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Liang Y, Huangfu X, Huang R, Han Z, Wu S, Wang J, Long X, Ma J, He Q. Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals. JOURNAL OF HAZARDOUS MATERIALS 2024; 472:134501. [PMID: 38735182 DOI: 10.1016/j.jhazmat.2024.134501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 05/14/2024]
Abstract
Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms. To further alleviate the challenge of limited sample size, we introduced a data combination strategy that improved prediction accuracy and mitigated overfitting by combining different datasets. The Light Gradient Boosting Machine (LightGBM) with MF and Random Forest (RF) with MD models based on the unified dataset were finally selected as the optimal models. The SHapley Additive exPlanations revealed insights: the MF-LightGBM model successfully captured the influence of electron-withdrawing/donating groups, while autocorrelation, walk count and information content descriptors in the MD-RF model were identified as key features. Furthermore, the important contribution of pH was emphasized. The results of the applicability domain analysis further supported that the developed model can make reliable predictions for query compounds across a broader range. Finally, a practical web application for logk calculations was built.
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Affiliation(s)
- Youheng Liang
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Xiaoliu Huangfu
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China.
| | - Ruixing Huang
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Zhenpeng Han
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Sisi Wu
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Jingrui Wang
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Xinlong Long
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
| | - Jun Ma
- State Key Laboratory of Urban Water Resources and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, China
| | - Qiang He
- Key Laboratory of Eco-Environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment, and Ecology, Chongqing University, Chongqing 400044, China
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2
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Masand VH, Al-Hussain SA, Alzahrani AY, Al-Mutairi AA, Hussien RA, Samad A, Zaki MEA. Estrogen Receptor Alpha Binders for Hormone-Dependent Forms of Breast Cancer: e-QSAR and Molecular Docking Supported by X-ray Resolved Structures. ACS OMEGA 2024; 9:16759-16774. [PMID: 38617692 PMCID: PMC11007693 DOI: 10.1021/acsomega.4c00906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 03/16/2024] [Accepted: 03/19/2024] [Indexed: 04/16/2024]
Abstract
Cancer, a life-disturbing and lethal disease with a high global impact, causes significant economic, social, and health challenges. Breast cancer refers to the abnormal growth of cells originating from breast tissues. Hormone-dependent forms of breast cancer, such as those influenced by estrogen, prompt the exploration of estrogen receptors as targets for potential therapeutic interventions. In this study, we conducted e-QSAR molecular docking and molecular dynamics analyses on a diverse set of inhibitors targeting estrogen receptor alpha (ER-α). The e-QSAR model is based on a genetic algorithm combined with multilinear regression analysis. The newly developed model possesses a balance between predictive accuracy and mechanistic insights adhering to the OECD guidelines. The e-QSAR model pointed out that sp2-hybridized carbon and nitrogen atoms are important atoms governing binding profiles. In addition, a specific combination of H-bond donors and acceptors with carbon, nitrogen, and ring sulfur atoms also plays a crucial role. The results are supported by molecular docking, MD simulations, and X-ray-resolved structures. The novel results could be useful for future drug development for ER-α.
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Affiliation(s)
- Vijay H Masand
- Department of Chemistry, Vidya Bharati Mahavidyalaya, Amravati 444 602, Maharashtra, India
| | - Sami A Al-Hussain
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Abdullah Y Alzahrani
- Department of Chemistry, Faculty of Science and Arts, King Khalid University, Mohail 61421, Saudi Arabia
| | - Aamal A Al-Mutairi
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
| | - Rania A Hussien
- Department of Chemistry, Faculty of Science, Al-Baha University, Al-Baha 65799, Kingdom of Saudi Arabia
| | - Abdul Samad
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Tishk International University, Erbil 44001, Iraq
| | - Magdi E A Zaki
- Department of Chemistry, College of Science, Imam Mohammad Ibn Saud Islamic University, Riyadh 11623, Saudi Arabia
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3
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Samanipour S, O’Brien JW, Reid MJ, Thomas KV, Praetorius A. From Molecular Descriptors to Intrinsic Fish Toxicity of Chemicals: An Alternative Approach to Chemical Prioritization. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17950-17958. [PMID: 36480454 PMCID: PMC10666547 DOI: 10.1021/acs.est.2c07353] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/27/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
The European and U.S. chemical agencies have listed approximately 800k chemicals about which knowledge of potential risks to human health and the environment is lacking. Filling these data gaps experimentally is impossible, so in silico approaches and prediction are essential. Many existing models are however limited by assumptions (e.g., linearity and continuity) and small training sets. In this study, we present a supervised direct classification model that connects molecular descriptors to toxicity. Categories can be driven by either data (using k-means clustering) or defined by regulation. This was tested via 907 experimentally defined 96 h LC50 values for acute fish toxicity. Our classification model explained ≈90% of the variance in our data for the training set and ≈80% for the test set. This strategy gave a 5-fold decrease in the frequency of incorrect categorization compared to a quantitative structure-activity relationship (QSAR) regression model. Our model was subsequently employed to predict the toxicity categories of ≈32k chemicals. A comparison between the model-based applicability domain (AD) and the training set AD was performed, suggesting that the training set-based AD is a more adequate way to avoid extrapolation when using such models. The better performance of our direct classification model compared to that of QSAR methods makes this approach a viable tool for assessing the hazards and risks of chemicals.
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Affiliation(s)
- Saer Samanipour
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam (UvA), 1090 GDAmsterdam, The Netherlands
- UvA
Data Science Center, University of Amsterdam, 1090 GDAmsterdam, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane, QLD4072, Australia
| | - Jake W. O’Brien
- Van
’t Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam (UvA), 1090 GDAmsterdam, The Netherlands
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane, QLD4072, Australia
| | - Malcolm J. Reid
- Norwegian
Institute for Water Research (NIVA), NO-0579Oslo, Norway
| | - Kevin V. Thomas
- Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Brisbane, QLD4072, Australia
| | - Antonia Praetorius
- Institute
for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, 1090 GDAmsterdam, The Netherlands
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Ghafoor N, Yildiz A. Targeting MDM2-p53 Axis through Drug Repurposing for Cancer Therapy: A Multidisciplinary Approach. ACS OMEGA 2023; 8:34583-34596. [PMID: 37779953 PMCID: PMC10536845 DOI: 10.1021/acsomega.3c03471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 09/01/2023] [Indexed: 10/03/2023]
Abstract
Cancer remains a major cause of morbidity and mortality worldwide, and while current therapies, such as chemotherapy, immunotherapy, and cell therapy, have been effective in many patients, the development of novel therapeutic options remains an urgent priority. Mouse double minute 2 (MDM2) is a key regulator of the tumor suppressor protein p53, which plays a critical role in regulating cellular growth, apoptosis, and DNA repair. Consequently, MDM2 has been the subject of extensive research aimed at developing novel cancer therapies. In this study, we employed a machine learning-based approach to establish a quantitative structure-activity relationship model capable of predicting the potential in vitro efficacy of small molecules as MDM2 inhibitors. Our model was used to screen 5883 FDA-approved drugs, resulting in the identification of promising hits that were subsequently evaluated using molecular docking and molecular dynamics simulations. Two antihistamine drugs, cetirizine (CZ) and rupatadine (RP), exhibited particularly favorable results in the initial in silico analyses. To further assess their potential use as the activators of the p53 pathway, we investigated the antiproliferative capability of the abovementioned drugs on human glioblastoma and neuroblastoma cell lines. Both the compounds exhibited significant antiproliferative effects on the abovementioned cell lines in a dose-dependent manner. The half-maximal inhibitory concentration (IC50) of CZ was found to be 697.87 and 941.37 μM on U87 and SH-SY5Y cell lines, respectively, while the IC50 of RP was found to be 524.28 and 617.07 μM on the same cell lines, respectively. Further investigation by quantitative reverse transcriptase polymerase chain reaction analysis revealed that the CZ-treated cell lines upregulate the expression of the p53-regulated genes involved in cell cycle arrest, apoptosis, and DNA damage response compared to their respective vehicle controls. These findings suggest that CZ activates the p53 pathway by inhibiting MDM2. Our results provide compelling preclinical evidence supporting the potential use of CZ as a modulator of the MDM2-p53 axis and its plausible repurposing for cancer treatment.
