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Nikolov NG, Nissen ACVE, Wedebye EB. A method for in vitro data and structure curation to optimize for QSAR modelling of minimum absolute potency levels and a comparative use case. ENVIRONMENTAL TOXICOLOGY AND PHARMACOLOGY 2023; 98:104069. [PMID: 36702390 DOI: 10.1016/j.etap.2023.104069] [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: 08/30/2022] [Revised: 01/16/2023] [Accepted: 01/18/2023] [Indexed: 06/18/2023]
Abstract
Large screening programs such as the US Tox21 are releasing experimental in vitro results for many endpoints of relevance for human health. In (Q)SAR modelling, it is essential to clearly define the endpoint (OECD QSAR Validation Principle 1) and extract the most robust data points according to the definition. We have developed a comprehensive data curation procedure to interpret in vitro experimental data sets for (Q)SAR development, with modules for selecting actives according to quality of curve fittings, magnitude of activity and 'absolute' potency cut-offs, requiring non-cytotoxicity at activity concentration; extracting only very robust inactives; selecting only substances tested in high purity; and accounting for assay signal interference. A structure curation procedure with uniform representation of tautomeric classes of substances is also developed. The detailed method and a use case of modelling Tox21 data for an estrogen receptor α agonism assay with and without use of the method is presented.
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Affiliation(s)
- Nikolai G Nikolov
- National Food Institute, Technical University of Denmark, Kemitorvet 2, 2800 Kgs., Lyngby, Denmark.
| | - Ana C V E Nissen
- National Food Institute, Technical University of Denmark, Kemitorvet 2, 2800 Kgs., Lyngby, Denmark.
| | - Eva B Wedebye
- National Food Institute, Technical University of Denmark, Kemitorvet 2, 2800 Kgs., Lyngby, Denmark.
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2
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Krebs J, McKeague M. Green Toxicology: Connecting Green Chemistry and Modern Toxicology. Chem Res Toxicol 2020; 33:2919-2931. [DOI: 10.1021/acs.chemrestox.0c00260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Johanna Krebs
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Department of Health Sciences and Technology, ETH Zürich, Universitätstrasse 2, Zurich, Switzerland CH 8092
| | - Maureen McKeague
- Pharmacology and Therapeutics, Faculty of Medicine, McGill University, 3655 Promenade Sir William Osler, Montreal, Quebec, Canada H3G 1Y6
- Faculty of Science, Chemistry, McGill University, 801 Sherbrooke Street West, Montreal, Quebec, Canada H3A 0B8
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3
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A compound attributes-based predictive model for drug induced liver injury in humans. PLoS One 2020; 15:e0231252. [PMID: 32294131 PMCID: PMC7159228 DOI: 10.1371/journal.pone.0231252] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/29/2020] [Indexed: 11/19/2022] Open
Abstract
Drug induced liver injury (DILI) is one of the key safety concerns in drug development. To assess the likelihood of drug candidates with potential adverse reactions of liver, we propose a compound attributes-based approach to predicting hepatobiliary disorders that are routinely reported to US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Specifically, we developed a support vector machine (SVM) model with recursive feature extraction, based on physicochemical and structural properties of compounds as model input. Cross validation demonstrates that the predictive model has a robust performance with averaged 70% of both sensitivity and specificity over 500 trials. An independent validation was performed on public benchmark drugs and the results suggest potential utility of our model for identifying safety alerts. This in silico approach, upon further validation, would ultimately be implemented, together with other in vitro safety assays, for screening compounds early in drug development.
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Goya-Jorge E, Doan TQ, Scippo ML, Muller M, Giner RM, Barigye SJ, Gozalbes R. Elucidating the aryl hydrocarbon receptor antagonism from a chemical-structural perspective. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:209-226. [PMID: 31916862 DOI: 10.1080/1062936x.2019.1708460] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 12/19/2019] [Indexed: 06/10/2023]
Abstract
The aryl hydrocarbon receptor (AhR) plays an important role in several biological processes such as reproduction, immunity and homoeostasis. However, little is known on the chemical-structural and physicochemical features that influence the activity of AhR antagonistic modulators. In the present report, in vitro AhR antagonistic activity evaluations, based on a chemical-activated luciferase gene expression (AhR-CALUX) bioassay, and an extensive literature review were performed with the aim of constructing a structurally diverse database of contaminants and potentially toxic chemicals. Subsequently, QSAR models based on Linear Discriminant Analysis and Logistic Regression, as well as two toxicophoric hypotheses were proposed to model the AhR antagonistic activity of the built dataset. The QSAR models were rigorously validated yielding satisfactory performance for all classification parameters. Likewise, the toxicophoric hypotheses were validated using a diverse set of 350 decoys, demonstrating adequate robustness and predictive power. Chemical interpretations of both the QSAR and toxicophoric models suggested that hydrophobic constraints, the presence of aromatic rings and electron-acceptor moieties are critical for the AhR antagonism. Therefore, it is hoped that the deductions obtained in the present study will contribute to elucidate further on the structural and physicochemical factors influencing the AhR antagonistic activity of chemical compounds.
