Early Identification of Adverse Reactions to Herbs

早期识别草药不良反应

• 批准号: 6562173
• 负责人: STEPHEN W BENT
• 金额: $ 12.12万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-12-16 至 2007-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6562173
• 关键词: St Johns wort; alternative medicine; angiosperms; anticoagulants; aspirin; clinical research; drug adverse effect; drug interactions; epidemiology; female; folk medicine; ginkgo biloba; heart disorder chemotherapy; hormone therapy; human data; information system analysis; medicinal plants; outcomes research; plant extracts; postmenopause; statistics /biometry

DESCRIPTION (provided by applicant): Herbal products have gained widespread popularity in the United States, with up to one-quarter of adults reporting use of an herb to treat a medical illness within the past year. Although herbs are commonly perceived as safe and "natural," numerous serious side effects have been reported, including myocardial infarction, stroke, liver failure, end-stage renal disease, and death. Unfortunately, surveillance systems to detect side effects of herbal products are extremely limited. Most serious reactions to herbs (including Chinese herb nephropathy and kava-kava related hepatitis) have been identified through case reporting, an inefficient system of monitoring that captures only a small percentage of all adverse events and only after large numbers of patients have been exposed. Current monitoring systems can be described as "passive," since action is only taken after enough case reports accumulate to raise concern of a possible dangerous adverse reaction. This career development program will provide the applicant with mentored training and research experience to examine the ability of "active" systems to detect adverse reactions to herbs at an early phase, before severe clinical outcomes occur. After completing an extensive curriculum in pharmacognosy, pharmacoepidemiology, and the analysis of complex cohort data, Dr. Bent will conduct four research projects that address two specific aims. The first specific aim is to determine if secondary data analysis is able to detect associations between specific herbs and laboratory and clinical outcomes that may represent adverse reactions. This will be addressed by three studies that examine 1) associations in a completed randomized controlled trial, 2) associations in an existing patient database, and 3) a comparison of the sensitivity and specificity of the two methods. The second specific aim is to determine the ability of a prospective cohort study to detect associations between specific herbs and laboratory/clinical outcomes that may represent adverse events. A cohort of patients at high risk for herb-drug interactions (patients on long-term anticoagulation) will be established to determine if herbal product use is associated with poor control of anticoagulation or related adverse clinical outcomes. Ultimately, this research strives to identify improved methods for the early detection of adverse events, providing patients with a safer environment to achieve the potential benefits of herbal products.

描述（由申请人提供）：草药产品在美国广泛流行，最多四分之一的成年人在过去一年内报告使用草药来治疗医疗疾病。尽管通常认为草药是安全和“自然”的，但已经报道了许多严重的副作用，包括心肌梗塞，中风，肝衰竭，终末期肾脏疾病和死亡。不幸的是，检测草药副作用的监视系统极为有限。通过病例报告，已经确定了对草药的最严重反应（包括中草药肾病和Kava-kava相关的肝炎），这是一种效率低下的监测系统，仅捕获所有不良事件的一小部分，并且仅在暴露了大量患者之后。当前的监测系统可以描述为“被动”，因为只有在足够的病例报告积累以引起对可能的危险不良反应的关注之后才采取行动。该职业发展计划将为申请人提供受过指导的培训和研究经验，以检查“活跃”系统在早期阶段检测不良反应的能力，然后再发生严重的临床结果。在完成药物认知，药物ePidemiology和复杂队列数据的分析中，Bent博士将进行四个研究项目，以解决两个具体目标。第一个具体目的是确定二级数据分析是否能够检测特定草药与实验室和临床结果之间的关联，这可能代表不良反应。这将通过三项研究来解决1）在完整的随机对照试验中的关联，2）现有患者数据库中的关联，以及3）比较两种方法的敏感性和特异性。第二个具体目的是确定前瞻性队列研究检测可能代表不良事件的特定草药与实验室/临床结果之间关联的能力。将建立一系列患有草药 - 药物相互作用（长期抗凝患者）的患者队列，以确定草药的使用是否与抗凝或相关的不良临床结果的控制不佳有关。最终，这项研究致力于确定改进的方法来早期发现不良事件，从而为患者提供更安全的环境，以实现草药产品的潜在益处。