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Affiliation(s)
- Naeem
Abdul Ghafoor
- Department
of Molecular Biology and Genetics, Graduate School of Natural and
Applied Sciences, Mugla Sitki Kocman University, 48000 Mugla, Turkey
| | - Aysegul Yildiz
- Department
of Molecular Biology and Genetics, Graduate School of Natural and
Applied Sciences, Mugla Sitki Kocman University, 48000 Mugla, Turkey
- Department
of Molecular Biology and Genetics, Faculty of Science, Mugla Sitki Kocman University, 48000 Mugla, Turkey
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Pacureanu L, Bora A, Crisan L. New Insights on the Activity and Selectivity of MAO-B Inhibitors through In Silico Methods. Int J Mol Sci 2023; 24:ijms24119583. [PMID: 37298535 DOI: 10.3390/ijms24119583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/12/2023] Open
Abstract
To facilitate the identification of novel MAO-B inhibitors, we elaborated a consolidated computational approach, including a pharmacophoric atom-based 3D quantitative structure-activity relationship (QSAR) model, activity cliffs, fingerprint, and molecular docking analysis on a dataset of 126 molecules. An AAHR.2 hypothesis with two hydrogen bond acceptors (A), one hydrophobic (H), and one aromatic ring (R) supplied a statistically significant 3D QSAR model reflected by the parameters: R2 = 0.900 (training set); Q2 = 0.774 and Pearson's R = 0.884 (test set), stability s = 0.736. Hydrophobic and electron-withdrawing fields portrayed the relationships between structural characteristics and inhibitory activity. The quinolin-2-one scaffold has a key role in selectivity towards MAO-B with an AUC of 0.962, as retrieved by ECFP4 analysis. Two activity cliffs showing meaningful potency variation in the MAO-B chemical space were observed. The docking study revealed interactions with crucial residues TYR:435, TYR:326, CYS:172, and GLN:206 responsible for MAO-B activity. Molecular docking is in consensus with and complementary to pharmacophoric 3D QSAR, ECFP4, and MM-GBSA analysis. The computational scenario provided here will assist chemists in quickly designing and predicting new potent and selective candidates as MAO-B inhibitors for MAO-B-driven diseases. This approach can also be used to identify MAO-B inhibitors from other libraries or screen top molecules for other targets involved in suitable diseases.
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Affiliation(s)
- Liliana Pacureanu
- "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania
| | - Alina Bora
- "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania
| | - Luminita Crisan
- "Coriolan Dragulescu" Institute of Chemistry, 24 Mihai Viteazu Ave., 300223 Timisoara, Romania
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Estrogenic flavonoids and their molecular mechanisms of action. J Nutr Biochem 2023; 114:109250. [PMID: 36509337 DOI: 10.1016/j.jnutbio.2022.109250] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 12/13/2022]
Abstract
Flavonoids are a major group of phytoestrogens associated with physiological effects, and ecological and social impacts. Although the estrogenic activity of flavonoids was reported by researchers in the fields of medical, environmental and food studies, their molecular mechanisms of action have not been comprehensively reviewed. The estrogenic activity of the respective classes of flavonoids, anthocyanidins/anthocyanins, 2-arylbenzofurans/3-arylcoumarins/α-methyldeoxybenzoins, aurones/chalcones/dihydrochalcones, coumaronochromones, coumestans, flavans/flavan-3-ols/flavan-4-ols, flavanones/dihydroflavonols, flavones/flavonols, homoisoflavonoids, isoflavans, isoflavanones, isoflavenes, isoflavones, neoflavonoids, oligoflavonoids, pterocarpans/pterocarpenes, and rotenone/rotenoids, was summarized through a comprehensive literature search, and their structure-activity relationship, biological activities, signaling pathways, and applications were discussed. Although the respective classes of flavonoids contained at least one chemical mimicking estrogen, the mechanisms varied, such as those with estrogenic, anti-estrogenic, non-estrogenic, and biphasic activities, and additional activities through crosstalk/bypassing, which exert biological activities through cell signaling pathways. Such mechanistic variations of estrogen action are not limited to flavonoids and are observed among other broad categories of chemicals, thus this group of chemicals can be termed as the "estrogenome". This review article focuses on the connection of estrogen action mainly between the outer and the inner environments, which represent variations of chemicals and biological activities/signaling pathways, respectively, and form the basis to understand their applications. The applications of chemicals will markedly progress due to emerging technologies, such as artificial intelligence for precision medicine, which is also true of the study of the estrogenome including estrogenic flavonoids.
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7
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Li J, Chen Y, Ye W, Zhang M, Zhu J, Zhi W, Cheng Q. Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level. PHOTOACOUSTICS 2023; 30:100483. [PMID: 37063308 PMCID: PMC10090435 DOI: 10.1016/j.pacs.2023.100483] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/20/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
Breast cancer threatens the health of women worldwide, and its molecular subtypes largely determine the therapy and prognosis of patients. However, an uncomplicated and accurate method to identify subtypes is currently lacking. This study utilized photoacoustic spectral analysis (PASA) based on the partial least squares discriminant algorithm (PLS-DA) to identify molecular breast cancer subtypes at the biomacromolecular level in vivo. The area of power spectrum density (APSD) was extracted to semi-quantify the biomacromolecule content. The feature wavelengths were obtained via the variable importance in projection (VIP) score and the selectivity ratio (Sratio), to identify the biomarkers. The PASA achieved an accuracy of 84%. Most of the feature wavelengths fell into the collagen-dominated absorption waveband, which was consistent with the histopathological results. This paper proposes a successful method for identifying molecular breast cancer subtypes and proves that collagen can be treated as a biomarker for molecular breast cancer subtyping.
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Affiliation(s)
- Jiayan Li
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, China
| | - Yingna Chen
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, China
| | - Wanli Ye
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, China
| | - Mengjiao Zhang
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, China
| | - Jingtao Zhu
- School of Physics Science and Engineering, Tongji University, Shanghai, China
| | - Wenxiang Zhi
- Department of Ultrasonography, Fudan University Shanghai Cancer Center, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qian Cheng
- Institute of Acoustics, School of Physics Science and Engineering, Tongji University, Shanghai, China
- Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai, China
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Ibrahim MM, Uzairu A, Ibrahim MT, Umar AB. Modelling PIP4K2A inhibitory activity of 1,7-naphthyridine analogues using machine learning and molecular docking studies. RSC Adv 2023; 13:3402-3415. [PMID: 36756602 PMCID: PMC9871732 DOI: 10.1039/d2ra07382j] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/12/2023] [Indexed: 01/26/2023] Open
Abstract
PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). PIP4K2A has been intricately linked to the inhibition of various types of tumors via reactive oxygen species-mediated apoptosis, making it an important therapeutic target. In the quest of finding biologically active substances with efficient PIP4K2A inhibitory activity, machine learning algorithms were used to investigate the quantitative relationship between structures and inhibitory activities of 1,7-naphthyridine analogues. Three machine learning algorithms (MLR, ANN, and SVM) were used to develop QSAR models that can effectively predict the PIP4K2A inhibitory activity of a library of 1,7-naphthyridine analogues. The cascaded feature selection method was performed by sequential application of GFA and MP5 algorithms to identify a molecular descriptor subset that can best describe the PIP4K2A inhibitory activity of 1,7-naphthyridine analogues. PIP4K2A inhibitory activities predicted by the ML models were strongly correlated with the experimental values. The QSAR Modelling indicates that the best-performing ML model was SVM with the RBF kernel function. The SVM model performed very well in predicting PIP4K2A inhibitory activity of the 1,7-naphthyridine analogues with RTR and QEX values of 0.9845 and 0.8793 respectively. To further gain more structural insight into the origin of PIP4K2A inhibitory activity of 1,7-naphthyridine analogues, molecular docking studies were performed. The results indicate that five compounds; 15, 25, 13, 09, and 28 were found to have a high binding affinity with the receptor molecules. Hydrogen bonding, pi-pi interaction, and pi-cation interactions were found to modulate the binding interaction of the inhibitors. Although the SVM gives essentially a black-box model which cannot be readily interpreted, using SVM in tandem with MLR and ANN provides a unique perspective in building robust QSAR predictive models. The superior predictive performance of the ML models and the explanatory power of MLR models were combined to provide a unique insight into the structure-activity relationship of 1,7-naphthyridine inhibitors. This is relevant in that it provides information that can be invaluable as guidelines for the design of novel PIP4K2A inhibitors.
