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Tan H, Wu J, Zhang R, Zhang C, Li W, Chen Q, Zhang X, Yu H, Shi W. Development, Validation, and Application of a Human Reproductive Toxicity Prediction Model Based on Adverse Outcome Pathway. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:12391-12403. [PMID: 35960020 DOI: 10.1021/acs.est.2c02242] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
A growing number of environmental contaminants have been proved to have reproductive toxicity to males and females. However, the unclear toxicological mechanism of reproductive toxicants limits the development of virtual screening methods. By consolidating androgen (AR)-/estrogen receptors (ERs)-mediated adverse outcome pathways (AOPs) with more than 8000 chemical substances, we uncovered relationships between chemical features, a series of pathway-related effects, and reproductive apical outcomes─changes in sex organ weights. An AOP-based computational model named RepTox was developed and evaluated to predict and characterize chemicals' reproductive toxicity for males and females. Results showed that RepTox has three outstanding advantages. (I) Compared with the traditional models (37 and 81% accuracy, respectively), AOP significantly improved the predictive robustness of RepTox (96.3% accuracy). (II) Compared with the application domain (AD) of models based on small in vivo datasets, AOP expanded the ADs of RepTox by 1.65-fold for male and 3.77-fold for female, respectively. (III) RepTox implied that hydrophobicity, cyclopentanol substructure, and several topological indices (e.g., hydrogen-bond acceptors) were important, unbiased features associated with reproductive toxicants. Finally, RepTox was applied to the inventory of existing chemical substances of China and identified 2100 and 7281 potential toxicants to the male and female reproductive systems, respectively.
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Affiliation(s)
- Haoyue Tan
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Jinqiu Wu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Rong Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Chi Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Wei Li
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Qinchang Chen
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Hongxia Yu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
| | - Wei Shi
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
- Jiangsu Province Ecology and Environment Protection Key Laboratory of Chemical Safety and Health Risk, Nanjing 210023, Jiangsu, China
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2
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Louis B, Agrawal VK. Quantitative Structure Activity Relationship Analysis of Antiviral Activity of PF74 Type HIV-1 Capsid Protein Inhibitors by Simplex Representation of Molecular Structure. Polycycl Aromat Compd 2022. [DOI: 10.1080/10406638.2022.2038215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Bruno Louis
- QSAR and Computer Chemical Laboratories, A.P.S. University, Rewa, India
| | - Vijay K. Agrawal
- QSAR and Computer Chemical Laboratories, A.P.S. University, Rewa, India
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3
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [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: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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4
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Drug design of new 5-HT 6R antagonists aided by artificial neural networks. J Mol Graph Model 2021; 104:107844. [PMID: 33529936 DOI: 10.1016/j.jmgm.2021.107844] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 11/23/2022]
Abstract
Alzheimer's Disease (AD) is the most frequent illness and cause of death amongst the age related-neurodegenerative disorders. The Alzheimer's Disease International (ADI) reported in 2019 that over 50 million people were living with dementia in the world and this number could potentially be around 152 million by 2050.5-hydroxtryptamine subtype 6 receptor (5-HT6R) has been identified as a potential anti-amnesic drug target and therefore, the administration of 5-HT6R antagonists can likely mitigate the memory loss and intellectual deterioration associated with AD. Herein, computational tools were applied to design new 5-HT6 antagonists and their biological activity values were predicted by our QSAR model obtained from Artificial Neural Networks (ANN). The proposed compounds here from the QSAR-ANN model presented significant biological activity values and some of them have achieved pKi above 9.00. Furthermore, our results suggest that the presence of halogen atoms (especially bromine) linked to the aromatic ring at para-position (HYD) contribute considerably to the increase of the biological activity values while bulky groups in the PI position do not culminate with the increase antagonist activity of compounds here analyzed. Finally, the ADME/Tox profile as well as the synthetic accessibility of new proposed compounds qualify them to go on further with experimental procedures and thenceforward their antagonist effects can be confirmed.
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Sedykh AY, Shah RR, Kleinstreuer NC, Auerbach SS, Gombar VK. Saagar-A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions. Chem Res Toxicol 2020; 34:634-640. [PMID: 33356152 DOI: 10.1021/acs.chemrestox.0c00464] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Molecular structure-based predictive models provide a proven alternative to costly and inefficient animal testing. However, due to a lack of interpretability of predictive models built with abstract molecular descriptors they have earned the notoriety of being black boxes. Interpretable models require interpretable descriptors to provide chemistry-backed predictive reasoning and facilitate intelligent molecular design. We developed a novel set of extensible chemistry-aware substructures, Saagar, to support interpretable predictive models and read-across protocols. Performance of Saagar in chemical characterization and search for structurally similar actives for read-across applications was compared with four publicly available fingerprint sets (MACCS (166), PubChem (881), ECFP4 (1024), ToxPrint (729)) in three benchmark sets (MUV, ULS, and Tox21) spanning ∼145 000 compounds and 78 molecular targets at 1%, 2%, 5%, and 10% false discovery rates. In 18 of the 20 comparisons, interpretable Saagar features performed better than the publicly available, but less interpretable and fixed-bit length, fingerprints. Examples are provided to show the enhanced capability of Saagar in extracting compounds with higher scaffold similarity. Saagar features are interpretable and efficiently characterize diverse chemical collections, thus making them a better choice for building interpretable predictive in silico models and read-across protocols.
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Affiliation(s)
| | - Ruchir R Shah
- Sciome LLC, Research Triangle Park, North Carolina 27709, United States
| | - Nicole C Kleinstreuer
- National Institute of Environmental Health Sciences (NIEHS), National Toxicology Program (NTP), Research Triangle Park, North Carolina 27709, United States
| | - Scott S Auerbach
- National Institute of Environmental Health Sciences (NIEHS), National Toxicology Program (NTP), Research Triangle Park, North Carolina 27709, United States
| | - Vijay K Gombar
- Sciome LLC, Research Triangle Park, North Carolina 27709, United States
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6
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Virtual screening and assessment of anticancer potential of selenium-based compounds against HL-60 and MCF7 cells. Future Med Chem 2020; 12:2191-2207. [PMID: 33243002 DOI: 10.4155/fmc-2020-0110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Aim: Selenium-based compounds have antitumor potential. We used a ligand-based virtual screening analysis to identify selenoglycolicamides with potential antitumor activity. Results & Conclusion: Compounds 3, 6, 7 and 8 were selected for in vitro cytotoxicity tests against various cell lines, according to spectrophotometry results. Compound 3 presented the best cytotoxicity results against a promyelocytic leukemia line (HL-60) and was able to induce cell death at a frequency similar to that observed for doxorubicin. The docking study showed that compound 3 has good interaction energies with the targets caspase-3, 7 and 8, which are components of the apoptotic pathway. These results suggested that selenium has significant pharmacological potential for the selective targeting of tumor cells, inducing molecular and cellular events that culminate in tumor cell death.
