High affinity restricts the localization and tumor penetration of single-chain fv antibody molecules.

单克隆抗体 抗体 化学 体内分布 抗原 分子生物学 免疫组织化学 体内 亲和力成熟 癌症研究 体外 生物 免疫学 生物化学 生物技术
作者
Gregory P. Adams,Robert Schier,Adrian M. McCall,Heidi H. Simmons,Eva Horak,R. Katherine Alpaugh,James D. Marks,L M Weiner
出处
期刊:PubMed [National Institutes of Health]
卷期号:61 (12): 4750-5 被引量:624
链接
标识
摘要

Antitumor monoclonal antibodies must bind to tumor antigens with high affinity to achieve durable tumor retention. This has spurred efforts to generate high affinity antibodies for use in cancer therapy. However, it has been hypothesized that very high affinity interactions between antibodies and tumor antigens may impair efficient tumor penetration of the monoclonal antibodies and thus diminish effective in vivo targeting (K. Fujimori et al., J. Nucl. Med., 31: 1191-1198, 1990). Here we show that intrinsic affinity properties regulate the quantitative delivery of antitumor single-chain Fv (scFv) molecules to solid tumors and the penetration of scFv from the vasculature into tumor masses. In biodistribution studies examining a series of radioiodinated scFv mutants with affinities ranging from 10(-7)-10(-11) M, quantitative tumor retention did not significantly increase with enhancements in affinity beyond 10(-9) M. Similar distribution patterns were observed when the scFv were evaluated in the absence of renal clearance in anephric mice, indicating that the rapid renal clearance of the scFv was not responsible for these observations. IHC and IF evaluations of tumor sections after the i.v. administration of scFv affinity mutants revealed that the lowest affinity molecule exhibited diffuse tumor staining whereas the highest affinity scFv was primarily retained in the perivascular regions of the tumor. These results indicate that antibody-based molecules with extremely high affinity have impaired tumor penetration properties that must be considered in the design of antibody-based cancer therapies.

科研通智能强力驱动
Strongly Powered by AbleSci AI
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
刚刚
1秒前
芦苇吖关注了科研通微信公众号
1秒前
chutong12345发布了新的文献求助10
1秒前
zzn完成签到,获得积分10
2秒前
yue4yue发布了新的文献求助10
2秒前
2秒前
wjx发布了新的文献求助10
2秒前
3秒前
111发布了新的文献求助10
4秒前
烟花应助wutong采纳,获得10
5秒前
小马甲应助faye采纳,获得10
5秒前
041发布了新的文献求助10
6秒前
CodeCraft应助满天星采纳,获得10
7秒前
小蘑菇应助相逢花梨落采纳,获得10
7秒前
7秒前
迷人莺完成签到,获得积分10
8秒前
Owen应助patrick7400采纳,获得20
9秒前
顺利的蛋挞完成签到,获得积分10
9秒前
yue4yue完成签到,获得积分10
10秒前
yjh123应助将月采纳,获得20
10秒前
核桃发布了新的文献求助10
11秒前
13秒前
科研小废物完成签到,获得积分10
13秒前
隐形曼青应助mukou采纳,获得80
13秒前
13秒前
14秒前
14秒前
Jihan应助陶醉的谷丝采纳,获得10
16秒前
天天快乐应助顺心晓凡采纳,获得10
16秒前
Owen应助111采纳,获得10
18秒前
zyy发布了新的文献求助10
18秒前
18秒前
hll发布了新的文献求助10
19秒前
核桃发布了新的文献求助10
20秒前
111关闭了111文献求助
20秒前
20秒前
孙亦沈完成签到,获得积分10
20秒前
一二三完成签到 ,获得积分10
20秒前
英吉利25发布了新的文献求助10
21秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
Dynamische Polarisation von H-1 und B-11 in (CH-3)-3NBH-3 500
CLSI M07 2024 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7249019
求助须知:如何正确求助?哪些是违规求助? 8871819
关于积分的说明 18720017
捐赠科研通 6928291
什么是DOI,文献DOI怎么找? 3198578
关于科研通互助平台的介绍 2373977
邀请新用户注册赠送积分活动 2173264