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      A semi‐mechanistic model based on glutathione depletion to describe intra‐individual reduction in busulfan clearance

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          Abstract

          Aim

          To develop a semi‐mechanistic model, based on glutathione depletion and predict a previously identified intra‐individual reduction in busulfan clearance to aid in more precise dosing.

          Methods

          Busulfan concentration data, measured as part of regular care for allogeneic hematopoietic cell transplantation (HCT) patients, were used to develop a semi‐mechanistic model and compare it to a previously developed empirical model. The latter included an empirically estimated time effect, where the semi‐mechanistic model included theoretical glutathione depletion. As older age has been related to lower glutathione levels, this was tested as a covariate in the semi‐mechanistic model. Lastly, a therapeutic drug monitoring (TDM) simulation was performed comparing the two models in target attainment.

          Results

          In both models, a similar clearance decrease of 7% (range −82% to 44%), with a proportionality to busulfan metabolism, was found. After 40 years of age, the time effect increased with 4% per year of age (0.6–8%, P = 0.009), causing the effect to increase more than a 2‐fold over the observed age‐range (0–73 years). Compared to the empirical model, the final semi‐mechanistic model increased target attainment from 74% to 76%, mainly through better predictions for adult patients.

          Conclusion

          These results suggest that the time‐dependent decrease in busulfan clearance may be related to gluthathione depletion. This effect increased with older age ( >40 years) and was proportional to busulfan metabolism. The newly constructed semi‐mechanistic model could be used to further improve TDM‐guided exposure target attainment of busulfan in patients undergoing HCT.

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          Most cited references33

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          Prediction-corrected visual predictive checks for diagnosing nonlinear mixed-effects models.

          Informative diagnostic tools are vital to the development of useful mixed-effects models. The Visual Predictive Check (VPC) is a popular tool for evaluating the performance of population PK and PKPD models. Ideally, a VPC will diagnose both the fixed and random effects in a mixed-effects model. In many cases, this can be done by comparing different percentiles of the observed data to percentiles of simulated data, generally grouped together within bins of an independent variable. However, the diagnostic value of a VPC can be hampered by binning across a large variability in dose and/or influential covariates. VPCs can also be misleading if applied to data following adaptive designs such as dose adjustments. The prediction-corrected VPC (pcVPC) offers a solution to these problems while retaining the visual interpretation of the traditional VPC. In a pcVPC, the variability coming from binning across independent variables is removed by normalizing the observed and simulated dependent variable based on the typical population prediction for the median independent variable in the bin. The principal benefit with the pcVPC has been explored by application to both simulated and real examples of PK and PKPD models. The investigated examples demonstrate that pcVPCs have an enhanced ability to diagnose model misspecification especially with respect to random effects models in a range of situations. The pcVPC was in contrast to traditional VPCs shown to be readily applicable to data from studies with a priori and/or a posteriori dose adaptations.
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            THE CONCISE GUIDE TO PHARMACOLOGY 2019/20: Introduction and Other Protein Targets

            The Concise Guide to PHARMACOLOGY 2019/20 is the fourth in this series of biennial publications. The Concise Guide provides concise overviews of the key properties of nearly 1800 human drug targets with an emphasis on selective pharmacology (where available), plus links to the open access knowledgebase source of drug targets and their ligands (http://www.guidetopharmacology.org/), which provides more detailed views of target and ligand properties. Although the Concise Guide represents approximately 400 pages, the material presented is substantially reduced compared to information and links presented on the website. It provides a permanent, citable, point‐in‐time record that will survive database updates. The full contents of this section can be found at http://onlinelibrary.wiley.com/doi/10.1111/bph.14747. In addition to this overview, in which are identified Other protein targets which fall outside of the subsequent categorisation, there are six areas of focus: G protein‐coupled receptors, ion channels, nuclear hormone receptors, catalytic receptors, enzymes and transporters. These are presented with nomenclature guidance and summary information on the best available pharmacological tools, alongside key references and suggestions for further reading. The landscape format of the Concise Guide is designed to facilitate comparison of related targets from material contemporary to mid‐2019, and supersedes data presented in the 2017/18, 2015/16 and 2013/14 Concise Guides and previous Guides to Receptors and Channels. It is produced in close conjunction with the International Union of Basic and Clinical Pharmacology Committee on Receptor Nomenclature and Drug Classification (NC‐IUPHAR), therefore, providing official IUPHAR classification and nomenclature for human drug targets, where appropriate.
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              Piraña and PCluster: a modeling environment and cluster infrastructure for NONMEM.