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7
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Korn D, Pervitsky V, Bobrowski T, Alves VM, Schmitt C, Bizon C, Baker N, Chirkova R, Cherkasov A, Muratov E, Tropsha A. COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:13289222. [PMID: 33269341 PMCID: PMC7709174 DOI: 10.26434/chemrxiv.13289222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Revised: 11/26/2020] [Indexed: 12/02/2022]
Abstract
Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at https://coke.mml.unc.edu/ and the code is available at https://github.com/DnlRKorn/CoKE.
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Affiliation(s)
- Daniel Korn
- Department of Computer Science, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Vera Pervitsky
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Vinicius M. Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Chris Bizon
- Renaissance Computing Institute, the University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7568, USA
| | - Nancy Baker
- ParlezChem, 123 W Union Street, Hillsborough, NC, 27278, USA
| | - Rada Chirkova
- Department of Computer Science, North Carolina State University, Raleigh, NC, 27606-5550
| | - Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, Canada
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, the University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
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8
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Egorova A, Bogner E, Novoselova E, Zorn KM, Ekins S, Makarov V. Dispirotripiperazine-core compounds, their biological activity with a focus on broad antiviral property, and perspectives in drug design (mini-review). Eur J Med Chem 2020; 211:113014. [PMID: 33218683 PMCID: PMC7658596 DOI: 10.1016/j.ejmech.2020.113014] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 10/26/2020] [Accepted: 11/08/2020] [Indexed: 12/31/2022]
Abstract
Viruses are obligate intracellular parasites and have evolved to enter the host cell. To gain access they come into contact with the host cell through an initial adhesion, and some viruses from different genus may use heparan sulfate proteoglycans for it. The successful inhibition of this early event of the infection by synthetic molecules has always been an attractive target for medicinal chemists. Numerous reports have yielded insights into the function of compounds based on the dispirotripiperazine scaffold. Analysis suggests that this is a structural requirement for inhibiting the interactions between viruses and cell-surface heparan sulfate proteoglycans, thus preventing virus entry and replication. This review summarizes our current knowledge about the early history of development, synthesis, structure-activity relationships and antiviral evaluation of dispirotripiperazine-based compounds and where they are going in the future.
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Affiliation(s)
- Anna Egorova
- Research Center of Biotechnology RAS, Leninsky Prospekt 33-2, 119071, Moscow, Russia
| | - Elke Bogner
- Institute of Virology, Charité Universitätsmedizin Berlin, Charité Campus Mitte, Chariteplatz 1, 10117, Berlin, Germany
| | - Elena Novoselova
- Research Center of Biotechnology RAS, Leninsky Prospekt 33-2, 119071, Moscow, Russia
| | - Kimberley M Zorn
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab, 3510, Raleigh, NC, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab, 3510, Raleigh, NC, USA
| | - Vadim Makarov
- Research Center of Biotechnology RAS, Leninsky Prospekt 33-2, 119071, Moscow, Russia.
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9
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Alves VM, Bobrowski T, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. QSAR Modeling of SARS-CoV M pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS-CoV-2. Mol Inform 2020; 40:e2000113. [PMID: 33405340 DOI: 10.1002/minf.202000113] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 07/28/2020] [Indexed: 12/22/2022]
Abstract
The main protease (Mpro) of the SARS-CoV-2 has been proposed as one of the major drug targets for COVID-19. We have identified the experimental data on the inhibitory activity of compounds tested against the closely related (96 % sequence identity, 100 % active site conservation) Mpro of SARS-CoV. We developed QSAR models of these inhibitors and employed these models for virtual screening of all drugs in the DrugBank database. Similarity searching and molecular docking were explored in parallel, but docking failed to correctly discriminate between experimentally active and inactive compounds, so it was not relied upon for prospective virtual screening. Forty-two compounds were identified by our models as consensus computational hits. Subsequent to our computational studies, NCATS reported the results of experimental screening of their drug collection in SARS-CoV-2 cytopathic effect assay (https://opendata.ncats.nih.gov/covid19/). Coincidentally, NCATS tested 11 of our 42 hits, and three of them, cenicriviroc (AC50 of 8.9 μM), proglumetacin (tested twice independently, with AC50 of 8.9 μM and 12.5 μM), and sufugolix (AC50 12.6 μM), were shown to be active. These observations support the value of our modeling approaches and models for guiding the experimental investigations of putative anti-COVID-19 drug candidates. All data and models used in this study are publicly available via Supplementary Materials, GitHub (https://github.com/alvesvm/sars-cov-mpro), and Chembench web portal (https://chembench.mml.unc.edu/).
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Affiliation(s)
- Vinicius M Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Cleber C Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Scott Auerbach
- Toxinformatics Group, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Beard Hall, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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10
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Borba JVB, Braga RC, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Andrade CH. Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers. Chem Res Toxicol 2020; 34:258-267. [PMID: 32673477 DOI: 10.1021/acs.chemrestox.0c00186] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.
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Affiliation(s)
- Joyce V B Borba
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil.,Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | | | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.,Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, Paraíba 58059, Brazil
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil
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11
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 312] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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12
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Yao J, Qi R, Pan Y, He H, Fan Y, Jiang J, Jiang J. Prediction of the flash points of binary biodiesel mixtures from molecular structures. J Loss Prev Process Ind 2020. [DOI: 10.1016/j.jlp.2020.104137] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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13
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Bobrowski T, Alves VM, Melo-Filho CC, Korn D, Auerbach S, Schmitt C, Muratov EN, Tropsha A. Computational Models Identify Several FDA Approved or Experimental Drugs as Putative Agents Against SARS-CoV-2. CHEMRXIV : THE PREPRINT SERVER FOR CHEMISTRY 2020:12153594. [PMID: 32511287 PMCID: PMC7252448 DOI: 10.26434/chemrxiv.12153594] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 04/22/2020] [Indexed: 01/09/2023]
Abstract
The outbreak of a novel human coronavirus (SARS-CoV-2) has evolved into global health emergency, infecting hundreds of thousands of people worldwide. We have identified experimental data on the inhibitory activity of compounds tested against closely related (96% sequence identity, 100% active site conservation) protease of SARS-CoV and employed this data to build QSAR models for this dataset. We employed these models for virtual screening of all drugs from DrugBank, including compounds in clinical trials. Molecular docking and similarity search approaches were explored in parallel with QSAR modeling, but molecular docking failed to correctly discriminate between experimentally active and inactive compounds. As a result of our studies, we recommended 41 approved, experimental, or investigational drugs as potential agents against SARS-CoV-2 acting as putative inhibitors of Mpro. Ten compounds with feasible prices were purchased and are awaiting the experimental validation. .
