Alessandro Boianelli (1),Stefan Scheuerer (1)
(1) Drug Discovery Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an der Riß, Germany
Objectives: Increased fructose consumption has been suggested to contribute strongly to the de novo lipogenesis and consequently to the non-alcoholic fatty liver disease, dyslipidemia, and insulin resistance. However a causal role of fructose in these metabolic diseases remains debated [1]. Quantitative systems pharmacology models can represent an important and useful tool to improve the holistic understanding of the correlation between the fructose metabolism and lipid accumulation via de novo lipogenesis pathways. To this purpose, the main objectives of this work are:
- develop a mechanistic mathematical model to better understand the acute and long term effect of high fructose diet on non–alcoholic fatty liver disease biomarkers in vivo;
- elucidate the relative importance of enzymatic pathways involved in fatty acids and triglycerides formation
- prioritize specific therapeutic targets in the de novo lipogenesis pathways
Methods: the quantitative systems pharmacology model based on the ordinary differential equations consider as compartments the portal vein, the plasma and the hepatocytes cytosol: This last compartment encompasses the major pathways of fructose and glucose metabolism in the liver considering also the effects of hormonal (insulin and glucagon) and allosteric regulations. The reaction rates of the enzymatic processes follow Michaelis-Menten and Hill function form. The kinetic parameters and the initial conditions for the metabolites included in the model were fixed according to the concentration values present in literature [2], [3] and BRENDA database [4]. Experimental data were generated using C57BL/6 mice receiving a 2 g/kg dose of fructose administered either orally or intraperitoneally. Fructose and fructose 1-phosphate, as the first biomarker in the fructose pathway, concentrations were measured in plasma, portal vein and liver every 15 min for 3 hours. Moreover, in order to evaluate the long term effect of fructose intake on NAFLD biomarkers (liver triglycerides, free fatty acids, glucose, lactate and glycogen), we simulated a high fructose diet for 12 weeks with two different intake rate of 0.8 g/kg and 2 g/kg every 2 hours respectively. All the model simulations for the acute and long term fructose intake were performed using MATLAB R2016b.
Results: Fructose and fructose 1-phosphate concentration in the liver were described adequately by the mathematical model. The fructose was already absorbed in the portal vein and in the liver within 15 min after fructose challenge and returned at steady state level in 2 hours. Consequently the fructose 1-phosphate concentration showed the same temporal profile as fructose. Moreover during the same observation time, model simulations revealed modest production of glucose, glycogen and lactate. The same pattern was also conserved for liver triglycerides and fatty acids. On the contrary, the long term high fructose diet simulations showed an increase of liver glucose from 5.5 mM to 6.8 mM and lactate concentrations changing from 1 mM to 1.2 mM. Furthermore, liver triglycerides level after the high fructose diet exhibited a steady state level of 48 mM compared to the physiological level considered (32 mM). Interestingly, the two fructose intake levels presented the same steady state levels for the biomarkers considered.
Conclusions: We developed a quantitative systems pharmacology model for the fructose metabolism in the liver. The model was able to reproduce experimental data obtained in mice. The long term high fructose diet simulations showed a severe increase of liver triglycerides, thus suggesting the possible role of fructose to contribute to the development of metabolic syndrome.
References:
[1] ter Horst, K. W., & Serlie, M. J. (2017). Fructose consumption, lipogenesis, and non-alcoholic fatty liver disease. Nutrients, 9(9), 981.
[2] Foguet, Carles, et al. “HepatoDyn: a dynamic model of hepatocyte metabolism that integrates 13C isotopomer data.” PLoS computational biology 12.4 (2016): e1004899.
[3] Bulik, Sascha, Hermann-Georg Holzhütter, and Nikolaus Berndt. “The relative importance of kinetic mechanisms and variable enzyme abundances for the regulation of hepatic glucose metabolism–insights from mathematical modeling.” BMC biology14.1 (2016): 15.
[4] Schomburg, I., Jeske, L., Ulbrich, M., Placzek, S., Chang, A., & Schomburg, D. (2017). The BRENDA enzyme information system–From a database to an expert system. Journal of biotechnology, 261, 194-206.
Reference: PAGE 27 (2018) Abstr 8491 [www.page-meeting.org/?abstract=8491]
Poster: Drug/Disease Modelling - Other Topics