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Affiliation(s)
- Muktar Musa Ibrahim
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University P. M. B 1045 Zaria Nigeria +234 6196 4053
| | - Adamu Uzairu
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University P. M. B 1045 Zaria Nigeria +234 6196 4053
| | - Muhammad Tukur Ibrahim
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University P. M. B 1045 Zaria Nigeria +234 6196 4053
| | - Abdullahi Bello Umar
- Department of Chemistry, Faculty of Physical Sciences, Ahmadu Bello University P. M. B 1045 Zaria Nigeria +234 6196 4053
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9
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Li W, Wu S. Challenges of halogenated polycyclic aromatic hydrocarbons in foods: Occurrence, risk, and formation. Trends Food Sci Technol 2022. [DOI: 10.1016/j.tifs.2022.11.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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10
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Tao C, Chen Y, Tao T, Cao Z, Chen W, Zhu T. Versatile in silico modeling of XAD-air partition coefficients for POPs based on abraham descriptor and temperature. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 311:119857. [PMID: 35944777 DOI: 10.1016/j.envpol.2022.119857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/17/2022] [Accepted: 07/23/2022] [Indexed: 06/15/2023]
Abstract
The concentration of persistent organic pollutants (POPs) makes remarkable difference to environmental fate. In the field of passive sampling, the partition coefficients between polystyrene-divinylbenzene resin (XAD) and air (i.e., KXAD-A) are indispensable to obtain POPs concentration, and the KXAD-A is generally thought to be governed by temperature and molecular structure of POPs. However, experimental determination of KXAD-A is unrealistic for countless and novel chemicals. Herein, the Abraham solute descriptors of poly parameter linear free energy relationship (pp-LFER) and temperature were utilized to develop models, namely pp-LFER-T, for predicting KXAD-A values. Two linear (MLR and LASSO) and four nonlinear (ANN, SVM, kNN and RF) machine learning algorithms were employed to develop models based on a data set of 307 sample points. For the aforementioned six models, R2adj and Q2ext were both beyond 0.90, indicating distinguished goodness-of-fit and robust generalization ability. By comparing the established models, the best model was observed as the RF model with R2adj = 0.991, Q2ext = 0.935, RMSEtra = 0.271 and RMSEext = 0.868. The mechanism interpretation revealed that the temperature, size of molecules and dipole-type interactions were the predominant factors affecting KXAD-A values. Concurrently, the developed models with the broad applicability domain provide available tools to fill the experimental data gap for untested chemicals. In addition, the developed models were helpful to preliminarily evaluate the environmental ecological risk and understand the adsorption behavior of POPs between XAD membrane and air.
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Affiliation(s)
- Cuicui Tao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Ying Chen
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Tianyun Tao
- College of Agriculture, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Zaizhi Cao
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China
| | - Wenxuan Chen
- School of Civil Engineering, Southeast University, Nanjing, 210096, Jiangsu, China
| | - Tengyi Zhu
- School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
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Pinacho-Castellanos SA, García-Jacas CR, Gilson MK, Brizuela CA. Alignment-Free Antimicrobial Peptide Predictors: Improving Performance by a Thorough Analysis of the Largest Available Data Set. J Chem Inf Model 2021; 61:3141-3157. [PMID: 34081438 DOI: 10.1021/acs.jcim.1c00251] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
In the last two decades, a large number of machine-learning-based predictors for the activities of antimicrobial peptides (AMPs) have been proposed. These predictors differ from one another in the learning method and in the training and testing data sets used. Unfortunately, the training data sets present several drawbacks, such as a low representativeness regarding the experimentally validated AMP space, and duplicated peptide sequences between negative and positive data sets. These limitations give a low confidence to most of the approaches to be used in prospective studies. To address these weaknesses, we propose novel modeling and assessing data sets from the largest experimentally validated nonredundant peptide data set reported to date. From these novel data sets, alignment-free quantitative sequence-activity models (AF-QSAMs) based on Random Forest are created to identify general AMPs and their antibacterial, antifungal, antiparasitic, and antiviral functional types. An applicability domain analysis is carried out to determine the reliability of the predictions obtained, which, to the best of our knowledge, is performed for the first time for AMP recognition. A benchmarking is undertaken between the models proposed and several models from the literature that are freely available in 13 programs (ClassAMP, iAMP-2L, ADAM, MLAMP, AMPScanner v2.0, AntiFP, AMPfun, PEPred-suite, AxPEP, CAMPR3, iAMPpred, APIN, and Meta-iAVP). The models proposed are those with the best performance in all of the endpoints modeled, while most of the methods from the literature have weak-to-random predictive agreements. The models proposed are also assessed through Y-scrambling and repeated k-fold cross-validation tests, demonstrating that the outcomes obtained by them are not given by chance. Three chemometric analyses also confirmed the relevance of the peptides descriptors used in the modeling. Therefore, it can be concluded that the models built by fixing the drawbacks existing in the literature contribute to identifying antibacterial, antifungal, antiparasitic, and antiviral peptides with high effectivity and reliability. Models are freely available via the AMPDiscover tool at https://biocom-ampdiscover.cicese.mx/.
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Affiliation(s)
- Sergio A Pinacho-Castellanos
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México.,Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI), Instituto Politécnico Nacional (IPN), 22435 Tijuana, Baja California, México
| | - César R García-Jacas
- Cátedras CONACYT-Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
| | - Michael K Gilson
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093, United States
| | - Carlos A Brizuela
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), 22860 Ensenada, Baja California, México
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12
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Emara Y, Fantke P, Judson R, Chang X, Pradeep P, Lehmann A, Siegert MW, Finkbeiner M. Integrating endocrine-related health effects into comparative human toxicity characterization. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 762:143874. [PMID: 33401053 DOI: 10.1016/j.scitotenv.2020.143874] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/10/2020] [Accepted: 11/11/2020] [Indexed: 06/12/2023]
Abstract
Endocrine-disrupting chemicals have the ability to interfere with and alter functions of the hormone system, leading to adverse effects on reproduction, growth and development. Despite growing concerns over their now ubiquitous presence in the environment, endocrine-related human health effects remain largely outside of comparative human toxicity characterization frameworks as applied for example in life cycle impact assessments. In this paper, we propose a new methodological framework to consistently integrate endocrine-related health effects into comparative human toxicity characterization. We present two quantitative and operational approaches for extrapolating towards a common point of departure from both in vivo and dosimetry-adjusted in vitro endocrine-related effect data and deriving effect factors as well as corresponding characterization factors for endocrine-active/endocrine-disrupting chemicals. Following the proposed approaches, we calculated effect factors for 323 chemicals, reflecting their endocrine potency, and related characterization factors for 157 chemicals, expressing their relative endocrine-related human toxicity potential. Developed effect and characterization factors are ready for use in the context of chemical prioritization and substitution as well as life cycle impact assessment and other comparative assessment frameworks. Endocrine-related effect factors were found comparable to existing effect factors for cancer and non-cancer effects, indicating that (1) the chemicals' endocrine potency is not necessarily higher or lower than other effect potencies and (2) using dosimetry-adjusted effect data to derive effect factors does not consistently overestimate the effect of potential endocrine disruptors. Calculated characterization factors span over 8-11 orders of magnitude for different substances and emission compartments and are dominated by the range in endocrine potencies.
