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基于建模和模拟框架优化 N,N-二甲基色胺输注率以达到目标致幻强度。

Optimized infusion rates for N,N-dimethyltryptamine to achieve a target psychedelic intensity based on a modeling and simulation framework.

机构信息

Unit for Pharmacokinetics and Drug Metabolism, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.

出版信息

CPT Pharmacometrics Syst Pharmacol. 2023 Oct;12(10):1398-1410. doi: 10.1002/psp4.13037. Epub 2023 Sep 7.

DOI:10.1002/psp4.13037
PMID:37675853
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10583250/
Abstract

N,N-dimethyltryptamine (DMT) is a psychedelic compound that is being studied as a therapeutic option in various psychiatric disorders. Due to its short half-life, continuous infusion of DMT has been proposed to extend the psychedelic experience and potential therapeutic effects. The primary aim of this work was to design an infusion protocol for DMT based on a desired level of psychedelic intensity using population pharmacokinetic/pharmacodynamic modeling. As a secondary aim, the impact of choosing a continuous variable or a bounded integer pharmacokinetic/pharmacodynamic model to inform such an infusion protocol was investigated. A previously published continuous variable model and two newly developed bounded integer models were used to assess optimal doses for achieving a target response. Simulations were performed to identify an optimal combination of a bolus dose and an infusion rate. Based on the simulations, optimal doses to achieve intensity ratings between 7 and 9 (possible range = 0-10) were a bolus dose of 16 mg DMT fumarate followed by an infusion rate of 1.4 mg/min based on the continuous variable model and 14 mg with 1.2 mg/min for the two bounded integer models. However, the proportion within target was low (<53%) for all models, indicating that individual dose adjustments would be necessary. Furthermore, some differences between the models were observed. The bounded integer models generally predicted lower proportions within a target of 7-9 with higher proportions exceeding target compared with the continuous variable model. However, results varied depending on target response with the major differences observed at the boundaries of the scale.

摘要

N,N-二甲基色胺(DMT)是一种迷幻化合物,正在各种精神疾病中作为治疗选择进行研究。由于其半衰期短,有人提出连续输注 DMT 以延长迷幻体验和潜在的治疗效果。这项工作的主要目的是设计一种基于所需迷幻强度的 DMT 输注方案,使用群体药代动力学/药效学建模。作为次要目标,研究了选择连续变量或有界整数药代动力学/药效学模型来为这种输注方案提供信息的影响。使用先前发表的连续变量模型和两个新开发的有界整数模型来评估实现目标反应的最佳剂量。进行了模拟以确定实现目标强度评分 7-9(可能范围为 0-10)的最佳剂量组合。基于模拟,最佳剂量为 16mg DMT 富马酸盐推注剂量,然后以连续变量模型以 1.4mg/min 的速度输注,以两个有界整数模型分别以 14mg 和 1.2mg/min 的速度输注。然而,所有模型的目标内比例都较低(<53%),这表明需要进行个体剂量调整。此外,还观察到模型之间存在一些差异。与连续变量模型相比,有界整数模型通常预测目标内 7-9 的比例较低,而超过目标的比例较高。然而,结果因目标反应而异,在量表的边界处观察到主要差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/7ee0d2e7ee6b/PSP4-12-1398-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/56285a44dc27/PSP4-12-1398-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/50e8c541a93d/PSP4-12-1398-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/857e4eaa3f74/PSP4-12-1398-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/c3244752b8f6/PSP4-12-1398-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/7ee0d2e7ee6b/PSP4-12-1398-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/56285a44dc27/PSP4-12-1398-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/50e8c541a93d/PSP4-12-1398-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/857e4eaa3f74/PSP4-12-1398-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/c3244752b8f6/PSP4-12-1398-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2c29/10583250/7ee0d2e7ee6b/PSP4-12-1398-g004.jpg

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本文引用的文献

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Eur J Drug Metab Pharmacokinet. 2023 May;48(3):311-327. doi: 10.1007/s13318-023-00822-y. Epub 2023 Apr 22.
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Population pharmacokinetic/pharmacodynamic modeling of the psychedelic experience induced by N,N-dimethyltryptamine - Implications for dose considerations.
人群药代动力学/药效动力学模型研究 N,N-二甲基色胺诱导的迷幻体验 - 对剂量考虑的影响。
Clin Transl Sci. 2022 Dec;15(12):2928-2937. doi: 10.1111/cts.13410. Epub 2022 Sep 27.
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