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Affiliation(s)
- Tesia Bobrowski
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Vinicius M. Alves
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Cleber C. Melo-Filho
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daniel Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Scott Auerbach
- Toxinformatics Group, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Charles Schmitt
- Office of Data Science, National Toxicology Program, NIEHS, Morrisville, NC, 27560, USA
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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14
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Chen CH, Tanaka K, Kotera M, Funatsu K. Comparison and improvement of the predictability and interpretability with ensemble learning models in QSPR applications. J Cheminform 2020; 12:19. [PMID: 33430997 PMCID: PMC7106596 DOI: 10.1186/s13321-020-0417-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 02/05/2020] [Indexed: 12/23/2022] Open
Abstract
Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine’s inability to interpret the predictions to researchers. In fact, many implementations of ensemble learning models are able to quantify the overall magnitude of each feature. For example, feature importance allows us to assess the relative importance of features and to interpret the predictions. However, different ensemble learning methods or implementations may lead to different feature selections for interpretation. In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting) for regression and binary classification modeling tasks. Then, the blending methods were built by summarizing four different ensemble learning methods. The blending method led to better performance and a unification interpretation by summarizing individual predictions from different learning models. The important features of two case studies which gave us some valuable information to compound properties were discussed in detail in this report. QSPR modeling with interpretable machine learning techniques can move the chemical design forward to work more efficiently, confirm hypothesis and establish knowledge for better results.
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Affiliation(s)
- Chia-Hsiu Chen
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Kenichi Tanaka
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Masaaki Kotera
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
| | - Kimito Funatsu
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.
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15
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Development of quantitative structure-property relationship (QSPR) models for predicting the thermal hazard of ionic liquids: A review of methods and models. J Mol Liq 2020. [DOI: 10.1016/j.molliq.2020.112471] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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16
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Andrade CH, Neves BJ, Melo-Filho CC, Rodrigues J, Silva DC, Braga RC, Cravo PVL. In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases. Curr Med Chem 2019. [DOI: 10.2174/0929867325666180309114824] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs)
have reached clinical trials in the last decades, underscoring the need for new, safe and effective
treatments. In such context, drug repositioning, which allows finding novel indications
for approved drugs whose pharmacokinetic and safety profiles are already known,
emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent
of the typical drug discovery process that involves the systematic screening of chemical
compounds against drug targets in high-throughput screening (HTS) efforts, for the identification
of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics
attempts to identify all potential ligands for all possible targets and diseases. In
this review, we summarize current methodological development efforts in drug repositioning
that use state-of-the-art computational ligand- and structure-based chemogenomics approaches.
Furthermore, we highlighted the recent progress in computational drug repositioning
for some NTDs, based on curation and modeling of genomic, biological, and chemical data.
Additionally, we also present in-house and other successful examples and suggest possible solutions
to existing pitfalls.
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Affiliation(s)
- Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Bruno Junior Neves
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Cleber Camilo Melo-Filho
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Juliana Rodrigues
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Diego Cabral Silva
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Rodolpho Campos Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Pedro Vitor Lemos Cravo
- Laboratory of Cheminformatics, Centro Universitario de Anapolis (UniEVANGELICA), Anapolis, GO, 75083-515, Brazil
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17
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Alves VM, Hwang D, Muratov E, Sokolsky-Papkov M, Varlamova E, Vinod N, Lim C, Andrade CH, Tropsha A, Kabanov A. Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs. SCIENCE ADVANCES 2019; 5:eaav9784. [PMID: 31249867 PMCID: PMC6594770 DOI: 10.1126/sciadv.aav9784] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 05/16/2019] [Indexed: 05/29/2023]
Abstract
Many drug candidates fail therapeutic development because of poor aqueous solubility. We have conceived a computer-aided strategy to enable polymeric micelle-based delivery of poorly soluble drugs. We built models predicting both drug loading efficiency (LE) and loading capacity (LC) using novel descriptors of drug-polymer complexes. These models were employed for virtual screening of drug libraries, and eight drugs predicted to have either high LE and high LC or low LE and low LC were selected. Three putative positives, as well as three putative negative hits, were confirmed experimentally (implying 75% prediction accuracy). Fortuitously, simvastatin, a putative negative hit, was found to have the desired micelle solubility. Podophyllotoxin and simvastatin (LE of 95% and 87% and LC of 43% and 41%, respectively) were among the top five polymeric micelle-soluble compounds ever studied experimentally. The success of the strategy described herein suggests its broad utility for designing drug delivery systems.
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Affiliation(s)
- Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Duhyeong Hwang
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Pharmaceutical Sciences, Federal University of Paraíba, Joao Pessoa, PB 58059, Brazil
| | - Marina Sokolsky-Papkov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ekaterina Varlamova
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Natasha Vinod
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- UNC/NC State Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chaemin Lim
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Alexander Kabanov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory of Chemical Design of Bionanomaterials, Faculty of Chemistry, M.V. Lomonosov Moscow State University, Moscow 119992, Russia
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18
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Gridina TL, Fedchuk AS, Basok SS, Artemenko AG, Ognichenko LN, Shitikova LI, Lutsyuk AF, Gruzevskii AA, Kuz’min VE. The effect of the structure of derivatives of nitrogen-containing heterocycles on their anti-influenza activity. Chem Heterocycl Compd (N Y) 2019. [DOI: 10.1007/s10593-019-02479-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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19
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Shen S, Pan Y, Ji X, Ni Y, Jiang J. Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures. Int J Mol Sci 2019; 20:ijms20092084. [PMID: 31035591 PMCID: PMC6539801 DOI: 10.3390/ijms20092084] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 04/11/2019] [Accepted: 04/23/2019] [Indexed: 11/17/2022] Open
Abstract
A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous “compounds out” strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.
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Affiliation(s)
- Shijing Shen
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Yong Pan
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Xianke Ji
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Yuqing Ni
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
| | - Juncheng Jiang
- Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control, College of Safety Science and Engineering, Nanjing Tech University, Nanjing 210009, China.