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Affiliation(s)
- Yasmine Emara
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Peter Fantke
- Quantitative Sustainability Assessment, Department of Technology, Management and Economics, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark.
| | - Richard Judson
- Office of Research and Development, Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
| | - Xiaoqing Chang
- Integrated Laboratory Systems, LLC., Morrisville, NC 27560, United States.
| | - Prachi Pradeep
- Biomolecular and Computational Toxicology Division, Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711.
| | - Annekatrin Lehmann
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Marc-William Siegert
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
| | - Matthias Finkbeiner
- Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany.
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13
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Lunghini F, Marcou G, Azam P, Bonachera F, Enrici MH, Van Miert E, Varnek A. Endocrine disruption: the noise in available data adversely impacts the models' performance. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2021; 32:111-131. [PMID: 33461329 DOI: 10.1080/1062936x.2020.1864468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/11/2020] [Indexed: 06/12/2023]
Abstract
This paper is devoted to the analysis of available experimental data and preparation of predictive models for binding affinity of molecules with respect to two nuclear receptors involved in endocrine disruption (ED): the oestrogen (ER) and the androgen (AR) receptors. The ED-relevant data were retrieved from multiple sources, including the CERAPP, CoMPARA, and the Tox21 projects as well as ChEMBL and PubChem databases. Data analysis performed with the help of generative topographic mapping revealed the problem of low agreement between experimental values from different sources. Collected data were used to train both classification models for ER and AR binding activities and regression models for relative binding affinity (RBA) and median inhibition concentration (IC50). These models displayed relatively poor performance in classification (sensitivities ER = 0.34, AR = 0.49) and in regression (determination coefficient r 2 for the RBA and IC50 models in external validation varied from 0.44 to 0.76). Our analysis demonstrates that low models' performance resulted from misinterpreted experimental endpoints or wrongly reported values, thus confirming the observations reported in CERAPP and CoMPARA studies. Developed models and collected data sets included of 6215 (ER) and 3789 (AR) unique compounds, which are freely available.
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Affiliation(s)
- F Lunghini
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
- Toxicological and Environmental Risk Assessment Unit, Solvay S.A ., St. Fons, France
| | - G Marcou
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
| | - P Azam
- Toxicological and Environmental Risk Assessment Unit, Solvay S.A ., St. Fons, France
| | - F Bonachera
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
| | - M H Enrici
- Toxicological and Environmental Risk Assessment Unit, Solvay S.A ., St. Fons, France
| | - E Van Miert
- Toxicological and Environmental Risk Assessment Unit, Solvay S.A ., St. Fons, France
| | - A Varnek
- Laboratory of Chemoinformatics, University of Strasbourg , Strasbourg, France
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14
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Piir G, Sild S, Maran U. Binary and multi-class classification for androgen receptor agonists, antagonists and binders. CHEMOSPHERE 2021; 262:128313. [PMID: 33182081 DOI: 10.1016/j.chemosphere.2020.128313] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/24/2020] [Accepted: 09/10/2020] [Indexed: 06/11/2023]
Abstract
Androgens and androgen receptor regulate a variety of biological effects in the human body. The impaired functioning of androgen receptor may have different adverse health effects from cancer to infertility. Therefore, it is important to determine whether new chemicals have any binding activity and act as androgen agonists or antagonists before commercial use. Due to the large number of chemicals that require experimental testing, the computational methods are a viable alternative. Therefore, the aim of the present study was to develop predictive QSAR models for classifying compounds according to their activity at the androgen receptor. A large data set of chemicals from the CoMPARA project was used for this purpose and random forest classification models have been developed for androgen binding, agonistic, and antagonistic activity. In addition, a unique effort has been made for multi-class approach that discriminates between inactive compounds, agonists and antagonists simultaneously. For the evaluation set, the classification models predicted agonists with 80% of accuracy and for the antagonists' and binders' the respective metrics were 72% and 78%. Combining agonists, antagonists and inactive compounds into a multi-class approach added complexity to the modelling task and resulted to 64% prediction accuracy for the evaluation set. Considering the size of the training data sets and their imbalance, the achieved evaluation accuracy is very good. The final classification models are available for exploring and predicting at QsarDB repository (https://doi.org/10.15152/QDB.236).
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Affiliation(s)
- Geven Piir
- University of Tartu, Institute of Chemistry, Ravila 14A, Tartu, 50411, Estonia
| | - Sulev Sild
- University of Tartu, Institute of Chemistry, Ravila 14A, Tartu, 50411, Estonia
| | - Uko Maran
- University of Tartu, Institute of Chemistry, Ravila 14A, Tartu, 50411, Estonia.
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15
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Abstract
At the end of her academic career, the author summarizes the main aspects of QSAR modeling, giving comments and suggestions according to her 23 years' experience in QSAR research on environmental topics. The focus is mainly on Multiple Linear Regression, particularly Ordinary Least Squares, using a Genetic Algorithm for variable selection from various theoretical molecular descriptors, but the comments can be useful also for other QSAR methods. The need for rigorous validation, also external, and for applicability domain check to guarantee predictivity and reliability of QSAR models is particularly highlighted. The commented approach is the “predictive” one, based on chemometrics, and is usefully applied to the prioritization of environmental pollutants. All the discussed points and the author's ideas are implemented in the software QSARINS, as a legacy to the QSAR community.
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16
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Gbeddy G, Egodawatta P, Goonetilleke A, Ayoko G, Chen L. Application of quantitative structure-activity relationship (QSAR) model in comprehensive human health risk assessment of PAHs, and alkyl-, nitro-, carbonyl-, and hydroxyl-PAHs laden in urban road dust. JOURNAL OF HAZARDOUS MATERIALS 2020; 383:121154. [PMID: 31525685 DOI: 10.1016/j.jhazmat.2019.121154] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Revised: 08/24/2019] [Accepted: 09/03/2019] [Indexed: 05/22/2023]
Abstract
The carcinogenic human health risks (CHHR) posed by the exposure to PAHs and transformed PAH products (TPPs) are currently inconclusive due to the lack of toxicity equivalency factors (TEFs) for most TPPs although some of these pollutants are more potent carcinogens. The applicability of quantitative structure-activity relationship (QSAR) model in predicting TEF of PAHs and TPPs to holistically evaluate the CHHR posed by the exposure to these pollutants in road dust from Gold Coast, Australia was examined. Statistical evaluation via ten metrics shows that partial least-squares regression (PLSR1) model has more statistical power in predicting TEF than multiple linear regression (MLR) within relevant applicability domain. For instance, the predicted residual sum of squares (PRESS) and standard deviation of error of prediction (SDEP) for PLSR is closer to zero than that of MLR. The total cancer risk estimated using the QSAR model derived TEFs and original TEFs for outliers gives a more holistic incremental lifetime cancer risk in relation to children and adults. Potential cancer risk exists for adults with this approach whereas reliance on only the originally available TEFs lead to a negligible risk diagnosis. The application of QSAR model in assessing CHHR due to PAHs and TPPs exposures is very viable.
