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The ingredients for an antimicrobial mathematical modelling broth.
Marcus Tindall1, Michael J Chappell2, James W T Yates3
1Department of Mathematics and Statistics, University of Reading, Reading, UK; Institute of Cardiovascular and Metabolic Research, University of Reading, Reading, UK.
International Journal of Antimicrobial Agents
|July 25, 2022
Summary
Mathematical modeling optimizes antimicrobial treatments by incorporating host immune responses. This study presents a novel model structure to quantify these crucial biological interactions for better treatment strategies.
Area of Science:
- Mathematical biology
- Pharmacodynamics
- Immunology
Background:
- Antimicrobial treatments require optimization for efficacy.
- Mathematical modeling is a key tool in this optimization process.
- Existing models may not fully capture host-pathogen-drug dynamics.
Purpose of the Study:
- To identify essential processes for mathematical models of antimicrobial treatment.
- To emphasize the need for quantifying host immune system responses.
- To introduce a novel model structure for this purpose.
Main Methods:
- Review of key processes in mathematical modeling for antimicrobial therapy.
- Development of a novel mathematical model structure.
- Illustration of model structure with host immune response quantification.
Main Results:
- Identified critical components for accurate antimicrobial treatment modeling.
- Demonstrated the necessity of including host immune system dynamics.
- Presented a new model framework for enhanced predictive power.
Conclusions:
- Accurate mathematical models for antimicrobial treatments must integrate host immune responses.
- The novel model structure provides a framework for quantifying these interactions.
- This approach can lead to improved optimization of antimicrobial therapies.
