1
|
Krupčíková S, Stiborek M, Kalousková P, Urík J, Šimek Z, Melymuk L, Muz M, Vrana B. Investigation of occurrence of aromatic amines in municipal wastewaters using passive sampling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 939:173196. [PMID: 38750764 DOI: 10.1016/j.scitotenv.2024.173196] [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: 03/20/2024] [Revised: 05/03/2024] [Accepted: 05/11/2024] [Indexed: 06/03/2024]
Abstract
Aromatic amines (AAs) are human-made compounds known for their mutagenic properties, entering surface waters from various sources, often originating as transformation products of dyes or pesticides. Despite their low concentrations in surface waters, AAs can exhibit mutagenicity. Our study focused on evaluating three passive samplers (PSs) for enriching these compounds from influent and effluent of a wastewater treatment plant (WWTP) in Brno, Czech Republic. The PSs tested included variants containing AttractSPE™ SDB-RPS sorbent disk, one with and one without a diffusive agarose hydrogel layer, and a modified Speedisk (Bakerbond Speedisk® H2O-Philic). PSs were deployed in wastewater (WW) for one to four weeks in various overlapping combinations, and the uptake of AAs to PSs was compared to their concentrations in 24-hour composite water samples. A targeted LC/MS analysis covered 42 amines, detecting 11 and 13 AAs in daily composite influent and effluent samples, respectively. In the influent, AAs ranged from 1.5 ng L-1 for 1-anilinonaphthalene to 1.0 μg L-1 for aniline, and the highest concentration among all measured amines was observed for cyclohexylamine at 2.9 μg L-1. In the effluent, concentrations ranged from 0.5 ng L-1 for 1-anilinonaphthalene to 88 ng L-1 for o-anisidine. PSs demonstrated comparable accumulation of amines, with integrative uptake up to 28 days in both influent and effluent and detection of up to 23 and 27 amines in influent and effluent, respectively; altogether 34 compounds were detected in the study. Sampling rates (Rs) were estimated for compounds present in at least 50 % of the samples and showing <40 % aqueous concentration variability, with robustness evaluated by comparing values for compounds in WWTP influent and effluent. Although all devices performed similarly, hydrogel-based PS exhibited superior performance in several criteria, including time integration and robustness of sampling rates, making it a suitable monitoring tool for AAs in WW.
Collapse
Affiliation(s)
- Simona Krupčíková
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 61137, Brno, Czech Republic.
| | - Marek Stiborek
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 61137, Brno, Czech Republic.
| | - Petra Kalousková
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 61137, Brno, Czech Republic.
| | - Jakub Urík
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 61137, Brno, Czech Republic.
| | - Zdeněk Šimek
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 61137, Brno, Czech Republic.
| | - Lisa Melymuk
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 61137, Brno, Czech Republic.
| | - Melis Muz
- Helmholtz Centre for Environmental Research GmbH-UFZ, Department Exposure Science, Permoserstraße 15, 04318 Leipzig, Germany.
| | - Branislav Vrana
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, 61137, Brno, Czech Republic.
| |
Collapse
|
2
|
Ta GH, Weng CF, Leong MK. Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA. Toxicology 2024; 503:153739. [PMID: 38307191 DOI: 10.1016/j.tox.2024.153739] [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: 12/18/2023] [Revised: 01/22/2024] [Accepted: 01/28/2024] [Indexed: 02/04/2024]
Abstract
Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.
Collapse
Affiliation(s)
- Giang H Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan
| | - Ching-Feng Weng
- Institute of Respiratory Disease Department of Basic Medical Science Xiamen Medical College, Xiamen 361023, Fujian, China
| | - Max K Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan.
| |
Collapse
|
3
|
Arora S, Satija S, Mittal A, Solanki S, Mohanty SK, Srivastava V, Sengupta D, Rout D, Arul Murugan N, Borkar RM, Ahuja G. Unlocking The Mysteries of DNA Adducts with Artificial Intelligence. Chembiochem 2024; 25:e202300577. [PMID: 37874183 DOI: 10.1002/cbic.202300577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 10/25/2023]
Abstract
Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.
