Model-based dietary optimization for late-stage, levodopa-treated, Parkinson’s disease patients

Abstract

Levodopa has been the gold standard for Parkinson’s disease treatment for more than 40 years. Its bioavailability is hindered by dietary amino acids, leading to fluctuations in the motor response particularly in late-stage (stage 3 and 4 on Hoehn and Yahr scale) patients. The routine dietary intervention consists of low-protein (<0.8 g/kg) diets or the redistribution of daily protein allowance to the last meal. Computational modeling was used to examine the fluctuation of gastrointestinal levodopa absorption under consideration of the diet by (i) identifying the group of patients that could benefit from dietary interventions, (ii) comparing existing diet recommendations for their impact on levodopa bioavailability, and (iii) suggesting a mechanism-based dietary intervention. We developed a multiscale computational model consisting of an ordinary differential equations-based advanced compartmentalized absorption and transit (ACAT) gut model and metabolic genome-scale small intestine epithelial cell model. We used this model to investigate complex spatiotemporal relationship between dietary amino acids and levodopa absorption. Our model predicted an improvement in bioavailability, as reflected by blood concentrations of levodopa with protein redistribution diet by 34% compared with a low-protein diet and by 11% compared with the ante cibum (a.c.) administration. These results are consistent with the reported better outcome in late-stage patients. A systematic analysis of the effect of different amino acids in the diet suggested that a serine-rich diet could improve the bioavailability by 22% compared with the a.c. administration. In addition, the slower gastric emptying rate in PD patients exacerbates the loss of levodopa due to competition. Optimizing dietary recommendations in quantity, composition, and intake time holds the promise to improve levodopa efficiency and patient’s quality of life based on highly detailed, mechanistic models of gut physiology endowed with improved extrapolative properties, thus paving the way for precision medical treatment.

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0

“…New flux balance methods, multi-scale model rescaling and multi-scale solvers, additional solver interfaces, thermodynamically feasible methods 7,53,129,133,142,143 .…”

Quantitative systems pharmacology and the personalized drug–microbiota–diet axis

“…The drug-dependent parameters allow for the mechanistic extrapolation of human pharmacokinetics from in vitro and in silico data via a “bottom-up” approach [31] . Whole-body PBPK models have been published for at least 50 drugs 32 , 33 , ∗∗34 , including 32 with an advanced compartment absorption and transit model (ACAT). The ACAT model [35] was based on a CAT model [36] , which did not consider the dissolution of solid particles.…”

“… [55] integrated a COBRA model of cellular liver metabolism, which consisted of 777 metabolites and 2539 reactions [57] , with a PBPK model of an human adult to demonstrate an increase in predictive accuracy and mechanistic understanding for allopurinol treatment, ammonia detoxification, and paracetamol toxication. In a subsequent study, we combined seven copies of a COBRA model for a small intestinal epithelial cell [18] , each consisting of 433 metabolites and 1318 reactions, with a physiology-based pharmacokinetic ACAT model [34] . We used this model to investigate the role of intestinal absorption on the bioavailability of levodopa, the predominant drug administered to patients with Parkinson's disease.…”

Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation

“… 31 While acetaminophen intoxication was already analyzed in this initial study, the drug-induced perturbation of the cellular metabolism was, however, rather qualitatively addressed through the impaired capability of a metabolic network to fulfill a set of predefined metabolic tasks. The previously established concept of combining PBPK and GSMN models has been further applied for modeling the interstitial uptake of levodopa, 32 cortisol signaling, 33 diabetes, 34 and the impact of phenytoin on estradiol metabolism. 35 In complementary approaches, stoichiometric models have been considered within a whole-body context by using static multi-tissue GSMN models to investigate the endogenous metabolic interplay in diabetes 36 and for the analysis of the impact of different diets on the human metabolism and xenobiotic reaction stoichiometry.…”