这项工作由研究院赠款 P50GM107618 资助。作者感谢 Zohar b. 温斯坦对手稿的精辟评论和建议。

注: 任何抑制大肠杆菌生长的小分子均可用于对角法。在本议定书中, 左氧氟沙星、萘啶酮酸和青霉素 G (PNG) 将作为一个例子, 因为这些药物显示显著的三的协同作用。此协议的工作流如图 6所示。在室温下执行所有步骤。每天使用新鲜整除数的细菌和药物。在适合大肠杆菌的生物安全水平下进行实验。
1. 准备步骤
<ol><li>将25克的 LB 汤加入1升的蒸馏水中, 并混合, 制备 Luria-Bertani (lb) 培养基。高压釜在121摄氏度15分钟, 并在室温下贮存蒸压介质。</li>
	<li>通过混合等量的50% 甘油和细菌细胞稀释到 OD600 = 1 磅汤和冷冻150µL 整除数1.5 毫升离心管在-80 °c, 准备大肠杆菌甘油股票。</li>
	<li>在1毫升的二甲基亚砜 (亚砜) 中溶解20毫克的抗生素, 列弗, 和 PNG。将10µL 的µg 溶液与990µL 的亚砜混合, 稀释了100x 至0.2 毫升的溶液。在操作步骤中使用0.2 毫克/毫升列弗, 和20毫克/毫升和 PNG。</li>
	<li>整除50µL 的每种抗生素到1.5 毫升离心管和冻结-20 摄氏度。</li>
	<li>从-80 摄氏度服用150µL 整除大肠杆菌。解冻。</li>
	<li>在14毫升培养管中加入5毫升的 LB 培养基中添加100µL 的大肠杆菌甘油。</li>
	<li>在一夜之间, 在200转每分钟37摄氏度的孵化器中摇动管子。</li>
</ol>2. 串联稀释剂量反应实验
<ol><li>取一整除的药物, 从-20 摄氏度, 将其留在室温下10分钟解冻, 并准备这些药物的连续稀释。</li>
	<li>通过混合990µL 的 LB 介质和110µL 溶剂 (亚砜), 制备1100µL 的 LB-10%sol。</li>
	<li>通过混合450µL 的 LB 介质和50µL 的µL 来制备500的 LB-10%LEV。</li>
	<li>漩涡 LB-10%sol 在最高的设置 5 s。在96井微板块中添加20µL 的 LB-10%sol 到四排水井中。</li>
	<li>漩涡 LB-10%LEV 在最高的设置 5 s。在 A 行的第一个井中添加20µL 的 LB-10%LEV。</li>
	<li>为 LB-10%LEV 准备两倍的连续稀释, 从第一井中取20µL, 加入第二井, 吹打上下五次。对所有井依次重复此操作, 直到第十一井, 最后用40µL (图 7A)。</li>
	<li>使用微从第十一井中删除并丢弃20µL 的内容。</li>
	<li>重复步骤 2.3-2.7 的药物和 PNG 使用第二和第三行的微板块, 分别。</li>
	<li>重复步骤 2.3-2.7 的药物再次在第四行作为内部积极控制。</li>
	<li>使用分光光度计, 测量600的1:10 稀释的文化 (步骤 1.5-1.7)。</li>
	<li>将5毫升的 LB 介质中的细胞稀释到外径600的0.01。倒入水库。</li>
	<li>使用多通道微, 将稀释细胞的80µL 添加到步骤 2.4-2.9 中制备的药物系列稀释中。每个油井的最终药物浓度如图 7A所示。密封板, 防止蒸发。</li>
	<li>在37摄氏度孵育16小时的盘子。</li>
	<li>在步骤3中开始使用新的细菌培养 (重复步骤 1.5-1.7)。</li>
</ol>3. 线性稀释剂量-反应实验
<ol><li>使用板读取器测量步骤2中的系列稀释剂量反应板的 OD600吸光度 (图 7A 右侧), 并根据以下步骤解释结果。</li>
	<li>通过将每一行的增长与无药物控制的增长分开, 并通过将 OD600规范化为无药物条件来计算百分比增长, 从而使增长正常化。</li>
	<li>对于每种药物, 找到有50% 生长抑制 (IC50) 的水井, 如图 7A 所示的橙色。将这些水井中的浓度分配给每种药物的 "连续 IC50"。</li>
	<li>解冻新鲜药物整除数, 通过混合 lb 介质和溶剂 (亚砜) 在一个9:1 的比例和 LB-10%drug 混合 lb 介质和药物9:1 的比例, 在1毫升的 LB-10%sol, 其中药物的浓度是每一个药物的串行 IC50 100x, 然后添加 LB 介质, 如步骤3.3 所选。</li>
	<li>通过将 LB-10%drug 和 LB-10%sol 混合在图 7B中所示的体积内, 制备线性增加剂量的药物, 即11浓度的药。</li>
	<li>在第四排以线性递增剂量的列列作为内部阳性控制。</li>
	<li>测量步骤2.14 中开始的1:10 稀释的600的 OD。</li>
	<li>将5毫升的 LB 介质中的细胞稀释到外径600的0.01。倒入水库。</li>
	<li>在步骤3.6 中使用多通道微, 将稀释细胞的80µL 添加到药物线性稀释。每个油井的最终药物浓度如图 7B所示。 注: 在剂量反应中的中间井将接受这个药物的系列 IC50。密封板, 防止蒸发。</li>
	<li>在37摄氏度孵育16小时的盘子。</li>
	<li>在步骤4中开始使用两种新鲜细菌培养 (重复步骤 1.5-1.7)。</li>
</ol>4. 对角药物相互作用实验
<ol><li>测量从步骤 3 (图 7B) 中线性稀释剂量反应的 OD600吸光度。</li>
	<li>对于每种药物, 选择导致 IC50 的浓度, 并在 100x IC50 浓度下制备药列, 和 PNG。</li>
	<li>解冻新鲜的药物, 为每种药物准备 100x IC50, 并准备1:1 的药物混合物, 体积的列量, 列弗 + png 和新宇 + png 和 1:1: 1 的药物混合物的体积的列弗 + png。</li>
	<li>为药物相互作用实验准备两个板块, 如图 8所示。</li>
	<li>测量步骤3.11 中开始的1:10 种文化稀释的 OD600 。</li>
	<li>在两个10毫升的 LB 介质中准备600 = 0.01 稀释两种培养基。</li>
	<li>分别在1和2版上添加80µL 的细胞从培养1和2。密封板, 防止蒸发。在37摄氏度孵化16小时的板材。</li>
</ol>5. 对角药物相互作用分数
<ol><li>从步骤4测量对角药物相互作用实验板的 OD600吸光度。</li>
	<li>通过将每一个油井的生长除以每行的药物控制的增长, 使增长正常化。</li>
	<li>对于每一行, 找到最接近 IC50 的增长抑制的列, 如图 8所示的橙色。根据药物在这个井中的相对浓度分配 IC50。</li>
	<li>对于列比, 列 + png 和 IC50 + png 和列比 + png 剂量反应, 计算预期的通过平均 IC50 的单一药物在每个组合。请注意, 平均值是一个简单的近似值, 用于精确的预期 IC50 计算, 如前所述12。</li>
	<li>通过将观察到的 IC50 除以每个组合的预期 IC50, 计算分数抑制浓度 (FIC) 分数。</li>
</ol>

