Name: high-throughput identification of synergistic drug combinations by the overlap2 method
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Description: Watch this Scientific Journal Video about High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method at JoVE.com

This work was supported by a startup grant from the Department of Pathology, University of Utah to J.C.S.B.

1. Identifying Synergy Prediction Mutants from Chemical-genetics Dataset by the Overlap2 Method (O2M)
NOTE: This is the method for identifying synergy prediction mutants using the published dataset from Nichols et al.17 in E. coli. However, this can be done on any chemical-genetics dataset and microorganism. These data sets contain a library of knockout mutants grown in the presence of more than 100 small molecules, giving a quantitative growth score for each mutant in each small molecule. One synergistic pair must be known, and there should be growth scores for both small molecules included in the dataset.
<ol>
	<li>Calculate the mean growth score and standard deviation for all mutants grown in the presence of a single concentration of each small molecule. 
	NOTE: This can be done using any spreadsheet software. The Brown lab uses Excel and the functions =AVERAGE(B3:B3981) and =STDEV(B3:B3981) for the calculations of each column of small molecule.
	<ol>
		<li>Calculate the means and standard deviations from each column of small molecule, though this orientation may differ if using a different dataset.</li>
	</ol>
	</li>
	<li>Calculate Z scores for each concentration of small molecule using the same dataset from Nichols et al.17 in a spreadsheet program. For this, negative z scores will be Z(2.5) = mean -2.5*standard deviation, and positive z scores will be Z(2.5) = mean + 2.5*standard deviation.</li>
	<li>Determine which gene mutants contain significant Z scores in the small molecules that make up the known synergistic pair, being sure to look at all concentrations of those molecules. If the growth score of a mutant is less than the negative Z (2.5) score or is greater than the positive Z (2.5) score for that small molecule, it is considered significant.</li>
	<li>Determine if any of the significant gene mutants belong to an operon. 
	NOTE: For E. coli, the authors recommend checking &#34;Ecoli wiki&#34; for information regarding if genes are transcribed as part of a polycistronic RNA.</li>
	<li>Identify gene/operon mutants that are common to the majority of the small molecules of interest at different concentrations (e.g., when looking for molecules that synergize with the antibiotic trimethoprim, identify mutants that show a significant Z score when grown in the presence of trimethoprim and its known synergistic partners such as sulfamethizole or sulfamethoxazole). These are putative synergy prediction mutants.</li>
</ol>
2. Predicting Synergizers within the Chemical-genetics Dataset by O2M
<ol>
	<li>Returning to the dataset17, identify each concentration of small molecule that elicits a significant growth score from the synergy prediction mutant. This is done by looking at the Z scores calculated for each concentration of small molecules. Again, a significant growth score is less than the negative Z score or greater than the positive Z score for that concentration of small molecule. 
	NOTE: If a molecule appears at even one of its concentrations, the molecule is considered a predicted synergizer. Any molecule that does not elicit a significant growth score is a predicted non-synergizer.</li>
</ol>
3. Validation of Predicted Synergistic Interactions
<ol>
	<li>Finding minimal inhibitory concentration (MIC) of predicted synergizers.
	<ol>
		<li>Grow an overnight culture of E. coli in M9 minimal media at 37 &#176;C. 
		NOTE: Any overnight culture can be grown in appropriately defined medium for the organism of interest. The Brown lab does not recommend rich media like LB or YPD. M9 minimal media consists of 10.5 g/L M9 broth, 0.2% casamino acids, 0.1 M CaCl2, 0.4% glucose, 1 M MgSO4, 0.25% nicotinic acid, and 0.33% thiamine in H2O.</li>
		<li>If no MIC data for pathogen of interest is found in the literature upon receiving the small molecules, dissolve in dimethyl sulfoxide (DMSO) at a high concentration (&#62;10 mg/mL).</li>
		<li>Using a 96-well plate, add 100 &#181;L of M9 media to each well. Add an extra 100 &#181;L of M9 media in column 1 so it contains a total of 200 &#181;L.</li>
		<li>Add an arbitrary amount (~10 &#181;L) of concentrated small molecule solution in column 1. One small molecule per well of column 1 (8 small molecules per plate). 
		NOTE: These are the small molecules of interest, that are predicted to synergize. The Brown lab generally adds 10 &#181;L of highly concentrated small molecule solution, which is below the amount of 100% DMSO that can inhibit E. coli.</li>
		<li>Once the small molecules have been added, dilute the small molecules across the x-axis of the plate. Take 100 &#181;L of solution from column 1, place in column 2 and mix. Take 100 &#181;L from column 2, place in column 3 and mix. Continue this process until 100 &#181;L are placed in column 11. Mix column 11, then take 100 &#181;L from that column and discard. Leave column 12 with just media to normalize the data.</li>
		<li>Inoculate the plate. Obtain the optical density (OD600) from an overnight culture. Prepare a solution with the culture and M9 media to an OD600 of 0.002 (or appropriate concentration that represents 500 cells/&#181;L for the desired organism of interest). Pipette 2 &#181;L of culture solution into each well of the plate so there are 1,000 cells per well.</li>
		<li>Gently shake the plate and obtain the OD600 for the 0 h reading. Let the plate grow at 37 &#176;C for 24 h. Obtain the OD600 for the 24 h reading. 
		NOTE: Use appropriate growing conditions for other organisms of interest.</li>
		<li>Calculate the net growth for each well using a spreadsheet program.</li>
		<li>Average the net growth of column 12 to obtain the average no drug growth. Normalize the other wells to the average of no drug growth using a spreadsheet software. Look for any well with less than 10% growth to identify the MIC90. Any well with less than 50% growth indicates the MIC50. Calculate the concentration of small molecule in those wells.</li>
	</ol>
	</li>
	<li>Checkerboard Assays for synergistic interactions
	<ol>
		<li>Grow an overnight culture of E. coli in M9 minimal media as before. 
		NOTE: Use appropriate growing conditions for other organisms.</li>
		<li>Add 100 &#181;L of M9 media to each well of a 96 well plate and an extra 100 &#181;L in column 1.</li>
		<li>Add the test small molecule (at ~8x MIC) to each well in column 1 and add double the concentration (~16x MIC) to well A1 (one small molecule tested per plate). 
		NOTE: This amount is determined from the MIC test completed prior and differs for each potential synergizer.</li>
		<li>Create a gradient dilution on the x-axis by taking 100 &#181;L from column 1 and transferring to column 2. Continue this process until 100 &#181;L is added to column 11. Mix, then take 100 &#181;L from column 11 and dispose. Do not add any drug to column 12 (Figure 1A).</li>
		<li>Set up the second drug gradient across the y-axis for the input drug. Add 100 &#181;L of media containing 2x MIC of the input molecule in row A. Mix and dilute as before, taking 100 &#181;L from row A and placing in B. Continue until 100 &#181;L is placed in row G. Mix, take 100 &#181;L and dispose, ensuring not to add any input drug in row H. This allows row H and column 12 to contain individual MICs of the drug pair tested (Figure 1B).</li>
