F. Liu (1), L. Aulin (1), H. Taghvafard (1), P.H. van der Graaf (1,2), J. Burggraaf (1,3), M. Moerland (3), J.G.C. van Hasselt (1)
(1) Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands, (2) Certara QSP, Canterbury Innovation House, Canterbury, UK, (3) Centre for Human Drug Research, Leiden, The Netherlands.
Objectives:
Sepsis is a life-threatening condition that arises when the body’s response to infection causes injury to its own tissues and organs. It is associated with high mortality and is typically treated with a broad-coverage antibiotic treatment. Sepsis is characterized by uncontrolled excessive production of pro- and anti-inflammatory cytokines, where tumor necrosis factor (TNF-α) and interleukin-6 (IL-6) are key pro-inflammatory mediators [1]. The immune response is typically triggered by activation of pathogen recognition receptors such as the Toll-like receptor 4 (TLR4) by lipopolysaccharide (LPS). Clinical challenge studies where LPS is administered to healthy volunteers to induce an inflammatory response are relevant to characterize immune response biomarker response profiles associated with TLR4 activation, although these studies should not be seen as a model for sepsis. Quantitative understanding of inflammatory biomarker dynamics during infection and sepsis is relevant to monitor disease progression and treatment response but remains currently poorly understood. We aimed to develop a dynamic model of immune biomarker for IL-6, IL-8, TNF-α and C-reactive protein (CRP) dynamics after administration of LPS to help quantitative interpretation of clinical sepsis biomarker studies.
Methods:
Previously reported LPS concentration-time profiles in healthy volunteers were used to obtain insight in the pharmacokinetics of LPS [2]. Data from a previously conducted clinical LPS challenge study in healthy male volunteers (3 cohorts of 8 subjects, LPS_placebo=6:2) who received a single ascending low dose of LPS (0.5, 1.0 or 2.0 ng/kg) was used to study the kinetics of inflammatory biomarkers [3]. For each volunteer, multiple blood samples were obtained prior to LPS injection and within 24 h after dosing, cytokines (IL-6, IL-8, TNFα) and C-reactive protein (CRP) were measured in plasma to investigate the in vivo inflammatory response. Vital signs were assessed by measurement of temperature, heart rate and blood pressure.
For LPS concentration-time profiles we evaluated different compartmental models. For the pharmacodynamic models we aimed to use indirect response models in combination with transit models to account for the delay between receptor activation and release of inflammatory biomarkers. Linear, exponential, power, Hill and Emax functions were tested to investigate the relationship between LPS and inflammatory markers. A prediction-corrected visual predictive check was used to evaluate model fit. Parameter precision was evaluated using bootstrap analysis. All diagnostic procedures were implemented by using PsN and all models were fitted in NONMEM version 7.3 using the First Order Conditional Estimation method.
Results:
A one-compartment PK model with first-order elimination was used to capture the time-concentration profile of LPS. Clearance (CL) and volume of distribution (V) were estimated to be 46.2 L/h and 6.62 L, respectively.
The LPS exposure-response relationship for IL-6, IL-8 and TNFα in relation to LPS exposure was described using an indirect response model in combination with a transit model to account for the delay in TLR4 signal transduction. The baseline value for IL-6, IL-8 and TNFα were estimated to 6.00, 6.40 and 3.31 pg/mL, respectively. Mean transfer time (MTT) within transit compartments were 2.38, 2.76 and 2.20 h-1, respectively. Slope factor between LPS and cytokines concentration were 4.73, 3.96 and 5.81, respectively. This model was then linked to a second LPS indirect response model to describe the relationship between IL-6 induced changes in CRP, with a baseline of 1.12 mg/L, an MTT of 10.4 h-1, a slope factor of 1.48, and an induction rate constant of 0.03 h-1. The final model described the observed concentration-time profiles accurately with no major model misspecification and with good predictive performance.
Conclusions:
We developed a model describing the dynamic relationship between LPS exposure, IL-6, IL-8 and TNF-α, and between IL-6 and CRP. The model can be used as a basis to quantitatively interpret inflammatory biomarker kinetics in patients with sepsis to guide treatment strategies.
References:
[1] Schulte W., Bernhagen J., Bucala R. Cytokines in Sepsis: Potent Immunoregulators and Potential Therapeutic Targets—An Updated View. Mediators Inflamm 2013. 2013: 165974.
[2] Van Deventer, S. J. H., Buller, H. R., Ten Cate, et.al. Experimental Endotoxemia in Humans: Analysis of Cytokine Release and Coagulation, Fibrinolytic, and Complement Pathways. Blood 1990. 76 (12): 2520-2526.
[3] Dilingh, M.S., Poelgeest, E.P., Malone, K.E, et.al. Characterization of inflammation and immune cell modulation induced by low-dose LPS administration to healthy volunteers. Journal of Inflammation 2014. 11:28
Reference: PAGE 28 (2019) Abstr 8937 [www.page-meeting.org/?abstract=8937]
Poster: Drug/Disease Modelling - Infection