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Affiliation(s)
- Gustav Gbeddy
- Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, 4001, Queensland, Australia.
| | - Prasanna Egodawatta
- Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, 4001, Queensland, Australia
| | - Ashantha Goonetilleke
- Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, 4001, Queensland, Australia
| | - Godwin Ayoko
- Science and Engineering Faculty, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, 4001, Queensland, Australia
| | - Lan Chen
- Institute for Future Environments, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, 4001, Queensland, Australia
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17
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Zhang YM, Wang T, Yang XS. An in vitro and in silico investigation of human pregnane X receptor agonistic activity of poly- and perfluorinated compounds using the heuristic method-best subset and comparative similarity indices analysis. CHEMOSPHERE 2020; 240:124789. [PMID: 31561157 DOI: 10.1016/j.chemosphere.2019.124789] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 09/01/2019] [Accepted: 09/05/2019] [Indexed: 06/10/2023]
Abstract
Poly- and perfluorinated compounds (PFCs) may induce potential endocrine-disrupting hormonal effects. However, the molecular mechanism of the toxicology of PFCs remains unclear, and the insufficient information is available on the biological activities of PFCs at present. In this study, the cell-based reporter gene assays were used to determine the agonistic activity of PFCs on the human pregnane X receptor (hPXR). The heuristic method combined with best subset modeling (HM-BSM) based on Dragon descriptors and comparative similarity indices analysis (CoMSIA) were employed to build classical quantitative structure-activity relationship (QSAR) and three-dimensional QSAR models, respectively. The applicability domain (AD) of the classical QSAR model was assessed. Both the HM-BSM and CoMSIA approaches demonstrated good robustness, predictive ability, and mechanistic interpretability. The r2 and leave-one-out cross-validation squared correlated coefficient (q2LOO) values were 0.872 and 0.759 for the HM-BSM, and 0.976 and 0.751 for the CoMSIA model, respectively. The hPXR agonistic activity of the PFCs predicted by the built HM-BSM and CoMSIA agreed well with experimental activity, with root mean square error (RMSE) values of 0.0803 and 0.117, respectively, and external validation squared correlated coefficients (q2EXT) of 0.972 and 0.932, respectively. The hPXR agonistic activity of PFCs was related to their molecular polarizability, charge and atomic mass. Hydrogen bonding and hydrophobic interactions constituted the primary intermolecular forces between PFCs and the hPXR. The developed models were used to screen the PFCs with high hPXR agonistic activity.
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Affiliation(s)
- Yi-Ming Zhang
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Wang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital, Nanjing Medical University, Nanjing, 210029, China
| | - Xu-Shu Yang
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China.
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18
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Papa E, Sangion A, Chirico N. Celebrating 40 Years of Career. Mol Inform 2019; 38:e1980831. [PMID: 31432627 DOI: 10.1002/minf.201980831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Ester Papa
- Department of Theoretical and Applied Sciences, University of Insubria, via J.H. Dunant, 3 -, 21100, Varese, Italy
| | - Alessandro Sangion
- Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail -, M1C 1A4, Toronto ON, Canada
| | - Nicola Chirico
- Department of Theoretical and Applied Sciences, University of Insubria, via J.H. Dunant, 3 -, 21100, Varese, Italy
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19
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Cultivar Classification of Single Sweet Corn Seed Using Fourier Transform Near-Infrared Spectroscopy Combined with Discriminant Analysis. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9081530] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000–2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.
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20
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Wang X, Han W, Li J. QSAR Analysis of a Series of Hydantoin-based Androgen Receptor Modulators and Corresponding Binding Affinities. Mol Inform 2019; 38:e1800147. [PMID: 30969473 DOI: 10.1002/minf.201800147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 03/04/2019] [Indexed: 11/06/2022]
Abstract
Androgen receptor (AR), a member of the nuclear hormone receptor superfamily of intracellular ligand-dependent transcription factors, plays an indispensable role in normal male development through the regulation of androgen through the binding with endogenous androgens. Inappropriate amounts of androgens have a severe adverse effect on men. Excessive androgen may contribute to accelerate prostatic hypertrophy, even prostate cancer, while the absence of androgen may result in reduced muscle mass and strength, decreased bone mass, low energy, diminished sexual function and an increased risk of osteoporosis and fracture. In these cases, androgen receptor modulators are important to maintain the normal biological function of AR. So androgen receptor modulators are necessary for human being to improve their happy life index. To explore the relationships between molecular structures and corresponding binding abilities to aid the new AR modulator design, multiple linear regressions (MLR) are employed to analyze a series of hydantoin analogues, which can bind to androgen receptor acting as AR modulators. The obtained optimum model presents wonderful reliabilities and strong predictive abilities with R2 =0.858, Q L O O 2 =0.822, Q L M O 2 =0.813, Q F 1 2 =0.840, Q F 2 2 =0.807, Q F 3 2 =0.814, CCC=0.893, respectively. The derived model can be used to predict the binding abilities of unknown chemicals and may help to design novel molecules with better AR affinity activity.
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Affiliation(s)
- Xin Wang
- School of Pharmacy, Lanzhou University, 199 West Donggang Rd., 730000, Lanzhou, China
| | - Wenya Han
- School of Pharmacy, Lanzhou University, 199 West Donggang Rd., 730000, Lanzhou, China
| | - Jiazhong Li
- School of Pharmacy, Lanzhou University, 199 West Donggang Rd., 730000, Lanzhou, China
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21
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Toropova AP, Toropov AA. Does the Index of Ideality of Correlation Detect the Better Model Correctly? Mol Inform 2019; 38:e1800157. [DOI: 10.1002/minf.201800157] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 01/18/2019] [Indexed: 12/17/2022]
Affiliation(s)
- Alla P. Toropova
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS Via La Masa 19 20156 Milan Italy
| | - Andrey A. Toropov
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS Via La Masa 19 20156 Milan Italy
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22
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Single-Kernel FT-NIR Spectroscopy for Detecting Supersweet Corn (Zea mays L. Saccharata Sturt) Seed Viability with Multivariate Data Analysis. SENSORS 2018; 18:s18041010. [PMID: 29597324 PMCID: PMC5948831 DOI: 10.3390/s18041010] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Revised: 03/27/2018] [Accepted: 03/27/2018] [Indexed: 01/27/2023]
Abstract
The viability and vigor of crop seeds are crucial indicators for evaluating seed quality, and high-quality seeds can increase agricultural yield. The conventional methods for assessing seed viability are time consuming, destructive, and labor intensive. Therefore, a rapid and nondestructive technique for testing seed viability has great potential benefits for agriculture. In this study, single-kernel Fourier transform near-infrared (FT-NIR) spectroscopy with a wavelength range of 1000-2500 nm was used to distinguish viable and nonviable supersweet corn seeds. Various preprocessing algorithms coupled with partial least squares discriminant analysis (PLS-DA) were implemented to test the performance of classification models. The FT-NIR spectroscopy technique successfully differentiated viable seeds from seeds that were nonviable due to overheating or artificial aging. Correct classification rates for both heat-damaged kernels and artificially aged kernels reached 98.0%. The comprehensive model could also attain an accuracy of 98.7% when combining heat-damaged samples and artificially aged samples into one category. Overall, the FT-NIR technique with multivariate data analysis methods showed great potential capacity in rapidly and nondestructively detecting seed viability in supersweet corn.
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23
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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.![]()
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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
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24
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Wong JC, Zidar J, Ho J, Wang Y, Lee KK, Zheng J, Sullivan MB, You X, Kriegel R. Assessment of several machine learning methods towards reliable prediction of hormone receptor binding affinity. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.cdc.2017.05.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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25
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Brezina E, Prasse C, Meyer J, Mückter H, Ternes TA. Investigation and risk evaluation of the occurrence of carbamazepine, oxcarbazepine, their human metabolites and transformation products in the urban water cycle. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2017; 225:261-269. [PMID: 28408188 DOI: 10.1016/j.envpol.2016.10.106] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 10/28/2016] [Accepted: 10/30/2016] [Indexed: 05/03/2023]
Abstract
Trace organic contaminants such as pharmaceuticals, personal care products and industrial chemicals are frequently detected in the urban water cycle, including wastewater, surface water and groundwater, as well as drinking water. These also include human metabolites (HMs), which are formed in the human body and then excreted via urine or feces, as well as transformation products (TPs) formed in engineered treatment systems and the aquatic environment. In the current study, the occurrence of HMs as well as their TPs of the anticonvulsants carbamazepine (CBZ) and oxcarbazepine (OXC) were investigated using LC tandem MS in effluents of wastewater treatment plants (WWTPs), surface water and groundwater. Highest concentrations were observed in raw wastewater for 10,11-dihydro-10,11-dihydroxycarbamazepine (DiOHCBZ), 10,11-dihydro-10-hydroxy-cabamazepine (10OHCBZ) and CBZ with concentrations ranging up to 2.7 ± 0.4, 1.7 ± 0.2 and 1.07 ± 0.06 μg L-1, respectively. Predictions of different toxicity endpoints using a Distributed Structure-Searchable Toxicity (DSSTox) expert system query indicated that several HMs and TPs, in particular 9-carboxy-acridine (9-CA-ADIN) and acridone (ADON), may exhibit an increased genotoxicity compared to the parent compound CBZ. As 9-CA-ADIN was also detected in groundwater, a detailed investigation of the genotoxicity of 9-CA-ADIN is warranted. Investigations of an advanced wastewater treatment plant further revealed that the discharge of the investigated compounds into the aquatic environment could be substantially reduced by ozonation followed by granular activated carbon (GAC) filtration.
