• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

用于预测口服剂型体内性能的生理药代动力学(PBPK)模型。

PBPK models for the prediction of in vivo performance of oral dosage forms.

作者信息

Kostewicz Edmund S, Aarons Leon, Bergstrand Martin, Bolger Michael B, Galetin Aleksandra, Hatley Oliver, Jamei Masoud, Lloyd Richard, Pepin Xavier, Rostami-Hodjegan Amin, Sjögren Erik, Tannergren Christer, Turner David B, Wagner Christian, Weitschies Werner, Dressman Jennifer

机构信息

Institute of Pharmaceutical Technology, Goethe University, Frankfurt/Main, Germany.

Centre for Applied Pharmacokinetic Research, Manchester Pharmacy School, The University of Manchester, United Kingdom.

出版信息

Eur J Pharm Sci. 2014 Jun 16;57:300-21. doi: 10.1016/j.ejps.2013.09.008. Epub 2013 Sep 21.

DOI:10.1016/j.ejps.2013.09.008
PMID:24060672
Abstract

Drug absorption from the gastrointestinal (GI) tract is a highly complex process dependent upon numerous factors including the physicochemical properties of the drug, characteristics of the formulation and interplay with the underlying physiological properties of the GI tract. The ability to accurately predict oral drug absorption during drug product development is becoming more relevant given the current challenges facing the pharmaceutical industry. Physiologically-based pharmacokinetic (PBPK) modeling provides an approach that enables the plasma concentration-time profiles to be predicted from preclinical in vitro and in vivo data and can thus provide a valuable resource to support decisions at various stages of the drug development process. Whilst there have been quite a few successes with PBPK models identifying key issues in the development of new drugs in vivo, there are still many aspects that need to be addressed in order to maximize the utility of the PBPK models to predict drug absorption, including improving our understanding of conditions in the lower small intestine and colon, taking the influence of disease on GI physiology into account and further exploring the reasons behind population variability. Importantly, there is also a need to create more appropriate in vitro models for testing dosage form performance and to streamline data input from these into the PBPK models. As part of the Oral Biopharmaceutical Tools (OrBiTo) project, this review provides a summary of the current status of PBPK models available. The current challenges in PBPK set-ups for oral drug absorption including the composition of GI luminal contents, transit and hydrodynamics, permeability and intestinal wall metabolism are discussed in detail. Further, the challenges regarding the appropriate integration of results from in vitro models, such as consideration of appropriate integration/estimation of solubility and the complexity of the in vitro release and precipitation data, are also highlighted as important steps to advancing the application of PBPK models in drug development. It is expected that the "innovative" integration of in vitro data from more appropriate in vitro models and the enhancement of the GI physiology component of PBPK models, arising from the OrBiTo project, will lead to a significant enhancement in the ability of PBPK models to successfully predict oral drug absorption and advance their role in preclinical and clinical development, as well as for regulatory applications.

摘要

药物从胃肠道(GI)的吸收是一个高度复杂的过程,取决于众多因素,包括药物的物理化学性质、制剂特性以及与胃肠道潜在生理特性的相互作用。鉴于制药行业当前面临的挑战,在药物产品开发过程中准确预测口服药物吸收的能力变得愈发重要。基于生理学的药代动力学(PBPK)建模提供了一种方法,能够根据临床前的体外和体内数据预测血浆浓度-时间曲线,从而为支持药物开发过程各个阶段的决策提供有价值的资源。虽然PBPK模型在识别体内新药开发的关键问题方面已经取得了不少成功,但仍有许多方面需要解决,以最大限度地发挥PBPK模型预测药物吸收的效用,包括加深我们对小肠下段和结肠情况的了解、考虑疾病对胃肠道生理学的影响以及进一步探究人群变异性背后的原因。重要的是,还需要创建更合适的体外模型来测试剂型性能,并简化从这些模型输入到PBPK模型的数据。作为口服生物制药工具(OrBiTo)项目的一部分,本综述总结了现有PBPK模型的现状。详细讨论了口服药物吸收的PBPK模型设置中当前面临的挑战,包括胃肠道腔内内容物的组成、转运和流体动力学、通透性以及肠壁代谢。此外,关于体外模型结果的适当整合所面临的挑战,如考虑溶解度的适当整合/估计以及体外释放和沉淀数据的复杂性,也被强调为推进PBPK模型在药物开发中应用的重要步骤。预计OrBiTo项目所带来的来自更合适体外模型的体外数据的“创新性”整合以及PBPK模型胃肠道生理学组成部分的增强,将显著提高PBPK模型成功预测口服药物吸收的能力,并提升其在临床前和临床开发以及监管应用中的作用。

