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In nonalcoholic fatty liver disease (NAFLD) the patatin-like phospholipase domain-containing 3 (PNPLA3) rs738409 variant is a contributor. In mice, the Pnpla3 148M variant accumulates on lipid droplets and probably leads to sequestration of a lipase cofactor leading to impaired mobilization of triglycerides. To advance our understanding of the localization and abundance of PNPLA3 protein in humans, we used liver biopsies from patients with NAFLD to investigate the link to NAFLD and the PNPLA3 148M genotype. We experimentally qualified an antibody against human PNPLA3. Hepatic PNPLA3 protein fractional area and localization were determined by immunohistochemistry in biopsies from a well-characterized NAFLD cohort of 67 patients. Potential differences in hepatic PNPLA3 protein levels among patients related to degree of steatosis, lobular inflammation, ballooning, and fibrosis, and PNPLA3 I148M gene variants were assessed. Immunohistochemistry staining in biopsies from patients with NAFLD showed that hepatic PNPLA3 protein was predominantly localized to the membranes of small and large lipid droplets in hepatocytes. PNPLA3 protein levels correlated strongly with steatosis grade (p = 0.000027) and were also significantly higher in patients with lobular inflammation (p = 0.009), ballooning (p = 0.022), and significant fibrosis (stage 2-4, p = 0.014). In addition, PNPLA3 levels were higher in PNPLA3 rs738409 148M (CG, GG) risk allele carriers compared to 148I (CC) nonrisk allele carriers (p = 0.0029). Conclusion: PNPLA3 protein levels were associated with increased hepatic lipid content and disease severity in patients with NAFLD and were higher in PNPLA3 rs738409 (148M) risk allele carriers. Our hypothesis that increased hepatic levels of PNPLA3 may be part of the pathophysiological mechanism of NAFLD is supported. Funding Agencies|ALF Grants, Region Ostergotland; Astra Zeneca; Forskningsradet i Sydostra Sverige

We investigated the effects of chronic oral administration of mineral oil, versus corn oil as control, on intestinal permeability, inflammatory markers, and plasma lipids in APOE*3-Leiden.CETP mice. Mice received mineral oil or corn oil 15 or 30 μL/mouse/day for 16 weeks (15 mice/group). Intestinal permeability was increased with mineral versus corn oil 30 µL/day, shown by increased mean plasma FITC-dextran concentrations 2 h post-administration (11 weeks: 1.5 versus 1.1 μg/ml, p = 0.02; 15 weeks: 1.7 versus 1.3 μg/ml, p = 0.08). Mean plasma lipopolysaccharide-binding protein levels were raised with mineral versus corn oil 30 µL/day (12 weeks: 5.8 versus 4.4 μg/ml, p = 0.03; 16 weeks: 5.8 versus 4.5 μg/ml, p = 0.09), indicating increased intestinal bacterial endotoxin absorption and potential pro-inflammatory effects. Plasma cholesterol and triglyceride concentrations were decreased with mineral oil, without affecting liver lipids among treated groups. Fecal neutral sterol measurements indicated increased fecal cholesterol excretion with mineral oil 30 µL/day (+16%; p = 0.04). Chronic oral administration of mineral oil in APOE*3-Leiden.CETP mice increased intestinal permeability, with potential pro-inflammatory effects, and decreased plasma cholesterol and triglyceride levels. Our findings may raise concerns about the use of mineral oil as a placebo in clinical studies.

We investigated the effects of mineral oil on statin pharmacokinetics and inflammatory markers in animal models. A new synthesis strategy produced regioisomers that facilitated the characterization of the main metabolite (M1) of atorvastatin, a lipophilic statin, in C57BL/6NCrl mice. The chemical structure of M1 in mice was confirmed as ortho-hydroxy β-oxidized atorvastatin. Atorvastatin and M1 pharmacokinetics and inflammatory markers were assessed in C57BL6/J mice given atorvastatin 5 mg/kg/day or 10 mg/kg/day, as a single dose or for 21 days, with or without 10 µL or 30 µL mineral oil. No consistent differences in plasma exposure of atorvastatin or M1 were observed in mice after single or repeat dosing of atorvastatin with or without mineral oil. However, mice administered atorvastatin 10 mg/kg with 30 µL mineral oil for 21 days had significantly increased plasma levels of serum amyloid A (mean 9.6 µg/mL vs 7.9 µg/mL without mineral oil; p0.01) and significantly increased proportions of C62L

Background and Purpose Risk of cardiac conduction slowing (QRS/PR prolongations) is assessed prior to clinical trials using in vitro and in vivo studies. Understanding the quantitative translation of these studies to the clinical situation enables improved risk assessment in the nonclinical phase. Experimental Approach Four compounds that prolong QRS and/or PR (AZD1305, flecainide, quinidine and verapamil) were characterized using in vitro (sodium/calcium channels), in vivo (guinea pigs/dogs) and clinical data. Concentration-matched translational relationships were developed based on in vitro and in vivo modelling, and the in vitro to clinical translation of AZD1305 was quantified using an in vitro model. Key Results Meaningful (10%) human QRS/PR effects correlated with low levels of in vitro Nav1.5 block (3–7%) and Cav1.2 binding (13–21%) for all compounds. The in vitro model developed using AZD1305 successfully predicted QRS/PR effects for the remaining drugs. Meaningful QRS/PR changes in humans correlated with small effects in guinea pigs and dogs (QRS 2.3–4.6% and PR 2.3–10%), suggesting that worst-case human effects can be predicted by assuming four times greater effects at the same concentration from dog/guinea pig data. Conclusion and Implications Small changes in vitro and in vivo consistently translated to meaningful PR/QRS changes in humans across compounds. Assuming broad applicability of these approaches to assess cardiovascular safety risk for non–arrhythmic drugs, this study provides a means of predicting human QRS/PR effects of new drugs from effects observed in nonclinical studies.