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Affiliation(s)
- Elena Brezina
- Federal Institute of Hydrology (BfG), Am Mainzer Tor 1, 56068 Koblenz, Germany
| | - Carsten Prasse
- Federal Institute of Hydrology (BfG), Am Mainzer Tor 1, 56068 Koblenz, Germany; Department of Civil & Environmental Engineering, University of California at Berkeley, Berkeley, CA, USA
| | - Johannes Meyer
- Walther-Straub-Institute, LMU, Goethestraße 33, 80336 Munich, Germany
| | - Harald Mückter
- Walther-Straub-Institute, LMU, Goethestraße 33, 80336 Munich, Germany
| | - Thomas A Ternes
- Federal Institute of Hydrology (BfG), Am Mainzer Tor 1, 56068 Koblenz, Germany.
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26
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Slavov SH, Beger RD. Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for ToxCast estrogen receptor data classification, validation, and feature extraction. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2017; 36:823-830. [PMID: 27509091 DOI: 10.1002/etc.3578] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 07/06/2016] [Accepted: 08/09/2016] [Indexed: 06/06/2023]
Abstract
The estrogenic potential (expressed as a score composite of 18 high throughput screening bioassays) of 1528 compounds from the ToxCast database was modeled by a 3-dimensional spectral data activity relationship approach (3D-SDAR). Due to a lack of 17 O nuclear magnetic resonance (NMR) simulation software, the most informative carbon-carbon 3D-SDAR fingerprints were augmented with indicator variables representing oxygen atoms from carbonyl and carboxamide, ester, sulfonyl, nitro, aliphatic hydroxyl, and phenolic hydroxyl groups. To evaluate the true predictive performance of the authors' model the United States Environmental Protection Agency provided them with a blind test set consisting of 2008 compounds. Of these, 543 had available literature data-their binding affinity served to estimate the external classification accuracy of the developed model: predictive accuracy of 0.62, sensitivity of 0.71, and specificity of 0.53 were obtained. Compared with alternative modeling techniques, the authors' model displayed very little reduction in performance between the modeling and the prediction set. A 3D-SDAR mapping technique allowed identification of structural features essential for estrogenicity: 1) the presence of a phenolic OH group or cyclohexenone, 2) a second aromatic or phenolic ring at a distance of 6 Å to 8 Å from the oxygen of the first phenol ring, 3) the presence of a methyl group approximately 6 Å away from the centroid of a phenol ring, and 4) a carbonyl group in close proximity (∼4 Å measured to the centroid) to 1 of the phenol rings. Environ Toxicol Chem 2017;36:823-830. Published 2016 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.
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Affiliation(s)
- Svetoslav H Slavov
- Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
| | - Richard D Beger
- Division of Systems Biology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA
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27
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Porta N, ra Roncaglioni A, Marzo M, Benfenati E. QSAR Methods to Screen Endocrine Disruptors. NUCLEAR RECEPTOR RESEARCH 2016. [DOI: 10.11131/2016/101203] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Nicola Porta
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Aless ra Roncaglioni
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Marco Marzo
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, IRCCS-Istituto di Ricerche Farmacologiche “Mario Negri”, Via La Masa 19, 20159 Milan, Italy
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Zhang YM, Chang MJ, Yang XS, Han X. In silico investigation of agonist activity of a structurally diverse set of drugs to hPXR using HM-BSM and HM-PNN. JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY. MEDICAL SCIENCES = HUA ZHONG KE JI DA XUE XUE BAO. YI XUE YING DE WEN BAN = HUAZHONG KEJI DAXUE XUEBAO. YIXUE YINGDEWEN BAN 2016; 36:463-468. [PMID: 27376821 DOI: 10.1007/s11596-016-1609-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2016] [Revised: 04/28/2016] [Indexed: 10/21/2022]
Abstract
The human pregnane X receptor (hPXR) plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards hPXR. Heuristic method (HM)-Best Subset Modeling (BSM) and HM-Polynomial Neural Networks (PNN) were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain (AD) of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved (for HM-BSM, r (2)=0.881, q LOO (2) =0.797, q EXT (2) =0.674; for HM-PNN, r (2)=0.882, q LOO (2) =0.856, q EXT (2) =0.655). The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to hPXR.
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Affiliation(s)
- Yi-Ming Zhang
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 210029, China
| | - Mei-Jia Chang
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China
| | - Xu-Shu Yang
- School of Pharmacy, Nanjing Medical University, Nanjing, 211166, China.
| | - Xiao Han
- School of Basic Medical Sciences, Nanjing Medical University, Nanjing, 210029, China.
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29
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Borhani TNG, Saniedanesh M, Bagheri M, Lim JS. QSPR prediction of the hydroxyl radical rate constant of water contaminants. WATER RESEARCH 2016; 98:344-53. [PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 02/09/2016] [Accepted: 04/15/2016] [Indexed: 05/24/2023]
Abstract
In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
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Affiliation(s)
- Tohid Nejad Ghaffar Borhani
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
| | - Mohammadhossein Saniedanesh
- Process Systems Engineering Centre (PROSPECT), Research Institute for Sustainable Environment, Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia
| | - Mehdi Bagheri
- Department of Chemical and Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, BC V6T 1Z3, Canada
| | - Jeng Shiun Lim
- Process Systems Engineering Centre (PROSPECT), Research Institute for Sustainable Environment, Faculty of Chemical and Energy Engineering, Universiti Teknologi Malaysia (UTM), 81310 UTM Johor Bahru, Johor, Malaysia
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30
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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
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31
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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.
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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
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Ruiz P, Ingale K, Wheeler JS, Mumtaz M. 3D QSAR studies of hydroxylated polychlorinated biphenyls as potential xenoestrogens. CHEMOSPHERE 2016; 144:2238-2246. [PMID: 26598992 PMCID: PMC8211363 DOI: 10.1016/j.chemosphere.2015.11.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 09/17/2015] [Accepted: 11/02/2015] [Indexed: 06/01/2023]
Abstract
Mono-hydroxylated polychlorinated biphenyls (OH-PCBs) are found in human biological samples and lack of data on their potential estrogenic activity has been a source of concern. We have extended our previous in silico 2D QSAR study through the application of advance techniques such as docking and 3D QSAR to gain insights into their estrogen receptor (ERα) binding. The results support our earlier findings that the hydroxyl group is the most important feature on the compounds; its position, orientation and surroundings in the structure are influential for the binding of OH-PCBs to ERα. This study has also revealed the following additional interactions that influence estrogenicity of these chemicals (a) the aromatic interactions of the biphenyl moieties with the receptor, (b) hydrogen bonding interactions of the p-hydroxyl group with key amino acids ARG394 and GLU353, (c) low or no electronegative substitution at para-positions of the p-hydroxyl group, (d) enhanced electrostatic interactions at the meta position on the B ring, and (e) co-planarity of the hydroxyl group on the A ring. In combination the 2D and 3D QSAR approaches have led us to the support conclusion that the hydroxyl group is the most important feature on the OH-PCB influencing the binding to estrogen receptors, and have enhanced our understanding of the mechanistic details of estrogenicity of this class of chemicals. Such in silico computational methods could serve as useful tools in risk assessment of chemicals.