Collapse
Affiliation(s)
- Sakshi Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Shiva Satija
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Aayushi Mittal
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Saveena Solanki
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Sanjay Kumar Mohanty
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Vaibhav Srivastava
- Division of Glycoscience, Department of Chemistry CBH School, Royal Institute of Technology (KTH) AlbaNova University Center, 10691, Stockholm, Sweden
| | - Debarka Sengupta
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Diptiranjan Rout
- Department of Transfusion Medicine National Cancer Institute, AIIMS, New Delhi, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, 110608, India
| | - Natarajan Arul Murugan
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| | - Roshan M Borkar
- Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research (NIPER)-Guwahati, Sila Katamur Halugurisuk P.O.: Changsari, Dist, Guwahati, Assam, 781101, India
| | - Gaurav Ahuja
- Department of Computational Biology, Indraprastha Institute of Information Technology (IIIT-Delhi) Okhla, Phase III, New Delhi, 110020, India
| |
Collapse
|
4
|
A Graph Convolution Network with Subgraph Embedding for Mutagenic Prediction in Aromatic Hydrocarbons. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
|
5
|
In Silico Prediction of Skin Permeability Using a Two-QSAR Approach. Pharmaceutics 2022; 14:pharmaceutics14050961. [PMID: 35631545 PMCID: PMC9143389 DOI: 10.3390/pharmaceutics14050961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/23/2022] [Accepted: 04/26/2022] [Indexed: 11/17/2022] Open
Abstract
Topical and transdermal drug delivery is an effective, safe, and preferred route of drug administration. As such, skin permeability is one of the critical parameters that should be taken into consideration in the process of drug discovery and development. The ex vivo human skin model is considered as the best surrogate to evaluate in vivo skin permeability. This investigation adopted a novel two-QSAR scheme by collectively incorporating machine learning-based hierarchical support vector regression (HSVR) and classical partial least square (PLS) to predict the skin permeability coefficient and to uncover the intrinsic permeation mechanism, respectively, based on ex vivo excised human skin permeability data compiled from the literature. The derived HSVR model functioned better than PLS as represented by the predictive performance in the training set, test set, and outlier set in addition to various statistical estimations. HSVR also delivered consistent performance upon the application of a mock test, which purposely mimicked the real challenges. PLS, contrarily, uncovered the interpretable relevance between selected descriptors and skin permeability. Thus, the synergy between interpretable PLS and predictive HSVR models can be of great use for facilitating drug discovery and development by predicting skin permeability.
Collapse
|
6
|
Chen C, Min Y, Li X, Chen D, Shen J, Zhang D, Sun H, Bian Q, Yuan H, Wang SL. Mutagenicity risk prediction of PAH and derivative mixtures by in silico simulations oriented from CYP compound I-mediated metabolic activation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 787:147596. [PMID: 33991922 DOI: 10.1016/j.scitotenv.2021.147596] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/26/2021] [Accepted: 05/01/2021] [Indexed: 06/12/2023]
Abstract
PAHs and their derivatives are the main sources of mutagenicity and carcinogenicity in airborne particular matter and cause serious public health and environmental problems. Risk assessment is challenging due to the mixed nature and deficiency of toxicity data of most PAHs and their derivatives. Cytochrome P450 enzymes (CYPs) play important roles in PAH-induced carcinogenicity via metabolic activation, and CYP conformations with compound I structures strongly influence metabolic sites and metabolite species. In this study, complexes of BaP with CYP1A1, CYP1B1 or CYP2C19 compound I were successfully simulated by QM/MM methods and verified by metabolic clearance, and the mutagenicity of chemicals was then predicted by the BaP-7,8-epoxide-related metabolic conformation fitness (MCF) approach, which was validated by Ames tests, showing satisfying accuracy (R2 = 0.46-0.66). Furthermore, a prediction model of the mutagenicity risk of PAH and derivative mixtures was established based on the relative potential factor (RPF) approach and the RPF calculated from the mathematical relationship between the minimum MCF (MCFmin) and RPF, which was successfully validated by the mutagenesis of PAH and derivative mixture mimic-simulating PM2.5 samples collected in eastern China. This study provides fast reliable tools for assessing risk of the complex components of environmental PAHs and their derivatives.
Collapse
Affiliation(s)
- Chao Chen
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Yue Min
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Xuxu Li
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; School of Nursing, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Dongyin Chen
- School of Pharmacy, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Jiemiao Shen
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Di Zhang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China
| | - Hong Sun
- Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Rd., Nanjing 210009, PR China
| | - Qian Bian
- Jiangsu Provincial Center for Disease Control and Prevention, 172 Jiangsu Rd., Nanjing 210009, PR China
| | - Haoliang Yuan
- State Key Laboratory of Natural Medicines and Center of Drug Discovery, China Pharmaceutical University, 639 Longmian Avenue, Nanjing 211198, PR China
| | - Shou-Lin Wang
- Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; State Key Laboratory of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China; School of Nursing, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, PR China.
