1
|
Khan MKH, Guo W, Liu J, Dong F, Li Z, Patterson TA, Hong H. Machine learning and deep learning for brain tumor MRI image segmentation. Exp Biol Med (Maywood) 2023; 248:1974-1992. [PMID: 38102956 PMCID: PMC10798183 DOI: 10.1177/15353702231214259] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2023] Open
Abstract
Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.
Collapse
Affiliation(s)
- Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Wenjing Guo
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| |
Collapse
|
2
|
Guo W, Liu J, Dong F, Song M, Li Z, Khan MKH, Patterson TA, Hong H. Review of machine learning and deep learning models for toxicity prediction. Exp Biol Med (Maywood) 2023; 248:1952-1973. [PMID: 38057999 PMCID: PMC10798180 DOI: 10.1177/15353702231209421] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2023] Open
Abstract
The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.
Collapse
Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Meng Song
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| |
Collapse
|
3
|
Liu J, Xu L, Guo W, Li Z, Khan MKH, Ge W, Patterson TA, Hong H. Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment. Exp Biol Med (Maywood) 2023; 248:1927-1936. [PMID: 37997891 PMCID: PMC10798185 DOI: 10.1177/15353702231209413] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 09/26/2023] [Indexed: 11/25/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.
Collapse
Affiliation(s)
| | | | - Wenjing Guo
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Zoe Li
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Md Kamrul Hasan Khan
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Weigong Ge
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food & Drug Administration, Jefferson, AR 72079, USA
| |
Collapse
|
4
|
Liu J, Guo W, Dong F, Aungst J, Fitzpatrick S, Patterson TA, Hong H. Machine learning models for rat multigeneration reproductive toxicity prediction. Front Pharmacol 2022; 13:1018226. [PMID: 36238576 PMCID: PMC9552001 DOI: 10.3389/fphar.2022.1018226] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 09/09/2022] [Indexed: 11/13/2022] Open
Abstract
Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%–61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals.
Collapse
Affiliation(s)
- Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Jason Aungst
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
| | - Suzanne Fitzpatrick
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
| | - Tucker A. Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
- *Correspondence: Huixiao Hong,
| |
Collapse
|
5
|
Guo W, Liu J, Dong F, Chen R, Das J, Ge W, Xu X, Hong H. Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:3376. [PMID: 36234502 PMCID: PMC9565823 DOI: 10.3390/nano12193376] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/24/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
Metal-organic frameworks (MOFs), a class of porous nanomaterials, have been widely used in gas adsorption-based applications due to their high porosities and chemical tunability. To facilitate the discovery of high-performance MOFs for different applications, a variety of machine learning models have been developed to predict the gas adsorption capacities of MOFs. Most of the predictive models are developed using traditional machine learning algorithms. However, the continuously increasing sizes of MOF datasets and the complicated relationships between MOFs and their gas adsorption capacities make deep learning a suitable candidate to handle such big data with increased computational power and accuracy. In this study, we developed models for predicting gas adsorption capacities of MOFs using two deep learning algorithms, multilayer perceptron (MLP) and long short-term memory (LSTM) networks, with a hypothetical set of about 130,000 structures of MOFs with methane and carbon dioxide adsorption data at different pressures. The models were evaluated using 10 iterations of 10-fold cross validations and 100 holdout validations. The MLP and LSTM models performed similarly with high prediction accuracy. The models for predicting gas adsorption at a higher pressure outperformed the models for predicting gas adsorption at a lower pressure. The deep learning models are more accurate than the random forest models reported in the literature, especially for predicting gas adsorption capacities at low pressures. Our results demonstrated that deep learning algorithms have a great potential to generate models that can accurately predict the gas adsorption capacities of MOFs.
Collapse
Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Fan Dong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Ru Chen
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Jayanti Das
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Weigong Ge
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Xiaoming Xu
- Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| |
Collapse
|
6
|
Ji Z, Guo W, Wood EL, Liu J, Sakkiah S, Xu X, Patterson TA, Hong H. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials. Chem Res Toxicol 2022; 35:125-139. [PMID: 35029374 DOI: 10.1021/acs.chemrestox.1c00310] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The wide application of nanomaterials in consumer and medical products has raised concerns about their potential adverse effects on human health. Thus, more and more biological assessments regarding the toxicity of nanomaterials have been performed. However, the different ways the evaluations were performed, such as the utilized assays, cell lines, and the differences of the produced nanoparticles, make it difficult for scientists to analyze and effectively compare toxicities of nanomaterials. Fortunately, machine learning has emerged as a powerful tool for the prediction of nanotoxicity based on the available data. Among different types of toxicity assessments, nanomaterial cytotoxicity was the focus here because of the high sensitivity of cytotoxicity assessment to different treatments without the need for complicated and time-consuming procedures. In this review, we summarized recent studies that focused on the development of machine learning models for prediction of cytotoxicity of nanomaterials. The goal was to provide insight into predicting potential nanomaterial toxicity and promoting the development of safe nanomaterials.
Collapse
Affiliation(s)
- Zuowei Ji
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Wenjing Guo
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Erin L Wood
- Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jie Liu
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Xiaoming Xu
- Office of Testing and Research, Office of Pharmaceutical Quality, Center for Drug Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Tucker A Patterson
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- National Center for Toxicological Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| |
Collapse
|
7
|
Liu J, Guo W, Sakkiah S, Ji Z, Yavas G, Zou W, Chen M, Tong W, Patterson TA, Hong H. Machine Learning Models for Predicting Liver Toxicity. Methods Mol Biol 2022; 2425:393-415. [PMID: 35188640 DOI: 10.1007/978-1-0716-1960-5_15] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.
Collapse
Affiliation(s)
- Jie Liu
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Wenjing Guo
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Sugunadevi Sakkiah
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Zuowei Ji
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Gokhan Yavas
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Wen Zou
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Minjun Chen
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Weida Tong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Tucker A Patterson
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.
| |
Collapse
|
8
|
Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020; 25:E5277. [PMID: 33198233 PMCID: PMC7696134 DOI: 10.3390/molecules25225277] [Citation(s) in RCA: 109] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/04/2020] [Accepted: 11/09/2020] [Indexed: 12/30/2022] Open
Abstract
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
Collapse
Affiliation(s)
- Lauv Patel
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Tripti Shukla
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, AR 72467, USA;
| | - David W. Ussery
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Shanzhi Wang
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| |
Collapse
|
9
|
Sakkiah S, Leggett C, Pan B, Guo W, Valerio LG, Hong H. Development of a Nicotinic Acetylcholine Receptor nAChR α7 Binding Activity Prediction Model. J Chem Inf Model 2020; 60:2396-2404. [PMID: 32159345 DOI: 10.1021/acs.jcim.0c00139] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.