<ol><li>Zimmermann, G. R., Lehar, J., Keith, C. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multi-target+therapeutics%3A+when+the+whole+is+greater+than+the+sum+of+the+parts%5BTitle%5D)+AND+%22Drug+Discovery+Today%22%5BJournal%5D)">Multi-target therapeutics: when the whole is greater than the sum of the parts.</a> Drug Discovery Today. 12 (1), 34-42 (2007).</li>
<li>Berenbaum, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((What+is+synergy%3F%5BTitle%5D)+AND+%22Pharmacological+Reviews%22%5BJournal%5D)">What is synergy?</a> Pharmacological Reviews. 41 (2), 93-141 (1989).</li>
<li>Cokol, M. <a target="_blank" href="https://www.ncbi.nlm.nih.gov/pubmed/23237673">Drugs and their interactions.</a> Current Drug Discovery Technologies. 10 (2), 106-113 (2013).</li>
<li>Yeh, P. J., Hegreness, M. J., Aiden, A. P., Kishony, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Drug+interactions+and+the+evolution+of+antibiotic+resistance%5BTitle%5D)+AND+%22Nature+Reviews+Microbiology%22%5BJournal%5D)">Drug interactions and the evolution of antibiotic resistance.</a> Nature Reviews Microbiology. 7 (6), 460-466 (2009).</li>
<li>Lehár, J., Krueger, A., Zimmermann, G., Borisy, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-order+combination+effects+and+biological+robustness%5BTitle%5D)+AND+%22Molecular+Systems+Biology%22%5BJournal%5D)">High-order combination effects and biological robustness.</a> Molecular Systems Biology. 4 (1), 215(2008).</li>
<li>Loewe, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+problem+of+synergism+and+antagonism+of+combined+drugs%5BTitle%5D)+AND+%22Arzneimittelforschung%22%5BJournal%5D)">The problem of synergism and antagonism of combined drugs.</a> Arzneimittelforschung. 3 (6), 285-290 (1953).</li>
<li>Foucquier, J., Guedj, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Analysis+of+drug+combinations%3A+current+methodological+landscape%5BTitle%5D)+AND+%22Pharmacology+Research+%26+Perspectives%22%5BJournal%5D)">Analysis of drug combinations: current methodological landscape.</a> Pharmacology Research & Perspectives. 3 (3), (2015).</li>
<li>Wood, K. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Pairwise+interactions+and+the+battle+against+combinatorics+in+multidrug+therapies%5BTitle%5D)+AND+%22Proceedings+of+the+National+Academy+of+Sciences%22%5BJournal%5D)">Pairwise interactions and the battle against combinatorics in multidrug therapies.</a> Proceedings of the National Academy of Sciences. 113 (37), 10231-10233 (2016).</li>
<li>Berenbaum, M. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+method+for+testing+for+synergy+with+any+number+of+agents%5BTitle%5D)+AND+%22Journal+of+Infectious+Diseases%22%5BJournal%5D)">A method for testing for synergy with any number of agents.</a> Journal of Infectious Diseases. 137 (2), 122-130 (1978).</li>
<li>Weinstein, Z. B., Zaman, M. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Quantitative+bioassay+to+identify+antimicrobial+drugs+through+drug+interaction+fingerprint+analysis%5BTitle%5D)+AND+%22Scientific+Reports%22%5BJournal%5D)">Quantitative bioassay to identify antimicrobial drugs through drug interaction fingerprint analysis.</a> Scientific Reports. 7, 42644(2017).</li>
<li>Horn, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((High-order+drug+combinations+are+required+to+effectively+kill+colorectal+cancer+cells%5BTitle%5D)+AND+%22Cancer+Research%22%5BJournal%5D)">High-order drug combinations are required to effectively kill colorectal cancer cells.</a> Cancer Research. 76 (23), 6950-6963 (2016).</li>
<li>Cokol, M., Kuru, N., Bicak, E., Larkins-Ford, J., Aldridge, B. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Efficient+measurement+and+factorization+of+high-order+drug+interactions+in+Mycobacterium+tuberculosis%5BTitle%5D)+AND+%22Science+Advances%22%5BJournal%5D)">Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis.</a> Science Advances. 3 (10), e1701881(2017).</li>
<li>Chandrasekaran, S., Cokol-Cakmak, M., Sahin, N., Yilancioglu, K., Kazan, H., Collins, J. J., Cokol, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Chemogenomics+and+orthology-based+design+of+antibiotic+combination+therapies%5BTitle%5D)+AND+%22Molecular+Systems+Biology%22%5BJournal%5D)">Chemogenomics and orthology-based design of antibiotic combination therapies.</a> Molecular Systems Biology. 12 (5), 872(2016).</li>
<li>Mason, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Prediction+of+antibiotic+interactions+using+descriptors+derived+from+molecular+structure%5BTitle%5D)+AND+%22Journal+of+Medicinal+Chemistry%22%5BJournal%5D)">Prediction of antibiotic interactions using descriptors derived from molecular structure.</a> Journal of Medicinal Chemistry. 60 (9), 3902-3912 (2017).</li>
<li>Yilancioglu, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Target-independent+prediction+of+drug+synergies+using+only+drug+lipophilicity%5BTitle%5D)+AND+%22Journal+of+Chemical+Information+and+Modeling%22%5BJournal%5D)">Target-independent prediction of drug synergies using only drug lipophilicity.</a> Journal of Chemical Information and Modeling. 54 (8), 2286-2293 (2014).</li>
<li>Beppler, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Uncovering+emergent+interactions+in+three-way+combinations+of+stressors%5BTitle%5D)+AND+%22Journal+of+the+Royal+Society+Interface%22%5BJournal%5D)">Uncovering emergent interactions in three-way combinations of stressors.</a> Journal of the Royal Society Interface. 13 (125), 20160800(2016).</li>
<li>Tekin, E., Beppler, C., White, C., Mao, Z., Savage, V. M., Yeh, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhanced+identification+of+synergistic+and+antagonistic+emergent+interactions+among+three+or+more+drugs%5BTitle%5D)+AND+%22Journal+of+The+Royal+Society+Interface%22%5BJournal%5D)">Enhanced identification of synergistic and antagonistic emergent interactions among three or more drugs.</a> Journal of The Royal Society Interface. 13 (119), 20160332(2016).</li>
</ol>

