This work was funded by NIGMS Grant P50GM107618. The authors thank Zohar B. Weinstein for the insightful comments and suggestions on the manuscript.

NOTE: Any small molecule that inhibits E. coli bacteria growth can be used for the diagonal method. In this protocol, levofloxacin (LEV), nalidixic acid (NAL), and Penicillin G (PNG) will be used as an example, since these drugs show a salient three-way synergy. The workflow of this protocol is shown as Figure 6. Carry out all steps at room temperature. Use fresh aliquots of bacteria and drugs each day. Perform the experiment under biosafety levels appropriate for E. coli.
1. Preparation Steps
<ol>
	<li>Prepare Luria-Bertani (LB) media by adding 25 g of LB broth to 1 L of distilled water and mix. Autoclave at 121&#160;&#176;C for 15 min and store the autoclaved media at room temperature.</li>
	<li>Prepare E. coli glycerol stocks by mixing equal volumes of sterile 50% glycerol and bacterial cells diluted to OD600 = 1 in LB broth and freeze 150&#160;&#181;L aliquots in 1.5 mL microcentrifuge tubes at -80&#160;&#176;C.</li>
	<li>Dissolve 20 mg of antibiotics, LEV, NAL and PNG in 1 mL of dimethyl sulfoxide (DMSO) each. Dilute LEV solution 100x to 0.2&#160;&#181;g/mL by mixing 10&#160;&#181;L of LEV solution with 990&#160;&#181;L of DMSO. Use 0.2 mg/mL LEV, and 20 mg/mL NAL and PNG in the proceeding steps.</li>
	<li>Aliquot 50&#160;&#181;L of each antibiotic to 1.5 mL microcentrifuge tubes and freeze at -20&#160;&#176;C.</li>
	<li>Take one 150&#160;&#181;L aliquot of E. coli from -80&#160;&#176;C. Thaw.</li>
	<li>Add 100&#160;&#181;L of E. coli glycerol stock in 5 mL of LB media in a 14 mL culture tube.</li>
	<li>Shake the tubes in a 37&#160;&#176;C incubator overnight at 200 rpm.</li>
</ol>
2. Serial Dilution Dose-Response Experiment
<ol>
	<li>Take one aliquot of drugs LEV, NAL and PNG from -20&#160;&#176;C, leave them at room temperature for 10 min to thaw and prepare for serial dilutions of these drugs.</li>
	<li>Prepare 1100&#160;&#181;L of LB-10%sol by mixing 990&#160;&#181;L of LB media and 110&#160;&#181;L of solvent (DMSO).</li>
	<li>Prepare 500&#160;&#181;L of LB-10%LEV by mixing 450&#160;&#181;L of LB media and 50&#160;&#181;L of LEV.</li>
	<li>Vortex LB-10%sol for 5 s at the highest setting. Add 20&#160;&#181;L of LB-10%sol to the top four rows of wells in a 96-well microplate.</li>
	<li>Vortex LB-10%LEV for 5 s at the highest setting. Add 20&#160;&#181;L of LB-10%LEV to the first well in row A.</li>
	<li>Prepare two-fold serial dilution for LB-10%LEV by taking 20&#160;&#181;L from the first well, adding to the second well, pipetting up and down five times. Repeat this for all wells sequentially until the 11th well, which ends up with 40&#160;&#181;L (Figure 7A).</li>
	<li>Remove and discard 20&#160;&#181;L of the content from the 11th well, using a micropipette.</li>
	<li>Repeat steps 2.3-2.7 for the drugs NAL and PNG using the second and third rows of the microplate, respectively.</li>
	<li>Repeat steps 2.3-2.7 for the drug LEV again on fourth row as an internal positive control.</li>
	<li>Using a spectrophotometer, measure the OD600 of a 1:10 dilution of the culture (steps 1.5-1.7).</li>
	<li>Dilute the cells in 5 mL of LB media to an OD600 of 0.01. Pour into a reservoir.</li>
	<li>Using a multichannel micropipette, add 80&#160;&#181;L of the diluted cells to the drug serial dilutions prepared in step 2.4-2.9. The final drug concentrations in each well is shown in Figure 7A. Seal the plate to prevent evaporation.</li>
	<li>Incubate the plate for 16 h at 37&#160;&#176;C.</li>
	<li>Start a new bacterial culture to use in step 3 (repeat steps 1.5-1.7).</li>
