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      Novel Population Pharmacokinetic Approach to Explain the Differences between Cystic Fibrosis Patients and Healthy Volunteers via Protein Binding

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          Abstract

          The pharmacokinetics in patients with cystic fibrosis (CF) has long been thought to differ considerably from that in healthy volunteers. For highly protein bound β-lactams, profound pharmacokinetic differences were observed between comparatively morbid patients with CF and healthy volunteers. These differences could be explained by body weight and body composition for β-lactams with low protein binding. This study aimed to develop a novel population modeling approach to describe the pharmacokinetic differences between both subject groups by estimating protein binding. Eight patients with CF (lean body mass [LBM]: 39.8 ± 5.4kg) and six healthy volunteers (LBM: 53.1 ± 9.5kg) received 1027.5 mg cefotiam intravenously. Plasma concentrations and amounts in urine were simultaneously modelled. Unscaled total clearance and volume of distribution were 3% smaller in patients with CF compared to those in healthy volunteers. After allometric scaling by LBM to account for body size and composition, the remaining pharmacokinetic differences were explained by estimating the unbound fraction of cefotiam in plasma. The latter was fixed to 50% in male and estimated as 54.5% in female healthy volunteers as well as 56.3% in male and 74.4% in female patients with CF. This novel approach holds promise for characterizing the pharmacokinetics in special patient populations with altered protein binding.

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          Physiological parameters in laboratory animals and humans.

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            Generating Robust and Informative Nonclinical In Vitro and In Vivo Bacterial Infection Model Efficacy Data To Support Translation to Humans

            In June 2017, the National Institute of Allergy and Infectious Diseases, part of the National Institutes of Health, organized a workshop entitled “Pharmacokinetics-Pharmacodynamics (PK/PD) for Development of Therapeutics against Bacterial Pathogens.” The aims were to discuss details of various PK/PD models and identify sound practices for deriving and utilizing PK/PD relationships to design optimal dosage regimens for patients. Workshop participants encompassed individuals from academia, industry, and government, including the United States Food and Drug Administration.
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              A survey of population analysis methods and software for complex pharmacokinetic and pharmacodynamic models with examples.

              An overview is provided of the present population analysis methods and an assessment of which software packages are most appropriate for various PK/PD modeling problems. Four PK/PD example problems were solved using the programs NONMEM VI beta version, PDx-MCPEM, S-ADAPT, MONOLIX, and WinBUGS, informally assessed for reasonable accuracy and stability in analyzing these problems. Also, for each program we describe their general interface, ease of use, and abilities. We conclude with discussing which algorithms and software are most suitable for which types of PK/PD problems. NONMEM FO method is accurate and fast with 2-compartment models, if intra-individual and interindividual variances are small. The NONMEM FOCE method is slower than FO, but gives accurate population values regardless of size of intra- and interindividual errors. However, if data are very sparse, the NONMEM FOCE method can lead to inaccurate values, while the Laplace method can provide more accurate results. The exact EM methods (performed using S-ADAPT, PDx-MCPEM, and MONOLIX) have greater stability in analyzing complex PK/PD models, and can provide accurate results with sparse or rich data. MCPEM methods perform more slowly than NONMEM FOCE for simple models, but perform more quickly and stably than NONMEM FOCE for complex models. WinBUGS provides accurate assessments of the population parameters, standard errors and 95% confidence intervals for all examples. Like the MCPEM methods, WinBUGS's efficiency increases relative to NONMEM when solving the complex PK/PD models.
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                Author and article information

                Journal
                Pharmaceutics
                Pharmaceutics
                pharmaceutics
                Pharmaceutics
                MDPI
                1999-4923
                18 June 2019
                June 2019
                : 11
                : 6
                : 286
                Affiliations
                [1 ]Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Orlando, FL 32827, USA; shah.nirav@ 123456cop.ufl.edu (N.R.S.); yyjiao@ 123456cop.ufl.edu (Y.J.); DSutaria@ 123456cop.ufl.edu (D.S.S.); tealingsxun@ 123456ufl.edu (X.T.)
                [2 ]IBMP—Institute for Biomedical and Pharmaceutical Research, Nürnberg-Heroldsberg 90562, Germany; Martina.Kinzig@ 123456gtf-online.de
                [3 ]Drug Delivery, Disposition and Dynamics, Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, VIC 3052, Australia; cornelia.landersdorfer@ 123456monash.edu
                [4 ]Institute of Clinical Hygiene, Medical Microbiology and Infectiology, Klinikum Nürnberg, Paracelsus Medical University, Nürnberg 90419, Germany; rai.hoehl@ 123456googlemail.com
                [5 ]Institute for Pharmacy and Food Chemistry, University of Würzburg, Würzburg 97074, Germany; ulrike.holzgrabe@ 123456uni-wuerzburg.de
                [6 ]Department of Pharmacology, University of Regensburg, Regensburg 93053, Germany; frieder.kees@ 123456chemie.uni-regensburg.de
                [7 ]Department of Pharmacology, University of Duisburg, Essen 47057, Germany
                Author notes
                [* ]Correspondence: jbulitta@ 123456cop.ufl.edu (J.B.B.); Fritz.Soergel@ 123456ibmp.net (F.S.); Tel.: +1-407-313-7010 (J.B.B.); +49-911-518-290 (F.S.)
                [†]

                Deceased.

                Author information
                https://orcid.org/0000-0001-7352-3097
                https://orcid.org/0000-0001-8149-3481
                https://orcid.org/0000-0002-0364-7278
                Article
                pharmaceutics-11-00286
                10.3390/pharmaceutics11060286
                6630667
                31216743
                6b9b587c-b08b-45b8-87f1-7a9ad6c8c070
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 31 March 2019
                : 17 May 2019
                Categories
                Article

                cystic fibrosis patients,healthy volunteers,cefotiam,beta-lactam antibiotics,population pharmacokinetics,protein binding,allometric scaling,body size,body composition,s-adapt

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