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      Drug Design, Development and Therapy (submit here)

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      Pharmacokinetics of Linezolid Dose Adjustment for Creatinine Clearance in Critically Ill Patients: A Multicenter, Prospective, Open-Label, Observational Study

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          Abstract

          Purpose

          The aim of this study is to use a population pharmacokinetic (PK) approach to evaluate the optimal dosing strategy for linezolid (LNZ) in critically ill patients.

          Methods

          This multicenter, prospective, open-label, observational study was conducted in 152 patients, and 117 of them were included in the PK model, whereas the rest were in the validation group. The percentage of therapeutic target attainment (PTTA) comprising two pharmacodynamic indices and one toxicity index was used to evaluate dosing regimens based on Monte Carlo simulations stratified by low, normal, and high renal clearance for MICs of 0.25–4 mg/L.

          Results

          A single-compartment model with a covariate creatinine clearance (CrCL) was chosen as the final model. The PK parameter estimates were clearance of 5.60 L/h, with CrCL adjustment factor of 0.386, and a distribution volume of 43.4 L. For MIC ≤2 mg/L, the standard dosing regimen (600 mg q12h) for patients with severe renal impairment (CrCL, 40 mL/min) and standard dosing or 900 mg q12h for patients with normal renal functions (CrCL, 80 mL/min) could achieve PTTA ≥74%. The dose of 2400 mg per 24-h continuous infusion was ideal for augmented renal clearance (ARC) with MIC ≤1 mg/L. For MICs >2 mg/L, rare optimal dose regimens were found regardless of renal function.

          Conclusion

          In critically ill patients, the standard dose of 600 mg q12h was sufficient for MIC ≤2 mg/L in patients without ARC. Moreover, a 2400 mg/day 24-h continuous infusion was recommended for ARC patients.

          Most cited references31

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          Prediction of Creatinine Clearance from Serum Creatinine

          A formula has been developed to predict creatinine clearance (C cr ) from serum creatinine (S cr ) in adult males: Ccr = (140 – age) (wt kg)/72 × S cr (mg/100ml) (15% less in females). Derivation included the relationship found between age and 24-hour creatinine excretion/kg in 249 patients aged 18–92. Values for C cr were predicted by this formula and four other methods and the results compared with the means of two 24-hour C cr’s measured in 236 patients. The above formula gave a correlation coefficient between predicted and mean measured Ccr·s of 0.83; on average, the difference between predicted and mean measured values was no greater than that between paired clearances. Factors for age and body weight must be included for reasonable prediction.
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            Computing normalised prediction distribution errors to evaluate nonlinear mixed-effect models: the npde add-on package for R.

            Pharmacokinetic/pharmacodynamic data are often analysed using nonlinear mixed-effect models, and model evaluation should be an important part of the analysis. Recently, normalised prediction distribution errors (npde) have been proposed as a model evaluation tool. In this paper, we describe an add-on package for the open source statistical package R, designed to compute npde. npde take into account the full predictive distribution of each individual observation and handle multiple observations within subjects. Under the null hypothesis that the model under scrutiny describes the validation dataset, npde should follow the standard normal distribution. Simulations need to be performed before hand, using for example the software used for model estimation. We illustrate the use of the package with two simulated datasets, one under the true model and one with different parameter values, to show how npde can be used to evaluate models. Model estimation and data simulation were performed using NONMEM version 5.1.
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              Modeling and Simulation Workbench for NONMEM: Tutorial on Pirana, PsN, and Xpose

              Several software tools are available that facilitate the use of the NONMEM software and extend its functionality. This tutorial shows how three commonly used and freely available tools, Pirana, PsN, and Xpose, form a tightly integrated workbench for modeling and simulation with NONMEM. During the tutorial, we provide some guidance on what diagnostics we consider most useful in pharmacokinetic model development and how to construct them using these tools.
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                Author and article information

                Journal
                Drug Des Devel Ther
                Drug Des Devel Ther
                dddt
                dddt
                Drug Design, Development and Therapy
                Dove
                1177-8881
                19 May 2021
                2021
                : 15
                : 2129-2141
                Affiliations
                [1 ]Department of Medical Sciences, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangdong Cardiovascular Institute , Guangzhou, 510080, People’s Republic of China
                [2 ]Department of Critical Care Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences , Guangzhou, 510080, Guangdong, People’s Republic of China
                [3 ]School of Biology and Biological Engineering, South China University of Technology , Guangzhou, Guangdong, 510080, People’s Republic of China
                [4 ]Department of Oncology, Maoming People’s Hospital , Maoming, 525000, Guangdong, People’s Republic of China
                [5 ]Department of Neurosurgery, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology , Guangzhou, Guangdong, 510180, People’s Republic of China
                [6 ]Department of Neurosurgery, Nanfang Hospital, Southern Medical University , Guangzhou, 510515, Guangdong, People’s Republic of China
                [7 ]Department of Intensive Care Unit, Guangdong 999 Brain Hospital , Guangzhou, 510510, Guangdong, People’s Republic of China
                [8 ]Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Laboratory of South China Structural Heart Disease , Guangzhou, 510080, Guangdong, People’s Republic of China
                [9 ]The Second School of Clinical Medicine, Southern Medical University , Guangzhou, 510000, Guangdong, People’s Republic of China
                Author notes
                Correspondence: Chunbo Chen Department of Intensive Care Unit of Cardiovascular Surgery, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Laboratory of South China Structural Heart Disease , 96 Dongchuan Road, Guangzhou, 510080, Guangdong, People’s Republic of China Email gghccm@163.com
                [*]

                These authors contributed equally to this work

                Author information
                http://orcid.org/0000-0001-5684-2475
                http://orcid.org/0000-0001-5662-497X
                Article
                303497
                10.2147/DDDT.S303497
                8142937
                34040351
                ed82067d-962a-4ff2-a522-c30bfc40d8fe
                © 2021 Wang et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 24 January 2021
                : 13 April 2021
                Page count
                Figures: 4, Tables: 5, References: 34, Pages: 13
                Funding
                Funded by: the National Natural Science Foundation of China;
                Funded by: the Guangzhou Science and Technology Program;
                Funded by: Guangdong Province High-Level Hospital Construction Project of Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences;
                Funded by: the National Natural Science Foundation of China;
                Funded by: the Science and Technology Program of Guangzhou;
                Funded by: the Guangdong Provincial Hospital Pharmaceutical Research Fund (ChiaTai Tianqing) of Guangdong Pharmaceutical Association;
                Funded by: Medical Scientific Research Foundation of Guangdong Province;
                Author Chunbo Chen is currently receiving a grant (#81671963) from the National Natural Science Foundation of China, a grant (#201803010058) from the Guangzhou Science and Technology Program, and a grant (No. DFJH2020028) under the major program of Summit Project, Guangdong Province High-Level Hospital Construction Project of Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences. Author Shenglong Chen is currently receiving a grant (#81701875) from the National Natural Science Foundation of China, and a grant (#201904010039) from the Science and Technology Program of Guangzhou. Author Xipei Wang is currently receiving a grant (#2018A04) from the Guangdong Provincial Hospital Pharmaceutical Research Fund (ChiaTai Tianqing) of Guangdong Pharmaceutical Association and a grant (#A2020002) from Medical Scientific Research Foundation of Guangdong Province of China.
                Categories
                Original Research

                Pharmacology & Pharmaceutical medicine
                critically ill patients,linezolid,population pharmacokinetic

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