A systems biology approach to dynamic modeling and inter-subject variability of statin pharmacokinetics in human hepatocytes

Systemic Lupus Erythematosus Activity Affects the Sinusoidal Uptake Transporter OATP1B1 Evaluated by the Pharmacokinetics of Atorvastatin

The present study assessed the effect of systemic lupus erythematosus (SLE) activity, a chronic and inflammatory autoimmune disease, on the sinusoidal uptake transporter OATP1B1 using atorvastatin (ATV) as a probe drug. Fifteen healthy subjects, 13 patients with controlled SLE (SLEDAI 0-4), and 12 patients with uncontrolled SLE (SLEDAI from 6 to 15), all women, were investigated. Apparent total clearance of midazolam (MDZ), a marker of CYP3A4 activity, did not vary among the three investigated groups. The controlled and uncontrolled SLE groups showed higher plasma concentrations of MCP-1 and TNF-α, while the uncontrolled SLE group also showed higher plasma concentrations of IL-10. The uncontrolled SLE group showed higher area under the curve (AUC) for ATV (60.47 (43.76-83.56) vs. 30.56 (22.69-41.15) ng⋅hour/mL) and its inactive metabolite ATV-lactone (98.74 (74.31-131.20) vs. 49.21 (34.89-69.42) ng⋅hour/mL), and lower apparent total clearance (330.7 (239.30-457.00) vs. 654.5 (486.00-881.4) L/hour) and apparent volume of distribution (2,609 (1,607-4,234) vs. 7,159 (4,904-10,450) L), when compared to the healthy subjects group (geometric mean and 95% confidence interval). The pharmacokinetics of ATV and its metabolites did not differ between the healthy subject group and the patients with controlled SLE group. In conclusion, uncontrolled SLE increased the systemic exposure to both ATV and ATV-lactone, inferring inhibition of OATP1B1 activity, once in vivo CYP3A4 activity assessed by oral clearance of MDZ was unaltered. The inflammatory state, not the disease itself, was responsible for the changes described in the uncontrolled SLE group as a consequence of inhibition of OATP1B1, because systemic exposure to ATV and its metabolites were not altered in patients with controlled SLE.

Visualizing metabolic network dynamics through time-series metabolomic data

Background

New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems—the human platelet and erythrocyte under cold storage for use in transfusion medicine.

Results

The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures.

Conclusions

The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user’s guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.

Predicting network modules of cell cycle regulators using relative protein abundance statistics

BACKGROUND

Parameter estimation in systems biology is typically done by enforcing experimental observations through an objective function as the parameter space of a model is explored by numerical simulations. Past studies have shown that one usually finds a set of "feasible" parameter vectors that fit the available experimental data equally well, and that these alternative vectors can make different predictions under novel experimental conditions. In this study, we characterize the feasible region of a complex model of the budding yeast cell cycle under a large set of discrete experimental constraints in order to test whether the statistical features of relative protein abundance predictions are influenced by the topology of the cell cycle regulatory network.

RESULTS

Using differential evolution, we generate an ensemble of feasible parameter vectors that reproduce the phenotypes (viable or inviable) of wild-type yeast cells and 110 mutant strains. We use this ensemble to predict the phenotypes of 129 mutant strains for which experimental data is not available. We identify 86 novel mutants that are predicted to be viable and then rank the cell cycle proteins in terms of their contributions to cumulative variability of relative protein abundance predictions. Proteins involved in "regulation of cell size" and "regulation of G1/S transition" contribute most to predictive variability, whereas proteins involved in "positive regulation of transcription involved in exit from mitosis," "mitotic spindle assembly checkpoint" and "negative regulation of cyclin-dependent protein kinase by cyclin degradation" contribute the least. These results suggest that the statistics of these predictions may be generating patterns specific to individual network modules (START, S/G2/M, and EXIT). To test this hypothesis, we develop random forest models for predicting the network modules of cell cycle regulators using relative abundance statistics as model inputs. Predictive performance is assessed by the areas under receiver operating characteristics curves (AUC). Our models generate an AUC range of 0.83-0.87 as opposed to randomized models with AUC values around 0.50.

