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Wang Z, Chen J, Hong H. Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:6857-6866. [PMID: 33914508 DOI: 10.1021/acs.est.0c07040] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Chemicals may cause adverse effects on human health through binding to peroxisome proliferator-activated receptor γ (PPARγ). Hence, binding affinity is useful for evaluating chemicals with potential endocrine-disrupting effects. Quantitative structure-activity relationship (QSAR) regression models with defined applicability domains (ADs) are important to enable efficient screening of chemicals with PPARγ binding activity. However, lack of large data sets hindered the development of QSAR models. In this study, based on PPARγ binding affinity data sets curated from various sources, 30 QSAR models were developed using molecular fingerprints, two-dimensional descriptors, and five machine learning algorithms. Structure-activity landscapes (SALs) of the training compounds were described by network-like similarity graphs (NSGs). Based on the NSGs, local discontinuity scores were calculated and found to be positively correlated with the cross-validation absolute prediction errors of the models using the different training sets, descriptors, and algorithms. Moreover, innovative ADs were defined based on pairwise similarities between compounds and were found to outperform some conventional ADs. The curated data sets and developed regression models could be useful for evaluating PPARγ-involved adverse effects of chemicals. The SAL analysis and the innovative ADs could facilitate understanding of prediction results from QSAR models.
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Affiliation(s)
- Zhongyu Wang
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Huixiao Hong
- National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas 72079, United States
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Novel QSAR Models for Molecular Initiating Event Modeling in Two Intersecting Adverse Outcome Pathways Based Pulmonary Fibrosis Prediction for Biocidal Mixtures. TOXICS 2021; 9:toxics9030059. [PMID: 33809804 PMCID: PMC8002424 DOI: 10.3390/toxics9030059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/19/2021] [Accepted: 03/12/2021] [Indexed: 12/31/2022]
Abstract
The adverse outcome pathway (AOP) was introduced as an alternative method to avoid unnecessary animal tests. Under the AOP framework, an in silico methods, molecular initiating event (MIE) modeling is used based on the ligand-receptor interaction. Recently, the intersecting AOPs (AOP 347), including two MIEs, namely peroxisome proliferator-activated receptor-gamma (PPAR-γ) and toll-like receptor 4 (TLR4), associated with pulmonary fibrosis was proposed. Based on the AOP 347, this study developed two novel quantitative structure-activity relationship (QSAR) models for the two MIEs. The prediction performances of different MIE modeling methods (e.g., molecular dynamics, pharmacophore model, and QSAR) were compared and validated with in vitro test data. Results showed that the QSAR method had high accuracy compared with other modeling methods, and the QSAR method is suitable for the MIE modeling in the AOP 347. Therefore, the two QSAR models based on the AOP 347 can be powerful models to screen biocidal mixture related to pulmonary fibrosis.
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Tanis SP, Colca JR, Parker TT, Artman GD, Larsen SD, McDonald WG, Gadwood RC, Kletzien RF, Zeller JB, Lee PH, Adams WJ. PPARγ-sparing thiazolidinediones as insulin sensitizers. Design, synthesis and selection of compounds for clinical development. Bioorg Med Chem 2018; 26:5870-5884. [DOI: 10.1016/j.bmc.2018.10.033] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 10/20/2018] [Accepted: 10/27/2018] [Indexed: 01/09/2023]
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Al Sharif M, Tsakovska I, Pajeva I, Alov P, Fioravanzo E, Bassan A, Kovarich S, Yang C, Mostrag-Szlichtyng A, Vitcheva V, Worth AP, Richarz AN, Cronin MT. The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation. Toxicology 2017; 392:140-154. [DOI: 10.1016/j.tox.2016.01.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2015] [Revised: 01/17/2016] [Accepted: 01/24/2016] [Indexed: 12/18/2022]
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Identification of novel peroxisome proliferator-activated receptor-gamma (PPARγ) agonists using molecular modeling method. J Comput Aided Mol Des 2014; 28:1143-51. [PMID: 25168706 DOI: 10.1007/s10822-014-9791-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Accepted: 08/23/2014] [Indexed: 10/24/2022]
Abstract
Peroxisome proliferator-activated receptor-gamma (PPARγ) plays a critical role in lipid and glucose homeostasis. It is the target of many drug discovery studies, because of its role in various disease states including diabetes and cancer. Thiazolidinediones, a synthetic class of agents that work by activation of PPARγ, have been used extensively as insulin-sensitizers for the management of type 2 diabetes. In this study, a combination of QSAR and docking methods were utilised to perform virtual screening of more than 25 million compounds in the ZINC library. The QSAR model was developed using 1,517 compounds and it identified 42,378 potential PPARγ agonists from the ZINC library, and 10,000 of these were selected for docking with PPARγ based on their diversity. Several steps were used to refine the docking results, and finally 30 potentially highly active ligands were identified. Four compounds were subsequently tested for their in vitro activity, and one compound was found to have a K i values of <5 μM.
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Maleki A, Daraei H, Alaei L, Faraji A. Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict K d of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2014. [DOI: 10.1134/s106816201306006x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Colca JR, Tanis SP, McDonald WG, Kletzien RF. Insulin sensitizers in 2013: new insights for the development of novel therapeutic agents to treat metabolic diseases. Expert Opin Investig Drugs 2013; 23:1-7. [DOI: 10.1517/13543784.2013.839659] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Jerry R Colca
- Metabolic Solutions Development Company,
161 E. Michigan Ave, Kalamazoo, 49007, USA
| | - Steven P Tanis
- PharmaChem Consulting LLC,
1750 Oriole Ct, Carlsbad, 92011, United States
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Vallianatou T, Lambrinidis G, Giaginis C, Mikros E, Tsantili-Kakoulidou A. Analysis of PPAR-α/γ Activity by Combining 2-D QSAR and Molecular Simulation. Mol Inform 2013; 32:431-45. [DOI: 10.1002/minf.201200117] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2012] [Accepted: 11/28/2012] [Indexed: 12/30/2022]
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Vishvakarma VK, Singh P, Dubey M, Kumari K, Chandra R, Pandey ND. Quantitative structure-activity relationship analysis of thiazolidineones: potent antidiabetic compounds. ACTA ACUST UNITED AC 2013; 28:31-47. [DOI: 10.1515/dmdi-2012-0036] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2012] [Accepted: 01/10/2013] [Indexed: 11/15/2022]
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Impact of X-Ray Structure on Predictivity of Scoring Functions: PPARγ Case Study. Mol Inform 2012; 31:631-3. [DOI: 10.1002/minf.201200040] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2012] [Accepted: 07/23/2012] [Indexed: 11/07/2022]
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Guasch L, Sala E, Valls C, Mulero M, Pujadas G, Garcia-Vallvé S. Development of docking-based 3D-QSAR models for PPARgamma full agonists. J Mol Graph Model 2012; 36:1-9. [DOI: 10.1016/j.jmgm.2012.03.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2011] [Revised: 03/02/2012] [Accepted: 03/06/2012] [Indexed: 10/28/2022]
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Rupp M, Schroeter T, Steri R, Proschak E, Hansen K, Zettl H, Rau O, Schubert-Zsilavecz M, Müller KR, Schneider G. Kernel learning for ligand-based virtual screening: discovery of a new PPARγ agonist. J Cheminform 2010. [PMCID: PMC2867160 DOI: 10.1186/1758-2946-2-s1-p27] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
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Rupp M, Schroeter T, Steri R, Zettl H, Proschak E, Hansen K, Rau O, Schwarz O, Müller-Kuhrt L, Schubert-Zsilavecz M, Müller KR, Schneider G. From Machine Learning to Natural Product Derivatives that Selectively Activate Transcription Factor PPARγ. ChemMedChem 2010; 5:191-4. [DOI: 10.1002/cmdc.200900469] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Rinnan Å, Christensen NJ, Engelsen SB. How the energy evaluation method used in the geometry optimization step affect the quality of the subsequent QSAR/QSPR models. J Comput Aided Mol Des 2009; 24:17-22. [DOI: 10.1007/s10822-009-9308-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2009] [Accepted: 10/29/2009] [Indexed: 10/20/2022]
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Giaginis C, Theocharis S, Tsantili-Kakoulidou A. A QSAR Study on Indole-Based PPAR-γ Agonists in Respect to Receptor Binding and Gene Transactivation Data. ACTA ACUST UNITED AC 2009. [DOI: 10.1002/qsar.200860185] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Sundriyal S, Bharatam PV. Important pharmacophoric features of pan PPAR agonists: common chemical feature analysis and virtual screening. Eur J Med Chem 2009; 44:3488-95. [PMID: 19268404 DOI: 10.1016/j.ejmech.2009.01.024] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2008] [Revised: 01/15/2009] [Accepted: 01/22/2009] [Indexed: 11/19/2022]
Abstract
HipHop program was used to generate a common chemical feature hypothesis for pan Peroxisome Proliferator-Activated Receptor (PPAR) agonists. The top scoring hypothesis (hypo-1) was found to differentiate the pan agonists (actives) from subtype-specific and dual PPAR agonists (inactives). The importance of individual features in hypo-1 was assessed by deleting a particular feature to generate a new hypothesis and observing its discriminating ability between 'actives' and 'inactives'. Deletion of aromatic features AR-1 (hypo-1b), AR-2 (hypo-1e) and a Hydrophobic feature HYD-1 (hypo-1c) individually did not affect the discriminating power of the hypo-1 significantly. However, deletion of a Hydrogen Bond Acceptor (HBA) feature (hypo-1f) in the hydrophobic tail group was found to be highly detrimental for the specificity of hypo-1 leading to high hit rate of 'inactives'. Since hypo-1 did not produce any useful hits from the database search, hypo-1b, hypo-1c and hypo-1e were used for virtual screening leading to the identification of new potential pan PPAR ligands. The docking studies were used to predict the binding pose of the proposed molecules in PPARgamma active site.