		<li>Inoculate the plate. Add 2 &#181;L of a E. coli culture of OD600 0.002 to each well (1,000 cells per well).</li>
		<li>Measure the OD600 of each well in the plate for the 0 h reading.</li>
		<li>Incubate the plate for 24 h at 37 &#176;C.</li>
		<li>Measure the OD600 of each well at 24 h.</li>
	</ol>
	</li>
	<li>Calculating the fractional inhibitory concentration index (FICI) from Checkerboards
	<ol>
		<li>Calculate the net growth for each well of the plate by subtracting the OD600 at 0 h from the OD600 at 24 h in a spreadsheet program.</li>
		<li>Normalize the growth in the software by dividing each well by well H12, which contains no small molecule.</li>
		<li>Find the MIC90 of the input molecule in column 12 and the MIC90 of the potential synergist in row H.</li>
		<li>Look for all wells that inhibit 90% of growth.</li>
		<li>Calculate the FICI90 for the well that is either the lowest below (synergistic) or highest above (antagonistic) both MIC90 values, using the equation: 
		<img alt="Equation 1" src="/files/ftp_upload/57241/57241eq1.jpg" /></li>
		<li>Consider any FICI &#8804; 0.5 as synergistic and FICI &#8805; 4 as antagonistic (Figure 2). 
		NOTE: This can also be done with MIC50 (50% inhibition of growth) to get an FICI50 based on those MIC values.</li>
	</ol>
	</li>
	<li>Bliss Independence Assay 
	NOTE: Bliss Independence is run with any potential synergist that did not have an MIC on its own.
	<ol>
		<li>Grow an overnight culture of E. coli in M9 media at 37 &#176;C, as before. 
		NOTE: Again, appropriate growing conditions for other organisms of interest can be used in this step.</li>
		<li>Prepare 96-well plates for testing the potential synergistic molecule, the input molecule, the combination, and a non-treated control.</li>
		<li>Create the potential synergist plate by adding 50 &#181;L of M9 media to each well. Add 50 &#181;L of media containing a set concentration (10 &#181;M or 100 &#181;M) of the potential synergist to each well of a row. Test 8 molecules per plate.</li>
		<li>Create the input molecule plate by adding 50 &#181;L of media to each well. Add 50 &#181;L containing 4x MIC of the input drug to each well in column 1. Create a gradient dilution along the x-axis similar to MIC plates, taking 50 &#181;L from column 1, dispersing and mixing in column 2. Continue this process until column 12, disposing 50 &#181;L from column 12. Add an additional 50 &#181;L of media to each well so the end total for each well is 100 &#181;L.</li>
		<li>Creating the combination plate by using a 96 well plate, add 50 &#181;L of media to each well. Add 50 &#181;L containing 4x MIC of the input drug to each well in column 1. Dilute along the x-axis similar to MIC plates, taking 50 &#181;L from column 1, dispersing and mixing in column 2. Continue this the whole way to column 12, disposing 50 &#181;L from column 12. Add 50 &#181;L of media containing the potential synergist at 10 &#181;M or 100 &#181;M to each well of a row. Again, there should be one concentration or drug per row.</li>
		<li>Create the non-treated control by adding 100 &#181;L of M9 media to all wells of the plate.</li>
		<li>Inoculate all plates by adding 2 &#181;L of bacterial culture with an OD600 of 0.002 or 1,000 cells to each well as before.</li>
		<li>Measure the OD600 of each well at 0 and 24 h.</li>
	</ol>
	</li>
	<li>Calculating the Bliss Independence Score
	<ol>
		<li>Calculate the net growth for each well of the plate by subtracting the OD600 at 0 h from the growth at 24 h using a spreadsheet program.</li>
		<li>Normalize the wells to the average growth from the no drug plate in a spreadsheet program.</li>
		<li>Using spreadsheet software, calculate the inhibition by subtracting the net growth from 1.</li>
		<li>Average the columns in the control plate containing only the input gradient in the spreadsheet software.</li>
		<li>Calculate the bliss score for each well by the equation: 
		Bliss Score = (well X + input column X) &#8211; Combined well X</li>
		<li>Look for negative scores in wells below the MIC90 of the input. 
		NOTE: Negative scores across multiple concentrations would indicate a synergistic interaction.</li>
	</ol>
	</li>
</ol>
4. High-throughput Screen with Synergy Prediction Mutants to Identify Novel Synergistic Pairs
<ol>
	<li>Identify synergy prediction mutants 
	NOTE: Step 1.5 identified putative synergy prediction mutants. Here, we determine which of these mutants will function best in a high-throughput screen for synergistic drugs. We performed this assay in a Bioscreen machine, but 96 well plate readers should also be acceptable.
	<ol>
		<li>Grow each putative synergy prediction mutants and wild-type cells in M9 minimal media.</li>
		<li>Set up the 100 well honeycomb plate (or 96 well plate) with the appropriate growth medium, growth medium + input drug, growth medium + known synergistic partner of the input drug, and growth medium + known non-synergistic drug. Ensure to include vehicle controls. 
		NOTE: We recommend four replicate wells for each strain/drug/concentration.</li>
		<li>Inoculate 1,000 cells per well, including blank control wells.</li>
		<li>Grow 48 h at 37 &#176;C, with shaking, and read OD600 every 20 min.</li>
		<li>Determine drug concentration and time point that shows the largest growth difference between wild-type and synergy prediction mutant cells in the presence of the input drug and its known synergistic partners. At the optimal time point and concentration, there should not be much growth difference between wild-type and synergy prediction mutant cells when grown in the presence of drugs known to not act synergistically with the input drug (Figure 3).</li>
	</ol>
	</li>
	<li>High-throughput screen for synergistic drugs
	<ol>
		<li>Grow wild-type cells and synergy prediction mutant cells in M9 minimal media. 
		NOTE: Use appropriate growth medium for organism of interest.</li>
		<li>Add drugs from the library to media so each well which contains 100 &#181;L media with small molecules at set concentration which was determined in the previous step. Include blank wells (without cells) and wells with vehicle controls (e.g., DMSO) to account for any growth inhibition by the drug dilution vehicle.
		<ol>
			<li>Prepare at least four vehicle control wells per plate, ideally scattered across the plate to account for well position effects. If assays are variable, then include at least one vehicle control well per row/column and use each row/column control well as the control to calculate Z scores for each row/column instead of for an entire plate (step 4.2.5).</li>
		</ol>
		</li>
		<li>Add 2 &#181;L of culture to each well as before. (One plate for wild type bacteria and one plate for predicted synergy mutant bacteria).</li>
		<li>Assess the OD600 at 0 and 18 h for E. coli or 48 h for C. neoformans.</li>
		<li>Using spreadsheet software, calculate the net growth by subtracting the OD600 of blank wells from the vehicle control wells. Identify wells with a Z-score of -2.5 (Z score = mean - 2.5*standard deviation). These wells contain a potential synergist small molecule.</li>
		<li>Validate screen hits using the methods outlined in step 3.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

