An in-silico model of lipoprotein metabolism and kinetics for the evaluation of targets and biomarkers in the reverse cholesterol transport pathway

An industry perspective on current QSP trends in drug development

2023 marks the 10th anniversary of Natpara's submission to the US FDA, which led to the first recorded regulatory interaction where a decision was supported by Quantitative and Systems Pharmacology (QSP) simulations. It had taken about 5 years for the timid QSP discipline to emerge as an effective Model-Informed Drug Development (MIDD) tool with visible impact in the pharmaceutical industry. Since then, the presence of QSP in the regulatory environment has continued to increase, to the point that the Agency reported 60 QSP submissions in 2020 alone, representing ~ 4% of their annual IND submissions [1]. What sort of industry mindset has enabled QSP to reach this level of success? How does QSP fit within the MIDD paradigm? Does QSP mean the same to Discovery and to Clinical Development projects? How do 'platforms' compare to 'fit-for-purpose' QSP models in an industrial setting? Can QSP and empirical Pharmacokinetic-Pharmacodynamic (PKPD) modelling be complementary? What level of validation is required to inform drug development decisions? This article reflects on all these questions, in particular addressing those audiences with limited line-of-sight into the drug industry decision-making machinery.

Computationally Modelling Cholesterol Metabolism and Atherosclerosis

Cardiovascular disease (CVD) is the leading cause of death globally. The underlying pathological driver of CVD is atherosclerosis. The primary risk factor for atherosclerosis is elevated low-density lipoprotein cholesterol (LDL-C). Dysregulation of cholesterol metabolism is synonymous with a rise in LDL-C. Due to the complexity of cholesterol metabolism and atherosclerosis mathematical models are routinely used to explore their non-trivial dynamics. Mathematical modelling has generated a wealth of useful biological insights, which have deepened our understanding of these processes. To date however, no model has been developed which fully captures how whole-body cholesterol metabolism intersects with atherosclerosis. The main reason for this is one of scale. Whole body cholesterol metabolism is defined by macroscale physiological processes, while atherosclerosis operates mainly at a microscale. This work describes how a model of cholesterol metabolism was combined with a model of atherosclerotic plaque formation. This new model is capable of reproducing the output from its parent models. Using the new model, we demonstrate how this system can be utilized to identify interventions that lower LDL-C and abrogate plaque formation.

Modeling Clinical Phenotype Variability: Consideration of Genomic Variations, Computational Methods, and Quantitative Proteomics

Advances in biomedical and computer technologies have presented the modeling community the opportunity for mechanistically modeling and simulating the variability in a disease phenotype or in a drug response. The capability to quantify response variability can inform a drug development program. Quantitative systems pharmacology scientists have published various computational approaches for creating virtual patient populations (VPops) to model and simulate drug response variability. Genomic variations can impact disease characteristics and drug exposure and response. Quantitative proteomics technologies are increasingly used to facilitate drug discovery and development and inform patient care. Incorporating variations in genomics and quantitative proteomics may potentially inform creation of VPops to model and simulate virtual patient trials, and may help account for, in a predictive manner, phenotypic variations observed clinically.

Mathematical Modelling of Material Transfer to High-Density Lipoprotein (HDL) upon Triglyceride Lipolysis by Lipoprotein Lipase: Relevance to Cardioprotective Role of HDL

High-density lipoprotein (HDL) contributes to lipolysis of triglyceride-rich lipoprotein (TGRL) by lipoprotein lipase (LPL) via acquirement of surface lipids, including free cholesterol (FC), released upon lipolysis. According to the reverse remnant-cholesterol transport (RRT) hypothesis recently developed by us, acquirement of FC by HDL is reduced at both low and extremely high HDL concentrations, potentially underlying the U-shaped relationship between HDL-cholesterol and cardiovascular disease. Mechanisms underlying impaired FC transfer however remain indeterminate. We developed a mathematical model of material transfer to HDL upon TGRL lipolysis by LPL. Consistent with experimental observations, mathematical modelling showed that surface components of TGRL, including FC, were accumulated in HDL upon lipolysis. The modelling successfully reproduced major features of cholesterol accumulation in HDL observed experimentally, notably saturation of this process over time and appearance of a maximum as a function of HDL concentration. The calculations suggested that the both phenomena resulted from competitive fluxes of FC through the HDL pool, including primarily those driven by FC concentration gradient between TGRL and HDL on the one hand and mediated by lecithin-cholesterol acyltransferase (LCAT) and cholesteryl ester transfer protein (CETP) on the other hand. These findings provide novel opportunities to revisit our view of HDL in the framework of RRT.

