Suppr超能文献

用于描述蛋白质治疗药物的吸收、分布、代谢和排泄的数学模型。

Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics.

机构信息

Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York.

Department of Pharmaceutical Sciences, School of Pharmacy and Pharmaceutical Sciences, The State University of New York at Buffalo, Buffalo, New York

出版信息

Drug Metab Dispos. 2022 Jun;50(6):867-878. doi: 10.1124/dmd.121.000460. Epub 2022 Feb 23.

Abstract

Therapeutic proteins (TPs) have ranked among the most important and fastest-growing classes of drugs in the clinic, yet the development of successful TPs is often limited by unsatisfactory efficacy. Understanding pharmacokinetic (PK) characteristics of TPs is key to achieving sufficient and prolonged exposure at the site of action, which is a prerequisite for eliciting desired pharmacological effects. PK modeling represents a powerful tool to investigate factors governing in vivo disposition of TPs. In this mini-review, we discuss many state-of-the-art models that recapitulate critical processes in each of the absorption, distribution, metabolism/catabolism, and excretion pathways of TPs, which can be integrated into the physiologically-based pharmacokinetic framework. Additionally, we provide our perspectives on current opportunities and challenges for evolving the PK models to accelerate the discovery and development of safe and efficacious TPs. SIGNIFICANCE STATEMENT: This minireview provides an overview of mechanistic pharmacokinetic (PK) models developed to characterize absorption, distribution, metabolism, and elimination (ADME) properties of therapeutic proteins (TPs), which can support model-informed discovery and development of TPs. As the next-generation of TPs with diverse physicochemical properties and mechanism-of-action are being developed rapidly, there is an urgent need to better understand the determinants for the ADME of TPs and evolve existing platform PK models to facilitate successful bench-to-bedside translation of these promising drug molecules.

摘要

治疗性蛋白(TPs)是临床应用中最重要且发展最快的药物类别之一,但成功开发 TPs 常常受到疗效不理想的限制。了解 TPs 的药代动力学(PK)特征是在作用部位获得足够和持久暴露的关键,这是产生所需药理作用的前提。PK 建模是研究影响 TPs 体内处置因素的有力工具。在这篇综述中,我们讨论了许多最新的模型,这些模型可以重现 TPs 在吸收、分布、代谢/分解和排泄途径中的关键过程,并可以整合到基于生理学的 PK 框架中。此外,我们还就当前的机会和挑战提出了看法,以推进 PK 模型的发展,从而加速安全有效的 TPs 的发现和开发。

意义陈述

本篇综述概述了为表征治疗性蛋白(TPs)的吸收、分布、代谢和消除(ADME)特性而开发的机制性 PK 模型,这些模型可以支持 TPs 的基于模型的发现和开发。随着具有不同理化性质和作用机制的下一代 TPs 迅速发展,我们迫切需要更好地了解 TPs 的 ADME 决定因素,并改进现有的平台 PK 模型,以促进这些有前途的药物分子从实验室到临床的成功转化。

相似文献

1
Mathematical Models to Characterize the Absorption, Distribution, Metabolism, and Excretion of Protein Therapeutics.
Drug Metab Dispos. 2022 Jun;50(6):867-878. doi: 10.1124/dmd.121.000460. Epub 2022 Feb 23.
2
Absorption, Distribution, Metabolism, and Excretion of Therapeutic Proteins: Current Industry Practices and Future Perspectives.
Drug Metab Dispos. 2022 Jun;50(6):837-845. doi: 10.1124/dmd.121.000461. Epub 2022 Feb 11.
5
Physiologically-based modeling of monoclonal antibody pharmacokinetics in drug discovery and development.
Drug Metab Pharmacokinet. 2019 Feb;34(1):3-13. doi: 10.1016/j.dmpk.2018.11.002. Epub 2018 Nov 22.
6
Pharmacokinetic and Pharmacodynamic Modeling of siRNA Therapeutics - a Minireview.
Pharm Res. 2022 Aug;39(8):1749-1759. doi: 10.1007/s11095-022-03333-8. Epub 2022 Jul 12.
9
Advances in Physiologically Based Pharmacokinetic (PBPK) Modeling of Nanomaterials.
ACS Pharmacol Transl Sci. 2024 Jul 12;7(8):2251-2279. doi: 10.1021/acsptsci.4c00250. eCollection 2024 Aug 9.
10
Absorption, Distribution, Metabolism, and Excretion of US Food and Drug Administration-Approved Antisense Oligonucleotide Drugs.
Drug Metab Dispos. 2022 Jun;50(6):888-897. doi: 10.1124/dmd.121.000417. Epub 2022 Feb 27.

引用本文的文献

1
Pharmacometric modeling of lipid nanoparticle-encapsulated mRNA therapeutics and vaccines: A systematic review.
Mol Ther Nucleic Acids. 2025 Aug 14;36(3):102686. doi: 10.1016/j.omtn.2025.102686. eCollection 2025 Sep 9.
2
Human nerve growth factor delivery to the retina: Quantitative methodology and mathematical modeling in preclinical settings.
PNAS Nexus. 2025 Aug 11;4(8):pgaf250. doi: 10.1093/pnasnexus/pgaf250. eCollection 2025 Aug.
3
Comparison of monoclonal antibody disposition predictions using different physiologically based pharmacokinetic modelling platforms.
J Pharmacokinet Pharmacodyn. 2024 Dec;51(6):639-651. doi: 10.1007/s10928-023-09894-4. Epub 2023 Nov 12.

本文引用的文献

1
Allometric scaling of therapeutic monoclonal antibodies in preclinical and clinical settings.
MAbs. 2021 Jan-Dec;13(1):1964935. doi: 10.1080/19420862.2021.1964935.
2
A multivalent biparatopic EGFR-targeting nanobody drug conjugate displays potent anticancer activity in solid tumor models.
Signal Transduct Target Ther. 2021 Sep 3;6(1):320. doi: 10.1038/s41392-021-00666-5.
3
Imaging Reveals Importance of Shape and Flexibility for Glomerular Filtration of Biologics.
Mol Cancer Ther. 2021 Oct;20(10):2008-2015. doi: 10.1158/1535-7163.MCT-21-0116. Epub 2021 Jul 26.
4
A Biparatopic Antibody-Drug Conjugate to Treat MET-Expressing Cancers, Including Those that Are Unresponsive to MET Pathway Blockade.
Mol Cancer Ther. 2021 Oct;20(10):1966-1976. doi: 10.1158/1535-7163.MCT-21-0009. Epub 2021 Jul 26.
6
FDA approves 100th monoclonal antibody product.
Nat Rev Drug Discov. 2021 Jul;20(7):491-495. doi: 10.1038/d41573-021-00079-7.
8
Top companies and drugs by sales in 2020.
Nat Rev Drug Discov. 2021 Apr;20(4):253. doi: 10.1038/d41573-021-00050-6.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验