Open Innovation for Phenotypic Drug Discovery: The PD2 Assay Panel

作者
Jonathan A. Lee,Shaoyou Chu,Francis S. Willard,Karen L. Cox,Rachelle J. Sells Galvin,Robert B. Peery,Sarah E. Oliver,Jennifer Oler,Tamika D. Meredith,Steven A. Heidler,Wendy H. Gough,Saba Husain,Alan D. Palkowitz,Christopher M. Moxham
出处
期刊: [Elsevier BV]
卷期号:16 (6): 588-602 被引量:54
标识
DOI:10.1177/1087057111405379
摘要

Phenotypic lead generation strategies seek to identify compounds that modulate complex, physiologically relevant systems, an approach that is complementary to traditional, target-directed strategies. Unlike gene-specific assays, phenotypic assays interrogate multiple molecular targets and signaling pathways in a target "agnostic" fashion, which may reveal novel functions for well-studied proteins and discover new pathways of therapeutic value. Significantly, existing compound libraries may not have sufficient chemical diversity to fully leverage a phenotypic strategy. To address this issue, Eli Lilly and Company launched the Phenotypic Drug Discovery Initiative (PD(2)), a model of open innovation whereby external research groups can submit compounds for testing in a panel of Lilly phenotypic assays. This communication describes the statistical validation, operations, and initial screening results from the first PD(2) assay panel. Analysis of PD(2) submissions indicates that chemical diversity from open source collaborations complements internal sources. Screening results for the first 4691 compounds submitted to PD(2) have confirmed hit rates from 1.6% to 10%, with the majority of active compounds exhibiting acceptable potency and selectivity. Phenotypic lead generation strategies, in conjunction with novel chemical diversity obtained via open-source initiatives such as PD(2), may provide a means to identify compounds that modulate biology by novel mechanisms and expand the innovation potential of drug discovery.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
大宝发布了新的文献求助10
1秒前
1秒前
村民完成签到 ,获得积分10
1秒前
1秒前
youtianyi发布了新的文献求助10
1秒前
国服懒羊羊应助sxf采纳,获得10
1秒前
Cece发布了新的文献求助10
1秒前
Qlake发布了新的文献求助10
1秒前
2秒前
3秒前
小两完成签到,获得积分10
3秒前
泡芙完成签到,获得积分10
3秒前
CodeCraft应助周一采纳,获得10
4秒前
干净白容完成签到,获得积分10
4秒前
4秒前
MONSTER发布了新的文献求助50
5秒前
科目三应助蓝若曦采纳,获得10
5秒前
5秒前
565656发布了新的文献求助10
5秒前
5秒前
wu完成签到,获得积分10
6秒前
二十八发布了新的文献求助10
6秒前
研友_VZG7GZ应助Tan采纳,获得10
7秒前
fanny发布了新的文献求助10
7秒前
YK发布了新的文献求助10
7秒前
百忧解发布了新的文献求助10
7秒前
SciGPT应助阿郑采纳,获得10
7秒前
11发布了新的文献求助10
8秒前
gdd完成签到,获得积分10
8秒前
8秒前
8秒前
精明觅荷完成签到,获得积分10
8秒前
852应助bsknkd采纳,获得10
8秒前
8秒前
9秒前
所所应助摆烂大王采纳,获得10
9秒前
9秒前
不安的败完成签到,获得积分10
9秒前
十七发布了新的文献求助10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Principles of town planning: translating concepts to applications 1000
Management and the Arts 510
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The Effective Clinical Neurologist 3ed 500
The Great Hymn to Šamaš 500
Moody's Ratings Rising AI spending narrows the gap, but US hyperscalers retain edge over Chinese peers 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7696747
求助须知:如何正确求助?哪些是违规求助? 9256795
关于积分的说明 20005185
捐赠科研通 7271220
什么是DOI,文献DOI怎么找? 3292836
关于科研通互助平台的介绍 2448408
邀请新用户注册赠送积分活动 2298634