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Affiliation(s)
- E Goya-Jorge
- CEEI (Centro Europeo de Empresas Innovadoras), ProtoQSAR SL, Parque Tecnológico de Valencia, Valencia, Spain
- Departament de Farmacologia, Facultat de Farmàcia, Universitat de València, Valencia, Spain
| | - T Q Doan
- Laboratory of Food Analysis, FARAH-Veterinary Public Health, ULiège, Liège, Belgium
| | - M L Scippo
- Laboratory of Food Analysis, FARAH-Veterinary Public Health, ULiège, Liège, Belgium
| | - M Muller
- Laboratory for Organogenesis and Regeneration, GIGA-Research, ULiège, Liège, Belgium
| | - R M Giner
- Departament de Farmacologia, Facultat de Farmàcia, Universitat de València, Valencia, Spain
| | - S J Barigye
- CEEI (Centro Europeo de Empresas Innovadoras), ProtoQSAR SL, Parque Tecnológico de Valencia, Valencia, Spain
| | - R Gozalbes
- CEEI (Centro Europeo de Empresas Innovadoras), ProtoQSAR SL, Parque Tecnológico de Valencia, Valencia, Spain
- R&D Department, MolDrug AI Systems SL, Valencia, Spain
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QSAR modelling of a large imbalanced aryl hydrocarbon activation dataset by rational and random sampling and screening of 80,086 REACH pre-registered and/or registered substances. PLoS One 2019; 14:e0213848. [PMID: 30870500 PMCID: PMC6417725 DOI: 10.1371/journal.pone.0213848] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 03/01/2019] [Indexed: 12/02/2022] Open
Abstract
The Aryl hydrocarbon receptor (AhR) plays important roles in many normal and pathological physiological processes, including endocrine homeostasis, foetal development, cell cycle regulation, cellular oxidation/antioxidation, immune regulation, metabolism of endogenous and exogenous substances, and carcinogenesis. An experimental data set for human in vitro AhR activation comprising 324,858 substances, of which 1,982 were confirmed actives, was used to test an in-house-developed approach to rationally select Quantitative Structure-Activity Relationship (QSAR) training set substances from an unbalanced data set. In the first iteration, active and inactive substances were selected by random to make QSAR models. Then, more inactive substances were added to the training set in two further iterations based on incorrect or out-of-domain predictions to produce larger models. The resulting ‘rational’ model, comprising 832 actives and four times as many inactives, i.e. 3,328, was compared to a model with a training set of same size and proportion of inactives chosen entirely by random. Both models underwent robust cross-validation and external validation showing good statistical performance, with the rational model having external validation sensitivity of 85.1% and specificity of 97.1%, compared to the random model with sensitivity 89.1% and specificity 91.3%. Furthermore, we integrated the training sets for both models with the 93 external validation test set actives and 372 randomly selected inactives to make two final models. They also underwent external validations for specificity and cross-validations, which confirmed that good predictivity was maintained. All developed models were applied to predict 80,086 EU REACH substances. The rational and random final models had 63.1% and 56.9% coverage of the REACH set, respectively, and predicted 1,256 and 3,214 substances as actives. The final models as well as predictions for AhR activation for 650,000 substances will be published in the Danish (Q)SAR Database and can, for example, be used for priority setting, in read-across predictions and in weight-of-evidence assessments of chemicals.
