DEVELOPMENT OF AN IN-HOUSE PROTON SPIN NETWORK DATABASE TO CHARACTERIZE THE PHARMACOPHORES OF CENTELLA ASIATICA FOR STANDARDIZATION AND QUALITY CONTROL

开发内部质子自旋网络数据库来表征积雪草药效团，以实现标准化和质量控制

• 批准号: 10415273
• 负责人: Liva Harinantenaina Rakotondraibe
• 金额: $ 7.88万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10415273
• 关键词: Acids; Adult; Aging; Alzheimer' s Disease; Amino Acids; Antioxidants; Apiaceae; Biological; Biological Markers; Botanical dietary supplements; Botanicals; Carbon; Cell Nucleus; Characteristics; Chemicals; Cognition Disorders; Communities; Complement; Complex Mixtures; Consumption; Coupled; Coupling; Data; Databases; Dimensions; Drug Kinetics; Engineering; Equipment; Face; Family; Feasibility Studies; Fingerprint; Funding; Goals; Gotu kola; Individual; Isomerism; Liquid Chromatography; Machine Learning; Manuals; Mass Spectrum Analysis; Metabolic; Methodology; Methods; Minor; Modeling; NMR Spectroscopy; National Center for Complementary and Integrative Health; Natural Products; Neurologic; Nuclear Magnetic Resonance; Ohio; Oxidative Stress; Physiologic pulse; Plants; Population; Procedures; Protons; Quality Control; Reference Standards; Reporting; Research; Safety; Sampling; Scanning; Shapes; Signal Transduction; Spectrum Analysis; Standardization; Structure; TOCSY; Techniques; Technology; United States; United States National Institutes of Health; Universities; Update; Water; analytical method; attenuation; base; cognitive benefits; cryogenics; dietary constituent; dietary supplements; experimental study; ionization technique; metabolomics; milligram; mitochondrial dysfunction; novel therapeutics; pharmacophore; preclinical study; resilience; response; three dimensional structure

PROJECT SUMMARY The benefits of dietary supplements (DS) including botanicals are well documented as they are consumed by about half of the adult population of the United States. For safety and batch-to batch consistency purposes, the identification and characterization of chemical constituents of DS of plant origin have to be performed although the complexity of the mixture and the possible activity contributions of many of the constituting components make the task challenging. Liquid chromatography coupled with mass spectrometry (LC-MS)-based methods are the most reliable so far in the identification and characterization of already known bioactive compounds in a botanical DS. More reliable and updated mass spectrometric databases and methodologies as well as new complementary methods are, however, still needed for rapid metabolite identification, activity consistency, and batch-to-batch quality controls. NMR spectroscopy has long been used with mass spectrometry in metabolomics profiling of natural products and to determine unambiguously structures of organic compounds. Most profiling in many NMR-based metabolomics studies, however, focus only on identifiable (or already known) major compounds, leaving the identification of overlapping signals arising from complex mixture of compounds as major challenges. Furthermore, pure authentic standard compounds display proton and carbon signals with chemical shifts slightly different from those of the same compounds in a mixture and these chemical shift differences make NMR-based metabolomics difficult if not inaccurate. Nevertheless, the shape and the splitting of the signals due to coupled protons in spin networks remain the same, despite mixture-enhanced resonance shifts. These unchanged spin network characteristics can be identified by selective one-dimensional TOCSY (S1DT) experiments, which use pulse sequences that show signal sensitivity increase especially when high-field strength NMR and high number of scans are used. Moreover, many isomers that are undiscernible in most MS analyses can be differentiated using their S1DT fingerprint generated characteristic spin networks. Our overall goal is to couple the identified S1DT fingerprint information with the existing mass spectrometric data information on Centella asiatica (gotu kola) at currently available at BENFRA Botanical Dietary Supplements Research Center (NIH/NCCIH U19 AT010829) and those of compounds isolated during the present study to complement LC-MS for batch-to batch quality control and activity consistencies. Aim 1 on comprehensive untargeted isolation will afford reference standard compounds and a robust LC-MS database that will be used for identification and standardization studies at the center and other research communities working on C. asiatica. In addition, a new application of S1DT that will help to accurately identify chemical constituents and their potential pharmacophores by comparing 1H spin network fingerprints identified in the isolated compounds with those derived from the 1H NMR spectra of the extract will be developed in Aim 2.

项目概要 包括植物药在内的膳食补充剂 (DS) 的益处已得到充分证明，因为它们的摄入量是 约占美国成年人口的一半。出于安全和批次间一致性的目的， 尽管必须对植物来源的 DS 的化学成分进行鉴定和表征 混合物的复杂性和许多组成成分可能的活性贡献使得 任务具有挑战性。基于液相色谱与质谱 (LC-MS) 的方法是 迄今为止在植物中已知生物活性化合物的鉴定和表征方面最可靠 DS。更可靠和更新的质谱数据库和方法以及新的 然而，仍然需要补充方法来快速鉴定代谢物、活性 一致性和批次间的质量控制。核磁共振波谱长期以来一直用于质量分析 天然产物代谢组学分析中的光谱分析并确定有机物的明确结构 化合物。然而，许多基于 NMR 的代谢组学研究中的大多数分析仅关注可识别的（或 已知）主要化合物，从而识别复杂混合物产生的重叠信号 化合物作为主要挑战。此外，纯正的标准化合物显示质子和碳 化学位移信号与混合物中相同化合物的信号略有不同，并且这些化学物质 位移差异使得基于 NMR 的代谢组学即使不准确，也变得困难。尽管如此，形状和 尽管混合增强，但自旋网络中耦合质子引起的信号分裂保持不变 共振位移。这些未改变的自旋网络特征可以通过选择性一维来识别 TOCSY (S1DT) 实验，使用脉冲序列显示信号灵敏度增加，尤其是在 使用高场强核磁共振和高扫描次数。此外，许多异构体在 大多数 MS 分析可以使用其 S1DT 指纹生成的特征自旋网络来区分。我们的 总体目标是将识别的 S1DT 指纹信息与现有的质谱数据结合起来 目前可在 BENFRA 植物膳食补充剂获取有关积雪草 (gotu kola) 的信息 研究中心 (NIH/NCCIH U19 AT010829) 以及在本研究中分离出的化合物 与 LC-MS 相辅相成，实现批次间的质量控制和活性一致性。目标1全面 非目标分离将提供参考标准化合物和强大的 LC-MS 数据库， 用于中心和其他研究团体的识别和标准化研究 研究 C. asiatica。此外，S1DT的新应用将有助于准确识别 通过比较 1H 自旋网络指纹识别化学成分及其潜在药效团 在分离的化合物中鉴定出的化合物与从提取物的 1H NMR 谱中衍生的化合物将是 在目标 2 中开发。