1
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Blouin M, Métras MÉ, El Hassani M, Yaliniz A, Marsot A. Optimization of Vancomycin Initial Dosing Regimen in Neonates Using an Externally Evaluated Population Pharmacokinetic Model. Ther Drug Monit 2024:00007691-990000000-00235. [PMID: 38857472 DOI: 10.1097/ftd.0000000000001226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/27/2024] [Indexed: 06/12/2024]
Abstract
BACKGROUND Vancomycin therapeutic monitoring guidelines were revised in March 2020, and a population pharmacokinetics-guided Bayesian approach to estimate the 24-hour area under the concentration-time curve to the minimum inhibitory concentration ratio has since been recommended instead of trough concentrations. To comply with these latest guidelines, we evaluated published population pharmacokinetic models of vancomycin using an external dataset of neonatal patients and selected the most predictive model to develop a new initial dosing regimen. METHODS The models were identified from the literature and tested using a retrospective dataset of Canadian neonates. Their predictive performance was assessed using prediction- and simulation-based diagnostics. Monte Carlo simulations were performed to develop the initial dosing regimen with the highest probability of therapeutic target attainment. RESULTS A total of 144 vancomycin concentrations were derived from 63 neonates in the external population. Five of the 28 models retained for evaluation were found predictive with a bias of 15% and an imprecision of 30%. Overall, the Grimsley and Thomson model performed best, with a bias of -0.8% and an imprecision of 20.9%; therefore, it was applied in the simulations. A novel initial dosing regimen of 15 mg/kg, followed by 11 mg/kg every 8 hours should favor therapeutic target attainment. CONCLUSIONS A predictive population pharmacokinetic model of vancomycin was identified after an external evaluation and used to recommend a novel initial dosing regimen. The implementation of these model-based tools may guide physicians in selecting the most appropriate initial vancomycin dose, leading to improved clinical outcomes.
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Affiliation(s)
- Mathieu Blouin
- STP Laboratory, Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
- Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
| | - Marie-Élaine Métras
- Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
- Department of Pharmacy, Centre Hospitalier Universitaire Sainte-Justine, Montréal (QC), Canada; and
| | - Mehdi El Hassani
- STP Laboratory, Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
- Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
| | - Aysenur Yaliniz
- STP Laboratory, Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
- Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
| | - Amélie Marsot
- STP Laboratory, Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
- Faculty of Pharmacy, Université de Montréal, Montréal (QC), Canada
- Research Center, Centre Hospitalier Universitaire Sainte-Justine, Montréal (QC), Canada
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2
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Li Y, Allegaert K. Why population pharmacokinetic studies are instrumental to boost clinical research in neonates, and suggestions on how clinicians should assess these papers. Pediatr Res 2024; 95:1181-1183. [PMID: 38195936 DOI: 10.1038/s41390-023-03011-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 12/15/2023] [Indexed: 01/11/2024]
Affiliation(s)
- Yan Li
- GCP office, Children's Hospital Affiliated to Zhengzhou University, 450018, Zhengzhou, China
- Department of Pharmacy, Children's Hospital Affiliated to Zhengzhou University, 450018, Zhengzhou, China
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
- Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.
- Department of Hospital Pharmacy, Erasmus Medical Center, Rotterdam, The Netherlands.
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3
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Aurélie L, Andréa B, Gauvind K, Olivier B, Raoul B, Dayan F, Sylvain B, Romain G. External Evaluation of Population Pharmacokinetics Models of Lithium in the Bipolar Population. Pharmaceuticals (Basel) 2023; 16:1627. [PMID: 38004492 PMCID: PMC10674621 DOI: 10.3390/ph16111627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
Lithium has been used in the treatment of bipolar disorder for several decades. Treatment optimization is recommended for this drug, due to its narrow therapeutic range and a large pharmacokinetics (PK) variability. In addition to therapeutic drug monitoring, attempts have been made to predict individual lithium doses using population pharmacokinetics (popPK) models. This study aims to assess the clinical applicability of published lithium popPK models by testing their predictive performance on two different external datasets. Available PopPK models were identified and their predictive performance was determined using a clinical dataset (46 patients/samples) and the literature dataset (89 patients/samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated, and the results of both external evaluations compared. Only one model met the acceptability criteria for both datasets. Overall, there was a lack of predictability of models; median PE and median absolute PE, respectively, ranged from -6.6% to 111.2% and from 24.4% to 111.2% for the literature dataset, and from -4.5% to 137.6% and from 24.9% to 137.6% for the clinical dataset. Most models underpredicted the observed concentrations (7 out of 10 models presented a negative bias). Renal status was included as a covariate of lithium's clearance in only two models. To conclude, most of lithium's PopPK models had limited predictive performances related to the absence of covariates of interest included, such as renal status. A solution to this problem could be to improve the models with methodologies such as metamodeling. This could be useful in the perspective of model-informed precision dosing.
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Affiliation(s)
- Lereclus Aurélie
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Inserm UMR 1106, 13385 Marseille, France (G.R.)
- EXACTCURE, 06000 Nice, France (F.D.)
| | | | - Kallée Gauvind
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, 13005 Marseille, France
| | - Blin Olivier
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Inserm UMR 1106, 13385 Marseille, France (G.R.)
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, 13005 Marseille, France
| | - Belzeaux Raoul
- Pôle Universitaire de Psychiatrie, CHU de Montpellier, 34000 Montpellier, France
| | | | | | - Guilhaumou Romain
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Inserm UMR 1106, 13385 Marseille, France (G.R.)
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, 13005 Marseille, France
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4
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Črček M, Grabnar I, Zdovc JA, Grosek Š, Kos MK. External validation of population pharmacokinetic models of gentamicin in paediatric population from preterm newborns to adolescents. ACTA PHARMACEUTICA (ZAGREB, CROATIA) 2023; 73:175-194. [PMID: 37307377 DOI: 10.2478/acph-2023-0027] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/04/2023] [Indexed: 06/14/2023]
Abstract
The aim of this study was to externally validate the predictive performance of published population pharmacokinetic models of gentamicin in all paediatric age groups, from preterm newborns to adolescents. We first selected published population pharmacokinetic models of gentamicin developed in the paediatric population with a wide age range. The parameters of the literature models were then re-estimated using the PRIOR subroutine in NONMEM®. The predictive ability of the literature and the tweaked models was evaluated. Retrospectively collected data from a routine clinical practice (512 concentrations from 308 patients) were used for validation. The models with covariates characterising developmental changes in clearance and volume of distribution had better predictive performance, which improved further after re-estimation. The tweaked model by Wang 2019 performed best, with suitable accuracy and precision across the complete paediatric population. For patients treated in the intensive care unit, a lower proportion of patients would be expected to reach the target trough concentration at standard dosing. The selected model could be used for model-informed precision dosing in clinical settings where the entire paediatric population is treated. However, for use in clinical practice, the next step should include additional analysis of the impact of intensive care treatment on gentamicin pharmacokinetics, followed by prospective validation.
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Affiliation(s)
- Mateja Črček
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
| | - Iztok Grabnar
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
| | - Jurij Aguiar Zdovc
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
| | - Štefan Grosek
- 2University of Ljubljana, Faculty of Medicine, Department of Pediatrics 1000 Ljubljana, Slovenia
- 3University Medical Centre Ljubljana Division of Obstetrics and Gynecology, Department of Perinatology Neonatology Section, 1000 Ljubljana Slovenia
- 4University Medical Centre Ljubljana Division of Paediatrics, Department of Paediatric Intensive Therapy, 1000 Ljubljana, Slovenia
| | - Mojca Kerec Kos
- 1University of Ljubljana, Faculty of Pharmacy, Department of Biopharmacy and Pharmacokinetics, 1000 Ljubljana Slovenia
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5
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Nguyen TA, Kirubakaran R, Schultz HB, Wong S, Reuter SE, McMullan B, Bolisetty S, Campbell C, Horvath AR, Stocker SL. Analytical and Non-Analytical Variation May Lead to Inappropriate Antimicrobial Dosing in Neonates: An In Silico Study. Clin Chem 2023:7146664. [PMID: 37116191 DOI: 10.1093/clinchem/hvad036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/01/2023] [Indexed: 04/30/2023]
Abstract
BACKGROUND Therapeutic drug monitoring (TDM) of aminoglycosides and vancomycin is used to prevent oto- and nephrotoxicity in neonates. Analytical and nonanalytical factors potentially influence dosing recommendations. This study aimed to determine the impact of analytical variation (imprecision and bias) and nonanalytical factors (accuracy of drug administration time, use of non-trough concentrations, biological variation, and dosing errors) on neonatal antimicrobial dosing recommendations. METHODS Published population pharmacokinetic models and the Australasian Neonatal Medicines Formulary were used to simulate antimicrobial concentration-time profiles in a virtual neonate population. Laboratory quality assurance data were used to quantify analytical variation in antimicrobial measurement methods used in clinical practice. Guideline-informed dosing recommendations based on drug concentrations were applied to compare the impact of analytical variation and nonanalytical factors on antimicrobial dosing. RESULTS Analytical variation caused differences in subsequent guideline-informed dosing recommendations in 9.3-12.1% (amikacin), 16.2-19.0% (tobramycin), 12.2-45.8% (gentamicin), and 9.6-19.5% (vancomycin) of neonates. For vancomycin, inaccuracies in drug administration time (45.6%), use of non-trough concentrations (44.7%), within-subject biological variation (38.2%), and dosing errors (27.5%) were predicted to result in more dosing discrepancies than analytical variation (12.5%). Using current analytical performance specifications, tolerated dosing discrepancies would be up to 14.8% (aminoglycosides) and 23.7% (vancomycin). CONCLUSIONS Although analytical variation can influence neonatal antimicrobial dosing recommendations, nonanalytical factors are more influential. These result in substantial variation in subsequent dosing of antimicrobials, risking inadvertent under- or overexposure. Harmonization of measurement methods and improved patient management systems may reduce the impact of analytical and nonanalytical factors on neonatal antimicrobial dosing.
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Affiliation(s)
- Thi A Nguyen
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
| | - Ranita Kirubakaran
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, NSW, Australia
- Seberang Jaya Hospital, Penang, Malaysia
| | - Hayley B Schultz
- UniSA: Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Sherilyn Wong
- UniSA: Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Stephanie E Reuter
- UniSA: Clinical & Health Sciences, University of South Australia, Adelaide, SA, Australia
| | - Brendan McMullan
- Department of Immunology and Infectious Diseases, Sydney Children's Hospital, Randwick, NSW, Australia
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Srinivas Bolisetty
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Craig Campbell
- NSW Health Pathology, Department of Chemical Pathology, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Andrea R Horvath
- Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- NSW Health Pathology, Department of Chemical Pathology, Prince of Wales Hospital, Sydney, NSW, Australia
| | - Sophie L Stocker
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Department of Clinical Pharmacology and Toxicology, St. Vincent's Hospital, Sydney, NSW, Australia
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6
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El Hassani M, Marsot A. External Evaluation of Population Pharmacokinetic Models for Precision Dosing: Current State and Knowledge Gaps. Clin Pharmacokinet 2023; 62:533-540. [PMID: 37004650 DOI: 10.1007/s40262-023-01233-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/27/2023] [Indexed: 04/04/2023]
Abstract
Predicting drug exposures using population pharmacokinetic models through Bayesian forecasting software can improve individual pharmacokinetic/pharmacodynamic target attainment. However, selecting the most adapted model to be used is challenging due to the lack of guidance on how to design and interpret external evaluation studies. The confusion around the choice of statistical metrics and acceptability criteria emphasises the need for further research to fill this methodological gap as there is an urgent need for the development of standards and guidelines for external evaluation studies. Herein we discuss the scientific challenges faced by pharmacometric researchers and opportunities for future research with a focus on antibiotics.
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Affiliation(s)
- Mehdi El Hassani
- Faculté de pharmacie, Université de Montréal, 2940 chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada.
- Laboratoire de suivi thérapeutique pharmacologique et pharmacocinétique, Faculté de pharmacie, Université de Montréal, Montréal, Canada.
| | - Amélie Marsot
- Faculté de pharmacie, Université de Montréal, 2940 chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada
- Laboratoire de suivi thérapeutique pharmacologique et pharmacocinétique, Faculté de pharmacie, Université de Montréal, Montréal, Canada
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7
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Lereclus A, Korchia T, Riff C, Dayan F, Blin O, Benito S, Guilhaumou R. Towards Precision Dosing of Clozapine in Schizophrenia: External Evaluation of Population Pharmacokinetic Models and Bayesian Forecasting. Ther Drug Monit 2022; 44:674-682. [PMID: 35385439 DOI: 10.1097/ftd.0000000000000987] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 02/21/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Therapeutic drug monitoring and treatment optimization of clozapine are recommended, owing to its narrow therapeutic range and pharmacokinetic (PK) variability. This study aims to assess the clinical applicability of published population PK models by testing their predictive performance in an external data set and to determine the effectiveness of Bayesian forecasting (BF) for clozapine treatment optimization. METHODS Available models of clozapine were identified, and their predictive performance was determined using an external data set (53 patients, 151 samples). The median prediction error (PE) and median absolute PE were used to assess bias and inaccuracy. The potential factors influencing model predictability were also investigated. The final concentration was reestimated for all patients using covariates or previously observed concentrations. RESULTS The 7 included models presented limited predictive performance. Only 1 model met the acceptability criteria (median PE ≤ ±20% and median absolute PE ≤30%). There was no difference between the data used for building the models (therapeutic drug monitoring or PK study) or the number of compartments in the models. A tendency for higher inaccuracy at low concentrations during treatment initiation was observed. Heterogeneities were observed in the predictive performances between the subpopulations, especially in terms of smoking status and sex. For the models included, BF significantly improved their predictive performance. CONCLUSIONS Our study showed that upon external evaluation, clozapine models provide limited predictive performance, especially in subpopulations such as nonsmokers. From the perspective of model-informed prediction dosing, model predictability should be improved using updating or metamodeling methods. Moreover, BF substantially improved model predictability and could be used for clozapine treatment optimization.
