Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs.
Ying Hong Li,Xiao Xu Li,Xiao Xu Li,Jia Jun Hong,Yun Xia Wang,Jian Bo Fu,Hong Yang,Chun Yan Yu,Feng Cheng Li,Jie Hu,Weiwei Xue,Yuyang Jiang,Yu Zong Chen,Feng Zhu,Feng Zhu +14 more
TLDR
A comprehensive analysis of all 89 innovative targets of first-in-class drugs approved in 2004–17 confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and revealed a simple rule for identifying the speedy human targets through clinical trials.Abstract:
Drugs produce their therapeutic effects by modulating specific targets, and there are 89 innovative targets of first-in-class drugs approved in 2004-17, each with information about drug clinical trial dated back to 1984. Analysis of the clinical trial timelines of these targets may reveal the trial-speed differentiating features for facilitating target assessment. Here we present a comprehensive analysis of all these 89 targets, following the earlier studies for prospective prediction of clinical success of the targets of clinical trial drugs. Our analysis confirmed the literature-reported common druggability characteristics for clinical success of these innovative targets, exposed trial-speed differentiating features associated to the on-target and off-target collateral effects in humans and further revealed a simple rule for identifying the speedy human targets through clinical trials (from the earliest phase I to the 1st drug approval within 8 years). This simple rule correctly identified 75.0% of the 28 speedy human targets and only unexpectedly misclassified 13.2% of 53 non-speedy human targets. Certain extraordinary circumstances were also discovered to likely contribute to the misclassification of some human targets by this simple rule. Investigation and knowledge of trial-speed differentiating features enable prioritized drug discovery and development.read more
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Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics
Yunxia Wang,Song Zhang,Fengcheng Li,Ying Zhou,Ying Zhang,Zhengwen Wang,Runyuan Zhang,Jiang Zhu,Yuxiang Ren,Ying Tan,Chu Qin,Yinghong Li,Xiaoxu Li,Yu Zong Chen,Feng Zhu +14 more
TL;DR: The Therapeutic Target Database (TTD) is constructed with expanded information about target-regulating microRNAs and transcription factors, target-interacting proteins, and patented agents and their targets, which can be conveniently retrieved and is further enriched with regulatory mechanisms or biochemical classes.
Journal ArticleDOI
Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents.
Ying Zhou,Yintao Zhang,Xichen Lian,Fengcheng Li,Chaoxin Wang,Feng Zhu,Yunqing Qiu,Yu Zong Chen,Yu Zong Chen +8 more
TL;DR: In this article, a major update of the Therapeutic Target Database, previously featured in NAR, was introduced, which includes poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and collective structureactivity and drug-likeness landscapes of enhanced drug feature.
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Consistent gene signature of schizophrenia identified by a novel feature selection strategy from comprehensive sets of transcriptomic data.
Qingxia Yang,Qingxia Yang,Bo Li,Jing Tang,Jing Tang,Xuejiao Cui,Xuejiao Cui,Yunxia Wang,Yunxia Wang,Xiaofeng Li,Xiaofeng Li,Jie Hu,Yu Zong Chen,Weiwei Xue,Yan Lou,Yunqing Qiu,Feng Zhu,Feng Zhu +17 more
TL;DR: The SCZ signature identified in this study would provide valuable clues for discovering diagnostic molecules and potential targets for SCZ and stood out among the popular methods by showing superior stability and differentiating ability.
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Crystal structure of the Jak3 kinase domain in complex with a staurosporine analog. Commentary
Titus J. Boggon,Yiqun Li,Paul W. Manley,Michael J. Eck,John J. O'Shea,Massimo Gadina,Xiaomin Che +6 more
TL;DR: In this paper, the crystal structure of the Jak3 kinase domain in complex with staurosporine analog AFN941 is presented, with both activation loop tyrosine residues phosphorylated, suggesting a direct mechanism by which the C-helix is induced by phosphorylation of the activation loop.
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NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.
Qingxia Yang,Qingxia Yang,Yunxia Wang,Ying Zhang,Fengcheng Li,Weiqi Xia,Ying Zhou,Yunqing Qiu,Honglin Li,Feng Zhu,Feng Zhu +10 more
TL;DR: NOREVA 2.0 is distinguished for its capability in identifying well-performing normalization method(s) for time-course and multi-class metabolomics, which makes it an indispensable complement to other available tools.
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