You may have heard the phrase that a team is better (or worse) than the sum of its parts. This phrase points out that social endeavors are more complicated than a simple math problem; sometimes individual people work really well together, and sometimes it is a terrible fit.

This kind of co-determinism also exists in the molecular world. Some antibiotics have no impact on each other. That is, they work the exact same in isolation as when paired together. However, some antibiotic pairings change the potency, making them either more or less potent at killing cells.

When using multiple antibiotics in cell culture, the combination can either be synergistic (enhanced potency), antagonistic (reduced potency), or additive (expected potency). It is important to test for changes in potency unless using antibiotics and cells with an established protocol . 

Let’s dive deeper into understanding these interactions, and then discuss how to test for antibiotic drug interactions and when testing is important.

 

Article Table of Contents

What is an antibiotic interaction?

Synergy vs. antagonism vs. additive

How to measure antibiotic interactions

Should I check for antibiotic interactions?

References

 

 

What is an antibiotic interaction?

An antibiotic interaction is a change in how an antibiotic works due to an external factor, like the presence of another antibiotic, certain types of food, or another pre-existing condition.

While antibiotic interactions are quite broad in the real world, in the context of cell culture we have a narrower focus. In this case, we’re interested in how antibiotics work together, which is the interaction between two, or more, antibiotics.

 

Synergy vs. antagonism vs. additive

Synergy means that an antibiotic is more potent in the presence of another antibiotic. In the next section we’ll discuss how exactly this is measured. But conceptually, the idea is that antibiotic 1 will work at a lower concentration when antibiotic 2 is also used, compared to when antibiotic 1 is used by itself.

Antagonism is the opposite. In this case antibiotic 1 works worse with antibiotic 2 than by itself. Meaning antibiotic 1 only works at a higher concentration when antibiotic 2 is present.

Most antibiotic pairings are additive, meaning they work together exactly as you would expect without being synergistic or antagonistic.

 

How to measure antibiotic interactions

Measuring the potency for a single antibiotic is done using a kill curve, which is essentially just a titration of that antibiotic against your cell (or cells) of interest.

The kill curve defines the minimal inhibitory concentration (MIC) that is needed for the antibiotic to kill the cells or effectively stall their growth (Figure 1).

Figure 1. Kill curves are used to define the minimum inhibitory concentration for an antibiotic and a given type of cells. This chart shows the minimum concentration, or the smallest concentration needed to kill all cells, indicated by a 0% survival rate.

To measure antibiotic interactions, you are going to do a two-dimensional kill curve and titrate both antibiotics against each other. This technique is commonly called a checkerboard assay because the two-dimensional setup is reminiscent of the grid in the board game checkers (Figure 2).

Figure 2. A checkerboard assay is used to test concentrations of two antibiotics that kill or stall the growth of a particular type of cell.

 

Each square in the checkerboard corresponds to a different combination of concentrations for the two antibiotics (Figure 3).

Figure 3. Each square in the checkerboard corresponds to a different combination of concentrations for antibiotic 1 and 2, indicated here by the shade of pink and blue. The top left corner indicates the highest concentration of antibiotic 2 tested, with the lowest concentration of antibiotic 1 tested. The bottom right corner is the opposite. The top right corner has the highest concentration of both antibiotics tested.

 

For antibiotics that are additive, the MICs will be a diagonal line across the checkerboard (Figure 4, left). It is not immediately intuitive to me why this is the case, so I find it helpful to do a quick thought experiment.

Let’s pretend that we’re actually using the same antibiotic on both axes of the titration. That is, both antibiotic 1 and antibiotic 2 are the same exact antibiotic. Each square along the diagonal line will have the same concentration of that antibiotic, which is why the cells stop growing at that concentration.

Of course, in the real checkerboard experiment, antibiotic 1 and 2 are not the same. So, in the real experiment this pattern is telling us the combinations of antibiotic 1 and antibiotic 2 that stop cell growth.

Figure 4. For additive antibiotics, the minimum inhibitory concentrations will be a diagonal line (left - dotted line). For synergistic antibiotics this line will bend towards lower concentrations (middle) and for antagonistic antibiotics it will bend towards higher concentrations (right).

The MICs for synergistic antibiotics will bend towards lower concentrations because when both antibiotics are present they more potently stop cell growth than either antibiotic would on its own (Figure 4, middle).

For antagonistic antibiotics the MICs will curve towards higher concentrations since the antibiotics are less potent together than individually (Figure 4, right).

 

Should I check for antibiotic interactions?

