ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, 3001 Leuven, Belgium.
Institute of Bioinformatics, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040 Linz, Austria.
Cell Chem Biol. 2018 May 17;25(5):611-618.e3. doi: 10.1016/j.chembiol.2018.01.015. Epub 2018 Mar 1.
In both academia and the pharmaceutical industry, large-scale assays for drug discovery are expensive and often impractical, particularly for the increasingly important physiologically relevant model systems that require primary cells, organoids, whole organisms, or expensive or rare reagents. We hypothesized that data from a single high-throughput imaging assay can be repurposed to predict the biological activity of compounds in other assays, even those targeting alternate pathways or biological processes. Indeed, quantitative information extracted from a three-channel microscopy-based screen for glucocorticoid receptor translocation was able to predict assay-specific biological activity in two ongoing drug discovery projects. In these projects, repurposing increased hit rates by 50- to 250-fold over that of the initial project assays while increasing the chemical structure diversity of the hits. Our results suggest that data from high-content screens are a rich source of information that can be used to predict and replace customized biological assays.
在学术界和制药行业，用于药物发现的大规模检测既昂贵又不切实际，尤其是对于越来越重要的生理相关模型系统，这些系统需要原代细胞、类器官、整个生物体，或者昂贵或稀有试剂。我们假设，即使针对不同途径或生物过程，来自单一高通量成像检测的数据也可以被重新用于预测其他检测中化合物的生物活性。实际上，从基于三通道显微镜的糖皮质激素受体易位筛选中提取的定量信息能够预测两个正在进行的药物发现项目中的特定于检测的生物活性。在这些项目中，重新利用将初始项目检测的命中率提高了 50 到 250 倍，同时增加了命中的化学结构多样性。我们的结果表明，高内涵筛选的数据是丰富的信息来源，可以用于预测和替代定制的生物学检测。
