A computational model of liver iron metabolism

A whole-body mechanistic physiologically-based pharmacokinetic modeling of intravenous iron

Iron is essential for every cell of the mammalian organism. Iron deficiency is a major public health issue worldwide. Intravenous (IV) iron therapy has been used to treat anemia. However, IV iron therapy is known far away from ideal because the quantitative relationship between the pharmacokinetics and biodistribution of IV iron under different iron statuses remains unclear. Patients are known to suffer adverse effects from excessive iron accumulation. Our objective was to develop a physiologically based pharmacokinetic (PBPK) model of iron in mice and validate its application for predicting iron disposition in rats and humans. Previously published data on iron were collected for constructing the PBPK model of iron in mice, and then extrapolated to rats and humans based on physiologically and chemically specific parameters relevant to each species. The PBPK model characterized the distribution of iron in mice successfully. The model based on extrapolation to rats accurately simulated the ferric carboxymaltose (FCM) PK profiles in rat tissues. Similarly, the observed and simulated serum PK of FCM in humans were in reasonable agreement. This mechanistic whole-body PBPK model is useful for understanding and predicting iron effects on different species. It also establishes a foundation for future research that incorporates iron kinetics and biodistribution, along with related clinical experiments. This approach could lead to the development of effective and personalized iron deficiency anemia treatments.

Mathematical modeling reveals ferritin as the strongest cellular driver of dietary iron transfer block in enterocytes

Intestinal mucosal block is the transient reduction in iron absorption ability of intestinal epithelial cells (enterocytes) in response to previous iron exposures that occur at the cell scale. The block characteristics have been shown to depend both on iron exposure magnitude and temporality, and understanding block control will enable deeper understanding of how intestinal iron absorption contributes to pathological iron states. Three biochemical mechanisms implicated in driving the block behavior are divalent metal transporter 1 endocytosis, ferritin iron sequestration, and iron regulatory protein regulation of iron related protein expression. In this work, a model of enterocyte iron metabolism is built based on published experimental data that is capable of reproducing the mucosal block phenomena. The model is then used to estimate the quantitative contribution of each of the three mechanisms on the properties of the mucosal block. Analysis reveals that ferritin and iron regulatory proteins are the main intracellular mechanisms contributing to the mucosal block, findings congruent with experimental predictions. Lastly, DMT1 endocytosis is shown to play a role in limiting total iron uptake by enterocytes but does not contribute to the decrease in total iron transfer across their basal membrane seen in the mucosal block.

A Systems Biology Approach Towards a Comprehensive Understanding of Ferroptosis

Ferroptosis is a regulated cell death process characterized by iron ion catalysis and reactive oxygen species, leading to lipid peroxidation. This mechanism plays a crucial role in age-related diseases, including cancer and cardiovascular and neurological disorders. To better mimic iron-induced cell death, predict the effects of various elements, and identify drugs capable of regulating ferroptosis, it is essential to develop precise models of this process. Such drugs can be tested on cellular models. Systems biology offers a powerful approach to studying biological processes through modeling, which involves accumulating and analyzing comprehensive research data. Once a model is created, it allows for examining the system's response to various stimuli. Our goal is to develop a modular framework for ferroptosis, enabling the prediction and screening of compounds with geroprotective and antiferroptotic effects. For modeling and analysis, we utilized BioUML (Biological Universal Modeling Language), which supports key standards in systems biology, modular and visual modeling, rapid simulation, parameter estimation, and a variety of numerical methods. This combination fulfills the requirements for modeling complex biological systems. The integrated modular model was validated on diverse datasets, including original experimental data. This framework encompasses essential molecular genetic processes such as the Fenton reaction, iron metabolism, lipid synthesis, and the antioxidant system. We identified structural relationships between molecular agents within each module and compared them to our proposed system for regulating the initiation and progression of ferroptosis. Our research highlights that no current models comprehensively cover all regulatory mechanisms of ferroptosis. By integrating data on ferroptosis modules into an integrated modular model, we can enhance our understanding of its mechanisms and assist in the discovery of new treatment targets for age-related diseases. A computational model of ferroptosis was developed based on a modular modeling approach and included 73 differential equations and 93 species.

