Assessment of the Health Effects of Chemicals in Humans: II. Construction of an Adverse Effects Database for QSAR Modeling

数量结构-活动关系 数据库 不利影响 药物反应 药品 药物警戒 药物不良反应 毒理 医学 药理学 计算机科学 数据挖掘 机器学习 生物
作者
Edwin J. Matthews,Naomi L. Kruhlak,James L. Weaver,R. Benz,Joseph F. Contrera
出处
期刊:Current Drug Discovery Technologies [Bentham Science Publishers]
卷期号:1 (4): 243-254 被引量:35
标识
DOI:10.2174/1570163043334794
摘要

The FDAs Spontaneous Reporting System (SRS) database contains over 1.5 million adverse drug reaction (ADR) reports for 8620 drugs / biologics that are listed for 1191 Coding Symbols for Thesaurus of Adverse Reaction (COSTAR) terms of adverse effects. We have linked the trade names of the drugs to 1861 generic names and retrieved molecular structures for each chemical to obtain a set of 1515 organic chemicals that are suitable for modeling with commercially available QSAR software packages. ADR report data for 631 of these compounds were extracted and pooled for the first five years that each drug was marketed. Patient exposure was estimated during this period using pharmaceutical shipping units obtained from IMS Health. Significant drug effects were identified using a Reporting Index (RI), where RI = ( ADR reports / shipping units) 1,000,000. MCASE / MC4PC software was used to identify the optimal conditions for defining a significant adverse effect finding. Results suggest that a significant effect in our database is characterized by ≥4 ADR reports and ≥20,000 shipping units during five years of marketing, and an RI ≥4.0. Furthermore, for a test chemical to be evaluated as active it must contain a statistically significant molecular structural alert, called a decision alert, in two or more toxicologically related endpoints. We also report the use of a composite module, which pools observations from two or more toxicologically related COSTAR term endpoints to provide signal enhancement for detecting adverse effects. Keywords: adr, adverse effect, fda, computational toxicology, predictive modeling, qsar, srs database, human clinical data
最长约 10秒,即可获得该文献文件

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
神勇的天问完成签到 ,获得积分10
刚刚
索大学术完成签到,获得积分10
3秒前
wang发布了新的文献求助10
5秒前
5秒前
5秒前
6秒前
ok123完成签到 ,获得积分0
8秒前
好的好的完成签到 ,获得积分10
9秒前
今后应助爱笑的保温杯采纳,获得10
11秒前
科研大师发布了新的文献求助10
12秒前
ao完成签到,获得积分10
12秒前
动听雨梅完成签到 ,获得积分10
16秒前
科研通AI6.4应助一二采纳,获得10
16秒前
slgzhangtao完成签到,获得积分10
17秒前
feiyue126发布了新的文献求助10
19秒前
洁净灭男完成签到,获得积分10
19秒前
小马甲应助开朗的以柳采纳,获得10
21秒前
xudaniel完成签到,获得积分10
21秒前
懵懂的弱完成签到,获得积分10
22秒前
26秒前
jin完成签到,获得积分10
28秒前
wsz131完成签到 ,获得积分10
28秒前
777完成签到 ,获得积分10
28秒前
29秒前
无语的怜梦完成签到,获得积分10
29秒前
潇洒的天与完成签到,获得积分10
30秒前
66发布了新的文献求助10
31秒前
葡萄完成签到,获得积分10
33秒前
cmuzf发布了新的文献求助10
34秒前
jake完成签到,获得积分10
34秒前
南歌子完成签到 ,获得积分10
36秒前
zz完成签到,获得积分10
37秒前
wang发布了新的文献求助10
39秒前
什么名字235完成签到,获得积分10
40秒前
41秒前
月军完成签到,获得积分10
42秒前
可靠铸海应助66采纳,获得10
42秒前
Tetryl完成签到,获得积分10
42秒前
找我办事要带李同学完成签到 ,获得积分10
43秒前
happyboy2008完成签到 ,获得积分10
46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
Health Psychology 800
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
Electric machines: theory, operating applications, and controls 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
When Is Two-Stage Sample Robust Optimization Asymptotically Optimal? 500
Discerning Saints: Moralization of Intrinsic Motivation and Selective Prosociality at Work 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 7592714
求助须知:如何正确求助?哪些是违规求助? 9169990
关于积分的说明 19626697
捐赠科研通 7170597
什么是DOI,文献DOI怎么找? 3267520
关于科研通互助平台的介绍 2432387
邀请新用户注册赠送积分活动 2260021