Collapse
Affiliation(s)
- Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Carmine Leggett
- Division of Nonclinical Science, Office of Science, Center for Tobacco Products, U.S. Food and Drug Administration, 11785 Beltsville Drive, Calverton, Maryland 20705, United States
| | - Bohu Pan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Wenjing Guo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Luis G Valerio
- Division of Nonclinical Science, Office of Science, Center for Tobacco Products, U.S. Food and Drug Administration, 11785 Beltsville Drive, Calverton, Maryland 20705, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| |
Collapse
|
10
|
Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:27002. [PMID: 32074470 DOI: 10.23645/epacomptox.5176876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
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™ 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™/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 ∼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. https://doi.org/10.1289/EHP5580.
Collapse
Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche "Mario Negri", IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)-INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen - German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique-UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| |
Collapse
|
11
|
Mansouri K, Kleinstreuer N, Abdelaziz AM, Alberga D, Alves VM, Andersson PL, Andrade CH, Bai F, Balabin I, Ballabio D, Benfenati E, Bhhatarai B, Boyer S, Chen J, Consonni V, Farag S, Fourches D, García-Sosa AT, Gramatica P, Grisoni F, Grulke CM, Hong H, Horvath D, Hu X, Huang R, Jeliazkova N, Li J, Li X, Liu H, Manganelli S, Mangiatordi GF, Maran U, Marcou G, Martin T, Muratov E, Nguyen DT, Nicolotti O, Nikolov NG, Norinder U, Papa E, Petitjean M, Piir G, Pogodin P, Poroikov V, Qiao X, Richard AM, Roncaglioni A, Ruiz P, Rupakheti C, Sakkiah S, Sangion A, Schramm KW, Selvaraj C, Shah I, Sild S, Sun L, Taboureau O, Tang Y, Tetko IV, Todeschini R, Tong W, Trisciuzzi D, Tropsha A, Van Den Driessche G, Varnek A, Wang Z, Wedebye EB, Williams AJ, Xie H, Zakharov AV, Zheng Z, Judson RS. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:27002. [PMID: 32074470 PMCID: PMC7064318 DOI: 10.1289/ehp5580] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/27/2019] [Accepted: 12/05/2019] [Indexed: 05/04/2023]
Abstract
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™ 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™/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 ∼ 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. https://doi.org/10.1289/EHP5580.
Collapse
Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
- ScitoVation LLC, Research Triangle Park, North Carolina, USA
- Integrated Laboratory Systems, Inc., Morrisville, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Ahmed M. Abdelaziz
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Domenico Alberga
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Brazil
| | - Fang Bai
- School of Pharmacy, Lanzhou University, China
| | - Ilya Balabin
- Information Systems & Global Solutions (IS&GS), Lockheed Martin, USA
| | - Davide Ballabio
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Emilio Benfenati
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | - Barun Bhhatarai
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Jingwen Chen
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | | | - Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Chris M. Grulke
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Dragos Horvath
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Xin Hu
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | | | - Jiazhong Li
- School of Pharmacy, Lanzhou University, China
| | - Xuehua Li
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | | | - Serena Manganelli
- Istituto di Ricerche Farmacologiche “Mario Negri”, IRCCS, Milan, Italy
| | | | - Uko Maran
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Gilles Marcou
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Todd Martin
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dac-Trung Nguyen
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | - Nikolai G. Nikolov
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Ulf Norinder
- Swedish Toxicology Sciences Research Center, Karolinska Institutet, Södertälje, Sweden
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Michel Petitjean
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Geven Piir
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Pavel Pogodin
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Vladimir Poroikov
- Institute of Biomedical Chemistry IBMC, 10 Building 8, Pogodinskaya st., Moscow 119121, Russia
| | - Xianliang Qiao
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Ann M. Richard
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | | | - Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Chetan Rupakheti
- National Risk Management Research Laboratory, U.S. EPA, Cincinnati, Ohio, USA
- Department of Biochemistry and Molecular Biophysics, University of Chicago, Chicago, Illinois, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Alessandro Sangion
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Varese, Italy
| | - Karl-Werner Schramm
- Technische Universität München, Wissenschaftszentrum Weihenstephan für Ernährung, Landnutzung und Umwelt, Department für Biowissenschaftliche Grundlagen, Weihenstephaner Steig 23, 85350 Freising, Germany
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | - Imran Shah
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Tartu, Estonia
| | - Lixia Sun
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Olivier Taboureau
- Computational Modeling of Protein-Ligand Interactions (CMPLI)–INSERM UMR 8251, INSERM ERL U1133, Functional and Adaptative Biology (BFA), Universite de Paris, Paris, France
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, Shanghai, China
| | - Igor V. Tetko
- BIGCHEM GmbH, Neuherberg, Germany
- Helmholtz Zentrum Muenchen – German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, Department of Earth and Environmental Sciences, University of Milano-Bicocca, Milan, Italy
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, Arkansas, USA
| | | | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - George Van Den Driessche
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chémoinformatique—UMR7140, University of Strasbourg/CNRS, Strasbourg, France
| | - Zhongyu Wang
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Eva B. Wedebye
- Division of Risk Assessment and Nutrition, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Antony J. Williams
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| | - Hongbin Xie
- School of Environmental Science and Technology, Dalian University of Technology, Dalian, China
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland, USA
| | - Ziye Zheng
- Chemistry Department, Umeå University, Umeå, Sweden
| | - Richard S. Judson
- National Center for Computational Toxicology, Office of Research and Development, U.S. Environmental Protection Agency (U.S. EPA), Research Triangle Park, North Carolina, USA
| |
Collapse
|
12
|
|
13
|
Sakkiah S, Guo W, Pan B, Kusko R, Tong W, Hong H. Computational prediction models for assessing endocrine disrupting potential of chemicals. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS 2019; 36:192-218. [PMID: 30633647 DOI: 10.1080/10590501.2018.1537132] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.
