Park Hye-Jung, Lee Sang-Ho, Kang Pureum, Cho Chang-Keun, Jang Choon-Gon, Lee Seok-Yong, Lee Yun Jeong, Bae Jung-Woo, Choi Chang-Ik
School of Pharmacy, Sungkyunkwan University, Suwon, 16419, Republic of Korea.
College of Pharmacy, Dongguk University-Seoul, Goyang, 10326, Republic of Korea.
Arch Pharm Res. 2025 Mar;48(3):234-250. doi: 10.1007/s12272-024-01528-8. Epub 2025 Jan 6.
Gliclazide is a sulfonylurea hypoglycemic agent used to treat type 2 diabetes. Cytochrome P450 (CYP) 2C9 and CYP2C19 are primarily involved in the hepatic metabolism of gliclazide. The two CYP isozymes are highly polymorphic, and their genetic polymorphisms are known to significantly impact the pharmacokinetics of gliclazide. In the present study, the physiologically based pharmacokinetic (PBPK) model was developed using data from subjects whose pharmacokinetic parameters were influenced by the genetic polymorphisms of the CYP metabolic enzymes. All predicted plasma concentration-time profiles generated by the model showed visual agreement with the observed data, and the pharmacokinetic results were within the twofold error range. Individual simulation results showed additional metrics: average fold error (- 0.19 to 0.07), geometric mean fold error (1.13-1.56), and mean relative deviation (1.18-1.58) for AUC, C, T, T, CL/F, and V values. These results met the standard evaluation criteria. The validation across a total of 8 studies and 7 races also satisfied the twofold error range for AUC, C, and T. Therefore, variations in gliclazide exposure according to individuals' CYP2C9 and CYP2C19 genotypes were properly captured through PBPK modeling in this study. This PBPK model may allow us to predict the gliclazide pharmacokinetics of patients with genetic polymorphisms in CYP2C9 and CYPC19 under various conditions, ultimately contributing to the realization of individualized drug therapy.
格列齐特是一种用于治疗2型糖尿病的磺脲类降糖药。细胞色素P450(CYP)2C9和CYP2C19主要参与格列齐特的肝脏代谢。这两种CYP同工酶具有高度多态性,已知其基因多态性会显著影响格列齐特的药代动力学。在本研究中,使用来自药代动力学参数受CYP代谢酶基因多态性影响的受试者的数据建立了基于生理的药代动力学(PBPK)模型。该模型生成的所有预测血浆浓度-时间曲线与观察数据在视觉上一致,药代动力学结果在两倍误差范围内。个体模拟结果还显示了其他指标:AUC、C、T、T、CL/F和V值的平均倍数误差(-0.19至0.07)、几何平均倍数误差(1.13 - 1.56)和平均相对偏差(1.18 - 1.58)。这些结果符合标准评估标准。在总共8项研究和7个种族中的验证也满足AUC、C和T的两倍误差范围。因此,在本研究中通过PBPK建模正确捕捉了根据个体CYP2C9和CYP2C19基因型的格列齐特暴露差异。这种PBPK模型可能使我们能够预测在各种条件下CYP2C9和CYPC19基因多态性患者的格列齐特药代动力学,最终有助于实现个体化药物治疗。