Kutchukian Peter S, Wassermann Anne Mai, Lindvall Mika K, Wright S Kirk, Ottl Johannes, Jacob Jaison, Scheufler Clemens, Marzinzik Andreas, Brooijmans Natasja, Glick Meir
Novartis Institutes for BioMedical Research, Cambridge, MA, USA Current address: Merck, Boston, MA, USA.
Novartis Institutes for BioMedical Research, Cambridge, MA, USA.
J Biomol Screen. 2015 Jun;20(5):588-96. doi: 10.1177/1087057114565080. Epub 2014 Dec 30.
A first step in fragment-based drug discovery (FBDD) often entails a fragment-based screen (FBS) to identify fragment "hits." However, the integration of conflicting results from orthogonal screens remains a challenge. Here we present a meta-analysis of 35 fragment-based campaigns at Novartis, which employed a generic 1400-fragment library against diverse target families using various biophysical and biochemical techniques. By statistically interrogating the multidimensional FBS data, we sought to investigate three questions: (1) What makes a fragment amenable for FBS? (2) How do hits from different fragment screening technologies and target classes compare with each other? (3) What is the best way to pair FBS assay technologies? In doing so, we identified substructures that were privileged for specific target classes, as well as fragments that were privileged for authentic activity against many targets. We also revealed some of the discrepancies between technologies. Finally, we uncovered a simple rule of thumb in screening strategy: when choosing two technologies for a campaign, pairing a biochemical and biophysical screen tends to yield the greatest coverage of authentic hits.
基于片段的药物发现（FBDD）的第一步通常需要进行基于片段的筛选（FBS）以识别片段“命中物”。然而，整合来自正交筛选的相互矛盾的结果仍然是一个挑战。在此，我们展示了对诺华公司35个基于片段的研究项目的荟萃分析，这些项目使用一个通用的1400片段库，针对不同的靶标家族采用了各种生物物理和生化技术。通过对多维FBS数据进行统计学分析，我们试图研究三个问题：（1）是什么使得一个片段适合进行FBS？（2）来自不同片段筛选技术和靶标类别的命中物相互之间如何比较？（3）将FBS分析技术配对的最佳方法是什么？通过这样做，我们确定了对特定靶标类别具有优势的子结构，以及对许多靶标具有真实活性的优势片段。我们还揭示了技术之间的一些差异。最后，我们在筛选策略中发现了一个简单的经验法则：在为一个研究项目选择两种技术时，将生化筛选和生物物理筛选配对往往会产生最大覆盖范围的真实命中物。