Putting square pegs in round holes: why traditional pharmacokinetic principles cannot universally be applied to iron-carbohydrate complexes

Intravenous iron-carbohydrate complexes are nanomedicines that are commonly used to treat iron deficiency and iron deficiency anemia of various etiologies. Many challenges remain regarding these complex drugs in the context of fully understanding their pharmacokinetic parameters. Firstly, the measurement of the intact iron nanoparticles versus endogenous iron concentration fundamentally limits the availability of data for computational modeling. Secondly, the models need to include several parameters to describe the iron metabolism which is not completely defined and those identified (e.g. ferritin) exhibit considerable interpatient variability. Additionally, modeling is further complicated by the lack of traditional receptor/enzyme interactions. The known parameters of bioavailability, distribution, metabolism, and excretion for iron-carbohydrate nanomedicines will be reviewed and future challenges that currently prevent the direct application of physiologically-based pharmacokinetic or other computational modeling techniques will be discussed.

Modeling the iron storage protein ferritin reveals how residual ferrihydrite iron determines initial ferritin iron sequestration kinetics

Computational models can be created more efficiently by composing them from smaller, well-defined sub-models that represent specific cellular structures that appear often in different contexts. Cellular iron metabolism is a prime example of this as multiple cell types tend to rely on a similar set of components (proteins and regulatory mechanisms) to ensure iron balance. One recurrent component, ferritin, is the primary iron storage protein in mammalian cells and is necessary for cellular iron homeostasis. Its ability to sequester iron protects cells from rising concentrations of ferrous iron limiting oxidative cell damage. The focus of the present work is establishing a model that tractably represents the ferritin iron sequestration kinetics such that it can be incorporated into larger cell models, in addition to contributing to the understanding of general ferritin iron sequestration dynamics within cells. The model's parameter values were determined from published kinetic and binding experiments and the model was validated against independent data not used in its construction. Simulation results indicate that FT concentration is the most impactful on overall sequestration dynamics, while the FT iron saturation (number of iron atoms sequestered per FT cage) fine tunes the initial rates. Finally, because this model has a small number of reactions and species, was built to represent important details of FT kinetics, and has flexibility to include subtle changes in subunit composition, we propose it to be used as a building block in a variety of specific cell type models of iron metabolism.

Influence of prazosin on systemic iron levels and the associated iron metabolic alterations in spontaneously hypertensive rats

The relationship between cardiovascular diseases and iron disorders has gained increasing attention; however, the effects of hypotensive drugs on iron metabolic alterations in hypertension are not well understood. The purpose of this study was to investigate iron metabolic changes after prazosin treatment of spontaneously hypertensive rats (SHRs) and Wistar–Kyoto (WKY) rats. Our second objective was to examine the effects of hypertension and anti‐hypertensive drugs on bone formation and resorption. SHRs and WKY rats were randomized into either prazosin‐treated groups (WKY + PZ and SHR + PZ) or untreated groups (WKY and SHR). After 7 days of intragastric prazosin administration, the rats were sacrificed for analysis; blood samples and organs (the duodenum, liver, kidneys, spleen, and femur) were collected. Both WKY + PZ and SHR groups exhibited iron deficiency in the serum and liver. Prazosin increased the iron levels in the bone tissue of SHRs. Prazosin stimulated the expression of hepcidin mRNA in the liver of SHRs and inhibited the expression of this iron‐regulatory hormone in WKY rats. FPN1 expression in the duodenum was increased significantly in SHRs, however markedly decreased after prazosin treatment. The expression of TLR4 and Ctsk was enhanced in the bone tissue of SHRs, whereas CLC‐7 expression was inhibited. Both hypotension and hypertension can lead to iron deficiency. Treatment with prazosin restored iron homeostasis in SHRs. The inverse impacts of prazosin on hepatic hepcidin expression in SHRs versus WKY rats indicates differing iron regulatory mechanisms between hypertensive and normal animals. The osteoclast activity was found to be enhanced in SHRs. Further study is needed to address whether the changes in osteoblast and osteoclast activity in SHRs correlates with the effects on iron metabolism.