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Affiliation(s)
- Aurélie Lereclus
- Aix Marseille Université, Institut de Neurosciences des Systèmes, Inserm UMR 1106, Marseille, France
- EXACTCURE, Nice, France
| | - Théo Korchia
- Département de Psychiatrie, Sainte Marguerite University Hospital, Assistance Publique-Hôpitaux de Marseille, Marseille, France; and
| | - Camille Riff
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, Marseille, France
| | | | - Olivier Blin
- Aix Marseille Université, Institut de Neurosciences des Systèmes, Inserm UMR 1106, Marseille, France
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, Marseille, France
| | | | - Romain Guilhaumou
- Aix Marseille Université, Institut de Neurosciences des Systèmes, Inserm UMR 1106, Marseille, France
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, Marseille, France
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8
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Aguiar Zdovc J, Vaupotič M, Marolt G, Knez L, Režonja Kukec R, Čufer T, Vovk T, Grabnar I. Population pharmacokinetics of cisplatin in small cell lung cancer patients guided with informative priors. Cancer Chemother Pharmacol 2022; 90:301-313. [DOI: 10.1007/s00280-022-04465-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 08/08/2022] [Indexed: 11/02/2022]
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9
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Role of fluid status markers as risk factors for suboptimal vancomycin concentration during continuous infusion in neonates: an observational study. Eur J Pediatr 2022; 181:2935-2942. [PMID: 35581390 DOI: 10.1007/s00431-022-04500-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 05/04/2022] [Accepted: 05/06/2022] [Indexed: 11/03/2022]
Abstract
UNLABELLED Vancomycin is widely used in neonatal sepsis but proportion of newborn reaching recommended concentration is variable. Fluid status impact on vancomycin level remains understudied. We aimed to study fluid factors impacting vancomycin concentration at 24 h of treatment. We performed a prospective and retrospective observational monocentric study of NICU patients requiring a vancomycin treatment. We used a continuous infusion protocol, with age-appropriate loading and maintenance doses. Vancomycin target serum concentration after 24 h (C24h) was above 20 mg/L. Demographic, infections, and organ failure variables were analyzed as potential predictors of C24h. Over the study period, 70 infective episodes in 52 patients were included. At treatment initiation, the median post-natal age was 12.5 days (IQR 7-23), post menstrual age 30 weeks (IQR 28-35), and median weight 1140 g (IQR 835-1722). Germs isolated were mainly gram-positive with 73.5% being coagulase-negative Staphylococci. Median C24h was 18.7 mg/L (IQR 15.4-22.4). Overall, 41 (58.6%) treatments had a C24h < 20 mg/L. After multivariate analysis, higher creatinine level (OR 1.03 (95% CI 1.002-1.06)) was associated with C24h ≥ 20 mg/L; weight gain the day before infection (OR 0.21 (95% CI 0.05-0.79)) and positive biomarkers of inflammation (OR 0.22 (0.05-0.94)) were associated with C24h < 20 mg/L. CONCLUSION Vancomycin C24h was underdosed in 60% of patients and factors linked to changes in vancomycin pharmacokinetic such as volume of distribution and clearance, linked to creatinine level, inflammation, or weight gain, were identified. WHAT IS KNOWN • Adjustment of vancomycin regimen remains difficult due to inter- and intra-individual variability of vancomycin pharmacokinetics. • Impact of fluid status on vancomycin concentration in critically ill neonates is incompletely studied. WHAT IS NEW • Proportion of patients with adequate vancomycin concentration using a target adapted to nosocomial gram-positive bacteria MIC is low. • We confirmed the role of creatinine level and report two new factors associated with low vancomycin concentration: presence of systemic inflammation and weight gain.
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10
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Han J, Sauberan J, Tran MT, Adler-Shohet FC, Michalik DE, Tien TH, Tran L, DO DH, Bradley JS, Le J. Implementation of Vancomycin Therapeutic Monitoring Guidelines: Focus on Bayesian Estimation Tools in Neonatal and Pediatric Patients. Ther Drug Monit 2022; 44:241-252. [PMID: 34145165 DOI: 10.1097/ftd.0000000000000910] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 05/24/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND The 2020 consensus guidelines for vancomycin therapeutic monitoring recommend using Bayesian estimation targeting the ratio of the area under the curve over 24 hours to minimum inhibitory concentration as an optimal approach to individualize therapy in pediatric patients. To support institutional guideline implementation in children, the objective of this study was to comprehensively assess and compare published population-based pharmacokinetic (PK) vancomycin models and available Bayesian estimation tools, specific to neonatal and pediatric patients. METHODS PubMed and Embase databases were searched from January 1994 to December 2020 for studies in which a vancomycin population PK model was developed to determine clearance and volume of distribution in neonatal and pediatric populations. Available Bayesian software programs were identified and assessed from published articles, software program websites, and direct communication with the software company. In the present review, 14 neonatal and 20 pediatric models were included. Six programs (Adult and Pediatric Kinetics, BestDose, DoseMeRx, InsightRx, MwPharm++, and PrecisePK) were evaluated. RESULTS Among neonatal models, Frymoyer et al and Capparelli et al used the largest PK samples to generate their models, which were externally validated. Among the pediatric models, Le et al used the largest sample size, with multiple external validations. Of the Bayesian programs, DoseMeRx, InsightRx, and PrecisePK used clinically validated neonatal and pediatric models. CONCLUSIONS To optimize vancomycin use in neonatal and pediatric patients, clinicians should focus on selecting a model that best fits their patient population and use Bayesian estimation tools for therapeutic area under the -curve-targeted dosing and monitoring.
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Affiliation(s)
- Jihye Han
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Louisiana Jolla
| | - Jason Sauberan
- Neonatal Research Institute, SHARP Mary Birch Hospital for Women and Newborns, San Diego
| | | | | | - David E Michalik
- MemorialCare Miller Children's and Women's Hospital Long Beach, Long Beach, California
| | | | - Lan Tran
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Louisiana Jolla
| | | | - John S Bradley
- Division of Infectious Diseases, University of California at San Diego, Louisiana Jolla; and
- Rady Children's Hospital-San Diego, San Diego, California
| | - Jennifer Le
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, Louisiana Jolla
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11
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Chen X, Du L, Liu M. Development, validation, and application of an UPLC-MS/MS method for vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib analysis in human plasma. Ann Clin Biochem 2022; 59:253-263. [PMID: 35209719 DOI: 10.1177/00045632221077183] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background Vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib are five commonly used drugs which are all recommended to therapeutic drug monitoring in clinical settings. However, the blood concentration monitoring of these drugs and the interpretations of the test results are limited to some extent due to the differences of testing instruments and testing methods. Methods We established an ultra-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS) method for simultaneous quantification of vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib in human plasma. The method was validated according to the guideline for bioanalytical method validation and applied in clinical therapy. Results The calibration ranges of vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib were 0.5–100 μg/mL, 0.5–100 μg/mL, 5–1000 ng/mL, 10–2000 ng/mL, and 5–500 ng/mL, respectively. Inaccuracy and imprecision of every drug were less than 15%. The internal standard normalized recovery rates of vancomycin and norvancomycin were about 45%, while which of methotrexate, paclitaxel, and imatinib were almost 100%. No obvious carryover effect was observed. Samples were stable for at least 24 h in the automatic sampler, 72 h at 4°C, and 1 week in −80°C. There were no differences of concentrations between plasma and serum for the five drugs. Moreover, there were positive correlations between methotrexate and vancomycin concentrations and creatinine, as well as positive correlation between imatinib concentration and age of the patient. Conclusions The UPLC-MS/MS method was competent for the simultaneous monitoring of vancomycin, norvancomycin, methotrexate, paclitaxel, and imatinib because of its short analysis time, high specificity, and accuracy.
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Affiliation(s)
- Xinran Chen
- Department of Pharmacy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, P.R. China
| | - Liying Du
- Department of Pharmacy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, P.R. China
| | - Mingfeng Liu
- Department of Pharmacy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, P.R. China
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12
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Alrahahleh D, Xu S, Luig M, Kim HY, Alffenaar JW. Dosing of vancomycin and target attainment in neonates: a systematic review. Int J Antimicrob Agents 2022; 59:106515. [PMID: 35031450 DOI: 10.1016/j.ijantimicag.2021.106515] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 12/03/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022]
Abstract
INTRODUCTION Neonatal infections caused by Gram-positive bacteria are commonly treated with vancomycin. However, there is a lack of agreement on the optimal vancomycin dosing regimen and corresponding vancomycin exposure to correlate with efficacy and toxicity. OBJECTIVES This review aimed to evaluate dosing of vancomycin in neonates, therapeutic target attainment and clinical toxicity and efficacy outcomes. METHODS Two electronic databases - Embase and PubMed (Medline) - were systematically searched between 1995-2020. Studies that reported dosing regimens, drug concentrations, toxicity, and efficacy of vancomycin in neonates were eligible for inclusion. Descriptive analysis and a narrative synthesis were performed. RESULTS The systematic review protocol was registered with the PROSPERO International Prospective Register of Systematic reviews in 2020 (registration number: CRD42020219568). Twenty-four studies were included for final analysis. Overall, the data from the included studies showed a great degree of heterogeneity. Therapeutic drug monitoring practices were different between institutions. Although most studies used trough concentration with a target range of 10-20 mg/L, target attainment was different across the studies. The probability of target attainment was < 80% in all tested dosing algorithms. Few studies reported on vancomycin efficacy and toxicity. CONCLUSION This is a comprehensive overview of dosing strategies of vancomycin in neonates. There was inadequate evidence to propose an optimal therapeutic regimen in the newborn population, based on the data obtained, due to the heterogeneity in the design and objectives of the included studies. Consistent and homogeneous comparative randomised clinical trials are needed to identify a dosing regimen with a probability of target attainment of > 90% without toxicity.
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Affiliation(s)
- Dua'A Alrahahleh
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia; Westmead Hospital, Westmead, NSW, Australia
| | - Sophia Xu
- Department of Pharmacy, Westmead Hospital, Westmead, NSW, Australia
| | - Melissa Luig
- Department of Neonatology, Westmead Hospital, Westmead, NSW, Australia
| | - Hannah Yejin Kim
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia; Westmead Hospital, Westmead, NSW, Australia; Sydney Institute for Infectious Diseases, The University of Sydney, Camperdown, NSW, Australia
| | - Jan-Willem Alffenaar
- Sydney Pharmacy School, Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia; Westmead Hospital, Westmead, NSW, Australia; Sydney Institute for Infectious Diseases, The University of Sydney, Camperdown, NSW, Australia.
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13
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Zang YN, Guo W, Niu MX, Bao S, Wang Q, Wang Y, Dong F, Li AN, Ruan CJ. Population pharmacokinetics of valproic acid in adult Chinese patients with bipolar disorder. Eur J Clin Pharmacol 2021; 78:405-418. [PMID: 34854947 DOI: 10.1007/s00228-021-03246-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/21/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE To develop and validate a population pharmacokinetic (PPK) model of valproic acid (VPA) in adult Chinese patients with bipolar disorder, and provide guidance for individualized therapy in this population. METHODS A total of 1104 serum concentrations from 272 patients were collected in this study. The data analysis was performed using a nonlinear mixed-effects modeling approach. Covariates included demographic parameters, biological characteristics, and concomitant medications. Bootstrap validation (1000 runs), normalized prediction distribution error (NPDE), and external validation of 50 patients were employed to evaluate the final model. RESULTS A one-compartment model with first-order absorption and elimination was developed for VPA extended-release tablets. VPA clearance was significantly influenced by three variables: sex (12% higher in male patients), daily dose (increasing with the 0.13 exponent), and body weight (increasing with the 0.56 exponent). Typical values for the absorption rate constant (Ka), apparent clearance (CL/F), and apparent distribution volume (V/F) for a female patient weighing 70 kg administered VPA 1000 mg/day were 0.18 h-1, 0.46 L/h, and 12.84 L, respectively. The results of model evaluation indicated a good stable and precise performance of the final model. CONCLUSIONS A qualified PPK model of VPA was developed in Chinese patients with bipolar disorder. This model could be used as a suitable tool for the personalization of VPA dosing for bipolar patients.
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Affiliation(s)
- Yan-Nan Zang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Wei Guo
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Meng-Xi Niu
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shuang Bao
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qian Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yan Wang
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Fang Dong
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - An-Ning Li
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Can-Jun Ruan
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China. .,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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14
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Fan Y, Wang Y, Chen M, Chen Y, Guo B, Liu X, Wu H, Li Y, Hu J, Zhang J. Degradation of vancomycin in external quality assessment samples is a factor to underestimate its concentration. Bioanalysis 2021; 13:1743-1750. [PMID: 34789003 DOI: 10.4155/bio-2021-0163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Aim: To compare the difference between the measured and target values in vancomycin external quality assessment (EQA) samples and to investigate the factors for underestimating its concentration. Materials & methods: A retrospective analysis of 195 international vancomycin EQA results was performed. Deviations of the concentrations determined by TDx fluorescence polarization immunoassay (FPIA), Axsym FPIA and Architect chemiluminescence microparticle immunoassay (CMIA) method were -2.43, -16.28 and -10.53%, respectively. Chromatographic peaks of the crystalling degradation products appeared in samples with large deviations. Vancomycin were degraded after long-term transporting and high temperature. Conclusion: Vancomycin concentrations measured by Axsym FPIA and Architect CMIA methods were likely to be underestimated. Long-term transporting resulted in low EQA results, suggesting that establishing a local EQA system for vancomycin is essential.
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Affiliation(s)
- Yaxin Fan
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yu Wang
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Mengting Chen
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yuancheng Chen
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Beining Guo
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Xiaofen Liu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Hailan Wu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yi Li
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Jiali Hu
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Jing Zhang
- Institute of Antibiotics, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Key Laboratory of Clinical Pharmacology of Antibiotics, National Population & Family Planning Commission, Shanghai, 200040, China
- National Clinical Research Center for Aging & Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
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15
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Maier C, de Wiljes J, Hartung N, Kloft C, Huisinga W. A continued learning approach for model-informed precision dosing: updating models in clinical practice. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 11:185-198. [PMID: 34779144 PMCID: PMC8846635 DOI: 10.1002/psp4.12745] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/28/2021] [Accepted: 10/28/2021] [Indexed: 11/12/2022]
Abstract
Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to include also altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, since only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step towards building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.
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Affiliation(s)
- Corinna Maier
- Institute of Mathematics, University of Potsdam, Germany.,Graduate Research Training Program PharMetrX: Pharmacometrics & Computational Disease Modelling, Freie Universität Berlin and University of Potsdam, Germany
| | - Jana de Wiljes
- Institute of Mathematics, University of Potsdam, Germany
| | - Niklas Hartung
- Institute of Mathematics, University of Potsdam, Germany
| | - Charlotte Kloft
- Department of Clinical Pharmacy and Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Germany
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16
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Does Prolonged Infusion Time Really Improve the Efficacy of Meropenem Therapy? A Prospective Study in Critically Ill Patients. Infect Dis Ther 2021; 11:201-216. [PMID: 34748194 PMCID: PMC8847520 DOI: 10.1007/s40121-021-00551-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/13/2021] [Indexed: 11/06/2022] Open
Abstract
Introduction Meropenem is a carbapenem antibiotic, which has demonstrated excellent antimicrobial activity against gram-negative clinical isolates. It is also commonly used in critically ill patients. This study aimed to determine the pharmacokinetics/pharmacodynamics of meropenem in critically ill patients and whether prolonged injection duration is really beneficial to meropenem therapy. Methods We included 209 samples in 64 patients in this prospective study. PPK analysis and Monte Carlo dosing simulations were developed using Phoenix. Results A two-compartment model described the data adequately. Clearance (CL), volume (V), clearance of peripheral compartment (CL2), and volume of peripheral compartment (V2) were 6.15 l/h, 2.83 l/h, 17.40 l, and 17.48 l, respectively. Creatinine clearance and uric acid were significant covariates. Patients with creatinine clearance ≤ 60 ml/min and uric acid > 400 μmol/l could achieve the target > 90% under the minimum inhibitory concentration (MIC) of 8 mg/l, even with the administration dose of 500 mg/8 h with a 2-h infusion. Prolonging the infusion time significantly improved the therapeutic effect when MIC < 4. However, for the pharmacodynamic (PD) effects of 100% fT > MIC and 100% fT > 4 MIC, no significant statistical difference was observed in critically ill patients. Conclusions Critically ill patients with lower creatinine clearance and higher uric acid levels tended to need a lower dosage of meropenem. Prolonged infusion time was not always beneficial for those who needed a higher therapeutic target (100% fT > MIC, 100% fT > 4 MIC) or with MIC > 4 mg/l. Increasing dose or alternative therapeutic strategies may be required for critically ill patients with drug-resistant or severe infections. The study is of great significance to guide the rational use of meropenem in critically ill patients. Trial Registration The trial was registered in the China Clinical Trial (ChiCTR1900020672). Registered on 12 January 2019. Supplementary Information The online version contains supplementary material available at 10.1007/s40121-021-00551-2. Meropenem is commonly used empirically or targeted in critically ill patients for bacterial infection. Many studies have reported that prolonged infusion time can improve the efficacy of meropenem therapy. However, we are skeptical about that. Meanwhile, prolonged injections can sometimes cause mobility problems for patients. A quantitative method is used to evaluate meropenem use. It is called the population pharmacokinetic model or pharmacodynamic study. Using this method, we found two significant influencing factors of meropenem metabolism: creatinine clearance and uric acid level. It is likely that patients with a lower level of creatinine clearance and a high uric acid level tend to require lower dosages of meropenem. As for the effect of infusion time, Monte Carlo simulation was used, which can do 3000 simulations on an individual. The result was complex. We found infusion time was beneficial only when bacteria were sensitive to meropenem. The evidence suggests that prolonged injection duration sometimes does not significantly improve the outcome of antimicrobial therapy.
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17
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Yang P, Liu W, Zheng J, Zhang Y, Yang L, He N, Zhai S. Predicting the Outcome of Voriconazole Individualized Medication Using Integrated Pharmacokinetic/Pharmacodynamic Model. Front Pharmacol 2021; 12:711187. [PMID: 34721012 PMCID: PMC8548711 DOI: 10.3389/fphar.2021.711187] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
Therapeutic drug monitoring is considered to be an effective tool for the individualized use of voriconazole. However, drug concentration measurement alone doesn’t take into account the susceptibility of the infecting microorganisms to the drug. Linking pharmacodynamic data with the pharmacokinetic profile of individuals is expected to be an effective method to predict the probability of a certain therapeutic outcome. The objective of this study was to individualize voriconazole regimens by integrating individual pharmacokinetic parameters and pathogen susceptibility data through Monte Carlo simulations The individual pharmacokinetic parameters of 35 hospitalized patients who received voriconazole were calculated based on a validated population pharmacokinetic model. The area under the concentration-time curve for free drug/minimal inhibitory concentration (fAUCss/MIC) > 25 was selected as the pharmacokinetic/pharmacodynamic (PK/PD) parameter predicting the efficacy of voriconazole. The cumulative fraction of response (CFR) of the target value was assessed. To verify this conclusion, a logistic regression analysis was used to explore the relationship between actual clinical efficiency and the CFR value. For the 35 patients, the area under the free drug concentration-time curve (fAUCss) was calculated to be 34.90 ± 21.67 mgh/L. According to the dualistic logistic regression analysis, the minimal inhibitory concentration (MIC) value of different kinds of fungi had a great influence on the effectiveness of clinical treatment. It also showed that the actual clinical efficacy and the CFR value of fAUCss/MIC had a high degree of consistency. The results suggest that it is feasible to individualize voriconazole dosing and predict clinical outcomes through the integration of data on pharmacokinetics and antifungal susceptibility.
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Affiliation(s)
- Ping Yang
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Wei Liu
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Jiajia Zheng
- Department of Laboratory Medicine, Peking University Third Hospital, Beijing, China
| | - Yuanyuan Zhang
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Li Yang
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Na He
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
| | - Suodi Zhai
- Department of Pharmacy, Peking University Third Hospital, Beijing, China
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18
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Pharmacokinetics of Antibiotics in Pediatric Intensive Care: Fostering Variability to Attain Precision Medicine. Antibiotics (Basel) 2021; 10:antibiotics10101182. [PMID: 34680763 PMCID: PMC8532953 DOI: 10.3390/antibiotics10101182] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 12/16/2022] Open
Abstract
Children show important developmental and maturational changes, which may contribute greatly to pharmacokinetic (PK) variability observed in pediatric patients. These PK alterations are further enhanced by disease-related, non-maturational factors. Specific to the intensive care setting, such factors include critical illness, inflammatory status, augmented renal clearance (ARC), as well as therapeutic interventions (e.g., extracorporeal organ support systems or whole-body hypothermia [WBH]). This narrative review illustrates the relevance of both maturational and non-maturational changes in absorption, distribution, metabolism, and excretion (ADME) applied to antibiotics. It hereby provides a focused assessment of the available literature on the impact of critical illness—in general, and in specific subpopulations (ARC, extracorporeal organ support systems, WBH)—on PK and potential underexposure in children and neonates. Overall, literature discussing antibiotic PK alterations in pediatric intensive care is scarce. Most studies describe antibiotics commonly monitored in clinical practice such as vancomycin and aminoglycosides. Because of the large PK variability, therapeutic drug monitoring, further extended to other antibiotics, and integration of model-informed precision dosing in clinical practice are suggested to optimise antibiotic dose and exposure in each newborn, infant, or child during intensive care.
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19
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Schräpel C, Kovar L, Selzer D, Hofmann U, Tran F, Reinisch W, Schwab M, Lehr T. External Model Performance Evaluation of Twelve Infliximab Population Pharmacokinetic Models in Patients with Inflammatory Bowel Disease. Pharmaceutics 2021; 13:pharmaceutics13091368. [PMID: 34575443 PMCID: PMC8468301 DOI: 10.3390/pharmaceutics13091368] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/26/2021] [Accepted: 08/27/2021] [Indexed: 01/12/2023] Open
Abstract
Infliximab is approved for treatment of various chronic inflammatory diseases including inflammatory bowel disease (IBD). However, high variability in infliximab trough levels has been associated with diverse response rates. Model-informed precision dosing (MIPD) with population pharmacokinetic models could help to individualize infliximab dosing regimens and improve therapy. The aim of this study was to evaluate the predictive performance of published infliximab population pharmacokinetic models for IBD patients with an external data set. The data set consisted of 105 IBD patients with 336 infliximab concentrations. Literature review identified 12 published models eligible for external evaluation. Model performance was evaluated with goodness-of-fit plots, prediction- and variability-corrected visual predictive checks (pvcVPCs) and quantitative measures. For anti-drug antibody (ADA)-negative patients, model accuracy decreased for predictions > 6 months, while bias did not increase. In general, predictions for patients developing ADA were less accurate for all models investigated. Two models with the highest classification accuracy identified necessary dose escalations (for trough concentrations < 5 µg/mL) in 88% of cases. In summary, population pharmacokinetic modeling can be used to individualize infliximab dosing and thereby help to prevent infliximab trough concentrations dropping below the target trough concentration. However, predictions of infliximab concentrations for patients developing ADA remain challenging.
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Affiliation(s)
- Christina Schräpel
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (U.H.); (M.S.)
| | - Lukas Kovar
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
| | - Dominik Selzer
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (U.H.); (M.S.)
| | - Florian Tran
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, 24105 Kiel, Germany;
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, 24105 Kiel, Germany
| | - Walter Reinisch
- Department of Internal Medicine III, Medical University of Vienna, 1090 Vienna, Austria;
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (U.H.); (M.S.)
- Departments of Clinical Pharmacology, Pharmacy and Biochemistry, University of Tübingen, 72076 Tübingen, Germany
| | - Thorsten Lehr
- Clinical Pharmacy, Saarland University, 66123 Saarbrücken, Germany; (C.S.); (L.K.); (D.S.)
- Correspondence: ; Tel.: +49-681-302-70255
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20
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External Evaluation of Population Pharmacokinetic Models and Bayes-Based Dosing of Infliximab. Pharmaceutics 2021; 13:pharmaceutics13081191. [PMID: 34452152 PMCID: PMC8398005 DOI: 10.3390/pharmaceutics13081191] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/28/2021] [Accepted: 07/29/2021] [Indexed: 12/29/2022] Open
Abstract
Despite the well-demonstrated efficacy of infliximab in inflammatory diseases, treatment failure remains frequent. Dose adjustment using Bayesian methods has shown in silico its interest in achieving target plasma concentrations. However, most of the published models have not been fully validated in accordance with the recommendations. This study aimed to submit these models to an external evaluation and verify their predictive capabilities. Eight models were selected for external evaluation, carried out on an independent database (409 concentrations from 157 patients). Each model was evaluated based on the following parameters: goodness-of-fit (comparison of predictions to observations), residual error model (population weighted residuals (PWRES), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE)), and predictive performances (prediction-corrected visual predictive checks (pcVPC) and Bayesian simulations). The performances observed during this external evaluation varied greatly from one model to another. The eight evaluated models showed a significant bias in population predictions (from -7.19 to 7.38 mg/L). Individual predictions showed acceptable bias and precision for six of the eight models (mean error of -0.74 to -0.29 mg/L and mean percent error of -16.6 to -0.4%). Analysis of NPDE and pcVPC confirmed these results and revealed a problem with the inclusion of several covariates (weight, concomitant immunomodulatory treatment, presence of anti-drug antibodies). This external evaluation showed satisfactory results for some models, notably models A and B, and highlighted several prospects for improving the pharmacokinetic models of infliximab for clinical-biological application.
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21
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Tang BH, Guan Z, Allegaert K, Wu YE, Manolis E, Leroux S, Yao BF, Shi HY, Li X, Huang X, Wang WQ, Shen AD, Wang XL, Wang TY, Kou C, Xu HY, Zhou Y, Zheng Y, Hao GX, Xu BP, Thomson AH, Capparelli EV, Biran V, Simon N, Meibohm B, Lo YL, Marques R, Peris JE, Lutsar I, Saito J, Burggraaf J, Jacqz-Aigrain E, van den Anker J, Zhao W. Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction. Clin Pharmacokinet 2021; 60:1435-1448. [PMID: 34041714 DOI: 10.1007/s40262-021-01033-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. OBJECTIVE The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. METHODS Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. RESULTS The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. CONCLUSION A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
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Affiliation(s)
- Bo-Hao Tang
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Zheng Guan
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Center, Leiden, The Netherlands
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium
| | - Yue-E Wu
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Efthymios Manolis
- Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands
| | | | - Bu-Fan Yao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Hai-Yan Shi
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Xiao Li
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Xin Huang
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.,Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Wen-Qi Wang
- Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - A-Dong Shen
- Key Laboratory of Major Diseases in Children and National Key Discipline of Pediatrics (Capital Medical University), Ministry of Education, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Xiao-Ling Wang
- Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Tian-You Wang
- Clinical Research Center, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Chen Kou
- Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, People's Republic of China
| | - Hai-Yan Xu
- Department of Pediatrics, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China
| | - Yue Zhou
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Yi Zheng
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China
| | - Bao-Ping Xu
- Department of Respiratory Diseases, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, People's Republic of China
| | - Alison H Thomson
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK
| | - Edmund V Capparelli
- Pediatric Pharmacology and Drug Discovery, University of California, San Diego, CA, USA
| | - Valerie Biran
- Neonatal Intensive Care Unit, Hospital Robert Debre, Paris, France
| | - Nicolas Simon
- Aix Marseille Univ, APHM, INSERM, IRD, SESSTIM, Hop Sainte Marguerite, Service de Pharmacologie Clinique, CAP-TV, Marseille, France
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yoke-Lin Lo
- Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.,School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia
| | - Remedios Marques
- Department of Pharmacy Services, La Fe Hospital, Valencia, Spain
| | - Jose-Esteban Peris
- Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Valencia, Spain
| | - Irja Lutsar
- Institute of Medical Microbiology, University of Tartu, Tartu, Estonia
| | - Jumpei Saito
- Department of Pharmacy, National Children's Hospital National Center for Child Health and Development, Tokyo, Japan
| | - Jacobus Burggraaf
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Center, Leiden, The Netherlands
| | - Evelyne Jacqz-Aigrain
- Department of Pediatric Pharmacology and Pharmacogenetics, Hospital Robert Debre, APHP, Paris, France.,Clinical Investigation Center CIC1426, Hoŝpital Robert Debre, Paris, France.,University Paris Diderot, Sorbonne Paris Cite, Paris, France
| | - John van den Anker
- Division of Clinical Pharmacology, Children's National Hospital, Washington, DC, USA.,Departments of Pediatrics, Pharmacology and Physiology, Genomics and Precision Medicine, George Washington University School of Medicine and Health Sciences, Washington, DC, USA.,Department of Paediatric Pharmacology and Pharmacometrics, University of Basel Children's Hospital, Basel, Switzerland
| | - Wei Zhao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People's Republic of China. .,Modelling and Simulation Working Party, European Medicines Agency, Amsterdam, The Netherlands. .,Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China. .,Clinical Research Center, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, People's Republic of China.
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22
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Population Pharmacokinetic Models of Vancomycin in Paediatric Patients: A Systematic Review. Clin Pharmacokinet 2021; 60:985-1001. [PMID: 34002357 DOI: 10.1007/s40262-021-01027-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/12/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Vancomycin is commonly used to treat gram-positive bacterial infections in the paediatric population, but dosing can be challenging. Population pharmacokinetic (popPK) modelling can improve individualization of dosing regimens. The primary objective of this study was to describe popPK models of vancomycin and factors that influence pharmacokinetic (PK) variability in paediatric patients. METHODS Systematic searches were conducted in the Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, International Pharmaceutical Abstracts and the grey literature without language or publication status restrictions from inception to 17 August 2020. Observational studies that described the development of popPK models of vancomycin in paediatric patients (< 18 years of age) were included. Risk of bias was assessed using the National Heart, Lung and Blood Institute Study Quality Assessment Tool for Case Series Studies. RESULTS Sixty-four observational studies (1 randomized controlled trial, 13 prospective studies and 50 retrospective studies of 9019 patients with at least 25,769 serum vancomycin concentrations) were included. The mean age was 2.5 years (range 1 day-18 years), serum creatinine was 47.1 ± 33.6 µmol/L, and estimated creatinine clearance was 97.4 ± 76 mL/min/1.73m2. Most studies found that vancomycin PK was best described by a one-compartment model (71.9%). There was a wide range of clearance and volume of distribution (Vd) values (range 0.014-0.27 L/kg/h and 0.43-1.46 L/kg, respectively) with interindividual variability as high as 49.7% for clearance and 136% for Vd, proportional residual variability up to 37.5% and additive residual variability up to 17.5 mg/L. The most significant covariates for clearance were weight, age, and serum creatinine or creatinine clearance, and weight for Vd. Variable dosing recommendations were suggested. CONCLUSION Numerous popPK models of vancomycin were derived, however external validation of suggested dosing regimens and analyses in subgroup paediatric populations such as dialysis patients are still needed before a popPK model with best predictive performance can be applied for dosing recommendations. Significant intraindividual and interindividual PK variability was present, which demonstrated the need for ongoing therapeutic drug monitoring and derivation of PK models for vancomycin for certain subgroup populations, such as dialysis patients.
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23
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Li X, Qi H, Jin F, Yao BF, Wu YE, Qi YJ, Kou C, Wu XR, Luo XJ, Shen YH, Zheng X, Wang YH, Xu F, Jiao WW, Li JQ, Xiao J, Dong YN, Du B, Shi HY, Xu BP, Shen AD, Zhao W. Population pharmacokinetics-pharmacodynamics of ceftazidime in neonates and young infants: Dosing optimization for neonatal sepsis. Eur J Pharm Sci 2021; 163:105868. [PMID: 33951483 DOI: 10.1016/j.ejps.2021.105868] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 03/01/2021] [Accepted: 04/25/2021] [Indexed: 01/22/2023]
Abstract
Ceftazidime is a third-generation cephalosporin with high activity against many pathogens. But the ambiguity and diversity of the dosing regimens in neonates and young infants impair access to effective treatment. Thus, we conducted a population pharmacokinetic study of ceftazidime in this vulnerable population and recommended a model-based dosage regimen to optimize sepsis therapy. Totally 146 neonates and young infants (gestational age (GA): 36-43.4 weeks, postnatal age (PNA): 1-81 days, current weight (CW): 900-4500 g) were enrolled based on inclusion and exclusion criteria. Ceftazidime bloods samples (203) were obtained using the opportunistic sampling strategy and determined by the high-performance liquid chromatography. The population pharmacokinetic-pharmacodynamic analysis was conducted by nonlinear mixed effects model (NONMEM). A one-compartment model with first-order elimination best described the pharmacokinetic data. Covariate analysis showed the significance of GA, PNA, and CW on developmental pharmacokinetics. Monte Carlo simulation was performed based on above covariates and minimum inhibitory concentration (MIC). In the newborns with PNA ≤ 3 days (MIC=8 mg/L), the dose regimen was 25 mg/kg twice daily (BID). For the newborns with PNA > 3 days (MIC=16 mg/L), the optimal dose was 30 mg/kg three times daily (TID) for those with GA ≤ 37 weeks and 40 mg/kg TID for those with GA > 37 weeks. Overall, on the basis of the developmental population pharmacokinetic-pharmacodynamic analysis covering the whole range of neonates and young infants, the evidence-based ceftazidime dosage regimens were proposed to optimize neonatal early-onset and late-onset sepsis therapy.
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Affiliation(s)
- Xue Li
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Hui Qi
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Fei Jin
- Neonatal intensive care unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Yu-Jie Qi
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Chen Kou
- Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100045, China
| | - Xi-Rong Wu
- Department of Respiratory Diseases, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Xiao-Jing Luo
- Neonatal intensive care unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Yan-Hua Shen
- Neonatal intensive care unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Xu Zheng
- Neonatal intensive care unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Yong-Hong Wang
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Fang Xu
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Wei-Wei Jiao
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Jie-Qiong Li
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Jing Xiao
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Yi-Ning Dong
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Bin Du
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Hai-Yan Shi
- Department of Clinical Pharmacy, Clinical Trial Center, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Bao-Ping Xu
- Department of Respiratory Diseases, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - A-Dong Shen
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China.
| | - Wei Zhao
- Department of Clinical Pharmacy, Key Laboratory of Chemical Biology (Ministry of Education), School of Pharmaceutical Sciences, Cheeloo College of Medicine, Shandong University, Jinan 250012, China; Department of Clinical Pharmacy, Clinical Trial Center, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China.
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24
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Lv C, Lu J, Jing L, Liu TT, Chen M, Zhang R, Li C, Zhou S, Wei Y, Chen Y. Systematic external evaluation of reported population pharmacokinetic models of vancomycin in Chinese children and adolescents. J Clin Pharm Ther 2021; 46:820-831. [PMID: 33751618 DOI: 10.1111/jcpt.13363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 12/08/2020] [Accepted: 01/01/2021] [Indexed: 01/09/2023]
Abstract
WHAT IS KNOWN AND OBJECTIVES Various population pharmacokinetic (PopPK) models for vancomycin in children and adolescents have been constructed to optimize the therapeutic regimen of vancomycin. However, little is known about their predictive performance when extrapolated to different clinical centres. Therefore, the aim of this study was to externally validate the predictability of vancomycin PopPK model when extrapolated to different clinical centres and verify its applicability in an independent data set. METHODS The published models were screened from the literature and evaluated using an external data set of a total of 451 blood concentrations of vancomycin measured in 220 Chinese paediatric patients. Prediction- and simulation-based diagnostics and Bayesian forecasting were performed to evaluate the predictive performance of the models. RESULTS Ten published PopPK models were assessed. Prediction-based diagnostics showed that none of the investigated models met all the standards (median prediction error (MDPE) ≤ ±20%, median absolute prediction error (MAPE) ≤30%, PE% within ±20% (F20 ) ≥35% and PE% within ±30% (F30 ) ≥50%), indicating unsatisfactory predictability. In simulation-based diagnostics, both the visual predictive checks (VPC) and the normalized prediction distribution error (NPDE) indicated misspecification in all models. Bayesian forecasting results showed that the accuracy and precision of individual predictions could be significantly improved with one or two prior observations, but frequent monitoring might not be necessary in the clinic, since Bayesian forecasting identified that greater number of samples did not significantly improve the predictability. Model 3 established by Moffett et al showed better predictability than other models. WHAT IS NEW AND CONCLUSION The 10 published models performed unsatisfactorily in prediction- and simulation-based diagnostics; none of the published models was suitable for designing the initial dosing regimens of vancomycin. Pharmacokinetic characteristics and covariates, such as weight, renal function, age and underlying disease should be taken into account when extrapolating the vancomycin model. Bayesian forecasting combined with therapeutic drug monitoring based on model 3 can be used to adjust vancomycin dosing regimens.
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Affiliation(s)
- Chunle Lv
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jiejiu Lu
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Li Jing
- Department of Pharmacy, The Affiliated Hospital of Guilin Medical University, Guilin, China
| | - Tao-Tao Liu
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Ming Chen
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Ren Zhang
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Chengxin Li
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Siru Zhou
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yinyi Wei
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yiyu Chen
- Department of Pharmacy, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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25
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Liu YX, Wen H, Niu WJ, Li JJ, Li ZL, Jiao Z. External Evaluation of Vancomycin Population Pharmacokinetic Models at Two Clinical Centers. Front Pharmacol 2021; 12:623907. [PMID: 33897418 PMCID: PMC8058705 DOI: 10.3389/fphar.2021.623907] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/15/2021] [Indexed: 01/19/2023] Open
Abstract
Background: Numerous vancomycin population pharmacokinetic models in neonates have been published; however, their predictive performances remain unknown. This study aims to evaluate their external predictability and explore the factors that might affect model performance. Methods: Published population pharmacokinetic models in neonates were identified from the literature and evaluated using datasets from two clinical centers, including 171 neonates with a total of 319 measurements of vancomycin levels. Predictive performance was assessed by prediction- and simulation-based diagnostics and Bayesian forecasting. Furthermore, the effect of model structure and a number of identified covariates was also investigated. Results: Eighteen published pharmacokinetic models of vancomycin were identified after a systematic literature search. Using prediction-based diagnostics, no model had a median prediction error of ≤ ± 15%, a median absolute prediction error of ≤30%, and a percentage of prediction error that fell within ±30% of >50%. A simulation-based visual predictive check of most models showed there were large deviations between observations and simulations. After Bayesian forecasting with one or two prior observations, the predicted performance improved significantly. Weight, age, and serum creatinine were identified as the most important covariates. Moreover, employing a maturation model based on weight and age as well as nonlinear model to incorporate serum creatinine level significantly improved predictive performance. Conclusion: The predictability of the pharmacokinetic models for vancomycin is closely related to the approach used for modeling covariates. Bayesian forecasting can significantly improve the predictive performance of models.
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Affiliation(s)
- Yi-Xi Liu
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Pharmacy, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haini Wen
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wan-Jie Niu
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing-Jing Li
- Department of Pharmacy, Suzhou Hospital Affiliated to Nanjing Medical University, Suzhou, China
| | - Zhi-Ling Li
- Department of Pharmacy, Shanghai Children’s Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
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26
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Ryu S, Jung WJ, Jiao Z, Chae JW, Yun HY. External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates. Br J Clin Pharmacol 2021; 87:3878-3889. [PMID: 33638184 DOI: 10.1111/bcp.14803] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 02/09/2021] [Accepted: 02/13/2021] [Indexed: 02/06/2023] Open
Abstract
AIM Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models. METHODS Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement. RESULTS A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.
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Affiliation(s)
- Sunae Ryu
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea.,National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, Republic of Korea
| | - Woo Jin Jung
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, P.R. China
| | - Jung-Woo Chae
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
| | - Hwi-Yeol Yun
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
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27
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Abdulla A, Edwina EE, Flint RB, Allegaert K, Wildschut ED, Koch BCP, de Hoog M. Model-Informed Precision Dosing of Antibiotics in Pediatric Patients: A Narrative Review. Front Pediatr 2021; 9:624639. [PMID: 33708753 PMCID: PMC7940353 DOI: 10.3389/fped.2021.624639] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2020] [Accepted: 02/03/2021] [Indexed: 12/17/2022] Open
Abstract
Optimal pharmacotherapy in pediatric patients with suspected infections requires understanding and integration of relevant data on the antibiotic, bacterial pathogen, and patient characteristics. Because of age-related physiological maturation and non-maturational covariates (e.g., disease state, inflammation, organ failure, co-morbidity, co-medication and extracorporeal systems), antibiotic pharmacokinetics is highly variable in pediatric patients and difficult to predict without using population pharmacokinetics models. The intra- and inter-individual variability can result in under- or overexposure in a significant proportion of patients. Therapeutic drug monitoring typically covers assessment of pharmacokinetics and pharmacodynamics, and concurrent dose adaptation after initial standard dosing and drug concentration analysis. Model-informed precision dosing (MIPD) captures drug, disease, and patient characteristics in modeling approaches and can be used to perform Bayesian forecasting and dose optimization. Incorporating MIPD in the electronic patient record system brings pharmacometrics to the bedside of the patient, with the aim of a consisted and optimal drug exposure. In this narrative review, we evaluated studies assessing optimization of antibiotic pharmacotherapy using MIPD in pediatric populations. Four eligible studies involving amikacin and vancomycin were identified from 418 records. Key articles, independent of year of publication, were also selected to highlight important attributes of MIPD. Although very little research has been conducted until this moment, the available data on vancomycin indicate that MIPD is superior compared to conventional dosing strategies with respect to target attainment. The utility of MIPD in pediatrics needs to be further confirmed in frequently used antibiotic classes, particularly aminoglycosides and beta-lactams.