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Rosenberg S, Watt E, Judson R, Simmons S, Paul Friedman K, Dybdahl M, Nikolov N, Wedebye E. QSAR models for thyroperoxidase inhibition and screening of U.S. and EU chemical inventories. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.comtox.2017.07.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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QSAR development and profiling of 72,524 REACH substances for PXR activation and CYP3A4 induction. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.comtox.2017.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Hsieh JH, Sedykh A, Huang R, Xia M, Tice RR. A Data Analysis Pipeline Accounting for Artifacts in Tox21 Quantitative High-Throughput Screening Assays. JOURNAL OF BIOMOLECULAR SCREENING 2015; 20:887-97. [PMID: 25904095 PMCID: PMC4568956 DOI: 10.1177/1087057115581317] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Accepted: 03/19/2015] [Indexed: 11/16/2022]
Abstract
A main goal of the U.S. Tox21 program is to profile a 10K-compound library for activity against a panel of stress-related and nuclear receptor signaling pathway assays using a quantitative high-throughput screening (qHTS) approach. However, assay artifacts, including nonreproducible signals and assay interference (e.g., autofluorescence), complicate compound activity interpretation. To address these issues, we have developed a data analysis pipeline that includes an updated signal noise-filtering/curation protocol and an assay interference flagging system. To better characterize various types of signals, we adopted a weighted version of the area under the curve (wAUC) to quantify the amount of activity across the tested concentration range in combination with the assay-dependent point-of-departure (POD) concentration. Based on the 32 Tox21 qHTS assays analyzed, we demonstrate that signal profiling using wAUC affords the best reproducibility (Pearson's r = 0.91) in comparison with the POD (0.82) only or the AC(50) (i.e., half-maximal activity concentration, 0.81). Among the activity artifacts characterized, cytotoxicity is the major confounding factor; on average, about 8% of Tox21 compounds are affected, whereas autofluorescence affects less than 0.5%. To facilitate data evaluation, we implemented two graphical user interface applications, allowing users to rapidly evaluate the in vitro activity of Tox21 compounds.
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Affiliation(s)
- Jui-Hua Hsieh
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | | | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, MD, USA
| | - Raymond R Tice
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
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Chen S, Hsieh JH, Huang R, Sakamuru S, Hsin LY, Xia M, Shockley KR, Auerbach S, Kanaya N, Lu H, Svoboda D, Witt KL, Merrick BA, Teng CT, Tice RR. Cell-Based High-Throughput Screening for Aromatase Inhibitors in the Tox21 10K Library. Toxicol Sci 2015; 147:446-57. [PMID: 26141389 DOI: 10.1093/toxsci/kfv141] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Multiple mechanisms exist for endocrine disruption; one nonreceptor-mediated mechanism is via effects on aromatase, an enzyme critical for maintaining the normal in vivo balance of androgens and estrogens. We adapted the AroER tri-screen 96-well assay to 1536-well format to identify potential aromatase inhibitors (AIs) in the U.S. Tox21 10K compound library. In this assay, screening with compound alone identifies estrogen receptor alpha (ERα) agonists, screening in the presence of testosterone (T) identifies AIs and/or ERα antagonists, and screening in the presence of 17β-estradiol (E2) identifies ERα antagonists. Screening the Tox-21 library in the presence of T resulted in finding 302 potential AIs. These compounds, along with 31 known AI actives and inactives, were rescreened using all 3 assay formats. Of the 333 compounds tested, 113 (34%; 63 actives, 50 marginal actives) were considered to be potential AIs independent of cytotoxicity and ER antagonism activity. Structure-activity analysis suggested the presence of both conventional (eg, 1, 2, 4, - triazole class) and novel AI structures. Due to their novel structures, 14 of the 63 potential AI actives, including both drugs and fungicides, were selected for confirmation in the biochemical tritiated water-release aromatase assay. Ten compounds were active in the assay; the remaining 4 were only active in high-throughput screen assay, but with low efficacy. To further characterize these 10 novel AIs, we investigated their binding characteristics. The AroER tri-screen, in high-throughput format, accurately and efficiently identified chemicals in a large and diverse chemical library that selectively interact with aromatase.