CONCLUSIONS

By using differential evolution and random forest modeling, we show that the model prediction statistics generate distinct network module-specific patterns within the cell cycle network.

Indirect protein quantification of drug-transforming enzymes using peptide group-specific immunoaffinity enrichment and mass spectrometry

Immunoaffinity enrichment of proteotypic peptides, coupled with selected reaction monitoring, enables indirect protein quantification. However the lack of suitable antibodies limits its widespread application. We developed a method in which multi-specific antibodies are used to enrich groups of peptides, thus facilitating multiplexed quantitative protein assays. We tested this strategy in a pharmacokinetic experiment by targeting a group of homologous drug transforming proteins in human hepatocytes. Our results indicate the generic applicability of this method to any biological system.

SBMLSimulator: A Java Tool for Model Simulation and Parameter Estimation in Systems Biology

The identification of suitable model parameters for biochemical reactions has been recognized as a quite difficult endeavor. Parameter values from literature or experiments can often not directly be combined in complex reaction systems. Nature-inspired optimization techniques can find appropriate sets of parameters that calibrate a model to experimentally obtained time series data. We present SBMLsimulator, a tool that combines the Systems Biology Simulation Core Library for dynamic simulation of biochemical models with the heuristic optimization framework EvA2. SBMLsimulator provides an intuitive graphical user interface with various options as well as a fully-featured command-line interface for large-scale and script-based model simulation and calibration. In a parameter estimation study based on a published model and artificial data we demonstrate the capability of SBMLsimulator to identify parameters. SBMLsimulator is useful for both, the interactive simulation and exploration of the parameter space and for the large-scale model calibration and estimation of uncertain parameter values.

Genetic variation in the UGT1A locus is associated with simvastatin efficacy in a clinical practice setting

Aim: Simvastatin is a lactone prodrug that exists in equilibrium with its active hydroxyacid through a process mediated by UGT1A enzymes. The UGT1A locus has been associated with simvastatin response and disposition in humans. Therefore, we fine-mapped the UGT1A locus to identify genetic variations contributing to simvastatin disposition and response variability. Methods: Using de-identified electronic medical records linked to a DNA biobank, we extracted information about dose and low-density lipo-protein cholesterol (LDL-C) concentrations for patients who received more than two different doses of simvastatin. Pharmacodynamic measures of simvastatin potency and efficacy were calculated from dose-response curves (E0 = baseline LDL-C, ED50 = dose yielding 50% maximum response, and Emax = maximum decrease in LDL-C) in 1100 patients. We selected 153 polymorphisms in UGT1A1 and UGT1A3 for genotyping and conducted genotype-phenotype associations using a prespecified additive model. Results: Two variants in UGT1A1 (rs2003569 and rs12052787) were associated with Emax (p = 0.0059 and 0.031, respectively; for rs2003569 the mean Emax was 59.3 ± 23.0, 62.0 ± 22.4, and 69.7 ± 24.8 mg/dl, for patients with 0, 1 or 2 copies of the minor A allele, respectively). When stratified by race, the difference in response was greater in African-Americans than in European Americans. Rs2003569 was also negatively associated with total serum bilirubin levels (p = 7.85 × 10-5). Four rare SNPs were nominally associated with E0 and ED50. Conclusion: We identified a UGT1A1 promoter variant (rs2003569) associated with simvastatin efficacy. Original submitted 26 March 2014; Revision submitted 26 August 2014.

