4.6 Article

Quantitative Prediction of Human Pharmacokinetics for mAbs Exhibiting Target-Mediated Disposition

期刊

AAPS JOURNAL
卷 17, 期 2, 页码 389-399

出版社

SPRINGER
DOI: 10.1208/s12248-014-9690-8

关键词

ADME of biologics; human translation; monoclonal antibodies; PK/PD modeling; TMDD

资金

  1. Pharmacokinetic/Pharmacodynamic Summer Intern at PDM Department in Pfizer
  2. NIH [GM 57980]
  3. NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM057980] Funding Source: NIH RePORTER

向作者/读者索取更多资源

Prediction of human pharmacokinetics (PK) can be challenging for monoclonal antibodies (mAbs) exhibiting target-mediated drug disposition (TMDD). In this study, we performed a quantitative analysis of a diverse set of six mAbs exhibiting TMDD to explore translational rules that can be utilized to predict human PK. A TMDD model with rapid-binding approximation was utilized to fit PK and PD (i.e., free and/or total target levels) data, and average absolute fold error (AAFE) was calculated for each model parameter. Based on the comparative analysis, translational rules were developed and applied to a test antibody not included in the original analysis. AAFE of less than two-fold was observed between monkey and human for baseline target levels (R (0)), body-weight (BW) normalized central elimination rate (K (el)/BW-0.25) and central volume (V (c)/BW1.0). AAFE of less than three-fold was estimated for the binding affinity constant (K (D)). The other four parameters, i.e., complex turnover rate (K (int)), target turnover rate (K (deg)), central to peripheral distribution rate constant (K (pt)) and peripheral to central rate constant (K (tp)) were poorly correlated between monkey and human. The projected human PK of test antibody based on the translation rules was in good agreement with the observed nonlinear PK. In conclusion, we recommend a TMDD model-based prediction approach that integrates in vitro human biomeasures and in vivo preclinical data using translation rules developed in this study.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据