• 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

重新审视体内效力——保持目标在视线范围内。

In vivo potency revisited - Keep the target in sight.

机构信息

Department of Biomedical Sciences and Veterinary Public Health, Division of Pharmacology and Toxicology, Swedish University of Agricultural Sciences, Box 7028, SE-750 07 Uppsala, Sweden.

Mathematical Institute, Leiden University, PB 9512, 2300, RA, Leiden, The Netherlands.

出版信息

Pharmacol Ther. 2018 Apr;184:177-188. doi: 10.1016/j.pharmthera.2017.10.011. Epub 2017 Oct 10.

DOI:10.1016/j.pharmthera.2017.10.011
PMID:29024741
Abstract

Potency is a central parameter in pharmacological and biochemical sciences, as well as in drug discovery and development endeavors. It is however typically defined in terms only of ligand to target binding affinity also in in vivo experimentation, thus in a manner analogous to in in vitro studies. As in vivo potency is in fact a conglomerate of events involving ligand, target, and target-ligand complex processes, overlooking some of the fundamental differences between in vivo and in vitro may result in serious mispredictions of in vivo efficacious dose and exposure. The analysis presented in this paper compares potency measures derived from three model situations. Model A represents the closed in vitro system, defining target binding of a ligand when total target and ligand concentrations remain static and constant. Model B describes an open in vivo system with ligand input and clearance (Cl), adding in parallel to the turnover (k, k) of the target. Model C further adds to the open in vivo system in Model B also the elimination of the target-ligand complex (k) via a first-order process. We formulate corresponding equations of the equilibrium (steady-state) relationships between target and ligand, and complex and ligand for each of the three model systems and graphically illustrate the resulting simulations. These equilibrium relationships demonstrate the relative impact of target and target-ligand complex turnover, and are easier to interpret than the more commonly used ligand-, target- and complex concentration-time courses. A new potency expression, labeled L, is then derived. L is the ligand concentration at half-maximal target and complex concentrations and is an amalgamation of target turnover, target-ligand binding and complex elimination parameters estimated from concentration-time data. L is then compared to the dissociation constant K (target-ligand binding affinity), the conventional Black & Leff potency estimate EC, and the derived Michaelis-Menten parameter K (target-ligand binding and complex removal) across a set of literature data. It is evident from a comparison between parameters derived from in vitro vs. in vivo experiments that L can be either numerically greater or smaller than the K (or K) parameter, primarily depending on the ratio of k-to-k. Contrasting the limit values of target R and target-ligand complex RL for ligand concentrations approaching infinity demonstrates that the outcome of the three models differs to a great extent. Based on the analysis we propose that a better understanding of in vivo pharmacological potency requires simultaneous assessment of the impact of its underlying determinants in the open system setting. We propose that L will be a useful parameter guiding predictions of the effective concentration range, for translational purposes, and assessment of in vivo target occupancy/suppression by ligand, since it also encompasses target turnover - in turn also subject to influence by pathophysiology and drug treatment. Different compounds may have similar binding affinity for a target in vitro (same K), but vastly different potencies in vivo. L points to what parameters need to be taken into account, and particularly that closed-system (in vitro) parameters should not be first choice when ranking compounds in vivo (open system).