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20
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de Morais e Silva L, Lorenzo VP, Lopes WS, Scotti L, Scotti MT. Predictive Computational Tools for Assessment of Ecotoxicological Activity of Organic Micropollutants in Various Water Sources in Brazil. Mol Inform 2019; 38:e1800156. [DOI: 10.1002/minf.201800156] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Accepted: 01/06/2019] [Indexed: 01/18/2023]
Affiliation(s)
- Luana de Morais e Silva
- Post-Graduate Program in Science and Environmental TechnologyDepartment of Sanitary and Environmental EngineeringState University of Paraíba 58429500 Campina Grande, PB Brazil
| | - Vitor Prates Lorenzo
- Federal Institute of Education, Science and Technology Sertão Pernambucano 56316686 Petrolina, Pernambuco Brazil
| | - Wilton Silva Lopes
- Post-Graduate Program in Science and Environmental TechnologyDepartment of Sanitary and Environmental EngineeringState University of Paraíba 58429500 Campina Grande, PB Brazil
| | - Luciana Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive ProductsFederal University of Paraíba 58051-900 João Pessoa, PB Brazil
| | - Marcus Tullius Scotti
- Post-Graduate Program in Natural and Synthetic Bioactive ProductsFederal University of Paraíba 58051-900 João Pessoa, PB Brazil
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21
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Wilm A, Kühnl J, Kirchmair J. Computational approaches for skin sensitization prediction. Crit Rev Toxicol 2018; 48:738-760. [DOI: 10.1080/10408444.2018.1528207] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Anke Wilm
- Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
- HITeC e.V, Hamburg, Germany
| | - Jochen Kühnl
- Front End Innovation, Beiersdorf AG, Hamburg, Germany
| | - Johannes Kirchmair
- Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
- Department of Chemistry, University of Bergen, Bergen, Norway
- Computational Biology Unit (CBU), University of Bergen, Bergen, Norway
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22
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Alves VM, Golbraikh A, Capuzzi SJ, Liu K, Lam WI, Korn DR, Pozefsky D, Andrade CH, Muratov EN, Tropsha A. Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure-Activity Relationship Models. J Chem Inf Model 2018; 58:1214-1223. [PMID: 29809005 DOI: 10.1021/acs.jcim.8b00124] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , GO 74605-170 , Brazil
| | - Alexander Golbraikh
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Stephen J Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Kammy Liu
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Wai In Lam
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Daniel Robert Korn
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Diane Pozefsky
- Department of Computer Science , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy , Federal University of Goias , Goiania , GO 74605-170 , Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States.,Department of Chemical Technology , Odessa National Polytechnic University , Odessa , 65000 , Ukraine
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , North Carolina 27599 , United States
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23
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Capuzzi SJ, Sun W, Muratov EN, Martínez-Romero C, He S, Zhu W, Li H, Tawa G, Fisher EG, Xu M, Shinn P, Qiu X, García-Sastre A, Zheng W, Tropsha A. Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors. J Med Chem 2018; 61:3582-3594. [PMID: 29624387 DOI: 10.1021/acs.jmedchem.8b00035] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The Ebola virus (EBOV) causes severe human infection that lacks effective treatment. A recent screen identified a series of compounds that block EBOV-like particle entry into human cells. Using data from this screen, quantitative structure-activity relationship models were built and employed for virtual screening of a ∼17 million compound library. Experimental testing of 102 hits yielded 14 compounds with IC50 values under 10 μM, including several sub-micromolar inhibitors, and more than 10-fold selectivity against host cytotoxicity. These confirmed hits include FDA-approved drugs and clinical candidates with non-antiviral indications, as well as compounds with novel scaffolds and no previously known bioactivity. Five selected hits inhibited BSL-4 live-EBOV infection in a dose-dependent manner, including vindesine (0.34 μM). Additional studies of these novel anti-EBOV compounds revealed their mechanisms of action, including the inhibition of NPC1 protein, cathepsin B/L, and lysosomal function. Compounds identified in this study are among the most potent and well-characterized anti-EBOV inhibitors reported to date.
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Affiliation(s)
- Stephen J Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry , UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill , North Carolina 27599 , United States
| | - Wei Sun
- National Center for Advancing Translational Sciences , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry , UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill , North Carolina 27599 , United States.,Department of Chemical Technology , Odessa National Polytechnic University , Odessa 65000 , Ukraine
| | - Carles Martínez-Romero
- Department of Microbiology , Icahn School of Medicine at Mount Sinai , New York , New York 10029 , United States.,Global Health and Emerging Pathogens Institute , Icahn School of Medicine at Mount Sinai , New York , New York 10029 , United States
| | - Shihua He
- Special Pathogens Program, National Microbiology Laboratory , Public Health Agency of Canada , 1015 Arlington Street , Winnipeg , Manitoba R3E 3R2 , Canada
| | - Wenjun Zhu
- Special Pathogens Program, National Microbiology Laboratory , Public Health Agency of Canada , 1015 Arlington Street , Winnipeg , Manitoba R3E 3R2 , Canada.,Department of Medical Microbiology , University of Manitoba , 745 Bannatyne Avenue , Winnipeg , Manitoba R3E 0J9 , Canada
| | - Hao Li
- National Center for Advancing Translational Sciences , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Gregory Tawa
- National Center for Advancing Translational Sciences , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Ethan G Fisher
- National Center for Advancing Translational Sciences , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Miao Xu
- National Center for Advancing Translational Sciences , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Paul Shinn
- National Center for Advancing Translational Sciences , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Xiangguo Qiu
- Special Pathogens Program, National Microbiology Laboratory , Public Health Agency of Canada , 1015 Arlington Street , Winnipeg , Manitoba R3E 3R2 , Canada.,Department of Medical Microbiology , University of Manitoba , 745 Bannatyne Avenue , Winnipeg , Manitoba R3E 0J9 , Canada
| | - Adolfo García-Sastre
- Department of Microbiology , Icahn School of Medicine at Mount Sinai , New York , New York 10029 , United States.,Global Health and Emerging Pathogens Institute , Icahn School of Medicine at Mount Sinai , New York , New York 10029 , United States.,Department of Medicine, Division of Infectious Diseases , Icahn School of Medicine at Mount Sinai , New York , New York 10029 , United States
| | - Wei Zheng
- National Center for Advancing Translational Sciences , National Institutes of Health , Bethesda , Maryland 20892 , United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry , UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill , Chapel Hill , North Carolina 27599 , United States
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24
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Dreher J, Scheiber J, Stiefl N, Baumann K. xMaP-An Interpretable Alignment-Free Four-Dimensional Quantitative Structure-Activity Relationship Technique Based on Molecular Surface Properties and Conformer Ensembles. J Chem Inf Model 2018; 58:165-181. [PMID: 29172519 DOI: 10.1021/acs.jcim.7b00419] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A novel alignment-free molecular descriptor called xMaP (flexible MaP descriptor) is introduced. The descriptor is the advancement of the previously published translationally and rotationally invariant three-dimensional (3D) descriptor MaP (mapping property distributions onto the molecular surface) to the fourth dimension (4D). In addition to MaP, xMaP is independent of the chosen starting conformation of the encoded molecules and is therefore entirely alignment-free. This is achieved by using ensembles of conformers, which are generated by conformational searches. This step of the procedure is similar to Hopfinger's 4D quantitative structure-activity relationship (QSAR). A five-step procedure is used to compute the xMaP descriptor. First, a conformational search for each molecule is carried out. Next, for each of the conformers an approximation to the molecular surface with equally distributed surface points is computed. Third, molecular properties are projected onto this surface. Fourth, areas of identical properties are clustered to so-called patches. Fifth, the spatial distribution of the patches is converted into an alignment-free descriptor that is based on the entire conformer ensemble. The resulting descriptor can be interpreted by superimposing the most important descriptor variables and the molecules of the data set. The most important descriptor variables are identified with chemometric regression tools. The novel descriptor was applied to several benchmark data sets and was compared to other descriptors and QSAR techniques comprising a binary fingerprint, a topological pharmacophore descriptor (Cats2D), and the field-based 3D-QSAR technique GRID/PLS which is alignment-dependent. The use of conformer ensembles renders xMaP very robust. It turns out that xMaP performs very well on (almost) all data sets and that the statistical results are comparable to GRID/PLS. In addition to that, xMaP can also be used to efficiently visualize the derived quantitative structure-activity relationships.
