Feedback modeling of non-esterified fatty acids in rats after nicotinic acid infusions

A feedback model was developed to describe the tolerance and oscillatory rebound seen in non-esterified fatty acid (NEFA) plasma concentrations following intravenous infusions of nicotinic acid (NiAc) to male Sprague-Dawley rats. NiAc was administered as an intravenous infusion over 30 min (0, 1, 5 or 20 μmol kg−1 of body weight) or over 300 min (0, 5, 10 or 51 μmol kg−1 of body weight), to healthy rats (n = 63), and serial arterial blood samples were taken for measurement of NiAc and NEFA plasma concentrations. Data were analyzed using nonlinear mixed effects modeling (NONMEM). The disposition of NiAc was described by a two-compartment model with endogenous turnover rate and two parallel capacity-limited elimination processes. The plasma concentration of NiAc was driving NEFA (R) turnover via an inhibitory drug-mechanism function acting on the formation of NEFA. The NEFA turnover was described by a feedback model with a moderator distributed over a series of transit compartments, where the first compartment (M1) inhibited the formation of R and the last compartment (MN) stimulated the loss of R. All processes regulating plasma NEFA concentrations were assumed to be captured by the moderator function. The potency, IC50, of NiAc was 45 nmol L−1, the fractional turnover rate kout was 0.41 L mmol−1 min−1 and the turnover rate of moderator ktol was 0.027 min−1. A lower physiological limit of NEFA was modeled as a NiAc-independent release (kcap) of NEFA into plasma and was estimated to 0.032 mmol L−1 min−1. This model can be used to provide information about factors that determine the time-course of NEFA response following different modes, rates and routes of administration of NiAc. The proposed model may also serve as a preclinical tool for analyzing and simulating drug-induced changes in plasma NEFA concentrations after treatment with NiAc or NiAc analogues

Determination of eflornithine enantiomers in plasma by precolumn derivatization with o-phthalaldehyde-N-acetyl-l-cysteine and liquid chromatography with UV detection

A bioanalytical method for indirect determination of eflornithine enantiomers in 75 muL human plasma has been developed and validated. l- and d-eflornithine were derivatized with o-phthalaldehyde and N-acetyl-L-cysteine to generate diastereomers which were separated on two serially connected Chromolith Performance columns (RP-18e 100 x 4.6 mm i.d.) by a isocratic flow followed by a gradient flow for elution of endogenous compounds. The diastereomers were detected with UV (340 nm). The between-day precisions for L- and D-eflornithine in plasma were 8.4 and 2.3% at 3 mum, 4.0 and 5.1% at 400 mum, and 2.0 and 3.7% at 1000 mum. The lower limit of quantification was determined to be 1.5 mum, at which precision was 14.9 and 9.9% for L- and D-eflornithine, respectively

Proteome deconvolution of liver biopsies reveals hepatic cell composition as an important marker of fibrosis

Human liver tissue is composed of heterogeneous mixtures of different cell types and their cellular stoichiometry can provide information on hepatic physiology and disease progression. Deconvolution algorithms for the identification of cell types and their proportions have recently been developed for transcriptomic data. However, no method for the deconvolution of bulk proteomics data has been presented to date. Here, we show that proteomes, which usually contain less data than transcriptomes, can provide useful information for cell type deconvolution using different algorithms. We demonstrate that proteomes from defined mixtures of cell lines, isolated primary liver cells, and human liver biopsies can be deconvoluted with high accuracy. In contrast to transcriptome-based deconvolution, liver tissue proteomes also provided information about extracellular compartments. Using deconvolution of proteomics data from liver biopsies of 56 patients undergoing Roux-en-Y gastric bypass surgery we show that proportions of immune and stellate cells correlate with inflammatory markers and altered composition of extracellular matrix proteins characteristic of early-stage fibrosis. Our results thus demonstrate that proteome deconvolution can be used as a molecular microscope for investigations of the composition of cell types, extracellular compartments, and for exploring cell-type specific pathological events. We anticipate that these findings will allow the refinement of retrospective analyses of the growing number of proteome datasets from various liver disease states and pave the way for AI-supported clinical and preclinical diagnostics

Proteomics‐Informed Prediction of Rosuvastatin Plasma Profiles in Patients with a Wide Range of Body Weight

Rosuvastatin is a frequently used probe to study transporter-mediated hepatic uptake. Pharmacokinetic models have therefore been developed to predict transporter impact on rosuvastatin disposition in vivo. However, the interindividual differences in transporter concentrations were not considered in these models, and the predicted transporter impact was compared with historical in vivo data. In this study, we investigated the influence of interindividual transporter concentrations on the hepatic uptake clearance of rosuvastatin in 54 patients covering a wide range of body weight. The 54 patients were given an oral dose of rosuvastatin the day before undergoing gastric bypass or cholecystectomy, and pharmacokinetic (PK) parameters were established from each patient’s individual time-concentration profiles. Liver biopsies were sampled from each patient and their individual hepatic transporter concentrations were quantified. We combined the transporter concentrations with in vitro uptake kinetics determined in HEK293-transfected cells, and developed a semimechanistic model with a bottom-up approach to predict the plasma concentration profiles of the single dose of rosuvastatin in each patient. The predicted PK parameters were evaluated against the measured in vivo plasma PKs from the same 54 patients. The developed model predicted the rosuvastatin PKs within two-fold error for rosuvastatin area under the plasma concentration versus time curve (AUC; 78% of the patients; average fold error (AFE): 0.96), peak plasma concentration (Cmax; 76%; AFE: 1.05), and terminal half-life (t1/2; 98%; AFE: 0.89), and captured differences in the rosuvastatin PKs in patients with the OATP1B1 521T<C polymorphism. This demonstrates that hepatic uptake clearance determined in transfected cell lines, together with proteomics scaling, provides a useful tool for prediction models, without the need for empirical scaling factors

Drug Disposition Protein Quantification in Matched Human Jejunum and Liver From Donors With Obesity

Mathematical models, such as physiologically-based pharmacokinetic models, are used to predict, for example, drug disposition and toxicity. However, populations differ in the abundance of proteins involved in these processes. To improve the building and refinement of such models, they must take into account these interindividual variabilities. In this study, we used global proteomics to characterize the protein composition of jejunum and liver from 37 donors with obesity enrolled in the COCKTAIL study. Liver protein levels from the 37 donors were further compared with those from donors without obesity. We quantified thousands of proteins and could present the expression of several drug-metabolizing enzymes, for the first time, in jejunum, many of which belong to the cytochrome P450 (CYP) (e.g., CYP2U1) and the amine oxidase (flavin-containing) (e.g., monoamine oxidase A (MAOA)) families. Although we show that many metabolizing enzymes had greater expression in liver, others had higher expression in jejunum (such as, MAOA and CES2), indicating the role of the small intestine in extrahepatic drug metabolism. We further show that proteins involved in drug disposition are not correlated in the two donor-matched tissues. These proteins also do not correlate with physiological factors such as body mass index, age, and inflammation status in either tissue. Furthermore, the majority of these proteins are not differently expressed in donors with or without obesity. Nonetheless, interindividual differences were considerable, with implications for personalized prediction models and systems pharmacology

A case‐study of model‐informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Abstract Here, we show model‐informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first‐in‐human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de‐risk a phase IIb study. The focus of the case‐study is on the cross‐functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD