Department of Chemistry and Biochemistry, Patricia A. Sullivan Science Building , University of North Carolina at Greensboro , Greensboro , North Carolina 27402 , United States.
Anal Chem. 2019 Sep 3;91(17):11297-11305. doi: 10.1021/acs.analchem.9b02377. Epub 2019 Aug 15.
In fields ranging from environmental toxicology to drug discovery, it is critical to identify how multiple chemical compounds interact to perturb biological systems. Isolation-based approaches fail to incorporate multiconstituent interactions, such as synergy. We have developed an approach called "Simplify", which identifies mixture constituents that interact to achieve biological effects. Simplify combines biological and mass spectrometric data sets and uses an "activity index" to predict mixture interactions. Using the plant as a case study, we employed Simplify to identify four individual constituents that contribute to antimicrobial activity, three additives and one synergist. Our study is the first to enable identification of unknown synergists prior to isolating them, demonstrating the ability of the Simplify workflow to predict key contributors to the biological effect of a complex mixture. While utilized for natural products discovery in this study, this approach is expected to prove useful across multiple disciplines that rely on mixture analysis.
在环境毒理学到药物发现等领域,确定多种化学化合物如何相互作用来干扰生物系统是至关重要的。基于分离的方法无法纳入多成分相互作用,如协同作用。我们开发了一种称为“Simplify”的方法,它可以识别相互作用以产生生物学效应的混合物成分。Simplify 结合了生物学和质谱数据集,并使用“活性指数”来预测混合物相互作用。我们使用植物作为案例研究,采用 Simplify 来鉴定出四种对抗菌活性有贡献的单个成分、三种添加剂和一种增效剂。我们的研究首次能够在分离增效剂之前识别未知的增效剂,证明了 Simplify 工作流程预测复杂混合物生物学效应关键贡献者的能力。虽然在本研究中用于天然产物发现,但预计这种方法将在依赖于混合物分析的多个学科中证明是有用的。