Issues of parameter identifiability of routinely used pharmacodynamics models are considered in this paper. The structural identifiability of 16 commonly applied pharmacodynamic model structures was analyzed analytically, using the input-output approach. Both fixed-effects versions (non-population, no between-subject variability) and mixed-effects versions (population, including between-subject variability) of each model structure were analyzed. All models were found to be structurally globally identifiable under conditions of fixing either one of two particular parameters. Furthermore, an example was constructed to illustrate the importance of sufficient data quality and show that structural identifiability is a prerequisite, but not a guarantee, for successful parameter estimation and practical parameter identifiability. This analysis was performed by generating artificial data of varying quality to a structurally identifiable model with known true parameter values, followed by re-estimation of the parameter values. In addition, to show the benefit of including structural identifiability as part of model development, a case study was performed applying an unidentifiable model to real experimental data. This case study shows how performing such an analysis prior to parameter estimation can improve the parameter estimation process and model performance. Finally, an unidentifiable model was fitted to simulated data using multiple initial parameter values, resulting in highly different estimated uncertainties. This example shows that although the standard errors of the parameter estimates often indicate a structural identifiability issue, reasonably “good” standard errors may sometimes mask unidentifiability issues.\ud \ud

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.

Introduction: Pharmacokinetic-pharmacodynamic (PKPD) modelling can improve safety assessment, but few PKPD models describing drug-induced QRS and PR prolongations have been published. This investigation aims to develop and evaluate PKPD models for describing QRS and PR effects in routine safety studies. Methods: Exposure and telemetry data from safety pharmacology studies in conscious beagle dogs were acquired. Mixed effects baseline and PK-QRS/PR models were developed for the antiarrhythmic compounds AZD1305, flecainide, quinidine and verapamil and the anti-muscarinic compounds AZD8683 and AZD9164. RR interval correction and circadian rhythms were investigated for predicting baseline variability. Individual PK predictions were used to drive the pharmacological effects evaluating linear and non-linear direct and effect compartment models. Results: Conduction slowing induced by the tested anti-arrhythmics was direct and proportional at low exposures, whilst time delays and non-linear effects were evident for the tested antimuscarinics. AZD1305, flecainide and quinidine induced QRS widening with 4.2, 10 and 5.6 % µM-1 unbound drug. AZD1305 and flecainide also prolonged PR with 13.5 and 11.5 % µM- 1 . PR prolongations induced by the anti-muscarinics and verapamil were best described by Emax models with maximal effects ranging from 55 to 95 %. RR interval correction and circadian rhythm improved PR but not QRS modelling. However, circadian rhythm had minor impact on estimated drug effects. Discussion: Baseline and drug-induced effects on QRS and PR intervals can be effectively described with PKPD models using routine data, providing quantitative safety information to support drug discovery and development.

Systems pharmacology modeling and pharmacokinetic-pharmacodynamic (PK/PD) analysis of drug-induced effects on cardiovascular (CV) function plays a crucial role in understanding the safety risk of new drugs. The aim of this review is to outline the current modeling and simulation (M&S) approaches to describe and translate drug-induced CV effects, with an emphasis on how this impacts drug safety assessment. Current limitations are highlighted and recommendations are made for future effort in this vital area of drug research.

Type 2 diabetes originates in an expanding adipose tissue that for unknown reasons becomes insulin resistant. Insulin resistance reflects impairments in insulin signaling, but mechanisms involved are unclear because current research is fragmented. We report a systems level mechanistic understanding of insulin resistance, using systems wide and internally consistent data from human adipocytes. Based on quantitative steady-state and dynamic time course data on signaling intermediaries, normally and in diabetes, we developed a dynamic mathematical model of insulin signaling. The model structure and parameters are identical in the normal and diabetic states of the model, except for three parameters that change in diabetes: (i) reduced concentration of insulin receptor, (ii) reduced concentration of insulin-regulated glucose transporter GLUT4, and (iii) changed feedback from mammalian target of rapamycin in complex with raptor (mTORC1). Modeling reveals that at the core of insulin resistance in human adipocytes is attenuation of a positive feedback from mTORC1 to the insulin receptor substrate-1, which explains reduced sensitivity and signal strength throughout the signaling network. Model simulations with inhibition of mTORC1 are comparable with experimental data on inhibition of mTORC1 using rapamycin in human adipocytes. We demonstrate the potential of the model for identification of drug targets, e.g. increasing the feedback restores insulin signaling, both at the cellular level and, using a multilevel model, at the whole body level. Our findings suggest that insulin resistance in an expanded adipose tissue results from cell growth restriction to prevent cell necrosis.