Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland.
inVentiv Health, Burlington, Ontario, Canada.
Pharmacotherapy. 2017 Aug;37(8):916-926. doi: 10.1002/phar.1972. Epub 2017 Jul 26.
Acetaminophen (APAP) overdose is the leading cause of acute liver injury in the United States. Patients with elevated plasma acetaminophen concentrations (PACs) require hepatoprotective treatment with N-acetylcysteine (NAC). These patients have been primarily risk-stratified using the Rumack-Matthew nomogram. Previous studies of acute APAP overdoses found that the nomogram failed to accurately predict the need for the antidote. The objectives of this study were to develop a population pharmacokinetic (PK) model for APAP following acute overdose and evaluate the utility of population PK model-based Bayesian forecasting in NAC administration decisions.
DESIGN, PATIENTS AND MEASUREMENTS: Limited APAP concentrations from a retrospective cohort of acute overdosed subjects from the Maryland Poison Center were used to develop the population PK model and to investigate the effect of type of APAP products and other prognostic factors. The externally validated population PK model was used a prior for Bayesian forecasting to predict the individual PK profile when one or two observed PACs were available. The utility of Bayesian forecasted APAP concentration-time profiles inferred from one (first) or two (first and second) PAC observations were also tested in their ability to predict the observed NAC decisions.
A one-compartment model with first-order absorption and elimination adequately described the data with single activated charcoal and APAP products as significant covariates on absorption and bioavailability. The Bayesian forecasted individual concentration-time profiles had acceptable bias (6.2% and 9.8%) and accuracy (40.5% and 41.9%) when either one or two PACs were considered, respectively. The sensitivity and negative predictive value of the Bayesian forecasted NAC decisions using one PAC were 84% and 92.6%, respectively.
The population PK analysis provided a platform for acceptably predicting an individual's concentration-time profile following acute APAP overdose with at least one PAC, and the individual's covariate profile, and can potentially be used for making early NAC administration decisions.
对乙酰氨基酚（APAP）过量是导致美国急性肝损伤的主要原因。血浆中对乙酰氨基酚浓度升高（PACs）的患者需要用 N-乙酰半胱氨酸（NAC）进行肝保护治疗。这些患者主要采用 Rumack-Matthew 列线图进行风险分层。先前对急性 APAP 过量的研究发现，列线图未能准确预测解毒剂的需求。本研究的目的是建立急性 APAP 过量后的群体药代动力学（PK）模型，并评估基于群体 PK 模型的贝叶斯预测在 NAC 给药决策中的效用。
设计、患者和测量：马里兰州中毒中心回顾性队列中急性过量服用者的有限 APAP 浓度用于建立群体 PK 模型，并研究 APAP 产品类型和其他预后因素的影响。外部验证的群体 PK 模型用于贝叶斯预测，以在只有一个或两个观察到的 PAC 时预测个体 PK 曲线。还测试了从一个（第一个）或两个（第一个和第二个）PAC 观察推断出的贝叶斯预测的 APAP 浓度-时间曲线在预测观察到的 NAC 决策方面的能力。
一个具有一级吸收和消除的单室模型可以很好地描述数据，单一活性炭和 APAP 产品是吸收和生物利用度的显著协变量。当考虑一个或两个 PAC 时，贝叶斯预测的个体浓度-时间曲线的偏差分别为 6.2%和 9.8%，准确度分别为 40.5%和 41.9%。使用一个 PAC 进行贝叶斯预测的 NAC 决策的灵敏度和阴性预测值分别为 84%和 92.6%。
该群体 PK 分析为至少一个 PAC 后预测个体的浓度-时间曲线提供了一个平台，同时也预测了个体的协变量特征，并且可以用于早期进行 NAC 给药决策。