| |
Collapse
|
7
|
Shamovsky I, Ripa L, Narjes F, Bonn B, Schiesser S, Terstiege I, Tyrchan C. Mechanism-Based Insights into Removing the Mutagenicity of Aromatic Amines by Small Structural Alterations. J Med Chem 2021; 64:8545-8563. [PMID: 34110134 DOI: 10.1021/acs.jmedchem.1c00514] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Aromatic and heteroaromatic amines (ArNH2) are activated by cytochrome P450 monooxygenases, primarily CYP1A2, into reactive N-arylhydroxylamines that can lead to covalent adducts with DNA nucleobases. Hereby, we give hands-on mechanism-based guidelines to design mutagenicity-free ArNH2. The mechanism of N-hydroxylation of ArNH2 by CYP1A2 is investigated by density functional theory (DFT) calculations. Two putative pathways are considered, the radicaloid route that goes via the classical ferryl-oxo oxidant and an alternative anionic pathway through Fenton-like oxidation by ferriheme-bound H2O2. Results suggest that bioactivation of ArNH2 follows the anionic pathway. We demonstrate that H-bonding and/or geometric fit of ArNH2 to CYP1A2 as well as feasibility of both proton abstraction by the ferriheme-peroxo base and heterolytic cleavage of arylhydroxylamines render molecules mutagenic. Mutagenicity of ArNH2 can be removed by structural alterations that disrupt geometric and/or electrostatic fit to CYP1A2, decrease the acidity of the NH2 group, destabilize arylnitrenium ions, or disrupt their pre-covalent transition states with guanine.
Collapse
|
8
|
Kumar R, Khan FU, Sharma A, Siddiqui MH, Aziz IB, Kamal MA, Ashraf GM, Alghamdi BS, Uddin MS. A deep neural network-based approach for prediction of mutagenicity of compounds. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:47641-47650. [PMID: 33895950 DOI: 10.1007/s11356-021-14028-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 04/16/2021] [Indexed: 02/05/2023]
Abstract
We are exposed to various chemical compounds present in the environment, cosmetics, and drugs almost every day. Mutagenicity is a valuable property that plays a significant role in establishing a chemical compound's safety. Exposure and handling of mutagenic chemicals in the environment pose a high health risk; therefore, identification and screening of these chemicals are essential. Considering the time constraints and the pressure to avoid laboratory animals' use, the shift to alternative methodologies that can establish a rapid and cost-effective detection without undue over-conservation seems critical. In this regard, computational detection and identification of the mutagens in environmental samples like drugs, pesticides, dyes, reagents, wastewater, cosmetics, and other substances is vital. From the last two decades, there have been numerous efforts to develop the prediction models for mutagenicity, and by far, machine learning methods have demonstrated some noteworthy performance and reliability. However, the accuracy of such prediction models has always been one of the major concerns for the researchers working in this area. The mutagenicity prediction models were developed using deep neural network (DNN), support vector machine, k-nearest neighbor, and random forest. The developed classifiers were based on 3039 compounds and validated on 1014 compounds; each of them encoded with 1597 molecular feature vectors. DNN-based prediction model yielded highest prediction accuracy of 92.95% and 83.81% with the training and test data, respectively. The area under the receiver's operating curve and precision-recall curve values were found to be 0.894 and 0.838, respectively. The DNN-based classifier not only fits the data with better performance as compared to traditional machine learning algorithms, viz., support vector machine, k-nearest neighbor, and random forest (with and without feature reduction) but also yields better performance metrics. In current work, we propose a DNN-based model to predict mutagenicity of compounds.
Collapse
Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, Uttar Pradesh, India.
| | - Farhat Ullah Khan
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak, Malaysia
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh, India
| | - Mohammed Haris Siddiqui
- Department of Bioengineering, Integral University, Dasauli, P.O. Basha, Kursi Road, Lucknow, Uttar Pradesh, India
| | - Izzatdin Ba Aziz
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak, Malaysia
| | - Mohammad Amjad Kamal
- West China School of Nursing / Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China
- King Fahd Medical Research Center, King Abdulaziz University, P. O. Box 80216, Jeddah 21589, Saudi Arabia
- Enzymoics, Novel Global Community Educational Foundation, Hebersham, New South Wales, Australia
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.
- Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
| | - Badrah S Alghamdi
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Physiology, Neuroscience Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Md Sahab Uddin
- Department of Pharmacy, Southeast University, Dhaka, Bangladesh.
- Pharmakon Neuroscience Research Network, Dhaka, Bangladesh.
| |
Collapse
|
9
|
Ta GH, Jhang CS, Weng CF, Leong MK. Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability. Pharmaceutics 2021; 13:pharmaceutics13020174. [PMID: 33525340 PMCID: PMC7911528 DOI: 10.3390/pharmaceutics13020174] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/09/2021] [Accepted: 01/21/2021] [Indexed: 12/26/2022] Open
Abstract
Drug absorption is one of the critical factors that should be taken into account in the process of drug discovery and development. The human colon carcinoma cell layer (Caco-2) model has been frequently used as a surrogate to preliminarily investigate the intestinal absorption. In this study, a quantitative structure–activity relationship (QSAR) model was generated using the innovative machine learning-based hierarchical support vector regression (HSVR) scheme to depict the exceedingly confounding passive diffusion and transporter-mediated active transport. The HSVR model displayed good agreement with the experimental values of the training samples, test samples, and outlier samples. The predictivity of HSVR was further validated by a mock test and verified by various stringent statistical criteria. Consequently, this HSVR model can be employed to forecast the Caco-2 permeability to assist drug discovery and development.