A computational model for prediction of ferritin and haemoglobin levels in blood donors

Blood donors are at risk of iron deficiency anaemia. While this risk is decreased through ferritin-based deferral, ideally ferritin monitoring should also aid in optimising donation frequencies. We extended an existing model of haemoglobin (Hb) synthesis with iron homeostasis and validated the model on a cohort of 300 new donors whose ferritin levels were measured from stored blood samples collected over a 2-year period. We then used the donor's gender, body weight, height, and baseline Hb and ferritin levels to predict subsequent Hb and ferritin levels. The prediction error was within measurement variability in 88% of Hb level predictions and 64% of ferritin level predictions. A sensitivity analysis of the model revealed that baseline ferritin level was the most important in predicting future ferritin levels. Finally, we used the model to calculate the annual donation frequency at which donors would keep their ferritin level >15 ng/ml when measured after donating for 2 years. The mean annual donation frequency would then be 1.9 for women and 4.1 for men. The computational model, requiring baseline values only, can predict future Hb and ferritin levels remarkably well. This enables determination of optimal donation frequencies for individual donors at the start of their donation career.

Deciphering signal transduction networks in the liver by mechanistic mathematical modelling

In health and disease, liver cells are continuously exposed to cytokines and growth factors. While individual signal transduction pathways induced by these factors were studied in great detail, the cellular responses induced by repeated or combined stimulations are complex and less understood. Growth factor receptors on the cell surface of hepatocytes were shown to be regulated by receptor interactions, receptor trafficking and feedback regulation. Here, we exemplify how mechanistic mathematical modelling based on quantitative data can be employed to disentangle these interactions at the molecular level. Crucial is the analysis at a mechanistic level based on quantitative longitudinal data within a mathematical framework. In such multi-layered information, step-wise mathematical modelling using submodules is of advantage, which is fostered by sharing of standardized experimental data and mathematical models. Integration of signal transduction with metabolic regulation in the liver and mechanistic links to translational approaches promise to provide predictive tools for biology and personalized medicine.

Application of Magnetic Resonance Imaging in Liver Biomechanics: A Systematic Review

MRI-based biomechanical studies can provide a deep understanding of the mechanisms governing liver function, its mechanical performance but also liver diseases. In addition, comprehensive modeling of the liver can help improve liver disease treatment. Furthermore, such studies demonstrate the beginning of an engineering-level approach to how the liver disease affects material properties and liver function. Aimed at researchers in the field of MRI-based liver simulation, research articles pertinent to MRI-based liver modeling were identified, reviewed, and summarized systematically. Various MRI applications for liver biomechanics are highlighted, and the limitations of different viscoelastic models used in magnetic resonance elastography are addressed. The clinical application of the simulations and the diseases studied are also discussed. Based on the developed questionnaire, the papers' quality was assessed, and of the 46 reviewed papers, 32 papers were determined to be of high-quality. Due to the lack of the suitable material models for different liver diseases studied by magnetic resonance elastography, researchers may consider the effect of liver diseases on constitutive models. In the future, research groups may incorporate various aspects of machine learning (ML) into constitutive models and MRI data extraction to further refine the study methodology. Moreover, researchers should strive for further reproducibility and rigorous model validation and verification.

An NFκB Activity Calculator to Delineate Signaling Crosstalk: Type I and II Interferons Enhance NFκB via Distinct Mechanisms

Nuclear factor kappa B (NFκB) is a transcription factor that controls inflammation and cell survival. In clinical histology, elevated NFκB activity is a hallmark of poor prognosis in inflammatory disease and cancer, and may be the result of a combination of diverse micro-environmental constituents. While previous quantitative studies of NFκB focused on its signaling dynamics in single cells, we address here how multiple stimuli may combine to control tissue level NFκB activity. We present a novel, simplified model of NFκB (SiMoN) that functions as an NFκB activity calculator. We demonstrate its utility by exploring how type I and type II interferons modulate NFκB activity in macrophages. Whereas, type I IFNs potentiate NFκB activity by inhibiting translation of IκBα and by elevating viral RNA sensor (RIG-I) expression, type II IFN amplifies NFκB activity by increasing the degradation of free IκB through transcriptional induction of proteasomal cap components (PA28). Both cross-regulatory mechanisms amplify NFκB activation in response to weaker (viral) inducers, while responses to stronger (bacterial or cytokine) inducers remain largely unaffected. Our work demonstrates how the NFκB calculator can reveal distinct mechanisms of crosstalk on NFκB activity in interferon-containing microenvironments.