</ol>
3. Linear Dilution Dose-Response Experiment
<ol>
	<li>Measure the OD600 absorbance for serial dilution dose-response plate from step 2 using a plate reader&#160;(Figure 7A right) and interpret the results based on the following steps.</li>
	<li>Normalize the growth by dividing the growth in each well with the growth in the no drug control for each row and compute percent growth by normalizing OD600to no drug condition.</li>
	<li>For each drug, locate the wells that have ~50% growth inhibition (IC50), shown in orange in Figure 7A right. Assign the concentration in these wells as &#34;serial IC50&#34; for each drug.</li>
	<li>Thaw fresh drug aliquots, prepare 1 mL of LB-10%sol by mixing LB media and solvent (DMSO) in a 9:1 ratio and LB-10%drug by mixing LB media and drug in a 9:1 ratio, where the drug&#39;s concentration is 100x of each drug&#39;s serial IC50 before adding the LB media, as chosen at step 3.3.</li>
	<li>Prepare linearly increasing doses of drugs LEV, NAL and PNG in 11 concentrations, by mixing LB-10%drug and LB-10%sol in volumes shown in Figure 7B.</li>
	<li>Prepare linearly increasing doses of LEV on fourth row as an internal positive control.</li>
	<li>Measure the OD600 of the 1:10 dilution of the culture started in step 2.14.</li>
	<li>Dilute the cells in 5 mL of LB media to an OD600 of 0.01. Pour into a reservoir.</li>
	<li>Add 80&#160;&#181;L of the diluted cells on to the drug linear dilutions prepared in step 3.6 using a multichannel micropipette. The final drug concentrations in each well is shown in Figure 7B. 
	NOTE: The middle well in the dose-response will receive the serial IC50 for this drug. Seal plate to prevent evaporation.</li>
	<li>Incubate the plate for 16 h at 37&#160;&#176;C.</li>
	<li>Start two fresh bacterial cultures to use in step 4 (repeat steps 1.5-1.7).</li>
</ol>
4. Diagonal Drug Interaction Experiment
<ol>
	<li>Measure the OD600 absorbance for linear dilution dose-response from step 3 (Figure 7B).</li>
	<li>For each drug, choose the concentration that resulted in IC50 and prepare drug LEV, NAL and PNG in 100x IC50 concentrations.</li>
	<li>Thaw fresh drugs, prepare 100x IC50 for each drug and prepare 1:1 drug mixtures by volume of LEV+NAL, LEV+PNG and NAL+PNG and 1:1:1 drug mixture by volume of LEV+NAL+PNG.</li>
	<li>Prepare two plates for drug interaction experiments as shown in Figure 8.</li>
	<li>Measure the OD600 of the 1:10 dilution of the cultures started in step 3.11.</li>
	<li>Prepare OD600 = 0.01 dilutions of two cultures in two 10&#160;mL of LB media.</li>
	<li>Add 80&#160;&#181;L of the cells from culture 1 and 2 on plates 1 and 2, respectively. Seal the plates to prevent evaporation. Incubate the plates for 16 h at 37&#160;&#176;C.</li>
</ol>
5. Diagonal Drug Interaction Scores
<ol>
	<li>Measure the OD600 absorbance for diagonal drug interaction experiment plates from step 4.</li>
	<li>Normalize the growth by dividing the growth in each well with the growth in the no drug control well for each row.</li>
	<li>For each row, locate the column that has growth inhibition closest to IC50, shown in orange in Figure 8 right. Assign IC50 based on the relative concentration of drug in this well.</li>
	<li>For LEV+NAL, LEV+PNG and NAL+PNG and LEV+NAL+PNG dose-responses, calculate expected IC50 by averaging the IC50 of the single drugs in each combination. Note that averaging is a simple approximation for exact expected IC50 calculation as described before12.</li>
	<li>Compute Fractional Inhibitory Concentration (FIC) scores by dividing the observed IC50 by the expected IC50 in each combination.</li>
</ol>

