Liu Dongyang, Zhang Yi, Jiang Ji, Choi John, Li Xuening, Zhu Dalong, Xiao Dawei, Ding Yanhua, Fan Hongwei, Chen Li, Hu Pei
Clinical Pharmacology Research Center, Peking Union Medical College Hospital and Chinese Academy of Medical Sciences, Beijing, 100032, China.
HuaMedicine (Shanghai) Ltd., Shanghai, China.
Clin Pharmacokinet. 2017 Aug;56(8):925-939. doi: 10.1007/s40262-016-0484-2.
Pharmacokinetic/pharmacodynamic modeling and simulation can aid clinical drug development by dynamically integrating key system- and drug-specific information into predictive profiles. In this study, we propose a methodology to predict pharmacokinetic/pharmacodynamic profiles of sinogliatin (HMS-5552, RO-5305552), a novel glucokinase activator to treat diabetes mellitus, for first-in-patient (FIP) studies.
Initially, pharmacokinetic/pharmacodynamic profiles of sinogliatin and another glucokinase activator (US2) previously acquired from healthy subjects were fitted using Model A incorporating an indirect response mechanism. The pharmacokinetic/pharmacodynamic profiles of US2 in patients with type 2 diabetes mellitus (T2DM) were then fitted using Model B incorporating circadian rhythm and food effects after thoughtful research on the difference between healthy subjects and T2DM patients. The differences in results between the two US2 modeling populations were used to scale the values of the pharmacodynamic parameters and refine the pharmacodynamic model of sinogliatin, which was then utilized to project pharmacokinetic/pharmacodynamic profiles of sinogliatin in T2DM patients after an 8-day simulated treatment. Results showed that the projected pharmacokinetic/pharmacodynamic values of five parameters were within 70-130% of values fitted from observed clinical data while the other two remaining projected parameters were within a twofold error. Population pharmacokinetic/pharmacodynamic analysis conducted for sinogliatin also suggested that age and sex were significantly correlated to pharmacokinetic/pharmacodynamic characteristics. Additionally, Model B was combined with a glycosylated hemoglobin (HbA) compartment to form Model C, which was then used to project serum HbA levels in patients after a 1-month simulated treatment of sinogliatin. The predicted HbA changes were nearly identical to observed clinical values (0.82 vs. 0.78%).
Model-based drug development methods utilizing a learn-research-confirm cycle may accurately project pharmacokinetic/pharmacodynamic profiles of new drugs in FIP studies.
药代动力学/药效学建模与模拟可通过将关键的系统和药物特异性信息动态整合到预测模型中，辅助临床药物研发。在本研究中，我们提出一种方法，用于预测西格列汀（HMS - 5552，RO - 5305552）的药代动力学/药效学特征，西格列汀是一种用于治疗糖尿病的新型葡萄糖激酶激活剂，用于首次人体试验（FIP）研究。
首先，使用包含间接反应机制的模型A对先前从健康受试者获得的西格列汀和另一种葡萄糖激酶激活剂（US2）的药代动力学/药效学特征进行拟合。在深入研究健康受试者与2型糖尿病（T2DM）患者之间的差异后，使用包含昼夜节律和食物效应的模型B对US2在T2DM患者中的药代动力学/药效学特征进行拟合。将两个US2建模群体的结果差异用于缩放药效学参数值并完善西格列汀的药效学模型，然后利用该模型预测西格列汀在T2DM患者中进行8天模拟治疗后的药代动力学/药效学特征。结果表明，五个参数的预测药代动力学/药效学值在从观察到的临床数据拟合值的70 - 130%范围内，而其余两个预测参数的误差在两倍以内。对西格列汀进行的群体药代动力学/药效学分析还表明，年龄和性别与药代动力学/药效学特征显著相关。此外，模型B与糖化血红蛋白（HbA）隔室相结合形成模型C，然后用于预测西格列汀进行1个月模拟治疗后患者的血清HbA水平。预测的HbA变化与观察到的临床值几乎相同（0.82%对0.78%）。
利用学习 - 研究 - 验证循环的基于模型的药物研发方法可能在FIP研究中准确预测新药的药代动力学/药效学特征。
