Zhu Feng, Han LianYi, Zheng ChanJuan, Xie Bin, Tammi Martti T, Yang ShengYong, Wei YuQuan, Chen YuZong
Bioinformatics and Drug Design Group, Center for Computational Science and Engineering, Department of Pharmacy, National University of Singapore, 18 Science Dr. 4, Singapore 117543.
J Pharmacol Exp Ther. 2009 Jul;330(1):304-15. doi: 10.1124/jpet.108.149955. Epub 2009 Apr 8.
Low target discovery rate has been linked to inadequate consideration of multiple factors that collectively contribute to druggability. These factors include sequence, structural, physicochemical, and systems profiles. Methods individually exploring each of these profiles for target identification have been developed, but they have not been collectively used. We evaluated the collective capability of these methods in identifying promising targets from 1019 research targets based on the multiple profiles of up to 348 successful targets. The collective method combining at least three profiles identified 50, 25, 10, and 4% of the 30, 84, 41, and 864 phase III, II, I, and nonclinical trial targets as promising, including eight to nine targets of positive phase III results. This method dropped 89% of the 19 discontinued clinical trial targets and 97% of the 65 targets failed in high-throughput screening or knockout studies. Collective consideration of multiple profiles demonstrated promising potential in identifying innovative targets.
低靶点发现率与对共同影响成药可能性的多种因素考虑不足有关。这些因素包括序列、结构、物理化学和系统特征。已经开发了分别探索这些特征以进行靶点识别的方法，但尚未将它们综合使用。我们基于多达348个成功靶点的多种特征，评估了这些方法从1019个研究靶点中识别有前景靶点的综合能力。结合至少三种特征的综合方法将30个III期、84个II期、41个I期和864个非临床试验靶点中的50%、2%、10%和4%识别为有前景的靶点，其中包括8至9个III期结果为阳性的靶点。该方法排除了19个已终止临床试验靶点中的89%以及65个在高通量筛选或基因敲除研究中失败的靶点中的97%。对多种特征的综合考虑在识别创新靶点方面显示出有前景的潜力。
