Department of Clinical Pharmacy, Institute of Pharmacy, Ernst-Moritz-Arndt-University of Greifswald, Greifswald, Germany.
Department of Internal Medicine, Division of Oncology/Hematology and Pneumology, Paracelsus Medical University, Klinikum Nuernberg, Nuernberg, Germany.
Eur J Clin Pharmacol. 2019 Sep;75(9):1237-1248. doi: 10.1007/s00228-019-02700-6. Epub 2019 Jun 1.
To develop a system to estimate the risk of herb-drug interactions that includes the available evidence from clinical and laboratory studies, transparently delineates the algorithm for the risk estimation, could be used in practice settings and allows for adaptation and update.
We systematically searched Drugbank, Transformer, Drug Information Handbook, European and German Pharmacopoeia and MEDLINE for studies on herb-drug interactions of five common medicinal plants (coneflower, ginseng, milk thistle, mistletoe and St. John's wort). A diverse set of data were independently extracted by two researchers and subsequently analysed by a newly developed algorithm. Results are displayed in the form of interaction risk categories. The development of the algorithm was guided by an expert panel consensus process.
From 882 publications retrieved by the search, 154 studies were eligible and provided 529 data sets on herbal interactions. The developed algorithm prioritises results from clinical trials over case reports over in vitro investigations and considers type of study, consistency of study results and study outcome for clinical trials as well as identification, permeability, bioavailability, and interaction potency of an identified herbal perpetrator for in vitro investigations. Risk categories were assigned to and dynamically visualised in a colour-coded matrix format.
The novel algorithm allows to transparently generate and dynamically display herb-drug interaction risks based on the available evidence from clinical and laboratory pharmacologic studies. It provides health professionals with readily available and easy updatable information about the risk of pharmacokinetic interactions between herbs and oncologic drugs.
开发一种能够评估草药-药物相互作用风险的系统，该系统应包含来自临床和实验室研究的现有证据，明确界定风险评估算法，可在实际环境中使用，并允许进行调整和更新。
我们系统地检索了 Drugbank、Transformer、Drug Information Handbook、欧洲和德国药典以及 MEDLINE 中关于五种常见药用植物（松果菊、人参、奶蓟草、槲寄生和贯叶连翘）的草药-药物相互作用的研究。两位研究人员独立提取了各种数据，然后使用新开发的算法进行分析。结果以交互风险类别形式呈现。算法的开发由专家小组共识过程指导。
从搜索中检索到的 882 篇出版物中，有 154 项研究符合条件，提供了 529 组草药相互作用数据。开发的算法优先考虑临床试验结果，其次是病例报告，然后是体外研究，并考虑研究类型、研究结果的一致性以及临床试验的研究结果，以及体外研究中确定的草药肇事者的识别、通透性、生物利用度和相互作用效力。风险类别被分配并以彩色编码矩阵格式动态可视化。
新算法允许根据来自临床和实验室药理学研究的现有证据透明地生成和动态显示草药-药物相互作用风险。它为医疗保健专业人员提供了关于草药和肿瘤药物之间药代动力学相互作用风险的现成且易于更新的信息。