Collapse
Affiliation(s)
- Sugunadevi Sakkiah
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Wenjing Guo
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Bohu Pan
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Rebecca Kusko
- b Immuneering Corporation , Cambridge , Massachusetts , USA
| | - Weida Tong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| | - Huixiao Hong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , Arkansas , USA
| |
Collapse
|
14
|
Sakkiah S, Kusko R, Pan B, Guo W, Ge W, Tong W, Hong H. Structural Changes Due to Antagonist Binding in Ligand Binding Pocket of Androgen Receptor Elucidated Through Molecular Dynamics Simulations. Front Pharmacol 2018; 9:492. [PMID: 29867496 PMCID: PMC5962723 DOI: 10.3389/fphar.2018.00492] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Accepted: 04/25/2018] [Indexed: 01/28/2023] Open
Abstract
When a small molecule binds to the androgen receptor (AR), a conformational change can occur which impacts subsequent binding of co-regulator proteins and DNA. In order to accurately study this mechanism, the scientific community needs a crystal structure of the Wild type AR (WT-AR) ligand binding domain, bound with antagonist. To address this open need, we leveraged molecular docking and molecular dynamics (MD) simulations to construct a structure of the WT-AR ligand binding domain bound with antagonist bicalutamide. The structure of mutant AR (Mut-AR) bound with this same antagonist informed this study. After molecular docking analysis pinpointed the suitable binding orientation of a ligand in AR, the model was further optimized through 1 μs of MD simulations. Using this approach, three molecular systems were studied: (1) WT-AR bound with agonist R1881, (2) WT-AR bound with antagonist bicalutamide, and (3) Mut-AR bound with bicalutamide. Our structures were very similar to the experimentally determined structures of both WT-AR with R1881 and Mut-AR with bicalutamide, demonstrating the trustworthiness of this approach. In our model, when WT-AR is bound with bicalutamide, Val716/Lys720/Gln733, or Met734/Gln738/Glu897 move and thus disturb the positive and negative charge clumps of the AF2 site. This disruption of the AF2 site is key for understanding the impact of antagonist binding on subsequent co-regulator binding. In conclusion, the antagonist induced structural changes in WT-AR detailed in this study will enable further AR research and will facilitate AR targeting drug discovery.
Collapse
Affiliation(s)
- Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Rebecca Kusko
- Immuneering Corporation, Cambridge, MA, United States
| | - Bohu Pan
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Wenjing Guo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| |
Collapse
|
15
|
Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents. Oncotarget 2018; 9:16899-16916. [PMID: 29682193 PMCID: PMC5908294 DOI: 10.18632/oncotarget.24458] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2017] [Accepted: 02/01/2018] [Indexed: 12/21/2022] Open
Abstract
The detrimental health effects associated with tobacco use constitute a major public health concern. The addiction associated with nicotine found in tobacco products has led to difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the targets of nicotine and are responsible for addiction to tobacco products. However, it is unknown if the other >8000 tobacco constituents are addictive. Since it is time-consuming and costly to experimentally assess addictive potential of such larger number of chemicals, computationally predicting human nAChRs binding is important for in silico evaluation of addiction potential of tobacco constituents and needs structures of human nAChRs. Therefore, we constructed three-dimensional structures of the ligand binding domain of human nAChR α7 subtype and then developed a predictive model based on the constructed structures to predict human nAChR α7 binding activity of tobacco constituents. The predictive model correctly predicted 11 out of 12 test compounds to be binders of nAChR α7. The model is a useful tool for high-throughput screening of potential addictive tobacco constituents. These results could inform regulatory science research by providing a new validated predictive tool using cutting-edge computational methodology to high-throughput screen tobacco additives and constituents for their binding interaction with the human α7 nicotinic receptor. The tool represents a prediction model capable of screening thousands of chemicals found in tobacco products for addiction potential, which improves the understanding of the potential effects of additives.
Collapse
|
16
|
Naveja JJ, Norinder U, Mucs D, López-López E, Medina-Franco JL. Chemical space, diversity and activity landscape analysis of estrogen receptor binders. RSC Adv 2018; 8:38229-38237. [PMID: 35559115 PMCID: PMC9089822 DOI: 10.1039/c8ra07604a] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 11/05/2018] [Indexed: 11/21/2022] Open
Abstract
Understanding the structure–activity relationships (SAR) of endocrine-disrupting chemicals has a major importance in toxicology. Despite the fact that classifiers and predictive models have been developed for estrogens for the past 20 years, to the best of our knowledge, there are no studies of their activity landscape or the identification of activity cliffs. Herein, we report the first SAR of a public dataset of 121 chemicals with reported estrogen receptor binding affinities using activity landscape modeling. To this end, we conducted a systematic quantitative and visual analysis of the chemical space of the 121 chemicals. The global diversity of the dataset was characterized by means of Consensus Diversity Plot, a recently developed method. Adding pairwise activity difference information to the chemical space gave rise to the activity landscape of the data set uncovering a heterogeneous SAR, in particular for some structural classes. At least eight compounds were identified with high propensity to form activity cliffs. The findings of this work further expand the current knowledge of the underlying SAR of estrogenic compounds and can be the starting point to develop novel and potentially improved predictive models. Global diversity and activity landscape analysis of endocrine-disrupting chemicals identifies activity cliffs that are rationalized at the structure level.![]()
Collapse
Affiliation(s)
- J. Jesús Naveja
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| | - Ulf Norinder
- Swetox
- Karolinska Institutet
- Unit of Toxicology Sciences
- SE-151 36 Södertälje
- Sweden
| | - Daniel Mucs
- Swetox
- Karolinska Institutet
- Unit of Toxicology Sciences
- SE-151 36 Södertälje
- Sweden
| | - Edgar López-López
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| | - Josė L. Medina-Franco
- Department of Pharmacy
- School of Chemistry
- Universidad Nacional Autónoma de México
- Mexico City
- Mexico
| |
Collapse
|
17
|
Thakkar S, Chen M, Fang H, Liu Z, Roberts R, Tong W. The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury. Expert Rev Gastroenterol Hepatol 2018; 12:31-38. [PMID: 28931315 DOI: 10.1080/17474124.2018.1383154] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can be used to assess and predict DILI. Among much information available, several reference drug lists with annotated human DILI risk are of important. The LTKB DILI classification data include DILI severity concern determined by the FDA drug labeling, DILI severity score from the NIH LiverTox database, and other DILI classification schemes from the literature. Overall, ~1000 drugs were annotated with at least one classification scheme, of which around 750 drugs were flagged for some degree of DILI risk. Expert commentary: The LTKB provides a centralized repository of information for DILI study and predictive model development. The DILI classification data in LTKB could be a useful resource for developing biomarkers, predictive models and assessing data from emerging technologies such as in silico, high-throughput and high-content screening methodologies. In coming years, streamlining the prediction process by including DILI predictive models for both DILI severity and types in LTKB would enhance the identification of compounds with the DILI potential earlier in drug development and risk assessment.
