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McCann S, Helfer VE, Balevic SJ, Hornik CD, Goldstein SL, Autmizguine J, Meyer M, Al-Uzri A, Anderson SG, Payne EH, Turdalieva S, Gonzalez D. Using Real-World Data to Externally Evaluate Population Pharmacokinetic Models of Dexmedetomidine in Children and Infants. J Clin Pharmacol 2024; 64:963-974. [PMID: 38545761 DOI: 10.1002/jcph.2434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/04/2024] [Indexed: 07/30/2024]
Abstract
Dexmedetomidine is a sedative used in both adults and off-label in children with considerable reported pharmacokinetic (PK) interindividual variability affecting drug exposure across populations. Several published models describe the population PKs of dexmedetomidine in neonates, infants, children, and adolescents, though very few have been externally evaluated. A prospective PK dataset of dexmedetomidine plasma concentrations in children and young adults aged 0.01-19.9 years was collected as part of a multicenter opportunistic PK study. A PubMed search of studies reporting dexmedetomidine PK identified five population PK models developed with data from demographically similar children that were selected for external validation. A total of 168 plasma concentrations from 102 children were compared with both population (PRED) and individualized (IPRED) predicted values from each of the five published models by quantitative and visual analyses using NONMEM (v7.3) and R (v4.1.3). Mean percent prediction errors from observed values ranged from -1% to 120% for PRED, and -24% to 60% for IPRED. The model by James et al, which was developed using similar "real-world" data, nearly met the generalizability criteria from IPRED predictions. Other models developed using clinical trial data may have been limited by inclusion/exclusion criteria and a less racially diverse population than this study's opportunistic dataset. The James model may represent a useful, but limited tool for model-informed dosing of hospitalized children.
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Affiliation(s)
- Sean McCann
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Victória E Helfer
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stephen J Balevic
- Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | - Chi D Hornik
- Department of Pediatrics, Duke University Medical Center, Durham, NC, USA
- Duke Clinical Research Institute, Durham, NC, USA
| | | | - Julie Autmizguine
- Department of Pediatrics, Center Hospitalier Universitaire Sainte-Justine, Montreal, Quebec, Canada
| | - Marisa Meyer
- Critical Care Medicine, Nemours Children's Hospital, Delaware, Wilmington, DE, USA
| | - Amira Al-Uzri
- Oregon Health and Science University, Portland, OR, USA
| | | | | | | | - Daniel Gonzalez
- Duke Clinical Research Institute, Durham, NC, USA
- Division of Clinical Pharmacology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
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Ding Y, Liu A, Wang Y, Zhao S, Huang S, Zhu H, Ma L, Han L, Shu S, Zheng L, Chen X. Genetic polymorphisms are associated with individual susceptibility to dexmedetomidine. Front Genet 2023; 14:1187415. [PMID: 37693312 PMCID: PMC10483403 DOI: 10.3389/fgene.2023.1187415] [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: 03/17/2023] [Accepted: 08/09/2023] [Indexed: 09/12/2023] Open
Abstract
Introduction: Dexmedetomidine (DXM) is widely used as an adjuvant to anesthesia or a sedative medicine, and differences in individual sensitivity to the drug exist. This study aimed to investigate the effect of genetic polymorphisms on these differences. Methods: A total of 112 patients undergoing hand surgery were recruited. DXM 0.5 μg/kg was administered within 10 min and then continuously injected (0.4 μg/kg/h). Narcotrend index, effective dose and onset time of sedation, MAP, and HR were measured. Forty-five single nucleotide polymorphisms (SNPs) were selected for genotype. Results: We observed individual differences in the sedation and hemodynamics induced by DXM. ABCG2 rs2231142, CYP2D6 rs16947, WBP2NL rs5758550, KATP rs141294036, KCNMB1 rs11739136, KCNMA1 rs16934182, ABCC9 rs11046209, ADRA2A rs1800544, and ADRB2 rs1042713 were shown to cause statistically significant (p < 0.05) influence on the individual variation of DXM on sedation and hemodynamics. Moreover, the multiple linear regression analysis indicated sex, BMI, and ADRA2A rs1800544 are statistically related to the effective dose of DXM sedation. Discussion: The evidence suggests that the nine SNPs involved in transport proteins, metabolic enzymes, and target proteins of DXM could explain the individual variability in the sedative and hemodynamic effects of DXM. Therefore, with SNP genotyping, these results could guide personalized medication and promote clinical and surgical management.
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Affiliation(s)
- Yuanyuan Ding
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Aiqing Liu
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yafeng Wang
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuai Zhao
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shiqian Huang
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongyu Zhu
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lulin Ma
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Linlin Han
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shaofang Shu
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lidong Zheng
- Department of Anesthesiology, Lu’an Hospital Affiliated to Anhui Medical University, Lu’an, China
| | - Xiangdong Chen
- Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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James NT, Breeyear JH, Caprioli R, Edwards T, Hachey B, Kannankeril PJ, Keaton JM, Marshall MD, Van Driest SL, Choi L. Population pharmacokinetic analysis of dexmedetomidine in children using real-world data from electronic health records and remnant specimens. Br J Clin Pharmacol 2022; 88:2885-2898. [PMID: 34957589 PMCID: PMC9106818 DOI: 10.1111/bcp.15194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/18/2021] [Accepted: 12/14/2021] [Indexed: 11/30/2022] Open
Abstract
AIMS Our objectives were to perform a population pharmacokinetic analysis of dexmedetomidine in children using remnant specimens and electronic health records (EHRs) and explore the impact of patient's characteristics and pharmacogenetics on dexmedetomidine clearance. METHODS Dexmedetomidine dosing and patient data were gathered from EHRs and combined with opportunistically sampled remnant specimens. Population pharmacokinetic models were developed using nonlinear mixed-effects modelling. Stage 1 developed a model without genotype variables; Stage 2 added pharmacogenetic effects. RESULTS Our final study population included 354 post-cardiac surgery patients aged 0-22 years (median 16 mo). The data were best described with a 2-compartment model with allometric scaling for weight and Hill maturation function for age. Population parameter estimates and 95% confidence intervals were 27.3 L/h (24.0-31.1 L/h) for total clearance, 161 L (139-187 L) for central compartment volume of distribution, 26.0 L/h (22.5-30.0 L/h) for intercompartmental clearance and 7903 L (5617-11 119 L) for peripheral compartment volume of distribution. The estimate for postmenstrual age when 50% of adult clearance is achieved was 42.0 weeks (41.5-42.5 weeks) and the Hill coefficient estimate was 7.04 (6.99-7.08). Genotype was not statistically or clinically significant. CONCLUSION Our study demonstrates the use of real-world EHR data and remnant specimens to perform a population pharmacokinetic analysis and investigate covariate effects in a large paediatric population. Weight and age were important predictors of clearance. We did not find evidence for pharmacogenetic effects of UGT1A4 or UGT2B10 genotype or CYP2A6 risk score.
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Affiliation(s)
- Nathan T. James
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Joseph H. Breeyear
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Richard Caprioli
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Todd Edwards
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Brian Hachey
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Prince J. Kannankeril
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN
- Center for Pediatric Precision Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jacob M. Keaton
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Matthew D. Marshall
- Department of Pharmaceutical Services, Vanderbilt University Medical Center, Nashville, TN
| | - Sara L. Van Driest
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN
- Center for Pediatric Precision Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Leena Choi
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
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