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Affiliation(s)
- Shiuan Chen
- *Department of Cancer Biology, Beckman Research Institute of the City of Hope, Duarte, California 91010;
| | - Jui-Hua Hsieh
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
| | - Ruili Huang
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850; and
| | - Srilatha Sakamuru
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850; and
| | - Li-Yu Hsin
- *Department of Cancer Biology, Beckman Research Institute of the City of Hope, Duarte, California 91010
| | - Menghang Xia
- National Center for Advancing Translational Sciences, National Institutes of Health, Rockville, Maryland 20850; and
| | - Keith R Shockley
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
| | - Scott Auerbach
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
| | - Noriko Kanaya
- *Department of Cancer Biology, Beckman Research Institute of the City of Hope, Duarte, California 91010
| | - Hannah Lu
- *Department of Cancer Biology, Beckman Research Institute of the City of Hope, Duarte, California 91010
| | - Daniel Svoboda
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
| | - Kristine L Witt
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
| | - B Alex Merrick
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
| | - Christina T Teng
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
| | - Raymond R Tice
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina 27709
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An evaluation of in-house and off-the-shelf in silico models: implications on guidance for mutagenicity assessment. Regul Toxicol Pharmacol 2015; 71:388-97. [PMID: 25656493 DOI: 10.1016/j.yrtph.2015.01.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Revised: 01/20/2015] [Accepted: 01/21/2015] [Indexed: 11/22/2022]
Abstract
The evaluation of impurities for genotoxicity using in silico models are commonplace and have become accepted by regulatory agencies. Recently, the ICH M7 Step 4 guidance was published and requires two complementary models for genotoxicity assessments. Over the last ten years, many companies have developed their own internal genotoxicity models built using both public and in-house chemical structures and bacterial mutagenicity data. However, the proprietary nature of internal structures prevents sharing of data and the full OECD compliance of such models. This analysis investigated whether using in-house internal compounds for training models is needed and substantially impacts the results of in silico genotoxicity assessments, or whether using commercial-off-the-shelf (COTS) packages such as Derek Nexus or Leadscope provide adequate performance. We demonstrated that supplementation of COTS packages with a Support Vector Machine (SVM) QSAR model trained on combined in-house and public data does, in fact, improve coverage and accuracy, and reduces the number of compounds needing experimental assessment, i.e., the liability load. This result indicates that there is added value in models trained on both internal and public structures and incorporating such models as part of a consensus approach improves the overall evaluation. Lastly, we optimized an in silico consensus decision-making approach utilizing two COTS models and an internal (SVM) model to minimize false negatives.
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11
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Nikolov NG, Dybdahl M, Jónsdóttir SÓ, Wedebye EB. hERG blocking potential of acids and zwitterions characterized by three thresholds for acidity, size and reactivity. Bioorg Med Chem 2014; 22:6004-13. [DOI: 10.1016/j.bmc.2014.09.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2014] [Revised: 08/26/2014] [Accepted: 09/05/2014] [Indexed: 02/01/2023]
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12
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Defining and characterizing drug/compound function. Biochem Pharmacol 2014; 87:40-63. [DOI: 10.1016/j.bcp.2013.07.033] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 07/22/2013] [Indexed: 12/25/2022]
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Valerio LG, Balakrishnan S, Fiszman ML, Kozeli D, Li M, Moghaddam S, Sadrieh N. Development of cardiac safety translational tools for QT prolongation and torsade de pointes. Expert Opin Drug Metab Toxicol 2013; 9:801-15. [DOI: 10.1517/17425255.2013.783819] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Abstract
Use of predictive technologies is an important aspect of many efforts in today's research, development, and regulatory landscapes. Computational methods as predictive tools for supporting drug safety assessments is of widespread interest as the field of in silico assessments rapidly changes with emerging technologies and the large amount of existing data available for modeling. There are challenges associated with application of in silico analyses for drug toxicity predictions and need for strategies and harmonization to enable an acceptable in silico evaluation for prediction of specific toxicity assay outcomes. This chapter will provide an overview focused on computational tools using structure-activity relationships and will highlight initiatives for use of computational assessments and realistic applications for predictive modeling in evaluating potential toxicities of drug-related molecules.
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Cho MH, Song JS, Kim HJ, Park SG, Jung G. Structure-based design and biochemical evaluation of sulfanilamide derivatives as hepatitis B virus capsid assembly inhibitors. J Enzyme Inhib Med Chem 2012; 28:916-25. [PMID: 22803663 DOI: 10.3109/14756366.2012.694879] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
Virus capsid structure is essential in virion maturation and durability, so disrupting capsid assembly could be an effective way to reduce virion count and cure viral diseases. However, currently there is no known antiviral which affects capsid inhibition, and only a small number of assembly inhibitors were experimentally successful. In this present study, we aimed to find hepatitis B virus (HBV) capsid assembly inhibitor which binds to the HBV core protein and changes protein conformation. Several candidate molecules were found to bind to certain structure in core protein with high specificity. Furthermore, these molecules significantly changed the protein conformation and reduced assembly affinity of core protein, leading to decrease of the number of assembled capsid or virion, both in vitro and in vivo. In addition, prediction also suggests that improvements in inhibition efficiency could be possible by changing functional groups and ring structures.