Synergistic small molecule pairs can be a powerful tool in treating microbial infections, yet they have not reached their full clinical potential because synergistic pairs are challenging to identify. This paper describes a method for identifying synergistic pairs much faster than simple pairwise combinations. By using chemical-genetics datasets, O2M identifies mutants with gene knockouts that can then be used as a readout to screen large libraries of small molecules in order to predict synergistic pairs. The ability to predict small molecules allows for the high scalability of screens, which in turn makes for largescale identification of synergistic partners. After identifying synergistic pairs, one can elucidate the molecular mechanism underlying synergistic interactions, then rationally design additional synergistic pairs11.
O2M requires a chemical-genetic dataset and a known synergistic pair. Fortunately, chemical-genetics datasets are common for a variety of microbes19,20,21. The Brown lab previously demonstrated that O2M can successfully find synergistic pairs in both C. neoformans and E. coli11,12. This demonstrates that the broad impact O2M has as a scalable method that is broadly applicable to a variety of organisms. All steps listed here can be altered for the growth of a different microorganism with relatively similar results, thus making O2M a valuable tool for general identification of synergistic pairs. Several other groups are identifying synergistic combinations from chemical-genetics datasets by different analytical methods, although O2M requires the least programming knowledge of any of these methods. Each method identifies different sets of synergistic antibiotics or antifungals 18,22,23,24, suggesting that a large number of synergistic pairs remain to be discovered. O2M and other synergy prediction methods are also potentially applicable to mammalian systems, including identifying cancer drug combinations.
In sum, this method describes a fast way to screen for synergistic pairs from a chemical-genetics dataset. This method and others help synergistic pairs become a more feasible treatment option in clinics. Additionally, O2M&#39;s fast and generalized method proves it a valuable tool when seeking out synergistic small molecule pairs.

