Omega 3 Improves Both apoB100-containing Lipoprotein Turnover and their Sphingolipid Profile in Hypertriglyceridemia

Context Evidence for an association between sphingolipids and metabolic disorders is increasingly reported. Omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFAs) improve apolipoprotein B100 (apoB100)-containing lipoprotein metabolism, but their effects on the sphingolipid content in lipoproteins remain unknown. Objectives In subjects with hypertriglyceridemia, we analyzed the effect of n-3 LC-PUFAs on the turnover apoB100-containing lipoproteins and on their sphingolipid content and looked for the possible association between these lipid levels and apoB100-containing lipoprotein turnover parameters. Methods Six subjects underwent a kinetic study before and after n-3 supplementation for 2 months with 1 g of fish oil 3 times day containing 360 mg of eicosapentaenoic acid (EPA) and 240 mg of docosahexaenoic acid (DHA) in the form of triglycerides. We examined apoB100-containing lipoprotein turnover by primed perfusion labeled [5,5,5-2H3]-leucine and determined kinetic parameters using a multicompartmental model. We quantified sphingolipid species content in lipoproteins using mass spectrometry. Results Supplementation decreased very low-density lipoprotein (VLDL), triglyceride, and apoB100 concentrations. The VLDL neutral and polar lipids showed increased n-3 LC-PUFA and decreased n-6 LC-PUFA content. The conversion rate of VLDL1 to VLDL2 and of VLDL2 to LDL was increased. We measured a decrease in total apoB100 production and VLDL1 production. Supplementation reduced the total ceramide concentration in VLDL while the sphingomyelin content in LDL was increased. We found positive correlations between plasma palmitic acid and VLDL ceramide and between VLDL triglyceride and VLDL ceramide, and inverse correlations between VLDL n-3 LC-PUFA and VLDL production. Conclusion Based on these results, we hypothesize that the improvement in apoB100 metabolism during n-3 LC-PUFA supplementation is contributed to by changes in sphingolipids

Mining Small Routine Clinical Data: A Population Pharmacokinetic Model and Optimal Sampling Times of Capecitabine and its Metabolites

Purpose: The present study was performed to demonstrate that small amounts of routine clinical data allow to generate valuable knowledge. Concretely, the aims of this research were to build a joint population pharmacokinetic model for capecitabine and three of its metabolites (5-DFUR, 5-FU and 5-FUH2) and to determine optimal sampling times for therapeutic drug monitoring. Methods: We used data of 7 treatment cycles of capecitabine in patients with metastatic colorectal cancer. The population pharmacokinetic model was built as a multicompartmental model using NONMEM and was internally validated by visual predictive check. Optimal sampling times were estimated using PFIM 4.0 following D-optimality criterion. Results: The final model was a multicompartmental model which represented the sequential transformations from capecitabine to its metabolites 5-DFUR, 5-FU and 5-FUH2 and was correctly validated. The optimal sampling times were 0.546, 0.892, 1.562, 4.736 and 8 hours after the administration of the drug. For its correct implementation in clinical practice, the values were rounded to 0.5, 1, 1.5, 5 and 8 hours after the administration of the drug. Conclusions: Capecitabine, 5-DFUR, 5-FU and 5-FUH2 can be correctly described by the joint multicompartmental model presented in this work. The aforementioned times are optimal to maximize the information of samples. Useful knowledge can be obtained for clinical practice from small databases.

Abstract 30: ApoE and ApoCIII Interact to Modulate the Metabolism of HDL ApoA-I in Humans