Modeling cholesterol metabolism and atherosclerosis

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low-density lipoprotein cholesterol (LDL-C) remains the gold standard. Cholesterol metabolism at the cellular and whole-body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future. This article is categorized under: Cardiovascular Diseases > Computational Models.

A quantitative systems pharmacology modeling platform for evaluating triglyceride profiles in patients with high triglycerides receiving evinacumab

A model to quantitatively characterize the effect of evinacumab, an investigational monoclonal antibody against angiopoietin‐like protein 3 (ANGPTL3) on lipid trafficking is needed. A quantitative systems pharmacology (QSP) approach was developed to predict the transient responses of different triglyceride (TG)‐rich lipoprotein particles in response to evinacumab administration. A previously published hepatic lipid model was modified to address specific queries relevant to the mechanism of evinacumab and its effect on lipid metabolism. Modifications included the addition of intermediate‐density lipoprotein and low‐density lipoprotein compartments to address the modulation of lipoprotein lipase (LPL) activity by evinacumab, ANGPTL3 biosynthesis and clearance, and a target‐mediated drug disposition model. A sensitivity analysis guided the creation of virtual patients (VPs). The drug‐free QSP model was found to agree well with clinical data published with the initial hepatic liver model over simulations ranging from 20 to 365 days in duration. The QSP model, including the interaction between LPL and ANGPTL3, was validated against clinical data for total evinacumab, total ANGPTL3, and TG concentrations as well as inhibition of apolipoprotein CIII. Free ANGPTL3 concentration and LPL activity were also modeled. In total, seven VPs were created; the lipid levels of the VPs were found to match the range of responses observed in evinacumab clinical trial data. The QSP model results agreed with clinical data for various subjects and was shown to characterize known TG physiology and drug effects in a range of patient populations with varying levels of TGs, enabling hypothesis testing of evinacumab effects on lipid metabolism.

Blocking endothelial lipase with monoclonal antibody MEDI5884 durably increases high density lipoprotein in nonhuman primates and in a phase 1 trial

Cardiovascular disease (CVD) is the leading global cause of death, and treatments that further reduce CV risk remain an unmet medical need. Epidemiological studies have consistently identified low high-density lipoprotein cholesterol (HDL-C) as an independent risk factor for CVD, making HDL elevation a potential clinical target for improved CVD resolution. Endothelial lipase (EL) is a circulating enzyme that regulates HDL turnover by hydrolyzing HDL phospholipids and driving HDL particle clearance. Using MEDI5884, a first-in-class, EL-neutralizing, monoclonal antibody, we tested the hypothesis that pharmacological inhibition of EL would increase HDL-C by enhancing HDL stability. In nonhuman primates, MEDI5884 treatment resulted in lasting, dose-dependent elevations in HDL-C and circulating phospholipids, confirming the mechanism of EL action. We then showed that a favorable lipoprotein profile of elevated HDL-C and reduced low-density lipoprotein cholesterol (LDL-C) could be achieved by combining MEDI5884 with a PCSK9 inhibitor. Last, when tested in healthy human volunteers, MEDI5884 not only raised HDL-C but also increased HDL particle numbers and average HDL size while enhancing HDL functionality, reinforcing EL neutralization as a viable clinical approach aimed at reducing CV risk.

