Name: high-throughput identification of synergistic drug combinations by the overlap2 method
Uploaded: 2018-05-21T14:00:00.000+00:00
Duration: 7 min 51 s
Description: Watch this Scientific Journal Video about High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method at JoVE.com

这项工作得到了来自犹他州大学病理学系 j.c.s. B 的启动补助金的支持。

1. 通过重叠的2方法, 从化学遗传学数据集确定协同预测突变体 (O2M)
注意: 这是使用尼科尔斯et发布的数据集来识别协同预测突变体的方法。大肠杆菌中的17 。然而, 这可以在任何化学遗传学数据集和微生物。这些数据集包含一个在100多个小分子存在下的挖空突变体库, 为每个小分子中的每个突变体提供定量的生长评分。必须知道一个协同对, 并且在数据集中包含的两个小分子应该有增长分数。
<ol><li>
计算每个小分子在单个浓度下生长的所有突变体的平均生长分数和标准偏差。 注意: 这可以使用任何电子表格软件完成。棕色实验室使用 Excel 和函数 = 平均值 (B3: B3981) 和 = STDEV (B3: B3981) 为小分子的每列的计算。<ol>
<li>计算每列小分子的方法和标准偏差, 但如果使用不同的数据集, 这个方向可能会有所不同。</li>
	</ol>
</li>
	<li>使用来自尼科尔斯et al的相同数据集计算每个小分子浓度的 Z 分数。电子表格程序中的17 。为此, 负 z 分数将是 z (2.5) = 平均-2.5 * 标准偏差, 和正 z 分数将 z (2.5) = 平均值 + 2.5 * 标准偏差。</li>
	<li>确定哪些基因突变体在组成已知协同对的小分子中含有显著的 Z 分数, 一定要观察这些分子的所有浓度。如果突变体的生长评分小于负 z (2.5) 评分或大于正 z (2.5) 分数的小分子, 它被认为是重要的。</li>
	<li>确定是否有任何重要的基因突变体属于操纵。 注: 对于大肠杆菌, 作者建议检查 "Ecoli wiki", 以了解有关基因是否转录为 polycistronic RNA 的一部分的信息。</li>
	<li>在不同浓度的小分子中, 发现基因/操纵突变体 (e. g), 在寻找与抗生素甲氧苄啶协同的分子时, 识别出显着 Z 的突变体。在甲氧苄啶的存在和其已知的协同合作伙伴如 sulfamethizole 或磺胺) 中生长时得分。这些都是假定的协同预测突变体。</li>
</ol>2. 通过 O2M 预测化学遗传学数据集内的 Synergizers
<ol><li>返回到数据集17, 确定每个小分子的浓度, 从协同预测突变体中引出显著的生长分数。这是通过观察计算每个小分子浓度的 Z 分数来完成的。同样, 一个显著的增长分数小于负 z 分数或大于正 z 分数为该浓度的小分子。 注: 如果一个分子出现在其浓度的一个, 分子被认为是预测的 synergizer。任何不引起显著生长评分的分子都是预测的非 synergizer。</li>
</ol>3. 预测的协同相互作用的验证
<ol><li>寻找预测 synergizers 的最小抑制浓度 (MIC)。
	<ol>
<li>在 M9 最小介质中, 在摄氏37摄氏度处生长一夜之间的大肠杆菌文化。 注意: 任何隔夜文化都可以用适当定义的培养基为感兴趣的有机体生长。布朗实验室不推荐像 LB 或 YPD 这样的富媒体。M9 最小介质包括10.5 克/升 M9 汤, 0.2% casamino 酸, 0.1 米 CaCl2, 0.4% 葡萄糖, 1 m MgSO4, 0.25% 烟酸, 0.33% 硫胺在 H2O。</li>
		<li>如果在接受小分子时, 在文献中没有发现感兴趣的病菌的 MIC 数据, 则在高浓度 (> 10 毫克/毫升) 中溶解二甲基亚砜 (亚砜)。</li>
		<li>使用96井板, 添加100µL 的 M9 介质到每个井。在1栏中添加额外的100µL M9 介质, 因此它总共包含200µL。</li>
		<li>在1栏中添加任意量 (~ 10 µL) 的浓缩小分子溶液。一个小分子每井专栏 1 (8 个小分子每板材)。 注: 这些是感兴趣的小分子, 预测协同。布朗实验室通常增加10µL 的高度集中的小分子溶液, 它低于100% 亚砜的数量, 可以抑制大肠杆菌。</li>