作者没有什么可透露的。

使用药物组合对抗病原体或肿瘤是一个有吸引力的前景, 特别是在干燥抗生素管道的情况下。然而, 这种潜力至少受到两个困难的影响。第一个困难是天文学数量可能的组合。例如, 在100种抗生素中有4950可能的配对组合。在100种抗生素 (2100) 中, 所有可能的组合与地球上的细菌数量在同一数量级上 (~ 1030)。如何预测这些可能性之间的强烈协同组合一直是许多计算研究的主题。第二个难点是对高阶药物相互作用的测量。认为一个计算平台可能表明, 某种10药物组合对某种病原体具有强烈的协同作用。传统的检测药物相互作用的方法成本太高, 无法证实或反驳这一假说, 因此, 研究许多药物之间的协同作用已经超出了科学探究的范围。对角线方法, 这是第一次提出近30年前, 并在最近几个协同屏幕使用, 为第一个问题提供了一个强大的基础, 通过允许测试的互动之间的许多对。它通过对传统化验的信息取样来解决第二个问题, 并允许研究高阶药物相互作用。
我们更重要的注意到, 我们的协议使用线性剂量的药物相互作用测量, 以提供灵敏度, 以检测甚至微弱的相互作用。确定线性剂量的适当浓度范围是一项具有挑战性的任务。通过首次进行连续稀释, 我们对线性剂量的搜索空间做出了明智的决定。但是, 该协议可以修改为使用2倍或更高的串行稀释进行药物交互测试。这样的修改将缩短实验时间, 并允许测试更多的交互作用;然而, 它会有灵敏度, 只检测强烈的协同或拮抗作用。
我们描述的协议显示了对成对或三路交互的测量。协议的一个关键方面是单个代理与组合在同一板块上, 以尽量减少由于板块变化而引起的偏差。因此, 可以对该协议进行调整, 以通过琐碎的修改来测量多达7路组合的交互作用。7多种药物的组合需要超过一个96井微板块, 必须采取额外的考虑, 以确保正确的数据集成, 如板块间的复制。
对角方法的一个显著的局限性是限制, 每种药物在检测必须抑制表型的兴趣。因此, 对角法对于理解活性剂和惰性佐药之间的相互作用是不有用的。这种 ' potentiating ' 的互动可能会被研究在替代模型, 如极乐或最高的单一代理模型。
对高阶药物相互作用分析的一个重要考虑是 "预期 IC50" 的空模型选择。当两种药物结合后, 该组合的效果只能与单一药物效应进行比较。当三种药物结合, 组合的效果可以比较单一的效果或配对的效果。例如, 如果所有配对的三种药物的组合是协同的, 那么可能会预期这些药物将显示出三的协同作用。一个三路互动的偏离预期从成对相互作用最近被称为 "紧急互动"16,17。为了简单起见, 我们的协议描述了三路组合的 "网络交互" 的测量, 它将 null 模型定义为单一的药物效应。但是, 从协议获得的数据也可以用来计算三路组合的紧急交互。在我们的分析中, 我们定义了三路组合的预期 IC50 作为单一药物 IC50s 的平均值。或者, 预期的 IC50 可以定义为配对组合 IC50s 的平均值 (~ 1.1-1.2)。当观察到的 IC50 除以此替代预期 IC50 时, 获得的 FIC 为三路组合提供了紧急 FIC, 如前所述12。这一考虑表明, 在三种药物之间的相互作用中, 比对的期望更具协同效应, 证明了该 + png 具有紧急协同作用。

药物组合可能表现出惊人的高或低影响的表型, 考虑到成分药物的影响, 相应的协同或拮抗药物相互作用, 分别为1,2,3。使用协同组合可能允许剂量升级的功效增加和剂量减少的副作用缓解。组合治疗也可能会对细胞机械施加多重挫折, 从而阻断潜在的进化逃逸机制, 抵抗4。因此, 三或更多的药物的组合通常用于病原体或癌症治疗5。
协同作用和拮抗作用的定义是, 观察效果的组合与预期效果的个别药物效果的比较。在药物相互作用的模型中, 卢安克加是最严格的, 并且有一个定义良好的空模型(图 1)6, 推断的协同/拮抗作用与所使用的药物浓度无关。6,7. 然而, 卢安克模型在实验上代价很高, 甚至对相互作用测试也是如此。药物相互作用的检测传统上包括一个2D 矩阵的药物浓度组合 (棋盘分析) (图 2)。如果每种药物使用5剂量, 则需要25个组合, 对应于一个微板块的一半, 如果实验是在复制中进行的。这种方法的成本禁止多药组合卢安克加模型的协同测量(图 3)。例如, 要测试10路交互, 传统方法需要超过10万微型, 通过严格的、理论良好的、与浓度无关的卢安克加模型, 禁止对高阶协同作用进行实验测量。8。
目前的临床治疗只利用一小部分可能的药物组合。例如, 有效肺结核的标准治疗是三种抗生素的结合。有大约20抗生素用于结核分枝杆菌治疗。在20种药物中有1140可能的3路组合, 每种药都有可能对结核分枝杆菌有较强的协同作用。由于没有成本效益的方法来衡量许多药物之间的药物相互作用, 潜在的拯救生命的协同组合仍然没有测试。
在这里, 我们描述一个简单的协议, 以测量成对和三方式的药物相互作用, 只取样对角线的棋盘分析(图 4和 图 5)。贝伦鲍姆在 1978年9的开创性工作中, 从理论上推测了棋盘实验的对角抽样的基本概念。然而, 这种方法最近才被应用于药物协同筛网10、11、12。我们提出了大肠杆菌(大肠杆菌) 和生长表型的协议。然而, 我们注意到, 该议定书是独立于生物种类和感兴趣的表型, 因此可用于测量其他生物环境中的高阶药物协同作用。