Cholesterol Homeostasis: An In Silico Investigation into How Aging Disrupts Its Key Hepatic Regulatory Mechanisms

The dysregulation of intracellular cholesterol homeostasis is associated with several age-related diseases, most notably cardiovascular disease (CVD). Research in this area has benefitted from using computational modelling to study the inherent complexity associated with the regulation of this system. In addition to facilitating hypothesis exploration, the utility of modelling lies in its ability to represent an array of rate limiting enzymatic reactions, together with multiple feedback loops, which collectively define the dynamics of cholesterol homeostasis. However, to date no model has specifically investigated the effects aging has on this system. This work addresses this shortcoming by explicitly focusing on the impact of aging on hepatic intracellular cholesterol homeostasis. The model was used to investigate the experimental findings that reactive oxygen species induce the total activation of 3-hydroxy-3-methylglutaryl-coenzyme A (HMG-CoA) reductase (HMGCR). Moreover, the model explored the impact of an age-related decrease in hepatic acetyl-CoA acetyltransferase 2 (ACAT2). The model suggested that an increase in the activity of HMGCR does not have as significant an impact on cholesterol homeostasis as a decrease in hepatic ACAT2 activity. According to the model, a decrease in the activity of hepatic ACAT2 raises free cholesterol (FC) and decreases low-density lipoprotein cholesterol (LDL-C) levels. Increased acetyl CoA synthesis resulted in a reduction in the number of hepatic low-density lipoprotein receptors, and increased LDL-C, FC, and cholesterol esters. The rise in LDL-C was restricted by elevated hepatic FC accumulation. Taken together these findings have important implications for healthspan. This is because emerging clinical data suggest hepatic FC accumulation is relevant to the pathogenesis of non-alcoholic fatty liver disease (NAFLD), which is associated with an increased risk of CVD. These pathophysiological changes could, in part, help to explain the phenomenon of increased mortality associated with low levels of LDL-C which have been observed in certain studies involving the oldest old (≥85 years).

Comparative quantitative systems pharmacology modeling of anti-PCSK9 therapeutic modalities in hypercholesterolemia

Since the discovery of proprotein convertase subtilisin/kexin type 9 (PCSK9) as an attractive target in the treatment of hypercholesterolemia, multiple anti-PCSK9 therapeutic modalities have been pursued in drug development. The objective of this research is to set the stage for the quantitative benchmarking of two anti-PCSK9 pharmacological modality classes, monoclonal antibodies (mAbs) and small interfering RNA (siRNA). To this end, we developed an integrative mathematical model of lipoprotein homeostasis describing the dynamic interplay between PCSK9, LDL-cholesterol (LDL-C), VLDL-cholesterol, HDL-cholesterol (HDL-C), apoB, lipoprotein a [Lp(a)], and triglycerides (TGs). We demonstrate that LDL-C decreased proportionally to PCSK9 reduction for both mAb and siRNA modalities. At marketed doses, however, treatment with mAbs resulted in an additional ∼20% LDL-C reduction compared with siRNA. We further used the model as an evaluation tool and determined that no quantitative differences were observed in HDL-C, Lp(a), TG, or apoB responses, suggesting that the disruption of PCSK9 synthesis would provide no additional effects on lipoprotein-related biomarkers in the patient segment investigated. Predictive model simulations further indicate that siRNA therapies may reach reductions in LDL-C levels comparable to those achieved with mAbs if the current threshold of 80% PCSK9 inhibition via siRNA could be overcome.

Translational Quantitative Systems Pharmacology in Drug Development: from Current Landscape to Good Practices

Systems pharmacology approaches have the capability of quantitatively linking the key biological molecules relevant to a drug candidate's mechanism of action (drug-induced signaling pathways) to the clinical biomarkers associated with the proposed target disease, thereby quantitatively facilitating its development and life cycle management. In this review, the model attributes of published quantitative systems pharmacology (QSP) modeling for lowering cholesterol, treating salt-sensitive hypertension, and treating rare diseases as well as describing bone homeostasis and related pharmacological effects are critically reviewed with respect to model quality, calibration, validation, and performance. We further reviewed the common practices in optimizing QSP modeling and prediction. Notably, leveraging genetics and genomic studies for model calibration and validation is common. Statistical and quantitative assessment of QSP prediction and handling of model uncertainty are, however, mostly lacking as are the quantitative and statistical criteria for assessing QSP predictions and the covariance matrix of coefficients between the parameters in a validated virtual population. To accelerate advances and application of QSP with consistent quality, a list of key questions is proposed to be addressed when assessing the quality of a QSP model in hopes of stimulating the scientific community to set common expectations. The common expectations as to what constitutes the best QSP modeling practices, which the scientific community supports, will advance QSP modeling in the realm of informed drug development. In the long run, good practices will extend the life cycles of QSP models beyond the life cycles of individual drugs.