Collapse
Affiliation(s)
- Shraddha Thakkar
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Minjun Chen
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Hong Fang
- b Office of Scientific Coordination , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Zhichao Liu
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Ruth Roberts
- c ApconiX Ltd , Alderley Edge , UK.,d School of Biosciences, University of Birmingham , Birmingham , UK
| | - Weida Tong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| |
Collapse
|
18
|
Sakkiah S, Wang T, Zou W, Wang Y, Pan B, Tong W, Hong H. Endocrine Disrupting Chemicals Mediated through Binding Androgen Receptor Are Associated with Diabetes Mellitus. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 15:ijerph15010025. [PMID: 29295509 PMCID: PMC5800125 DOI: 10.3390/ijerph15010025] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2017] [Revised: 12/13/2017] [Accepted: 12/20/2017] [Indexed: 02/06/2023]
Abstract
Endocrine disrupting chemicals (EDCs) can mimic natural hormone to interact with receptors in the endocrine system and thus disrupt the functions of the endocrine system, raising concerns on the public health. In addition to disruption of the endocrine system, some EDCs have been found associated with many diseases such as breast cancer, prostate cancer, infertility, asthma, stroke, Alzheimer’s disease, obesity, and diabetes mellitus. EDCs that binding androgen receptor have been reported associated with diabetes mellitus in in vitro, animal, and clinical studies. In this review, we summarize the structural basis and interactions between androgen receptor and EDCs as well as the associations of various types of diabetes mellitus with the EDCs mediated through androgen receptor binding. We also discuss the perspective research for further understanding the impact and mechanisms of EDCs on the risk of diabetes mellitus.
Collapse
Affiliation(s)
- Sugunadevi Sakkiah
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Tony Wang
- Department of Biology, Arkansas University, Fayetteville, AR 72701, USA.
| | - Wen Zou
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Yuping Wang
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Bohu Pan
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weida Tong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Huixiao Hong
- National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, USA.
| |
Collapse
|
19
|
Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs. Sci Rep 2017; 7:17311. [PMID: 29229971 PMCID: PMC5725422 DOI: 10.1038/s41598-017-17701-7] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Accepted: 11/30/2017] [Indexed: 12/11/2022] Open
Abstract
Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA’s Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.
Collapse
|
20
|
Sakkiah S, Selvaraj C, Gong P, Zhang C, Tong W, Hong H. Development of estrogen receptor beta binding prediction model using large sets of chemicals. Oncotarget 2017; 8:92989-93000. [PMID: 29190972 PMCID: PMC5696238 DOI: 10.18632/oncotarget.21723] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 08/27/2017] [Indexed: 12/31/2022] Open
Abstract
We developed an ERβ binding prediction model to facilitate identification of chemicals specifically bind ERβ or ERα together with our previously developed ERα binding model. Decision Forest was used to train ERβ binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ERβ binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ERβ binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ERβ binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ERα prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ERβ or ERα.
Collapse
Affiliation(s)
- Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS, USA
| | - Chaoyang Zhang
- School of Computer Science, University of Southern Mississippi, Hattiesburg, MS, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA
| |
Collapse
|
21
|
Selvaraj C, Sakkiah S, Tong W, Hong H. Molecular dynamics simulations and applications in computational toxicology and nanotoxicology. Food Chem Toxicol 2017; 112:495-506. [PMID: 28843597 DOI: 10.1016/j.fct.2017.08.028] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2017] [Revised: 08/08/2017] [Accepted: 08/22/2017] [Indexed: 12/13/2022]
Abstract
Nanotoxicology studies toxicity of nanomaterials and has been widely applied in biomedical researches to explore toxicity of various biological systems. Investigating biological systems through in vivo and in vitro methods is expensive and time taking. Therefore, computational toxicology, a multi-discipline field that utilizes computational power and algorithms to examine toxicology of biological systems, has gained attractions to scientists. Molecular dynamics (MD) simulations of biomolecules such as proteins and DNA are popular for understanding of interactions between biological systems and chemicals in computational toxicology. In this paper, we review MD simulation methods, protocol for running MD simulations and their applications in studies of toxicity and nanotechnology. We also briefly summarize some popular software tools for execution of MD simulations.
Collapse
Affiliation(s)
- Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicology Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| |
Collapse
|
22
|
Hong H, Rua D, Sakkiah S, Selvaraj C, Ge W, Tong W. Consensus Modeling for Prediction of Estrogenic Activity of Ingredients Commonly Used in Sunscreen Products. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13100958. [PMID: 27690075 PMCID: PMC5086697 DOI: 10.3390/ijerph13100958] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Revised: 09/16/2016] [Accepted: 09/20/2016] [Indexed: 11/16/2022]
Abstract
Sunscreen products are predominantly regulated as over-the-counter (OTC) drugs by the US FDA. The "active" ingredients function as ultraviolet filters. Once a sunscreen product is generally recognized as safe and effective (GRASE) via an OTC drug review process, new formulations using these ingredients do not require FDA review and approval, however, the majority of ingredients have never been tested to uncover any potential endocrine activity and their ability to interact with the estrogen receptor (ER) is unknown, despite the fact that this is a very extensively studied target related to endocrine activity. Consequently, we have developed an in silico model to prioritize single ingredient estrogen receptor activity for use when actual animal data are inadequate, equivocal, or absent. It relies on consensus modeling to qualitatively and quantitatively predict ER binding activity. As proof of concept, the model was applied to ingredients commonly used in sunscreen products worldwide and a few reference chemicals. Of the 32 chemicals with unknown ER binding activity that were evaluated, seven were predicted to be active estrogenic compounds. Five of the seven were confirmed by the published data. Further experimental data is needed to confirm the other two predictions.
Collapse
Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Diego Rua
- Division of Nonprescription Drug Products, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Chandrabose Selvaraj
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR 72079, USA.
| |
Collapse
|
23
|
sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides. Sci Rep 2016; 6:32115. [PMID: 27558848 PMCID: PMC4997263 DOI: 10.1038/srep32115] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Accepted: 08/02/2016] [Indexed: 12/19/2022] Open
Abstract
Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.
Collapse
|
24
|
Liu H, Yang X, Lu R. Development of classification model and QSAR model for predicting binding affinity of endocrine disrupting chemicals to human sex hormone-binding globulin. CHEMOSPHERE 2016; 156:1-7. [PMID: 27156209 DOI: 10.1016/j.chemosphere.2016.04.077] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Revised: 04/13/2016] [Accepted: 04/20/2016] [Indexed: 06/05/2023]
Abstract
Disturbing the transport process is a crucial pathway for endocrine disrupting chemicals (EDCs) to disrupt endocrine function. However, this mechanism has not gotten enough attention, compared with that of hormone receptors and synthetase up to now, especially for the sex hormone transport process. In this study, we selected sex hormone-binding globulin (SHBG) and EDCs as a model system and the relative competing potency of a chemical with testosterone binding to SHBG (log RBA) as the endpoints, to develop classification models and quantitative structure-activity relationship (QSAR) models. With the classification model, a satisfactory model with nR09, nR10 and RDF155v as the most relevant variables was screened. Statistic results indicated that the model had the sensitivity, specificity, accuracy of 86.4%, 80.0%, 84.4% and 85.7%, 87.5%, 86.2% for the training set and validation set, respectively, highlighting a high classification performance of the model. With the QSAR model, a satisfactory model with statistical parameters, specifically, an adjusted determination coefficient (Radj(2)) of 0.810, a root mean square error (RMSE) of 0.616, a leave-one-out cross-validation squared correlation coefficient (QLOO(2)) of 0.777, a bootstrap method (QBOOT(2)) of 0.756, an external validation coefficient (Qext(2)) of 0.544 and a RMSEext of 0.859, were obtained, which implied satisfactory goodness of fit, robustness and predictive ability. The applicability domain of the current model covers a large number of structurally diverse chemicals, especially a few classes of nonsteroidal compounds.