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Affiliation(s)
- Jan Dreher
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Josef Scheiber
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Nikolaus Stiefl
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
| | - Knut Baumann
- Institute of Medicinal and Pharmaceutical Chemistry, University of Technology Braunschweig , Beethovenstrasse 55, D 38106 Braunschweig, Germany
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25
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Polishchuk P. Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future. J Chem Inf Model 2017; 57:2618-2639. [DOI: 10.1021/acs.jcim.7b00274] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Pavel Polishchuk
- Institute of Molecular and
Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and University Hospital in Olomouc, Hněvotínská
1333/5, 779 00 Olomouc, Czech Republic
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Gomes MN, Muratov EN, Pereira M, Peixoto JC, Rosseto LP, Cravo PVL, Andrade CH, Neves BJ. Chalcone Derivatives: Promising Starting Points for Drug Design. Molecules 2017; 22:E1210. [PMID: 28757583 PMCID: PMC6152227 DOI: 10.3390/molecules22081210] [Citation(s) in RCA: 199] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2017] [Revised: 07/11/2017] [Accepted: 07/14/2017] [Indexed: 12/20/2022] Open
Abstract
Medicinal chemists continue to be fascinated by chalcone derivatives because of their simple chemistry, ease of hydrogen atom manipulation, straightforward synthesis, and a variety of promising biological activities. However, chalcones have still not garnered deserved attention, especially considering their high potential as chemical sources for designing and developing new effective drugs. In this review, we summarize current methodological developments towards the design and synthesis of new chalcone derivatives and state-of-the-art medicinal chemistry strategies (bioisosterism, molecular hybridization, and pro-drug design). We also highlight the applicability of computer-assisted drug design approaches to chalcones and address how this may contribute to optimizing research outputs and lead to more successful and cost-effective drug discovery endeavors. Lastly, we present successful examples of the use of chalcones and suggest possible solutions to existing limitations.
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Affiliation(s)
- Marcelo N Gomes
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Setor Leste Universitário, Goiânia 74605-510, Brazil.
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27955-7568, USA.
| | - Maristela Pereira
- Laboratório de Biologia Molecular, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia 74001-970, Brazil.
| | - Josana C Peixoto
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
| | - Lucimar P Rosseto
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
| | - Pedro V L Cravo
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
- GHTM/Instituto de Higiene e Medicina Tropical, Universidade Nova de Lisboa, 1349-008 Lisboa, Portugal.
| | - Carolina H Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Setor Leste Universitário, Goiânia 74605-510, Brazil.
| | - Bruno J Neves
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Setor Leste Universitário, Goiânia 74605-510, Brazil.
- Laboratório de Biologia Molecular, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia 74001-970, Brazil.
- Programa de Pós-Graduação em Sociedade, Tecnologia e Meio Ambiente, Centro Universitário de Anápolis-UniEVANGÉLICA, Anápolis 75083-515, Brazil.
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Alves VM, Muratov EN, Zakharov A, Muratov NN, Andrade CH, Tropsha A. Chemical toxicity prediction for major classes of industrial chemicals: Is it possible to develop universal models covering cosmetics, drugs, and pesticides? Food Chem Toxicol 2017; 112:526-534. [PMID: 28412406 DOI: 10.1016/j.fct.2017.04.008] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 03/16/2017] [Accepted: 04/10/2017] [Indexed: 01/15/2023]
Abstract
Computational models have earned broad acceptance for assessing chemical toxicity during early stages of drug discovery or environmental safety assessment. The majority of publicly available QSAR toxicity models have been developed for datasets including mostly drugs or drug-like compounds. We have evaluated and compared chemical spaces occupied by cosmetics, drugs, and pesticides, and explored whether current computational models of toxicity endpoints can be universally applied to all these chemicals. Our analysis of the chemical space overlap and applicability domain (AD) of models built previously for twenty different toxicity endpoints showed that most of these models afforded high coverage (>90%) for all three classes of compounds analyzed herein. Only T. pyriformis models demonstrated lower coverage for drugs and pesticides (38% and 54%, respectively). These results show that, for the most part, historical QSAR models built with data available for different toxicity endpoints can be used for toxicity assessment of novel chemicals irrespective of the intended commercial use; however, the AD restriction is necessary to assure the expected prediction accuracy. Local models may need to be developed to capture chemicals that appear as outliers with respect to global models.
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA; Laboratory of Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, 74605-170, Brazil
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA; Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Alexey Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD, 20850, USA
| | - Nail N Muratov
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Carolina H Andrade
- Laboratory of Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO, 74605-170, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.
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Consensus of classification trees for skin sensitisation hazard prediction. Toxicol In Vitro 2016; 36:197-209. [DOI: 10.1016/j.tiv.2016.07.014] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 07/08/2016] [Accepted: 07/21/2016] [Indexed: 11/20/2022]
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Alves V, Muratov E, Capuzzi S, Politi R, Low Y, Braga R, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade C, Kuz'min V, Fourches D, Tropsha A. Alarms about structural alerts. GREEN CHEMISTRY : AN INTERNATIONAL JOURNAL AND GREEN CHEMISTRY RESOURCE : GC 2016; 18:4348-4360. [PMID: 28503093 PMCID: PMC5423727 DOI: 10.1039/c6gc01492e] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.
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Affiliation(s)
- Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yen Low
- Netflix, San Francisco, CA 94123, USA
| | - Rodolpho Braga
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD 20850, USA
| | | | - Elena Mokshyna
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Carolina Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Victor Kuz'min
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Neves BJ, Muratov E, Machado RB, Andrade CH, Cravo PVL. Modern approaches to accelerate discovery of new antischistosomal drugs. Expert Opin Drug Discov 2016; 11:557-67. [PMID: 27073973 DOI: 10.1080/17460441.2016.1178230] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects. AREAS COVERED Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors. EXPERT OPINION Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.
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Affiliation(s)
- Bruno Junior Neves
- a LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil
| | - Eugene Muratov
- b Laboratory for Molecular Modeling, Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , NC , USA
| | - Renato Beilner Machado
- c GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil
| | - Carolina Horta Andrade
- a LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil
| | - Pedro Vitor Lemos Cravo
- c GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil.,d Instituto de Higiene e Medicina Tropical , Universidade Nova de Lisboa , Lisbon , Portugal
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Mokshyna EG, Polishchuk PG, Nedostup VI, Kuz’min VE. QSPR modeling of critical properties of organic binary mixtures. RUSSIAN JOURNAL OF ORGANIC CHEMISTRY 2016. [DOI: 10.1134/s1070428016010024] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Zakharov AV, Varlamova EV, Lagunin AA, Dmitriev AV, Muratov EN, Fourches D, Kuz'min VE, Poroikov VV, Tropsha A, Nicklaus MC. QSAR Modeling and Prediction of Drug-Drug Interactions. Mol Pharm 2016; 13:545-56. [PMID: 26669717 DOI: 10.1021/acs.molpharmaceut.5b00762] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.
