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      Population Pharmacokinetic and Pharmacodynamic Target Attainment Analysis of Cefazolin in the Serum and Hip Joint Capsule of Patients Undergoing Total Hip Arthroplasty

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          Abstract

          The objectives of this study were to evaluate the population pharmacokinetics of prophylactic cefazolin (CFZ) from its serum and hip joint capsule concentrations in patients undergoing total hip arthroplasty and to establish the pharmacodynamic target concentration exceeding the MIC for designing an effective dosing regimen for serum and the hip joint capsule. We analyzed 249 serum samples and 125 hip joint capsule samples from 125 individuals using a nonlinear mixed-effects model.

          ABSTRACT

          The objectives of this study were to evaluate the population pharmacokinetics of prophylactic cefazolin (CFZ) from its serum and hip joint capsule concentrations in patients undergoing total hip arthroplasty and to establish the pharmacodynamic target concentration exceeding the MIC for designing an effective dosing regimen for serum and the hip joint capsule. We analyzed 249 serum samples and 125 hip joint capsule samples from 125 individuals using a nonlinear mixed-effects model. The pharmacodynamic index target value obtained from our results indicates the probability of maintaining CFZ trough and hip joint capsule concentrations exceeding the MIC of 1 mg/liter to account for methicillin-susceptible S. aureus (MSSA). We estimated the population pharmacokinetics using a two-compartment model. The estimated population pharmacokinetic parameters were as follows: clearance (CL) (liters/h) = 1.46 × (creatinine clearance [CL cr ] [ml/min]/77) 0.891 , volume of distribution of the central compartment (V c ) (liters) = 7.5, central-hip joint capsule compartment clearance (Q) (liters/h) = 3.38, and volume of distribution in the hip joint capsule compartment (V JC ) (liters) = 36.1. The probability of achieving concentrations exceeding the MIC 90 for MSSA was approximately 100% for serum and 100% for the hip joint capsule at 3 h after the initial dose. Our findings suggest that population-based parameters are useful for evaluating CFZ pharmacokinetics and that individual dosages should be determined based on the dosage regimen that achieves and maintains adequate tissue CFZ concentration.

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          Most cited references 16

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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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            PsN-Toolkit--a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM.

            PsN-Toolkit is a collection of statistical tools for pharmacometric data analysis using the non-linear mixed effect modeling software NONMEM. The toolkit is object oriented and written in the programming language Perl using the programming library Perl-speaks-NONMEM (PsN). Five methods: the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building are included as separate classes and may be used in user-written Perl scripts or through stand-alone command line applications. The tools are designed with the ability to cooperate and with an emphasis on common structures for workflow and result handling. Parallel execution of independent tool sections is supported on shared memory multiprocessor (SMP) computers, Mosix/openMosix clusters and distributed computing environments following the NorduGrid standard. In conclusion, PsN-Toolkit makes it easier to use the Bootstrap, the Jackknife, Log-likelihood Profiling, Case-deletion Diagnostics and Stepwise Covariate Model building in pharmacometric data analysis.
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              Clinical practice guidelines for antimicrobial prophylaxis in surgery.

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Antimicrobial Agents and Chemotherapy
                Antimicrob Agents Chemother
                American Society for Microbiology
                0066-4804
                1098-6596
                March 18 2021
                March 18 2021
                : 65
                : 4
                Affiliations
                [1 ]Department of Pharmacy, Kitasato University Hospital, Kanagawa, Japan
                [2 ]Department of Pharmacy, Medical Hospital, Tokyo Medical and Dental University, Tokyo, Japan
                [3 ]Department of Orthopaedic Surgery, School of Medicine, Kitasato University, Kanagawa, Japan
                [4 ]Laboratory of Clinical Pharmacokinetics, Research and Education Center for Clinical Pharmacy, Kitasato University School of Pharmacy, Tokyo, Japan
                [5 ]Department of Infection Control and Infectious Diseases, Research and Development Center for New Medical Frontiers, Kitasato University School of Medicine, Kanagawa, Japan
                [6 ]Department of Infection Control and Prevention, Kitasato University Hospital, Kanagawa, Japan
                [7 ]Infection Control Research Center, Kitasato Institute for Life Sciences, Kitasato University, Tokyo, Japan
                [8 ]Pharmacy Practice and Science I, Research and Education Center for Clinical Pharmacy, Kitasato University School of Pharmacy, Kanagawa, Japan
                Article
                10.1128/AAC.02114-20
                © 2021

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