[Paper Review] Personalized Mathematical Model Predicting Endotoxin-Induced Inflammatory Responses in Young Men

Renee Brady‐Nicholls, Dennis O. Frank‐Ito+7 more|arXiv (Cornell University)|Sep 6, 2016

Immune Response and Inflammation29 references3 citations

TL;DR

This study develops a personalized mathematical model to predict endotoxin-induced inflammatory responses in young men using experimental data from 20 healthy male subjects exposed to low-dose lipopolysaccharide (LPS). By calibrating the model to individual cytokine dynamics—IL-6, TNF-α, CXCL8, and IL-10—over 8 hours, it accurately captures inter-individual variability and identifies abnormal inflammatory responses, offering a quantitative tool for assessing sepsis risk or surgical susceptibility.

ABSTRACT

The initial reaction of the body to pathogenic microbial infection or severe tissue trauma is an acute inflammatory response. The magnitude of such a response is of critical importance, since an uncontrolled response can cause further tissue damage, sepsis, and ultimately death, while an insufficient response can result in inadequate clearance of pathogens. A normal inflammatory response helps to annihilate threats posed by microbial pathogenic ligands, such as endotoxins, and thus, restore the body to a healthy state. Using a personalized mathematical model, comprehension and a detailed description of the interactions between pro- and anti-inflammatory cytokines can provide important insight in the evaluation of a patient with sepsis or a susceptible patient in surgery. Our model is calibrated to experimental data obtained from experiments measuring pro-inflammatory cytokines (interleukin-6 (IL-6), tumor necrosis factor (TNF-), and chemokine ligand-8 (CXCL8)) and the anti-inflammatory cytokine interleukin-10 (IL-10) over 8 hours in 20 healthy young male subjects, given a low dose intravenous injection of lipopolysaccharide (LPS), resulting in endotoxin-stimulated inflammation. Through the calibration process, we created a personalized mathematical model that can accurately determine individual differences between subjects, as well as identify those who showed an abnormal response.

Motivation & Objective

• To develop a personalized mathematical model that captures individual variations in inflammatory responses to endotoxin challenge.
• To understand the dynamic interactions between pro-inflammatory (IL-6, TNF-α, CXCL8) and anti-inflammatory (IL-10) cytokines in young men.
• To identify subjects with abnormal inflammatory responses using quantitative modeling.
• To provide a systems-level tool for evaluating sepsis susceptibility or surgical outcomes in clinical settings.

Proposed method

• A system of ordinary differential equations (ODEs) models the temporal dynamics of pro- and anti-inflammatory cytokines following LPS challenge.
• Model parameters are calibrated to experimental data from 20 healthy young male subjects receiving intravenous LPS.
• The model incorporates feedback regulation between cytokines, including negative feedback from IL-10.
• Personalization is achieved by fitting the model to individual subject data, allowing inter-individual variation to be quantified.
• Sensitivity analysis and parameter uncertainty quantification are used to validate model robustness and individual predictions.

Experimental results

Research questions

• RQ1 How do pro- and anti-inflammatory cytokine levels dynamically interact in response to LPS challenge in young men?
• RQ2 To what extent can a personalized mathematical model capture inter-individual differences in inflammatory responses?
• RQ3 Can the model identify subjects with abnormal inflammatory responses based on cytokine kinetics?
• RQ4 What are the key regulatory mechanisms governing the resolution of inflammation in healthy individuals?

Key findings

• The personalized model accurately reproduces observed cytokine profiles (IL-6, TNF-α, CXCL8, IL-10) across all 20 subjects over 8 hours post-LPS injection.
• The model successfully identified subjects with abnormal inflammatory responses, including those with delayed or excessive cytokine production.
• Inter-individual variability in cytokine dynamics was quantitatively captured through subject-specific parameter estimation.
• The model revealed that IL-10-mediated negative feedback plays a critical role in resolving inflammation in most subjects.