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Affiliation(s)
- Min-Hyung Cho
- Department of Biological Sciences, Seoul National University , Seoul , Republic of Korea
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16
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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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17
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Valerio, LG, Cross KP. Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*. Toxicol Appl Pharmacol 2012; 260:209-21. [DOI: 10.1016/j.taap.2012.03.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2011] [Revised: 02/24/2012] [Accepted: 03/02/2012] [Indexed: 10/28/2022]
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Valerio LG. Application of advancedin silicomethods for predictive modeling and information integration. Expert Opin Drug Metab Toxicol 2012; 8:395-8. [DOI: 10.1517/17425255.2012.664636] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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19
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Kulemina LV, Ostrov DA. Prediction of off-target effects on angiotensin-converting enzyme 2. ACTA ACUST UNITED AC 2011; 16:878-85. [PMID: 21859683 DOI: 10.1177/1087057111413919] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The authors describe a structure-based strategy to identify therapeutically beneficial off-target effects by screening a chemical library of Food and Drug Administration (FDA)-approved small-molecule drugs matching pharmacophores defined for specific target proteins. They applied this strategy to angiotensin-converting enzyme 2 (ACE2), an enzyme that generates vasodilatory peptides and promotes protection from hypertension-associated cardiovascular disease. The conformation-based structural selection method by molecular docking using DOCK allowed them to identify a series of FDA-approved drugs that enhance catalytic efficiency of ACE2 in vitro. These data demonstrate that libraries of approved drugs can be rapidly screened to identify potential side effects due to interactions with specific proteins other than the intended targets.
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Affiliation(s)
- Lidia V Kulemina
- Department of Chemistry, University of Florida, Gainesville, FL 32611, USA
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20
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Abstract
In silico toxicology methods are practical, evidence-based and high throughput, with varying accuracy. In silico approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects--including maximising use of prior test data and the potential for minimising animal use for future toxicity testing--the primary use of in silico toxicology methods in the pharmaceutical sciences are as decision support information. It is possible for in silico toxicology methods to complement and strengthen the evidence for certain regulatory review processes, and to enhance risk management by supporting a more informed decision regarding priority setting for additional toxicological testing in research and product development. There are also several challenges with these continually evolving methods which clearly must be considered. This mini-review describes in silico methods that have been researched as Critical Path Initiative toolkits for predicting toxicities early in drug development based on prior knowledge derived from preclinical and clinical data at the US Food and Drug Administration, Center for Drug Evaluation and Research.
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Affiliation(s)
- Luis G Valerio
- Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, White Oak 51, Room 4128, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.
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Abstract
Computational scientific tools involving construction and testing of models, screening and data mining for drug and chemical induced toxicities and metabolism have significantly grown in experimental use to help guide product development and assist by enhancing certain areas of regulatory decision making. This themed issue of the journal entitled Computational Science in Drug Metabolism & Toxicology contains state-of-the-art review articles and perspectives covering a diversity of in silico approaches. Computational science tools have a strong potential for expediting our further understanding of drug metabolism and toxicity and are continually being developed and validated. The reader will gain an understanding of the current state of in silico tools and modeling approaches aimed at reducing these liabilities. In addition, how these tools are tested and developed for use in drug safety to support drug development efforts and a review of how they are used to predict genotoxic liabilities are covered in this issue. Computational science tools when properly validated and used judiciously can lend themselves as enablers to support drug safety assessment in investigative and applied settings.
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Affiliation(s)
- Luis G Valerio
- Center for Drug Evaluation and Research, US Food and Drug Administration, Office of Pharmaceutical Science, White Oak 51 Room 4128, 10903 New Hampshire Ave., Silver Spring, MD 20993-0002, USA.
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Wang YJ, Dou J, Cross KP, Valerio LG. Computational analysis for hepatic safety signals of constituents present in botanical extracts widely used by women in the United States for treatment of menopausal symptoms. Regul Toxicol Pharmacol 2011; 59:111-24. [DOI: 10.1016/j.yrtph.2010.09.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2010] [Revised: 09/23/2010] [Accepted: 09/25/2010] [Indexed: 10/19/2022]
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Naven RT, Louise-May S, Greene N. The computational prediction of genotoxicity. Expert Opin Drug Metab Toxicol 2010; 6:797-807. [DOI: 10.1517/17425255.2010.495118] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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