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The authors have nothing to disclose.

The use of drug combinations against pathogens or tumors is an attractive prospect, especially under the circumstances of the drying antibiotic pipeline. However, this potential is hampered by at least two difficulties. The first difficulty is the astronomical number of possible combinations. There are, for example, 4950 possible pairwise combinations among 100 antibiotics. All possible combinations among 100 antibiotics (2100) is on the same order of magnitude with the number of bacteria on Earth (~1030). How to predict strongly synergistic combinations among these possibilities has been the subject of numerous computational studies. The second difficulty is the measurement of high-order drug interactions. Consider that a computational platform may suggest that a certain 10-drug combination is strongly synergistic against a certain pathogen. Traditional methods to test drug interactions is too costly to verify or refute this hypothesis, therefore the study of synergy among many drugs has been outside the boundaries of scientific inquiry. The diagonal method, which was first proposed almost 30 years ago and was used in a few recent synergy screens provide a strong foundation for the first problem, by allowing testing of the interaction among many pairs. It solves the second problem by an informative sampling of the traditional assays and allows the study of high-order drug interactions.
We importantly note that our protocol uses a linear dosing for drug interaction measurements, to provide the sensitivity for detecting even weak interactions. Establishing the right concentration range for linear dosing is a challenging task. By first performing a serial dilution, we make an informed decision about the search space for linear dosing. However, the protocol can be modified to use 2-fold or higher serial dilutions for drug interaction testing. Such a modification would shorten the experiment time and allow the testing of more interactions; however, it would have sensitivity to detect only strongly synergistic or antagonistic interactions.
The protocol we described shows the measurement of pairwise or three-way interactions. A critical aspect of the protocol is that single agents are on the same plate as the combination, to minimize bias due to plate variations. Therefore, the protocol can be adjusted to measure interactions up to 7-way combinations by trivial modifications. Combinations of more than 7 drugs will require more than one 96-well microplate and additional considerations must be taken to ensure correct data integration, such as inter-plate replicates.
A notable limitation of the diagonal method is the restriction that each drug in the assay must inhibit the phenotype of interest. Therefore, the diagonal method is not useful for understanding the interactions among active agents and inert adjuvants. Such &#39;potentiating&#39; interactions may be studied under alternative models such as Bliss or Highest Single Agent models.
An important consideration for the analysis of high-order drug interactions is the null model choice for the &#34;expected IC50.&#34; When two drugs are combined, the combination&#39;s effect can only be compared to the single drug effects. When three drugs are combined, the combination&#39;s effect can be compared to the single effects or pairwise effects. For example, if all pairwise combinations of three drugs are synergistic, then it may be expected that these drugs will show a three-way synergy. A three-way interaction&#39;s deviation from what is expected from pairwise interactions has been recently dubbed &#34;emergent interaction&#34;16,17. For simplicity, our protocol describes the measurement of the three-way combination&#39;s &#34;net interaction,&#34; which defines the null model as single drug effects. However, the data that is obtained from the protocol may also be used to compute the emergent interaction of the three-way combination. In our analysis, we defined the expected IC50 of the three-way combination as the average of the single drug IC50s. Alternatively, the expected IC50 can be defined as the average of the IC50s of pairwise combinations (~1.1-1.2). When the observed IC50 is divided by this alternative expected IC50, the obtained FIC provides the emergent FIC for the three-way combination, as previously described12. This consideration reveals that LEV+NAL+PNG is more synergistic than what would be expected from the pairwise interactions among three drugs, demonstrating that LEV+NAL+PNG has emergent synergy.