Collapse
Affiliation(s)
- Giang Huong Ta
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
| | - Cin-Syong Jhang
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
| | - Ching-Feng Weng
- Department of Physiology, School of Basic Medical Science, Xiamen Medical College, Xiamen 361023, China;
| | - Max K. Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 974301, Taiwan; (G.H.T.); (C.-S.J.)
- Correspondence: ; Tel.: +886-3-890-3609
| |
Collapse
|
10
|
In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression. Int J Mol Sci 2020; 21:ijms21103582. [PMID: 32438630 PMCID: PMC7279352 DOI: 10.3390/ijms21103582] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 05/14/2020] [Accepted: 05/17/2020] [Indexed: 11/17/2022] Open
Abstract
The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 53, r2 = 0.93, q CV 2 = 0.84, RMSE = 0.17, s = 0.08), test set (n = 13, q2 = 0.75-0.89, RMSE = 0.26, s = 0.14), and even outlier set (n = 8, q2 = 0.78-0.92, RMSE = 0.19, s = 0.09). The built HSVR model consistently met the most stringent criteria when subjected to various statistical assessments. A mock test also assured the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.
Collapse
|
11
|
Jillella GK, Khan K, Roy K. Application of QSARs in identification of mutagenicity mechanisms of nitro and amino aromatic compounds against Salmonella typhimurium species. Toxicol In Vitro 2020; 65:104768. [PMID: 31926304 DOI: 10.1016/j.tiv.2020.104768] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Revised: 12/19/2019] [Accepted: 01/06/2020] [Indexed: 11/25/2022]
Abstract
In an attempt to describe the underlying causes of mutagenicity mainly due to organic chemicals, quantitative structure-activity relationship (QSAR) models have been developed using two different Salmonella typhimurium mutagenicity endpoints with or without presence of liver metabolic microsomal enzymes (S9) namely TA98-S9 and TA98 + S9. The models were developed using simple 2D variables having definite physicochemical meaning calculated from Dragon, SiRMS, and PaDEL-descriptor software tools. Stepwise regression followed by partial least squares (PLS) regression was used in model development following the strict OECD guidelines for QSAR model development and validation. The models were validated using coefficient of determination R2, cross-validation coefficient Q2LOO (leave one out) while the test set predictions were analyzed using Q2F1 (coefficient of determination for the test set). Several other internationally accepted validation metrics like MAE95%train, average rm(LOO)2 and Δrm(LOO)2 (for the training set) were used to check model robustness while predictive efficiency was evaluated using MAE95%test, average rm2 and Δrm2 (for the test set). The scope of predictions was defined by applicability domain analysis using the DModX approach, a recommended tool for PLS models. The major contributing features related to mutagenicity include lipophilicity, electronegativity, branching and unsaturation, etc. The present manuscript is the first attempt to undertake modeling of two different endpoints (TA98-S9 and TA98 + S9) in order to explore major contributing molecular features linked directly or indirectly to mutagenicity.
Collapse
Affiliation(s)
- Gopala Krishna Jillella
- Department of Pharmacoinformatics, National Institute of Pharmaceutical Educational and Research (NIPER), Chunilal Bhawan, 168, Manikata Main Road, 700054 Kolkata, India
| | - Kabiruddin Khan
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, 188 Raja S C Mullick Road, 700032 Kolkata, India.
| |
Collapse
|
12
|
In Silico Prediction of PAMPA Effective Permeability Using a Two-QSAR Approach. Int J Mol Sci 2019; 20:ijms20133170. [PMID: 31261723 PMCID: PMC6651837 DOI: 10.3390/ijms20133170] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Revised: 06/12/2019] [Accepted: 06/26/2019] [Indexed: 12/15/2022] Open
Abstract
Oral administration is the preferred and predominant route of choice for medication. As such, drug absorption is one of critical drug metabolism and pharmacokinetics (DM/PK) parameters that should be taken into consideration in the process of drug discovery and development. The cell-free in vitro parallel artificial membrane permeability assay (PAMPA) has been adopted as the primary screening to assess the passive diffusion of compounds in the practical applications. A classical quantitative structure–activity relationship (QSAR) model and a machine learning (ML)-based QSAR model were derived using the partial least square (PLS) scheme and hierarchical support vector regression (HSVR) scheme to elucidate the underlying passive diffusion mechanism and to predict the PAMPA effective permeability, respectively, in this study. It was observed that HSVR executed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical assessments. When applied to the mock test, which was designated to mimic real challenges, HSVR also showed better predictive performance. PLS, conversely, cannot cover some mechanistically interpretable relationships between descriptors and permeability. Accordingly, the synergy of predictive HSVR and interpretable PLS models can be greatly useful in facilitating drug discovery and development by predicting passive diffusion.
