Pharmacometrician Chugai Pharmaceutical Co., Ltd., Japan
Disclosure(s):
Chika Ogami: No relevant disclosure to display
Objectives: Pathogen infection sometimes induces multi-organ dysfunction including sepsis, characterized by excessive inflammation and coagulation abnormalities. Coagulation abnormalities, including thrombocytopenia and thrombosis, have been reported as prognostic factors predicting poor clinical outcomes in sepsis patients (1). We developed a quantitative systems model describing the dynamics of inflammation and coagulation in the early phase of pathogen infection to help understand the quantitative disease course of sepsis.
Methods: The model was composed of three parts: (i) neutrophils in blood decrease by migrating to infected tissue and subsequently increase through immune response; (ii) platelets in blood are consumed while activating the coagulation cascade triggered by increased tissue factor (TF) (2); and (iii) dynamics of circulating biomarkers for inflammation and coagulation abnormalities (e.g., interleukin-6 (IL-6) and thrombin-antithrombin complex). Parameter calibration was performed based on clinical and in vivo data reported in published literatures (1, 3-5). To evaluate the contribution of each parameter and biomarker to early platelet decrease, sensitivity analysis (SA) and simulations with inhibiting IL-6, tumor necrosis factor alpha (TNFα), and TF were performed.
Results: The predictions of neutrophil counts, platelet counts, and circulating biomarker levels by our model captured the clinical time course following pathogen infection. Simulated platelet counts reached their nadir approximately 48 hours after pathogen infection. SA evaluating the contribution of each parameter to platelet counts at 48 hours suggested that parameters related to platelet activation by vascular damage had higher contributions to early platelet decrease compared to other parameters. Simulation results with 90% inhibition of IL-6, TNFα, or TF showed that inhibition of a single factor had slight impact on platelet decrease within 48 hours, but combined inhibition of all these factors reduced platelet decrease from 73.9% to 62.0%.
Conclusions: The present quantitative systems model could simulate dynamic changes in blood markers of inflammation and coagulation quantitatively and will be useful for informing drug development strategies for sepsis patients.
Citations: [1] Ye Q, Wang X, Xu X, et al. Serial platelet count as a dynamic prediction marker of hospital mortality among septic patients. Burns Trauma. 2024;12:tkae016. [2] Williams B, Zou L, Pittet JF, Chao W. Sepsis-Induced Coagulopathy: A Comprehensive Narrative Review of Pathophysiology, Clinical Presentation, Diagnosis, and Management Strategies. Anesth Analg. 2024;138(4):696-711. [3] Malkin AD, Sheehan RP, Mathew S, et al. A Neutrophil Phenotype Model for Extracorporeal Treatment of Sepsis. PLoS Comput Biol. 2015;11(10):e1004314. [4] Liu F, Aulin LBS, Guo T, et al. Modelling inflammatory biomarker dynamics in a human lipopolysaccharide (LPS) challenge study using delay differential equations. Br J Clin Pharmacol. 2022;88(12):5420-7. [5] de Jonge E, Dekkers PEP, Creasey AA, et al. Tissue factor pathway inhibitor dose-dependently inhibits coagulation activation without influencing the fibrinolytic and cytokine response during human endotoxemia. Blood. 2000;95(4):1124-9.