Drug combinations may exhibit surprisingly high or low effect on a phenotype given the effects of constituent drugs, corresponding to synergistic or antagonistic drug interactions, respectively1,2,3. The use of synergistic combinations may allow dose-escalation for efficacy increase and dose-reduction for side-effect relief. Combination treatments may also apply multiple setbacks to cellular machinery, thereby blocking potential evolutionary escape mechanisms to resistance4. Therefore, combinations of three or more drugs are routinely used in pathogen or cancer treatment5.
Synergy and antagonism are defined by a comparison between the observed effect of a combination versus an expected effect given individual drug effects. Among the models for drug interactions, Loewe additivity is the most stringent and has a well-defined null model (Figure 1)6, and the inferred synergy/antagonism interaction is independent of the drug concentration used6,7. However, the Loewe model is experimentally costly even for a pairwise interaction test. Drug interaction assays traditionally comprise of a 2D matrix of drug concentration combinations (a checkerboard assay) (Figure 2). If 5 doses are used for each drug, then 25 combinations are required, corresponding to one half of a microplate if experiments are conducted in replicate. The cost of this approach prohibits synergy measurement by the Loewe additivity model for multi-drug combinations (Figure 3). For example, to test a 10-way interaction, traditional methods would require more than 100 thousand microplates, barring experimental measurement of high-order synergies by the stringent, well-theorized and concentration-independent Loewe additivity model8.
Current clinical treatments utilize only a fraction of possible drug combinations. For example, the standard treatment of active tuberculosis is a combination of three antibiotics. There are approximately 20 antibiotics used in Mycobacterium tuberculosis (Mtb) treatment. There are 1140 possible 3-way combinations among 20 drugs, each with the potential to have a strong synergy against Mtb. As there has been no cost-effective method to measure drug interactions among many drugs, potentially life-saving synergistic combinations remain untested.
Here, we describe a simple protocol to measure pairwise and three-way drug interactions by sampling only the diagonal of a checkerboard assay (Figure 4 and Figure 5). The underlying concept of sampling the diagonal of a checkerboard experiment was theorized by Berenbaum in his seminal work in 19789. Yet, this approach has only recently been applied to drug synergy screens10,11,12. We present our protocol with Escherichia coli (E. coli) and the growth phenotype. However, we note that the protocol is independent of the biological species and phenotype of interest, and hence may be applied to the measurement of high-order drug synergy in other biological contexts.

<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

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.