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Affiliation(s)
- Sandeep Sundriyal
- Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160 062, India
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Giaginis C, Theocharis S, Tsantili-Kakoulidou A. Quantitative Structure-Activity Relationships for PPAR-γ Binding and Gene Transactivation of Tyrosine-Based Agonists Using Multivariate Statistics. Chem Biol Drug Des 2008; 72:257-64. [DOI: 10.1111/j.1747-0285.2008.00701.x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Abstract
y-Randomization is a tool used in validation of QSPR/QSAR models, whereby the performance of the original model in data description (r2) is compared to that of models built for permuted (randomly shuffled) response, based on the original descriptor pool and the original model building procedure. We compared y-randomization and several variants thereof, using original response, permuted response, or random number pseudoresponse and original descriptors or random number pseudodescriptors, in the typical setting of multilinear regression (MLR) with descriptor selection. For each combination of number of observations (compounds), number of descriptors in the final model, and number of descriptors in the pool to select from, computer experiments using the same descriptor selection method result in two different mean highest random r2 values. A lower one is produced by y-randomization or a variant likewise based on the original descriptors, while a higher one is obtained from variants that use random number pseudodescriptors. The difference is due to the intercorrelation of real descriptors in the pool. We propose to compare an original model's r2 to both of these whenever possible. The meaning of the three possible outcomes of such a double test is discussed. Often y-randomization is not available to a potential user of a model, due to the values of all descriptors in the pool for all compounds not being published. In such cases random number experiments as proposed here are still possible. The test was applied to several recently published MLR QSAR equations, and cases of failure were identified. Some progress also is reported toward the aim of obtaining the mean highest r2 of random pseudomodels by calculation rather than by tedious multiple simulations on random number variables.
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Giaginis C, Theocharis S, Tsantili-Kakoulidou A. A consideration of PPAR-gamma ligands with respect to lipophilicity: current trends and perspectives. Expert Opin Investig Drugs 2007; 16:413-7. [PMID: 17371190 DOI: 10.1517/13543784.16.4.413] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The fact that PPAR-gamma is expressed dramatically higher in fat, regulating gene transcription in response to small lipophilic ligands, supports an essential role of increased lipophilicity for those ligands. On the other hand, the skepticism concerning high lipophilicity as a characteristic associated with undesirable effects and formulation problems raises the question of how much lipophilicity should be incorporated in the ligand molecules so that they comply with generally accepted guidelines. A survey on the lipophilic behavior of thiazolidinediones and tyrosine-based derivatives with well-established PPAR-gamma affinity and functional activity suggests that excessive lipophilicity may not be favorable for their action.
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Affiliation(s)
- Costas Giaginis
- University of Athens, Department of Pharmaceutical Chemistry, School of Pharmacy, Panepistimiopolis, Zografou, Athens 157 71, Greece
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Scarsi M, Podvinec M, Roth A, Hug H, Kersten S, Albrecht H, Schwede T, Meyer UA, Rücker C. Sulfonylureas and glinides exhibit peroxisome proliferator-activated receptor gamma activity: a combined virtual screening and biological assay approach. Mol Pharmacol 2006; 71:398-406. [PMID: 17082235 DOI: 10.1124/mol.106.024596] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Most drugs currently employed in the treatment of type 2 diabetes either target the sulfonylurea receptor stimulating insulin release (sulfonylureas, glinides), or target the peroxisome proliferator-activated receptor (PPARgamma) improving insulin resistance (thiazolidinediones). Our work shows that sulfonylureas and glinides additionally bind to PPARgamma and exhibit PPARgamma agonistic activity. This activity was predicted in silico by virtual screening and confirmed in vitro in a binding assay, a transactivation assay, and by measuring the expression of PPARgamma target genes. Among the measured compounds, gliquidone and glipizide (two sulfonylureas), as well as nateglinide (a glinide), exhibit PPARgamma agonistic activity at concentrations comparable with those reached under pharmacological treatment. The most active of these compounds, gliquidone, is shown to be as potent as pioglitazone at inducing PPARgamma target gene expression. This dual mode of action of sulfonylureas and glinides may open new perspectives for the molecular pharmacology of antidiabetic drugs, because it provides evidence that drugs can be designed that target both the sulfonylurea receptor and PPARgamma. Targeting both receptors could increase pancreatic insulin secretion and improve insulin resistance. Glinides, sulfonylureas, and other acidified sulfonamides may be promising leads in the development of new PPARgamma agonists. In addition, we provide a unified concept of the PPARgamma binding ability of seemingly disparate compound classes.
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Affiliation(s)
- Marco Scarsi
- Biozentrum, University of Basel, Klingelbergstr. 50-70, CH-4056 Basel, Switzerland.
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