Collapse
Affiliation(s)
- Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu Province, China.
| | - Xianhai Yang
- Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Jiang-wang-miao Street, Nanjing, 210042, China
| | - Rui Lu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu Province, China
| |
Collapse
|
25
|
Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:ijerph13070705. [PMID: 27420082 PMCID: PMC4962246 DOI: 10.3390/ijerph13070705] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 07/01/2016] [Accepted: 07/05/2016] [Indexed: 01/23/2023]
Abstract
Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials.
Collapse
|
26
|
Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES 2016; 124:1023-33. [PMID: 26908244 PMCID: PMC4937869 DOI: 10.1289/ehp.1510267] [Citation(s) in RCA: 221] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 10/05/2015] [Accepted: 02/08/2016] [Indexed: 05/18/2023]
Abstract
BACKGROUND Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing. CONCLUSION This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points. CITATION Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016. CERAPP Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.
Collapse
Affiliation(s)
- Kamel Mansouri
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA
| | - Ahmed Abdelaziz
- Institute of Structural Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health (GmbH), Neuherberg, Germany
| | | | - Alessandra Roncaglioni
- Environmental Chemistry and Toxicology Laboratory, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico)-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alexandre Varnek
- Laboratoire de Chemoinformatique, University of Strasbourg, Strasbourg, France
| | - Alexey Zakharov
- National Cancer Institute, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Bethesda, Maryland, USA
| | - Andrew Worth
- Institute for Health and Consumer Protection (IHCP), Joint Research Centre of the European Commission in Ispra, Ispra, Italy
| | - Ann M. Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Christopher M. Grulke
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | | | - Denis Fourches
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Dragos Horvath
- Laboratoire de Chemoinformatique, University of Strasbourg, Strasbourg, France
| | - Emilio Benfenati
- Environmental Chemistry and Toxicology Laboratory, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico)-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Eugene Muratov
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Eva Bay Wedebye
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Francesca Grisoni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | | | - Giuseppina M. Incisivo
- Environmental Chemistry and Toxicology Laboratory, IRCCS (Istituto di Ricovero e Cura a Carattere Scientifico)-Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (USDA), Jefferson, Arizona, USA
| | - Hui W. Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration (USDA), Jefferson, Arizona, USA
| | - Igor V. Tetko
- Institute of Structural Biology, Helmholtz Zentrum Muenchen-German Research Center for Environmental Health (GmbH), Neuherberg, Germany
- BigChem GmbH, Neuherberg, Germany
| | - Ilya Balabin
- High Performance Computing, Lockheed Martin, Research Triangle Park, North Carolina, USA
| | - Jayaram Kancherla
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
| | - Jie Shen
- Research Institute for Fragrance Materials, Inc., Woodcliff Lake, New Jersey, USA
| | - Julien Burton
- Institute for Health and Consumer Protection (IHCP), Joint Research Centre of the European Commission in Ispra, Ispra, Italy
| | - Marc Nicklaus
- National Cancer Institute, National Institutes of Health (NIH), Department of Health and Human Services (DHHS), Bethesda, Maryland, USA
| | - Matteo Cassotti
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Nikolai G. Nikolov
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Orazio Nicolotti
- Department of Pharmacy-Drug Sciences, University of Bari, Bari, Italy
| | | | - Qingda Zang
- Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, USDA, Jefferson, Arizona, USA
| | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, Italy
| | - Ruili Huang
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Sherif Farag
- Laboratory for Molecular Modeling, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sine A. Rosenberg
- Division of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Copenhagen, Denmark
| | - Svetoslav Slavov
- Integrated Laboratory Systems, Inc., Research Triangle Park, North Carolina, USA
| | - Xin Hu
- National Center for Advancing Translational Sciences, NIH, DHHS, Bethesda, Maryland, USA
| | - Richard S. Judson
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA
- Address correspondence to R.S. Judson, U.S. EPA, National Center for Computational Toxicology, 109 T.W. Alexander Dr., Research Triangle Park, NC 27711 USA. Telephone: (919) 541-3085. E-mail:
| |
Collapse
|
27
|
Sakkiah S, Ng HW, Tong W, Hong H. Structures of androgen receptor bound with ligands: advancing understanding of biological functions and drug discovery. Expert Opin Ther Targets 2016; 20:1267-82. [PMID: 27195510 DOI: 10.1080/14728222.2016.1192131] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Androgen receptor (AR) is a ligand-dependent transcription factor and a member of the nuclear receptor superfamily. It plays a vital role in male sexual development and regulates gene expression in various tissues, including prostate. Androgens are compounds that exert their biological effects via interaction with AR. Binding of androgens to AR initiates conformational changes in AR that affect binding of co-regulator proteins and DNA. AR agonists and antagonists are widely used in a variety of clinical applications (i.e. hypogonadism and prostate cancer therapy). AREAS COVERED This review provides a close look at structures of AR-ligand complexes and mutations in the receptor that have been revealed, discusses current challenges in the field, and sheds light on future directions. EXPERT OPINION AR is one of the primary targets for the treatment of prostate cancer, as AR antagonists inhibit prostate cancer growth. However, these drugs are not effective for long-term treatment and lead to castration-resistant prostate cancer. The structures of AR-ligand complexes are an invaluable scientific asset that enhances our understanding of biological functions and mechanisms of androgenic and anti-androgenic chemicals as well as promotes the discovery of superior drug candidates.