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Affiliation(s)
- Alexey V Zakharov
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
| | - Ekaterina V Varlamova
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine.,Chemical-Technological Department, Odessa National Polytechnic University , 1 Shevchenko Ave, Odessa 65000, Ukraine
| | - Alexey A Lagunin
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia.,Medico-Biological Department, Pirogov Russian National Research Medical University , Ostrovitianov str. 1, Moscow 117997, Russia
| | - Alexander V Dmitriev
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh, North Carolina 27695, United States
| | - Victor E Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine , Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - Vladimir V Poroikov
- Institute of Biochemical Chemistry , 10/8, Pogodinskaya street, 119121 Moscow, Russia
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina , Beard Hall 301, CB#7568, Chapel Hill, North Carolina 27599, United States
| | - Marc C Nicklaus
- Computer-Aided Drug Design Group, Chemical Biology Laboratory, Center for Cancer Research, National Cancer Institute, National Institutes of Health, DHHS, NCI-Frederick , 376 Boyles Street, Frederick, Maryland 21702, United States
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Polishchuk PG, Samoylenko GV, Khristova TM, Krysko OL, Kabanova TA, Kabanov VM, Kornylov AY, Klimchuk O, Langer T, Andronati SA, Kuz'min VE, Krysko AA, Varnek A. Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents. J Med Chem 2015; 58:7681-94. [PMID: 26367138 DOI: 10.1021/acs.jmedchem.5b00865] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
This article describes design, virtual screening, synthesis, and biological tests of novel αIIbβ3 antagonists, which inhibit platelet aggregation. Two types of αIIbβ3 antagonists were developed: those binding either closed or open form of the protein. At the first step, available experimental data were used to build QSAR models and ligand- and structure-based pharmacophore models and to select the most appropriate tool for ligand-to-protein docking. Virtual screening of publicly available databases (BioinfoDB, ZINC, Enamine data sets) with developed models resulted in no hits. Therefore, small focused libraries for two types of ligands were prepared on the basis of pharmacophore models. Their screening resulted in four potential ligands for open form of αIIbβ3 and four ligands for its closed form followed by their synthesis and in vitro tests. Experimental measurements of affinity for αIIbβ3 and ability to inhibit ADP-induced platelet aggregation (IC50) showed that two designed ligands for the open form 4c and 4d (IC50 = 6.2 nM and 25 nM, respectively) and one for the closed form 12b (IC50 = 11 nM) were more potent than commercial antithrombotic Tirofiban (IC50 = 32 nM).
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Affiliation(s)
- Pavel G Polishchuk
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Georgiy V Samoylenko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Tetiana M Khristova
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine.,Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
| | - Olga L Krysko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Tatyana A Kabanova
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Vladimir M Kabanov
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Alexander Yu Kornylov
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Olga Klimchuk
- Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
| | - Thierry Langer
- Department of Pharmaceutical Chemistry, Faculty of Life Sciences, University of Vienna , Althanstraße 14, 1090 Vienna, Austria
| | - Sergei A Andronati
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Victor E Kuz'min
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Andrei A Krysko
- A.V. Bogatsky Physico-Chemical Institute of National Academy of Sciences of Ukraine , Lustdorfskaya doroga 86, Odessa 65080, Ukraine
| | - Alexandre Varnek
- Laboratory of Chemoinformatics (UMR 7140 CNRS/UniStra), University of Strasbourg , 1, rue B. Pascal, Strasbourg 67000, France
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicol Appl Pharmacol 2015; 284:262-72. [PMID: 25560674 PMCID: PMC4546933 DOI: 10.1016/j.taap.2014.12.014] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/14/2014] [Accepted: 12/21/2014] [Indexed: 12/20/2022]
Abstract
Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71-88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation.
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Affiliation(s)
- Vinicius M Alves
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Judy Strickland
- ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Nicole Kleinstreuer
- ILS/Contractor Supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Carolina H Andrade
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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Alves VM, Muratov E, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicol Appl Pharmacol 2015; 284:273-80. [PMID: 25560673 PMCID: PMC4408226 DOI: 10.1016/j.taap.2014.12.013] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 12/14/2014] [Accepted: 12/21/2014] [Indexed: 12/02/2022]
Abstract
Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R2=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q2ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential.
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Affiliation(s)
- Vinicius M Alves
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil; Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA; Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Judy Strickland
- ILS/Contractor supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Nicole Kleinstreuer
- ILS/Contractor supporting the NTP Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), P.O. Box 13501, Research Triangle Park, NC 27709, USA
| | - Carolina H Andrade
- Laboratory of Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-220, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA.
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Thompson CG, Sedykh A, Nicol MR, Muratov E, Fourches D, Tropsha A, Kashuba AD. Short communication: cheminformatics analysis to identify predictors of antiviral drug penetration into the female genital tract. AIDS Res Hum Retroviruses 2014; 30:1058-64. [PMID: 24512359 DOI: 10.1089/aid.2013.0254] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The exposure of oral antiretroviral (ARV) drugs in the female genital tract (FGT) is variable and almost unpredictable. Identifying an efficient method to find compounds with high tissue penetration would streamline the development of regimens for both HIV preexposure prophylaxis and viral reservoir targeting. Here we describe the cheminformatics investigation of diverse drugs with known FGT penetration using cluster analysis and quantitative structure-activity relationships (QSAR) modeling. A literature search over the 1950-2012 period identified 58 compounds (including 21 ARVs and representing 13 drug classes) associated with their actual concentration data for cervical or vaginal tissue, or cervicovaginal fluid. Cluster analysis revealed significant trends in the penetrative ability for certain chemotypes. QSAR models to predict genital tract concentrations normalized to blood plasma concentrations were developed with two machine learning techniques utilizing drugs' molecular descriptors and pharmacokinetic parameters as inputs. The QSAR model with the highest predictive accuracy had R(2)test=0.47. High volume of distribution, high MRP1 substrate probability, and low MRP4 substrate probability were associated with FGT concentrations ≥1.5-fold plasma concentrations. However, due to the limited FGT data available, prediction performances of all models were low. Despite this limitation, we were able to support our findings by correctly predicting the penetration class of rilpivirine and dolutegravir. With more data to enrich the models, we believe these methods could potentially enhance the current approach of clinical testing.