相似文献

1
PBPK models for the prediction of in vivo performance of oral dosage forms.用于预测口服剂型体内性能的生理药代动力学(PBPK)模型。
Eur J Pharm Sci. 2014 Jun 16;57:300-21. doi: 10.1016/j.ejps.2013.09.008. Epub 2013 Sep 21.
2
In vitro models for the prediction of in vivo performance of oral dosage forms.用于预测口服剂型体内性能的体外模型。
Eur J Pharm Sci. 2014 Jun 16;57:342-66. doi: 10.1016/j.ejps.2013.08.024. Epub 2013 Aug 27.
3
Oral biopharmaceutics tools - time for a new initiative - an introduction to the IMI project OrBiTo.口服生物药剂学工具——开启新计划的时机——IMI项目OrBiTo介绍
Eur J Pharm Sci. 2014 Jun 16;57:292-9. doi: 10.1016/j.ejps.2013.10.012. Epub 2013 Nov 1.
4
In silico predictions of gastrointestinal drug absorption in pharmaceutical product development: application of the mechanistic absorption model GI-Sim.在药物产品开发中的胃肠道药物吸收的计算预测:机制吸收模型 GI-Sim 的应用。
Eur J Pharm Sci. 2013 Jul 16;49(4):679-98. doi: 10.1016/j.ejps.2013.05.019. Epub 2013 May 29.
5
IMI - Oral biopharmaceutics tools project - Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies.IMI - 口腔生物药剂学工具项目 - 自下而上 PBPK 预测成功评估第 4 部分:改进数据和建模策略下的预测准确性和软件比较。
Eur J Pharm Biopharm. 2020 Nov;156:50-63. doi: 10.1016/j.ejpb.2020.08.006. Epub 2020 Aug 14.
6
Regional intestinal drug permeation: biopharmaceutics and drug development.区域肠道药物渗透:生物药剂学与药物开发
Eur J Pharm Sci. 2014 Jun 16;57:333-41. doi: 10.1016/j.ejps.2013.08.025. Epub 2013 Aug 27.
7
In Vivo Predictive Dissolution (IPD) and Biopharmaceutical Modeling and Simulation: Future Use of Modern Approaches and Methodologies in a Regulatory Context.体内预测性溶出(IPD)与生物药剂学建模和模拟:现代方法与技术在监管背景下的未来应用
Mol Pharm. 2017 Apr 3;14(4):1307-1314. doi: 10.1021/acs.molpharmaceut.6b00824. Epub 2017 Mar 1.
8
Current status and future opportunities for incorporation of dissolution data in PBPK modeling for pharmaceutical development and regulatory applications: OrBiTo consortium commentary.在药物开发和监管应用的 PBPK 建模中纳入溶出数据的现状和未来机遇:OrBiTo 联盟评论。
Eur J Pharm Biopharm. 2020 Oct;155:55-68. doi: 10.1016/j.ejpb.2020.08.005. Epub 2020 Aug 8.
9
Coupling biorelevant dissolution methods with physiologically based pharmacokinetic modelling to forecast in-vivo performance of solid oral dosage forms.将生物相关溶解方法与基于生理学的药代动力学模型相结合,以预测固体口服剂型的体内性能。
J Pharm Pharmacol. 2013 Jul;65(7):937-52. doi: 10.1111/jphp.12059. Epub 2013 Mar 25.
10
Paediatric oral biopharmaceutics: key considerations and current challenges.儿科口腔生物药剂学:关键考虑因素和当前挑战。
Adv Drug Deliv Rev. 2014 Jun;73:102-26. doi: 10.1016/j.addr.2013.10.006. Epub 2013 Nov 1.