Collapse
|
13
|
Toropov AA, Toropova AP. Use of the index of ideality of correlation to improve predictive potential for biochemical endpoints. Toxicol Mech Methods 2018; 29:43-52. [PMID: 30064284 DOI: 10.1080/15376516.2018.1506851] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The CORAL software is a tool to build up quantitative structure-property/activity relationships (QSPRs/QSARs). The project of updated version of the CORAL software is discussed in terms of practical applications for building up various models. The updating is the possibility to improve the predictive potential of models using the so-called Index of Ideality of Correlation (IIC) as a criterion of the predictive potential for QSPR/QSAR models. Efficacy of the IIC is examined with three examples of building up QSARs: (i) models for anticancer activity; (ii) models for mutagenicity; and (iii) models for toxicity of psychotropic drugs. The validation of these models has been carried out with several splits into the training, invisible training, calibration, and validation sets. The ability of IIC to be an indicator of predictive potential of QSAR models is confirmed. The updated version of the CORAL software (CORALSEA-2017, http://www.insilico.eu/coral ) is available on the Internet.
Collapse
Affiliation(s)
- Andrey A Toropov
- a Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology , Istituto di Ricerche Farmacologiche Mario Negri IRCCS , Milano , Italy
| | - Alla P Toropova
- a Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology , Istituto di Ricerche Farmacologiche Mario Negri IRCCS , Milano , Italy
| |
Collapse
|
14
|
Hou TY, Weng CF, Leong MK. Insight Analysis of Promiscuous Estrogen Receptor α-Ligand Binding by a Novel Machine Learning Scheme. Chem Res Toxicol 2018; 31:799-813. [PMID: 30019586 DOI: 10.1021/acs.chemrestox.8b00130] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Estrogen receptor α (ERα) plays a significant role in occurrence of breast cancer and may cause various adverse side-effects when ERα is an off-target protein. A theoretical model was derived to predict the binding affinity of ERα using the pharmacophore ensemble/support vector machine (PhE/SVM) scheme to consider the promiscuous characteristic of ERα. The estimations by PhE/SVM were discovered to be in good agreement with the observed values for those training molecules ( n = 31, r2 = 0.80, qCV2 = 0.77, RMSE = 0.57, s = 0.58), test molecules ( n = 179, q2 = 0.91-0.96, RMSE = 0.33, s = 0.26) and outliers ( n = 15, q2 = 0.80-0.86, RMSE = 0.56, s = 0.49). When subjected to various statistical validations, the PhE/SVM model consistently fulfilled the strictest criteria. A mock test also asserted its predictivity. When compared with crystal structures, the calculated results are consistent with the reported ERα-ligand co-complex structure, and the plasticity nature of ERα is also disclosed. Consequently, this precise, fast, and robust model can be adopted to predict ERα-ligand binding affinities and to design safer non-ERα-targeted pharmaceuticals in the process of drug discovery and development.
Collapse
|
15
|
Chen C, Lee MH, Weng CF, Leong MK. Theoretical Prediction of the Complex P-Glycoprotein Substrate Efflux Based on the Novel Hierarchical Support Vector Regression Scheme. Molecules 2018; 23:E1820. [PMID: 30037151 PMCID: PMC6100076 DOI: 10.3390/molecules23071820] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Revised: 07/19/2018] [Accepted: 07/20/2018] [Indexed: 12/13/2022] Open
Abstract
P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood⁻brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure⁻activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r² = 0.96, qCV2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q² = 0.80⁻0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.
Collapse
Affiliation(s)
- Chun Chen
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan.
| | - Ming-Han Lee
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan.
| | - Ching-Feng Weng
- Department of Life Science and Institute of Biotechnology, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan.
| | - Max K Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan.
- Department of Life Science and Institute of Biotechnology, National Dong Hwa University, Shoufeng, Hualien 97401, Taiwan.
| |
Collapse
|
16
|
Computational identification of structural factors affecting the mutagenic potential of aromatic amines: study design and experimental validation. Arch Toxicol 2018; 92:2369-2384. [PMID: 29779177 DOI: 10.1007/s00204-018-2216-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 05/03/2018] [Indexed: 01/03/2023]
Abstract
A grid-based, alignment-independent 3D-SDAR (three-dimensional spectral data-activity relationship) approach based on simulated 13C and 15N NMR chemical shifts augmented with through-space interatomic distances was used to model the mutagenicity of 554 primary and 419 secondary aromatic amines. A robust modeling strategy supported by extensive validation including randomized training/hold-out test set pairs, validation sets, "blind" external test sets as well as experimental validation was applied to avoid over-parameterization and build Organization for Economic Cooperation and Development (OECD 2004) compliant models. Based on an experimental validation set of 23 chemicals tested in a two-strain Salmonella typhimurium Ames assay, 3D-SDAR was able to achieve performance comparable to 5-strain (Ames) predictions by Lhasa Limited's Derek and Sarah Nexus for the same set. Furthermore, mapping of the most frequently occurring bins on the primary and secondary aromatic amine structures allowed the identification of molecular features that were associated either positively or negatively with mutagenicity. Prominent structural features found to enhance the mutagenic potential included: nitrobenzene moieties, conjugated π-systems, nitrothiophene groups, and aromatic hydroxylamine moieties. 3D-SDAR was also able to capture "true" negative contributions that are particularly difficult to detect through alternative methods. These include sulphonamide, acetamide, and other functional groups, which not only lack contributions to the overall mutagenic potential, but are known to actively lower it, if present in the chemical structures of what otherwise would be potential mutagens.