		<li>一旦添加了小分子, 就会将小分子分散在平板的 x 轴上。采取100µL 的解决方案从1栏, 地方在2栏和混合。从2栏中取100µL, 在3栏中放置并混合。继续此过程, 直到将100µL 放在11列中。混合11列, 然后从该列中取100µL 并丢弃。将12栏与仅介质保持在一起, 使数据正常化。</li>
		<li>给盘子接种疫苗。从隔夜文化中获取光学密度 (OD600)。用区域性和 M9 介质准备一个解决方案, 使其达到600的 0.002 (或适当的浓度, 表示所需的感兴趣的有机体的500个单元格/µL)。吸管2µL 的培养溶液到每一个井的板块, 所以有1000个细胞每井。</li>
		<li>轻轻摇动盘子, 获得0小时读数的 OD600 。让板材生长在37°c 为 24 h. 获取 24 h 读数的 OD600 。 注: 对其他感兴趣的生物体使用适当的生长条件。</li>
		<li>使用电子表格程序计算每个井的净增长。</li>
		<li>平均12栏净增长, 获得平均无药物增长。使用电子表格软件将其他油井正常化为无药物增长的平均值。寻找任何与少于10% 的增长, 以确定 MIC90 的好。任何低于50% 生长的井表明 MIC50。计算小分子在这些油井中的浓度。</li>
	</ol></li>
	<li>协同作用的棋盘测定方法
	<ol>
<li>像以前一样, 在 M9 最小的介质中长出一夜之间的大肠杆菌文化。 注: 对其他生物体使用适当的生长条件。</li>
		<li>将100µL M9 介质添加到96井板的每一个井中, 在1列中增加100µL。</li>
		<li>将测试小分子 (8 x 麦克风) 添加到1列中的每个井中, 并增加两个浓度 (~ 16 x 麦克风) 到良好的 A1 (每板测试一个小分子)。 注: 这笔金额是从麦克风测试完成之前, 并不同的每一个潜在的 synergizer。</li>
		<li>通过从1列中取100µL 并转移到2列, 在 x 轴上创建渐变稀释。继续此过程, 直到将100µL 添加到11列。混合, 然后采取100µL 从11栏和处置。不要将任何药物添加到12栏 (图 1A)。</li>
		<li>为输入药物在 y 轴上设置第二个药物梯度。添加100µL, 其中包含输入分子的2x 麦克风在 a 行混合和稀释为以前, 采取100µL 从 a 行和放置在 b. 继续直到100µL 放在列 g 混合, 采取100µL 和处置, 确保不添加任何输入药物在行 H。这允许行 H 和12列包含测试的药物对的单个麦克风 (图 1B)。</li>
		<li>给盘子接种疫苗。将 OD 600 0.002 的大肠杆菌区域性的2µL 添加到每个井 (每井1000个单元格)。</li>
		<li>测量0小时读数板中每个井的 OD600 。</li>
		<li>在37摄氏度孵育24小时的盘子。</li>
		<li>在24小时测量每个井的 OD600 。</li>
	</ol></li>
	<li>棋盘中分数抑制浓度指数 (五指) 的计算
	<ol>
<li>通过在电子表格程序中, 从外径600的 od600减去0小时, 计算板中每个井的净增长。</li>
		<li>通过 H12, 不包含小分子的井分, 使软件的增长正常化。</li>
		<li>找出12栏中输入分子的 MIC90 和 MIC90 中潜在增效剂的产生。</li>
		<li>寻找所有能抑制90% 增长的水井。</li>
		<li>计算 FICI90 的井, 这是最低以下 (协同) 或最高以上 (对抗性) 两个 MIC90 值, 使用等式: <img alt="Equation 1" src="/files/ftp_upload/57241/57241eq1.jpg">
</li>
		<li>考虑任何五指≤0.5 作为协同作用和五指≥4作为对抗性 (图 2)。 注: 这也可以做与 MIC50 (50% 抑制增长) 得到一个 FICI50 基于这些麦克风的价值。</li>
	</ol></li>
	<li>极乐独立化验 注: 极乐独立是运行与任何潜在的增效剂, 没有麦克风本身。<ol>
<li>像以前一样, 在 M9 介质中, 在37摄氏度的环境中生长出一夜之间的大肠杆菌文化。 注意: 再一次, 在这个步骤中可以使用适合其他感兴趣有机体的适当生长条件。</li>
		<li>准备96井板, 用于测试潜在的协同分子、输入分子、组合和未处理的控制。</li>
		<li>通过增加50µL 的 M9 介质, 创造潜在的增效剂板块。添加50µL 的介质, 其中包含集浓度 (10 µM 或100µM) 的潜在增效剂的每一个井。每板测试8个分子。</li>
