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<p><i>Clostridioides difficile</i> infection (CDI) poses significant challenges due to antibiotic-linked dysbiosis and recurrence. This study introduces a delay differential equation model to simulate host-microbiome-immune interactions during CDI, integrating time delays in immune activation, microbial competition, and inflammatory feedback. Calibrated with experimental data and global sensitivity analysis, the model identifies anti-toxin antibodies as the most effective single therapy for reducing pathogen load and inflammation. Combination therapy (fecal microbiota transplantation + anti-toxin antibodies) outperforms monotherapies, with immune balance modulation proving critical for recovery. The model emphasizes immune regulation in CDI management and offers a streamlined yet biologically grounded framework for optimizing treatments. Its balance of simplicity and accuracy enables practical applications in personalized medicine and in silico drug testing, advancing therapeutic strategy development.</p>

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A Delay Differential Equation Model to Improve Treatment Methods in Clostridioides difficile Infection

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Abazar Arabameri,
  • Davud Asemani,
  • Pegah Teymourpour

摘要

Clostridioides difficile infection (CDI) poses significant challenges due to antibiotic-linked dysbiosis and recurrence. This study introduces a delay differential equation model to simulate host-microbiome-immune interactions during CDI, integrating time delays in immune activation, microbial competition, and inflammatory feedback. Calibrated with experimental data and global sensitivity analysis, the model identifies anti-toxin antibodies as the most effective single therapy for reducing pathogen load and inflammation. Combination therapy (fecal microbiota transplantation + anti-toxin antibodies) outperforms monotherapies, with immune balance modulation proving critical for recovery. The model emphasizes immune regulation in CDI management and offers a streamlined yet biologically grounded framework for optimizing treatments. Its balance of simplicity and accuracy enables practical applications in personalized medicine and in silico drug testing, advancing therapeutic strategy development.