Moving From Point-Based Analysis to Systems-Based Modeling: Knowledge Integration to Address Antimicrobial...

Area of Science:

• Pharmacology
• Microbiology
• Computational Science
• Clinical Medicine

Background:

• Optimizing antibiotic therapy is complex, requiring a comprehensive bench-to-bedside strategy.
• Interdisciplinary collaboration among pharmacologists, clinicians, microbiologists, and computational scientists is crucial.

Purpose of the Study:

• To highlight how integrating novel experimental models and multiomics data with computational approaches can enhance antibiotic therapy.
• To emphasize the development of systems-level views for improved drug development and clinical decision-making.

Main Methods:

• Utilizing novel experimental models to study drug-pathogen interactions in host environments.
• Employing multiomics data to understand molecular mechanisms of bacterial responses.
• Applying pharmacometrics and machine learning to build in silico models.

Main Results:

• Experimental models and multiomics data provide insights into drug-pathogen interactions and bacterial responses.
• Pharmacometrics and machine learning enable the integration of these insights into predictive in silico models.
• A systems-level view facilitates informed drug development and clinical decision-making.

Conclusions:

• A holistic, interdisciplinary approach combining experimental and computational methods is key to optimizing antibiotic therapy.
• Effective integration of diverse data and modeling techniques can personalize antibiotic treatment (right drug, time, dose, duration).
• This integrated strategy promises to advance antimicrobial drug development and clinical practice.