Superbugs, or drug-resistant bacteria, have become a significant health threat, currently killing more Americans than HIV. Illnesses such as pneumonia and urinary tract infections are increasingly difficult to control due to antibiotic resistance. To tackle this problem, researchers at Duke University are utilizing computerized algorithms to study the behavior of these microorganisms and devise strategies to stay ahead of the ever-evolving bacteria.
Understanding Bacterial Behaviors
Bruce Donald, a researcher at Duke University, believes that studying how bacteria are likely to mutate and spread can help researchers predict their future behavior. This knowledge can then be used to design new antibiotic drugs or re-engineer existing ones, in order to counteract these life-threatening bacteria.
Pablo Gainza-Cirauqui, another researcher at Duke, echoes this sentiment, stating that the ability to predict the bacterial resistance trajectory would enable researchers to alter drugs in advance, plan the development of newer and more effective drugs, or discard therapies that are unlikely to remain effective long-term.
The History of Antibiotics and Superbugs
Since the invention and deployment of antibiotics in the 1940s, bacteria have consistently evolved and found new ways to resist each new class of antibiotics. The rapid pace of resistance presents a significant challenge for the development and efficacy of antibiotics. Bruce Donald highlights this concern, sharing that some antibiotics given to his children when they were younger are no longer effective.
One example of an increasingly resistant bacteria is Staphylococcus aureus. In 1975, just over 2% of infections were resistant to antibiotics. By 1991, that figure increased to 29%. Currently, over 55% of staph infections are resistant to antibiotics, resulting in over 11,000 annual deaths in the United States.
A New Approach to Tackle Resistance
Duke researchers have developed the OSPREY protein design algorithm that can identify DNA sequences enabling bacteria to become resistant to antibiotics. This algorithm is expected to help predict drug resistance mutations in various diseases, which will significantly impact the fight against resistant illnesses such as certain cancers, HIV, and influenza, in addition to bacterial infections.
The Role of Prevention and Alternative Treatments
While the use of algorithms to predict and combat bacterial resistance shows promise, it’s important not to overlook the role of prevention and alternative treatments in addressing the superbug crisis. Avoiding the overuse and misuse of antibiotics can help slow down resistance development, buy time for the state-of-the-art development of new drugs, and maintain the effectiveness of current antibiotics.
Encouraging patients to complete the full course of prescribed antibiotics is a simple and vital step—this prevents the development of partially treated, drug-resistant bacteria. Furthermore, healthcare providers can prioritize antibiotic stewardship programs, which focus on the responsible use of these drugs as dictated by guidelines such as dose, duration, and infection type.
Alternative treatments may also play a part in addressing the superbug crisis. For example, natural remedies like honey and essential oils like oregano and tea tree have been shown to have antimicrobial properties and can help alleviate symptoms when antibiotics are not necessary or as a supportive treatment to traditional medical measures.
Conclusion
The widespread issue of antibiotic-resistant bacteria has led to the development of increasingly sophisticated methods to tackle this health crisis. By utilizing computer algorithms like Duke University’s OSPREY, researchers can gain insight into bacterial behavior and develop new strategies for combating resistance, potentially saving countless lives in the process. However, it’s essential to remember that prevention and alternative treatments also play a crucial role in preserving the effectiveness of current antibiotics and ensuring that new drugs have a better chance of success.