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Affiliation(s)
- Corbin G. Thompson
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina
| | - Alexander Sedykh
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina
| | - Melanie R. Nicol
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina
| | - Denis Fourches
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina
| | - Angela D.M. Kashuba
- Division of Pharmacotherapy and Experimental Therapeutics, University of North Carolina Eshelman School of Pharmacy, Chapel Hill, North Carolina
- School of Medicine, University of North Carolina, Chapel Hill, North Carolina
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Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz'min VE, Cramer R, Benigni R, Yang C, Rathman J, Terfloth L, Gasteiger J, Richard A, Tropsha A. QSAR modeling: where have you been? Where are you going to? J Med Chem 2014; 57:4977-5010. [PMID: 24351051 PMCID: PMC4074254 DOI: 10.1021/jm4004285] [Citation(s) in RCA: 1023] [Impact Index Per Article: 102.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.
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Affiliation(s)
- Artem Cherkasov
- Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Alexandre Varnek
- Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France
| | - Igor I. Baskin
- Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mark Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - John Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK
| | - Paola Gramatica
- Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy
| | | | - Roberto Todeschini
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Viviana Consonni
- Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy
| | - Victor E. Kuz'min
- Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine
| | | | - Romualdo Benigni
- Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy
| | | | - James Rathman
- Altamira LLC, Columbus OH 43235, USA
- Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA
| | | | | | - Ann Richard
- National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27519, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Tin’kov OV, Muratov EN, Artemenko AG, Kuz’min VE. Analysis and Prediction of the Reproductive Toxicity of Organic Compounds of Different Classes using 2D Simplex Representations of Molecular Structure. Pharm Chem J 2013. [DOI: 10.1007/s11094-013-0974-6] [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]
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Polishchuk PG, Kuz'min VE, Artemenko AG, Muratov EN. Universal Approach for Structural Interpretation of QSAR/QSPR Models. Mol Inform 2013; 32:843-53. [DOI: 10.1002/minf.201300029] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Accepted: 07/29/2013] [Indexed: 11/07/2022]
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Fourches D, Muratov E, Ding F, Dokholyan NV, Tropsha A. Predicting binding affinity of CSAR ligands using both structure-based and ligand-based approaches. J Chem Inf Model 2013; 53:1915-22. [PMID: 23809015 PMCID: PMC3779696 DOI: 10.1021/ci400216q] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
We report on the prediction accuracy of ligand-based (2D QSAR) and structure-based (MedusaDock) methods used both independently and in consensus for ranking the congeneric series of ligands binding to three protein targets (UK, ERK2, and CHK1) from the CSAR 2011 benchmark exercise. An ensemble of predictive QSAR models was developed using known binders of these three targets extracted from the publicly available ChEMBL database. Selected models were used to predict the binding affinity of CSAR compounds toward the corresponding targets and rank them accordingly; the overall ranking accuracy evaluated by Spearman correlation was as high as 0.78 for UK, 0.60 for ERK2, and 0.56 for CHK1, placing our predictions in the top 10% among all the participants. In parallel, MedusaDock, designed to predict reliable docking poses, was also used for ranking the CSAR ligands according to their docking scores; the resulting accuracy (Spearman correlation) for UK, ERK2, and CHK1 were 0.76, 0.31, and 0.26, respectively. In addition, performance of several consensus approaches combining MedusaDock- and QSAR-predicted ranks altogether has been explored; the best approach yielded Spearman correlation coefficients for UK, ERK2, and CHK1 of 0.82, 0.50, and 0.45, respectively. This study shows that (i) externally validated 2D QSAR models were capable of ranking CSAR ligands at least as accurately as more computationally intensive structure-based approaches used both by us and by other groups and (ii) ligand-based QSAR models can complement structure-based approaches by boosting the prediction performances when used in consensus.
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Affiliation(s)
- Denis Fourches
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, USA
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Oprisiu I, Varlamova E, Muratov E, Artemenko A, Marcou G, Polishchuk P, Kuz'min V, Varnek A. QSPR Approach to Predict Nonadditive Properties of Mixtures. Application to Bubble Point Temperatures of Binary Mixtures of Liquids. Mol Inform 2012; 31:491-502. [DOI: 10.1002/minf.201200006] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 04/23/2012] [Indexed: 11/11/2022]
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Muratov EN, Varlamova EV, Artemenko AG, Polishchuk PG, Kuz'min VE. Existing and Developing Approaches for QSAR Analysis of Mixtures. Mol Inform 2012; 31:202-21. [PMID: 27477092 DOI: 10.1002/minf.201100129] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2011] [Accepted: 02/04/2012] [Indexed: 11/10/2022]
Abstract
This review is devoted to the critical analysis of advantages and disadvantages of existing mixture descriptors and their usage in various QSAR/QSPR tasks. We describe good practices for the QSAR modeling of mixtures, data sources for mixtures, a discussion of various mixture descriptors and their application, recommendations about proper external validation specific for mixture QSAR modeling, and future perspectives of this field. The biggest problem in QSAR of mixtures is the lack of reliable data about the mixtures' properties. Various mixture descriptors are used for the modeling of different endpoints. However, these descriptors have certain disadvantages, such as applicability only to 1 : 1 binary mixtures, and additive nature. The field of QSAR of mixtures is still under development, and existing efforts could be considered as a foundation for future approaches and studies. The usage of non-additive mixture descriptors, which are sensitive to interaction effects, in combination with best practices of QSAR model development (e.g., thorough data collection and curation, rigorous external validation, etc.) will significantly improve the quality of QSAR studies of mixtures.
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Affiliation(s)
- Eugene N Muratov
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394. , .,Laboratory for Molecular Modeling, Division of Medicinal Chemistry and Natural Products, Eshelman School of Pharmacy, University of North Carolina, Beard Hall 301, CB#7568, Chapel Hill, NC, 27599, USA tel: +19199663459, fax: +19199660204. ,
| | - Ekaterina V Varlamova
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Anatoly G Artemenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Pavel G Polishchuk
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
| | - Victor E Kuz'min
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A. V. Bogatsky Physical Chemical Institute, National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine tel: +380487662394, fax: +380487662394
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Ognichenko LN, Kuz'min VE, Gorb L, Hill FC, Artemenko AG, Polischuk PG, Leszczynski J. QSPR Prediction of Lipophilicity for Organic Compounds Using Random Forest Technique on the Basis of Simplex Representation of Molecular Structure. Mol Inform 2012; 31:273-80. [PMID: 27477097 DOI: 10.1002/minf.201100102] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2011] [Accepted: 02/05/2012] [Indexed: 11/08/2022]
Abstract
The relationship between the octanol-water partition coefficient for more than twelve thousand organic compounds and their structures was investigated using a QSPR approach based on Simplex Representation of Molecular Structure (SiRMS). The dataset used in our study included 10973 compounds with experimental values of lipophilicity (LogKow ) for different chemical compounds. Random Forest (RF) method was used for statistical modeling at the 2D level of representation of molecular structure. Developed models are adequate and successfully validated with external test sets. Proposed models have clear interpretation due to the use of simplex representation of molecular structure and predict the LogKow values with the accuracy of the best modern models. Thus QSPR models proposed in this study represent powerful and easy-to use virtual screening tool that can be recommended for prediction of octanol-water partition coefficient.