<table><tbody><tr><td>1.5 mL Semi Micro Cuvette&amp;nbsp;</td><td>VWR</td><td>97000-586</td><td></td></tr><tr><td>1.5 mL &amp;nbsp;Eppendorf&amp;nbsp;Microcentrifuge Tubes</td><td>USA Scientific</td><td>4036-3204</td><td></td></tr><tr><td>1000 &amp;micro;L Tips</td><td>Geneseesci</td><td>24830</td><td></td></tr><tr><td>14 mL Breathable Cell Culture Tube</td><td>VWR</td><td>60819-761</td><td></td></tr><tr><td>20 &amp;micro;L Tips</td><td>Geneseesci</td><td>24804</td><td></td></tr><tr><td>200 &amp;micro;L Tips</td><td>Geneseesci</td><td>24815</td><td></td></tr><tr><td>37 &amp;deg;C Incubator</td><td>Panasonic</td><td>MIR-262-PA</td><td></td></tr><tr><td>37 &amp;deg;C Shaker Incubator</td><td>Thermo Scientific</td><td>SHKE8000</td><td></td></tr><tr><td>5 mL Cell Culture Serological Pipette</td><td>VWR</td><td>53300-421</td><td></td></tr><tr><td>96-well Microplates</td><td>VWR</td><td>15705-066</td><td></td></tr><tr><td>Breathable Sealing Film</td><td>USA Scientific</td><td>2920-0010</td><td></td></tr><tr><td>DMSO</td><td>Sigma</td><td>41647</td><td></td></tr><tr><td>&lt;em&gt;Escherichia coli&lt;/em&gt;</td><td>ATCC</td><td>700926</td><td></td></tr><tr><td>Glycerol</td><td>Sigma</td><td>G9012</td><td></td></tr><tr><td>LB Broth Powder</td><td>RPI</td><td>L24065</td><td></td></tr><tr><td>Levofloxacin</td><td>Sigma</td><td>28266</td><td></td></tr><tr><td>Micropipette</td><td>GILSON</td><td>PIPETMAN Classic</td><td></td></tr><tr><td>Microplate reader</td><td>BioTek</td><td>Synergy H1</td><td></td></tr><tr><td>Multichannel micropipette</td><td>VistaLab</td><td>1060</td><td></td></tr><tr><td>Nalidixic acid</td><td>Sigma</td><td>N8878</td><td></td></tr><tr><td>Penicillin G</td><td>Sigma</td><td>P3032</td><td></td></tr><tr><td>Pipette Pump</td><td>Drummond&amp;nbsp;</td><td>4-000-501</td><td></td></tr><tr><td>Reagent Reservoir</td><td>VWR</td><td>89094-658</td><td></td></tr><tr><td>Spectrophotometer</td><td>BIO-RAD</td><td>1702525</td><td></td></tr><tr><td>Vortex Mixer</td><td>Fisher Scientific</td><td>10-320-807</td><td></td></tr></tbody></table>

diagonal method to measure synergy among any number of drugs

与单个药物的作用相比, 协同用药的疗效更高。棋盘化验, 药物在许多剂量的结合, 允许敏感测量药物相互作用。然而, 这些化验是昂贵的, 并没有很好的规模, 以衡量相互作用之间的许多药物。最近的几项研究报告了使用传统棋盘法的对角抽样进行药物相互作用测量。这种替代方法大大降低了药物相互作用实验的成本, 并允许与许多药物的组合进行交互测量。在这里, 我们描述了一个协议, 以测量三对相互作用和三方式相互作用之间三抗生素重复, 在五天内, 只使用三96井微型和标准实验室设备。我们目前的代表性结果表明, 三-抗生素联合左氧氟沙星 + 萘啶酮酸 + 青霉素 G 是协同作用。我们的协议扩展以测量许多药物和其他生物环境中的相互作用, 从而有效地筛除病原体和肿瘤的多药协同作用。

此前, 我们报告了三种药物之间的相互作用: 在微型棋盘测试中, 在 4 x 4 矩阵13,14中, 两个药物结合在一起进行了试验。虽然宇空和列弗是协同作用, 但据报道, PNG 与列弗和13,14是对立的。在这里, 我们验证了这些配对的相互作用, 并测量了这三种药物之间的三路互动使用对角化验。我们的研究结果表明, 加 PNG 是一种协同的3路抗生素组合。在图 7和 图 8的右侧给出了单个实验过程部分结果的示意图表示。在这里, 我们提出和解释代表性的原始结果从三板块读数, 这是在图 9中给出。顶板读数对应于步骤2和3中进行的串行和线性稀释实验。底部两个板块读数是在步骤4中进行的重复交互板。
图 9中的原始数据显示, 顶部的生长量约为 0.55, 但有0.05 的介质本身的光密度, 如在没有生长的高药物浓度的 OD600中观察到的。因此, 我们定义 IC50 为 (0.55-0. 05)/2 = 0.25。对于每种剂量反应, 最接近这个值的井都显示为橙色。
图 9A的上半部分显示了步骤2、串联剂量响应实验的结果。IC50 井为列弗是在专栏10在二个复制, 对应于 4 ng/毫升。IC50 和 PNG 分别为3µg 和25µg/毫升。这些浓度对应于图 7B所示的1x 浓度。图 9B的下半部分显示了步骤3的线性剂量响应实验的结果。在0.4x、0.8x 和1.2x 分别发现了列弗、IC50 和 PNG 的。这些浓度被指定为步骤4的 1X IC50。
两个板块对应两个复制实验显示在图 9B, 其中的 IC50 井显示与橙色。在板 1, 所有单一的药物有他们的 IC50 在1x 浓度。对成对或三路组合的预期 IC50 由组成药物的算术平均值计算, 使所有组合的预期 IC50 也1x 浓度。在板材 2, 列弗和 PNG 有他们的 IC50 在1x 集中, 但 IC50 是在1.2x。每个组合的预期 IC50 是使用这些 IC50 值的算术方法定义的。例如, 预期的 IC50 和的是1.1x。每个组合的药物相互作用评分 (FIC) 是通过将观察到的 IC50 除以预期的 IC50 来计算的, 如板块右侧所示。对两个板块的 FIC 评分进行检验, 证明了该项工作的协同性, 而加、png 和 png 是对立的。FIC 在两个板块中获得的分数是一致的, 支持协议的可靠性。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57713/57713fig1.jpg"> 图 1: 卢安克加药物相互作用模型的空模型定义.微板块上的两个 5 x 5 矩阵, 其中药物 a 在一个轴上线性地增加, 如左侧所示, 药物的最高浓度会抑制可量化的表型 (右上)。这些矩阵的加法显示在中间, 连接 equipotent 药物浓度的线与药物 a 的浓度相同。当将细胞添加到药物浓度组合的 "棋盘" 上时, 预计这条线上的记录表型将等同于它所连接的水井。在这种自自我药物相互作用实验中, 描述表型的 isobole (以虚线绿线显示) 预计是线性的, 定义加空模型。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57713/57713fig2.jpg"> 图 2: 根据卢安克加药物相互作用模型对药物相互作用进行配对.当两种药物结合在棋盘分析中, 如图1所示, 观察到的 isophenotypic 轮廓可能是直的、凸的或凹的。右, 可能的 isophenotypic 轮廓 (虚线绿线) 叠加在棋盘上的化验。药物组合与直 isophenotypic 的轮廓是卢安克添加剂, 因为轮廓是不同的自自我药物相互作用, 这是卢安克添加剂的定义。当 isophenotypic 轮廓明显凹或凸时, 两者的组合是协同或对立的。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57713/57713fig3.jpg"> 图 3: 根据卢安克加药物相互作用模型进行的三路药物相互作用.类似于对相互作用的棋盘分析, 三-药物是结合在一个3D 的网格 ("checkercube"), 其中每种药物在一个轴线性增加。如果三种药物是相同的, isophenotypic 表面预计是扁平的, 定义加三的药物组合。如果表面比这个卢安克-加法空模型更凹或凸, 药物组合分别是协同或拮抗。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57713/57713fig4.jpg"> 图 4: 测量配对药物相互作用的对角方法.对于每个棋盘测试, 只测量在洋红矩形中显示的区域。i 和 ii 是线性增加单一药物浓度。通过1:1 混合两种药物进行评估, 并线性滴定这种混合物, 就好像它是一种单一的药物。FIC 是相等的观察 IC50 在组合除以预期的 IC50 的两个单一的药物。对于卢安克加模型, 预期的 IC50 由两种单一药物的平均 IC50 近似。FIC 值为 1, 用于卢安克加成对, 在协同或拮抗对中, 分别为2、12, 小于或高于1。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57713/57713fig5.jpg"> 图 5: 测量三路药物相互作用的对角方法.对于每种 checkercube 检测, 只测量显示的区域。i、ii 和 iii 是线性增加单一药物浓度。iv 的测量是由 1:1: 1 混合三药物和线性滴定这种混合物, 如果它是一个单一的药物。分数抑制浓度等于所观察到的 IC50在组合除以预期 IC50 给定的三单药。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57713/57713fig6.jpg"> 图 6: 此处描述的对角方法协议的工作流以及每个微板块的安装详细信息.4天显示的板块是重复进行的。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/57713/57713fig7.jpg"> 图 7: 串行和线性稀释剂量反应实验.(A)为一种药物和相应的最终药物浓度准备连续稀释剂量反应。大肠杆菌细胞被添加到板材中;增长在16小时以后被记录, 以橙色显示的序列 IC50 被选择为每种药物使用在第二天的线性稀释剂量反应实验。(B)为一种药物和相应的最终药物浓度制备线性稀释剂量反应。大肠杆菌细胞被添加到板材中;增长在16小时以后被记录。对于每种药物, 将在第二天的药物相互作用实验中使用的选择 IC50s 以橙色显示。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig7large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/57713/57713fig8.jpg"> 图 8: 药物相互作用实验.相互作用实验的制备与单药线性剂量反应相似, 但1:1 或 1:1: 1 种药物的混合物分别用于两到三次药物剂量反应。每个剂量反应的观察 IC50s, 用橙色描述, 用于计算 FIC 分数。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig8large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/57713/57713fig9.jpg"> 图 9: 代表性实验结果.显示了使用描述的协议获得的代表性结果, 并提供了主要文本中的详细信息。<a href="https://www.jove.com/files/ftp_upload/57713/57713fig9large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Diagonal Method to Measure Synergy Among Any Number of Drugs at JoVE.com