A mathematical model of iron import and trafficking in wild-type and Mrs3/4ΔΔ yeast cells

Background

Iron plays crucial roles in the metabolism of eukaryotic cells. Much iron is trafficked into mitochondria where it is used for iron-sulfur cluster assembly and heme biosynthesis. A yeast strain in which Mrs3/4, the high-affinity iron importers on the mitochondrial inner membrane, are deleted exhibits a slow-growth phenotype when grown under iron-deficient conditions. However, these cells grow at WT rates under iron-sufficient conditions. The object of this study was to develop a mathematical model that could explain this recovery on the molecular level.

Results

A multi-tiered strategy was used to solve an ordinary-differential-equations-based mathematical model of iron import, trafficking, and regulation in growing Saccharomyces cerevisiae cells. At the simplest level of modeling, all iron in the cell was presumed to be a single species and the cell was considered to be a single homogeneous volume. Optimized parameters associated with the rate of iron import and the rate of dilution due to cell growth were determined. At the next level of complexity, the cell was divided into three regions, including cytosol, mitochondria, and vacuoles, each of which was presumed to contain a single form of iron. Optimized parameters associated with import into these regions were determined. At the final level of complexity, nine components were assumed within the same three cellular regions. Parameters obtained at simpler levels of complexity were used to help solve the more complex versions of the model; this was advantageous because the data used for solving the simpler model variants were more reliable and complete relative to those required for the more complex variants. The optimized full-complexity model simulated the observed phenotype of WT and Mrs3/4ΔΔ cells with acceptable fidelity, and the model exhibited some predictive power.

Conclusions

The developed model highlights the importance of an Fe^II mitochondrial pool and the necessary exclusion of O₂ in the mitochondrial matrix for eukaryotic iron-sulfur cluster metabolism. Similar multi-tiered strategies could be used for any micronutrient in which concentrations and metabolic forms have been determined in different organelles within a growing eukaryotic cell.

Electronic supplementary material

The online version of this article (10.1186/s12918-019-0702-2) contains supplementary material, which is available to authorized users.

Iron regulatory protein 2 deficiency may correlate with insulin resistance

Iron is known to be a crucial regulator of glucose, and several studies have demonstrated that iron overload is one of the risk factors for insulin resistance and diabetes; however, the mechanism has not yet been clarified. To investigate the effect of iron overload on glucose metabolism and the underlying mechanism, Irp2 knockout (Irp2^-/-) mice (endogenous iron overload model) were used. We found that Irp2^-/- mice exhibited hyperglycemia and iron overload in the liver and skeletal muscle. Increased MDA, decreased SOD levels, and increased cell apoptosis were also found in the liver and muscle of Irp2^-/- mice. Glucose concentrations were significantly higher in Irp2^-/- mice in insulin tolerance tests. However, early-phase insulin secretion was not altered in Irp2^-/- mice. The expression of hepatic IRS2 and muscle GLUT4 was declined in Irp2^-/- mice at both mRNA and protein levels when compared with those of wild-type control. In conclusions, Irp2^-/- mice showed hyperglycemia, which might due to insulin resistance rather than due to impaired insulin secretion.

