Modelling inflammatory biomarker dynamics in a human lipopolysaccharide (LPS) challenge study using delay...

Area of Science:

• Pharmacology and Toxicology
• Systems Biology
• Mathematical Biology

Background:

• Lipopolysaccharide (LPS) challenge studies in healthy volunteers are crucial for understanding Toll-like receptor 4 (TLR4)-mediated inflammation.
• Characterizing the dynamics and variability of inflammatory biomarkers is essential for interpreting these studies.

Purpose of the Study:

• To develop a quantitative mathematical modeling framework to characterize the dynamics and inter-individual variability of inflammatory biomarkers following LPS challenge.
• To provide a tool for informing the design and translation of clinical LPS challenge studies.

Main Methods:

• Utilized individual-level time-course data for tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), interleukin-8 (IL-8), and C-reactive protein (CRP) from existing LPS challenge studies.
• Employed a one-compartment model for LPS pharmacokinetics and indirect response (IDR) models to link LPS to biomarker responses.
• Incorporated delay differential equations to quantify biomarker response delays.

Main Results:

• The model successfully captured the dynamics of multiple inflammatory biomarkers, including LPS kinetics with estimated clearance of 35.7 L/h and volume of distribution of 6.35 L.
• Estimated time delays for biomarker secretion: TNF-α (0.924 h), IL-6 (1.46 h), and IL-8 (1.48 h).
• A second IDR model described CRP induction relative to IL-6 with a 4.2 h delay.

Conclusions:

• The developed quantitative models effectively characterize inflammatory biomarker dynamics and variability in LPS challenge studies.
• These models can optimize the design of future clinical LPS challenge studies.
• The framework may facilitate the translation of preclinical LPS challenge findings to human studies.