              Pharmacokinetic-pharmacodynamic modeling using non-linear mixed effects modeling (NONMEM) is a powerful yet challenging technique, as the software is generally accessed from the command line. A graphical user interface, Piraña, was developed that offers a complete modeling environment for NONMEM, enabling both novice and advanced users to increase efficiency of their workflow. Piraña provides features for the management and creation of model files, the overview of modeling results, creation of run reports and handling of datasets and output tables, and the running of custom R scripts on model output. Through the secure shell (SSH) protocol, Piraña can also be used to connect to Linux clusters (SGE, MOSIX) for distribution of workload. Modeling with NONMEM is computationally burdensome, which may be alleviated by distributing runs to computer clusters. A solution to this problem is offered here, called PCluster. This platform is easy to set up, runs in standard network environments, and can be extended with additional nodes if needed. The cluster supports the modeling toolkit Perl speaks NONMEM (PsN), and can include dedicated or non-dedicated PCs. A daemon script, written in Perl, was designed to run in the background on each node in the cluster, and to manage job distribution. The PCluster can be accessed from Piraña, and both software products have extensively been tested on a large academic network. The software is available under an open-source license. 2011 Elsevier Ireland Ltd. All rights reserved.
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                Author and article information

                Contributors
                e.m.vanmaarseveen@umcutrecht.nl
                Journal
                Br J Clin Pharmacol
                Br J Clin Pharmacol
                10.1111/(ISSN)1365-2125
                BCP
                British Journal of Clinical Pharmacology
                John Wiley and Sons Inc. (Hoboken )
                0306-5251
                1365-2125
                10 March 2020
                August 2020
                10 March 2020
                : 86
                : 8 ( doiID: 10.1111/bcp.v86.8 )
                : 1499-1509
                Affiliations
                [ 1 ] Laboratory of Translational Immunology University Medical Centre Utrecht, Utrecht University Utrecht The Netherlands
                [ 2 ] Model‐informed Drug Development Consultant, Pharmetheus AB Uppsala Sweden
                [ 3 ] Hospital Pharmacy, St Jansdal Hospital Harderwijk The Netherlands
                [ 4 ] Hospital Pharmacy, Albert Schweitzer Hospital Dordrecht The Netherlands
                [ 5 ] Department of Clinical Pharmacy, University Medical Centre Utrecht Utrecht University Utrecht The Netherlands
                [ 6 ] Department of Hematology, University Medical Centre Utrecht Utrecht University Utrecht The Netherlands
                [ 7 ] Department of Pharmacoepidemiology & Clinical Pharmacology, Faculty of Science Utrecht University, Utrecht, The Netherlands
                [ 8 ] Stem Cell Transplant and Cellular Therapies; Pediatrics, Memorial Sloan Kettering Cancer Centre New York City, New York USA
                [ 9 ] Department of Pharmacy & Pharmacology Netherlands Cancer Institute Amsterdam The Netherlands
                Author notes
                [*] [* ] Correspondence

                Erik van Maarseveen, Department of Clinical Pharmacy, University Medical Centre Utrecht, Utrecht, Utrecht University, Heidelberglaan 100: D.00.X, 3584 CX, Utrecht, The Netherlands.

                Email: e.m.vanmaarseveen@ 123456umcutrecht.nl

                Author information
                https://orcid.org/0000-0001-6593-3002
                Article
                BCP14256 MP-00750-19.R2
                10.1111/bcp.14256
                7373715
                32067250
                eb9dd494-3f5a-44f8-9565-1067614b1c34
                © 2020 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 30 September 2019
                : 04 February 2020
                : 05 February 2020
                Page count
                Figures: 6, Tables: 2, Pages: 11, Words: 6753
                Funding
                Funded by: Stichting Kinderen Kankervrij , open-funder-registry 10.13039/501100006244;
                Award ID: 190
                Categories
                Original Article
                Original Articles
                Custom metadata
                2.0
                August 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.5 mode:remove_FC converted:21.07.2020

                Pharmacology & Pharmaceutical medicine
                chemotherapy – oncology,drug safety – clinical pharmacology,pharmacokinetics,therapeutic drug monitoring – clinical pharmacology

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