Polypyrimidine tract-binding protein blocks miRNA-124 biogenesis to enforce its neuronal-specific expression in the mouse

MicroRNA (miRNA)-124 is expressed in neurons, where it represses genes inhibitory for neuronal differentiation, including the RNA binding protein PTBP1. PTBP1 maintains nonneuronal splicing patterns of mRNAs that switch to neuronal isoforms upon neuronal differentiation. We find that primary (pri)-miR-124-1 is expressed in mouse embryonic stem cells where mature miR-124 is absent. PTBP1 binds to this precursor RNA upstream of the miRNA stem-loop to inhibit mature miR-124 expression in vivo and DROSHA cleavage of pri-miR-124-1 in vitro. This function for PTBP1 in repressing miR-124 biogenesis defines an additional regulatory loop in the already intricate interplay between these two molecules. Applying mathematical modeling to examine the dynamics of this regulation, we find that the pool of pri-miR-124 whose maturation is blocked by PTBP1 creates a robust and self-reinforcing transition in gene expression as PTBP1 is depleted during early neuronal differentiation. While interlocking regulatory loops are often found between miRNAs and transcriptional regulators, our results indicate that miRNA targeting of posttranscriptional regulators also reinforces developmental decisions. Notably, induction of neuronal differentiation observed upon PTBP1 knockdown likely results from direct derepression of miR-124, in addition to indirect effects previously described.

No effects without causes: the Iron Dysregulation and Dormant Microbes hypothesis for chronic, inflammatory diseases

Since the successful conquest of many acute, communicable (infectious) diseases through the use of vaccines and antibiotics, the currently most prevalent diseases are chronic and progressive in nature, and are all accompanied by inflammation. These diseases include neurodegenerative (e.g. Alzheimer's, Parkinson's), vascular (e.g. atherosclerosis, pre-eclampsia, type 2 diabetes) and autoimmune (e.g. rheumatoid arthritis and multiple sclerosis) diseases that may appear to have little in common. In fact they all share significant features, in particular chronic inflammation and its attendant inflammatory cytokines. Such effects do not happen without underlying and initially 'external' causes, and it is of interest to seek these causes. Taking a systems approach, we argue that these causes include (i) stress-induced iron dysregulation, and (ii) its ability to awaken dormant, non-replicating microbes with which the host has become infected. Other external causes may be dietary. Such microbes are capable of shedding small, but functionally significant amounts of highly inflammagenic molecules such as lipopolysaccharide and lipoteichoic acid. Sequelae include significant coagulopathies, not least the recently discovered amyloidogenic clotting of blood, leading to cell death and the release of further inflammagens. The extensive evidence discussed here implies, as was found with ulcers, that almost all chronic, infectious diseases do in fact harbour a microbial component. What differs is simply the microbes and the anatomical location from and at which they exert damage. This analysis offers novel avenues for diagnosis and treatment.

Whole-body iron transport and metabolism: Mechanistic, multi-scale model to improve treatment of anemia in chronic kidney disease

Iron plays vital roles in the human body including enzymatic processes, oxygen-transport via hemoglobin and immune response. Iron metabolism is characterized by ~95% recycling and minor replenishment through diet. Anemia of chronic kidney disease (CKD) is characterized by a lack of synthesis of erythropoietin leading to reduced red blood cell (RBC) formation and aberrant iron recycling. Treatment of CKD anemia aims to normalize RBC count and serum hemoglobin. Clinically, the various fluxes of iron transport and accumulation are not measured so that changes during disease (e.g., CKD) and treatment are unknown. Unwanted iron accumulation in patients is known to lead to adverse effects. Current whole-body models lack the mechanistic details of iron transport related to RBC maturation, transferrin (Tf and TfR) dynamics and assume passive iron efflux from macrophages. Hence, they are not predictive of whole-body iron dynamics and cannot be used to design individualized patient treatment. For prediction, we developed a mechanistic, multi-scale computational model of whole-body iron metabolism incorporating four compartments containing major pools of iron and RBC generation process. The model accounts for multiple forms of iron in vivo, mechanisms involved in iron uptake and release and their regulation. Furthermore, the model is interfaced with drug pharmacokinetics to allow simulation of treatment dynamics. We calibrated our model with experimental and clinical data from peer-reviewed literature to reliably simulate CKD anemia and the effects of current treatment involving combination of epoietin-alpha and iron dextran. This in silico whole-body model of iron metabolism predicts that a year of treatment can potentially lead to 90% downregulation of ferroportin (FPN) levels, 15-fold increase in iron stores with only a 20% increase in iron flux from the reticulo-endothelial system (RES). Model simulations quantified unmeasured iron fluxes, previously unknown effects of treatment on FPN-level and iron stores in the RES. This mechanistic whole-body model can be the basis for future studies that incorporate iron metabolism together with related clinical experiments. Such an approach could pave the way for development of effective personalized treatment of CKD anemia.