A mathematical model to estimate cholesterylester transfer protein (CETP) triglycerides flux in human plasma

Background

Cholesterylester transfer protein (CETP) modulates the composition of various lipoproteins associated with cardiovascular disease. Despite its central role in lipoprotein metabolism, its mode of action is still not fully understood. Here we present a simple way to estimate CETP-mediated lipid fluxes between different lipoprotein fractions.

Results

The model derived adequately describes the observed findings, especially regarding low- and high dense lipoproteins (LDL and HDL), delivering correlation coefficients of R² = 0.567 (p < 0.001) and R² = 0.466 (p < 0.001), respectively. These estimated fluxes correlate best among all other measured concentrations and ‘lipid per lipoprotein’ ratios to the observed fluxes.

Conclusion

Our model approach is independent of CETP-action’s exact mechanistic mode. It is simple and easy to apply, and may be a useful tool in revealing CETP’s ambiguous role in lipid metabolism. The model mirrors a diffusion-like exchange of triglycerides between lipoproteins. Cholesteryl ester and triglyceride concentrations measured in HDL, LDL and VLDL are sufficient to apply the model on a plasma sample.

Electronic supplementary material

The online version of this article (10.1186/s12918-019-0679-x) contains supplementary material, which is available to authorized users.

In vivo and in silico dynamics of the development of Metabolic Syndrome

The Metabolic Syndrome (MetS) is a complex, multifactorial disorder that develops slowly over time presenting itself with large differences among MetS patients. We applied a systems biology approach to describe and predict the onset and progressive development of MetS, in a study that combined in vivo and in silico models. A new data-driven, physiological model (MINGLeD: Model INtegrating Glucose and Lipid Dynamics) was developed, describing glucose, lipid and cholesterol metabolism. Since classic kinetic models cannot describe slowly progressing disorders, a simulation method (ADAPT) was used to describe longitudinal dynamics and to predict metabolic concentrations and fluxes. This approach yielded a novel model that can describe long-term MetS development and progression. This model was integrated with longitudinal in vivo data that was obtained from male APOE*3-Leiden.CETP mice fed a high-fat, high-cholesterol diet for three months and that developed MetS as reflected by classical symptoms including obesity and glucose intolerance. Two distinct subgroups were identified: those who developed dyslipidemia, and those who did not. The combination of MINGLeD with ADAPT could correctly predict both phenotypes, without making any prior assumptions about changes in kinetic rates or metabolic regulation. Modeling and flux trajectory analysis revealed that differences in liver fluxes and dietary cholesterol absorption could explain this occurrence of the two different phenotypes. In individual mice with dyslipidemia dietary cholesterol absorption and hepatic turnover of metabolites, including lipid fluxes, were higher compared to those without dyslipidemia. Predicted differences were also observed in gene expression data, and consistent with the emergence of insulin resistance and hepatic steatosis, two well-known MetS co-morbidities. Whereas MINGLeD specifically models the metabolic derangements underlying MetS, the simulation method ADAPT is generic and can be applied to other diseases where dynamic modeling and longitudinal data are available.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

A Quantitative Systems Pharmacology Platform to Investigate the Impact of Alirocumab and Cholesterol-Lowering Therapies on Lipid Profiles and Plaque Characteristics

Reduction in low-density lipoprotein cholesterol (LDL-C) is associated with decreased risk for cardiovascular disease. Alirocumab, an antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9), significantly reduces LDL-C. Here, we report development of a quantitative systems pharmacology (QSP) model integrating peripheral and liver cholesterol metabolism, as well as PCSK9 function, to examine the mechanisms of action of alirocumab and other lipid-lowering therapies, including statins. The model predicts changes in LDL-C and other lipids that are consistent with effects observed in clinical trials of single or combined treatments of alirocumab and other treatments. An exploratory model to examine the effects of lipid levels on plaque dynamics was also developed. The QSP platform, on further development and qualification, may support dose optimization and clinical trial design for PCSK9 inhibitors and lipid-modulating drugs. It may also improve our understanding of factors affecting therapeutic responses in different phenotypes of dyslipidemia and cardiovascular disease.