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Affiliation(s)
- Liudmyla N Ognichenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Victor E Kuz'min
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Leonid Gorb
- Badger Technical Services, LLC, Vicksburg, Mississippi, USA
| | - Frances C Hill
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA
| | - Anatoly G Artemenko
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Pavel G Polischuk
- Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute, National Academy of Science of Ukraine, Ukraine, Odessa, 65080, Lustdorfskaya Doroga 86
| | - Jerzy Leszczynski
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA. .,Interdisciplinary Center for Nanotoxicity, Department of Chemistry and Biochemistry, Jackson State University, Jackson, Mississippi, 39217, USA.
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Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Fourches D, Zhu H, Rusyn I, Tropsha A. Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches. Chem Res Toxicol 2011; 24:1251-62. [PMID: 21699217 PMCID: PMC4281093 DOI: 10.1021/tx200148a] [Citation(s) in RCA: 160] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( http://toxico.nibio.go.jp/datalist.html ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.
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Affiliation(s)
- Yen Low
- Laboratory for Molecular Modeling, University of North Carolina, Chapel Hill, North Carolina 27599
- Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Takeki Uehara
- Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, North Carolina 27599
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Asagi, Osaka, Japan
| | - Yohsuke Minowa
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Asagi, Osaka, Japan
| | - Hiroshi Yamada
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Asagi, Osaka, Japan
| | - Yasuo Ohno
- National Institute of Health Sciences, Kamiyoga, Tokyo, Japan
| | - Tetsuro Urushidani
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Asagi, Osaka, Japan
- Doshisha Women's College of Liberal Arts, Kodo, Kyoto, Japan
| | - Alexander Sedykh
- Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Eugene Muratov
- Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, North Carolina 27599
- A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, Ukraine
| | - Denis Fourches
- Laboratory for Molecular Modeling, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Hao Zhu
- Laboratory for Molecular Modeling, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Ivan Rusyn
- Department of Environmental Sciences & Engineering, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, University of North Carolina, Chapel Hill, North Carolina 27599
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QSAR analysis of [(biphenyloxy)propyl]isoxazoles: agents against coxsackievirus B3. Future Med Chem 2011; 3:15-27. [PMID: 21428823 DOI: 10.4155/fmc.10.278] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Antiviral drugs are urgently needed for the treatment of acute and chronic diseases caused by enteroviruses such as coxsackievirus B3 (CVB3). The main goal of this study is quantitative structure-activity relationship (QSAR) analysis of anti-CVB3 activity (clinical CVB3 isolate 97927 [log IC50, µM]) and investigation of the selectivity of 25 ([biphenyloxy]propyl)isoxazoles, followed by computer-aided design and virtual screening of novel active compounds. DISCUSSION The 2D QSAR obtained models are quite satisfactory (R(2) = 0.84-0.99, Q(2) = 0.76-0.92, R(2)(ext) = 0.62-0.79). Compounds with high antiviral activity and selectivity have to contain 5-trifluoromethyl-[1,2,4]oxadiazole or 2,4-difluorophenyl fragments. Insertion of 2,5-dimethylbenzene, napthyl and especially biphenyl substituents into investigated compounds substantially decreases both their antiviral activity and selectivity. Several compounds were proposed as a result of design and virtual screening. A high level of activity of 2-methoxy-1-phenyl-1H-imidazo[4,5-c]pyridine (sm428) was confirmed experimentally. CONCLUSION Simplex representation of molecular structure allows successful QSAR analysis of anti-CVB3 activity of ([biphenyloxy]propyl)isoxazole derivatives. Two possible ways of battling CVB3 are considered as a future perspective.
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Kuz'min VE, Polishchuk PG, Artemenko AG, Andronati SA. Interpretation of QSAR Models Based on Random Forest Methods. Mol Inform 2011; 30:593-603. [DOI: 10.1002/minf.201000173] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2010] [Accepted: 05/13/2011] [Indexed: 11/07/2022]
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Artemenko A, Muratov EN, Kuz’min V, Muratov N, Varlamova E, Kuz'mina A, Gorb LG, Golius A, Hill F, Leszczynski J, Tropsha A. QSAR analysis of the toxicity of nitroaromatics in Tetrahymena pyriformis: structural factors and possible modes of action. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2011; 22:575-601. [PMID: 21714735 PMCID: PMC3442116 DOI: 10.1080/1062936x.2011.569950] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The Hierarchical Technology for Quantitative Structure-Activity Relationships (HiT QSAR) was applied to 95 diverse nitroaromatic compounds (including some widely known explosives) tested for their toxicity (50% inhibition growth concentration, IGC₅₀) against the ciliate Tetrahymena pyriformis. The dataset was divided into subsets according to putative mechanisms of toxicity. The Classification and Regression Trees (CART) approach implemented within HiT QSAR has been used for prediction of mechanism of toxicity for new compounds. The resulting models were shown to have ~80% accuracy for external datasets indicating that the mechanistic dataset division was sensible. The Partial Least Squares (PLS) statistical approach was then used to develop 2D QSAR models. Validated PLS models were explored to: (1) elucidate the effects of different substituents in nitroaromatic compounds on toxicity; (2) differentiate compounds by probable mechanisms of toxicity based on their structural descriptors; and (3) analyse the role of various physical-chemical factors responsible for compounds' toxicity. Models were interpreted in terms of molecular fragments promoting or interfering with toxicity. It was also shown that mutual influence of substituents in benzene ring plays the determining role in toxicity variation. Although chemical mechanism based models were statistically significant and externally predictive (r²(ext) = 0.64 for the external set of 63 nitroaromatics identified after all calculations have been completed), they were also shown to have limited coverage (57% for modelling and 76% for external set).
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Affiliation(s)
- A.G. Artemenko
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - E. N. Muratov
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - V.E. Kuz’min
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - N.N. Muratov
- Odessa National Polytechnic University, 1 Shevchenko Ave., Odessa, 65000, Ukraine
| | - E.V. Varlamova
- A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Lustdorfskaya Doroga 86, Odessa 65080, Ukraine
| | - A.V. Kuz'mina
- Odessa National Medicinal University, 2 Ol'gievskaya Str, Odessa, 65000, Ukraine
| | - L. G. Gorb
- Badger Technical Services, LLC, Vicksburg, Mississippi, USA
| | - A. Golius
- Kharkiv National V.N. Karazin University, Department of Radophysics, Karkiv, 61077, Ukraine
| | - F.C. Hill
- US Army ERDC, 3532 Manor Dr, Vicksburg, Mississippi, 39180, USA
| | - J. Leszczynski
- Interdisciplinary Nanotoxicity Center, Jackson State University, 1400 J.R. Lynch Str., Jackson, Mississippi, 39217 USA
| | - A. Tropsha
- Division of Medicinal Chemistry and Natural Products, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
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