{"title":"A translational physiologically-based pharmacokinetic model for MMAE-based antibody-drug conjugates.","authors":"Hsuan-Ping Chang, Dhaval K Shah","doi":"10.1007/s10928-025-09978-3","DOIUrl":null,"url":null,"abstract":"<p><p>The objective of this work was to develop a translational physiologically-based pharmacokinetic (PBPK) model for antibody-drug conjugates (ADCs), using monomethyl auristatin E (MMAE)-based ADCs. A previously established dual-structured whole-body PBPK model for MMAE-based ADCs in mice was scaled to higher species (i.e., rats and monkeys) and humans. Species-specific physiological and drug-related parameters for the payload and antibody backbone of ADCs were obtained from literature. Parameters associated with payload release, including the deconjugation rate, were optimized using an allometric scaling approach, and antibody degradation rate was adjusted to account for the enhanced clearance of ADCs due to conjugation across different species. The translational PBPK model predicted the PK profiles for various ADC analytes in rats, monkeys, and humans reasonably well. The optimized PBPK model suggested decreased rate of deconjugation for ADCs in higher species, whereas the effects of payload conjugation on ADC clearance were more pronounced in higher species and humans. The translational PBPK model presented here may enable prediction of different ADC analyte PK at the site-of-action, offering valuable insights for the development of exposure-response relationships for ADCs. The modeling framework presented here can also serve as a platform for the development of PBPK model for other ADCs.</p>","PeriodicalId":16851,"journal":{"name":"Journal of Pharmacokinetics and Pharmacodynamics","volume":"52 3","pages":"27"},"PeriodicalIF":2.2000,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12053227/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Pharmacokinetics and Pharmacodynamics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10928-025-09978-3","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 0
Abstract
The objective of this work was to develop a translational physiologically-based pharmacokinetic (PBPK) model for antibody-drug conjugates (ADCs), using monomethyl auristatin E (MMAE)-based ADCs. A previously established dual-structured whole-body PBPK model for MMAE-based ADCs in mice was scaled to higher species (i.e., rats and monkeys) and humans. Species-specific physiological and drug-related parameters for the payload and antibody backbone of ADCs were obtained from literature. Parameters associated with payload release, including the deconjugation rate, were optimized using an allometric scaling approach, and antibody degradation rate was adjusted to account for the enhanced clearance of ADCs due to conjugation across different species. The translational PBPK model predicted the PK profiles for various ADC analytes in rats, monkeys, and humans reasonably well. The optimized PBPK model suggested decreased rate of deconjugation for ADCs in higher species, whereas the effects of payload conjugation on ADC clearance were more pronounced in higher species and humans. The translational PBPK model presented here may enable prediction of different ADC analyte PK at the site-of-action, offering valuable insights for the development of exposure-response relationships for ADCs. The modeling framework presented here can also serve as a platform for the development of PBPK model for other ADCs.
这项工作的目的是建立一个基于翻译生理的药代动力学(PBPK)模型,用于抗体-药物偶联物(adc),使用单甲基aurisatin E (MMAE)为基础的adc。先前建立的基于mmae的adc小鼠双结构全身PBPK模型被扩展到更高物种(即大鼠和猴子)和人类。从文献中获得adc的有效载荷和抗体骨架的物种特异性生理和药物相关参数。利用异速缩放法优化了与有效载荷释放相关的参数,包括解偶联率,并调整了抗体降解率,以考虑由于不同物种的偶联而增强的adc清除率。翻译PBPK模型可以很好地预测各种ADC分析物在大鼠、猴子和人类中的PK谱。优化后的PBPK模型表明,高等物种中ADC的解偶联率降低,而有效载荷偶联对ADC清除率的影响在高等物种和人类中更为明显。本文提出的平移PBPK模型可以预测不同ADC分析物在作用部位的PK,为ADC暴露-反应关系的发展提供有价值的见解。本文提出的建模框架也可以作为开发其他adc的PBPK模型的平台。
期刊介绍:
Broadly speaking, the Journal of Pharmacokinetics and Pharmacodynamics covers the area of pharmacometrics. The journal is devoted to illustrating the importance of pharmacokinetics, pharmacodynamics, and pharmacometrics in drug development, clinical care, and the understanding of drug action. The journal publishes on a variety of topics related to pharmacometrics, including, but not limited to, clinical, experimental, and theoretical papers examining the kinetics of drug disposition and effects of drug action in humans, animals, in vitro, or in silico; modeling and simulation methodology, including optimal design; precision medicine; systems pharmacology; and mathematical pharmacology (including computational biology, bioengineering, and biophysics related to pharmacology, pharmacokinetics, orpharmacodynamics). Clinical papers that include population pharmacokinetic-pharmacodynamic relationships are welcome. The journal actively invites and promotes up-and-coming areas of pharmacometric research, such as real-world evidence, quality of life analyses, and artificial intelligence. The Journal of Pharmacokinetics and Pharmacodynamics is an official journal of the International Society of Pharmacometrics.