引用本文的文献

1
A Mechanistic Physiologically Based Pharmacokinetic (PBPK) modeling approach for fexofenadine: predictive pharmacokinetic insights in humans.非索非那定的基于生理的药代动力学(PBPK)机制建模方法:对人体药代动力学的预测性见解
Saudi Pharm J. 2025 Jul 9;33(4):24. doi: 10.1007/s44446-025-00024-4.
2
In Silico Evaluation of the Biopharmaceutical and Pharmacokinetic Behavior of Metronidazole from Coated Colonic Release Matrix Tablets.甲硝唑包衣结肠定位释放骨架片的生物药剂学及药代动力学行为的计算机模拟评价
Pharmaceutics. 2025 May 14;17(5):647. doi: 10.3390/pharmaceutics17050647.
3
NNKcat: deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling.
NNKcat:通过整合蛋白质序列和底物结构并增强数据不平衡处理来预测催化常数(Kcat)的深度神经网络。
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf212.
4
Molecular Precision Medicine: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions Between Lidocaine and Rocuronium/Propofol/Paracetamol.分子精准医学:基于生理药代动力学模型预测利多卡因与罗库溴铵/丙泊酚/对乙酰氨基酚之间药物相互作用的应用
Int J Mol Sci. 2025 Feb 11;26(4):1506. doi: 10.3390/ijms26041506.
5
Permeability Benchmarking: Guidelines for Comparing , , and Measurements.渗透率基准测试:比较、和测量的指南。 (注:原文中“,, and ”部分内容缺失,翻译只能按现有内容进行)
J Chem Inf Model. 2025 Feb 10;65(3):1067-1084. doi: 10.1021/acs.jcim.4c01815. Epub 2025 Jan 17.
6
In vitro-in vivo correlation (IVIVC) development for long-acting injectable drug products based on poly(lactide-co-glycolide).基于聚(丙交酯-乙交酯)的长效注射用药物产品的体外-体内相关性(IVIVC)研究
J Control Release. 2025 Jan 10;377:186-196. doi: 10.1016/j.jconrel.2024.11.021. Epub 2024 Nov 19.
7
Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!将新方法(NAMs)数据纳入剂量反应评估:未来已来!
J Toxicol Environ Health B Crit Rev. 2025 Jan 2;28(1):28-62. doi: 10.1080/10937404.2024.2412571. Epub 2024 Oct 10.
8
Preclinical pharmacokinetic studies and prediction of human PK profiles for Deg-AZM, a clinical-stage new transgelin agonist.临床前药代动力学研究及临床阶段新型凝溶胶蛋白激动剂Deg-AZM的人体药代动力学特征预测
Front Pharmacol. 2024 Aug 26;15:1423175. doi: 10.3389/fphar.2024.1423175. eCollection 2024.
9
Mathematical Modeling of the Gastrointestinal System for Preliminary Drug Absorption Assessment.用于初步药物吸收评估的胃肠道系统数学建模
Bioengineering (Basel). 2024 Aug 9;11(8):813. doi: 10.3390/bioengineering11080813.
10
The Use of Global Sensitivity Analysis to Assess the Oral Absorption of Weakly Basic Compounds: A Case Example of Dipyridamole.应用全局敏感性分析评估弱碱性化合物的口服吸收:以双嘧达莫为例。
Pharm Res. 2024 May;41(5):877-890. doi: 10.1007/s11095-024-03688-0. Epub 2024 Mar 27.