Predicting individual responses to pravastatin using a physiologically based kinetic model for plasma cholesterol concentrations

We used a previously developed physiologically based kinetic (PBK) model to analyze the effect of individual variations in metabolism and transport of cholesterol on pravastatin response. The PBK model is based on kinetic expressions for 21 reactions that interconnect eight different body cholesterol pools including plasma HDL and non-HDL cholesterol. A pravastatin pharmacokinetic model was constructed and the simulated hepatic pravastatin concentration was used to modulate the reaction rate constant of hepatic free cholesterol synthesis in the PBK model. The integrated model was then used to predict plasma cholesterol concentrations as a function of pravastatin dose. Predicted versus observed values at 40 mg/d pravastatin were 15 versus 22 % reduction of total plasma cholesterol, and 10 versus 5.6 % increase of HDL cholesterol. A population of 7,609 virtual subjects was generated using a Monte Carlo approach, and the response to a 40 mg/d pravastatin dose was simulated for each subject. Linear regression analysis of the pravastatin response in this virtual population showed that hepatic and peripheral cholesterol synthesis had the largest regression coefficients for the non-HDL-C response. However, the modeling also showed that these processes alone did not suffice to predict non-HDL-C response to pravastatin, contradicting the hypothesis that people with high cholesterol synthesis rates are good statin responders. In conclusion, we have developed a PBK model that is able to accurately describe the effect of pravastatin treatment on plasma cholesterol concentrations and can be used to provide insight in the mechanisms behind individual variation in statin response.

A multi-scale modeling framework for individualized, spatiotemporal prediction of drug effects and toxicological risk

In this study, we focus on a novel multi-scale modeling approach for spatiotemporal prediction of the distribution of substances and resulting hepatotoxicity by combining cellular models, a 2D liver model, and whole body model. As a case study, we focused on predicting human hepatotoxicity upon treatment with acetaminophen based on in vitro toxicity data and potential inter-individual variability in gene expression and enzyme activities. By aggregating mechanistic, genome-based in silico cells to a novel 2D liver model and eventually to a whole body model, we predicted pharmacokinetic properties, metabolism, and the onset of hepatotoxicity in an in silico patient. Depending on the concentration of acetaminophen in the liver and the accumulation of toxic metabolites, cell integrity in the liver as a function of space and time as well as changes in the elimination rate of substances were estimated. We show that the variations in elimination rates also influence the distribution of acetaminophen and its metabolites in the whole body. Our results are in agreement with experimental results. What is more, the integrated model also predicted variations in drug toxicity depending on alterations of metabolic enzyme activities. Variations in enzyme activity, in turn, reflect genetic characteristics or diseases of individuals. In conclusion, this framework presents an important basis for efficiently integrating inter-individual variability data into models, paving the way for personalized or stratified predictions of drug toxicity and efficacy.

A multi-route model of nicotine-cotinine pharmacokinetics, pharmacodynamics and brain nicotinic acetylcholine receptor binding in humans

The pharmacokinetics of nicotine, the pharmacologically active alkaloid in tobacco responsible for addiction, are well characterized in humans. We developed a physiologically based pharmacokinetic/pharmacodynamic model of nicotine pharmacokinetics, brain dosimetry and brain nicotinic acetylcholine receptor (nAChRs) occupancy. A Bayesian framework was applied to optimize model parameters against multiple human data sets. The resulting model was consistent with both calibration and test data sets, but in general underestimated variability. A pharmacodynamic model relating nicotine levels to increases in heart rate as a proxy for the pharmacological effects of nicotine accurately described the nicotine related changes in heart rate and the development and decay of tolerance to nicotine. The PBPK model was utilized to quantitatively capture the combined impact of variation in physiological and metabolic parameters, nicotine availability and smoking compensation on the change in number of cigarettes smoked and toxicant exposure in a population of 10,000 people presented with a reduced toxicant (50%), reduced nicotine (50%) cigarette Across the population, toxicant exposure is reduced in some but not all smokers. Reductions are not in proportion to reductions in toxicant yields, largely due to partial compensation in response to reduced nicotine yields. This framework can be used as a key element of a dosimetry-driven risk assessment strategy for cigarette smoke constituents.