An in silico model of retinal cholesterol dynamics (RCD model): insights into the pathophysiology of dry AMD

We developed an in silico mathematical model of retinal cholesterol (Ch) dynamics (RCD) to quantify the physiological rate of Ch turnover in the rod outer segment (ROS), the lipoprotein transport mechanisms by which Ch enters and leaves the outer retina, and the rates of drusen growth and macrophage-mediated clearance in dry age-related macular degeneration. Based on existing experimental data and mechanistic hypotheses, we estimated the Ch turnover rate in the ROS to be 1–6 pg/mm²/min, dependent on the rate of Ch recycling in the outer retina, and found comparable rates for LDL receptor-mediated endocytosis of Ch by the retinal pigment epithelium (RPE), ABCA1-mediated Ch transport from the RPE to the outer retina, ABCA1-mediated Ch efflux from the RPE to the choroid, and the secretion of 70 nm ApoB-Ch particles from the RPE. The drusen growth rate is predicted to increase from 0.7 to 4.2 μm/year in proportion to the flux of ApoB-Ch particles. The rapid regression of drusen may be explained by macrophage-mediated clearance if the macrophage density reaches ∼3,500 cells/mm². The RCD model quantifies retinal Ch dynamics and suggests that retinal Ch turnover and recycling, ApoB-Ch particle efflux, and macrophage-mediated clearance may explain the dynamics of drusen growth and regression.

Quantitative Systems Pharmacology: A Case for Disease Models

Quantitative systems pharmacology (QSP) has emerged as an innovative approach in model‐informed drug discovery and development, supporting program decisions from exploratory research through late‐stage clinical trials. In this commentary, we discuss the unique value of disease‐scale “platform” QSP models that are amenable to reuse and repurposing to support diverse clinical decisions in ways distinct from other pharmacometrics strategies.

Assessment of dietary exposure and effect in humans: The role of NMR

In human nutritional science progress has always depended strongly on analytical measurements for establishing relationships between diet and health. This field has undergone significant changes as a result of the development of NMR and mass spectrometry methods for large scale detection, identification and quantification of metabolites in body fluids. This has allowed systematic studies of the metabolic fingerprints that biological processes leave behind, and has become the research field of metabolomics. As a metabolic profiling technique, NMR is at its best when its unbiased nature, linearity and reproducibility are exploited in well-controlled nutritional intervention and cross-sectional population screening studies. Although its sensitivity is less good than that of mass spectrometry, NMR has maintained a strong position in metabolomics through implementation of standardisation protocols, hyphenation with mass spectrometry and chromatographic techniques, accurate quantification and spectral deconvolution approaches, and high-throughput automation. Thus, NMR-based metabolomics has contributed uniquely to new insights into dietary exposure, in particular by unravelling the metabolic fates of phytochemicals and the discovery of dietary intake markers. NMR profiling has also contributed to the understanding of the subtle effects of diet on central metabolism and lipoprotein metabolism. In order to hold its ground in nutritional metabolomics, NMR will need to step up its performance in sensitivity and resolution; the most promising routes forward are the analytical use of dynamic nuclear polarisation and developments in microcoil construction and automated fractionation.

