In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim.

BACKGROUND: The last few decades have seen the approval of many new treatment options for Relapsing-Remitting Multiple Sclerosis (RRMS), as well as advances in diagnostic methodology and criteria. These developments have greatly improved the available treatment options for today's Relapsing-Remitting Multiple Sclerosis patients. This increased availability of disease modifying treatments, however, has implications for clinical trial design in this therapeutic area. The availability of better diagnostics and more treatment options have not only contributed to progressively decreasing relapse rates in clinical trial populations but have also resulted in the evolution of control arms, as it is often no longer sufficient to show improvement from placebo. As a result, not only have clinical trials become longer and more expensive but comparing the results to those of "historical" trials has also become more difficult. METHODS: In order to aid design of clinical trials in RRMS, we have developed a simulator called MS TreatSim which can simulate the response of customizable, heterogeneous groups of patients to four common Relapsing-Remitting Multiple Sclerosis treatment options. MS TreatSim combines a mechanistic, agent-based model of the immune-based etiology of RRMS with a simulation framework for the generation and virtual trial simulation of populations of digital patients. RESULTS: In this study, the product was first applied to generate diverse populations of digital patients. Then we applied it to reproduce a phase III trial of natalizumab as published 15 years ago as a use case. Within the limitations of synthetic data availability, the results showed the potential of applying MS TreatSim to recreate the relapse rates of this historical trial of natalizumab. CONCLUSIONS: MS TreatSim's synergistic combination of a mechanistic model with a clinical trial simulation framework is a tool that may advance model-based clinical trial design.

Altered bile acid kinetics contribute to postprandial hypoglycaemia after Roux-en-Y gastric bypass surgery.

BACKGROUND/OBJECTIVES: Bile acids (BA) act as detergents in intestinal fat absorption and as modulators of metabolic processes via activation of receptors such as FXR and TGR5. Elevated plasma BA as well as increased intestinal BA signalling to promote GLP-1 release have been implicated in beneficial health effects of Roux-en-Y gastric bypass surgery (RYGB). Whether BA also contribute to the postprandial hypoglycaemia that is frequently observed post-RYGB is unknown. METHODS: Plasma BA, fibroblast growth factor 19 (FGF19), 7α-hydroxy-4-cholesten-3-one (C4), GLP-1, insulin and glucose levels were determined during 3.5 h mixed-meal tolerance tests (MMTT) in subjects after RYGB, either with (RYGB, n = 11) or without a functioning gallbladder due to cholecystectomy (RYGB-CC, n = 11). Basal values were compared to those of age, BMI and sex-matched obese controls without RYGB (n = 22). RESULTS: Fasting BA as well as FGF19 levels were elevated in RYGB and RYGB-CC subjects compared to non-bariatric controls, without significant differences between RYGB and RYGB-CC. Postprandial hypoglycaemia was observed in 8/11 RYGB-CC and only in 3/11 RYGB. Subjects who developed hypoglycaemia showed higher postprandial BA levels coinciding with augmented GLP-1 and insulin responses during the MMTT. The nadir of plasma glucose concentrations after meals showed a negative relationship with postprandial BA peaks. Plasma C4 was lower during MMTT in subjects experiencing hypoglycaemia, indicating lower hepatic BA synthesis. Computer simulations revealed that altered intestinal transit underlies the occurrence of exaggerated postprandial BA responses in hypoglycaemic subjects. CONCLUSION: Altered BA kinetics upon ingestion of a meal, as frequently observed in RYGB-CC subjects, appear to contribute to postprandial hypoglycaemia by stimulating intestinal GLP-1 release.

A computational model for the analysis of lipoprotein distributions in the mouse: translating FPLC profiles to lipoprotein metabolism.