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Affiliation(s)
- Alan Abdulla
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Elma E Edwina
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Robert B Flint
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands.,Division of Neonatology, Department of Pediatrics, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Karel Allegaert
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands.,Department of Pharmaceutical and Pharmacological Sciences, KU Leuven, Leuven, Belgium.,Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Enno D Wildschut
- Department of Pediatric Intensive Care, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Birgit C P Koch
- Department of Hospital Pharmacy, Erasmus University Medical Center, Rotterdam, Netherlands
| | - Matthijs de Hoog
- Department of Pediatric Intensive Care, Sophia Children's Hospital, Erasmus University Medical Center, Rotterdam, Netherlands
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28
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Tauzin M, Tréluyer JM, Nabbout R, Billette de Villemeur T, Desguerre I, Aboura R, Gana I, Zheng Y, Benaboud S, Bouazza N, Chenevier-Gobeaux C, Freihuber C, Hirt D. Dosing Recommendations for Lamotrigine in Children: Evaluation Based on Previous and New Population Pharmacokinetic Models. J Clin Pharmacol 2020; 61:677-687. [PMID: 33244764 DOI: 10.1002/jcph.1791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 11/19/2020] [Indexed: 12/30/2022]
Abstract
Lamotrigine is a broad-spectrum antiepileptic drug with high interindividual variability in serum concentrations in children. The aims of this study were to evaluate the predictive performance of pediatric population pharmacokinetic (PPK) models published on lamotrigine, to build a new model with our monitoring data and to evaluate the current recommended doses. A validation cohort included patients treated with lamotrigine who had a serum level assayed during therapeutic drug monitoring (TDM). PPK models published in the literature were first applied to the validation cohort. We assessed their predictive performance using mean prediction errors, root mean squared errors, and visual predictive checks. A new model was then built using the data. Dose simulations were performed to evaluate the doses recommended. We included 270 lamotrigine concentrations ranging from 0.5 to 17.9 mg/L from 175 patients. The median (range) age and weight were 11.8 years (0.8-18 years) and 32.7 kg (8-110 kg). We tested 6 PPK models; most had acceptable bias and precision but underestimated the variability of the cohort. We built a 1-compartment model with first-order absorption and elimination, allometric scaling, and effects of inhibitor and inducer comedications. In our cohort, 22.6% of trough concentrations were below 2.5 mg/L. In conclusion, we proposed a PPK model that can be used for TDM of lamotrigine in children. In our population, a high percentage of children had low trough concentrations of lamotrigine. As the intervals of recommended doses are large, we suggest aiming at the higher range of doses to reach the target concentration.
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Affiliation(s)
- Manon Tauzin
- Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France
- Réanimation néonatale et néonatologie, Centre Hospitalier Intercommunal de Créteil, Créteil, France
| | - Jean-Marc Tréluyer
- Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France
- EA 7323, Université Paris Descartes Sorbonne Paris Cité, Paris, France
- Unité de recherche Clinique, Hôpital Universitaire Necker-Enfants Malades, APHP, Université Paris Descartes, Paris, France
| | - Rima Nabbout
- Centre de référence épilepsies rares, Service de Neurologie pédiatrique, Hôpital Necker Enfants Malades, APHP, Paris, France
| | - Thierry Billette de Villemeur
- Sorbonne Université, UPMC, GRC ConCer-LD and AP-HP, Hôpital Trousseau, Service de Neuropédiatrie - Pathologie du développement, Centre de référence des déficits intellectuels de causes rares, Paris, France
| | - Isabelle Desguerre
- Centre de référence épilepsies rares, Service de Neurologie pédiatrique, Hôpital Necker Enfants Malades, APHP, Paris, France
| | - Radia Aboura
- Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France
| | - Ines Gana
- Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France
| | - Yi Zheng
- Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France
| | - Sihem Benaboud
- Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France
- EA 7323, Université Paris Descartes Sorbonne Paris Cité, Paris, France
| | - Naim Bouazza
- EA 7323, Université Paris Descartes Sorbonne Paris Cité, Paris, France
| | - Camille Chenevier-Gobeaux
- Service de Diagnostic Biologique Automatisé, Hôpital Cochin, Hôpitaux Universitaires Paris Centre (HUPC), Assistance Publique des Hôpitaux de Paris (APHP), Paris, France
| | - Cécile Freihuber
- Sorbonne Université, UPMC, GRC ConCer-LD and AP-HP, Hôpital Trousseau, Service de Neuropédiatrie - Pathologie du développement, Centre de référence des déficits intellectuels de causes rares, Paris, France
| | - Déborah Hirt
- Service de Pharmacologie Clinique, Hôpital Cochin, APHP, Paris, France
- EA 7323, Université Paris Descartes Sorbonne Paris Cité, Paris, France
- Inserm 1018 CESP, Hôpital Bicêtre, Le Kremlin-Bicêtre, France
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29
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Cheng Y, Wang CY, Li ZR, Pan Y, Liu MB, Jiao Z. Can Population Pharmacokinetics of Antibiotics be Extrapolated? Implications of External Evaluations. Clin Pharmacokinet 2020; 60:53-68. [PMID: 32960439 DOI: 10.1007/s40262-020-00937-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND OBJECTIVE External evaluation is an important issue in the population pharmacokinetic analysis of antibiotics. The purpose of this review was to summarize the current approaches and status of external evaluations and discuss the implications of external evaluation results for the future individualization of dosing regimens. METHODS We systematically searched the PubMed and EMBASE databases for external evaluation studies of population analysis and extracted the relevant information from these articles. A total of 32 studies were included in this review. RESULTS Vancomycin was investigated in 17 (53.1%) articles and was the most studied drug. Other studied drugs included gentamicin, tobramycin, amikacin, amoxicillin, ceftaroline, meropenem, fluconazole, voriconazole, and rifampicin. Nine (28.1%) studies were prospective, and the sample size varied widely between studies. Thirteen (40.6%) studies evaluated the population pharmacokinetic models by systematically searching for previous studies. Seven (21.9%) studies were multicenter studies, and 27 (84.4%) adopted the sparse sampling strategy. Almost all external evaluation studies of antibiotics (93.8%) used metrics for prediction-based diagnostics, while relatively fewer studies were based on simulations (46.9%) and Bayesian forecasting (25.0%). CONCLUSION The results of external evaluations in previous studies revealed the poor extrapolation performance of existing models of prediction- and simulation-based diagnostics, whereas the posterior Bayesian method could improve predictive performance. There is an urgent need for the development of standards and guidelines for external evaluation studies.
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Affiliation(s)
- Yu Cheng
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China.,Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Road, Gulou, Fuzhou, 350001, China
| | - Chen-Yu Wang
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China
| | - Zi-Ran Li
- College of Pharmacy, Fudan University, Shanghai, China
| | - Yan Pan
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China
| | - Mao-Bai Liu
- Department of Pharmacy, Fujian Medical University Union Hospital, 29 Xin Quan Road, Gulou, Fuzhou, 350001, China.
| | - Zheng Jiao
- Department of Pharmacy, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, 200040, China.
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30
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Jacqz-Aigrain E, Leroux S, Thomson AH, Allegaert K, Capparelli EV, Biran V, Simon N, Meibohm B, Lo YL, Marques R, Peris JE, Lutsar I, Saito J, Nakamura H, van den Anker JN, Sharland M, Zhao W. Population pharmacokinetic meta-analysis of individual data to design the first randomized efficacy trial of vancomycin in neonates and young infants. J Antimicrob Chemother 2020; 74:2128-2138. [PMID: 31049551 DOI: 10.1093/jac/dkz158] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 02/26/2019] [Accepted: 03/16/2019] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES In the absence of consensus, the present meta-analysis was performed to determine an optimal dosing regimen of vancomycin for neonates. METHODS A 'meta-model' with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0-24 of 400 mg·h/L at steady-state in at least 80% of neonates. RESULTS A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if <35 weeks PMA and 15 mg/kg q8h if ≥35 weeks PMA) achieved the AUC0-24 target earlier than a standard 'Blue Book' dosage regimen in >89% of the treated patients. CONCLUSIONS The results of a population meta-analysis of vancomycin data have been used to develop a new dosing regimen for neonatal use and to assist in the design of the model-based, multinational European trial, NeoVanc.
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Affiliation(s)
- Evelyne Jacqz-Aigrain
- Department of Pediatric Pharmacology and Pharmacogenetics, Hôpital Robert Debré, APHP, Paris, France.,Clinical Investigation Center CIC1426, Hôpital Robert Debré, Paris, France.,University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Stéphanie Leroux
- Department of Pediatric Pharmacology and Pharmacogenetics, Hôpital Robert Debré, APHP, Paris, France.,Clinical Investigation Center CIC1426, Hôpital Robert Debré, Paris, France.,Division of Neonatology, Department of Child and Adolescent Medicine, CHU de Rennes, Rennes, France
| | - Alison H Thomson
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, UK.,Pharmacy Department, Glasgow Royal Infirmary, Glasgow, UK
| | - Karel Allegaert
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium.,Intensive Care, Erasmus MC - Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Edmund V Capparelli
- Pediatric Pharmacology and Drug Discovery, University of California, San Diego, CA, USA
| | - Valérie Biran
- Neonatal Intensive Care Unit, Hôpital Robert Debré, Paris, France
| | - Nicolas Simon
- Department of Pharmacology, Hôpital de la Timone, APHM, Université de la Méditerranée, Marseille, France.,Service de Pharmacologie Clinique, Hôpital Sainte marguerite, CAP-TV, 13274 Marseille, France.,Aix Marseille University, INSERM, IRD, SESSTIM, Marseille, France
| | - Bernd Meibohm
- Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Yoke-Lin Lo
- Department of Pharmacy, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia.,School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia
| | - Remedios Marques
- Department of Pharmacy Services, La Fe Hospital, Valencia, Spain
| | - José-Esteban Peris
- Department of Pharmacy and Pharmaceutical Technology, University of Valencia, Valencia, Spain
| | - Irja Lutsar
- Institute of Medical Microbiology, University of Tartu, Tartu, Estonia
| | - Jumpei Saito
- Department of Pharmacy, National Children's Hospital National Center for Child Health and Development, Tokyo, Japan
| | - Hidefumi Nakamura
- Department of Development Strategy, Center for Clinical Research and Development, National Center for Child Health and Development, Tokyo, Japan
| | - Johannes N van den Anker
- Pharmacy Department, Glasgow Royal Infirmary, Glasgow, UK.,Division of Clinical Pharmacology, Children's National Medical Center, Washington, DC, USA.,Departments of Pediatrics, Pharmacology & Physiology, George Washington University, School of Medicine and Health Sciences, Washington, DC, USA.,Department of Paediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel, Basel, Switzerland
| | - Mike Sharland
- Paediatric Infectious Disease Unit, St George's Hospital, London, UK
| | - Wei Zhao
- Department of Pediatric Pharmacology and Pharmacogenetics, Hôpital Robert Debré, APHP, Paris, France.,Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China.,Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
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31
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van Beek SW, Ter Heine R, Keizer RJ, Magis-Escurra C, Aarnoutse RE, Svensson EM. Personalized Tuberculosis Treatment Through Model-Informed Dosing of Rifampicin. Clin Pharmacokinet 2020; 58:815-826. [PMID: 30671890 DOI: 10.1007/s40262-018-00732-2] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
BACKGROUND AND OBJECTIVE This study proposes a model-informed approach for therapeutic drug monitoring (TDM) of rifampicin to improve tuberculosis (TB) treatment. METHODS Two datasets from pulmonary TB patients were used: a pharmacokinetic study (34 patients, 373 samples), and TDM data (96 patients, 391 samples) collected at Radboud University Medical Center, The Netherlands. Nine suitable population pharmacokinetic models of rifampicin were identified in the literature and evaluated on the datasets. A model developed by Svensson et al. was found to be the most suitable based on graphical goodness of fit, residual diagnostics, and predictive performance. Prediction of individual area under the concentration-time curve from time zero to 24 h (AUC24) and maximum concentration (Cmax) employing various sampling strategies was compared with a previously established linear regression TDM strategy, using sampling at 2, 4, and 6 h, in terms of bias and precision (mean error [ME] and root mean square error [RMSE]). RESULTS A sampling strategy using 2- and 4-h blood collection was selected to be the most suitable. The bias and precision of the two strategies were comparable, except that the linear regression strategy was more biased in prediction of the AUC24 than the model-informed approach (ME of 9.9% and 1.5%, respectively). A comparison of resulting dose advice, using predictions on a simulated dataset, showed no significant difference in sensitivity or specificity between the two methods. The model was successfully implemented in the InsightRX precision dosing platform. CONCLUSION Blood sampling at 2 and 4 h, combined with model-based prediction, can be used instead of the currently used linear regression strategy, shortening the sampling by 2 h and one sampling point without performance loss while simultaneously offering flexibility in sampling times.
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Affiliation(s)
- Stijn W van Beek
- Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rob Ter Heine
- Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Cecile Magis-Escurra
- Department of Respiratory Diseases, Radboud University Medical Center-Dekkerswald, Groesbeek, The Netherlands
| | - Rob E Aarnoutse
- Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Elin M Svensson
- Department of Pharmacy, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands. .,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden.
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Population Pharmacokinetics of Vancomycin in the Pediatric Ventricular Assist Device Population. Pediatr Crit Care Med 2020; 21:e566-e571. [PMID: 32224831 DOI: 10.1097/pcc.0000000000002349] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
OBJECTIVES Determine the pharmacokinetic disposition of vancomycin in the pediatric ventricular assist device population. DESIGN A retrospective, population pharmacokinetic study. SETTING Large, quaternary care children's hospital. PATIENTS Less than 19 years old initiated on vancomycin while undergoing ventricular assist device therapy from 2011 to 2018 in our institution. INTERVENTIONS Patient data were summarized by using descriptive statistical methods, and population pharmacokinetic analysis was performed by using NONMEM (Icon, PLC, Dublin, Ireland). Simulation was performed to identify a vancomycin dosing strategy that resulted in a trough concentration less than 15 mg/L and an area under the curve0-24:minimum inhibitory concentration ratio of greater than 400. MEASUREMENTS AND MAIN RESULTS A total of 69 patients (male 50.7%, median age 7.1 years [interquartile range, 2.4-11.9]) met study criteria (HeartWare [Framingham, MA] = 37, Berlin Heart [Berlin, Germany] = 22, Impella [Abiomed, Danvers, MA] = 4, RotaFlow [Maquet, Hirrlingen, Germany] right ventricular assist device = 3, HeartMate II [Abbott Laboratories, Abbott Park, IL] = 2, Berlin Heart biventricular assist device = 1). Patients received a median of 21 doses (interquartile range, 13-44 doses) of IV vancomycin (14.8 ± 1.8 mg/kg/dose) along with vancomycin as an intrathoracic irrigation (n = 48; 69.6%). The mean serum concentration was 12.2 ± 5.2 mg/L at 11.2 ± 6.9 hours after a dose. A one-compartment pharmacokinetic model best fit the data with allometric scaling on clearance and volume of distribution. Clearance was characterized by total body weight and serum creatinine, and volume of distribution was characterized by total body weight. Simulation identified doses greater than 15 mg/kg/dose with extended intervals were necessary to achieve endpoints. CONCLUSIONS Vancomycin dosing in pediatric ventricular assist device patients should be altered in comparison to nonventricular assist device patients and should be accompanied with frequent serum concentration monitoring.