Collapse
Affiliation(s)
- Sugunadevi Sakkiah
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Hui Wen Ng
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Weida Tong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| | - Huixiao Hong
- a Division of Bioinformatics and Biostatistics , National Center for Toxicological Research, U.S. Food and Drug Administration , Jefferson , AR , USA
| |
Collapse
|
28
|
Norinder U, Boyer S. Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays. Chem Res Toxicol 2016; 29:1003-10. [DOI: 10.1021/acs.chemrestox.6b00037] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Ulf Norinder
- Swedish Toxicology Sciences Research Center, SE-151
36 Södertälje, Sweden
| | - Scott Boyer
- Swedish Toxicology Sciences Research Center, SE-151
36 Södertälje, Sweden
| |
Collapse
|
29
|
Norinder U, Rybacka A, Andersson PL. Conformal prediction to define applicability domain - A case study on predicting ER and AR binding. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2016; 27:303-316. [PMID: 27088868 DOI: 10.1080/1062936x.2016.1172665] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
A fundamental element when deriving a robust and predictive in silico model is not only the statistical quality of the model in question but, equally important, the estimate of its predictive boundaries. This work presents a new method, conformal prediction, for applicability domain estimation in the field of endocrine disruptors. The method is applied to binders and non-binders related to the oestrogen and androgen receptors. Ensembles of decision trees are used as statistical method and three different sets (dragon, rdkit and signature fingerprints) are investigated as chemical descriptors. The conformal prediction method results in valid models where there is an excellent balance in quality between the internally validated training set and the corresponding external test set, both in terms of validity and with respect to sensitivity and specificity. With this method the level of confidence can be readily altered by the user and the consequences thereof immediately inspected. Furthermore, the predictive boundaries for the derived models are rigorously defined by using the conformal prediction framework, thus no ambiguity exists as to the level of similarity needed for new compounds to be in or out of the predictive boundaries of the derived models where reliable predictions can be expected.
Collapse
Affiliation(s)
- U Norinder
- a Swedish Toxicology Sciences Research Center , Södertälje , Sweden
- b Department of Computer and Systems Sciences , Stockholm University , Kista , Sweden
| | - A Rybacka
- c Department of Chemistry , Umeå University , Umeå , Sweden
| | - P L Andersson
- c Department of Chemistry , Umeå University , Umeå , Sweden
| |
Collapse
|
30
|
Hong H, Shen J, Ng HW, Sakkiah S, Ye H, Ge W, Gong P, Xiao W, Tong W. A Rat α-Fetoprotein Binding Activity Prediction Model to Facilitate Assessment of the Endocrine Disruption Potential of Environmental Chemicals. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2016; 13:372. [PMID: 27023588 PMCID: PMC4847034 DOI: 10.3390/ijerph13040372] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 03/10/2016] [Accepted: 03/22/2016] [Indexed: 11/21/2022]
Abstract
Endocrine disruptors such as polychlorinated biphenyls (PCBs), diethylstilbestrol (DES) and dichlorodiphenyltrichloroethane (DDT) are agents that interfere with the endocrine system and cause adverse health effects. Huge public health concern about endocrine disruptors has arisen. One of the mechanisms of endocrine disruption is through binding of endocrine disruptors with the hormone receptors in the target cells. Entrance of endocrine disruptors into target cells is the precondition of endocrine disruption. The binding capability of a chemical with proteins in the blood affects its entrance into the target cells and, thus, is very informative for the assessment of potential endocrine disruption of chemicals. α-fetoprotein is one of the major serum proteins that binds to a variety of chemicals such as estrogens. To better facilitate assessment of endocrine disruption of environmental chemicals, we developed a model for α-fetoprotein binding activity prediction using the novel pattern recognition method (Decision Forest) and the molecular descriptors calculated from two-dimensional structures by Mold² software. The predictive capability of the model has been evaluated through internal validation using 125 training chemicals (average balanced accuracy of 69%) and external validations using 22 chemicals (balanced accuracy of 71%). Prediction confidence analysis revealed the model performed much better at high prediction confidence. Our results indicate that the model is useful (when predictions are in high confidence) in endocrine disruption risk assessment of environmental chemicals though improvement by increasing number of training chemicals is needed.
Collapse
Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Jie Shen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Sugunadevi Sakkiah
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Ping Gong
- Environmental Laboratory, U.S. Army Engineer Research and Development Center, 3909 Halls Ferry Road, Vicksburg, MS 39180, USA.
| | - Wenming Xiao
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA.
| |
Collapse
|
31
|
Abstract
Quantitative structure-activity relationship (QSAR) has been used in the scientific research community for many decades and applied to drug discovery and development in the industry. QSAR technologies are advancing fast and attracting possible applications in regulatory science. To facilitate the development of reliable QSAR models, the FDA had invested a lot of efforts in constructing chemical databases with a variety of efficacy and safety endpoint data, as well as in the development of computational algorithms. In this chapter, we briefly describe some of the often used databases developed at the FDA such as EDKB (Endocrine Disruptor Knowledge Base), EADB (Estrogenic Activity Database), LTKB (Liver Toxicity Knowledge Base), and CERES (Chemical Evaluation and Risk Estimation System) and the technologies adopted by the agency such as Mold(2) program for calculation of a large and diverse set of molecular descriptors and decision forest algorithm for QSAR model development. We also summarize some QSAR models that have been developed for safety evaluation of the FDA-regulated products.
Collapse
Affiliation(s)
- Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA.
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Jefferson, AR, 72079, USA
| |
Collapse
|
32
|
Ng HW, Doughty SW, Luo H, Ye H, Ge W, Tong W, Hong H. Development and Validation of Decision Forest Model for Estrogen Receptor Binding Prediction of Chemicals Using Large Data Sets. Chem Res Toxicol 2015; 28:2343-51. [PMID: 26524122 DOI: 10.1021/acs.chemrestox.5b00358] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Some chemicals in the environment possess the potential to interact with the endocrine system in the human body. Multiple receptors are involved in the endocrine system; estrogen receptor α (ERα) plays very important roles in endocrine activity and is the most studied receptor. Understanding and predicting estrogenic activity of chemicals facilitates the evaluation of their endocrine activity. Hence, we have developed a decision forest classification model to predict chemical binding to ERα using a large training data set of 3308 chemicals obtained from the U.S. Food and Drug Administration's Estrogenic Activity Database. We tested the model using cross validations and external data sets of 1641 chemicals obtained from the U.S. Environmental Protection Agency's ToxCast project. The model showed good performance in both internal (92% accuracy) and external validations (∼ 70-89% relative balanced accuracies), where the latter involved the validations of the model across different ER pathway-related assays in ToxCast. The important features that contribute to the prediction ability of the model were identified through informative descriptor analysis and were related to current knowledge of ER binding. Prediction confidence analysis revealed that the model had both high prediction confidence and accuracy for most predicted chemicals. The results demonstrated that the model constructed based on the large training data set is more accurate and robust for predicting ER binding of chemicals than the published models that have been developed using much smaller data sets. The model could be useful for the evaluation of ERα-mediated endocrine activity potential of environmental chemicals.
