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Akinola LK, Uzairu A, Shallangwa GA, Abechi SE, Umar AB. Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models. Curr Res Toxicol 2024; 6:100158. [PMID: 38435023 PMCID: PMC10907392 DOI: 10.1016/j.crtox.2024.100158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/22/2024] [Accepted: 02/22/2024] [Indexed: 03/05/2024] Open
Abstract
Identification of estrogen receptor (ER) agonists among environmental toxicants is essential for assessing the potential impact of toxicants on human health. Using 2D autocorrelation descriptors as predictor variables, two binary logistic regression models were developed to identify active ER agonists among hydroxylated polychlorinated biphenyls (OH-PCBs). The classifications made by the two models on the training set compounds resulted in accuracy, sensitivity and specificity of 95.9 %, 93.9 % and 97.6 % for ERα dataset and 91.9 %, 90.9 % and 92.7 % for ERβ dataset. The areas under the ROC curves, constructed with the training set data, were found to be 0.985 and 0.987 for the two models. Predictions made by models I and II correctly classified 84.0 % and 88.0 % of the test set compounds and 89.8 % and 85.8% of the cross-validation set compounds respectively. The two classification-based QSAR models proposed in this paper are considered robust and reliable for rapid identification of ERα and ERβ agonists among OH-PCB congeners.
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Affiliation(s)
- Lukman K. Akinola
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
- Department of Chemistry, Bauchi State University, Gadau, Nigeria
| | - Adamu Uzairu
- Department of Chemistry, Ahmadu Bello University, Zaria, Nigeria
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Idarubicin combats abiraterone and enzalutamide resistance in prostate cells via targeting XPA protein. Cell Death Dis 2022; 13:1034. [PMID: 36509750 PMCID: PMC9744908 DOI: 10.1038/s41419-022-05490-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 11/13/2022] [Accepted: 12/01/2022] [Indexed: 12/15/2022]
Abstract
Although second-generation therapies like abiraterone (ABI) and enzalutamide (ENZ) benefit patients with castration-resistant prostate cancer (CRPC), drug resistance frequently occurs, eventually resulting in therapy failure. In this study, we used two libraries, FDA-approved drug library and CRISP/Cas9 knockout (GeCKO) library to screen for drugs that overcome treatment resistance and to identify the potential drug-resistant genes involved in treatment resistance. Our screening results showed that the DNA-damaging agent idarubicin (IDA) overcame abiraterone and enzalutamide resistance in prostate cancer cells. IDA treatment inhibited the DNA repair protein XPA expression in a transcription-independent manner. Consistently, XPA knockout sensitized prostate cancer cells to abiraterone and enzalutamide treatment. In conclusion, IDA combats abiraterone and enzalutamide resistance by reducing XPA protein level in prostate cancer.
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Xu X, Wang C, Gui B, Yuan X, Li C, Zhao Y, Martyniuk CJ, Su L. Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor. ENVIRONMENTAL RESEARCH 2022; 212:113175. [PMID: 35351457 DOI: 10.1016/j.envres.2022.113175] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 03/04/2022] [Accepted: 03/22/2022] [Indexed: 06/14/2023]
Abstract
With the promotion of carbon neutrality, it is also important to synchronously promote the assessment and sustainable management of chemicals so as to protect public health. Humans and animals are possibly exposed to endocrine disruptors that have inhibitory effects on thyroid stimulating hormone receptor (TSHR). As such, it is important to identify chemicals that inhibit TSHR and to develop models to predict their inhibitory activity. In this study, 5952 compounds derived from a cyclic adenosine monophosphate (cAMP) analysis, a key signaling pathway in thyrocytes, were used to establish a binary classification model comparing methods that included random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR). The prediction model based on RF showed the highest identification accuracy for revealing chemicals that may inhibit TSHR. For the RF model, recall was calculated at 0.89, balance accuracy was 0.85, and its receiver operating characteristic (ROC) curve-area under (AUC) was 0.92, indicating that the model had very high predictive capacity. The lowest CDocker energy (CE) and CDocker interaction energy (CIE) for chemicals and TSHR were determined and were subsequently introduced into the predictive model as descriptors. A regression model, extreme gradient boosting-Regression (XGBR), was successfully established yielding an R2 = 0.65 to predict inhibitory activity for active compounds. Parameters that included dissociation characteristics, molecular structure, and binding energy were all key factors in the predictive model. We demonstrate that QSAR models are useful approaches, not only for identifying chemicals that inhibit TSHR, but for predicting inhibitory activity of active compounds.
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Affiliation(s)
- Xiaotian Xu
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, 130117, PR China
| | - Chen Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, 130117, PR China
| | - Bingxin Gui
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, 130117, PR China
| | - Xiangyi Yuan
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, 130117, PR China
| | - Chao Li
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, 130117, PR China
| | - Yuanhui Zhao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, 130117, PR China
| | - Christopher J Martyniuk
- Center for Environmental and Human Toxicology, Department of Physiological Sciences, College of Veterinary Medicine, UF Genetics Institute, Interdisciplinary Program in Biomedical Sciences Neuroscience, University of Florida, Gainesville, FL, 32611, USA
| | - Limin Su
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, Changchun, 130117, PR China.
