Antibiotic resistance: 8,000 new drug combinations are effective

Antibiotic, or antimicrobial, resistance occurs when bacteria or viruses — sometimes called superbugs — genetically mutate and become immune to drugs.

The World Health Organization (WHO) call the phenomenon “an increasingly serious threat to global public health that requires action across all government sectors and society.”

In the United States, antibiotic resistance is also a major public health concern. Every year, at least 2 million people in the U.S. contract a treatment-resistant bacterial infection, and more than 23,000 people die as a result.

Now, researchers may have come up with a strategy for tackling it. New research led by scientists at the University of California, Los Angeles (UCLA) reveals that combining four or five antibiotics can prove surprisingly effective in killing off or slowing down the progression of drug-resistant bacteria.

The findings go against the prevalent view that such drug combinations are ineffective, or that mixing different antibiotics leads to the drugs’ benefits canceling each other out.

Pamela Yeh, an assistant professor of ecology and evolutionary biology at UCLA, supervised the new study in collaboration with Van Savage, a professor of ecology, evolutionary biology, and biomathematics at UCLA.

Yeh comments on the findings, saying, “There is a tradition of using just one drug, maybe two.”

“We’re offering an alternative that looks very promising. We shouldn’t limit ourselves to just single drugs or two-drug combinations in our medical toolbox. We expect several of these combinations, or more, will work much better than existing antibiotics.”

Pamela Yeh

The researchers published their findings in the journal npj Systems Biology and Applications. Elif Tekin is the first author of the paper.

Discovering 8,000 effective combinations

The team carried out many experiments in the laboratory and designed a mathematical framework — called mathematical analysis for general interactions of components (MAGIC) — that enabled them to study multiple drug combinations and anticipate their results.

As Tekin explains, “We think MAGIC is a generalizable tool that can be applied to other diseases — including cancers — and in many other areas with three or more interacting components, to better understand how a complex system works.”

Using these tools, Tekin and colleagues examined how every possible combination of four and five antibiotics affected a strain of Escherichia coli. In total, the researchers tested 18,278 combinations.

They expected some of these combinations to perform well against bacteria — but surprisingly, they also found 1,676 four-drug combinations and 6,443 five-drug combinations to be equally effective.

“I was blown away by how many effective combinations there are as we increased the number of drugs,” says Prof. Savage.

On the flip side, the researchers also found that 2,331 four-drug combinations and 5,199 five-drug combinations were less effective than predicted. Using an analogy, Prof. Savage explains why.

“Some drugs attack the cell walls, others attack the DNA inside,” he explains. “It’s like attacking a castle or fortress. Combining different methods of attacking may be more effective than just a single approach.”

‘More promising antibiotic combinations’

Michael Kurilla, the director of the Division of Clinical Innovation at the National Institutes of Health (NIH), comments on the significance of the findings in the context of the antibiotic resistance crisis.

He claims, “With the specter of antibiotic resistance threatening to turn back healthcare to the pre-antibiotic era, the ability to more judiciously use combinations of existing antibiotics that singly are losing potency is welcome.”

“This work will accelerate the testing in humans of promising antibiotic combinations for bacterial infections that we are ill-equipped to deal with today.”

Michael Kurilla

Yeh cautions that taking the new findings from a laboratory setting and turning them into viable treatments in a clinical setting will likely take years.

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