因子浓缩物切换时预测血友病患者药代动力学的方法比较。
A comparison of methods for prediction of pharmacokinetics across factor concentrate switching in hemophilia patients.
机构信息
School of Pharmacy, University of Waterloo, Waterloo, Ontario, Canada.
McMaster-Bayer Endowed Research Chair for Clinical Epidemiology of Congenital Bleeding Disorders, Department of Medicine, Department of Health Research Methods, Evidence and Impact, McMaster University, Ontario, Canada.
出版信息
Thromb Res. 2019 Dec;184:31-37. doi: 10.1016/j.thromres.2019.10.023. Epub 2019 Oct 29.
INTRODUCTION
This study proposes a method to predict individual pharmacokinetics of a future product by using the individual pharmacokinetic profile on the current product and the PopPK models of the current and future product.
METHODS
Individual dense data was collected from two PK crossover studies, one enrolling 29 patients switching from Advate to Eloctate and one enrolling 15 patients switching from Advate to Novoeight. Three methods were designed to predict the second product's individual PK parameters (CL, V1, Q, and V2). Method 1 used the second product's typical population value of PK parameters from its PopPK model. Method 2 used the second product's calculated PK parameters based on individual covariates and its PopPK model. Method 3 used method 2, along with the predicted η-values of CL and V1 from the first product and its PopPK model. Each method was used to assess PK prediction during switching from Advate to Novoeight, Novoeight to Advate, and Advate to Eloctate.
RESULTS
The three methods produced different outcomes. The mean absolute relative errors for half-life were lowest for method 3 for each study (11.6%, 13.1%, 13.6%). The regression line between predicted and observed half-life for method 3 was closest to the line of identity for each study (0.84, 0.67, 0.66).
CONCLUSION
Taking into account individual PK from a previous clotting factor product was shown to provide better means of estimating individual PK for a new product. This may improve regimen design across switches and reduce the time to tailor optimal dose of FVIII products.
简介
本研究提出了一种通过使用当前产品的个体药代动力学曲线和当前及未来产品的群体药代动力学模型来预测未来产品个体药代动力学的方法。
方法
从两项 PK 交叉研究中收集个体密集数据，一项研究招募了 29 名从 Advate 转为 Eloctate 的患者，另一项研究招募了 15 名从 Advate 转为 Novoeight 的患者。设计了三种方法来预测第二种产品的个体 PK 参数（CL、V1、Q 和 V2）。方法 1 使用第二个产品的典型群体 PK 参数值从其群体药代动力学模型。方法 2 使用第二个产品的计算 PK 参数基于个体协变量及其群体药代动力学模型。方法 3 使用方法 2，以及第一个产品及其群体药代动力学模型预测的 CL 和 V1 的η值。每种方法都用于评估从 Advate 转换为 Novoeight、Novoeight 转换为 Advate 和 Advate 转换为 Eloctate 时的 PK 预测。
结果
三种方法产生了不同的结果。每种研究中半衰期的平均绝对相对误差均为方法 3 最低（11.6%、13.1%、13.6%）。对于每种研究，方法 3 预测的半衰期与观察的半衰期之间的回归线最接近身份线（0.84、0.67、0.66）。
结论
考虑到先前凝血因子产品的个体 PK，表明提供了更好的方法来估计新产品的个体 PK。这可能会改善各次转换之间的方案设计并减少调整 FVIII 产品最佳剂量的时间。
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