Evaluation of Antibiotic Therapy from Discovery to Modern Challenges
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Bacterial infections obtained in hospitals result in longer hospital stays and higher mortality rates. Antibioticresistant bacteria worsen the issue by impeding or delaying effective therapy. Treatment regimens that use multiple antibiotics may be employed to manage antibiotic resistance and guarantee effective therapy. This includes giving patients medicine mixtures (combination therapy), randomly allocating pharmaceuticals to various patients (mixing), and routinely changing the hospital's default medication from drug A to drug B and back (cycling). The potential of antibiotic combination therapy, mixing, and cycling has been evaluated for over two decades using mathematical models. However, despite trends in their rankings among research, the picture is still shockingly ambiguous and inconsistent, with no clear consensus on which strategy most effectively limits the emergence and spread of antibiotic resistance. In this paper, we examine previous modeling research and illustrate through examples how methodological considerations make it more difficult to produce a coherent image. These elements include the model's implementation and analysis, as well as the selection of the criterion used to compare the protocols' impacts. After that, we talk about how to advance and offer ideas for future modeling paths, emphasizing the need for standardized evaluation metrics, realistic clinical assumptions, integration of empirical data, and interdisciplinary collaboration to better inform evidence-based antibiotic stewardship policies.
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