Dose Optimization of Aditoprim-Sulfamethoxazole Combinations Against Trueperella pyogenes From Patients With Clinical Endometritis by Using Semi-mechanistic PK/PD Model

Virulence factors and therapeutic methods of Trueperella pyogenes: A review

Trueperella pyogenes is a prevalent opportunistic pathogen responsible for a wide range of infections in livestock and wildlife, such as in cattle, pigs, European bison and forest musk deer. Much of the successful infection of T. pyogenes relies on its virulence factors, including pyolysin as well as adhesion factors. The swift rise of bacterial resistance has highlighted the urgent need for developing new therapeutic strategies. Currently, virulence factor-mediated vaccine development and other therapeutic approaches are widely regarded as the primary interventions for addressing diseases associated with this pathogen. This review examines the broader virulence potential of T. pyogenes, focusing on haemolysin, host cell adhesion proteins, the prevalence of antibiotic resistance, and the development of vaccines mediated by virulence factors. Additionally, it discusses current and future approaches aimed at improving therapeutic interventions.

Prediction of Synergistic Antibiotic Combinations by Graph Learning

Antibiotic resistance is a major public health concern. Antibiotic combinations, offering better efficacy at lower doses, are a useful way to handle this problem. However, it is difficult for us to find effective antibiotic combinations in the vast chemical space. Herein, we propose a graph learning framework to predict synergistic antibiotic combinations. In this model, a network proximity method combined with network propagation was used to quantify the relationships of drug pairs, and we found that synergistic antibiotic combinations tend to have smaller network proximity. Therefore, network proximity can be used for building an affinity matrix. Subsequently, the affinity matrix was fed into a graph regularization model to predict potential synergistic antibiotic combinations. Compared with existing methods, our model shows a better performance in the prediction of synergistic antibiotic combinations and interpretability.