		<li>通过将50µL 的介质添加到每个井中, 创建输入分子板。在1栏中添加50µL 包含输入药物的4x 麦克风。沿 x 轴创建渐变稀释, 类似于 MIC 板, 从1列中取50µL, 在2列中分散和混合。继续此过程直到12栏, 从12列中处理50µL。增加一个额外的50µL 的介质, 每一个井, 所以每个井的结束总数是100µL。</li>
		<li>使用96井板创建组合板, 每井添加50µL 介质。在1栏中添加50µL 包含输入药物的4x 麦克风。沿 x 轴稀释类似于 MIC 板, 从1列中取50µL, 在2列中分散和混合。继续这整个方式到专栏 12, 处理50µL 从专栏12。添加50µL 的介质, 其中含有10µM 或100µM 的潜在增效剂。同样, 每排应该有一个浓度或药物。</li>
		<li>通过将100µL M9 介质添加到板的所有井中, 创建未处理的控制。</li>
		<li>通过添加2µL 的细菌培养, 将所有的盘子接种到0.002 或1000细胞的 OD600 , 并与以前一样。</li>
		<li>在0和 24 h 测量每个井的 OD600 。</li>
	</ol></li>
	<li>计算极乐独立评分
	<ol>
<li>通过使用电子表格程序, 从 24 h 的增长中减去0小时的 OD600 , 计算板中每个井的净增长。</li>
		<li>将油井正常化为电子表格程序中没有药物板块的平均生长。</li>
		<li>使用电子表格软件, 通过减去1的净增长来计算抑制。</li>
		<li>平均控制板中仅包含电子表格软件中的输入渐变的列。</li>
		<li>用等式计算每个井的极乐分数: 极乐评分= (井 x + 输入列 x) -组合井 x
</li>
		<li>在输入的 MIC90 下面寻找井下的负分数。 注意: 跨多个浓度的负分数将表明协同作用。</li>
	</ol></li>
</ol>4. 具有协同预测突变体的高通量屏幕, 以识别新的协同对
<ol><li>
识别协同预测突变体 注: 步骤1.5 确定了假定的协同预测突变体。在这里, 我们确定哪些突变体将在高通量屏幕中发挥最佳作用, 用于协同用药。我们在 Bioscreen 机上进行了这种检测, 但是96台平板阅读器也应该是可以接受的。<ol>
<li>在 M9 极小的培养基中生长每个假定的协同预测突变体和野生型细胞。</li>
		<li>建立100井蜂窝板 (或96井板) 与适当的生长培养基, 生长培养基 + 输入药物, 生长培养基 + 已知的协同合作伙伴的输入药物, 和生长培养基 + 已知的非协同用药。确保包括车辆控制。 注: 我们建议每种应变/药物/浓度的四个复制井。</li>
		<li>每井接种1000个细胞, 包括空白控制井。</li>
		<li>增长48小时在37°c, 与震动, 并且读 OD600每20分钟。</li>
		<li>确定药物浓度和时间点, 表明在输入药物及其已知的协同伙伴存在下, 野生型和协同预测突变细胞之间最大的生长差异。在最佳的时间点和浓度, 野生类型和协同预测突变细胞之间不应该有太大的增长差异, 在已知的药物存在不与输入药物协同作用 (图 3)。</li>
	</ol>
</li>
	<li>
用于协同用药的高通量屏幕
	<ol>
<li>在 M9 最小介质中生长野生型细胞和协同预测突变细胞。 注: 为感兴趣的生物体使用适当的生长培养基。</li>
		<li>将药物从库中添加到介质中, 每一个井中都含有100µL 介质, 在设定浓度上有小分子, 在上一步中确定。包括空白井 (无单元格) 和与车辆控制 (e. g, 亚砜) 的水井, 以说明药物稀释车的任何生长抑制。<ol>
<li>每板至少准备四辆车辆控制井, 理想情况下分散在板上, 以说明井位效应。如果检测是可变的, 那么每行/列至少包含一个车辆控制井, 并使用每行/列控件以及控件来计算每行/列的 Z 分数, 而不是整个板块 (步骤 4.2.5)。</li>
		</ol>
</li>
		<li>将2µL 的文化添加到以前的井中。(一盘为野生类型细菌和一个板材为预测的协同突变细菌)。</li>
		<li>评估大肠杆菌或 48 h 用于C. 隐球菌的 OD600为0和 18 h。</li>
		<li>使用电子表格软件, 通过从车辆控制井中减去空白井的 OD600计算净增长。识别与 z 评分为-2.5 的井 (z 分数 = 平均 2.5 * 标准偏差)。这些井含有一个潜在的增效剂小分子。</li>
		<li>使用步骤3中概述的方法验证屏幕命中。</li>
	</ol>
</li>
</ol>

