Kutilek Victoria D, Andrews Christine L, Richards Matthew P, Xu Zangwei, Sun Tianxiao, Chen Yiping, Hashke Andrew, Smotrov Nadya, Fernandez Rafael, Nickbarg Elliott B, Chamberlin Chad, Sauvagnat Berengere, Curran Patrick J, Boinay Ryan, Saradjian Peter, Allen Samantha J, Byrne Noel, Elsen Nathaniel L, Ford Rachael E, Hall Dawn L, Kornienko Maria, Rickert Keith W, Sharma Sujata, Shipman Jennifer M, Lumb Kevin J, Coleman Kevin, Dandliker Peter J, Kariv Ilona, Beutel Bruce
Department of Pharmacology, Screening and Protein Sciences, Merck & Co, Kenilworth, NJ, USA
Department of Pharmacology, Screening and Protein Sciences, Merck & Co, Kenilworth, NJ, USA.
J Biomol Screen. 2016 Jul;21(6):608-19. doi: 10.1177/1087057116637353. Epub 2016 Mar 11.
The primary objective of early drug discovery is to associate druggable target space with a desired phenotype. The inability to efficiently associate these often leads to failure early in the drug discovery process. In this proof-of-concept study, the most tractable starting points for drug discovery within the NF-κB pathway model system were identified by integrating affinity selection-mass spectrometry (AS-MS) with functional cellular assays. The AS-MS platform Automated Ligand Identification System (ALIS) was used to rapidly screen 15 NF-κB proteins in parallel against large-compound libraries. ALIS identified 382 target-selective compounds binding to 14 of the 15 proteins. Without any chemical optimization, 22 of the 382 target-selective compounds exhibited a cellular phenotype consistent with the respective target associated in ALIS. Further studies on structurally related compounds distinguished two chemical series that exhibited a preliminary structure-activity relationship and confirmed target-driven cellular activity to NF-κB1/p105 and TRAF5, respectively. These two series represent new drug discovery opportunities for chemical optimization. The results described herein demonstrate the power of combining ALIS with cell functional assays in a high-throughput, target-based approach to determine the most tractable drug discovery opportunities within a pathway.
早期药物发现的主要目标是将可成药靶点空间与期望的表型联系起来。无法有效地建立这种联系常常导致在药物发现过程的早期就遭遇失败。在这项概念验证研究中,通过将亲和选择质谱法(AS-MS)与细胞功能分析相结合,在核因子κB(NF-κB)信号通路模型系统中确定了药物发现最易处理的起始点。利用AS-MS平台自动配体识别系统(ALIS)对15种NF-κB蛋白针对大型化合物文库进行了快速平行筛选。ALIS鉴定出382种与15种蛋白中的14种结合的靶点选择性化合物。在未进行任何化学优化的情况下,382种靶点选择性化合物中的22种表现出与ALIS中各自相关靶点一致的细胞表型。对结构相关化合物的进一步研究区分出两个化学系列,它们分别呈现出初步的构效关系,并证实了对NF-κB1/p105和肿瘤坏死因子受体相关因子5(TRAF5)的靶点驱动细胞活性。这两个系列代表了进行化学优化的新的药物发现机会。本文所述结果证明了将ALIS与细胞功能分析相结合,以高通量、基于靶点的方法来确定信号通路内最易处理的药物发现机会的强大作用。