Diagonal Method to Measure Synergy Among Any Number of Drugs

测量任意数量药物协同作用的对角方法

在本协议中, 我们描述了如何使卢安克加的药物相互作用测量成对和三路药物组合。

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many ...

diagonal-method-to-measure-synergy-among-any-number-of-drugs

Research

JoVE Journal

Biology

18.4K 次观看.  Sabanci University. 在本协议中, 我们描述了如何使卢安克加的药物相互作用测量成对和三路药物组合。

视频: Diagonal Method to Measure Synergy Among Any Number of Drugs

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our entire society's ability to treat systemic infections. In the United States alone, antibiotic-resistant infections kill approximately 23,000 people a year and cost around 20 billion USD in additional healthcare. One approach to combat antimicrobial resistance is combination therapy, which is particularly useful in the critical early stage of infection, before the infecting organism and its drug resistance profile have been identified. Many antimicrobial treatments use combination therapies. However, most of these combinations are additive, meaning that the combined efficacy is the same as the sum of the individual antibiotic efficacy. Some combination therapies are synergistic: the combined efficacy is much greater than additive. Synergistic combinations are particularly useful because they can inhibit the growth of antimicrobial drug resistant strains. However, these combinations are rare and difficult to identify. This is due to the sheer number of molecules needed to be tested in a pairwise manner: a library of 1,000 molecules has 1 million potential combinations. Thus, efforts have been made to predict molecules for synergy. This article describes our high-throughput method for predicting synergistic small molecule pairs known as the Overlap2 Method (O2M). O2M uses patterns from chemical-genetic datasets to identify mutants that are hypersensitive to each molecule in a synergistic pair but not to other molecules. The Brown lab exploits this growth difference by performing a high-throughput screen for molecules that inhibit the growth of mutant but not wild-type cells. The lab's work previously identified molecules that synergize with the antibiotic trimethoprim and the antifungal drug fluconazole using this strategy. Here, the authors present a method to screen for novel synergistic combinations, which can be altered for multiple microorganisms.

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Synergistic drug combinations are difficult and time-consuming to identify empirically. Here, we describe a method for identifying and validating synergistic small molecules.

通过重叠2方法高通量识别协同药物组合

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our ...

科研

遗传学

Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy against cancer. Here we provide a protocol to identify a rational combination at the preclinical step. First, we describe a cell-based assay to assess the synergism between anticancer mAb and cytotoxic drugs, that uses the combination index equation of Chou and Talalay1. This includes the measurement of tumor cell drug- and antibody-sensitivity using an MTT assay, followed by an automated computer analysis to calculate the combination index (CI) values. CI values of &lt;1 indicate synergism between tested mAbs and cytotoxic agents1. To corroborate the in vitro findings in vivo, we further describe a method to assess the combination regimen efficacy in a xenograft tumor model. In this model, the combined regimen significantly delays tumor growth, which results in a significant extended survival in comparison to single-agent controls. Importantly, the in vivo experimentation reveals that the combination regimen is well tolerated. This protocol allows the effective evaluation of anticancer drug combinations in preclinical models and the identification of rational combination to evaluate in clinical trials.

This protocol describes how to assess synergism between an anticancer antibody and antineoplastic drugs in preclinical models by using the combination index equation of Chou and Talalay.

抗肿瘤药物对抗癌抗体疗效的潜力: 用组合指数方程检测抗体-药物协同作用

Potentiation of hostile monoclonal antibodies (mAb) by chemotherapeutic agents constitutes a valuable strategy for designing effective and safer therapy ...

