Winter 2018

A Better Way to Measure Drug Interaction

Research shows a method that’s more efficient and less expensive than traditional testing.

By Kim Thurler

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Drug interactions are measured by the shape of the contour on a checkerboard of dose combinations. “Studying such interactions is challenging because of the sheer number of combinations,” said Bree Aldridge. Photo: Cokol, et al., Sci. Adv. 2017;3: e1701881

Cancer, tuberculosis, and HIV are among the many serious diseases that are frequently treated with combinations of three or more drugs, over months or even years. Developing the most effective therapies for such diseases requires understanding how combining drugs affects their efficacy. If the drugs synergistically reinforce one another, doctors may be able to lower doses, potentially relieving side effects, reducing treatment time, and improving patient quality of life. But if they work against each other, efficacy will be reduced.

Now researchers at Tufts, along with colleagues at Harvard University and Turkey’s Sabanci University, have developed a new method to measure how drugs act in combination. The new methodology is more efficient and less expensive than traditional testing, and provides a framework for systematic testing of any dose-dependent therapeutic agent.

“Identifying synergies early in the preclinical drug-development process can help us prioritize drug combinations for further development,” said Bree Aldridge, assistant professor of molecular biology and microbiology at Tufts School of Medicine and adjunct assistant professor of biomedical engineering. “But studying such drug interactions is challenging because of the sheer number of combinations and the current method of measurement.”

Such testing has historically been done on pairs of drugs through a “checkerboard” methodology using an iPhone-sized plate containing a grid of tiny wells, typically 96 or 384 of them. A bacterium—or other target organism—is placed into each well along with a carefully calibrated dose of the two drugs in varying strengths. Bacterial growth in each well is measured to determine its response to the drugs. The complexity and cost of testing increase exponentially with the number of drugs being examined. To determine the synergy of five drugs, for example, would require measuring 100,000 cell response combinations on 1,000 plates. As a result, combinations of more than two drugs—called high-order combinations—rarely undergo such testing.

The new method, though, doesn’t require an exhaustive analysis of all cell behaviors in all possible dose combinations. Instead, in order to predict which high-order combinations are most likely to be synergistic, it targets only the most information-rich combinations. In experiments using Mycobacterium tuberculosis, which causes tuberculosis, Aldridge and her collaborators found that measuring only certain wells in the grid mirrored the results obtained by testing all the wells.

Aldridge employs the analogy of assessing metropolitan rush-hour traffic. “Instead of monitoring traffic in every neighborhood and on every road, if you look at traffic at multiple key points—such as the Massachusetts Turnpike and the airport tunnels in Boston—you’ll be able to get a pretty good picture of whether commuting will be a breeze or a nightmare.”

The new proof-of-concept study, recently reported in the journal Science Advances, analyzed pairwise and combination interactions among nine drugs now used against M. tuberculosis. Aldridge, whose work merges molecular and mathematical approaches to the study of mycobacteria, hopes to test additional drugs in future studies of the method, dubbed DiaMOND (diagonal measurement of n-way drug interactions).

Aldridge stresses that drug synergy should be only one consideration in developing effective patient therapies. “Synergies observed in the lab are not always associated with optimum clinical treatments,” she said. For example, it may make sense to include less synergistic combinations in a regimen in order to help combat drug resistance. But, she added, “DiaMOND can play an important role by enabling us to do a much better job of identifying potentially valuable synergies among candidate drugs in the pipeline.”

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