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33
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Chen CY, Li MY, Ma LY, Zhai XY, Luo DH, Zhou Y, Liu ZM, Cui YM. Precision and accuracy of commercial assays for vancomycin therapeutic drug monitoring: evaluation based on external quality assessment scheme. J Antimicrob Chemother 2020; 75:2110-2119. [PMID: 32464644 DOI: 10.1093/jac/dkaa150] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 03/26/2020] [Accepted: 03/27/2020] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Vancomycin remains a mainstay of the treatment of Gram-positive bacterial infections. It is crucial to accurately determine vancomycin serum concentration for adequate dose adjustment. OBJECTIVES To evaluate the precision and accuracy of commercial assay techniques for vancomycin concentration and to assess the comparability of vancomycin detection methods in Chinese laboratories. METHODS Human serum samples spiked with known concentrations of vancomycin were provided to laboratories participating in the external quality assessment scheme (EQAS). Assay methods included chemiluminescence, enzyme immunoassay (EIA) and so on. The dispersion of the measurements was analysed and the robust coefficient of variation (rCV), relative percentage difference (RPD) and satisfactory rate for method groups were calculated. Moreover, performance of the Chinese laboratories was assessed. RESULTS A total of 657 results from 75 laboratories were collected, including 84 samples from 10 Chinese laboratories. The median rCV, median RPD and satisfactory rates classified by methods ranged from 1.85% to 15.87%, -14.75% to 13.34% and 94.59% to 100.00%, respectively. Significant differences were seen in precision, between kinetic interaction of microparticles in solution (KIMS) and other methods, and in accuracy, between enzyme-multiplied immunoassay technique (EMIT), fluorescence polarization immunoassay (FPIA) and other techniques. Vancomycin detection in China mainly depended on the chemiluminescence and EMIT methods, which tended to result in lower measurements. CONCLUSIONS Although almost all assays in this study achieved an acceptable performance for vancomycin serum concentration monitoring, obvious inconsistencies between methods were still observed. Chinese laboratories were more likely to underestimate vancomycin concentrations. Thus, recognizing inconsistencies between methods and regular participation in vancomycin EQAS are essential.
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Affiliation(s)
- Chao-Yang Chen
- Department of Pharmacy, Peking University First Hospital, Beijing, China
| | - Meng-Ya Li
- Department of Pharmacy, Peking University First Hospital, Beijing, China.,Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
| | - Ling-Yun Ma
- Department of Pharmacy, Peking University First Hospital, Beijing, China
| | - Xing-Yu Zhai
- Department of Pharmacy, Peking University First Hospital, Beijing, China.,Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
| | - Dao-Huang Luo
- Department of Pharmacy, Peking University First Hospital, Beijing, China.,Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
| | - Ying Zhou
- Department of Pharmacy, Peking University First Hospital, Beijing, China.,Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
| | - Zhen-Ming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
| | - Yi-Min Cui
- Department of Pharmacy, Peking University First Hospital, Beijing, China.,Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University Health Science Center, Beijing, China
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34
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Germovsek E, Osborne L, Gunaratnam F, Lounis SA, Busquets FB, Standing JF, Sinha AK. Development and external evaluation of a population pharmacokinetic model for continuous and intermittent administration of vancomycin in neonates and infants using prospectively collected data. J Antimicrob Chemother 2020; 74:1003-1011. [PMID: 30668696 DOI: 10.1093/jac/dky525] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 11/05/2018] [Accepted: 11/16/2018] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Vancomycin is commonly used for nosocomial bacterial pathogens causing late-onset septicaemia in preterm infants. We prospectively collected pharmacokinetic data aiming to describe pharmacokinetics and determine covariates contributing to the variability in neonatal vancomycin pharmacokinetics. Further, we aimed to use the model to compare the ratio of AUC24 at steady-state to the MIC (AUC24,ss/MIC) of several intermittent and continuous dosing regimens. METHODS Newborns receiving vancomycin for suspected or confirmed late-onset sepsis were included. Peak and trough concentrations for intermittent vancomycin dosing and steady-state concentrations for continuous vancomycin dosing were measured. NONMEM 7.3 was used for population pharmacokinetic analysis. Monte Carlo simulations were performed to compare dosing schemes. RESULTS Data from 54 infants were used for model development and from 34 infants for the model evaluation {corrected gestational age [median (range)] = 29 (23.7-41.9) weeks and 28 (23.4-41.7) weeks, respectively}. The final model was a one-compartment model. Weight and postmenstrual age were included a priori, and then no additional covariate significantly improved the model fit. Final model parameter estimates [mean (SEM)]: CL = 5.7 (0.3) L/h/70 kg and V = 39.3 (3.7) L/70 kg. Visual predictive check of the evaluation dataset confirmed the model can predict external data. Simulations using MIC of 1 mg/L showed that for neonates with gestational age ≤25 weeks and postnatal age ≤2 weeks AUC24,ss/MIC was lower with the intermittent regimen (median 482 versus 663). CONCLUSIONS A population pharmacokinetic model for continuous and intermittent vancomycin administration in infants was developed. Continuous administration might be favourable for treating infections caused by resistant microorganisms in very young and immature infants.
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Affiliation(s)
- Eva Germovsek
- Inflammation, Infection and Rheumatology Section, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London, UK.,Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Leanne Osborne
- Neonatal Unit, Royal London Hospital, Barts Health NHS Trust, Whitechapel Road, Whitechapel, London, UK
| | - Flora Gunaratnam
- Centre for Genomics and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, UK
| | - Shehrazed A Lounis
- Centre for Genomics and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, UK
| | - Ferran Bossacoma Busquets
- Inflammation, Infection and Rheumatology Section, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London, UK.,Hospital Sant Joan de Deu, Passeig Hospital Sant Joan de Deu 2, Barcelona, Spain
| | - Joseph F Standing
- Inflammation, Infection and Rheumatology Section, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London, UK
| | - Ajay K Sinha
- Neonatal Unit, Royal London Hospital, Barts Health NHS Trust, Whitechapel Road, Whitechapel, London, UK.,Centre for Genomics and Child Health, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, London, UK
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35
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Ter Heine R, Keizer RJ, van Steeg K, Smolders EJ, van Luin M, Derijks HJ, de Jager CPC, Frenzel T, Brüggemann R. Prospective validation of a model-informed precision dosing tool for vancomycin in intensive care patients. Br J Clin Pharmacol 2020; 86:2497-2506. [PMID: 32415710 PMCID: PMC7688533 DOI: 10.1111/bcp.14360] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Revised: 04/24/2020] [Accepted: 05/01/2020] [Indexed: 02/04/2023] Open
Abstract
AIMS Vancomycin is an important antibiotic for critically ill patients with Gram-positive bacterial infections. Critically ill patients typically have severely altered pathophysiology, which leads to inefficacy or toxicity. Model-informed precision dosing may aid in optimizing the dose, but prospectively validated tools are not available for this drug in these patients. We aimed to prospectively validate a population pharmacokinetic model for purpose model-informed precision dosing of vancomycin in critically ill patients. METHODS We first performed a systematic evaluation of various models on retrospectively collected pharmacokinetic data in critically ill patients and then selected the best performing model. This model was implemented in the Insight Rx clinical decision support tool and prospectively validated in a multicentre study in critically ill patients. The predictive performance was obtained as mean prediction error and relative root mean squared error. RESULTS We identified 5 suitable population pharmacokinetic models. The most suitable model was carried forward to a prospective validation. We found in a prospective multicentre study that the selected model could accurately and precisely predict the vancomycin pharmacokinetics based on a previous measurement, with a mean prediction error and relative root mean squared error of respectively 8.84% (95% confidence interval 5.72-11.96%) and 19.8% (95% confidence interval 17.47-22.13%). CONCLUSION Using a systematic approach, with a retrospective evaluation and prospective verification we showed the suitability of a model to predict vancomycin pharmacokinetics for purposes of model-informed precision dosing in clinical practice. The presented methodology may serve a generic approach for evaluation of pharmacometric models for the use of model-informed precision dosing in the clinic.
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Affiliation(s)
- Rob Ter Heine
- Radboud Institute for Health Sciences, Department of Pharmacy, Radboud university medical center, Nijmegen, The Netherlands
| | | | - Krista van Steeg
- Department of Clinical Pharmacy, Ziekenhuisgroep Twente, Almelo, The Netherlands
| | - Elise J Smolders
- Department of Pharmacy, Isala Hospital, Zwolle, The Netherlands & Department of Pharmacy, Radboud University Medical Center, Radboud Institute of Health Sciences, Nijmegen, The Netherlands
| | - Matthijs van Luin
- Department of Clinical Pharmacy, Rijnstate Hospital, Arnhem, The Netherlands
| | - Hieronymus J Derijks
- Department of Pharmacy, Jeroen Bosch Hospital, 's-Hertogenbosch, The Netherlands/Department of Pharmacy, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Cornelis P C de Jager
- Department of Intensive Care Medicine, Jeroen Bosch Hospital, 's-Hertogenbosch, the Netherlands
| | - Tim Frenzel
- Department of Intensive Care Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Roger Brüggemann
- Radboud Institute for Health Sciences, Department of Pharmacy, Radboud university medical center, Nijmegen, The Netherlands
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36
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Wang YL, Guilhaumou R, Blin O, Velly L, Marsot A. External evaluation of population pharmacokinetic models for continuous administration of meropenem in critically ill adult patients. Eur J Clin Pharmacol 2020; 76:1281-1289. [PMID: 32495084 DOI: 10.1007/s00228-020-02922-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 05/29/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE Beta-lactams (BL), the most commonly prescribed class of antibiotics, are recommended as the first-line therapy for multiple indications in infectious disease guidelines. Meropenem (MERO) is frequently used in intensive care units (ICU) to treat bacterial infections with or without sepsis. The pharmacokinetics of MERO display a large variability in patients admitted to ICUs due to altered pathophysiology. The aim of this study was to perform an external evaluation of published population pharmacokinetic models of MERO in order to test their predictive performance in a cohort of ICU adult patients. METHODS A literature search in PubMed/Medline database was made following the PRISMA statement. External evaluation was performed using NONMEM software, and the bias and inaccuracy values were calculated. RESULTS An external validation dataset from the Timone Hospital in Marseille, France, included 84 concentration samples from 27 patients. Four models of MERO were identified according to the inclusion criteria of the study. None of the models presented acceptable values of bias and inaccuracy. CONCLUSION While performing external evaluations on some populations may confirm a model's suitability to diverse groups of patients, there is still some variability that cannot be explained nor solved by the procedure. This brings to light the difficulty to develop only one model for ICU patients and the need to develop one specific model to each population of critically ill patients.
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Affiliation(s)
- Y L Wang
- Laboratoire de Suivi Thérapeutique Pharmacologique et Pharmacocinétique, Faculté de Pharmacie, Université de Montréal, Pavillon Jean-Coutu, 2940 chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada.,Faculté de Pharmacie, Université de Montréal, Montréal, QC, Canada
| | - R Guilhaumou
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, Assistance Publique des Hôpitaux de Marseille, Marseille, France.,Pharmacologie intégrée et interface clinique et industrielle, Institut de Neuroscience des systèmes, CNRS 7289, Aix Marseille Université, 13385, Marseille, France
| | - O Blin
- Service de Pharmacologie Clinique et Pharmacovigilance, Hôpital de la Timone, Assistance Publique des Hôpitaux de Marseille, Marseille, France.,Pharmacologie intégrée et interface clinique et industrielle, Institut de Neuroscience des systèmes, CNRS 7289, Aix Marseille Université, 13385, Marseille, France
| | - L Velly
- Service d'Anesthésie-Réanimation, Hôpital de la Timone, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - Amélie Marsot
- Laboratoire de Suivi Thérapeutique Pharmacologique et Pharmacocinétique, Faculté de Pharmacie, Université de Montréal, Pavillon Jean-Coutu, 2940 chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada. .,Faculté de Pharmacie, Université de Montréal, Montréal, QC, Canada. .,Centre de Recherche, CHU Sainte Justine, Montréal, QC, Canada.
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37
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Maharaj AR, Wu H, Hornik CP, Arrieta A, James L, Bhatt-Mehta V, Bradley J, Muller WJ, Al-Uzri A, Downes KJ, Cohen-Wolkowiez M. Use of normalized prediction distribution errors for assessing population physiologically-based pharmacokinetic model adequacy. J Pharmacokinet Pharmacodyn 2020; 47:199-218. [PMID: 32323049 PMCID: PMC7293575 DOI: 10.1007/s10928-020-09684-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Accepted: 03/27/2020] [Indexed: 12/19/2022]
Abstract
Currently employed methods for qualifying population physiologically-based pharmacokinetic (Pop-PBPK) model predictions of continuous outcomes (e.g., concentration-time data) fail to account for within-subject correlations and the presence of residual error. In this study, we propose a new method for evaluating Pop-PBPK model predictions that account for such features. The approach focuses on deriving Pop-PBPK-specific normalized prediction distribution errors (NPDE), a metric that is commonly used for population pharmacokinetic model validation. We describe specific methodological steps for computing NPDE for Pop-PBPK models and define three measures for evaluating model performance: mean of NPDE, goodness-of-fit plots, and the magnitude of residual error. Utility of the proposed evaluation approach was demonstrated using two simulation-based study designs (positive and negative control studies) as well as pharmacokinetic data from a real-world clinical trial. For the positive-control simulation study, where observations and model simulations were generated under the same Pop-PBPK model, the NPDE-based approach denoted a congruency between model predictions and observed data (mean of NPDE = - 0.01). In contrast, for the negative-control simulation study, where model simulations and observed data were generated under different Pop-PBPK models, the NPDE-based method asserted that model simulations and observed data were incongruent (mean of NPDE = - 0.29). When employed to evaluate a previously developed clindamycin PBPK model against prospectively collected plasma concentration data from 29 children, the NPDE-based method qualified the model predictions as successful (mean of NPDE = 0). However, when pediatric subpopulations (e.g., infants) were evaluated, the approach revealed potential biases that should be explored.