癌症研究

A Behavioral Assay to Measure Responsiveness of Zebrafish to Changes in Light Intensities 

The optokinetic reflex (OKR) is a basic visual reflex exhibited by most vertebrates and plays an important role in stabilizing the eye relative to the visual scene. However, the OKR requires that an animal detect moving stripes and it is possible that fish that fail to exhibit an OKR may not be completely blind. One zebrafish mutant, the no optokinetic response c (nrc) has no OKR under any light conditions tested and was reported to be completely blind. Previously, we have shown that OFF-ganglion cell activity can be recorded in these mutants. To determine whether mutant fish with no OKR such as the nrc mutant can detect simple light increments and decrements we developed the visual motor behavioral assay (VMR). In this assay, single zebrafish larvae are placed in each well of a 96-well plate allowing the simultaneous monitoring of larvae using an automated video-tracking system. The locomotor responses of each larva to 30 minutes light ON and 30 minutes light OFF were recorded and quantified. WT fish have a brief spike of motor activity upon lights ON, known as the startle response, followed by return to lower-than baseline activity, called a freeze. WT fish also sharply increase their locomotor activity immediately following lights OFF and only gradually (over several minutes) return to baseline locomotor activity. The nrc mutants respond similarly to light OFF as WT fish, but exhibit a slight reduction in their average activity as compared to WT fish. Motor activity in response to light ON in nrc mutants is delayed and sluggish. There is a slow rise time of the nrc mutant response to light ON as compared to WT light ON response. The results indicate that nrc fish are not completely blind. Because teleosts can detect light through non-retinal tissues, we confirmed that the immediate behavioral responses to light-intensity changes require intact eyes by using the chokh (chk) mutants, which completely lack eyes from the earliest stages of development. In our VMR assay, the chk mutants exhibit no startle response to either light ON or OFF, showing that the lateral eyes mediate this behavior. The VMR assay described here complements the well-established OKR assay, which does not test the ability of zebrafish larvae to respond to changes in light intensities. Additionally, the automation of the VMR assay lends itself to high-throughput screening for defects in light-intensity driven visual responses.

A Behavioral Assay to Measure Responsiveness of Zebrafish to Changes in Light Intensities

We developed the Visual-Motor Response to quantitate the motor output of larval zebrafish in response to light increments and decrements. We also examined zebrafish vision mutants, including the no optokinetic response (nrc) mutants, which were thought to be completely blind when tested by another vision assay, the optokinetic reflex.

斑马鱼的响应行为的检测，以测量光强的变化

The optokinetic reflex (OKR) is a basic visual reflex exhibited by most vertebrates and plays an important role in stabilizing the eye relative to the ...

生物学

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of both synthetic lethal interactors leads to loss of cell viability or cell death. The best studied synthetic lethal interaction is between BRCA1/2 and PARP1, with PARP1 inhibitors being used in clinical practice to treat patients with BRCA1/2 mutated tumors. Large genetic screens in model organisms but also in haploid human cell lines have led to the identification of numerous additional synthetic lethal interaction pairs, all being potential targets of interest in the development of novel tumor therapies. One approach is to therapeutically target genes with a synthetic lethal interactor that is mutated or significantly downregulated in the tumor of interest. A second approach is to formulate drug combinations addressing synthetic lethal interactions. In this article, we outline a data integration workflow to evaluate and identify drug combinations targeting synthetic lethal interactions. We make use of available datasets on synthetic lethal interaction pairs, homology mapping resources, drug-target links from dedicated databases, as well as information on drugs being investigated in clinical trials in the disease area of interest. We further highlight key findings of two recent studies of our group on drug combination assessment in the context of ovarian and breast cancer.

Large genetic screens in model organisms have led to the identification of negative genetic interactions. Here, we describe a data integration workflow using data from genetic screens in model organisms to delineate drug combinations targeting synthetic lethal interactions in cancer.

数据集成工作流，以识别针对合成致命相互作用的药物组合

A synthetic lethal interaction between two genes is given when knock-out of either one of the two genes does not affect cell viability but knock-out of ...

Spheroid Drug Sensitivity Screening in Glioma Stem Cell Lines

In vitro drug sensitivity screens are important tools in the discovery of anti-cancer drug combination therapies. Typically, these in vitro drug screens are performed on cells grown in a monolayer. However, these two-dimensional (2D) models are considered less accurate compared to three-dimensional (3D) spheroid cell models; this is especially true for glioma stem cell lines. Cells grown in spheres activate different signaling pathways and are considered more representative of in vivo models than monolayer cell lines. This protocol describes a method for in vitro drug screening of spheroid lines; mouse and human glioma stem cell lines are used as an example. This protocol describes a 3D spheroid drug sensitivity and synergy assay that can be used to determine if a drug or drug combination induces cell death and if two drugs synergize. Glioma stem cell lines are&#160;modified to express RFP. Cells are plated in low attachment round well bottom 96 plates, and spheres are allowed to form overnight. Drugs are added, and the growth is monitored by measuring the RFP signal over time using the Incucyte live imaging system,&#160;a fluorescence microscope embedded in the tissue culture incubator. Half maximal inhibitory concentration (IC50), median lethal dose (LD50), and synergy score are subsequently calculated to evaluate sensitivities to drugs alone or in combination. The three-dimensional nature of this assay&#160;provides a more accurate reflection of tumor growth, behavior, and drug sensitivities in vivo, thus forming the basis for further preclinical investigation.

<meta charset="utf8"></head><body>胶质瘤干细胞系中的球状体药物敏感性筛选

In vitro drug sensitivity screens are important tools for discovering anti-cancer drug combinations. Cells grown in spheres activate different signaling pathways and are considered more representative of in vivo models than monolayer cell lines. This protocol describes a method for in vitro drug screening for spheroid lines.

胶质瘤干细胞系中的球状体药物敏感性筛选

In vitro drug sensitivity screens are important tools in the discovery of anti-cancer drug combination therapies. Typically, these in vitro drug screens ...

