Scholarlink

<p>The typically low, molecule-dependent permeability of antibodies across the blood–brain barrier (BBB) has driven the development of engineered constructs with optimized BBB transcytosis to facilitate efficient brain delivery. Physiologically-based pharmacokinetic (PBPK) modeling provides a framework to predict brain disposition and inform drug development; however, current models lack true <i>a priori</i> predictive capability and remain dependent on <i>in-vivo</i> data. <i>In-vitro</i> brain endothelial cell permeability (P<sub>app</sub>) assays provide antibody-specific estimates of brain transport, though these assays have yet to be integrated into PBPK models. This study developed and cross-validated an integrated <i>in-vitro–in-vivo</i>-extrapolation (IVIVE) PBPK framework that uses P<sub>app</sub> values to predict antibody-specific cerebrospinal fluid (CSF) concentrations <i>a priori</i>. Seven antibodies were evaluated, including four standard (non-FC5-fused) and three TMEM30A-binding constructs (FC5-fused). PBPK modeling was conducted using PK-Sim®/MoBi®. P<sub>app</sub> values were used to estimate antibody-specific brain transport parameters. For comparison, a conventional modeling approach was implemented, where brain transport was inferred to be similar to a reference antibody (trastuzumab). Model performance was assessed by comparing predicted <i>versus </i>observed CSF exposures (area-under-the-concentration–time-curve) in rats following intravenous administration. Integration of P<sub>app</sub> data substantially improved brain exposure predictions, reducing the absolute average percentage prediction error for CSF exposure from 296.1% (conventional approach) to 53.4% (IVIVE-PBPK). The framework accurately captured the brain disposition of both standard and FC5-fused antibodies without requiring pre-existing <i>in-vivo</i> CSF data. Overall, this P<sub>app</sub>-informed PBPK&#xa0;modeling approach enables <i>a priori</i>, mechanistic prediction of antibody brain exposure, supporting candidate selection and reducing reliance on animal studies in brain drug development.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrating In-vitro Permeability Assays within PBPK Modeling to Predict CNS Distribution of Standard and Engineered Antibodies

  • 期刊论文

  • 发表日期：

    2026年4月18日

查看全文
  • Seunghyun Kim,
  • Etienne Lessard,
  • Binbing Ling,
  • Kerry Rennie,
  • Arsalan Haqqani,
  • Anil R. Maharaj

摘要

The typically low, molecule-dependent permeability of antibodies across the blood–brain barrier (BBB) has driven the development of engineered constructs with optimized BBB transcytosis to facilitate efficient brain delivery. Physiologically-based pharmacokinetic (PBPK) modeling provides a framework to predict brain disposition and inform drug development; however, current models lack true a priori predictive capability and remain dependent on in-vivo data. In-vitro brain endothelial cell permeability (Papp) assays provide antibody-specific estimates of brain transport, though these assays have yet to be integrated into PBPK models. This study developed and cross-validated an integrated in-vitro–in-vivo-extrapolation (IVIVE) PBPK framework that uses Papp values to predict antibody-specific cerebrospinal fluid (CSF) concentrations a priori. Seven antibodies were evaluated, including four standard (non-FC5-fused) and three TMEM30A-binding constructs (FC5-fused). PBPK modeling was conducted using PK-Sim®/MoBi®. Papp values were used to estimate antibody-specific brain transport parameters. For comparison, a conventional modeling approach was implemented, where brain transport was inferred to be similar to a reference antibody (trastuzumab). Model performance was assessed by comparing predicted versus observed CSF exposures (area-under-the-concentration–time-curve) in rats following intravenous administration. Integration of Papp data substantially improved brain exposure predictions, reducing the absolute average percentage prediction error for CSF exposure from 296.1% (conventional approach) to 53.4% (IVIVE-PBPK). The framework accurately captured the brain disposition of both standard and FC5-fused antibodies without requiring pre-existing in-vivo CSF data. Overall, this Papp-informed PBPK modeling approach enables a priori, mechanistic prediction of antibody brain exposure, supporting candidate selection and reducing reliance on animal studies in brain drug development.

Graphical Abstract