Trace Elements and Healthcare: A Bioinformatics Perspective

Biological trace elements are essential for human health. Imbalance in trace element metabolism and homeostasis may play an important role in a variety of diseases and disorders. While the majority of previous researches focused on experimental verification of genes involved in trace element metabolism and those encoding trace element-dependent proteins, bioinformatics study on trace elements is relatively rare and still at the starting stage. This chapter offers an overview of recent progress in bioinformatics analyses of trace element utilization, metabolism, and function, especially comparative genomics of several important metals. The relationship between individual elements and several diseases based on recent large-scale systematic studies such as genome-wide association studies and case-control studies is discussed. Lastly, developments of ionomics and its recent application in human health are also introduced.

Modeling the dynamics of mouse iron body distribution: hepcidin is necessary but not sufficient

BACKGROUND

Iron is an essential element of most living organisms but is a dangerous substance when poorly liganded in solution. The hormone hepcidin regulates the export of iron from tissues to the plasma contributing to iron homeostasis and also restricting its availability to infectious agents. Disruption of iron regulation in mammals leads to disorders such as anemia and hemochromatosis, and contributes to the etiology of several other diseases such as cancer and neurodegenerative diseases. Here we test the hypothesis that hepcidin alone is able to regulate iron distribution in different dietary regimes in the mouse using a computational model of iron distribution calibrated with radioiron tracer data.

RESULTS

A model was developed and calibrated to the data from adequate iron diet, which was able to simulate the iron distribution under a low iron diet. However simulation of high iron diet shows considerable deviations from the experimental data. Namely the model predicts more iron in red blood cells and less iron in the liver than what was observed in experiments.

CONCLUSIONS

These results suggest that hepcidin alone is not sufficient to regulate iron homeostasis in high iron conditions and that other factors are important. The model was able to simulate anemia when hepcidin was increased but was unable to simulate hemochromatosis when hepcidin was suppressed, suggesting that in high iron conditions additional regulatory interactions are important.

Activated Oncogenic Pathway Modifies Iron Network in Breast Epithelial Cells: A Dynamic Modeling Perspective

Dysregulation of iron metabolism in cancer is well documented and it has been suggested that there is interdependence between excess iron and increased cancer incidence and progression. In an effort to better understand the linkages between iron metabolism and breast cancer, a predictive mathematical model of an expanded iron homeostasis pathway was constructed that includes species involved in iron utilization, oxidative stress response and oncogenic pathways. The model leads to three predictions. The first is that overexpression of iron regulatory protein 2 (IRP2) recapitulates many aspects of the alterations in free iron and iron-related proteins in cancer cells without affecting the oxidative stress response or the oncogenic pathways included in the model. This prediction was validated by experimentation. The second prediction is that iron-related proteins are dramatically affected by mitochondrial ferritin overexpression. This prediction was validated by results in the pertinent literature not used for model construction. The third prediction is that oncogenic Ras pathways contribute to altered iron homeostasis in cancer cells. This prediction was validated by a combination of simulation experiments of Ras overexpression and catalase knockout in conjunction with the literature. The model successfully captures key aspects of iron metabolism in breast cancer cells and provides a framework upon which more detailed models can be built.

Iron is required for cellular metabolism and growth, but can be toxic due to its ability to cause high oxidative stress and consequently DNA damage. To prevent damage, all organisms that require iron have developed mechanisms to tightly control iron levels. Dysregulation of iron metabolism is detrimental and can contribute to a wide range of diseases, including cancer. This paper presents a predictive mathematical model of iron regulation linked to iron utilization, oxidative stress, and the oncogenic response specific to normal breast epithelial cells. The model uses a discrete modeling framework to generate novel biological hypotheses for an investigation of how normal breast cells become malignant cells, capturing a breast cancer phenotype of iron homeostasis through overexpression and knockout simulations. The new biology discovered is (1) IRP2 overexpression alters the iron homeostasis pathway in breast cells, without affecting the oxidative stress response or oncogenic pathways, (2) an activated oncogenic pathway disrupts iron regulation in breast cancer cells.