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A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+discovery+of+multicomponent+therapeutics.">Systematic discovery of multicomponent therapeutics.</a> Proc Natl Acad Sci USA. 100 (13), 7977-7982 (2003).</li><li>Tang, J., Wennerberg, K., Aittokallio, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+is+synergy?+The+Saariselk&#228;+agreement+revisited.">What is synergy? The Saariselk&#228; agreement revisited.</a> Front Pharmacol. 6, 181 (2015).</li><li>Hsieh, M. H., Yu, C. M., Yu, V. L., Chow, J. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synergy+assessed+by+checkerboard.+A+critical+analysis.">Synergy assessed by checkerboard. A critical analysis.</a> Diagn Microbiol Infect Dis. 16 (4), 343-349 (1993).</li><li>Nichols, R. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phenotypic+Landscape+of+a+Bacterial+Cell.">Phenotypic Landscape of a Bacterial Cell.</a> Cell. 144 (1), 143-156 (2011).</li><li>Chandrasekaran, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemogenomics+and+orthology-based+design+of+antibiotic+combination+therapies.">Chemogenomics and orthology-based design of antibiotic combination therapies.</a> Mol Syst Biol. 12 (5), (2016).</li><li>Pradhan, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Chemogenomic+profiling+of+Plasmodium+falciparum+as+a+tool+to+aid+antimalarial+drug+discovery.">Chemogenomic profiling of Plasmodium falciparum as a tool to aid antimalarial drug discovery.</a> Sci Rep. 5, 15930 (2015).</li><li>Pethe, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+chemical+genetic+screen+in+Mycobacterium+tuberculosis+identifies+carbon-source-dependent+growth+inhibitors+devoid+of+in+vivo+efficacy.">A chemical genetic screen in Mycobacterium tuberculosis identifies carbon-source-dependent growth inhibitors devoid of in vivo efficacy.</a> Nat Commun. 1 (5), 1-8 (2010).</li><li>Diezmann, S., Michaut, M., Shapiro, R. S., Bader, G. D., Cowen, L. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mapping+the+Hsp90+Genetic+Interaction+Network+in+Candida+albicans+Reveals+Environmental+Contingency+and+Rewired+Circuitry.">Mapping the Hsp90 Genetic Interaction Network in Candida albicans Reveals Environmental Contingency and Rewired Circuitry.</a> PLoS Genetics. 8 (3), e1002562 (2012).</li><li>Robbins, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+Antifungal+Combination+Matrix+Identifies+a+Rich+Pool+of+Adjuvant+Molecules+that+Enhance+Drug+Activity+Against+Diverse+Fungal+Pathogens.">An Antifungal Combination Matrix Identifies a Rich Pool of Adjuvant Molecules that Enhance Drug Activity Against Diverse Fungal Pathogens.</a> Cell reports. 13 (7), 1481-1492 (2015).</li><li>Wildenhain, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+Synergism+from+Chemical-Genetic+Interactions+by+Machine+Learning.">Prediction of Synergism from Chemical-Genetic Interactions by Machine Learning.</a> Cell Syst. 1 (6), 383-395 (2015).</li><li>Spitzer, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cross-species+discovery+of+syncretic+drug+combinations+that+potentiate+the+antifungal+fluconazole.">Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole.</a> Mol Syst Biol. 7, 499-499 (2011).</li></ol>