Collapse
|
17
|
Basant N, Gupta S. QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2017; 24:14430-14444. [PMID: 28435990 DOI: 10.1007/s11356-017-8903-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2017] [Accepted: 03/20/2017] [Indexed: 06/07/2023]
Abstract
The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.
Collapse
Affiliation(s)
| | - Shikha Gupta
- CSIR-National Botanical Research Institute, Rana Pratap Marg, Lucknow, 226001, India
| |
Collapse
|
18
|
Toropov AA, Toropova AP. The index of ideality of correlation: A criterion of predictive potential of QSPR/QSAR models? MUTATION RESEARCH-GENETIC TOXICOLOGY AND ENVIRONMENTAL MUTAGENESIS 2017. [PMID: 28622828 DOI: 10.1016/j.mrgentox.2017.05.008] [Citation(s) in RCA: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The index of ideality of correlation (IIC) is a new criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs). This IIC is calculated with using of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The mutagenicity is well-known important characteristic of substances from ecological point of view. Consequently, the estimation of the IIC for mutagenicity is well motivated. It is confirmed that the utilization of this criterion significantly improves the predictive potential of QSAR models of mutagenicity. The new criterion can be used for other endpoints.
Collapse
Affiliation(s)
- Andrey A Toropov
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, 20156, Italy
| | - Alla P Toropova
- IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, Milano, 20156, Italy.
| |
Collapse
|
19
|
Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme. Sci Rep 2017; 7:40053. [PMID: 28059133 PMCID: PMC5216401 DOI: 10.1038/srep40053] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Accepted: 11/30/2016] [Indexed: 01/24/2023] Open
Abstract
The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r2 = 0.928–0.988, = 0.894–0.954, RMSE = 0.002–0.412, s = 0.001–0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967, = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.
Collapse
|
20
|
In silico prediction of the mutagenicity of nitroaromatic compounds using a novel two-QSAR approach. Toxicol In Vitro 2016; 40:102-114. [PMID: 28027902 DOI: 10.1016/j.tiv.2016.12.013] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2016] [Revised: 11/13/2016] [Accepted: 12/21/2016] [Indexed: 11/20/2022]
Abstract
Certain drugs are nitroaromatic compounds, which are potentially toxic. As such, it is of practical importance to assess and predict their mutagenic potency in the process of drug discovery. A classical quantitative structure-activity relationship (QSAR) model was developed using the linear partial least square (PLS) scheme to understand the underline mutagenic mechanism and a non-classical QSAR model was derived using the machine learning-based hierarchical support vector regression (HSVR) to predict the mutagenicity of nitroaromatic compounds based on a series of mutagenicity data (TA98-S9). It was observed that HSVR performed better than PLS as manifested by the predictions of the samples in the training set, test set, and outlier set as well as various statistical validations. A mock test designated to mimic real challenges also confirmed the better performance of HSVR. Furthermore, HSVR exhibited superiority in predictivity, generalization capabilities, consistent performance, and robustness when compared with various published predictive models. PLS, conversely, revealed some mechanistically interpretable relationships between descriptors and mutagenicity. Thus, this two-QSAR approach using the predictive HSVR and interpretable PLS models in a synergistic fashion can be adopted to facilitate drug discovery and development by designing safer drug candidates with nitroaromatic moiety.
Collapse
|
21
|
Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s40808-016-0132-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
|
22
|
Dearden JC, Rowe PH. Use of artificial neural networks in the QSAR prediction of physicochemical properties and toxicities for REACH legislation. Methods Mol Biol 2015; 1260:65-88. [PMID: 25502376 DOI: 10.1007/978-1-4939-2239-0_5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
With the introduction of the REACH legislation in the European Union, there is a requirement for property and toxicity data on chemicals produced in or imported into the EU at levels of 1 tonne/year or more. This has meant an increase in the in silico prediction of such data. One of the chief predictive approaches is QSAR (quantitative structure-activity relationships), which is widely used in many fields. A QSAR approach that is increasingly being used is that of artificial neural networks (ANNs), and this chapter discusses its application to the range of physicochemical properties and toxicities required by REACH. ANNs generally outperform the main QSAR approach of multiple linear regression (MLR), although other approaches such as support vector machines sometimes outperform ANNs. Most ANN QSARs reported to date comply with only two of the five OECD Guidelines for the Validation of (Q)SARs.