摘要

效价是药理学和生物化学科学、药物发现和开发工作中的一个核心参数。然而,即使在体内实验中,它也仅仅是根据配体与靶标结合亲和力来定义的,因此与体外研究类似。由于体内效价实际上是涉及配体、靶标和靶标-配体复合物过程的一系列事件的总和,如果忽略了体内和体外之间的一些基本区别,可能会导致对体内有效剂量和暴露量的严重预测错误。本文介绍的分析比较了来自三种模型情况的效价测量值。模型 A 代表封闭的体外系统,定义了当总靶标和配体浓度保持静态和恒定时配体与靶标的结合。模型 B 描述了一个开放的体内系统,其中有配体输入和清除(Cl),同时与靶标的周转率(k,k)平行增加。模型 C 进一步在模型 B 的开放体内系统中添加了通过一级过程消除靶标-配体复合物(k)。我们为三个模型系统中的每个系统制定了相应的平衡(稳态)关系的目标和配体之间,以及复合物和配体之间的方程,并以图形方式说明了由此产生的模拟。这些平衡关系展示了靶标和靶标-配体复合物周转率的相对影响,并且比更常用的配体、靶标和复合物浓度-时间曲线更容易解释。然后得出一个新的效价表达,标记为 L。L 是靶标和复合物浓度达到半最大值时的配体浓度,是从浓度-时间数据中估计的靶标周转率、靶标-配体结合和复合物消除参数的混合体。然后将 L 与解离常数 K(靶标-配体结合亲和力)、传统的 Black & Leff 效价估计 EC 以及从文献数据中得出的米氏常数 K(靶标-配体结合和复合物去除)进行比较。从比较体内和体外实验得出的参数可以明显看出,L 可以大于或小于 K(或 K)参数,主要取决于 k 与 k 的比值。对比趋近于无穷大的配体浓度下目标 R 和目标-配体复合物 RL 的极限值表明,三个模型的结果有很大的不同。基于我们的分析,我们提出,要更好地理解体内药理学效价,需要同时评估开放系统环境中其潜在决定因素的影响。我们提出,L 将是一个有用的参数,用于指导翻译目的的有效浓度范围的预测,并评估配体对体内靶标占据/抑制的作用,因为它还包括靶标周转率——靶标周转率也可能受到病理生理学和药物治疗的影响。不同的化合物在体外可能对同一靶标具有相似的结合亲和力(相同的 K),但在体内的效价却大不相同。L 指出了需要考虑哪些参数,特别是在对体内化合物进行排序时,不应首先选择封闭系统(体外)参数。

相似文献

1
In vivo potency revisited - Keep the target in sight.重新审视体内效力——保持目标在视线范围内。
Pharmacol Ther. 2018 Apr;184:177-188. doi: 10.1016/j.pharmthera.2017.10.011. Epub 2017 Oct 10.
2
Lost in translation: What's in an EC? Innovative PK/PD reasoning in the drug development context.迷失在翻译中:EC 中有什么?药物开发背景下的创新 PK/PD 推理。
Eur J Pharmacol. 2018 Sep 15;835:154-161. doi: 10.1016/j.ejphar.2018.07.037. Epub 2018 Jul 21.
3
Comparisons of basic target-mediated drug disposition (TMDD) and ligand facilitated target removal (LFTR).基本的靶点介导药物处置(TMDD)与配体促进靶点清除(LFTR)的比较。
Eur J Pharm Sci. 2021 Jul 1;162:105835. doi: 10.1016/j.ejps.2021.105835. Epub 2021 Apr 10.
4
New Equilibrium Models of Drug-Receptor Interactions Derived from Target-Mediated Drug Disposition.从基于靶点的药物处置推导的药物-受体相互作用的新平衡模型。
AAPS J. 2018 May 14;20(4):69. doi: 10.1208/s12248-018-0221-x.
5
Turn On, Tune In, Turnover! Target Biology Impacts In Vivo Potency, Efficacy, and Clearance.开启、调谐、更替!靶标生物学影响体内效力、疗效和清除率。
Pharmacol Rev. 2023 May;75(3):416-462. doi: 10.1124/pharmrev.121.000524. Epub 2023 Jan 10.
6
Quantifying biological activity in chemical terms: a pharmacology primer to describe drug effect.从化学角度量化生物活性:描述药物效应的药理学入门知识。
ACS Chem Biol. 2009 Apr 17;4(4):249-60. doi: 10.1021/cb800299s. Epub 2009 Feb 4.
7
Pharmacokinetic Steady-States Highlight Interesting Target-Mediated Disposition Properties.药代动力学稳态突出了有趣的靶点介导的处置特性。
AAPS J. 2017 May;19(3):772-786. doi: 10.1208/s12248-016-0031-y. Epub 2017 Jan 31.
8
Modeling and design of challenge tests: Inflammatory and metabolic biomarker study examples.激发试验的建模与设计:炎症和代谢生物标志物研究实例
Eur J Pharm Sci. 2015 Jan 25;67:144-159. doi: 10.1016/j.ejps.2014.11.006. Epub 2014 Nov 28.
9
Impact of enzyme turnover on the dynamics of the Michaelis-Menten model.酶周转率对米氏动力学模型动态的影响。
Math Biosci. 2022 Apr;346:108795. doi: 10.1016/j.mbs.2022.108795. Epub 2022 Mar 4.
10
Impact of mathematical pharmacology on practice and theory: four case studies.数学药理学对实践和理论的影响:四个案例研究。
J Pharmacokinet Pharmacodyn. 2018 Feb;45(1):3-21. doi: 10.1007/s10928-017-9539-8. Epub 2017 Sep 7.