Modelling Systemic Iron Regulation during Dietary Iron Overload and Acute Inflammation: Role of Hepcidin-Independent Mechanisms

Systemic iron levels must be maintained in physiological concentrations to prevent diseases associated with iron deficiency or iron overload. A key role in this process plays ferroportin, the only known mammalian transmembrane iron exporter, which releases iron from duodenal enterocytes, hepatocytes, or iron-recycling macrophages into the blood stream. Ferroportin expression is tightly controlled by transcriptional and post-transcriptional mechanisms in response to hypoxia, iron deficiency, heme iron and inflammatory cues by cell-autonomous and systemic mechanisms. At the systemic level, the iron-regulatory hormone hepcidin is released from the liver in response to these cues, binds to ferroportin and triggers its degradation. The relative importance of individual ferroportin control mechanisms and their interplay at the systemic level is incompletely understood. Here, we built a mathematical model of systemic iron regulation. It incorporates the dynamics of organ iron pools as well as regulation by the hepcidin/ferroportin system. We calibrated and validated the model with time-resolved measurements of iron responses in mice challenged with dietary iron overload and/or inflammation. The model demonstrates that inflammation mainly reduces the amount of iron in the blood stream by reducing intracellular ferroportin transcription, and not by hepcidin-dependent ferroportin protein destabilization. In contrast, ferroportin regulation by hepcidin is the predominant mechanism of iron homeostasis in response to changing iron diets for a big range of dietary iron contents. The model further reveals that additional homeostasis mechanisms must be taken into account at very high dietary iron levels, including the saturation of intestinal uptake of nutritional iron and the uptake of circulating, non-transferrin-bound iron, into liver. Taken together, our model quantitatively describes systemic iron metabolism and generated experimentally testable predictions for additional ferroportin-independent homeostasis mechanisms.

The importance of iron in many physiological processes relies on its ability to participate in reduction-oxidation reactions. This property also leads to potential toxicity if concentrations of free iron are not properly managed by cells and tissues. Multicellular organisms therefore evolved intricate regulatory mechanisms to control systemic iron levels. A central regulatory mechanism is the binding of the hormone hepcidin to the iron exporter ferroportin, which controls the major fluxes of iron into blood plasma. Here, we present a mathematical model that is fitted and validated against experimental data to simulate the iron content in different organs following dietary changes and/or inflammatory states, or genetic perturbation of the hepcidin/ferroportin regulatory system. We find that hepcidin mediated ferroportin control is essential, but not sufficient to quantitatively explain several of our experimental findings. Thus, further regulatory mechanisms had to be included in the model to reproduce reduced serum iron levels in response to inflammation, the preferential accumulation of iron in the liver in the case of iron overload, or the maintenance of physiological serum iron concentrations if dietary iron levels are very high. We conclude that hepcidin-independent mechanisms play an important role in maintaining systemic iron homeostasis.

Pharmacology-based toxicity assessment: towards quantitative risk prediction in humans

Despite ongoing efforts to better understand the mechanisms underlying safety and toxicity, ~30% of the attrition in drug discovery and development is still due to safety concerns. Changes in current practice regarding the assessment of safety and toxicity are required to reduce late stage attrition and enable effective development of novel medicines. This review focuses on the implications of empirical evidence generation for the evaluation of safety and toxicity during drug development. A shift in paradigm is needed to (i) ensure that pharmacological concepts are incorporated into the evaluation of safety and toxicity; (ii) facilitate the integration of historical evidence and thereby the translation of findings across species as well as between in vitro and in vivo experiments and (iii) promote the use of experimental protocols tailored to address specific safety and toxicity questions. Based on historical examples, we highlight the challenges for the early characterisation of the safety profile of a new molecule and discuss how model-based methodologies can be applied for the design and analysis of experimental protocols. Issues relative to the scientific rationale are categorised and presented as a hierarchical tree describing the decision-making process. Focus is given to four different areas, namely, optimisation, translation, analytical construct and decision criteria. From a methodological perspective, the relevance of quantitative methods for estimation and extrapolation of risk from toxicology and safety pharmacology experimental protocols, such as points of departure and potency, is discussed in light of advancements in population and Bayesian modelling techniques (e.g. non-linear mixed effects modelling). Their use in the evaluation of pharmacokinetics (PK) and pharmacokinetic-pharmacodynamic relationships (PKPD) has enabled great insight into the dose rationale for medicines in humans, both in terms of efficacy and adverse events. Comparable benefits can be anticipated for the assessment of safety and toxicity profile of novel molecules.

