Daniela Trisciuzzi1, Domenico Alberga2, Kamel Mansouri3, Richard Judson3, Saverio Cellamare1, Marco Catto1, Angelo Carotti1, Emilio Benfenati4, Ettore Novellino5, Giuseppe Felice Mangiatordi1, Orazio Nicolotti1. 1. Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona, 4, Bari I-70126, Italy. 2. Dipartimento Interateneo di Fisica 'M. Merlin', Università degli Studi di Bari 'Aldo Moro', INFN, Via E. Orabona, 4, Bari I-70126, Italy. 3. National Center for Computational Toxicology, US Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA. 4. IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via Privata Giuseppe La Masa, 19, 20156 Milano, Italy. 5. Dipartimento di Farmacia - Università degli Studi di Napoli 'Federico II' Corso Umberto I, 40 - 80138 Napoli, Italy.
Abstract
BACKGROUND: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals. RESULTS: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals. CONCLUSION: The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.
BACKGROUND: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals. RESULTS: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals. CONCLUSION: The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.
Authors: Teresa Maria Creanza; Pietro Delre; Nicola Ancona; Giovanni Lentini; Michele Saviano; Giuseppe Felice Mangiatordi Journal: J Chem Inf Model Date: 2021-09-10 Impact factor: 6.162
Authors: Kamel Mansouri; Ahmed Abdelaziz; Aleksandra Rybacka; Alessandra Roncaglioni; Alexander Tropsha; Alexandre Varnek; Alexey Zakharov; Andrew Worth; Ann M Richard; Christopher M Grulke; Daniela Trisciuzzi; Denis Fourches; Dragos Horvath; Emilio Benfenati; Eugene Muratov; Eva Bay Wedebye; Francesca Grisoni; Giuseppe F Mangiatordi; Giuseppina M Incisivo; Huixiao Hong; Hui W Ng; Igor V Tetko; Ilya Balabin; Jayaram Kancherla; Jie Shen; Julien Burton; Marc Nicklaus; Matteo Cassotti; Nikolai G Nikolov; Orazio Nicolotti; Patrik L Andersson; Qingda Zang; Regina Politi; Richard D Beger; Roberto Todeschini; Ruili Huang; Sherif Farag; Sine A Rosenberg; Svetoslav Slavov; Xin Hu; Richard S Judson Journal: Environ Health Perspect Date: 2016-02-23 Impact factor: 9.031
Authors: Chaitanya K Jaladanki; Yang He; Li Na Zhao; Sebastian Maurer-Stroh; Lit-Hsin Loo; Haiwei Song; Hao Fan Journal: Arch Toxicol Date: 2020-09-09 Impact factor: 5.153