Interspecies scaling of host inflammatory response during infection
A.M. (Marinda) van der Kuijl (1), F. Liu (1), C.A.J. Knibbe (1,2), E.H.J. Krekels (1), J.G.C. van Hasselt (1)
(1)Division of Systems Pharmacology and Pharmacy, Leiden Academic Centre for Drug Research, Leiden University, Leiden, the Netherlands, (2)Department of Clinical Pharmacy, St. Antonius Hospital, Nieuwegein, The Netherlands
Introduction: Sepsis is defined as a life-threatening condition that is caused by an disregulated host response to an infection, and which is associated with a high mortality[1]. Therapeutics strategies targeting the hosts’ inflammatory response offer a potential strategy for treatment of sepsis besides antibiotic treatments[2]. However, development of such treatments in sepsis have not been successful. This lack of success may be attributed to a lack of understanding of inter-species differences in inflammatory response in sepsis[2], [3]. Experimental lipopolysaccharide (LPS) challenge models in animals and healthy volunteers are commonly used to study the inflammatory response in sepsis. We hypothesize that inter-species differences in the pharmacodynamic response to LPS could be used to improve translational predictions between species. In this project, we aim to apply a model-based strategy to identify inter-species differences and similarities in the inflammatory response induced by LPS, which may help to address translational gaps in context of drug development.
Methods: We searched the literature for LPS challenge studies in animals, which included multiple time course samples for the inflammatory biomarkers, i.e., interleukin 6 (IL-6) and/or tumor necrosis factor alpha (TNF-α). Extracted metadata included LPS dose, LPS type, time course and concentrations of inflammatory biomarkers, limit of quantification of the biomarkers, and species characteristics. The inflammatory biomarker kinetics was extracted from tables of figures using GetData Graph Digitizer[4]. A systematic curation procedure was applied to the data to confirm accuracy and reliability.
A K-PD model was applied to capture the LPS kinetics and biomarker dynamics per species, since pharmacokinetic data is not available for LPS in animals. Turn-over models with zero order production and first order degradation were employed to describe the biomarker dynamics. Parameterization of the turn-over model was optimized before different effect models were included. LPS was assumed to stimulate the production of the biomarkers, for which different effect models (e.g. linear, non-linear) were tested. Delay differential equations (DDEs) were implemented to describe the delay between LPS exposure and stimulation of cytokine production[5]. The key estimated parameters, e.g. the production rate (), the degradation rate (), delay (τ) and effect parameters were compared to analyze inter-species differences and similarities.
Results: TNF-α and IL-6 concentration-time courses in mice, rats and pigs were obtained from 14 studies, with LPS doses ranged from 0.0005 to 20 mg/kg, 0.001 to 1 mg/kg and 0.063 to 16 µg/kg/h in mice, rats and pigs, respectively. A total of 538 samples were collected, of which 166 (31%) were of IL-6 and 372 (69%) of TNF-α. The sampling times ranged from 4 to 24 hours after LPS dose across all species. A non-linear relationship between LPS exposure and TNF-α and IL-6 response was observed for mice and pigs, and a linear relationship for rats. Across all species, a larger time delay in response is seen in IL-6 (3 hours) than in TNF-α (1 hour) with no differences between the species.
Conclusions: Qualitative and quantitative analysis of the models on IL-6 and TNF-α concentrations over time can help understand the differences among species and translate between species, ultimately to help improve the drug development for sepsis.
References:
[1] M. Singer et al., ‘The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)’, JAMA, vol. 315, no. 8, pp. 801–810, Feb. 2016, doi: 10.1001/jama.2016.0287.
[2] J. Cavaillon, M. Singer, and T. Skirecki, ‘Sepsis therapies: learning from 30 years of failure of translational research to propose new leads’, EMBO Mol Med, vol. 12, no. 4, Apr. 2020, doi: 10.15252/emmm.201810128.
[3] A. Dyson and M. Singer, ‘Animal models of sepsis: Why does preclinical efficacy fail to translate to the clinical setting?’:, Critical Care Medicine, vol. 37, no. Supplement, pp. S30–S37, Jan. 2009, doi: 10.1097/CCM.0b013e3181922bd3.
[4] Fedorov, S., ‘GetData Graph Digitizer’. [Software]. Available: http://getdata-graph-digitizer.com
[5] F. Liu et al., ‘Modelling inflammatory biomarker dynamics in a human lipopolysaccharide (LPS) challenge study using delay differential equations’, British Journal of Clinical Pharmacology, vol. n/a, no. n/a, 2022, doi: 10.1111/bcp.15476.