Disturbances of lipoprotein metabolism are recognized as indicators of cardiometabolic disease risk. Lipoprotein size and composition, measured in a lipoprotein profile, are considered to be disease risk markers. However, the measured profile is a collective result of complex metabolic interactions, which complicates the identification of changes in metabolism. In this study we aim to develop a method which quantitatively relates murine lipoprotein size, composition and concentration to the molecular mechanisms underlying lipoprotein metabolism. We introduce a computational framework which incorporates a novel kinetic model of murine lipoprotein metabolism. The model is applied to compute a distribution of plasma lipoproteins, which is then related to experimental lipoprotein profiles through the generation of an in silico lipoprotein profile. The model was first applied to profiles obtained from wild-type C57Bl/6J mice. The results provided insight into the interplay of lipoprotein production, remodelling and catabolism. Moreover, the concentration and metabolism of unmeasured lipoprotein components could be determined. The model was validated through the prediction of lipoprotein profiles of several transgenic mouse models commonly used in cardiovascular research. Finally, the framework was employed for longitudinal analysis of the profiles of C57Bl/6J mice following a pharmaceutical intervention with a liver X receptor (LXR) agonist. The multifaceted regulatory response to the administration of the compound is incompletely understood. The results explain the characteristic changes of the observed lipoprotein profile in terms of the underlying metabolic perturbation and resultant modifications of lipid fluxes in the body. The Murine Lipoprotein Profiler (MuLiP) presented here is thus a valuable tool to assess the metabolic origin of altered murine lipoprotein profiles and can be applied in preclinical research performed in mice for analysis of lipid fluxes and lipoprotein composition.

Model-based data analysis of individual human postprandial plasma bile acid responses indicates a major role for the gallbladder and intestine.

BACKGROUND: Bile acids are multifaceted metabolic compounds that signal to cholesterol, glucose, and lipid homeostasis via receptors like the Farnesoid X Receptor (FXR) and transmembrane Takeda G protein-coupled receptor 5 (TGR5). The postprandial increase in plasma bile acid concentrations is therefore a potential metabolic signal. However, this postprandial response has a high interindividual variability. Such variability may affect bile acid receptor activation. METHODS: In this study, we analyzed the inter- and intraindividual variability of fasting and postprandial bile acid concentrations during three identical meals on separate days in eight healthy lean male subjects using a statistical and mathematical approach. MAIN FINDINGS: The postprandial bile acid responses exhibited large interindividual and intraindividual variability. The individual mathematical models, which represent the enterohepatic circulation of bile acids in each subject, suggest that interindividual variability results from quantitative and qualitative differences of distal active uptake, colon transit, and microbial bile acid transformation. Conversely, intraindividual variations in gallbladder kinetics can explain intraindividual differences in the postprandial responses. CONCLUSIONS: We conclude that there is considerable inter- and intraindividual variation in postprandial plasma bile acid levels. The presented personalized approach is a promising tool to identify unique characteristics of underlying physiological processes and can be applied to investigate bile acid metabolism in pathophysiological conditions.

In Silico Analysis Identifies Intestinal Transit as a Key Determinant of Systemic Bile Acid Metabolism.

Bile acids fulfill a variety of metabolic functions including regulation of glucose and lipid metabolism. Since changes of bile acid metabolism accompany obesity, Type 2 Diabetes Mellitus and bariatric surgery, there is great interest in their role in metabolic health. Here, we developed a mathematical model of systemic bile acid metabolism, and subsequently performed in silico analyses to gain quantitative insight into the factors determining plasma bile acid measurements. Intestinal transit was found to have a surprisingly central role in plasma bile acid appearance, as was evidenced by both the necessity of detailed intestinal transit functions for a physiological description of bile acid metabolism as well as the importance of the intestinal transit parameters in determining plasma measurements. The central role of intestinal transit is further highlighted by the dependency of the early phase of the dynamic response of plasma bile acids after a meal to intestinal propulsion.

Model-Based Quantification of the Systemic Interplay between Glucose and Fatty Acids in the Postprandial State.

In metabolic diseases such as Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, the systemic regulation of postprandial metabolite concentrations is disturbed. To understand this dysregulation, a quantitative and temporal understanding of systemic postprandial metabolite handling is needed. Of particular interest is the intertwined regulation of glucose and non-esterified fatty acids (NEFA), due to the association between disturbed NEFA metabolism and insulin resistance. However, postprandial glucose metabolism is characterized by a dynamic interplay of simultaneously responding regulatory mechanisms, which have proven difficult to measure directly. Therefore, we propose a mathematical modelling approach to untangle the systemic interplay between glucose and NEFA in the postprandial period. The developed model integrates data of both the perturbation of glucose metabolism by NEFA as measured under clamp conditions, and postprandial time-series of glucose, insulin, and NEFA. The model can describe independent data not used for fitting, and perturbations of NEFA metabolism result in an increased insulin, but not glucose, response, demonstrating that glucose homeostasis is maintained. Finally, the model is used to show that NEFA may mediate up to 30-45% of the postprandial increase in insulin-dependent glucose uptake at two hours after a glucose meal. In conclusion, the presented model can quantify the systemic interactions of glucose and NEFA in the postprandial state, and may therefore provide a new method to evaluate the disturbance of this interplay in metabolic disease.