作者没有什么可透露的。

协同小分子对是治疗微生物感染的有力工具, 但由于协同对识别的挑战, 它们尚未达到充分的临床潜力。本文介绍了一种比简单配对组合更快地识别协同副的方法。通过使用化学遗传学数据集, O2M 识别突变体的基因敲除, 然后可以用来作为一个读数, 以筛选大型图书馆的小分子, 以预测协同对。预测小分子的能力允许屏幕的高可伸缩性, 这反过来又使得对协同合作伙伴的大规模识别。在确定协同对的?...

根据 CDC1, 耐药性细菌每年在美国造成200万多人感染和2.3万例死亡。需要新的治疗方法来克服这些感染。确定这些新治疗方法的策略包括开发新的抗菌药物或为其他条件批准的小分子重新调整用途, 以治疗微生物感染2,3,4。然而, 新药的发现是非常昂贵和耗时的。重新用途药物可能不确定新的药物或药物目标5,6。我们的实验室专注于第三种称为协同组合疗法的策略。当两个小分子在一起的功效大于其个体功效7的附加效果时, 协同组合就会发生。此外, 协同组合可以有效对抗抗性的病原体中的一个小分子, 除了有较少的不必要的离靶效果, 使他们很大的潜力8,9, 10。
协同对是罕见的, 发生在大约4-10% 的药物组合中11,12,13。因此, 传统的技术, 如配对屏幕是挑战和耗时, 数以千计的潜在组合, 从一个小图书馆的100分子。此外, 协同作用通常不能从化合物的活动中预测14。但是, 作者开发了一种高吞吐量的方法来筛选协同对, 称为重叠2方法 (O2M)12。这里描述的这种方法允许更快、更有效地识别这些协同对。O2M 需要使用已知的协同对和化学遗传学数据集。化学遗传学数据集是在许多不同的小分子存在的情况下产生的。如果一个已知的协同配对中的一个分子诱导相同的表型从一个特定的淘汰赛突变体作为第二个协同分子, 任何其他小分子, 诱发表型从同一变种人也应该协同与每个成员的已知协同对。这一基本原理已用于布朗实验室, 以确定协同抗菌对大肠杆菌(大肠杆菌)和协同抗真菌药物对致病性真菌隐球菌(C。隐球菌)11,12。O2M 不仅能适应各种病原体, 而且可以方便快速地筛选大型分子库, 以识别协同对。通过 O2M 识别的基因突变体进行筛选, 我们可以只验证那些预测协同作用的小分子。因此, 测试一个2000分子的图书馆配对将需要几个月, 而如果只有20分子在该库预测协同, 现在的协同测试需要几天时间。O2M 不需要编程技能, 在大多数实验室或核心设施中都可以使用所需的设备。除了对药物组合感兴趣的研究人员之外, O2M 分析对于所有已经完成了药物筛选并希望通过确定重要的药物-药物相互作用来扩大其命中范围的人感兴趣。下面是识别细菌中的协同小分子的协议, 以及验证已知化验中预测的协同作用15,16。

