Uitdehaag Joost C M, Sünnen Cecile M, van Doornmalen Antoon M, de Rouw Nikki, Oubrie Arthur, Azevedo Rita, Ziebell Michael, Nickbarg Elliott, Karstens Willem-Jan, Ruygrok Simone
Merck Research Labs, Oss, the Netherlands.
J Biomol Screen. 2011 Oct;16(9):1007-17. doi: 10.1177/1087057111418113. Epub 2011 Aug 26.
Over the past years, improvements in high-throughput screening (HTS) technology and compound libraries have resulted in a dramatic increase in the amounts of good-quality screening hits, and there is a growing need for follow-on hit profiling assays with medium throughput to further triage hits. Here the authors present such assays for the colony-stimulating factor 1 receptor (CSF1R, Fms), including tests for cellular activity and a homogeneous assay to measure affinity for inactive CSF1R. They also present a high-throughput assay to measure target residence time, which is based on competitive binding kinetics. To better fit k(off) rates, they present a modified mathematical model for competitive kinetics. In all assays, they profiled eight reference inhibitors (imatinib, sorafenib, sunitinib, tandutinib, dasatinib, GW2580, Ki20227, and J&J's pyrido[2,3-d]pyrimidin-5-one). Using the known biochemical selectivities of these inhibitors, which can be quantified using metrics such as the selectivity entropy, the authors have determined which assay readout best predicts hit selectivity. Their profiling shows surprisingly that imatinib has a preference for the active form of CSF1R and that Ki20227 has an unusually slow target dissociation rate. This confirms that follow-on hit profiling is essential to ensure that the best hits are selected for lead optimization.
在过去几年中,高通量筛选(HTS)技术和化合物库的改进使得高质量筛选命中物的数量大幅增加,因此越来越需要具有中等通量的后续命中物分析方法来进一步筛选命中物。本文作者介绍了针对集落刺激因子1受体(CSF1R,Fms)的此类分析方法,包括细胞活性测试和一种用于测量对无活性CSF1R亲和力的均相分析方法。他们还介绍了一种基于竞争结合动力学来测量靶点驻留时间的高通量分析方法。为了更好地拟合解离速率常数(k(off)),他们提出了一种用于竞争动力学的改进数学模型。在所有分析中,他们对8种参考抑制剂(伊马替尼、索拉非尼、舒尼替尼、坦度替尼、达沙替尼、GW2580、Ki20227和强生公司的吡啶并[2,3-d]嘧啶-5-酮)进行了分析。利用这些抑制剂已知的生化选择性(可以使用选择性熵等指标进行量化),作者确定了哪种分析读数最能预测命中物的选择性。他们的分析令人惊讶地表明,伊马替尼对CSF1R的活性形式具有偏好,而Ki20227具有异常缓慢的靶点解离速率。这证实了后续命中物分析对于确保选择最佳命中物进行先导优化至关重要。