PK/PD analysis of a novel pH-dependent antigen-binding antibody using a dynamic antibody–antigen binding model

抗体 抗原 化学 药代动力学 动力学 分子生物学 免疫学 生物 药理学 量子力学 物理
作者
Kenta Haraya,Tatsuhiko Tachibana,Yuki Iwayanagi,Atsuhiko Maeda,Kazuhisa Ozeki,Jun‐ichi Nezu,Masaki Ishigai,Tomoyuki Igawa
出处
期刊:Drug Metabolism and Pharmacokinetics [Elsevier]
卷期号:31 (2): 123-132 被引量:11
标识
DOI:10.1016/j.dmpk.2015.12.007
摘要

Previously, we have reported novel engineered antibody with pH-dependent antigen-binding (recycling antibody), and with both pH-dependent antigen-binding and increased FcRn-binding at neutral pH (sweeping antibody). The purpose of this study is to perform PK/PD predictions to better understand the potential applications of the antibodies as therapeutics. To demonstrate the applicability of recycling and sweeping antibodies over conventional antibodies, PK/PD analyses were performed. PK/PD parameters for antibody and antigen dynamics were estimated from the results of a pharmacokinetic study in human FcRn transgenic mice. A simulation study was performed using the estimated PK/PD parameters with various target antigen profiles. In comparison to conventional antibody, recycling antibody enhanced antibody-antigen complex clearance by 3 folds, while sweeping antibody accelerated antigen clearance by 10 folds in a pharmacokinetic study. Simulation results showed that recycling and sweeping antibodies can improve dosage frequency and reduce the required dose for target antigens with various clearances, plasma concentrations or binding kinetics. Moreover, importance of the association rate constant to enhance the beneficial effect of antibodies was shown. These results support the conclusion that recycling and sweeping antibodies can be applied to various target antigens with different profiles, and expand the number of antigens that antibodies can target.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI

祝大家在新的一年里科研腾飞
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
CodeCraft应助XS_QI采纳,获得10
1秒前
3秒前
JiangYifan完成签到 ,获得积分10
3秒前
3秒前
天天发布了新的文献求助30
4秒前
ABCD发布了新的文献求助30
4秒前
JamesPei应助鲜于诗霜采纳,获得10
7秒前
积极问晴发布了新的文献求助10
7秒前
领导范儿应助等待的慕梅采纳,获得10
7秒前
西方末完成签到 ,获得积分10
9秒前
CodeCraft应助时尚嚓茶采纳,获得10
10秒前
16秒前
16秒前
lizishu应助YoungLee采纳,获得10
19秒前
星辰大海应助美琦采纳,获得10
21秒前
ponowang完成签到,获得积分10
22秒前
24秒前
qianqian发布了新的文献求助10
24秒前
26秒前
27秒前
木子发布了新的文献求助10
30秒前
momona完成签到 ,获得积分10
30秒前
qianqian完成签到,获得积分10
31秒前
34秒前
田様应助qianqian采纳,获得10
38秒前
美琦发布了新的文献求助10
39秒前
森源海完成签到,获得积分10
39秒前
宇宙无敌超人完成签到,获得积分10
40秒前
40秒前
脑洞疼应助程昱采纳,获得10
41秒前
asata发布了新的文献求助10
41秒前
光电很亮发布了新的文献求助10
43秒前
符靖苑完成签到 ,获得积分10
43秒前
45秒前
45秒前
45秒前
45秒前
45秒前
45秒前
46秒前
高分求助中
Operational Bulk Evaporation Duct Model for MORIAH Version 1.2 1200
Yangtze Reminiscences. Some Notes And Recollections Of Service With The China Navigation Company Ltd., 1925-1939 800
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 600
Signals, Systems, and Signal Processing 510
Discrete-Time Signals and Systems 510
Industrial Organic Chemistry, 5th Edition 400
Multiple Regression and Beyond An Introduction to Multiple Regression and Structural Equation Modeling 4th Edition 400
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5847541
求助须知:如何正确求助?哪些是违规求助? 6227303
关于积分的说明 15620489
捐赠科研通 4964224
什么是DOI,文献DOI怎么找? 2676489
邀请新用户注册赠送积分活动 1621042
关于科研通互助平台的介绍 1576969