Multichannel imaging to quantify four classes of pharmacokinetic distribution in tumors.
Multichannel imaging to quantify four classes of pharmacokinetic distribution in tumors.
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DOI：
10.1002/jps.24086
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发表时间：
2014-10
影响因子：
3.8
通讯作者：
Thurber, Greg M.
中科院分区：
文献类型：
--
作者：
Bhatnagar, Sumit;Deschenes, Emily;Liao, Jianshan;Cilliers, Cornelius;Thurber, Greg M.
关键词：
药物和成像剂向肿瘤的低和不均匀递送导致降低的功效和差的成像结果。全身递送涉及药物性质和生理因素的复杂相互作用，并且肿瘤微环境的异质性使得预测和克服这些限制异常困难。理论模型表明，根据分布的基本步骤，组织中有四种不同的药代动力学行为。为了研究这些限制行为，我们使用多通道荧光显微镜和拼接高分辨率图像来检查四种药物在相同肿瘤微环境中的分布。使用具有图形用户界面的经验证的通用偏微分方程模型来选择表现出这四类行为的荧光剂，并且成像结果与预测一致。BODIPY-FL在高血流量组织中的浓度较高，西妥昔单抗的血管周围分布受到渗透性的限制，高血浆蛋白和靶点结合导致Hoechst 33342的扩散受限分布，Integrisense 680受到组织中结合位点数量的限制。总之，探针和模拟可用于研究其他肿瘤模型中的分布，预测肿瘤药物分布曲线，以及设计和解释体内实验。
Low and heterogeneous delivery of drugs and imaging agents to tumors results in decreased efficacy and poor imaging results. Systemic delivery involves a complex interplay of drug properties and physiological factors, and heterogeneity in the tumor microenvironment makes predicting and overcoming these limitations exceptionally difficult. Theoretical models have indicated that there are four different classes of pharmacokinetic behavior in tissue, depending on the fundamental steps in distribution. In order to study these limiting behaviors, we used multichannel fluorescence microscopy and stitching of high-resolution images to examine the distribution of four agents in the same tumor microenvironment. A validated generic partial differential equation model with a graphical user interface was used to select fluorescent agents exhibiting these four classes of behavior, and the imaging results agreed with predictions. BODIPY-FL exhibited higher concentrations in tissue with high blood flow, cetuximab gave perivascular distribution limited by permeability, high plasma protein and target binding resulted in diffusion-limited distribution for Hoechst 33342, and Integrisense 680 was limited by the number of binding sites in the tissue. Together, the probes and simulations can be used to investigate distribution in other tumor models, predict tumor drug distribution profiles, and design and interpret in vivo experiments.
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影响因子：
10.3
作者：
Dreher, MR;Liu, WG;Chilkoti, A
通讯作者：
Chilkoti, A
DOI：
10.1073/pnas.1113466109
发表时间：
2012-03-27
影响因子：
11.1
作者：
Devaraj, Neal K.;Thurber, Greg M.;Weissleder, Ralph
通讯作者：
Weissleder, Ralph
影响因子：
33.6
作者：
Goel S;Duda DG;Xu L;Munn LL;Boucher Y;Fukumura D;Jain RK
通讯作者：
Jain RK
影响因子：
38.3
作者：
通讯作者：
--
影响因子：
5.8
作者：
BLUMENTHAL, RD;FAND, I;GOLDENBERG, DM
通讯作者：
GOLDENBERG, DM