Cholesterol metabolism: A review of how ageing disrupts the biological mechanisms responsible for its regulation

Cholesterol plays a vital role in the human body as a precursor of steroid hormones and bile acids, in addition to providing structure to cell membranes. Whole body cholesterol metabolism is maintained by a highly coordinated balancing act between cholesterol ingestion, synthesis, absorption, and excretion. The aim of this review is to discuss how ageing interacts with these processes. Firstly, we will present an overview of cholesterol metabolism. Following this, we discuss how the biological mechanisms which underpin cholesterol metabolism are effected by ageing. Included in this discussion are lipoprotein dynamics, cholesterol absorption/synthesis and the enterohepatic circulation/synthesis of bile acids. Moreover, we discuss the role of oxidative stress in the pathological progression of atherosclerosis and also discuss how cholesterol biosynthesis is effected by both the mammalian target of rapamycin and sirtuin pathways. Next, we examine how diet and alterations to the gut microbiome can be used to mitigate the impact ageing has on cholesterol metabolism. We conclude by discussing how mathematical models of cholesterol metabolism can be used to identify therapeutic interventions.

A Six-Stage Workflow for Robust Application of Systems Pharmacology

Quantitative and systems pharmacology (QSP) is increasingly being applied in pharmaceutical research and development. One factor critical to the ultimate success of QSP is the establishment of commonly accepted language, technical criteria, and workflows. We propose an integrated workflow that bridges conceptual objectives with underlying technical detail to support the execution, communication, and evaluation of QSP projects.

In silico modeling of the dynamics of low density lipoprotein composition via a single plasma sample

Lipoproteins play a key role in the development of CVD, but the dynamics of lipoprotein metabolism are difficult to address experimentally. This article describes a novel two-step combined in vitro and in silico approach that enables the estimation of key reactions in lipoprotein metabolism using just one blood sample. Lipoproteins were isolated by ultracentrifugation from fasting plasma stored at 4°C. Plasma incubated at 37°C is no longer in a steady state, and changes in composition may be determined. From these changes, we estimated rates for reactions like LCAT (56.3 µM/h), β-LCAT (15.62 µM/h), and cholesteryl ester (CE) transfer protein-mediated flux of CE from HDL to IDL/VLDL (21.5 µM/h) based on data from 15 healthy individuals. In a second step, we estimated LDL's HL activity (3.19 pools/day) and, for the very first time, selective CE efflux from LDL (8.39 µM/h) by relying on the previously derived reaction rates. The estimated metabolic rates were then confirmed in an independent group (n = 10). Although measurement uncertainties do not permit us to estimate parameters in individuals, the novel approach we describe here offers the unique possibility to investigate lipoprotein dynamics in various diseases like atherosclerosis or diabetes.

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology Models

Quantitative systems pharmacology models mechanistically describe a biological system and the effect of drug treatment on system behavior. Because these models rarely are identifiable from the available data, the uncertainty in physiological parameters may be sampled to create alternative parameterizations of the model, sometimes termed "virtual patients." In order to reproduce the statistics of a clinical population, virtual patients are often weighted to form a virtual population that reflects the baseline characteristics of the clinical cohort. Here we introduce a novel technique to efficiently generate virtual patients and, from this ensemble, demonstrate how to select a virtual population that matches the observed data without the need for weighting. This approach improves confidence in model predictions by mitigating the risk that spurious virtual patients become overrepresented in virtual populations.

Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach

The recent failures of cholesteryl ester transport protein inhibitor drugs to decrease CVD risk, despite raising HDL cholesterol (HDL-C) levels, suggest that pharmacologic increases in HDL-C may not always reflect elevations in reverse cholesterol transport (RCT), the process by which HDL is believed to exert its beneficial effects. HDL-modulating therapies can affect HDL properties beyond total HDL-C, including particle numbers, size, and composition, and may contribute differently to RCT and CVD risk. The lack of validated easily measurable pharmacodynamic markers to link drug effects to RCT, and ultimately to CVD risk, complicates target and compound selection and evaluation. In this work, we use a systems pharmacology model to contextualize the roles of different HDL targets in cholesterol metabolism and provide quantitative links between HDL-related measurements and the associated changes in RCT rate to support target and compound evaluation in drug development. By quantifying the amount of cholesterol removed from the periphery over the short-term, our simulations show the potential for infused HDL to treat acute CVD. For the primary prevention of CVD, our analysis suggests that the induction of ApoA-I synthesis may be a more viable approach, due to the long-term increase in RCT rate.