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Affiliation(s)
- Anil R Maharaj
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Huali Wu
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Christoph P Hornik
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA
| | - Antonio Arrieta
- Children's Hospital of Orange County Research Institute, Orange, CA, USA
| | - Laura James
- Arkansas Children's Hospital Research Center, Little Rock, AR, USA
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Varsha Bhatt-Mehta
- University of Michigan College of Pharmacy and Michigan Medicine, Ann Arbor, MI, USA
| | - John Bradley
- Rady Children's Hospital and Health Center, San Diego, CA, USA
| | - William J Muller
- Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Amira Al-Uzri
- Oregon Health and Science University, Portland, OR, USA
| | - Kevin J Downes
- Division of Infectious Diseases, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Cohen-Wolkowiez
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA.
- Department of Pediatrics, Duke University School of Medicine, Durham, NC, USA.
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Thirion DJG, Pasche V, Matouk E, Marsot A. Amikacin nomogram for treatment of adult cystic fibrosis exacerbations based on an external evaluation of a population pharmacokinetic model. Pediatr Pulmonol 2020; 55:1154-1160. [PMID: 32119197 DOI: 10.1002/ppul.24689] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Accepted: 02/04/2020] [Indexed: 01/10/2023]
Abstract
BACKGROUND In patients with cystic fibrosis (CF), amikacin is the alternative for the treatment of acute pulmonary exacerbations associated with pathogens resistant to tobramycin. Population pharmacokinetic (PK) models of amikacin in adult patients with CF have been previously published. However, current dosing recommendations remain disputed (Illamola et al. Clin Pharmacokinet. 2018;57(10):1217-1228). We perform here the first external evaluation of a published amikacin adult CF population PK model and propose a dosing nomogram for initial dosing. METHODS We retrospectively collected demographic, biological, and clinical data from the medical records of adult patients who had received intravenous amikacin. To assess the predictive performance of this model we applied visual comparison of predictions to observations, calculation of bias and inaccuracy, and simulation-based diagnostics. Monte Carlo simulations from the evaluated model were used to compare maximum concentration/minimum inhibitory concentration achieved with different dosing regimens. RESULTS A total of 91 concentrations from 19 adult patients with CF were collected for external evaluation. The model predicted amikacin concentrations with reasonable bias (7.2% [95% confidence interval, CI: -0.7% to 15.0%]) and inaccuracy (18.2% [95% CI: 12.0%-24.4%]). Our simulations with this model suggest that administered amikacin doses must be adjusted to creatinine clearance and also adjusted to body weight (doses from 20 to 45 mg/kg/d). According to these simulations, we developed the Montreal amikacin nomogram to optimize amikacin dosing regimens in patients with CF. CONCLUSION In conclusion, we developed the first nomogram to optimize initial amikacin dosing regimens in patients with CF based on this external evaluation of a recently published amikacin population PK model.
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Affiliation(s)
- Daniel J G Thirion
- Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada.,Department of Pharmacy, McGill University Health Center, Montréal, Québec, Canada
| | - Valérian Pasche
- Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada.,Laboratoire de suivi thérapeutique pharmacologique et pharmacocinétique, Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada
| | - Elias Matouk
- Adult Cystic Fibrosis Clinic, Montreal Chest Institute, McGill University, Montréal, Québec, Canada.,McGill University Health Center Research Institute, Montréal, Québec, Canada.,Department of Medicine, McGill University, Montréal, Québec, Canada
| | - Amélie Marsot
- Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada.,Laboratoire de suivi thérapeutique pharmacologique et pharmacocinétique, Faculté de Pharmacie, Université de Montréal, Montréal, Québec, Canada
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Dao K, Guidi M, André P, Giannoni E, Basterrechea S, Zhao W, Fuchs A, Pfister M, Buclin T, Csajka C. Optimisation of vancomycin exposure in neonates based on the best level of evidence. Pharmacol Res 2020; 154:104278. [DOI: 10.1016/j.phrs.2019.104278] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 05/16/2019] [Accepted: 05/16/2019] [Indexed: 12/16/2022]
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40
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Zhao Y, Yao BF, Kou C, Xu HY, Tang BH, Wu YE, Hao GX, Zhang XP, Zhao W. Developmental Population Pharmacokinetics and Dosing Optimization of Cefepime in Neonates and Young Infants. Front Pharmacol 2020; 11:14. [PMID: 32116695 PMCID: PMC7010644 DOI: 10.3389/fphar.2020.00014] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/07/2020] [Indexed: 11/13/2022] Open
Abstract
Objective Cefepime is used to treat severe infections in neonates. Pharmacokinetic data have only been evaluated among preterm neonates and population pharmacokinetic model lacked external validation. Hence, our aim is to obtain the population pharmacokinetic parameters of cefepime with large sampling and optimize the cefepime dosage regimen for neonatal infection based on developmental pharmacokinetics–pharmacodynamics. Methods Blood samples from neonates and young infants treated with cefepime were collected using the opportunistic sampling design. The concentration of cefepime was determined using high performance liquid chromatography with ultraviolet detection. The population pharmacokinetic model was established using NONMEM software. Results One hundred blood samples from eighty-five neonates were analyzed. The population pharmacokinetics of cefepime were described by a one-compartment model with first-order elimination. Covariate analysis indicated that serum creatinine concentration, postmenstrual age and current weight had significant impact on the pharmacokinetic parameters of cefepime. Monte Carlo simulation results showed that the current dosage regimen (30 mg/kg, q12 h) had a high risk of insufficient dose. For 70% of neonates to obtain a higher free drug concentration than the minimum inhibitory concentration during 70% of the dosing interval, 50 mg/kg q12 h was needed with a susceptibility breakpoint of 4 mg/l. For a minimum inhibitory concentration of 8 mg/l, 40 mg/kg q8 h was recommended for all neonates. Conclusion A population pharmacokinetic model of cefepime in neonates and young infants was established. According to simulation results based on the developmental pharmacokinetics–pharmacodynamics, different dosage regimens should be given depending on pathogens and the postmenstrual age.
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Affiliation(s)
- Yang Zhao
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bu-Fan Yao
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Chen Kou
- Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Hai-Yan Xu
- Department of Neonatology, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Bo-Hao Tang
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Guo-Xiang Hao
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Xin-Ping Zhang
- School of Medicine and Health Management, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Zhao
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China.,Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
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Talaie H, Mousavizadeh A, Emami H, Gachkar L, Barari B, Alijanzadeh MH. High sensitivity versus low level of vancomycin needs to be concern for another alternative anti- Staphylococcus aureus as the first- line antibiotic. ACTA BIO-MEDICA : ATENEI PARMENSIS 2019; 90:498-503. [PMID: 31910175 PMCID: PMC7233789 DOI: 10.23750/abm.v90i4.7646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 11/23/2019] [Indexed: 11/23/2022]
Abstract
Background and aim: Vancomycin has been the first-line therapy for MRSA infection disease for many years. According to standard guidelines, the therapeutic vancomycin trough concentration should be above 10 mg/L and optimally between 15-20 mg/L. The aim of this study was to evaluate vancomycin trough level concentration in patients infected with MRSA.Methods: This cross- sectional study included a sample of 170 patients admitted to the ICU of Loghman hospital. We used a standard questionnaire, then applied appropriate statistical tests. All collected data had been analyzed and interpreted by IBM SPSS Statistics 19.0.Results: Among this study population, 71.8% was male. Just 20.8% of the patients can reach the therapeutic level trough even after changing the dose. It should be noted that a significant percentage of toxicity was observed after increasing the dose. Conclusions: Even though high sensitivity against vancomycin disc has been seen in antibiogram tests, sufficient efficiency has not been distinguished, in the sense that, just a few patients by low trough level concentration, reached to therapeutic level after the dose change. Based on some sources, because of the side effects and limited safe range of vancomycin, we should consider a new approach to the alternative antibiotics. (www.actabiomedica.it)
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Affiliation(s)
- Haleh Talaie
- Toxicological Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran..
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Gu JQ, Guo YP, Jiao Z, Ding JJ, Li GF. How to Handle Delayed or Missed Doses: A Population Pharmacokinetic Perspective. Eur J Drug Metab Pharmacokinet 2019; 45:163-172. [DOI: 10.1007/s13318-019-00598-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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43
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Le J, Bradley JS. Optimizing Antibiotic Drug Therapy in Pediatrics: Current State and Future Needs. J Clin Pharmacol 2019; 58 Suppl 10:S108-S122. [PMID: 30248202 DOI: 10.1002/jcph.1128] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Accepted: 03/01/2018] [Indexed: 12/19/2022]
Abstract
The selection of the right antibiotic and right dose necessitates clinicians understand the contribution of pharmacokinetic variability stemming from age-related physiologic maturation and the pharmacodynamics to optimize drug exposure for clinical response. The complexity of selecting the right dose arises from the multiplicity of pediatric age groups, from premature neonates to adolescents. Body size and age (which relate to organ function) must be incorporated to optimize antibiotic dosing in this vulnerable population. In the effort to optimize and individualize drug dosing regimens, clinical pharmacometrics that incorporate population-based pharmacokinetic modeling, Bayesian estimation, and Monte Carlo simulations are utilized as a quantitative approach to understanding and predicting the pharmacology and clinical and microbiologic efficacy of antibiotics. In addition, opportunistic study designs and alternative blood sampling strategies can serve as practical approaches to ensure successful conduct of pediatric studies. This review article examines relevant literature on optimization of antibiotic pharmacotherapy in pediatric populations published within the last decade. Specific pediatric antibiotic data, including beta-lactam antibiotics, aminoglycosides, and vancomycin, are critically evaluated.
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Affiliation(s)
- Jennifer Le
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California at San Diego, La Jolla, CA, USA
| | - John S Bradley
- Department of Pediatrics, Division of Infectious Diseases, University of California at San Diego, La Jolla, CA, USA.,Rady Children's Hospital San Diego, San Diego, CA, USA
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Allegaert K, Flint R, Smits A. Pharmacokinetic modelling and Bayesian estimation-assisted decision tools to optimize vancomycin dosage in neonates: only one piece of the puzzle. Expert Opin Drug Metab Toxicol 2019; 15:735-749. [PMID: 31402708 DOI: 10.1080/17425255.2019.1655540] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Introduction: Vancomycin is commonly administered to neonates, while observational data on therapeutic drug monitoring (TDM, trough levels) suggest that vancomycin exposure and dosage remain substandard. Area covered: Data on vancomycin pharmacokinetics (PK) and its covariates are abundant. Consequently, modeling is an obvious tool to improve targeted exposure, with a shift from TDM trough levels to area under the curve (AUC24h) targets, as in adults. Continuous administration appeared as a practice to facilitate AUC24h target attainment, while Bayesian model-supported targeting emerged as a novel tool. However, the AUC24h/MIC (minimal inhibitory concentration) target itself should consider neonate-specific aspects (bloodstream infections, coagulase-negative staphylococci, protein binding, underexplored causes of variability, like assays, preparation and administration inaccuracies, or missing covariates). Expert opinion: To improve targeted exposure in neonates, initial vancomycin prescription should be based on 'a priori model-based individual dosing' using validated dosing regimens, followed by further tailoring by dosing optimization applying Bayesian estimation-assisted TDM. Future research should focus on the feasibility to integrate these tools (individualized dosing, Bayesian models) in clinical practice, and to perform PK/PD studies in the relevant animal models and human neonatal setting (coagulase-negative staphylococci, bloodstream infections).
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Affiliation(s)
- Karel Allegaert
- Department of Pediatrics, Division of Neonatology, Erasmus MC Sophia Children's Hospital, University Medical Center Rotterdam , Rotterdam , the Netherlands.,Department of Development and Regeneration, KU Leuven , Leuven , Belgium
| | - Robert Flint
- Department of Pediatrics, Division of Neonatology, Erasmus MC Sophia Children's Hospital, University Medical Center Rotterdam , Rotterdam , the Netherlands.,Department of Pharmacy, Erasmus University Medical Center , Rotterdam , The Netherlands
| | - Anne Smits
- Department of Development and Regeneration, KU Leuven , Leuven , Belgium.,Neonatal Intensive Care Unit, University Hospitals Leuven , Leuven , Belgium
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45
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Wang DD, Chen X, Fu M, Zheng QS, Xu H, Li ZP. Model extrapolation to a real-world dataset: evaluation of tacrolimus population pharmacokinetics and drug interaction in pediatric liver transplantation patients. Xenobiotica 2019; 50:371-379. [PMID: 31192749 DOI: 10.1080/00498254.2019.1631505] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
1. Numerous tacrolimus population pharmacokinetic (PPK) models in pediatric liver transplantation patients have been established to define an optimal dose schedule. However, the applicability of extrapolating these PPK models to our clinical center remains unknown. The goals of the present study was to evaluate model external predictiveness and establish a new model applicable to traditional therapeutic drug monitoring data.2. Published PPK models were collected from the literature and assessed using our real-world dataset including 41 pediatric liver transplantation patients via the individual prediction error method. The establishment of a new model was characterized using non-linear mixed-effects modeling.3. Nine published pediatric liver transplantation PPK models were identified, three of which could be applied to our real-world dataset. However, these models were dissatisfactory in terms of individual prediction error and hence, inadequate for extrapolation. Finally, a new model applicable to our real-world dataset was established as follows: CL/F = 22.9 × (WT/70)0.75 × (1 - WZ × 0.264) × (1 - FCZ × 0.338) × (1 + ASPI × 0.281) × (POD/41)0.0486 L/h; V/F = 906 × (WT/70) L. Where WT, WZ, FCZ, ASPI and POD were weight, Wuzhi capsule, fluconazole, aspirin and post-transplantation day, respectively. In conclusion, published models were inadequate for application to our real-world dataset. The present study produced a new model applicable to our real-world study data.