A Dormant Microbial Component in the Development of Preeclampsia

Preeclampsia (PE) is a complex, multisystem disorder that remains a leading cause of morbidity and mortality in pregnancy. Four main classes of dysregulation accompany PE and are widely considered to contribute to its severity. These are abnormal trophoblast invasion of the placenta, anti-angiogenic responses, oxidative stress, and inflammation. What is lacking, however, is an explanation of how these themselves are caused. We here develop the unifying idea, and the considerable evidence for it, that the originating cause of PE (and of the four classes of dysregulation) is, in fact, microbial infection, that most such microbes are dormant and hence resist detection by conventional (replication-dependent) microbiology, and that by occasional resuscitation and growth it is they that are responsible for all the observable sequelae, including the continuing, chronic inflammation. In particular, bacterial products such as lipopolysaccharide (LPS), also known as endotoxin, are well known as highly inflammagenic and stimulate an innate (and possibly trained) immune response that exacerbates the inflammation further. The known need of microbes for free iron can explain the iron dysregulation that accompanies PE. We describe the main routes of infection (gut, oral, and urinary tract infection) and the regularly observed presence of microbes in placental and other tissues in PE. Every known proteomic biomarker of "preeclampsia" that we assessed has, in fact, also been shown to be raised in response to infection. An infectious component to PE fulfills the Bradford Hill criteria for ascribing a disease to an environmental cause and suggests a number of treatments, some of which have, in fact, been shown to be successful. PE was classically referred to as endotoxemia or toxemia of pregnancy, and it is ironic that it seems that LPS and other microbial endotoxins really are involved. Overall, the recognition of an infectious component in the etiology of PE mirrors that for ulcers and other diseases that were previously considered to lack one.

Quantitative combination of natural anti-oxidants prevents metabolic syndrome by reducing oxidative stress

Insulin resistance and abdominal obesity are present in the majority of people with the metabolic syndrome. Antioxidant therapy might be a useful strategy for type 2 diabetes and other insulin-resistant states. The combination of vitamin C (Vc) and vitamin E has synthetic scavenging effect on free radicals and inhibition effect on lipid peroxidation. However, there are few studies about how to define the best combination of more than three anti-oxidants as it is difficult or impossible to test the anti-oxidant effect of the combination of every concentration of each ingredient experimentally. Here we present a math model, which is based on the classical Hill equation to determine the best combination, called Fixed Dose Combination (FDC), of several natural anti-oxidants, including Vc, green tea polyphenols (GTP) and grape seed extract proanthocyanidin (GSEP). Then we investigated the effects of FDC on oxidative stress, blood glucose and serum lipid levels in cultured 3T3-L1 adipocytes, high fat diet (HFD)-fed rats which serve as obesity model, and KK-ay mice as diabetic model. The level of serum malondialdehyde (MDA) in the treated rats was studied and Hematoxylin-Eosin (HE) staining or Oil red slices of liver and adipose tissue in the rats were examined as well. FDC shows excellent antioxidant and anti-glycation activity by attenuating lipid peroxidation. FDC determined in this investigation can become a potential solution to reduce obesity, to improve insulin sensitivity and be beneficial for the treatment of fat and diabetic patients. It is the first time to use the math model to determine the best ratio of three anti-oxidants, which can save much more time and chemical materials than traditional experimental method. This quantitative method represents a potentially new and useful strategy to screen all possible combinations of many natural anti-oxidants, therefore may help develop novel therapeutics with the potential to ameliorate the worldwide metabolic abnormalities.