Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
Novartis Institutes for BioMedical Research Inc., 250 Massachusetts Avenue, Cambridge, MA 02139, USA.
Cell Chem Biol. 2016 Jul 21;23(7):862-874. doi: 10.1016/j.chembiol.2016.05.016. Epub 2016 Jul 14.
The use of potent and selective chemical tools with well-defined targets can help elucidate biological processes driving phenotypes in phenotypic screens. However, identification of selective compounds en masse to create targeted screening sets is non-trivial. A systematic approach is needed to prioritize probes, which prevents the repeated use of published but unselective compounds. Here we performed a meta-analysis of integrated large-scale, heterogeneous bioactivity data to create an evidence-based, quantitative metric to systematically rank tool compounds for targets. Our tool score (TS) was then tested on hundreds of compounds by assessing their activity profiles in a panel of 41 cell-based pathway assays. We demonstrate that high-TS tools show more reliably selective phenotypic profiles than lower-TS compounds. Additionally we highlight frequently tested compounds that are non-selective tools and distinguish target family polypharmacology from cross-family promiscuity. TS can therefore be used to prioritize compounds from heterogeneous databases for phenotypic screening.
使用作用强且选择性高的化学工具，并以明确的靶标为对象，有助于阐明表型筛选中表型相关的生物过程。然而，大规模地筛选出具有靶向性的化合物来构建有针对性的筛选集并非易事。需要采用系统的方法来确定探针的优先级，从而避免重复使用已发表但无选择性的化合物。在此，我们对整合的大型、异质生物活性数据进行了荟萃分析，创建了一个基于证据的、定量的指标，用于系统地对靶向工具化合物进行排序。然后，我们通过评估 41 种基于细胞的通路测定法中数百种化合物的活性谱，在数百种化合物上测试了我们的工具评分（TS）。我们证明，高 TS 的工具比低 TS 的化合物更可靠地显示出有选择性的表型特征。此外，我们还强调了经常测试的非选择性工具化合物，并区分了靶家族的多药理学和跨家族的混杂性。因此，TS 可用于从异质数据库中为表型筛选挑选化合物。
