Approved Research

Development of Antibiotic Response Prediction Model: Integrating Clinical and Genetic Factors for Personalized Pharmaceutical Care Professor Woorim Kim

Lay summary

This research project focuses on the use of antibiotics, like penicillin/beta-lactamase inhibitor combinations, for treating various infections such as pneumonia, urinary tract infections, abdominal infections, and bacterial sepsis. While these drugs are crucial for curing these diseases, they can sometimes lead to adverse reactions, like digestive problems, skin issues, kidney complications, and severe bone marrow suppression. These adverse reactions can even be severe, leading to patients stopping their treatment or, in rare cases, death. Surprisingly, there hasn't been enough research to understand why these reactions happen. Most previous studies focused on how these drugs move within the body but didn't consider genetic factors. Many studies also concentrated on specific racial groups, ignoring the wider genetic diversity that can influence how people react to these drugs. Understanding these adverse reactions is vital because they involve complex interactions between clinical and genetic factors. These reactions also differ among various races. A field called pharmacogenomics, which examines how genetic differences affect drug responses, has shown that different people react to antibiotics in unique ways, based on their gender, race, and genetics. Despite advances in understanding human genetics and finding genetic variations, research into these differences in antibiotic reactions is limited.

Our research aims to find the clinical and genetic factors that contribute to these adverse reactions. Our ultimate goal is to create a model that predicts how patients will respond to antibiotics. This model won't be limited to just one type of antibiotic; it can be applied to antibiotics more broadly. We'll also be using artificial intelligence-based tools to develop algorithms for this purpose. These algorithms will help us make predictions about antibiotic responses, which we'll assess using specific measures. In essence, our approach combines a comprehensive analysis of patient data, exploring genetics, and sophisticated statistical techniques to uncover the many factors influencing antibiotic-related adverse events and treatment effectiveness.

This project is important for the public because it aims to create precise models that predict how individuals will react to antibiotics, considering their unique clinical and genetic traits. This personalization will make treatments safer, reducing the risk of adverse reactions. Ultimately, our goal is to enhance our understanding of antibiotic-induced adverse reactions, potentially changing how we manage infectious diseases, which would significantly benefit public health.