Let’s imagine a scenario where you have two antibiotics, and you know the ideal concentration for each of them individually in a given type of cell. Do you have to check for an antibiotic interaction? It would be convenient to just assume the two antibiotics don’t interact and not have to worry about this additional experiment, right?

But there are two main questions I would ask here to determine if you need to check for an antibiotic interaction or if you can proceed without checking:

·         Is there any protocol in your lab or in literature for combining these two antibiotics in the specific cell type you’re using?

·         How long will you be treating your cells with the antibiotics?

Common antibiotic pairings are pretty well established for expressing two proteins in common Escherichia coli strains, for example (Table 1). Additionally, this is a relatively short protocol typically lasting two days or less from starter culture to harvesting the cell pellet, so you don’t have to worry about the effect of extended exposure of antibiotics to the cells. So, if you are using the same antibiotic combination and expression strain then you likely don’t need to check for antibiotic interactions and can use an established protocol.

 

Table 1. Common antibiotic combinations for dual selection in E. coli.

Antibiotic 1	Antibiotic 2
Ampicillin	Kanamycin
Ampicillin	Chloramphenicol
Kanamycin	Chloramphenicol

 

In contrast, an extensive selection protocol in mammalian or bacterial cells will take weeks or months. If you mess up the concentration, it could lead to false positives if your antibiotic concentration is too low, or overly harsh selection and rampant side effects if the antibiotic concentration is too high. If this is a new combination of antibiotics and cell line, then I would definitely check for antibiotic interactions first, to give your experiment the best chance for success.

Antibiotics have different potencies in different types of cells, which is why it is so important to run a kill curve when investigating an antibiotic in a new cell line (Delrue et al, 2018). There are lots of ideas about what leads to antibiotic interactions, however these ideas are not always generally applicable and can be completely wrong with slight experimental differences (Gil-Gil & Berryhill, 2025; Ocampo et al., 2014).

Larger studies looking at antibiotic interactions suggest that they are relatively rare, occurring in less than 10% of pairings. Additionally, antibiotic interactions are cell-type specific meaning that a pair of antibiotics will be antagonistic in one type of cell and have no interaction in other types of cells (Brochado et al, 2018).

Altogether, these findings indicate that you shouldn’t guess whether a pair of antibiotics will interact in a given setup unless it has been tested in a nearly identical experimental setup. Even then, it doesn’t hurt to make sure you can replicate those results with your cells in your lab with your aliquots of antibiotics.

The general take-away is that antibiotics don’t always act the same in combination as they do in isolation. If you’re using multiple antibiotics in your cell culture, and there aren’t established protocols for the antibiotics and cells that you’re using, then it is probably worth performing a checkerboard to see if there’s an interaction between the antibiotics.

 

GoldBio sells lots of reliable and affordable research reagents, including a wide-variety of antibiotics. Check out our links below, and throughout the article, for a few of our more popular antibiotics.

 

 

 

References

Brochado, A. R., Telzerow, A., Bobonis, J., Banzhaf, M., Mateus, A., Selkrig, J., Huth, E., Bassler, S., Zamarreño Beas, J., Zietek, M., Ng, N., Foerster, S., Ezraty, B., Py, B., Barras, F., Savitski, M. M., Bork, P., Göttig, S., & Typas, A. (2018). Species-specific activity of antibacterial drug combinations. Nature, 559(7713), 259–263. https://doi.org/10.1038/s41586-018-0278-9

Delrue, I., Pan, Q., Baczmanska, A. K., Callens, B. W., & Verdoodt, L. L. M. (2018). Determination of the Selection Capacity of Antibiotics for Gene Selection. Biotechnology journal, 13(8), e1700747. https://doi.org/10.1002/biot.201700747

Gil-Gil T, Berryhill BA. 2025. Antibiotic killing of drug-induced bacteriostatic cells. Antimicrob Agents Chemother69:e00156-25.https://doi.org/10.1128/aac.00156-25

Ocampo, P. S., Lázár, V., Papp, B., Arnoldini, M., Abel zur Wiesch, P., Busa-Fekete, R., Fekete, G., Pál, C., Ackermann, M., & Bonhoeffer, S. (2014). Antagonism between bacteriostatic and bactericidal antibiotics is prevalent. Antimicrobial agents and chemotherapy, 58(8), 4573–4582. https://doi.org/10.1128/AAC.02463-14

Tags

antibiotics cell culture Simon Currie

• Share :
• Deel
• X
• LinkedIn

---