Systems Modeling of Interactions between Mucosal Immunity and the Gut Microbiome during Clostridium difficile Infection

Clostridium difficile infections are associated with the use of broad-spectrum antibiotics and result in an exuberant inflammatory response, leading to nosocomial diarrhea, colitis and even death. To better understand the dynamics of mucosal immunity during C. difficile infection from initiation through expansion to resolution, we built a computational model of the mucosal immune response to the bacterium. The model was calibrated using data from a mouse model of C. difficile infection. The model demonstrates a crucial role of T helper 17 (Th17) effector responses in the colonic lamina propria and luminal commensal bacteria populations in the clearance of C. difficile and colonic pathology, whereas regulatory T (Treg) cells responses are associated with the recovery phase. In addition, the production of anti-microbial peptides by inflamed epithelial cells and activated neutrophils in response to C. difficile infection inhibit the re-growth of beneficial commensal bacterial species. Computational simulations suggest that the removal of neutrophil and epithelial cell derived anti-microbial inhibitions, separately and together, on commensal bacterial regrowth promote recovery and minimize colonic inflammatory pathology. Simulation results predict a decrease in colonic inflammatory markers, such as neutrophilic influx and Th17 cells in the colonic lamina propria, and length of infection with accelerated commensal bacteria re-growth through altered anti-microbial inhibition. Computational modeling provides novel insights on the therapeutic value of repopulating the colonic microbiome and inducing regulatory mucosal immune responses during C. difficile infection. Thus, modeling mucosal immunity-gut microbiota interactions has the potential to guide the development of targeted fecal transplantation therapies in the context of precision medicine interventions.

Analysis of "On/Off" Kinetics of a CETP Inhibitor Using a Mechanistic Model of Lipoprotein Metabolism and Kinetics

RG7232 is a potent inhibitor of cholesteryl-ester transfer protein (CETP). Daily oral administration of RG7232 produces a dose- and time-dependent increase in high-density lipoprotein-cholesterol (HDL-C) and apolipoproteinA-I (ApoA-I) levels and a corresponding decrease in low-density lipoprotein-cholesterol (LDL-C) and apolipoproteinB (ApoB) levels. Due to its short plasma half-life (∼3 hours), RG7232 transiently inhibits CETP activity during each dosing interval ("on/off" kinetics), as reflected by the temporal effects on HDL-C and LDL-C. The influence of RG7232 on lipid-poor ApoA-I (i.e., pre-β 1) levels and reverse cholesterol transport rates is unclear. To investigate this, a published model of lipoprotein metabolism and kinetics was combined with a pharmacokinetic model of RG7232. After calibration and validation of the combined model, the effect of RG7232 on pre-β 1 levels was simulated. A dose-dependent oscillation of pre-β 1, driven by the "on/off" kinetics of RG7232 was observed. The possible implications of these findings are discussed.

Computationally Modeling Lipid Metabolism and Aging: A Mini-review

One of the greatest challenges in biology is to improve the understanding of the mechanisms which underpin aging and how these affect health. The need to better understand aging is amplified by demographic changes, which have caused a gradual increase in the global population of older people. Aging western populations have resulted in a rise in the prevalence of age-related pathologies. Of these diseases, cardiovascular disease is the most common underlying condition in older people. The dysregulation of lipid metabolism due to aging impinges significantly on cardiovascular health. However, the multifaceted nature of lipid metabolism and the complexities of its interaction with aging make it challenging to understand by conventional means. To address this challenge computational modeling, a key component of the systems biology paradigm is being used to study the dynamics of lipid metabolism. This mini-review briefly outlines the key regulators of lipid metabolism, their dysregulation, and how computational modeling is being used to gain an increased insight into this system.

Control of cholesterol homeostasis by entero-hepatic bile transport – the role of feedback mechanisms

Cholesterol homeostasis is achieved through a tight regulation between synthesis, dietary absorption, utilization of bile salts, and excretion in the entero-hepatic compartment.