Antibiotic-resistant bacteria cause more than 2 million infections and 23,000 deaths annually in the United States according to the CDC1. New treatments are needed to overcome these infections. Strategies to identify these new treatments include the development of new antimicrobial drugs or the repurposing of small molecules approved for other conditions to treat microbial infections2,3,4. However, new drug discovery is very costly and time-consuming. Repurposing drugs may not identify novel drugs or drug targets5,6. Our lab focuses on a third strategy known as synergistic combination therapies. Synergistic combinations occur when two small molecules together have an efficacy greater than the additive effect of their individual efficacies7. Additionally, synergistic combinations can be effective against a pathogen resistant to one of the small molecules in the pair in addition to having less unwanted off-target effects, rendering them great potential8,9,10.
Synergistic pairs are rare, occurring in approximately 4-10% of drug combinations11,12,13. Thus, traditional techniques such as pairwise screens are challenging and time-consuming, with thousands of potential combinations from a small library of a hundred molecules. Moreover, synergistic interactions usually cannot be predicted from the activity of the compounds14. However, the authors developed a high-throughput approach to screen for synergistic pairs, called the Overlap2 Method (O2M)12. This method, described here, allows for faster, more efficient identification of these synergistic pairs. O2M requires the use of a known synergistic pair and a chemical-genetics dataset. Chemical-genetics datasets are generated when a library of knockout mutants is grown in the presence of many different small molecules. If one molecule in a known synergistic pair induces the same phenotype from a particular knockout mutant as the second synergistic molecule, any other small molecule that elicits the phenotype from that same mutant should also synergize with each member of the known synergistic pair. This rationale has been used in the Brown lab to identify synergistic antibiotic pairs active against Escherichia coli (E. coli) and synergistic antifungal drug pairs active against the pathogenic fungus Cryptococcus neoformans (C. neoformans)11,12. O2M is not only adaptable for various pathogens, but allows for the screening of large libraries of molecules to identify synergistic pairs easily and rapidly. Screening with the genetic mutant identified by O2M allows us to validate only those small molecules predicted for synergy. Thus, testing a 2,000-molecule library pairwise would take months, whereas if there were only 20 molecules in that library predicted to synergize, testing for synergy now takes a matter of days. O2M does not require programming skills, and the required equipment is available in most labs or core facilities. In addition to researchers interested in drug combinations, O2M analysis is of interest to anyone who has completed a drug screen and wants to expand their hits by identifying important drug-drug interactions. Below is the protocol for identifying synergistic small molecules in bacteria, as well as validating the predicted synergistic interactions in well-known assays15,16.