ApoE has potential roles in HDL metabolism by promoting enlargement and clearance, and apoCIII could delay apoE-mediated clearance by the liver as it does for VLDL metabolism. To determine whether apoE and apoCIII modulate the kinetics of apoA-I HDL, we compared the metabolism of apoA-I in HDL subspecies that have apoE, apoCIII, both, or neither. We recruited 10 participants (4M, 6F) with low HDL-C (range 24-54 mg/dl) and BMI between 25-35 kg/m 2 . They were given an IV bolus of d3-leucine and blood collected up to 46hr. HDL was isolated from plasma by anti-apoA-I immunoaffinity chromatography, separated by sequential anti-apoE and anti-apoCIII chromatography, and size-separated using NDPAGE into alpha-1, alpha-2, alpha-3, and prebeta-1 HDL. ApoA-I was purified from HDL subspecies on SDS-PAGE, and pool size of apoA-I was determined from the protein bands, adjusted to plasma total apoA-I. D3-leucine enrichment was measured by GC-MS. We used SAAM-II modeling software to compute apoA-I fractional catabolic rates (FCR) and fluxes for each HDL subspecies using a published multicompartmental model. The main findings from our preliminary model investigation are: - The liver secretes a range of HDL sizes for each of these HDL subspecies. About 2-6% of plasma HDL apoA-I is associated with apoE and/or apoCIII. Regardless of size, apoE- and apoCIII-containing HDL are detectable in the circulation slightly earlier after tracer administration than HDL containing neither apoE nor apoCIII. - HDL that contains apoE but not apoCIII is especially active in size conversions, such as generating prebeta-1 HDL. Prebeta-1 HDL types are not a universal precursor of larger size HDL. - HDL that contains apoE but not apoCIII has about a 4-fold increased FCR (range 1.3-8.8) across all sizes of HDL compared to other HDL subspecies, consistent with the role of apoE as a liver receptor ligand. When coexisting with apoE, apoCIII abolished the apoE-accelerated clearance, making the FCR similar to that of HDL that does not have apoE. But, when apoCIII is present on HDL that does not have apoE, there is no reduction in clearance compared to HDL containing neither apolipoprotein. In conclusion, these results suggest that apoE accelerates the metabolism of HDL apoA-I, whereas apoCIII impedes this process.

Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part II: Multi-Compartmental Modeling

This is the second article of a two-part paper, combining high-resolution computer simulation results of inhaled nanoparticle deposition in a human airway model (Kolanjiyil and Kleinstreuer, 2013, “Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part I: Whole-Lung Aerosol Dynamics,” ASME J. Biomech. Eng., 135(12), p. 121003) with a new multicompartmental model for insoluble nanoparticle barrier mass transfer into systemic regions. Specifically, it allows for the prediction of temporal nanoparticle accumulation in the blood and lymphatic systems and in organs. The multicompartmental model parameters were determined from experimental retention and clearance data in rat lungs and then the validated model was applied to humans based on pharmacokinetic cross-species extrapolation. This hybrid simulator is a computationally efficient tool to predict the nanoparticle kinetics in the human body. The study provides critical insight into nanomaterial deposition and distribution from the lungs to systemic regions. The quantitative results are useful in diverse fields such as toxicology for exposure-risk analysis of ubiquitous nanomaterial and pharmacology for nanodrug development and targeting.

Nanoparticle Mass Transfer From Lung Airways to Systemic Regions—Part I: Whole-Lung Aerosol Dynamics

This is a two-part paper describing inhaled nanoparticle (NP) transport and deposition in a model of a human respiratory tract (Part I) as well as NP-mass transfer across barriers into systemic regions (Part II). Specifically, combining high-resolution computer simulation results of inhaled NP deposition in the human airways (Part I) with a multicompartmental model for NP-mass transfer (Part II) allows for the prediction of temporal NP accumulation in the blood and lymphatic systems as well as in organs. An understanding of nanoparticle transport and deposition in human respiratory airways is of great importance, as exposure to nanomaterial has been found to cause serious lung diseases, while the use of nanodrugs may have superior therapeutic effects. In Part I, the fluid-particle dynamics of a dilute NP suspension was simulated for the entire respiratory tract, assuming steady inhalation and planar airways. Thus, a realistic airway configuration was considered from nose/mouth to generation 3, and then an idealized triple-bifurcation unit was repeated in series and parallel to cover the remaining generations. Using the current model, the deposition of NPs in distinct regions of the lung, namely extrathoracic, bronchial, bronchiolar, and alveolar, was calculated. The region-specific NP-deposition results for the human lung model were used in Part II to determine the multicompartmental model parameters from experimental retention and clearance data in human lungs. The quantitative, experimentally validated results are useful in diverse fields, such as toxicology for exposure-risk analysis of ubiquitous nanomaterial as well as in pharmacology for nanodrug development and targeting.