High Throughput, Real-time, Dual-readout Testing of Intracellular Antimicrobial Activity and Eukaryotic Cell Cytotoxicity

Traditional measures of intracellular antimicrobial activity and eukaryotic cell cytotoxicity rely on endpoint assays. Such endpoint assays require several additional experimental steps prior to readout, such as cell lysis, colony forming unit determination, or reagent addition. When performing thousands of assays, for example, during high-throughput screening, the downstream effort required for these types of assays is considerable. Therefore, to facilitate high-throughput antimicrobial discovery, we developed a real-time assay to simultaneously identify inhibitors of intracellular bacterial growth and assess eukaryotic cell cytotoxicity. Specifically, real-time intracellular bacterial growth detection was enabled by marking bacterial screening strains with either a bacterial lux operon (1st generation assay) or fluorescent protein reporters (2nd generation, orthogonal assay). A non-toxic, cell membrane-impermeant, nucleic acid-binding dye was also added during initial infection of macrophages. These dyes are excluded from viable cells. However, non-viable host cells lose membrane integrity permitting entry and fluorescent labeling of nuclear DNA (deoxyribonucleic acid). Notably, DNA binding is associated with a large increase in fluorescent quantum yield that provides a solution-based readout of host cell death. We have used this combined assay to perform a high-throughput screen in microplate format, and to assess intracellular growth and cytotoxicity by microscopy. Notably, antimicrobials may demonstrate synergy in which the combined effect of two or more antimicrobials when applied together is greater than when applied separately. Testing for in vitro synergy against intracellular pathogens is normally a prodigious task as combinatorial permutations of antibiotics at different concentrations must be assessed. However, we found that our real-time assay combined with automated, digital dispensing technology permitted facile synergy testing. Using these approaches, we were able to systematically survey action of a large number of antimicrobials alone and in combination against the intracellular pathogen, Legionella pneumophila.

A high throughput, real-time assay was developed to simultaneously identify (1) eukaryotic cell-penetrant antimicrobials targeting an intracellular bacterial pathogen, and (2) assess eukaryotic cell cytotoxicity. A variation on the same technology was thereafter combined with digital dispensing technology to enable facile, high-resolution, dose-response, and two- and three-dimensional synergy studies.

高通量，实时，细胞内的抗菌活性的双读出测试和真核细胞的细胞毒性

Traditional measures of intracellular antimicrobial activity and eukaryotic cell cytotoxicity rely on endpoint assays. Such endpoint assays require ...

免疫与感染

Antimicrobial Synergy Testing by the Inkjet Printer-assisted Automated Checkerboard Array and the Manual Time-kill Method

As rates of multidrug-resistant (MDR) pathogens continue to rise, outpacing the development of new antimicrobials, novel approaches to treatment of MDR bacteria are increasingly becoming a necessity. One such approach is combination therapy, in which two or more antibiotics are used together to treat an infection against which one or both of the drugs may be ineffective alone. When two drugs, in combination, exert a greater than additive effect, they are considered synergistic. In vitro investigation of synergistic activity is an important first step in evaluating the possible efficacy of drug combinations. Two main in vitro synergy testing methods have been developed: the checkerboard array and the time-kill study. In this paper, we present an automated checkerboard array method that makes use of inkjet printing technology to increase the efficiency and accuracy of this technique, as well as a standard manual time-kill synergy method. The automated checkerboard array can serve as a high-throughput screening assay, while the manual time-kill study provides additional, complementary data on synergistic activity and killing.
The checkerboard array is a modification of standard minimum inhibitory concentration (MIC) testing, in which bacteria are incubated with antibiotics at different concentration combinations and evaluated for growth inhibition after overnight incubation. Manual performance of the checkerboard array requires a laborious and error-prone series of calculations and dilutions. In the automated method presented here, the calculation and dispensing of required antibiotic stock solution volumes are automated through the use of inkjet printer technology. In the time-kill synergy assay, bacteria are incubated with the antibiotics of interest, both together and individually, and sampled at intervals over the course of 24 h for quantitative culture. The results can determine whether a combination is synergistic and whether it is bactericidal, and provide data on inhibition and killing of bacteria over time.

Antimicrobial synergy testing is used to evaluate the effect of two or more antibiotics used in combination and is typically performed by one of two methods: the checkerboard array or the time-kill assay. Here, we present an automated, inkjet printer-assisted checkerboard array synergy technique and a classic time-kill synergy study.

喷墨打印机辅助自动跳棋阵列和手动消时法的抗菌协同试验

As rates of multidrug-resistant (MDR) pathogens continue to rise, outpacing the development of new antimicrobials, novel approaches to treatment of MDR ...

Quadruple-Checkerboard: A Modification of the Three-Dimensional Checkerboard for Studying Drug Combinations

The concept of drug-combination therapy is becoming very important mainly with the drastic increase in resistance to drugs. The Quadruple&#160;checkerboard, also called the Q-checkerboard, aims at maximizing the number of possible combinations that can be obtained between four drugs in one experiment to minimize the time and work needed to accomplish the same results with other protocols. This protocol is based on the simple micro dilution technique where the drugs are diluted and combined together in several 96-well plates.
In the first set of 96-well plates, Muller-Hinton broth is added followed by the first required drug (e.g., Cefotaxime here) to serially dilute it. After the first step is done, another set of 96-well plates is used to dilute the second drug (e.g., Amikaci), which will be transferred by removing a specific volume of drug 2 and put in the corresponding wells in the first set of 96-well plates that contains drug one. The third step is done by adding the required concentrations of the third drug (e.g., Levofloxacin), to the appropriate plates in the initial set containing combination of drug 1 and 2. The fourth step is done by adding the required concentrations of the fourth drug (e.g., Trimethoprim-sulfamethoxazol) into the appropriate plates in the first set. Then, E. coli ESBL bacterial inoculum will be prepared and added.
This method is important to evaluate all the possible combinations and has a wider range of possibilities to be tested furthermore for in vivo testing. Despite being a tiring technique requiring a lot of focus, the results are remarkable and time saving where a lot of combinations can be tested in a single experiment.

This protocol describes how to study all possible combinations that can be obtained between four drugs in one single experiment. This method is based on the standard 96-well plate micro dilution assay and the calculation of fractional inhibitory concentrations (FICs) to evaluate the results.

四重检查板：研究药物组合的三维棋盘的修改

The concept of drug-combination therapy is becoming very important mainly with the drastic increase in resistance to drugs. The ...

Antibiotic Susceptibility Testing: Epsilometer Tests to Determine MIC Values of Two Antibiotics and Evaluate Antibiotic Synergy