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Affiliation(s)
- Patricia Ruiz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, 1600 Clifton Road, MS-F57, Atlanta, GA, 30333, USA.
| | - Kundan Ingale
- VLife Sciences Tech. Pvt. Ltd., Plot No-05, Survey No 131/1b/2/11, Ram Indu Park, Baner Road, Pune, 411045, India
| | - John S Wheeler
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, 1600 Clifton Road, MS-F57, Atlanta, GA, 30333, USA
| | - Moiz Mumtaz
- Computational Toxicology and Methods Development Laboratory, Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, 1600 Clifton Road, MS-F57, Atlanta, GA, 30333, USA
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Prasse C, Stalter D, Schulte-Oehlmann U, Oehlmann J, Ternes TA. Spoilt for choice: A critical review on the chemical and biological assessment of current wastewater treatment technologies. WATER RESEARCH 2015; 87:237-70. [PMID: 26431616 DOI: 10.1016/j.watres.2015.09.023] [Citation(s) in RCA: 158] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Revised: 09/02/2015] [Accepted: 09/11/2015] [Indexed: 05/28/2023]
Abstract
The knowledge we have gained in recent years on the presence and effects of compounds discharged by wastewater treatment plants (WWTPs) brings us to a point where we must question the appropriateness of current water quality evaluation methodologies. An increasing number of anthropogenic chemicals is detected in treated wastewater and there is increasing evidence of adverse environmental effects related to WWTP discharges. It has thus become clear that new strategies are needed to assess overall quality of conventional and advanced treated wastewaters. There is an urgent need for multidisciplinary approaches combining expertise from engineering, analytical and environmental chemistry, (eco)toxicology, and microbiology. This review summarizes the current approaches used to assess treated wastewater quality from the chemical and ecotoxicological perspective. Discussed chemical approaches include target, non-target and suspect analysis, sum parameters, identification and monitoring of transformation products, computational modeling as well as effect directed analysis and toxicity identification evaluation. The discussed ecotoxicological methodologies encompass in vitro testing (cytotoxicity, genotoxicity, mutagenicity, endocrine disruption, adaptive stress response activation, toxicogenomics) and in vivo tests (single and multi species, biomonitoring). We critically discuss the benefits and limitations of the different methodologies reviewed. Additionally, we provide an overview of the current state of research regarding the chemical and ecotoxicological evaluation of conventional as well as the most widely used advanced wastewater treatment technologies, i.e., ozonation, advanced oxidation processes, chlorination, activated carbon, and membrane filtration. In particular, possible directions for future research activities in this area are provided.
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Affiliation(s)
- Carsten Prasse
- Federal Institute of Hydrology (BfG), Department of Aquatic Chemistry, Koblenz, Germany; Department of Civil & Environmental Engineering, University of California at Berkeley, Berkeley, United States.
| | - Daniel Stalter
- National Research Centre for Environmental Toxicology, The University of Queensland, Queensland, Australia; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf, Switzerland
| | | | - Jörg Oehlmann
- Goethe University Frankfurt, Department Aquatic Ecotoxicology, Frankfurt, Germany
| | - Thomas A Ternes
- Federal Institute of Hydrology (BfG), Department of Aquatic Chemistry, Koblenz, Germany
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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.
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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
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35
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Vuorinen A, Odermatt A, Schuster D. Reprint of "In silico methods in the discovery of endocrine disrupting chemicals". J Steroid Biochem Mol Biol 2015; 153:93-101. [PMID: 26291836 DOI: 10.1016/j.jsbmb.2015.08.015] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2012] [Revised: 04/03/2013] [Accepted: 04/07/2013] [Indexed: 12/18/2022]
Abstract
The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.
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Affiliation(s)
- Anna Vuorinen
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck - CMBI, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
| | - Alex Odermatt
- Swiss Center for Applied Human Toxicology and Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056 Basel, Switzerland
| | - Daniela Schuster
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck - CMBI, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria.
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36
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Gallampois CMJ, Schymanski EL, Krauss M, Ulrich N, Bataineh M, Brack W. Multicriteria approach to select polyaromatic river mutagen candidates. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:2959-68. [PMID: 25635928 DOI: 10.1021/es503640k] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The identification of unknown compounds remains one of the most challenging tasks to link observed toxic effects in complex environmental mixtures to responsible toxicants in effect-directed analysis (EDA). Here, a workflow is presented based on nontarget liquid chromatography-high resolution mass spectrometry (LC-HRMS) starting with molecular formulas determined in a previous study. A compound database search (ChemSpider) was performed to retrieve candidates for each formula. Subsequently, the number of candidates was reduced by applying MS-, physical-chemical, and chromatography-based selection criteria including HRMS/MS fragmentation and plausibility, ionization efficiency with different ion sources and detection modes, acid/base behavior, octanol/water partitioning, retention time prediction and finally toxic effects (mutagenicity caused by aromatic amines). The workflow strongly decreased the number of possible candidates and resulted in the tentative identification of possible mutagens and the positive identification of the nonmutagen benzyl(diphenyl) phosphine oxide in a mutagenic fraction. The positive identification of mutagens was hampered by a lack of commercially available standards. The workflow is an innovative and promising approach and forms an excellent basis for possible further advancements.
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Affiliation(s)
- Christine M J Gallampois
- UFZ - Helmholtz Centre for Environmental Research , Department of Effect-Directed Analysis, Permoserstr. 15, D-04318 Leipzig, Germany
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37
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Vuorinen A, Odermatt A, Schuster D. In silico methods in the discovery of endocrine disrupting chemicals. J Steroid Biochem Mol Biol 2013; 137:18-26. [PMID: 23688835 DOI: 10.1016/j.jsbmb.2013.04.009] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2012] [Revised: 04/03/2013] [Accepted: 04/07/2013] [Indexed: 11/27/2022]
Abstract
The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.
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Affiliation(s)
- Anna Vuorinen
- Institute of Pharmacy/Pharmaceutical Chemistry and Center for Molecular Biosciences Innsbruck - CMBI, University of Innsbruck, Innrain 80-82, 6020 Innsbruck, Austria
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38
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Ghasemi JB, Hooshmand S. 3D-QSAR, docking and molecular dynamics for factor Xa inhibitors as anticoagulant agents. MOLECULAR SIMULATION 2013. [DOI: 10.1080/08927022.2012.741235] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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39
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Ruiz P, Myshkin E, Quigley P, Faroon O, Wheeler JS, Mumtaz MM, Brennan RJ. Assessment of hydroxylated metabolites of polychlorinated biphenyls as potential xenoestrogens: a QSAR comparative analysis∗. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2013; 24:393-416. [PMID: 23557136 DOI: 10.1080/1062936x.2013.781537] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Alternative methods, including quantitative structure-activity relationships (QSAR), are being used increasingly when appropriate data for toxicity evaluation of chemicals are not available. Approximately 40 mono-hydroxylated polychlorinated biphenyls (OH-PCBs) have been identified in humans. They represent a health and environmental concern because some of them have been shown to have agonist or antagonist interactions with human hormone receptors. This could lead to modulation of steroid hormone receptor pathways and endocrine system disruption. We performed QSAR analyses using available estrogenic activity (human estrogen receptor ER alpha) data for 71 OH-PCBs. The modelling was performed using multiple molecular descriptors including electronic, molecular, constitutional, topological, and geometrical endpoints. Multiple linear regressions and recursive partitioning were used to best fit descriptors. The results show that the position of the hydroxyl substitution, polarizability, and meta adjacent un-substituted carbon pairs at the phenolic ring contribute towards greater estrogenic activity for these chemicals. These comparative QSAR models may be used for predictive toxicity, and identification of health consequences of PCB metabolites that lack empirical data. Such information will help prioritize such molecules for additional testing, guide future basic laboratory research studies, and help the health/risk assessment community understand the complex nature of chemical mixtures.
