Drugs that are not antibiotics can also kill bacteria – New methods point out how

With the discovery of antibiotics in 1928, human history has changed forever. Infectious diseases such as pneumonia, tuberculosis and sepsis are widely and deadly until antibiotics can be treated. The surgical procedures that once had a high risk of infection became safer and more routine. Antibiotics mark a victorious moment in science, transforming medical practice and saving countless lives.

But antibiotics have an inherent warning: When overused, bacteria can evolve resistance to these drugs. The World Health Organization estimates that these superbacteria killed 1.27 million people in 2019 and could pose an increasingly threat to global public health in the coming years.

Recent discoveries are helping scientists face this challenge in innovative ways. Studies have found that nearly a quarter of drugs are usually not prescribed as antibiotics, such as drugs used to treat cancer, diabetes and depression, can kill bacteria at doses that are usually prescribed for people.

Understanding the mechanisms in which certain drugs are toxic to bacteria may have far-reaching effects on medicine. If non-antibiotic drugs target bacteria in a different way than standard antibiotics, they can serve as lead to develop new antibiotics. However, if non-antibiotics kill bacteria in a similar way to known antibiotics, their prolonged use (for example in the treatment of chronic diseases) may inadvertently promote antibiotic resistance.

In a paper published in 2024, my colleagues and I developed a new machine learning approach that not only determines how non-antibiotics kill bacteria, but also helps find new antibiotic targets for bacterial targets.

New ways to kill bacteria

Many scientists and doctors around the world are addressing drug resistance, including my colleagues and I in the Mitchell laboratory at UMass Chan Medical School. We use bacterial genetics to study whether mutations make bacteria resistant or more sensitive to drugs.

When my team and I learned about the broad antibacterial activity of non-antibiotics, the challenge we put in was consumed: figuring out how these drugs kill bacteria.

To answer this question, I used a genetic screening technique recently developed by my colleagues to study how anticancer drugs target bacteria. This method determines which specific genes and cellular processes change when bacteria are mutations. Monitoring how these changes affect bacteria's survival allows researchers to infer the mechanisms these drugs use to kill bacteria.

I collected and analyzed nearly 2 million toxic instances between 200 drugs and thousands of mutant bacteria. I developed using machine learning algorithms to infer similarities between different drugs, and I group these drugs together in the network based on how they affect mutant bacteria.

My map clearly shows that known antibiotics are grouped closely together through known killing mechanisms. For example, all antibiotics targeting the cell wall (the thick protective layer around bacterial cells) are grouped together and isolated from antibiotics that interfere with bacterial DNA replication.

Interestingly, when I added non-antibiotic drugs to my analysis, they formed a separate hub with the antibiotics. This suggests that non-antibiotics and antibiotic drugs have different ways to kill bacterial cells. Although these groups do not reveal how each drug specifically kills antibiotics, they suggest that those who gather together may work in a similar way.

The last part of the puzzle – whether we can find new drugs in bacteria to target them to kill them – from research by my colleague Carmen Li. She has grown hundreds of generations of bacteria that are exposed to different antibiotic drugs that are usually prescribed to treat anxiety, parasitic infections and cancer. The sequencing of bacterial genome evolved and adapted to the presence of these drugs, allowing us to point out that specific bacterial proteins, namely tritedanazole, a drug used to treat parasitic infections, targeted killing bacteria. Importantly, current antibiotics do not usually target this protein.

Furthermore, we found that two other non-antibiotics using a similar mechanism to the tribenzodiazole also target the same protein. This proves that my drug similarity graph is able to identify drugs with similar killing mechanisms, even if the mechanism is not yet clear.

Helps antibiotic discovery

Our findings offer researchers a variety of opportunities to study the different effects of non-antibiotics versus standard antibiotics. Our approach to mapping and testing drugs also has the potential to address key bottlenecks in the development of antibiotics.

Finding new antibiotics often involves sinking huge amounts of resources into screening thousands of chemicals that kill bacteria and figure out how they work. Most of these chemicals are similar to existing antibiotics and are discarded.

Our work shows that combining genetic screening with machine learning can help you discover chemical needles in haystacks, killing bacteria in ways researchers have never used before. There are many ways to kill bacteria that we haven’t used yet, and we can still take some paths to combat the threat of bacterial infections and antibiotic resistance.

The article was updated to show that antibiotic development is not only penicillin, making infectious diseases treatable.

This article is republished from the conversation, a non-profit independent news organization that brings you factual and trustworthy analysis to help you understand our complex world. It is written by: Mariana Noto Guillen Maumas Chen Medical College

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Mariana Noto Guillen does not work, consult, own shares for any company or organization that benefits from this article, and does not disclose any relevant affiliation except for academic appointments.