Drug combination in vivo using combination index method: Taxotere and T607 against colon carcinoma HCT-116 xenograft tumor in nude mice

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• 作者：Jianing Fu^a ; ¹ ; Ning Zhang^b ; ¹ ; Joseph H. Chou^c ; Hua-Jin Dong^d ; Shu-Fu Lin^e ; Gudrun S. Ulrich-Merzenich^f ; Ting-Chao Chou^g ; ^{ChouTC@PDScience.Org" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Synergy quantitation in vivo ; Drug combination ; Xenograft in nude mice ; Colon carcinoma ; Combination index ; CompuSyn simulation ; Median-effect equation ; Taxotere
• 刊名：Synergy
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：3
• 期：3
• 页码：15-30
• 全文大小：4701 K

文摘

The median-effect equation (MEE) of the mass-action law and the combination index (CI) theorem have been used for quantitative determination of synergism (CI <1), antagonism (CI >1) and additive effect (CI = 1) in animals in vivo. Experimental design, the theoretical algorithm and the CompuSyn software simulation have been used to illustrate step-by-step for the combination of two anti-cancer agents, Taxotere and T607 compound, with similar mode of actions of targeting microtubule polymerization, but with distinct chemical structures. These two compounds acted synergistically against human colon carcinoma HCT-116 xenograft tumor in athymic nude mice. In all, only 78 nude mice have been used. The synergy is especially significant (p < 0.01–0.05) following Q3Dx4, x3 i.v. treatments, at higher doses and at later stages of treatment. The MEE and the CI theorem of Chou-Talalay quantitatively determined synergism or antagonism at different doses and different effect levels as indicated by the Fa-CI plot and by isobolograms in CompuSyn simulation and automated graphics. The practical logistics on pre-experimental planning, scheme/design/layout, and precautions in terms of dose number, dose range, dose density, drug combination ratio, conservation of laboratory animals as well as regulatory and cost-effective considerations have been presented. The mass-action law based CI algorithm has been proven to be simple to use, economy to practice, even for in vivo experimentations. Most significantly, the mass-action law based algorithm provides quantitative indexed conclusions.