Collapse
Affiliation(s)
- Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Stephen W Doughty
- School of Pharmacy, University of Nottingham Malaysia Campus , Jalan Broga, 43500 Semenyih, Selangor, Malaysia
| | - Heng Luo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| |
Collapse
|
33
|
Ng HW, Shu M, Luo H, Ye H, Ge W, Perkins R, Tong W, Hong H. Estrogenic activity data extraction and in silico prediction show the endocrine disruption potential of bisphenol A replacement compounds. Chem Res Toxicol 2015; 28:1784-95. [PMID: 26308263 DOI: 10.1021/acs.chemrestox.5b00243] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Bisphenol A (BPA) replacement compounds are released to the environment and cause widespread human exposure. However, a lack of thorough safety evaluations on the BPA replacement compounds has raised public concerns. We assessed the endocrine disruption potential of BPA replacement compounds in the market to assist their safety evaluations. A literature search was conducted to ascertain the BPA replacement compounds in use. Available experimental estrogenic activity data of these compounds were extracted from the Estrogenic Activity Database (EADB) to assess their estrogenic potential. An in silico model was developed to predict the estrogenic activity of compounds lacking experimental data. Molecular dynamics (MD) simulations were performed to understand the mechanisms by which the estrogenic compounds bind to and activate the estrogen receptor (ER). Forty-five BPA replacement compounds were identified in the literature. Seven were more estrogenic and five less estrogenic than BPA, while six were nonestrogenic in EADB. A two-tier in silico model was developed based on molecular docking to predict the estrogenic activity of the 27 compounds lacking data. Eleven were predicted as ER binders and 16 as nonbinders. MD simulations revealed hydrophobic contacts and hydrogen bonds as the main interactions between ER and the estrogenic compounds.
Collapse
Affiliation(s)
- Hui Wen Ng
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Mao Shu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Heng Luo
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Hao Ye
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weigong Ge
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Roger Perkins
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| | - Huixiao Hong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration , 3900 NCTR Road, Jefferson, Arkansas 72079, United States
| |
Collapse
|
34
|
Doucet JP, Doucet-Panaye A. Structure-activity relationship study of trifluoromethylketone inhibitors of insect juvenile hormone esterase: comparison of several classification methods. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2014; 25:589-616. [PMID: 24884820 DOI: 10.1080/1062936x.2014.919959] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Juvenile hormone esterase (JHE) plays a key role in the development and metamorphosis of holometabolous insects. Its inhibitors could possibly be targeted for insect control. Conversely, JHE may also be involved in endocrine disruption by xenobiotics, resulting in detrimental effects in beneficial insects. There is therefore a need to know the structural characteristics of the molecules able to monitor JHE activity, and to develop SAR and QSAR studies to estimate their effectiveness. For a large diverse population of 181 trifluoromethylketones (TFKs) - the most potent JHE inhibitors known to date - we recently proposed a binary classification (active/inactive) using a support vector machine and Codessa structural descriptors. We have now examined, using the same data set and with the same descriptors, the applicability and performance of five other machine learning approaches. These have been shown able to handle high dimensional data (with descriptors possibly irrelevant or redundant) and to cope with complex mechanisms, but without delivering explicit directly exploitable models. Splitting the data into five batches (training set 80%, test set 20%) and carrying out leave-one-out cross-validation, led to good results of comparable performance, consistent with our previous support vector classifier (SVC) results. Accuracy was greater than 0.80 for all approaches. A reduced set of 15 descriptors common to all the investigated approaches showed good predictive ability (confirmed using a three-layer perceptron) and gives some clues regarding a mechanistic interpretation.
Collapse
Affiliation(s)
- J P Doucet
- a Itodys , Université Paris-Diderot , UMR 7086 , Paris , France
| | | |
Collapse
|
35
|
Kabankin AS, Radkevich LA. Collective recognition strategy for estimating hepatoprotector activity of various chemical compounds in increasing liver repair potential. Pharm Chem J 2013. [DOI: 10.1007/s11094-013-0922-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
36
|
Abstract
Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.
Collapse
|
37
|
Xu X, Yang W, Li Y, Wang Y. Discovery of estrogen receptor modulators: a review of virtual screening and SAR efforts. Expert Opin Drug Discov 2012; 5:21-31. [PMID: 22823969 DOI: 10.1517/17460440903490395] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
IMPORTANCE OF THE FIELD Virtual screening (VS) coupled with structural biology is a significantly important approach to increase the number and enhance the success of projects in lead identification stage of drug discovery process. Recent advances and future directions in estrogen therapy have resulted in great demand for identifying the potential estrogen receptor (ER) modulators with more activity and selectivity. AREAS COVERED IN THIS REVIEW This review presents the current state of the art in VS and structure-activity relationship of ER modulators in recent discovery, and discusses the strengths and weaknesses of the technology. WHAT THE READER WILL GAIN Readers will gain an overview of the current platforms of in silico screening for discovery of ER modulators; they will learn which structural information is significantly correlated with the bioactivity of ER modulators and what novel strategies should be considered for the creation of more effective chemical structures. TAKE HOME MESSAGE With the goal of reducing toxicity and/or improving efficacy, challenges to the successful modeling of endocrine agents are proposed, providing new paradigms for the design of ER inhibitors.
Collapse
Affiliation(s)
- Xue Xu
- Northwest A&F University, Center of Bioinformatics, Yangling, Shaanxi, 712100, China
| | | | | | | |
Collapse
|
38
|
Models for anti-inflammatory activity of 8-substituted-4-anilino-6-aminoquinoline-3-carbonitriles. Med Chem Res 2012. [DOI: 10.1007/s00044-011-9613-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
39
|
Tie Y, McPhail B, Hong H, Pearce BA, Schnackenberg LK, Ge W, Buzatu DA, Wilkes JG, Fuscoe JC, Tong W, Fowler BA, Beger RD, Demchuk E. Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme. Molecules 2012; 17:3407-60. [PMID: 22421793 PMCID: PMC6268819 DOI: 10.3390/molecules17033407] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Revised: 02/27/2012] [Accepted: 02/28/2012] [Indexed: 01/15/2023] Open
Abstract
Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D 13C-NMR and 1D 15N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.
Collapse
Affiliation(s)
- Yunfeng Tie
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
| | - Brooks McPhail
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
| | - Huixiao Hong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Bruce A. Pearce
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Laura K. Schnackenberg
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Weigong Ge
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Dan A. Buzatu
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Jon G. Wilkes
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - James C. Fuscoe
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Weida Tong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Bruce A. Fowler
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
| | - Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Eugene Demchuk
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
- Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506-9530, USA
- Author to whom correspondence should be addressed; ; Tel.: +1-770-488-3327; Fax: +1-404-248-4142
| |
Collapse
|
40
|
McPhail B, Tie Y, Hong H, Pearce BA, Schnackenberg LK, Ge W, Fuscoe JC, Tong W, Buzatu DA, Wilkes JG, Fowler BA, Demchuk E, Beger RD. Modeling chemical interaction profiles: I. Spectral data-activity relationship and structure-activity relationship models for inhibitors and non-inhibitors of cytochrome P450 CYP3A4 and CYP2D6 isozymes. Molecules 2012; 17:3383-406. [PMID: 22421792 PMCID: PMC6268752 DOI: 10.3390/molecules17033383] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Revised: 02/27/2012] [Accepted: 02/28/2012] [Indexed: 02/07/2023] Open
Abstract
An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutant—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13C and 1D 15N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13C-NMR and 15N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.