<table><tbody><tr><td>Bioscreen C</td><td>instrument</td><td>Growth Curves USA</td><td></td></tr><tr><td>Synergy H1</td><td>instrument</td><td>BioTek</td><td></td></tr><tr><td>M9 broth</td><td>reagent</td><td>Amresco</td><td>J863-500G</td></tr><tr><td>Casamino Acids</td><td>reagent</td><td>Fisher Scientific</td><td>BP1424-500</td></tr><tr><td>Glucose</td><td>reagent</td><td>Sigma</td><td>G7021-10KG</td></tr><tr><td>Nicotinic Acid</td><td>reagent</td><td>Alfa Aesar</td><td>A12683</td></tr><tr><td>Thiamine</td><td>reagent</td><td>Acros Organics</td><td>148991000</td></tr><tr><td>CaCl&lt;sub&gt;2 &lt;/sub&gt;Dihydrate</td><td>reagent</td><td>Fisher</td><td>C79-500</td></tr><tr><td>MgSO&lt;sub&gt;4 &lt;/sub&gt;Heptahydrate</td><td>reagent</td><td>Fisher</td><td>M63-500</td></tr><tr><td>chemical-genetics dataset</td><td>dataset</td><td>examples include Nichols et al., Cell, 2011, Brown et al, Cell, 2014, and others cited in the text.</td><td></td></tr><tr><td>trimethoprim (example input drug; any can be used)</td><td>reagent</td><td>Fisher Scientific</td><td>ICN19552701</td></tr><tr><td>sulfamethoxazole (example test drug; any can be used)</td><td>reagent</td><td>Fisher Scientific</td><td>ICN15671125</td></tr></tbody></table>

high-throughput identification of synergistic drug combinations by the overlap2 method

虽然抗菌药物大大提高了 20th世纪的寿命和生活质量, 但抗菌素耐药性威胁着我们整个社会治疗全身性感染的能力。仅在美国, 耐药性感染每年会杀死大约2.3万人, 额外的医疗费用约为200亿美元。一种对抗抗菌素耐药性的方法是组合疗法, 在感染性机体及其耐药性剖面确定之前, 这在早期的传染性疾病中特别有用。许多抗菌治疗使用组合疗法。然而, 这些组合的大部分是添加剂, 这意味着联合功效是相同的总和的个别抗生素功效。一些组合疗法是协同作用的: 联合功效远远大于添加剂。协同组合特别有用, 因为它们可以抑制抗菌耐药性菌株的生长。然而, 这些组合是罕见的和难以辨认。这是由于需要以配对的方式测试的分子数量太多: 1000 个分子的库有100万种潜在的组合。因此, 已作出努力预测分子的协同作用。本文介绍了用于预测协同小分子对 (称为重叠2方法 (O2M)) 的高通量方法。O2M 使用化学遗传数据集的模式来识别对协同对中的每个分子都敏感的突变体, 而不是其他分子。布朗实验室通过为抑制突变体而非野型细胞的生长而对分子进行高通量筛查来利用这种生长差异。实验室的工作以前确定的分子, 协同与抗生素甲氧苄啶和抗真菌药物氟康唑使用此策略。在此, 作者提出了一种新的协同组合的筛选方法, 可以改变多种微生物。

棋盘检测是一种测量协同相互作用的半定量方法。最后的分数输出, 五指, 确定药物组合是否被认为是协同的 (五指≤0.5), 不相互作用 (0.5 < 五指 < 4), 或拮抗 (五指≥4.0)。图 1阐释了如何在棋盘分析中设置药物渐变。图 2说明了常见的结果。考虑生长不到90% 的生长抑制 (紫色井)。在测量每一个盘子中每个井的 OD600后, 我们?...

Watch this Scientific Journal Video about High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method at JoVE.com

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

协同药物组合是困难和耗时的鉴定经验。在这里, 我们描述了一种识别和验证协同小分子的方法。

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 ...

