Integrating cellular metabolism into a multiscale whole-body model

Abstract

Cellular metabolism continuously processes an enormous range of external compounds into endogenous metabolites and is as such a key element in human physiology. The multifaceted physiological role of the metabolic network fulfilling the catalytic conversions can only be fully understood from a whole-body perspective where the causal interplay of the metabolic states of individual cells, the surrounding tissue and the whole organism are simultaneously considered. We here present an approach relying on dynamic flux balance analysis that allows the integration of metabolic networks at the cellular scale into standardized physiologically-based pharmacokinetic models at the whole-body level. To evaluate our approach we integrated a genome-scale network reconstruction of a human hepatocyte into the liver tissue of a physiologically-based pharmacokinetic model of a human adult. The resulting multiscale model was used to investigate hyperuricemia therapy, ammonia detoxification and paracetamol-induced toxication at a systems level. The specific models simultaneously integrate multiple layers of biological organization and offer mechanistic insights into pathology and medication. The approach presented may in future support a mechanistic understanding in diagnostics and drug development.

Figure 1. A bow-tie scheme illustrating the consideration of metabolic networks within a whole-body context.

(A) Schematic representation of the multiscale approach. PBPK models are used to quantitatively describe the tissue specific availability of exogenous and endogenous compounds at the organism level. The PBPK models are coupled with stoichiometric networks by means of exchange rates calculated with dFBA. Both consumption and formation of metabolites as well as regulatory effects can be simulated. (B) Possible fields of application, illustrating the broad applicability of the approach, are (i) multiscale PK/PD modeling, (ii) quantitative identification of disease specific or individualized biomarkers and (iii) analyses of drug-induced toxication.

Figure 2. Schematic representation of the feed-back loop used for direct coupling.

Flux distributions calculated by FBA are used to adjust clearance and production rates in the PBPK model. After simulating one time step in the PBPK model, new clearance rates constrain the next FBA step.

Figure 3. Reduction of uric acid production following multiple allopurinol administrations.

(A) Simulated venous plasma and intrahepatic concentration profiles of allopurinol and oxypurinol are in agreement with experimental PK data . (B) Prediction of venous plasma and intrahepatic concentration profiles of allopurinol and oxypurinol after multiple dosing based on the single application model. (C) Relative enzyme activity of xanthine oxidase (XO) following inhibition by a single dose of allopurinol. (D) Relative enzyme activity of XO following inhibition by multiple administration of allopurinol. (E) Simulated venous plasma and intrahepatic concentration profiles of uric acid following a single dose of allopurinol. (F) Simulated venous plasma and intrahepatic concentration profiles of uric acid following multiple dosing of allopurinol. Experimentally-measured venous plasma concentrations quantifying the hyperuricemic state (*) and the healthy uricemic state (**) after treatment with allopurinol are indicated.

Figure 4. Pathogenesis of a urea cycle disorder in ammonia plasma concentrations and metabolic exchange rates.

(A) Simulated venous plasma and intrahepatic concentration profiles of ammonia during development of a urea cycle disorder. The black dashed line represents the overall reduction in ornithine transcarbamylase activity. (B, C): Resulting exchange fluxes calculated with FBA during development of a urea cycle disorder. (B) Hepatic influx rates of the three substrates ammonia, oxygen and glucose. (C) Hepatic efflux rates of the three products urea, alanine and glutamine.

Figure 5. Determining the influence of inter-individual variability during development of a urea cycle disorder.

(A) Simulated venous plasma concentration profiles of ammonia in 100 individuals during development of a urea cycle disorder (single profiles and mean). (B) The distribution of ammonia concentrations as well as the cumulative sums in healthy and diseased individuals are significantly different (p<0.001).

Figure 6. Effect of a therapeutic dose and a toxic overdose of paracetamol on liver functionality.

(A) Simulated venous plasma concentration profiles of paracetamol and its three metabolites after an paracetamol dose of 1 g are in agreement with experimental PK data . (B) Prediction of venous plasma concentration profiles of paracetamol and its three metabolites after a paracetamol dose of 15 g based on the 1 g application model. (C, D) Relative enzyme activity of the three impaired enzymes THFDH, GDH, ATPS and the GSH depletion are also implemented as enzyme inhibition after the application of 1 g (C) and 15 g (D) paracetamol, respectively. (E, F) Effect of the paracetamol doses of 1 g (E) and 15 g (F), respectively, on liver functionality at three different time points. Bars represent the decrease of maximum values of every objective function which undergoes a change in its maximum value.

Figure 7. Comparison of two specific liver functions (production of oleate and cysteine, respectively).

Only fluxes which are nonzero at least in one of the three cases are compared. The number of fluxes which remain at their original values ( = ), become smaller or higher (<, >) or are non-zero (new) after application of 1 g and 15 g of paracetamol are indicated.. (A) Changes in fluxes after application of 1 g and 15 g paracetamol for production of oleate. (B) Changes in fluxes after application of 1 g and 15 g paracetamol for production of cysteine.