<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

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.

Checkerboard assays are a semi-quantitative method for measuring synergistic interactions. The final score output, FICI, determines if a drug combination is considered synergistic (FICI &#8804;0.5), non-interacting (0.5 &#60;FICI &#60;4), or antagonistic (FICI &#8805;4.0). Figure 1 illustrates how to set up the drug gradients in a checkerboard assay. Figure 2 illustrates common outcomes. Consider growth (purple wells) which display less than 90% growth inhibition. After measuring the OD600 of each well in a plate, we normalize everything to the no drug control well. Anything with a normalized value &#62;0.1 (10% of the OD600 of the control well) is scored as &#34;growth&#34;. FICI scores can vary depending on which well is picked; we select the well that gives the lowest FICI for each plate. In Figure 2A, that would be well F9 (columns are numbered, rows lettered), with an FICI = 0.07. In Figure 2B, the FICI is calculated from well C3 (FICI = 1.0). For antagonistic interactions, look for the highest FICI score possible. In Figure 2C, the FICI is calculated from well A1, which give an FICI of 8.0.
Optimal screening conditions will prevent a high number of false positives from the screen, which saves time and money. When we optimized the synergy prediction mutants for the fungus Cryptococcus neoformans, we tested the input drug (fluconazole), four known non-synergistic drugs (caffeine, climbazole, trimethoprim, and brefeldin A), and five of fluconazole&#39;s synergistic partners (rifamycin, myriocin, nigericin, rapamycin, and FK506, all discussed in Chandrasekaran, S. et al.18). Since we discovered these molecules through O2M analysis (sections 1 and 2), we expected the known synergizers to selectively inhibit the growth of synergy prediction mutant cells but not wild-type cells. To maximize the expected growth difference, we tested a range of concentrations for each small molecule, most of which were sub-inhibitory.
Example results are shown in Figure 3. A molecule that does not act synergistically with fluconazole, brefeldin A, inhibited wild-type and synergy prediction mutant growth only slightly, and approximately the same amount. Rifamycin (Figure 3B), the growth of the synergy prediction mutant (cnag_03917&#916;) was inhibited, but wild-type cell growth was not. The greatest growth difference was between 32 and 49 h post-inoculation, so the timepoint for screening was in this range. Growth curves of the other synergistic and non-synergistic molecules resembled those of rifamycin and brefeldin A, respectively.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57241/57241fig1.jpg" /> 
Figure 1: Depiction of Checkerboard Assay from step 3. The test drug and input drug gradients are illustrated in A and B, respectively. The final assay plate should appear as in C. <a href="/files/ftp_upload/57241/57241fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57241/57241fig2.jpg" /> 
Figure 2: Graphical results of Checkerboard Assay. Illustrations of synergistic, none, and antagonistic interactions are depicted in A, B, and C, respectively. <a href="/files/ftp_upload/57241/57241fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57241/57241fig3.jpg" /> 
Figure 3: Example data for identifying screening time. (A) Wild-type (green) and synergy prediction mutant (blue) of C. neoformans grown in the presence of a brefeldin A, a small molecule known to not synergize with fluconazole. Wild-type control (dark green) and drug treated (light green) have a similar difference in growth to the synergy prediction mutant control (dark blue) and drug treated (light blue). (B) Wild-type and synergy prediction mutant grown in the presence of rifamycin, a small molecule known to synergize with fluconazole. Wild-type control (dark green) and drug treated (light green) have a smaller growth difference than the synergy prediction mutant control (dark blue) and drug treated (light blue). The greatest difference is observed at 48 h for the known synergistic molecule and the difference is similar at that time point for the known non-synergistic molecule. <a href="/files/ftp_upload/57241/57241fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

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

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

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 ...

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Nanyang Technological University

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JoVE Journal

Genetics

11.6K Views.  University of Utah. Synergistic drug combinations are difficult and time-consuming to identify empirically. Here, we describe a method for identifying and validating synergistic small molecules. 

Video: 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.

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

Cancer Research

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.

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.

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.

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.

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.

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

Immunology and Infection

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.

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 ...

Medicine

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.

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.


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

Biology

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.

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

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.

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

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