Collapse
Affiliation(s)
- John C Dearden
- School of Pharmacy & Biomolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool, L3 3AF, UK,
| | | |
Collapse
|
23
|
Abstract
Carcinogenicity is an important toxicological endpoint which poses a great concern being the major determinants of cancers and tumours. Anilines possess such toxic properties as they can form various electrophilic intermediates and adducts with biological systems. In the present work, the molecular descriptors of anilines have been calculated with semi-empirical AM1 and E-dragon methods, and a quantitative structure–toxicity relationships (QSTR) model for carcinogenic potency (pTD50) model of anilines was developed with multiple linear regression (MLR) analysis. The validation results through the test set indicate that the proposed model is robust and satisfactory. The QSTR study suggests that the molecular structure and the electronegativity of chemicals are closely related to the Carcinogenicity.
Collapse
|
24
|
Ding YL, Shih YH, Tsai FY, Leong MK. In silico prediction of inhibition of promiscuous breast cancer resistance protein (BCRP/ABCG2). PLoS One 2014; 9:e90689. [PMID: 24614353 PMCID: PMC3948701 DOI: 10.1371/journal.pone.0090689] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2013] [Accepted: 02/03/2014] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Breast cancer resistant protein has an essential role in active transport of endogenous substances and xenobiotics across extracellular and intracellular membranes along with P-glycoprotein. It also plays a major role in multiple drug resistance and permeation of blood-brain barrier. Therefore, it is of great importance to derive theoretical models to predict the inhibition of both transporters in the process of drug discovery and development. Hitherto, very limited BCRP inhibition predictive models have been proposed as compared with its P-gp counterpart. METHODOLOGY/PRINCIPAL FINDINGS An in silico BCRP inhibition model was developed in this study using the pharmacophore ensemble/support vector machine scheme to take into account the promiscuous nature of BCRP. The predictions by the PhE/SVM model were found to be in good agreement with the observed values for those molecules in the training set (n= 22, r2 =0.82, qCV2=0.73, RMSE= 0.40, s = 0.24), test set (n =97, q2=0.75-0.89, RMSE= 0.31, s= 0.21), and outlier set (n= 16, q2 =0.72-0.91, RMSE= 0.29, s=0.17). When subjected to a variety of statistical validations, the developed PhE/SVM model consistently met the most stringent criteria. A mock test by HIV protease inhibitors also asserted its predictivity. CONCLUSIONS/SIGNIFICANCE It was found that this accurate, fast, and robust PhE/SVM model can be employed to predict the BCRP inhibition of structurally diverse molecules that otherwise cannot be carried out by any other methods in a high-throughput fashion to design therapeutic agents with insignificant drug toxicity and unfavorable drug-drug interactions mediated by BCRP to enhance clinical efficacy and/or circumvent drug resistance.
Collapse
Affiliation(s)
- Yi-Lung Ding
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan
| | - Yu-Hsuan Shih
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan
| | - Fu-Yuan Tsai
- Center for General Education, Chang Gung University, Taoyuan, Taiwan
| | - Max K Leong
- Department of Chemistry, National Dong Hwa University, Shoufeng, Hualien, Taiwan; Department of Life Science and Institute of Biotechnology, National Dong Hwa University, Shoufeng, Hualien, Taiwan; Department of Medical Research and Teaching, Mennonite Christian Hospital, Hualien, Taiwan
| |
Collapse
|
25
|
Quantitative structure–activity study of fluorides′ toxicity. J Fluor Chem 2013. [DOI: 10.1016/j.jfluchem.2013.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
26
|
Yuan J, Pu Y, Yin L. Liver Specificity of the Carcinogenicity of NOCs: A Chemical–Molecular Perspective. Chem Res Toxicol 2012; 25:2432-42. [DOI: 10.1021/tx3002912] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Jintao Yuan
- Key Laboratory
of Environmental Medicine Engineering,
Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China
| | - Yuepu Pu
- Key Laboratory
of Environmental Medicine Engineering,
Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China
| | - Lihong Yin
- Key Laboratory
of Environmental Medicine Engineering,
Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, China
| |
Collapse
|
27
|
Yuan J, Pu Y, Yin L. QSAR study of liver specificity of carcinogenicity of N-nitroso compounds. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 84:282-292. [PMID: 22910279 DOI: 10.1016/j.ecoenv.2012.07.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2012] [Revised: 07/21/2012] [Accepted: 07/24/2012] [Indexed: 06/01/2023]
Abstract
The quantitative structure-activity relationship (QSAR) of N-nitroso compounds (NOCs) for rat liver was developed by a topological sub-structural molecular-descriptors (TOPS-MODE) approach to predict non-liver-carcinogenic and liver-carcinogenic N-nitroso compounds based on a data set of 108 NOCs. Three descriptors calculated solely from the molecular structures of the compounds were selected by enhanced replacement method (ERM) and were weighted, respectively, with atomic weight, bond dipole moments and Abraham solute descriptor partition between water and aqueous solvent systems to indicate the importance of their roles in liver specificity. A detailed discussion on these three descriptors was carried out, and the contributions of different fragments to rat-liver specificity and the interactions among fragments were analyzed. Such results can offer some useful theoretical references for understanding the chemical structural and biological factors related to the liver-specific biological activity of NOCs.