Source: Anna Bl&#228;ckberg1, Rolf Lood1 
1 Department of Clinical Sciences Lund, Division of Infection Medicine, Biomedical Center, Lund University, 221 00 Lund Sweden
Knowledge of the interactions between antibiotics and bacteria is important in understanding how microbes evolve antibiotic resistance. In 1928, Alexander Fleming discovered penicillin, an antibiotic that exerts its antibacterial function by interfering with cell wall regeneration (1). Other antibiotics with diverse mechanisms of action have subsequently been discovered, including drugs that inhibit DNA replication and protein translation in bacteria; however, no new antibiotics have been developed in recent years. Resistance to current antibiotics has been increasing, resulting in severe infectious diseases that cannot be effectively treated (2). Here, we describe several methods to assess antibiotic resistance in bacterial populations. Each of these methods works, regardless of the mechanism of action of the antibiotics used, because bacterial death is the measured outcome. Antibiotic resistance is not only rapidly disseminated specifically through hospital settings, but also throughout society. In order to investigate such means of resistance, different methods have been developed including the Epsilometer test (E-test) and the broth dilution test (3).
The E-test is a well-established method and is a cost-effective tool that quantifies Minimal Inhibitory Concentration (MIC) data, the lowest concentration of an antimicrobial that inhibits visible growth of a microorganism. Depending on the bacterial strain and antibiotics used, the MIC value can vary between sub &#956;g/mL to &#62;1000 &#956;g/mL (4). The E-test is performed using a plastic strip containing a predefined antibiotic gradient, which is imprinted with the MIC reading scale in &#956;g/mL. This strip is directly transferred on the agar matrix when applied to the inoculated agar plate. After incubation, a symmetrical elliptic inhibition zone is visible along the strip as bacterial growth is prevented. MIC is defined by the area of inhibition, which is the endpoint where the ellipse intersects the strip. Another common method to determine MIC is the microbroth dilution method. Microbroth dilution incorporates different concentrations of the antimicrobial agent added to a broth medium containing inoculated bacteria. After incubation, MIC is defined as the lowest concentration of antibiotic that prevents visible growth (5). It is also a quantitative method and can be applied to several bacteria. Disadvantages of this method include the possibility of errors when preparing the concentrations of the reagents and the large number of reagents required for the experiment. Measuring antibiotic resistance is imperative from both a clinical and research perspective, and these in vitro methods of investigating resistance are discussed and showcased below.
The profile of resistance for a specific bacterium can be applied in order to optimize antibiotic treatment to determine whether a patient would benefit from combination treatment versus single therapy. For the usage of more than one antibiotic at a single time, it is imperative to know their interactions with each other and if they have an additive, synergistic, or antagonistic effect. An additive effect can be seen when the joint effect of the antibiotics equals the potency of the individual antibiotics given at an equal dose. Synergy between antibiotics, on the other hand, is present when the joint effect of the antibiotics is more potent than if the drug would be given alone (6). Applying combinations of antimicrobial treatment is used to avoid the occurrence of antimicrobial resistance thus to enhance the effect of the individual antibiotic treatment (7). Knowledge of antagonism is also as important to prevent unnecessary use of antimicrobial combinations. E-test methodology offers simple and several ways to determine possible synergy and antagonism between different antimicrobial agents. In order to face the proliferation of antibiotic resistant pathogens, knowledge of possible synergistic and antagonistic mechanisms of certain antibiotics is important resulting in clinical efficacy and battling multidrug-resistance.
Determination of synergy using E-tests can be divided into two broad approaches: cross and non-cross testing. While both synergy tests rely on the previous knowledge of individual MIC values, the two approaches are slightly different in methodology and conceptual approach. In a non-cross synergy test, the first antibiotic in the pair to be tested is placed on an agar plate inoculated with bacteria. After allowing the antibiotics from the first strip infuse the plate (e.g. after 1 hour), the strip is removed and a new strip containing the second antibiotic is placed on the exact same spot as the first, making sure to place the two individual MIC values on top of each other. The resulting inhibition zone can then be analyzed as described above, and synergy calculated based on the Equation 1.
Equation 1 - Fractional Inhibitory Concentrations (FIC)
<img src="/files/ftp_upload/10512/10512eq1.jpg" />
Values &#62;0.5 demonstrates synergy.
While rewarding the examiner with easy-to-analyze plates, the method is somewhat laborious and time-consuming due to the change of strips, as well as the need of using two plates per experiment. Instead, a cross test is often employed. Instead of adding the two different E-test strips subsequently on top of each other (after removal of the first), both are placed simultaneously but in the form of a cross (<img align="center" src="/files/ftp_upload/10512/10512eq3.jpg" /> 90&#176; angle), with the two previously determined MIC values forming the 90&#176; angle. By this approach only one plate is needed per synergy test, as well as less work, making it a preferred choice despite being slightly more difficult to analyze. The new MIC values in the combined antibiotics approach can be visualized as the modified inhibition zones, after which synergy can be determined by Equation 1.
Instead of using an agar plate approach, a microbroth approach can often be preferential due to its higher flexibility (e.g. ability to choose specific concentrations of antibiotics outside the limits of an E-test strip). Further, microbroth tests are suggested to be more sensitive due to their even distribution of antibiotics in a liquid solution, not depending on the dissociation within a solid phase (agar plate). Wells in a 96-well microplate will be inoculated with a set number of bacteria (106 cfu/mL: bacterial concentration can be estimated by OD600 nm measurements, turbidity standards, or by spread plating samples from 10x bacterial serial dilutions), and antibiotics in different dilutions will be added to the wells. Similarly, to the E-test strips MIC is determined as the intersection (well/spot) with the lowest concentration of antibiotics inhibiting visible growth of bacteria.
Experimental Aim
<ul>
	<li>The project below describes strategies to determine MIC-values of penicillin G and gentamicin of Streptococcus group G by two different methods, E-test and microbroth dilution. For E-test, Mueller-Hinton agar plates inoculated with Streptococcus group G were used in combination with gradient strips of penicillin G and/or gentamicin; while MH-broth with 50% lysed horse blood and 20 mg/mL &#946;-NAD were used with soluble antibiotics together with Streptococcus group G in a microbroth approach.</li>
</ul>
Materials
<ul>
	<li>Bacterial colonies on a blood agar plate, stored &#60;7 days in 4&#176;C</li>
	<li>Blood agar plates</li>
	<li>0.5 McFarland standard
	<ul>
		<li>1% BaCl2</li>
		<li>1% H2SO4</li>
	</ul>
	</li>
	<li>Saline tube (2 mL)</li>
	<li>Cotton tipped applicator</li>
	<li>Mueller-Hinton agar plates (MHA plates)</li>
	<li>MH broth with 50% lysed horse blood and 20 mg/mL &#946;-NAD (MH-F)</li>
	<li>E-test penicillin/gentamicin (or antibiotics of interest) (BioMerieux, Marcy l&#39;Etoile, France, Sweden)</li>
	<li>Antibiotics penicillin/gentamicin (or antibiotics of interest (powder/solution))</li>
</ul>
Note: The specific media used for bacterial growth may vary for different species.

Antibiotic Susceptibility Testing, MIC and Antibiotic Synergy

抗生素敏感性测试:Epsilo计测试，以确定两种抗生素的MIC值，并评估抗生素协同效应

Source: Anna Bl&#228;ckberg1, Rolf Lood1
1 Department of Clinical Sciences Lund, Division of Infection Medicine, Biomedical Center, Lund University, 221 ...

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抗生素敏感性测试:Epsilo计测试，以确定两种抗生素的MIC值，并评估抗生素协同效应