引用本文的文献

1
Target-Mediated Drug Disposition (TMDD) Revisited: High Versus Low-Affinity Approximations of the TMDD Model.再探靶点介导的药物处置(TMDD):TMDD模型的高亲和力与低亲和力近似值
CPT Pharmacometrics Syst Pharmacol. 2025 Jul;14(7):1262-1272. doi: 10.1002/psp4.70048. Epub 2025 May 27.
2
Discovery of a CK2α'-Biased ATP-Competitive Inhibitor from a High-Throughput Screen of an Allosteric-Inhibitor-Like Compound Library.从变构抑制剂样化合物库的高通量筛选中发现 CK2α′偏向性的 ATP 竞争性抑制剂。
ACS Chem Neurosci. 2024 Aug 7;15(15):2703-2718. doi: 10.1021/acschemneuro.4c00062. Epub 2024 Jun 22.
3
Unraveling the Mechanism of Epichaperome Modulation by Zelavespib: Biochemical Insights on Target Occupancy and Extended Residence Time at the Site of Action.
解析泽拉维司匹对表观伴侣组的调节机制:关于作用位点的靶点占据和延长停留时间的生化见解
Biomedicines. 2023 Sep 22;11(10):2599. doi: 10.3390/biomedicines11102599.
4
An integrated modelling approach for targeted degradation: insights on optimization, data requirements and PKPD predictions from semi- or fully-mechanistic models and exact steady state solutions.一种靶向降解的综合建模方法:从半机理或全机理模型和精确稳态解中获得关于优化、数据需求和 PKPD 预测的见解。
J Pharmacokinet Pharmacodyn. 2023 Oct;50(5):327-349. doi: 10.1007/s10928-023-09857-9. Epub 2023 Apr 29.
5
For whom the T cells troll? Bispecific T-cell engagers in glioblastoma.针对谁的 T 细胞在肆虐?双特异性 T 细胞接合器在神经胶质瘤中的作用。
J Immunother Cancer. 2021 Nov;9(11). doi: 10.1136/jitc-2021-003679.
6
Modeling Pharmacokinetics and Pharmacodynamics of Therapeutic Antibodies: Progress, Challenges, and Future Directions.治疗性抗体的药代动力学和药效学建模:进展、挑战与未来方向
Pharmaceutics. 2021 Mar 21;13(3):422. doi: 10.3390/pharmaceutics13030422.
7
Dissecting the impact of target-binding kinetics of protein binders on tumor localization.剖析蛋白质结合剂的靶点结合动力学对肿瘤定位的影响。
iScience. 2021 Jan 29;24(2):102104. doi: 10.1016/j.isci.2021.102104. eCollection 2021 Feb 19.
8
Proximate and ultimate causes of the bactericidal action of antibiotics.抗生素杀菌作用的近因和远因。
Nat Rev Microbiol. 2021 Feb;19(2):123-132. doi: 10.1038/s41579-020-00443-1. Epub 2020 Oct 6.
9
Does In Vitro Potency Predict Clinically Efficacious Concentrations?体外效力能否预测临床有效浓度?
Clin Pharmacol Ther. 2020 Aug;108(2):298-305. doi: 10.1002/cpt.1846. Epub 2020 May 10.
10
Understanding Buprenorphine for Use in Chronic Pain: Expert Opinion.理解丁丙诺啡在慢性疼痛中的应用:专家意见。
Pain Med. 2020 Apr 1;21(4):714-723. doi: 10.1093/pm/pnz356.