课题基金 / 基金详情

Pharmacokinetics and modeling of betamethasone therapy in threatened preterm birth

Pharmacokinetics and modeling of betamethasone therapy in threatened preterm birth
先兆早产倍他米松治疗的药代动力学和模型
批准号:
10174278
负责人:
DAVID M. HAAS
金额:
$26.53万
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-03-01 至 2022-02-28

项目摘要

项目成果

DAVID M. HAAS的其他基金

相关文献

中文摘要
翻译
项目总结 本项目的主要目标是表征阿司匹林的药代动力学分布和安全性。 加巴喷丁在剖宫产后哺乳期妇女中的应用。鉴于剖腹产是最多的 美国常见的外科手术,手术后的疼痛管理对 在康复方面,找到安全有效地控制疼痛和减少阿片类药物使用的方法是重要的。AS 许多中心现在正在为接受手术的妇女使用增强术后恢复方案 剖腹产将加巴喷丁作为其多模式疼痛控制策略的一部分,该项目 是及时和必要的。包括我们的初步数据在内的报告表明,加巴喷丁可以减少 剖宫产后妇女的阿片类药物使用情况。而LactMed认为加巴喷丁是“相容的” 关于母乳喂养,“数据很稀少,而且基于很少的案例。一个更全面的 需要进行药代动力学模型研究。此外,这项提案将通过以下方式增加目前的文献 询问参与者药物对婴儿的副作用,特别是嗜睡,并将 同时表征产后阿片类药物的使用,为女性增加更多的安全性和有效性数据 谁是母乳喂养的。我们将在很短的时间内完成这项补充建议,因为我们的 繁忙的劳动单位已经在为所有接受剖腹产的妇女使用加巴喷丁。我们会 招募计划母乳喂养的剖腹产妇女。我们的团队在 同意哺乳期妇女进行药代动力学研究,并能够采集相关母亲的血液, 母乳、婴儿血样采集成功。他们也有成功的记录 在分娩后保留招募的妇女队列。我们的分析核心实验室在药物方面经验丰富 我们在孕妇体内的药代动力学研究的测量。我们的治疗模特团队已经 在过去的几年里,我创建并报告了多种怀孕药物模型。这个团队是 完全有能力在以下时间内完成本行政补充提案中的工作 指定的时间。这项建议补充了母公司R01的工作,该公司还研究了 妊娠期药物动力学与个体化用药。近30%的 剖宫产后使用强化康复方案的机构正在使用加巴喷丁,这是 为了确保该药物被添加到更多的疼痛控制方案中,它是安全的,非常需要一项提案 要做到这一点。拟议的工作将填补文献中的一个重要空白,并可以作为一个队列 关于未来的其他童年结果,请关注。
英文摘要
PROJECT SUMMARY The primary objective of this project is to characterize the pharmacokinetic distribution and safety of gabapentin in lactating women after a cesarean delivery. Given that cesarean delivery is the most common surgical procedure in the United States and that pain management after surgery is crucial to recovery, finding ways to safely and effectively manage pain and reduce opioid use is important. As many centers are now using Enhanced Recovery After Surgery protocols for women undergoing a cesarean delivery which include gabapentin as part of their multimodal pain control strategy, this project is timely and needed. Reports, including our preliminary data, indicate that gabapentin can reduce opioid use in women after cesarean delivery. While LactMed considers gabapentin to be “compatible with breastfeeding,” the data are sparse and based on few cases. A more comprehensive pharmacokinetic modeling study is needed. In addition, this proposal will add to the current literature by asking participants about side effects of the drug on their baby, notably somnolence, and will characterize postpartum opioid use at the same time, adding more safety and efficacy data for women who breastfeed. We will accomplish this proposal in the short time for the supplement because our busy labor unit is already using gabapentin for all women undergoing a cesarean delivery. We will recruit women undergoing a cesarean delivery who plan to breastfeed. Our team is experienced in consenting lactating women for pharmacokinetic studies and able to collect linked maternal blood, breast milk, and infant blood samples successfully. They also have a track record of successfully retaining cohorts of recruited women after delivery. Our analytical core lab is experienced in drug measurement for our pharmacokinetic studies in pregnant women. Our therapeutic modeling team have been creating and reporting multiple pregnancy drug models over the last several years. The team is perfectly positioned to be able to accomplish the work in this administrative supplement proposal within the time specified. This proposal complements the work of the parent R01, which also studies pharmacokinetics and individualized pharmacotherapy in pregnancy. As nearly thirty-percent of institutions using Enhanced Recovery protocols after cesarean delivery are using gabapentin, this proposal is greatly needed to ensure that as the drug is added to more pain control regimens it is safe to do so. The proposed work will fill an important gap in the literature and can serve as a cohort to follow for other childhood outcomes in the future.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1016/j.ajog.2021.04.251
发表时间: 2021-11
期刊: American journal of obstetrics and gynecology
影响因子: 9.8
作者: [McKinzie AH, Yang Z, Teal E, Daggy JK, Tepper RS, Quinney SK, Rhoads E, Haneline LS, Haas DM]
通讯作者: Haas DM
Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomes
Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomes
Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomes
Pharmacokinetics and modeling of betamethasone therapy in threatened preterm birth