chinese Medicine(Ministry of Education), School of Pharmacy, Guizhou Medical University Guiyang 550004, China.
Zhongguo Zhong Yao Za Zhi. 2022 Dec;47(23):6308-6319. doi: 10.19540/j.cnki.cjcmm.20220914.201.
In the present study, a pharmacokinetics(PK)-pharmacodynamics(PD) model in the anti-inflammatory active components in Inula cappa extract was established based on the lipopolysaccharide(LPS)-induced in vitro inflammation model in order to clarify the relationship between the dynamic changes of anti-inflammatory active components in inflammatory cells and their efficacy. Firstly, the inflammation model in vitro was induced by 1 μg·mL(-1) LPS in RAW264.7 cells for 24 h. After treatment with 400 μg·mL(-1) I. cappa extract, the pharmacokinetics(PK) of five anti-inflammatory active components, including luteolin(LUT), chlorogenic acid(CA), cryptochlorogenic acid(CCA), 3,4-dicaffeoylquinic acid(3,4-DCQA), and 4,5-dicaffeoylquinic acid(4,5-DCQA), in normal cells and inflammatory cells was compared. Meanwhile, the PD study was carried out by measuring the inflammatory factors NO and TNF-α in the cell supernatant at each time point, which was fitted with PK by the Phoenix Model in the WinNonlin 8.2 to establish the PK-PD model for five components including LUT, CA, CCA, 3,4-DCQA, and 4,5-DCQA. The results showed that compared with normal cells, the model cells showed increased or decreased uptake of five components, advanced T_(max), faster absorption, prolonged MRT and t_(1/2), and increasing or decreasing trend of CL_(z/F) and V_(z/F). When NO was used as the efficacy index, the PK-PD model after the integration of the multi-effect components in I. cappa was E=7.45×[1-Ce(5.74)/(78.24(5.74)+Ce(5.74))], while with TNF-α as the efficacy index, the PK-PD model after the integration of the multi-effect components in I. cappa was E=79.28×[1-Ce(6.45)/(85.10(6.45)+Ce(6.45))]. The results of the study suggested that the inflammatory state could change the cellular PK of I. cappa. The anti-inflammatory effect of active components in I. cappa might be related to the down-regulation of the secretion of NO and TNF-α in inflammatory cells, and NO and TNF-α might serve as the anti-inflammatory targets of active components of I. cappa.
在本研究中，基于脂多糖（LPS）诱导的体外炎症模型，建立了大花旋覆花提取物抗炎活性成分的药代动力学（PK）-药效学（PD）模型，以阐明炎症细胞中抗炎活性成分的动态变化与其药效之间的关系。首先，用1μg·mL⁻¹ LPS诱导RAW264.7细胞建立体外炎症模型24小时。用400μg·mL⁻¹大花旋覆花提取物处理后，比较了木犀草素（LUT）、绿原酸（CA）、隐绿原酸（CCA）、3,4-二咖啡酰奎宁酸（3,4-DCQA）和4,5-二咖啡酰奎宁酸（4,5-DCQA）这五种抗炎活性成分在正常细胞和炎症细胞中的药代动力学（PK）。同时，通过测定各时间点细胞上清液中的炎症因子NO和TNF-α进行药效学（PD）研究，在WinNonlin 8.2中用Phoenix模型将其与PK拟合，建立了包括LUT、CA、CCA、3,4-DCQA和4,5-DCQA五种成分的PK-PD模型。结果表明，与正常细胞相比，模型细胞中五种成分的摄取量有增加或减少，Tₘₐₓ提前，吸收加快，平均驻留时间（MRT）和半衰期（t₁/₂）延长，清除率（CL₍z/F₎）和表观分布容积（V₍z/F₎）有增加或减少的趋势。当以NO作为药效指标时，大花旋覆花多效成分整合后的PK-PD模型为E = 7.45×[1 - Ce⁽⁵.⁷⁴⁾/(78.24⁽⁵.⁷⁴⁾ + Ce⁽⁵.⁷⁴⁾)]，而以TNF-α作为药效指标时，大花旋覆花多效成分整合后的PK-PD模型为E = 79.28×[1 - Ce⁽⁶.⁴⁵⁾/(85.10⁽⁶.⁴⁵⁾ + Ce⁽⁶.⁴⁵⁾)]。研究结果表明，炎症状态可改变大花旋覆花的细胞药代动力学。大花旋覆花活性成分的抗炎作用可能与炎症细胞中NO和TNF-α分泌的下调有关，NO和TNF-α可能是大花旋覆花活性成分的抗炎靶点。