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Research

JoVE Journal

Genetics

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

11.6K 次观看.  University of Utah. 协同药物组合是困难和耗时的鉴定经验。在这里, 我们描述了一种识别和验证协同小分子的方法。

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

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 ...

科研

癌症研究

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 ...

A Rapid Screening Workflow to Identify Potential Combination Therapy for GBM using Patient-Derived Glioma Stem Cells

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in glioblastoma (GBM), the most prevalent and devastating primary brain tumor. The presence of GSCs makes the GBM very refractory to most of individual targeted agents, so high-throughput screening methods are required to identify potential effective combination therapeutics. The protocol describes a simple workflow to enable rapid screening for potential combination therapy with synergistic interaction. The general steps of this workflow consist of establishing luciferase-tagged GSCs, preparing matrigel coated plates, combination drug screening, analyzing, and validating the results.

快速筛选工作流程，利用患者衍生胶质瘤干细胞确定GBM的潜在组合疗法

The glioma stem cells (GSCs) are a small fraction of cancer cells which play essential roles in tumor initiation, angiogenesis, and drug resistance in ...

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

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created "RE:fine Drugs," a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the "RE:fine Drugs" system and explores the different advanced options through a case study.

Here we describe a protocol using the web-based drug repurposing hypothesis generation tool: "RE:fine Drugs." This protocol can be modified to a user's preferences at the level of the query type (gene, drug or disease) and/or the range of available advanced options.

药品再利用产生假设使用"RE:精药品"系统

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The ...

医学

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 ...

Identification of Kinase-substrate Pairs Using High Throughput Screening

We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel signal transduction pathways. Our approach features the use of a library of purified GST-tagged human protein kinases and a recombinant protein substrate of interest. We have used this technology to identify MAP/microtubule affinity-regulating kinase 2 (MARK2) as the kinase for a glucose-regulated site on CREB-Regulated Transcriptional Coactivator 2 (CRTC2), a protein required for beta cell proliferation, as well as the Axl family of tyrosine kinases as regulators of cell metastasis by phosphorylation of the adaptor protein ELMO. We describe this technology and discuss how it can help to establish a comprehensive map of how cells respond to environmental stimuli.

Protein phosphorylation is a central feature of how cells interpret and respond to information in their extracellular milieu. Here, we present a high throughput screening protocol using kinases purified from mammalian cells to rapidly identify kinases that phosphorylate a substrate(s) of interest.

激酶底物对鉴定使用高通量筛选


We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel ...

生物学

Diagonal Method to Measure Synergy Among Any Number of Drugs

A synergistic drug combination has a higher efficacy compared to the effects of individual drugs. Checkerboard assays, where drugs are combined in many doses, allow sensitive measurement of drug interactions. However, these assays are costly and do not scale well for measuring interaction among many drugs. Several recent studies have reported drug interaction measurements using a diagonal sampling of the traditional checkerboard assay. This alternative methodology greatly decreases the cost of drug interaction experiments and allows interaction measurement for combinations with many drugs. Here, we describe a protocol to measure the three pairwise interactions and one three-way interaction among three antibiotics in duplicate, in five days, using only three 96-well microplates and standard laboratory equipment. We present representative results showing that the three-antibiotic combination of Levofloxacin + Nalidixic Acid + Penicillin G is synergistic. Our protocol scales up to measure interactions among many drugs and in other biological contexts, allowing for efficient screens for multi-drug synergies against pathogens and tumors.

In this protocol, we describe how to make Loewe additivity-based drug interaction measurements for pairwise and three-way drug combinations.

测量任意数量药物协同作用的对角方法

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

通过重叠²方法高通量识别协同药物组合

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此视频中的章节

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

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

快速筛选工作流程，利用患者衍生胶质瘤干细胞确定GBM的潜在组合疗法

胶质瘤干细胞系中的球状体药物敏感性筛选

胶质瘤干细胞系中的球状体药物敏感性筛选

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

药品再利用产生假设使用"RE:精药品"系统

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

激酶底物对鉴定使用高通量筛选

测量任意数量药物协同作用的对角方法