F. Liu (1), L. Aulin (1), T. Guo (1), H. Taghvafard (1), P.H. van der Graaf (1,2), 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: Bacterial infections and sepsis are associated with production of several inflammatory mediators including cytokines such as tumor necrosis factor α (TNF-α), interleukin-6 (IL-6) and interleukin-8 (IL-8), and acute phase proteins such as C-reactive protein (CRP) [1]. Production of these inflammatory mediators is triggered by activation of pathogen recognition receptors such as Toll-like receptor 4 (TLR4), which is activated by lipopolysaccharide (LPS) present on the cell wall of Gram-negative bacteria. Human healthy volunteer LPS challenge studies represent an important model to study the inflammatory response in various clinical conditions including sepsis. Quantitative understanding of the time course and inter-individual variation of inflammatory biomarker dynamics can help to better interpret the dynamics of these biomarkers in septic patients. Here, we aimed to develop a quantitative model to characterize the dynamics of immune biomarkers TNF-α, IL-6, IL-8 and CRP after LPS administration in healthy volunteers.
Methods: Data: Previously reported LPS concentration-time data in healthy volunteers were used to model the pharmacokinetics of LPS [2]. We obtained biomarker data from two previously conducted clinical LPS challenge studies in healthy male volunteers. Study 1 consisted of 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)[3]. Study 2 consisted of 4 healthy volunteers with a systemic challenge of 2.0 ng/kg LPS[4]. For each volunteer, multiple blood samples were drawn prior to LPS injection and within 24 h after dosing, cytokines (TNF-α, IL-6, IL-8) and CRP were measured in plasma to investigate the longitudinal inflammatory response in vivo.
Model development: We evaluated standard compartmental models to analyze the pharmacokinetics of LPS. The relationship between LPS exposure and biomarker concentrations were modeled using indirect response models, delay differential equations (DDE) were implemented to address the observed delayed effect of LPS on the biomarkers.. The M3 method was used to handle the data below the limit of quantification. All models were fitted using the stochastic approximation expectation maximization (SAEM) and importance sampling (IMP) methods with DDE solver system in NONMEM version 7.5.
Results: A one-compartment PK model with first-order elimination was used to described the pharmacokinetics of LPS. Clearance (CL) and volume of distribution (V) were estimated to be 35.7 L/h and 6.35 L, respectively.
The relationship between LPS and TNF-α, IL-6 and IL-8 was described by an indirect response model with a linear stimulation on the production of the biomarkers, in combination with DDEs to account for the delay in TLR4 signal transduction. The baseline values for TNF-α, IL-6 and IL-8 were estimated to be 2.14, 0.695 and 3.27 pg/mL, respectively. Delay factors were 0.924, 1.46 and 1.48 h, respectively. The slope factors accounting for the relationship between LPS exposure and different biomarkers concentrations were 52.8, 41.5 and 67.1 mL/pg, respectively. The production rate of IL-6 was modeled according to a time-dependent exponential decay factor of 0.038 h-1. CRP concentration was modeled assuming IL-6 instead of LPS stimulates the production rate of CRP, with a baseline of 0.381 mg/L, an time delay factor of 4.2 h, and a slope factor of 19.2 L/mg.
Conclusions: We successfully developed quantitative models to describe the dynamic relationship between LPS and cytokines TNF-α, IL-6 and IL-8, as well as between IL-6 and CRP. The time delay between LPS concentration and biomarker concentration was adequately captured using DDEs. The developed models may be used as a basis to quantitatively interpret inflammatory biomarker dynamics in patients.
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 SJ, Büller HR, ten Cate JW, 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. J Inflamm 2014;11:28
[4] Monnet E, Lapeyre G, Poelgeest EV, et al. Evidence of NI-0101 pharmacological activity, an anti-TLR4 antibody, in a randomized phase I dose escalation study in healthy volunteers receiving LPS. Clin Pharmacol Ther. 2017;101(2):200-208.
Reference: PAGE 29 (2021) Abstr 9794 [www.page-meeting.org/?abstract=9794]
Poster: Drug/Disease Modelling - Infection