Biostatistics and Bioinformatics Unit, IMDEA Food CEI UAM+CSIC, E28049 Madrid, Spain.
J Agric Food Chem. 2020 Aug 19;68(33):8812-8824. doi: 10.1021/acs.jafc.0c02521. Epub 2020 Aug 5.
The mechanistic understanding of the biological effects of foods involves the testing of food compounds in biochemical and biological assays. Positive results in these assays can be artifactual due to some properties of the compound: namely chemical reactivity, membrane disruption, redox cycling, etc., or through the formation of colloidal aggregates. Within the drug discovery field, a wide set of so-called "nuisance" filters have been developed to identify substructures prone to assay artifacts and/or promiscuity, e.g., the pan-assay interference compounds (PAINS) and others. In the subarea of natural products, a similar concept is the so-called invalid metabolic panaceas (IMPs). Finally, tools to identify putative aggregators have also been developed. Here, we analyzed the presence of nuisance substructures, IMPs, and aggregators in a large database of food compounds (the FooDB), which should be useful to the researchers working in the field, in order to be aware of possible artifact/promiscuity issues in their assays.
食品生物效应的机制理解包括在生化和生物测定中测试食物化合物。由于化合物的某些性质：即化学反应性、膜破坏、氧化还原循环等，或者通过胶体聚集物的形成，这些测定中的阳性结果可能是人为的。在药物发现领域，已经开发了广泛的所谓“干扰”过滤器，以识别易于产生测定干扰和/或混杂的亚结构，例如泛测定干扰化合物（PAINS）和其他化合物。在天然产物的子领域中，类似的概念是所谓的无效代谢万能药（IMPs）。最后，还开发了用于识别潜在聚集剂的工具。在这里，我们分析了大量食品化合物数据库（FooDB）中干扰亚结构、IMPs 和聚集剂的存在，这对于从事该领域研究的人员应该是有用的，以便在他们的测定中意识到可能的人为/混杂问题。