Previously, we have reported the pairwise interactions among three drugs: LEV, NAL and PNG based on testing in miniaturized checkerboard assays, where two drugs were combined in a 4 x 4 matrix13,14. While NAL and LEV were synergistic, PNG was reported to be antagonistic with both LEV and NAL13,14. Here, we verified these pairwise interactions and measured the three-way interaction among these three drugs using a diagonal assay. Our results demonstrate that LEV+NAL+PNG is a synergistic 3-way antibiotic combination. Schematic representations for the results of the individual experimental procedure sections were given on the right side of Figure 7 and Figure 8. Here, we present and interpret representative raw results from three plate readings, which are given in Figure 9. The top plate reading corresponds to serial and linear dilution experiments conducted in steps 2 and 3. The bottom two plate readings are duplicate interaction plates conducted in step 4.
The raw data in Figure 9 shows that top growth is around 0.55, but there is a 0.05 optical density of the media itself, as observed in the OD600 of the high drug concentrations where there is no growth. Therefore, we define IC50 as (0.55-0.05)/2 = 0.25. For each dose-response, the wells located closest to this value are shown with orange.
The upper half of Figure 9A shows the results from step 2, serial dose-response experiment. The IC50 wells for LEV is at column 10 in two replicates, which correspond to 4 ng/mL. The IC50 for NAL and PNG are at 3 &#181;g/mL and 25 &#181;g/mL, respectively. These concentrations correspond to the 1x concentration shown in Figure 7B. The lower half of Figure 9B shows the results from step 3, linear dose-response experiments. LEV, NAL and PNG&#39;s IC50 are found at 0.4x, 0.8x and 1.2x, respectively. These concentrations are assigned as the 1X IC50 for step 4.
Two plates corresponding to two replicate experiments are shown in Figure 9B, where the IC50 wells are shown with orange. In plate 1, all single drugs have their IC50 at 1x concentration. The expected IC50 for the pairwise or three-way combination is calculated by the arithmetic mean of constituent drugs, making expected IC50 for all combinations also 1x concentration. In plate 2, LEV and PNG have their IC50 at the 1x concentration, but NAL IC50 is at 1.2x. The expected IC50 for each combination is defined using the arithmetic means of these IC50 values. For example, the expected IC50 for LEV+NAL and NAL+PNG is 1.1x. The drug interaction score (FIC) for each combination is calculated by dividing observed IC50 with the expected IC50, as shown on the right side of the plates. Inspection of the FIC scores of the two plates demonstrates that LEV+NAL and LEV+NAL+PNG are synergistic, while LEV+PNG and NAL+PNG are antagonistic. FIC scores obtained in the two plates are in agreement, supporting the reliability of the protocol.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57713/57713fig1.jpg" /> 
Figure 1: Null model definition for Loewe additivity drug interaction model. Two 5 x 5 matrices on a microplate, where drug A is linearly increased in one axis as shown at left, and a top concentration of drug inhibits a quantifiable phenotype (top right). The addition of these matrices is shown in the middle, where the lines connecting equipotent drug A concentrations in each single drug have the same concentration as drug A. When cells are added on this &#34;checkerboard&#34; of drug concentration combinations, it is expected that the recorded phenotype on this line will be equivalent for the wells it connects. In this self-self drug interaction experiment, the isobole depicting a phenotype (shown with a dashed green line) is expected to be linear, defining the additivity null model. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig1large.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/57713/57713fig2.jpg" /> 
Figure 2: Pairwise drug interactions according to Loewe additivity drug interaction model. When two drugs are combined in a checkerboard assay as in Figure 1, the observed isophenotypic contour may be straight, convex or concave. At right, possible isophenotypic contours (dashed green lines) are superimposed on checkerboard assays. Drug combinations with straight isophenotypic contours are Loewe-additive, as the contours are not different from a self-self drug interaction, which is Loewe-additive by definition. When the isophenotypic contour is significantly concave or convex, the combination is synergistic or antagonistic, respectively. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig2large.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/57713/57713fig3.jpg" /> 
Figure 3: Three-way drug interactions according to Loewe additivity drug interaction model. Similar to the checkerboard assay for pairwise interactions, three-drugs are combined in a 3D grid (a &#34;checkercube&#34;), where each drug is linearly increased in one axis. If the three drugs were identical, the isophenotypic surface is expected to be flat, defining the additivity for three drug combinations. If the surface is more concave or convex than this Loewe-additive null model, drug combinations are synergistic or antagonistic, respectively. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57713/57713fig4.jpg" /> 
Figure 4: Diagonal method to measure pairwise drug interactions. For each checkerboard assay, only the regions shown in magenta rectangles are measured. i and ii are linearly increasing single drug concentrations. iii is assessed by making a 1:1 mixture of two drugs and linearly titrating this mixture as if it was a single drug. The FIC is equal to the observed IC50 in the combination divided by the expected IC50 of two single drugs. For the Loewe additivity model, the expected IC50 is approximated by the average IC50 of the two single drugs. A FIC value is 1 for Loewe-additive pairs and is lower or higher than 1 for synergistic or antagonistic pairs, respectively2,12. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57713/57713fig5.jpg" /> 
Figure 5: Diagonal method to measure three-way drug interactions. For each checkercube assay, only the regions shown are measured. i, ii and iii are linearly increasing single drug concentrations. iv is measured by making a 1:1:1 mixture of three drugs and linearly titrating this mixture as if it were a single drug. The Fractional Inhibitory Concentration is equal to the observed IC50 in the combination divided by the expected IC50 given three single drugs. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57713/57713fig6.jpg" /> 
Figure 6: Workflow for the diagonal method protocol described herein and the setup details for each microplate. The plate shown in Day 4 is conducted in duplicate. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/57713/57713fig7.jpg" /> 
Figure 7: Serial and linear dilution dose-response experiments. (A) Preparation of serial dilution dose-response for one drug and corresponding final drug concentrations. E. coli cells are added to the plate; growth is recorded after 16 h. Serial IC50, shown in orange, is selected for each drug for use in the following day&#39;s linear dilution dose-response experiments. (B) Preparation of linear dilution dose-response for one drug and corresponding final drug concentrations. E. coli cells are added to the plate; growth is recorded after 16 h. For each drug, the selected IC50s that will be used in the following day&#39;s drug interaction experiments are shown in orange. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/57713/57713fig8.jpg" /> 
Figure 8: Drug interaction experiments. Preparation of interaction experiments is similar to single drug linear dose-responses, except that a 1:1 or 1:1:1 mixture of drugs is used for two or three drug dose-responses, respectively. Observed IC50s for each dose-response, depicted in orange, are used to calculate FIC scores. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/57713/57713fig9.jpg" /> 
Figure 9: Representative experiment results. Representative results obtained using the described protocol are shown, with details provided in the main text. <a href="https://www.jove.com/files/ftp_upload/57713/57713fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</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