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Affiliation(s)
- P Ruiz
- Division of Toxicology and Human Health Sciences, Agency for Toxic Substances and Disease Registry, Atlanta, USA.
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40
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Li J, Liu H, Huo X, Gramatica P. Structure-Activity Relationship Analysis of the Thermal Stabilities of Nitroaromatic Compounds Following Different Decomposition Mechanisms. Mol Inform 2013; 32:193-202. [DOI: 10.1002/minf.201200089] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2012] [Accepted: 12/06/2012] [Indexed: 11/11/2022]
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Abstract
The fundamental and more critical steps that are necessary for the development and validation of QSAR models are presented in this chapter as best practices in the field. These procedures are discussed in the context of predictive QSAR modelling that is focused on achieving models of the highest statistical quality and with external predictive power. The most important and most used statistical parameters needed to verify the real performances of QSAR models (of both linear regression and classification) are presented. Special emphasis is placed on the validation of models, both internally and externally, as well as on the need to define model applicability domains, which should be done when models are employed for the prediction of new external compounds.
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Affiliation(s)
- Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Theoretical and Applied Sciences, University of Insubria, via Dunant 3, Varese, Italy.
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Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E. QSAR Modeling is not "Push a Button and Find a Correlation": A Case Study of Toxicity of (Benzo-)triazoles on Algae. Mol Inform 2012; 31:817-35. [PMID: 27476736 DOI: 10.1002/minf.201200075] [Citation(s) in RCA: 136] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2012] [Accepted: 09/21/2012] [Indexed: 11/07/2022]
Abstract
A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR-OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL-Descriptor and QSPR-THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL-Descriptor software.
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Affiliation(s)
- Paola Gramatica
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Via Dunant 3, 21100, Varese, Italy, http://www.qsar.it.
| | - Stefano Cassani
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Via Dunant 3, 21100, Varese, Italy, http://www.qsar.it
| | - Partha Pratim Roy
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Via Dunant 3, 21100, Varese, Italy, http://www.qsar.it.,Present Address: Guru Ghasidas University, Bilaspur, Koni, India
| | - Simona Kovarich
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Via Dunant 3, 21100, Varese, Italy, http://www.qsar.it
| | - Chun Wei Yap
- Department of Pharmacy, Pharmaceutical Data Exploration Laboratory, National University of Singapore, Singapore
| | - Ester Papa
- QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Department of Theoretical and Applied Sciences, University of Insubria, Via Dunant 3, 21100, Varese, Italy, http://www.qsar.it
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Hechinger M, Leonhard K, Marquardt W. What is Wrong with Quantitative Structure–Property Relations Models Based on Three-Dimensional Descriptors? J Chem Inf Model 2012; 52:1984-93. [DOI: 10.1021/ci300246m] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- M. Hechinger
- AVT-Process
Systems Engineering and ‡Chair of Technical Thermodynamics, RWTH Aachen University, 52064 Aachen, Germany
| | - K. Leonhard
- AVT-Process
Systems Engineering and ‡Chair of Technical Thermodynamics, RWTH Aachen University, 52064 Aachen, Germany
| | - W. Marquardt
- AVT-Process
Systems Engineering and ‡Chair of Technical Thermodynamics, RWTH Aachen University, 52064 Aachen, Germany
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Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products. Molecules 2012; 17:8982-9001. [PMID: 22842643 PMCID: PMC6269063 DOI: 10.3390/molecules17088982] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2012] [Revised: 07/19/2012] [Accepted: 07/23/2012] [Indexed: 11/17/2022] Open
Abstract
Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (LD₅₀) for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.
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Myshkin E, Brennan R, Khasanova T, Sitnik T, Serebriyskaya T, Litvinova E, Guryanov A, Nikolsky Y, Nikolskaya T, Bureeva S. Prediction of Organ Toxicity Endpoints by QSAR Modeling Based on Precise Chemical-Histopathology Annotations. Chem Biol Drug Des 2012; 80:406-16. [DOI: 10.1111/j.1747-0285.2012.01411.x] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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Li F, Wu H, Li L, Li X, Zhao J, Peijnenburg WJGM. Docking and QSAR study on the binding interactions between polycyclic aromatic hydrocarbons and estrogen receptor. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 80:273-279. [PMID: 22503158 DOI: 10.1016/j.ecoenv.2012.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2011] [Revised: 03/15/2012] [Accepted: 03/18/2012] [Indexed: 05/31/2023]
Abstract
Little is known about the estrogenic activities of polycyclic aromatic hydrocarbons (PAHs) and the underlying mechanisms on estrogenic activities are still unclear. Molecular docking and quantitative structure-activity relationship (QSAR) were used to understand the relationship between molecular structural features and estrogenic activity, and to predict the binding affinity of PAHs to estrogen receptor α (ERα). From molecular docking analysis, hydrogen bonding as well as hydrophobic and π interactions were found between PAHs and ERα. Based on the docking results, appropriate molecular structural parameters were adopted to develop a QSAR model. Five descriptors were included in the QSAR model, which indicated that the estrogenic activity was related to molecular size, van der Waals volumes, shape profiles, polarizabilities and electropological states were significant parameters explaining the estrogenicity. Comparatively, the developed QSAR model had good robustness, predictive ability and mechanistic interpretability. Moreover, the applicability domain of the model was described.
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Affiliation(s)
- Fei Li
- Key Laboratory of Coastal Zone Environmental Processes, Yantai Institute of Coastal Zone Research (YIC), Chinese Academy of Sciences (CAS), Shandong Provincial Key Laboratory of Coastal Zone Environmental Processes, YICCAS, Yantai Shandong 264003, PR China
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Akyüz L, Sarıpınar E. Conformation depends on 4D-QSAR analysis using EC-GA method: pharmacophore identification and bioactivity prediction of TIBOs as non-nucleoside reverse transcriptase inhibitors. J Enzyme Inhib Med Chem 2012; 28:776-91. [PMID: 22591319 DOI: 10.3109/14756366.2012.684051] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The electron conformational and genetic algorithm methods (EC-GA) were integrated for the identification of the pharmacophore group and predicting the anti HIV-1 activity of tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepinone (TIBO) derivatives. To reveal the pharmacophore group, each conformation of all compounds was arranged by electron conformational matrices of congruity. Multiple comparisons of these matrices, within given tolerances for high active and low active TIBO derivatives, allow the identification of the pharmacophore group that refers to the electron conformational submatrix of activity. The effects of conformations, internal and external validation were investigated by four different models based on an ensemble of conformers and a single conformer, both with and without a test set. Model 1 using an ensemble of conformers for the training (39 compounds) and test sets (13 compounds), obtained by the optimum seven parameters, gave satisfactory results (R²(training) = 0.878, R²(test)= 0.910, q² = 0.840, q²(ext1) = 0.926 and q²(ext2) = 0.900).
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Affiliation(s)
- Lalehan Akyüz
- Faculty of Science, Department of Chemistry, Erciyes University, Kayseri, Turkey
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48
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QSAR prediction of antagonistic activity of PCBs towards human PXR by using heuristic method and best subset modeling. Sci China Chem 2012. [DOI: 10.1007/s11426-011-4409-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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49
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Rao H, Zeng X, Wang Y, He H, Zhu F, Li Z, Chen Y. Identification of DNA adduct formation of small molecules by molecular descriptors and machine learning methods. MOLECULAR SIMULATION 2012. [DOI: 10.1080/08927022.2011.616891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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50
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Bagheri M, Rajabi M, Mirbagheri M, Amin M. BPSO-MLR and ANFIS based modeling of lower flammability limit. J Loss Prev Process Ind 2012. [DOI: 10.1016/j.jlp.2011.10.005] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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