文献类型：期刊文献

英文题名：CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity

作者：Mansouri, Kamel[1,2,3];Kleinstreuer, Nicole[4];Abdelaziz, Ahmed M.[5];Alberga, Domenico[6];Alves, Vinicius M.[7,8];Andersson, Patrik L.[9];Andrade, Carolina H.[7];Bai, Fang[10];Balabin, Ilya[11];Ballabio, Davide[12];Benfenati, Emilio[14];Bhhatarai, Barun[15];Boyer, Scott[16];Chen, Jingwen[17];Consonni, Viviana[12];Farag, Sherif[8];Fourches, Denis[18];Garcia-Sosa, Alfonso T.[19];Gramatica, Paola[15];Grisoni, Francesca[12];Grulke, Chris M.[1];Hong, Huixiao[20];Horvath, Dragos[21];Hu, Xin[22];Huang, Ruili[22];Jeliazkova, Nina[23];Li, Jiazhong[10];Li, Xuehua[17];Liu, Huanxiang[10];Manganelli, Serena[14,32];Mangiatordi, Giuseppe F.[6,33];Maran, Uko[19];Marcou, Gilles[21];Martin, Todd[24];Muratov, Eugene[8];Dac-Trung Nguyen[22];Nicolotti, Orazio[6];Nikolov, Nikolai G.[13];Norinder, Ulf[16];Papa, Ester[15];Petitjean, Michel[25];Piir, Geven[19];Pogodin, Pavel[26];Poroikov, Vladimir[26];Qiao, Xianliang[17];Richard, Ann M.[1];Roncaglioni, Alessandra[14];Ruiz, Patricia[27];Rupakheti, Chetan[24,28];Sakkiah, Sugunadevi[20];Sangion, Alessandro[15];Schramm, Karl-Werner[5];Selvaraj, Chandrabose[20];Shah, Imran[1];Sild, Sulev[19];Sun, Lixia[29];Taboureau, Olivier[25];Tang, Yun[29];Tetko, Igor, V[30,31];Todeschini, Roberto[12];Tong, Weida[20];Trisciuzzi, Daniela[6];Tropsha, Alexander[8];Van Den Driessche, George[18];Varnek, Alexandre[21];Wang, Zhongyu[17];Wedebye, Eva B.[13];Williams, Antony J.[1];Xie, Hongbin[17];Zakharov, Alexey, V[22];Zheng, Ziye[9];Judson, Richard S.[1]

机构：[1]US EPA, Natl Ctr Computat Toxicol, Off Res & Dev, Res Triangle Pk, NC USA;[2]ScitoVation LLC, Res Triangle Pk, NC USA;[3]Integrated Lab Syst Inc, Morrisville, NC USA;[4]Natl Inst Environm Hlth Sci, Interagcy Ctr Evaluat Alternat Toxicol Methods NI, Natl Toxicol Program, Res Triangle Pk, NC USA;[5]Tech Univ Munich, Dept Biowissensch Grundlagen, Wissenschaftszentrum Weihenstephan Ernahrung Land, Weihenstephaner Steig 23, D-85350 Freising Weihenstephan, Germany;[6]Univ Bari, Dept Pharm Drug Sci, Bari, Italy;[7]Univ Fed Goias, Fac Pharm, Lab Mol Modeling & Drug Design, Goiania, Go, Brazil;[8]Univ N Carolina, Lab Mol Modeling, Chapel Hill, NC 27515 USA;[9]Umea Univ, Chem Dept, Umea, Sweden;[10]Lanzhou Univ, Sch Pharm, Lanzhou, Peoples R China;[11]Lockheed Martin, IS&GS, Washington, DC USA;[12]Univ Milano Bicocca, Dept Earth & Environm Sci, Milano Chemometr & QSAR Res Grp, Milan, Italy;[13]Tech Univ Denmark, Natl Food Inst, Div Risk Assessment & Nutr, Copenhagen, Denmark;[14]IRCCS, Ist Ric Farmacol Mario Negri, Milan, Italy;[15]Univ Insubria, Dept Theoret & Appl Sci, QSAR Res Unit Environm Chem & Ecotoxicol, Varese, Italy;[16]Karolinska Inst, Swedish Toxicol Sci Res Ctr, Sodertalje, Sweden;[17]Dalian Univ Technol, Sch Environm Sci & Technol, Dalian, Peoples R China;[18]North Carolina State Univ, Bioinformat Res Ctr, Dept Chem, Raleigh, NC USA;[19]Univ Tartu, Inst Chem, Tartu, Estonia;[20]US FDA, Natl Ctr Toxicol Res, Div Bioinformat & Biostat, Jefferson, AR 72079 USA;[21]Univ Strasbourg, Lab Chemoinformat UMR7140, CNRS, Strasbourg, France;[22]Natl Ctr Advancing Translat Sci, NIH, Rockville, MD USA;[23]IdeaConsult Ltd, Sofia, Bulgaria;[24]US EPA, Natl Risk Management Res Lab, Cincinnati, OH 45268 USA;[25]Univ Paris, INSERM ERL U1133, Funct & Adaptat Biol BFA, INSERM UMR 8251,CMPLI, Paris, France;[26]Inst Biomed Chem IBMC, 10 Bldg 8,Pogodinskaya St, Moscow 119121, Russia;[27]Ctr Dis Control & Prevent, Computat Toxicol & Methods Dev Lab, Div Toxicol & Human Hlth Sci, Agcy Tox Subst & Dis Registry, Atlanta, GA USA;[28]Univ Chicago, Dept Biochem & Mol Biophys, Chicago, IL 60637 USA;[29]East China Univ Sci & Technol, Sch Pharm, Dept Pharmaceut Sci, Shanghai, Peoples R China;[30]BIGCHEM GmbH, Neuherberg, Germany;[31]German Res Ctr Environm Hlth GmbH, Helmholtz Zentrum Muenchen, Neuherberg, Germany;[32]Nestle Res, Chem Food Safety Grp, Lausanne, Switzerland;[33]CNR, Ist Cristallog, Via G Amendola 122-O, I-70126 Bari, Italy

年份：2020

卷号：128

期号：2

外文期刊名：ENVIRONMENTAL HEALTH PERSPECTIVES

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000518589800006)】；

基金：This work was supported by Oak Ridge Institute for Science and Education (ORISE) Research Participation Program at the EPA, the Lush Prize (Young Researcher) (2017), and The Intramural Research Program of National Institute of Environmental Health Sciences (NIEHS). Technical support was provided by Integrated Laboratory Systems Inc. under MFRS contract HHSN273201500010C. S.S., G.P., A.T.G.S. and U.M. from University of Tartu arc grateful for support from the Ministry of Education and Research, Republic of Estonia (grant number IUT34-14) and the European Union European Regional Development Fund through Foundation Archimedes (grant number TK143, Centre of Excellence in Molecular Cell Engineering).

语种：英文

摘要：BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accuracy assessment. This implementation was used to screen the entire EPA DSSTox database of similar to 875,000 chemicals, and their predicted AR activities have been made available on the EPA CompTox Chemicals dashboard and National Toxicology Program's Integrated Chemical Environment.