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 ...

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18.3K Views.  Sabanci University. In this protocol, we describe how to make Loewe additivity-based drug interaction measurements for pairwise and three-way drug combinations.

Video: 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.

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

Genetics

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 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.

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 ...

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 ...

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

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 ...

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.

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

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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Agonism and Antagonism: Quantification

When drugs are administered, they can elicit either an agonist or antagonist effect on the body. Agonism occurs when a drug activates a specific receptor, triggering a biological response. On the other hand, antagonism happens when a drug binds to the same receptors but blocks their activation, thereby preventing a biological response.
To quantify these effects, researchers use a dose-response curve, which provides valuable information about the potency and efficacy of a drug. Potency refers to the amount of drug required to produce a particular response, while efficacy measures the maximum therapeutic effect achievable with the drug.
In addition to agonism and antagonism, drug interactions play a significant role in determining the overall effect. Drug interactions can be additive or synergistic. Additive effects occur when the combined effect of two drugs equals the sum of their individual effects. For example, if one drug lowers blood pressure by ten units and another drug lowers it by fifteen units, their combined effect would be a reduction of twenty-five units in blood pressure.
Synergism, on the other hand, happens when the combined effect of two drugs exceeds the sum of their individual effects. This is commonly observed in the treatment of heart disease, where the combined effect of aspirin and clopidogrel on preventing blood clots surpasses what either drug could achieve alone.
To visually represent these interactions, researchers often use isobolograms. Isobolograms provide a graphical representation of the quantitative relationship between two drugs and their combined effect. This visual tool enables researchers to analyze and understand the nature of drug interactions more effectively.
Understanding the dynamics of drug effects and interactions is crucial in developing safe and effective treatments. Researchers can optimize drug therapy regimens, minimize adverse effects, and maximize therapeutic outcomes by comprehending the mechanisms of agonism, antagonism, and synergism.

Diagonal Method to Measure Synergy Among Any Number of Drugs

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Chapters in this 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

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

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

Spheroid Drug Sensitivity Screening in Glioma Stem Cell Lines

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

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

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

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

Agonism and Antagonism: Quantification