Collapse
Affiliation(s)
- Brooks McPhail
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
| | - Yunfeng Tie
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
| | - Huixiao Hong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Bruce A. Pearce
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Laura K. Schnackenberg
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Weigong Ge
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - James C. Fuscoe
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Weida Tong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Dan A. Buzatu
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Jon G. Wilkes
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| | - Bruce A. Fowler
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
| | - Eugene Demchuk
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (B.M.); (Y.T.); (B.A.F.)
- Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506-9530, USA
- Author to whom correspondence should be addressed; ; Tel.: +1-770-488-3327; Fax: +1-404-248-4142
| | - Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (J.C.F.); (W.T.); (D.A.B.); (J.G.W.); (R.D.B.)
| |
Collapse
|
41
|
Zhang S. Application of Machine Leaning in Drug Discovery and Development. Mach Learn 2012. [DOI: 10.4018/978-1-60960-818-7.ch517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.
Collapse
Affiliation(s)
- Shuxing Zhang
- The University of Texas at M.D. Anderson Cancer Center, USA
| |
Collapse
|
42
|
Liew CY, Lim YC, Yap CW. Mixed learning algorithms and features ensemble in hepatotoxicity prediction. J Comput Aided Mol Des 2011; 25:855-71. [DOI: 10.1007/s10822-011-9468-3] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2011] [Accepted: 08/23/2011] [Indexed: 12/22/2022]
|
43
|
Abstract
Molecular biomarkers are used for various purposes, including disease diagnosis and prognosis, prediction and assessment of treatment response, and safety assessment. There has been a significant increase in the number of US FDA-approved drug labels containing information on molecular biomarkers over the last decade. Almost every pharmaceutical company has been developing molecular biomarker programs, either alone, through partnerships or other ventures. More molecular biomarkers are expected to be identified and validated in drug development, and used to support approval of drug products. This article summarizes the current status of molecular biomarkers used for FDA-approved drug products, and discusses the challenges and future perspectives for the identification and qualification of molecular biomarkers. Specific FDA programs and research projects related to molecular biomarkers are also discussed for supporting regulatory review in the future.
Collapse
Affiliation(s)
- Huixiao Hong
- Center for Toxicoinformatics, Division of Systems Toxicology, National Center for Toxicological Research, US FDA 3900 NCTR Road, Jefferson, AR, USA.
| | | | | | | |
Collapse
|
44
|
Hong H, Xie Q, Ge W, Qian F, Fang H, Shi L, Su Z, Perkins R, Tong W. Mold(2), molecular descriptors from 2D structures for chemoinformatics and toxicoinformatics. J Chem Inf Model 2008; 48:1337-44. [PMID: 18564836 DOI: 10.1021/ci800038f] [Citation(s) in RCA: 180] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Research applications in chemoinformatics and toxicoinformatics increasingly use representations of molecules in the form of numerical descriptors that capture the structural characteristics and properties of molecules. These representations are useful for ADME/toxicity prediction, diversity analysis, library design, QSAR/QSPR, virtual screening, and other purposes. Molecular descriptors have ranged from relatively simple forms calculated from simple two-dimensional (2D) chemical structures to more complex forms representing three-dimensional (3D) chemical structures or complex molecular fingerprints consisting of numerous bit positions to represent specific chemical information. The Mold (2) software was developed to enable the rapid calculation of a large and diverse set of descriptors encoding two-dimensional chemical structure information. Comparative analysis of Mold (2) descriptors with those calculated by Cerius (2), Dragon, and Molconn-Z on several data sets using Shannon entropy analysis demonstrated that Mold (2) descriptors convey a similar amount of information. In addition, using the same classification method, slightly better models were generated using Mold (2) descriptors compared to those generated using descriptors from the compared commercial software packages. The low computing cost for Mold (2) makes it suitable not only for small data sets, such as in QSAR, but also for large databases in virtual screening. High reproducibility and reliability are expected because Mold (2) does not require 3D structures. Mold (2) is freely available to the public ( http://www.fda.gov/nctr/science/centers/toxicoinformatics/index.htm).
Collapse
Affiliation(s)
- Huixiao Hong
- Center for Toxicoinformatics, Division of Systems Toxicology, National Center for Toxicological Research, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
45
|
Cronin M, Worth A. (Q)SARs for Predicting Effects Relating to Reproductive Toxicity. ACTA ACUST UNITED AC 2008. [DOI: 10.1002/qsar.200710118] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
46
|
Schultz TW, Carlson RE, Cronin MTD, Hermens JLM, Johnson R, O'Brien PJ, Roberts DW, Siraki A, Wallace KB, Veith GD. A conceptual framework for predicting the toxicity of reactive chemicals: modeling soft electrophilicity. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:413-28. [PMID: 16920662 DOI: 10.1080/10629360600884371] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Although the literature is replete with QSAR models developed for many toxic effects caused by reversible chemical interactions, the development of QSARs for the toxic effects of reactive chemicals lacks a consistent approach. While limitations exit, an appropriate starting-point for modeling reactive toxicity is the applicability of the general rules of organic chemical reactions and the association of these reactions to cellular targets of importance in toxicology. The identification of plausible "molecular initiating events" based on covalent reactions with nucleophiles in proteins and DNA provides the unifying concept for a framework for reactive toxicity. This paper outlines the proposed framework for reactive toxicity. Empirical measures of the chemical reactivity of xenobiotics with a model nucleophile (thiol) are used to simulate the relative rates at which a reactive chemical is likely to bind irreversibly to cellular targets. These measures of intrinsic reactivity serve as correlates to a variety of toxic effects; what's more they appear to be more appropriate endpoints for QSAR modeling than the toxicity endpoints themselves.
Collapse
Affiliation(s)
- T W Schultz
- Department of Comparative Medicine, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN 37996-4543, USA
| | | | | | | | | | | | | | | | | | | |
Collapse
|
47
|
Devillers J, Marchand-Geneste N, Carpy A, Porcher JM. SAR and QSAR modeling of endocrine disruptors. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2006; 17:393-412. [PMID: 16920661 DOI: 10.1080/10629360600884397] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.
Collapse
Affiliation(s)
- J Devillers
- CTIS, 3 Chemin de la Gravière, 69140 Rillieux La Pape, France.
| | | | | | | |
Collapse
|