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药代动力学-药效学建模对改善小儿麻醉实践的贡献。

Pharmacokinetic Pharmacodynamic Modelling Contributions to Improve Paediatric Anaesthesia Practice.

作者信息

Morse James D, Cortinez Luis Ignacio, Anderson Brian J

机构信息

Department of Anaesthesiology, University of Auckland, Park Road, Auckland 1023, New Zealand.

División Anestesiología, Escuela de Medicina, Pontificia Universidad Católica de Chile, San Diego de Chile 8331150, Chile.

出版信息

J Clin Med. 2022 May 26;11(11):3009. doi: 10.3390/jcm11113009.

DOI:10.3390/jcm11113009
PMID:35683399
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9181587/
Abstract

The use of pharmacokinetic-pharmacodynamic models has improved anaesthesia practice in children through a better understanding of dose-concentration-response relationships, developmental pharmacokinetic changes, quantification of drug interactions and insights into how covariates (e.g., age, size, organ dysfunction, pharmacogenomics) impact drug prescription. Simulation using information from these models has enabled the prediction and learning of beneficial and adverse effects and decision-making around clinical scenarios. Covariate information, including the use of allometric size scaling, age and consideration of fat mass, has reduced population parameter variability. The target concentration approach has rationalised dose calculation. Paediatric pharmacokinetic-pharmacodynamic insights have led to better drug delivery systems for total intravenous anaesthesia and an expectation about drug offset when delivery is stopped. Understanding concentration-dependent adverse effects have tempered dose regimens. Quantification of drug interactions has improved the understanding of the effects of drug combinations. Repurposed drugs (e.g., antiviral drugs used for COVID-19) within the community can have important effects on drugs used in paediatric anaesthesia, and the use of simulation educates about these drug vagaries.

摘要

药代动力学-药效学模型的应用通过更好地理解剂量-浓度-反应关系、发育性药代动力学变化、药物相互作用的量化以及对协变量(如年龄、体型、器官功能障碍、药物基因组学)如何影响药物处方的深入了解,改善了儿童麻醉实践。利用这些模型的信息进行模拟,能够预测和了解有益和不良反应,并围绕临床情况进行决策。协变量信息,包括使用异速生长体型缩放、年龄以及对脂肪量的考虑,减少了群体参数的变异性。目标浓度法使剂量计算更加合理。儿科药代动力学-药效学的见解促成了用于全静脉麻醉的更好的给药系统,并对停止给药时的药物消除有了预期。对浓度依赖性不良反应的了解调整了给药方案。药物相互作用的量化提高了对药物组合效果的认识。社区内重新利用的药物(如用于治疗COVID-19的抗病毒药物)可能对儿科麻醉中使用的药物产生重要影响,而模拟的应用有助于了解这些药物的多变性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/b502721a3f68/jcm-11-03009-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/3be0f523f5c3/jcm-11-03009-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/e4d47550f046/jcm-11-03009-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/32b01fa35365/jcm-11-03009-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/4ab2cced8c53/jcm-11-03009-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/b502721a3f68/jcm-11-03009-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/3be0f523f5c3/jcm-11-03009-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/e4d47550f046/jcm-11-03009-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/32b01fa35365/jcm-11-03009-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/4ab2cced8c53/jcm-11-03009-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/db08/9181587/b502721a3f68/jcm-11-03009-g005.jpg

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