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姜黄素、橙黄G和白藜芦醇对β-淀粉样肽抑制动力学的建模

Modeling the Inhibition Kinetics of Curcumin, Orange G, and Resveratrol with Amyloid-β Peptide.

作者信息

Madhuranthakam Chandra Mouli R, Shakeri Arash, Rao Praveen P N

机构信息

Chemical Engineering Department, Abu Dhabi University, P.O. Box 59911, Abu Dhabi, UAE.

School of Pharmacy, Health Sciences Campus, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada.

出版信息

ACS Omega. 2021 Mar 19;6(12):8680-8686. doi: 10.1021/acsomega.1c00610. eCollection 2021 Mar 30.

Abstract

The β-amyloid (Aβ) protein aggregation into toxic forms is one of the major factors in the Alzheimer's disease (AD) pathology. Screening compound libraries as inhibitors of Aβ-aggregation is a common strategy to discover novel molecules as potential therapeutics in AD. In this regard, thioflavin T (ThT)-based fluorescence spectroscopy is a widely used in vitro method to identify inhibitors of Aβ aggregation. However, conventional data processing of the ThT assay experimental results generally provides only qualitative output and lacks inhibitor-specific quantitative data, which can offer a number of advantages such as identification of critical inhibitor-specific parameters required to design superior inhibitors and reduce the need to conduct extensive in vitro kinetic studies. Therefore, we carried out mathematical modeling based on logistic equation and power law (PL) model to correlate the experimental results obtained from the ThT-based Aβ40 aggregation kinetics for small-molecule inhibitors curcumin, orange G, and resveratrol and quantitatively fit the data in a logistic equation. This approach provides important inhibitor-specific parameters such as lag time λ, inflection point τ, maximum slope , and apparent rate constant , which are particularly useful in comparing the effectiveness of potential Aβ40 aggregation inhibitors and can be applied in drug discovery campaigns to compare and contrast Aβ40 inhibition data for large compound libraries.

摘要

β-淀粉样蛋白(Aβ)聚集成毒性形式是阿尔茨海默病(AD)病理的主要因素之一。筛选化合物库作为Aβ聚集的抑制剂是发现新型分子作为AD潜在治疗药物的常用策略。在这方面,基于硫黄素T(ThT)的荧光光谱法是一种广泛用于体外鉴定Aβ聚集抑制剂的方法。然而,ThT检测实验结果的传统数据处理通常只提供定性输出,缺乏抑制剂特异性的定量数据,而这些数据可以提供许多优势,例如识别设计优质抑制剂所需的关键抑制剂特异性参数,并减少进行广泛体外动力学研究的必要性。因此,我们基于逻辑方程和幂律(PL)模型进行了数学建模,以关联从小分子抑制剂姜黄素、橙黄G和白藜芦醇的基于ThT的Aβ40聚集动力学获得的实验结果,并将数据定量拟合到逻辑方程中。这种方法提供了重要的抑制剂特异性参数,如滞后时间λ、拐点τ、最大斜率和表观速率常数,这些参数在比较潜在Aβ40聚集抑制剂的有效性时特别有用,并且可应用于药物发现活动,以比较和对比大型化合物库的Aβ40抑制数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/781e/8015079/978d199111a2/ao1c00610_0002.jpg

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