Collapse
Affiliation(s)
- Jintao Yuan
- Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, 87 Dingjiaqiao, Nanjing 210009, China.
| | | | | |
Collapse
|
28
|
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]
|
29
|
McCarren P, Springer C, Whitehead L. An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential. J Cheminform 2011; 3:51. [PMID: 22107807 PMCID: PMC3277490 DOI: 10.1186/1758-2946-3-51] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2011] [Accepted: 11/22/2011] [Indexed: 11/29/2022] Open
Abstract
Background In drug discovery, a positive Ames test for bacterial mutation presents a significant hurdle to advancing a drug to clinical trials. In a previous paper, we discussed success in predicting the genotoxicity of reagent-sized aryl-amines (ArNH2), a structure frequently found in marketed drugs and in drug discovery, using quantum mechanics calculations of the energy required to generate the DNA-reactive nitrenium intermediate (ArNH:+). In this paper we approach the question of what molecular descriptors could improve these predictions and whether external data sets are appropriate for further training. Results In trying to extend and improve this model beyond this quantum mechanical reaction energy, we faced considerable difficulty, which was surprising considering the long history and success of QSAR model development for this test. Other quantum mechanics descriptors were compared to this reaction energy including AM1 semi-empirical orbital energies, nitrenium formation with alternative leaving groups, nitrenium charge, and aryl-amine anion formation energy. Nitrenium formation energy, regardless of the starting species, was found to be the most useful single descriptor. External sets used in other QSAR investigations did not present the same difficulty using the same methods and descriptors. When considering all substructures rather than just aryl-amines, we also noted a significantly lower performance for the Novartis set. The performance gap between Novartis and external sets persists across different descriptors and learning methods. The profiles of the Novartis and external data are significantly different both in aryl-amines and considering all substructures. The Novartis and external data sets are easily separated in an unsupervised clustering using chemical fingerprints. The chemical differences are discussed and visualized using Kohonen Self-Organizing Maps trained on chemical fingerprints, mutagenic substructure prevalence, and molecular weight. Conclusions Despite extensive work in the area of predicting this particular toxicity, work in designing and publishing more relevant test sets for compounds relevant to drug discovery is still necessary. This work also shows that great care must be taken in using QSAR models to replace experimental evidence. When considering all substructures, a random forest model, which can inherently cover distinct neighborhoods, built on Novartis data and previously reported external data provided a suitable model.
Collapse
Affiliation(s)
- Patrick McCarren
- Novartis Institutes for Biomedical Research, 100 Technology Square, Cambridge, MA 02139, USA.
| | | | | |
Collapse
|
30
|
Predicting Activation of the Promiscuous Human Pregnane X Receptor by Pharmacophore Ensemble/Support Vector Machine Approach. Chem Res Toxicol 2011; 24:1765-78. [DOI: 10.1021/tx200310j] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
31
|
McCarren P, Bebernitz GR, Gedeck P, Glowienke S, Grondine MS, Kirman LC, Klickstein J, Schuster HF, Whitehead L. Avoidance of the Ames test liability for aryl-amines via computation. Bioorg Med Chem 2011; 19:3173-82. [PMID: 21524589 DOI: 10.1016/j.bmc.2011.03.066] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2011] [Revised: 03/25/2011] [Accepted: 03/30/2011] [Indexed: 11/19/2022]
Abstract
Aryl-amines are commonly used synthons in modern drug discovery, however a minority of these chemical templates have the potential to cause toxicity through mutagenicity. The toxicity mostly arises through a series of metabolic steps leading to a reactive electrophilic nitrenium cation intermediate that reacts with DNA nucleotides causing mutation. Highly detailed in silico calculations of the energetics of chemical reactions involved in the metabolic formation of nitrenium cations have been performed. This allowed a critical assessment of the accuracy and reliability of using a theoretical formation energy of the DNA-reactive nitrenium intermediate to correlate with the Ames test response. This study contains the largest data set reported to date, and presents the in silico calculations versus the in vitro Ames response data in the form of beanplots commonly used in statistical analysis. A comparison of this quantum mechanical approach to QSAR and knowledge-based methods is also reported, as well as the calculated formation energies of nitrenium ions for thousands of commercially available aryl-amines generated as a watch-list for medicinal chemists in their synthetic optimization strategies.
Collapse
Affiliation(s)
- Patrick McCarren
- Novartis Institutes for Biomedical Research, Cambridge, MA 02139, USA
| | | | | | | | | | | | | | | | | |
Collapse
|