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Affiliation(s)
- Dong-Dong Wang
- Department of Pharmacy, Children's Hospital of Fudan University, Shanghai, China
| | - Xiao Chen
- Department of Pharmacy, Children's Hospital of Fudan University, Shanghai, China
| | - Meng Fu
- Department of Pharmacy, Children's Hospital of Fudan University, Shanghai, China
| | - Qing-Shan Zheng
- Center for Drug of Clinical Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hong Xu
- Department of Nephrology, Children's Hospital of Fudan University, Shanghai, China
| | - Zhi-Ping Li
- Department of Pharmacy, Children's Hospital of Fudan University, Shanghai, China
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46
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Leroux S, van den Anker JN, Smits A, Pfister M, Allegaert K. Maturational changes in vancomycin protein binding affect vancomycin dosing in neonates. Br J Clin Pharmacol 2019; 85:865-867. [PMID: 30834552 PMCID: PMC6475736 DOI: 10.1111/bcp.13899] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 02/04/2019] [Accepted: 02/07/2019] [Indexed: 11/29/2022] Open
Affiliation(s)
- Stéphanie Leroux
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital BaselUniversity of BaselBaselSwitzerland
- Neonatal Intensive Care UnitUniversity Hospital RennesRennesFrance
| | - Johannes N. van den Anker
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital BaselUniversity of BaselBaselSwitzerland
- Division of Clinical PharmacologyChildren's National Health SystemWashingtonDCUSA
| | - Anne Smits
- Neonatal Intensive Care UnitUniversity Hospitals LeuvenLeuvenBelgium
- Department of Development and RegenerationKU LeuvenLeuvenBelgium
| | - Marc Pfister
- Pediatric Pharmacology and Pharmacometrics, University Children's Hospital BaselUniversity of BaselBaselSwitzerland
- Certara LPPrincetonNJUSA
| | - Karel Allegaert
- Department of Development and RegenerationKU LeuvenLeuvenBelgium
- Department of Pediatrics, Division of Neonatology, Erasmus MC Sophia Children's HospitalUniversity Medical Center RotterdamRotterdamThe Netherlands
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Rational Use of Antibiotics in Neonates: Still in Search of Tailored Tools. Healthcare (Basel) 2019; 7:healthcare7010028. [PMID: 30781454 PMCID: PMC6473895 DOI: 10.3390/healthcare7010028] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 01/31/2019] [Accepted: 02/14/2019] [Indexed: 12/13/2022] Open
Abstract
Rational medicine use in neonates implies the prescription and administration of age-appropriate drug formulations, selecting the most efficacious and safe dose, all based on accurate information on the drug and its indications in neonates. This review illustrates that important uncertainties still exist concerning the different aspects (when, what, how) of rational antibiotic use in neonates. Decisions when to prescribe antibiotics are still not based on robust decision tools. Choices (what) on empiric antibiotic regimens should depend on the anticipated pathogens, and the available information on the efficacy and safety of these drugs. Major progress has been made on how (beta-lactam antibiotics, aminoglycosides, vancomycin, route and duration) to dose. Progress to improve rational antibiotic use necessitates further understanding of neonatal pharmacology (short- and long-term safety, pharmacokinetics, duration and route) and the use of tailored tools and smarter practices (biomarkers, screening for colonization, and advanced therapeutic drug monitoring techniques). Implementation strategies should not only facilitate access to knowledge and guidelines, but should also consider the most effective strategies (‘skills’) and psychosocial aspects involved in the prescription process: we should be aware that both the decision not to prescribe as well as the decision to prescribe antibiotics is associated with risks and benefits.
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Zhang HX, Sheng CC, Liu LS, Luo B, Fu Q, Zhao Q, Li J, Liu YF, Deng RH, Jiao Z, Wang CX. Systematic external evaluation of published population pharmacokinetic models of mycophenolate mofetil in adult kidney transplant recipients co-administered with tacrolimus. Br J Clin Pharmacol 2019; 85:746-761. [PMID: 30597603 DOI: 10.1111/bcp.13850] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 12/03/2018] [Accepted: 12/19/2018] [Indexed: 12/15/2022] Open
Abstract
AIMS Various mycophenolate mofetil (MMF) population pharmacokinetic (popPK) models have been developed to describe its PK characteristics and facilitate its optimal dosing in adult kidney transplant recipients co-administered with tacrolimus. However, the external predictive performance has been unclear. Thus, this study aimed to comprehensively evaluate the external predictability of published MMF popPK models in such populations and investigate the potential influencing factors. METHODS The external predictability of qualified popPK models was evaluated using an independent dataset. The evaluation included prediction- and simulation-based diagnostics, and Bayesian forecasting. In addition, factors influencing model predictability, especially the impact of structural models, were investigated. RESULTS Fifty full PK profiles from 45 patients were included in the evaluation dataset and 11 published popPK models were identified and evaluated. In prediction-based diagnostics, the prediction error within ±30% was less than 50% in most published models. The prediction- and variability-corrected visual predictive check and posterior predictive check showed large discrepancies between the observations and simulations in most models. Moreover, the normalized prediction distribution errors of all models did not follow a normal distribution. Bayesian forecasting demonstrated an improvement in the model predictability. Furthermore, the predictive performance of two-compartment (2CMT) models incorporating the enterohepatic circulation (EHC) process was not superior to that of conventional 2CMT models. CONCLUSIONS The published models showed large variability and unsatisfactory predictive performance, which indicated that therapeutic drug monitoring was necessary for MMF clinical application. Further studies incorporating potential covariates need to be conducted to investigate the key factors influencing model predictability of MMF.
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Affiliation(s)
- Huan-Xi Zhang
- Organ Transplant Centre, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chang-Cheng Sheng
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China.,Department of Pharmacy, Guizhou Provincial People's Hospital, Guiyang, China
| | - Long-Shan Liu
- Organ Transplant Centre, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Bi Luo
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Qian Fu
- Organ Transplant Centre, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qun Zhao
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Li
- Organ Transplant Centre, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yan-Feng Liu
- Department of urology, Shenzhen People's Hospital, Shenzhen, China
| | - Rong-Hai Deng
- Organ Transplant Centre, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zheng Jiao
- Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China
| | - Chang-Xi Wang
- Organ Transplant Centre, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory on Organ Donation and Transplant Immunology, Guangzhou, China
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Tang BH, Wu YE, Kou C, Qi YJ, Qi H, Xu HY, Leroux S, Huang X, Zhou Y, Zheng Y, Jacqz-Aigrain E, Shen AD, Zhao W. Population Pharmacokinetics and Dosing Optimization of Amoxicillin in Neonates and Young Infants. Antimicrob Agents Chemother 2019; 63:e02336-18. [PMID: 30509939 PMCID: PMC6355568 DOI: 10.1128/aac.02336-18] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Accepted: 11/27/2018] [Indexed: 12/19/2022] Open
Abstract
Amoxicillin is widely used to treat bacterial infections in neonates. However, considerable intercenter variability in dosage regimens of antibiotics exists in clinical practice. The pharmacokinetics of amoxicillin has been described in only a few preterm neonates. Thus, we aimed to evaluate the population pharmacokinetics of amoxicillin through a large sample size covering the entire age range of neonates and young infants and to establish evidence-based dosage regimens based on developmental pharmacokinetics-pharmacodynamics. This is a prospective, multicenter, pharmacokinetic study using an opportunistic sampling design. Amoxicillin plasma concentrations were determined using high-performance liquid chromatography. Population pharmacokinetic analysis was performed using NONMEM. A total of 224 pharmacokinetic samples from 187 newborns (postmenstrual age range, 28.4 to 46.3 weeks) were available for analysis. A two-compartment model with first-order elimination was used to describe population pharmacokinetics. Covariate analysis showed that current weight, postnatal age, and gestational age were significant covariates. The final model was further validated for predictive performance in an independent cohort of patients. Monte Carlo simulation demonstrated that for early-onset sepsis, the currently used dosage regimen (25 mg/kg twice daily [BID]) resulted in 99.0% of premature neonates and 87.3% of term neonates achieving the pharmacodynamic target (percent time above MIC), using a MIC breakpoint of 1 mg/liter. For late-onset sepsis, 86.1% of premature neonates treated with 25 mg/kg three times a day (TID) and 79.0% of term neonates receiving 25 mg/kg four times a day (QID) reached the pharmacodynamic target, using a MIC breakpoint of 2 mg/liter. The population pharmacokinetics of amoxicillin was assessed in neonates and young infants. A dosage regimen was established based on developmental pharmacokinetics-pharmacodynamics.
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Affiliation(s)
- Bo-Hao Tang
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Yue-E Wu
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Chen Kou
- Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, China
| | - Yu-Jie Qi
- Neonatal Intensive Care Unit, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Hui Qi
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Hai-Yan Xu
- Department of Neonatology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | | | - Xin Huang
- Department of Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
| | - Yue Zhou
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Yi Zheng
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
| | - Evelyne Jacqz-Aigrain
- Department of Pediatric Pharmacology and Pharmacogenetics, Hôpital Robert Debré, APHP, Paris, France
| | - A-Dong Shen
- Beijing Key Laboratory of Pediatric Respiratory Infection Diseases, Key Laboratory of Major Diseases in Children, Ministry of Education, National Clinical Research Center for Respiratory Diseases, National Key Discipline of Pediatrics (Capital Medical University), Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Wei Zhao
- Department of Clinical Pharmacy, School of Pharmaceutical Sciences, Shandong University, Jinan, China
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Tauzin M, Cohen R, Durrmeyer X, Dassieu G, Barre J, Caeymaex L. Continuous-Infusion Vancomycin in Neonates: Assessment of a Dosing Regimen and Therapeutic Proposal. Front Pediatr 2019; 7:188. [PMID: 31139607 PMCID: PMC6527807 DOI: 10.3389/fped.2019.00188] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 04/24/2019] [Indexed: 12/12/2022] Open
Abstract
Introduction: Vancomycin remains the reference antibiotic in neonates for care-related infections caused by ß-lactam-resistant Gram-positive bacteria. Achieving the optimal serum vancomycin level is challenging because of high inter-individual variability and the drug's narrow therapeutic window. Continuous infusion might offer pharmacokinetic and practical advantages, but we lack consensus on the dosing regimen. The aim was to determine the proportion of neonates achieving an optimal therapeutic vancomycin level at the first vancomycin concentration assay and which dosing regimen is the most suitable for neonates. Methods: All neonates receiving continuous-infusion vancomycin (loading dose 15 mg/kg and maintenance dose 30 mg/kg/d) in a neonatal intensive care unit were retrospectively analyzed. The proportion of neonates reaching the target serum vancomycin level was calculated. After reviewing the literature to identify all published articles proposing a dosing regimen for continuous-infusion vancomycin for neonates, regimens were theoretically applied to our population by using maintenance doses according to covariate(s) proposed in the original publication. Results: Between January 2013 and December 2014, 75 neonates received 91 vancomycin courses by continuous infusion. Median gestational age, birth weight, and postnatal age were 27 weeks (interquartile range 26-30.5), 815 g (685-1,240), and 15 days (9-33). At the first assay, only 28/91 (30.8%) courses resulted in vancomycin levels between 20 and 30 mg/L (target level), 23/91 (25.3%) >30 mg/L and 40/91 (43.9%) <20 mg/L. We applied six published dosing regimens to our patients. One of these dosing regimens based on corrected gestational age (CGA) and serum creatinine level (SCR) would have allowed us to prescribe lower doses to neonates with high vancomycin levels and higher doses to neonates with low levels. Conclusions: A simplified dosing regimen of continuous-infusion vancomycin did not achieve therapeutic ranges in neonates; a patient-tailored dosing regimen taking into account CGA and SCR level or an individualized pharmacokinetic model can help to anticipate the inter-individual variability in neonates and would have been more suitable.
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Affiliation(s)
- Manon Tauzin
- Neonatal Intensive Care Unit, Centre Hospitalier Intercommunal de Créteil, Créteil, France
| | - Robert Cohen
- ACTIV, Association Clinique et Thérapeutique Infantile du Val de Marne, Saint-Maur des Fossés, France.,Université Paris Est, IMRB- GRC GEMINI, Créteil, France.,Clinical Research Center, Centre Hospitalier Intercommunal de Créteil, Créteil, France.,Groupe de Pathologie Infectieuse Pédiatrique, Paris, France.,Unité Court Séjour, Petits Nourrissons, Service de Néonatologie, Centre Hospitalier Intercommunal de Créteil, Créteil, France
| | - Xavier Durrmeyer
- Neonatal Intensive Care Unit, Centre Hospitalier Intercommunal de Créteil, Créteil, France.,Université Paris Est, IMRB- GRC GEMINI, Créteil, France.,Inserm, U1153, Obstetrical, Perinatal and Pediatric Epidemiology Team, Epidemiology and Biostatistics Sorbonne, Paris Descartes University, Paris, France
| | - Gilles Dassieu
- Neonatal Intensive Care Unit, Centre Hospitalier Intercommunal de Créteil, Créteil, France
| | - Jérôme Barre
- Clinical Research Center, Centre Hospitalier Intercommunal de Créteil, Créteil, France.,Department of Pharmacology, Centre Hospitalier Intercommunal de Créteil, Créteil, France
| | - Laurence Caeymaex
- Neonatal Intensive Care Unit, Centre Hospitalier Intercommunal de Créteil, Créteil, France.,Clinical Research Center, Centre Hospitalier Intercommunal de Créteil, Créteil, France.,Department of Research in Ethics EA1610 Studies on Science and Technics, Paris Est University, Créteil, France
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