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Song WS, Koh DH, Kim EY. Orthogonal assay for validation of Tox21 PPARγ data and applicability to in silico prediction model. Toxicol In Vitro 2022; 84:105445. [PMID: 35863590 DOI: 10.1016/j.tiv.2022.105445] [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: 02/17/2022] [Revised: 07/01/2022] [Accepted: 07/13/2022] [Indexed: 11/28/2022]
Abstract
High-throughput screening data from the Tox21 database is used for prioritizing hazardous chemicals and building in silico-based toxicity prediction models. One of the Tox21 dataset, peroxisome proliferator-activated receptor-gamma (PPARγ), a nuclear receptor superfamily, identified various endpoints in HEK293 cells. PPARγ mediates various toxic effects when its receptors are activated or inhibited by ligands such as thiazolidinedione and GW9662. In this study, an orthogonal assay was constructed to verify the effectiveness of the Tox21 PPARγ data, and the effect of highly reliable data on in silico model construction was investigated. The orthogonal assay was a reporter gene assay based on the PPARγ ligand binding domain in CV-1 cells. Only 39% of agonists and 55% of antagonists had similar responses in CV-1 and HEK293 cells. Thus, the effectiveness of Tox21 data on PPARγ may vary depending on the cell line. However, in silico PLS-DA analysis with only high-reliability data (i.e., the same response in both cell lines), yielded more accurate prediction of the activity of potential chemical ligands, despite the small number of samples. Thus, obtaining reliable chemical screening data for PPARγ through orthogonal analysis, even for only limited chemicals, supports the construction of highly predictive in silico models with improved screening efficiency.
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Affiliation(s)
- Woo-Seon Song
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Hoegi-Dong, Dongdaemun-Gu, Seoul 130-701, Republic of Korea
| | - Dong-Hee Koh
- Department of Biology, Kyung Hee University, Hoegi-Dong, Dongdaemun-Gu, Seoul 130-701, Republic of Korea
| | - Eun-Young Kim
- Department of Biomedical and Pharmaceutical Sciences, Kyung Hee University, Hoegi-Dong, Dongdaemun-Gu, Seoul 130-701, Republic of Korea; Department of Biology, Kyung Hee University, Hoegi-Dong, Dongdaemun-Gu, Seoul 130-701, Republic of Korea.
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Gui B, Wang C, Xu X, Li C, Zhao Y, Su L. Identification of active or inactive agonists of tumor suppressor protein based on Tox21 library. Toxicology 2022; 474:153224. [PMID: 35659517 DOI: 10.1016/j.tox.2022.153224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/15/2022] [Accepted: 05/25/2022] [Indexed: 11/18/2022]
Abstract
Exposure of cells to xenobiotic human-made products can lead to genotoxicity and cause DNA damage. It is an urgent need to quickly identify the chemicals that cause DNA damage, and their toxicity should be predicted. In this study, recursive partitioning (RP), binary logistic regression, and one machine learning approach, namely, random forest (RF) classifier, were used to predict the active and inactive compounds of a total 5036 data based on the assay conducted by a β-lactamase reporter gene under control of the p53 response element (p53RE) from Tox21 library. Results show that the binary logistic regression model with a threshold of 0.5 has a high accuracy rate (83%) to distinguish active and inactive compounds. The RF classifier method has satisfactory results, with an accuracy rate (84.38%) approximately higher than that of binary logistic regression. The models established can identify compounds that induce DNA damage and activate p53, and provide a scientific basis for the risk assessment of organic chemicals in the environment.
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Affiliation(s)
- Bingxin Gui
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, 2555 Jingyue Street, Changchun 130117 Jilin, PR China
| | - Chen Wang
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, 2555 Jingyue Street, Changchun 130117 Jilin, PR China
| | - Xiaotian Xu
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, 2555 Jingyue Street, Changchun 130117 Jilin, PR China
| | - Chao Li
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, 2555 Jingyue Street, Changchun 130117 Jilin, PR China
| | - Yuanhui Zhao
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, 2555 Jingyue Street, Changchun 130117 Jilin, PR China
| | - Limin Su
- State Environmental Protection Key Laboratory of Wetland Ecology and Vegetation Restoration, School of Environment, Northeast Normal University, 2555 Jingyue Street, Changchun 130117 Jilin, PR China.
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Matsuzaka Y, Totoki S, Handa K, Shiota T, Kurosaki K, Uesawa Y. Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure-Activity Relationship System. Int J Mol Sci 2021; 22:10821. [PMID: 34639159 PMCID: PMC8509615 DOI: 10.3390/ijms221910821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 12/11/2022] Open
Abstract
In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure-activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system-which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations-we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.
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Affiliation(s)
- Yasunari Matsuzaka
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
- Center for Gene and Cell Therapy, Division of Molecular and Medical Genetics, The Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
| | - Shin Totoki
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Kentaro Handa
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Tetsuyoshi Shiota
- Fujitsu Limited, Kawasaki-shi, Kanagawa 211-8588, Japan; (S.T.); (K.H.); (T.S.)
| | - Kota Kurosaki
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
| | - Yoshihiro Uesawa
- Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose, Tokyo 204-8588, Japan; (Y.M.); (K.K.)
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