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Bębenek E, Rzepka Z, Hermanowicz JM, Chrobak E, Surażyński A, Beberok A, Wrześniok D. Synthesis, Pharmacokinetic Profile, Anticancer Activity and Toxicity of the New Amides of Betulonic Acid-In Silico and In Vitro Study. Int J Mol Sci 2024; 25:4517. [PMID: 38674101 PMCID: PMC11050400 DOI: 10.3390/ijms25084517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/17/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024] Open
Abstract
Betulonic acid (B(O)A) is a pentacyclic lupane-type triterpenoid that widely exists in plants. There are scientific reports indicating anticancer activity of B(O)A, as well as the amides and esters of this triterpenoid. In the first step of the study, the synthesis of novel amide derivatives of B(O)A containing an acetylenic moiety was developed. Subsequently, the medium-soluble compounds (EB171 and EB173) and the parent compound, i.e., B(O)A, were investigated for potential cytotoxic activity against breast cancer (MCF-7 and MDA-MB-231) and melanoma (C32, COLO 829 and A375) cell lines, as well as normal human fibroblasts. Screening analysis using the WST-1 test was applied. Moreover, the lipophilicity and ADME parameters of the obtained derivatives were determined using experimental and in silico methods. The toxicity assay using zebrafish embryos and larvae was also performed. The study showed that the compound EB171 exhibited a significant cytotoxic effect on cancer cell lines: MCF-7, A-375 and COLO 829, while it did not affect the survival of normal cells. Moreover, studies on embryos and larvae showed no toxicity of EB171 in an animal model. Compared to EB171, the compound EB173 had a weaker effect on all tested cancer cell lines and produced less desirable effects against normal cells. The results of the WST-1 assay obtained for B(O)A revealed its strong cytotoxic activity on the examined cancer cell lines, but also on normal cells. In conclusion, this article describes new derivatives of betulonic acid-from synthesis to biological properties. The results allowed to indicate a promising direction for the functionalization of B(O)A to obtain derivatives with selective anticancer activity and low toxicity.
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Affiliation(s)
- Ewa Bębenek
- Department of Organic Chemistry, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia in Katowice, 4 Jagiellońska, 41-200 Sosnowiec, Poland; (E.B.); (E.C.)
| | - Zuzanna Rzepka
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia in Katowice, 4 Jagiellońska, 41-200 Sosnowiec, Poland; (Z.R.); (A.B.)
| | - Justyna Magdalena Hermanowicz
- Department of Pharmacodynamics, Medical University of Bialystok, Mickiewicza 2c, 15-222 Bialystok, Poland;
- Department of Clinical Pharmacy, Medical University of Bialystok, Mickiewicza 2c, 15-222 Bialystok, Poland
| | - Elwira Chrobak
- Department of Organic Chemistry, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia in Katowice, 4 Jagiellońska, 41-200 Sosnowiec, Poland; (E.B.); (E.C.)
| | - Arkadiusz Surażyński
- Department of Medicinal Chemistry, Medical University of Bialystok, Kilinskiego 1, 15-089 Bialystok, Poland;
| | - Artur Beberok
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia in Katowice, 4 Jagiellońska, 41-200 Sosnowiec, Poland; (Z.R.); (A.B.)
| | - Dorota Wrześniok
- Department of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia in Katowice, 4 Jagiellońska, 41-200 Sosnowiec, Poland; (Z.R.); (A.B.)
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Cai Y, Tian T, Huang Y, Yao H, Qi X, Fan J, Kuang Y, Chen J, Li X, Kadokami K. Occurrence and Health Risks of Organic Micropollutants in Tap Water in Dalian. Chem Res Toxicol 2023; 36:1938-1946. [PMID: 38039423 DOI: 10.1021/acs.chemrestox.3c00221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2023]
Abstract
Organic micropollutants (OMPs) in tap water may pose risks to human health. Previous studies on the potential health risks of OMPs in tap water may have underestimated the potential health risks of OMPs due to their limited coverage in target pollutants and incomplete toxicity data. In this study, tap water samples were collected in 37 sampling sites in Dalian, China. More than 1,200 target pollutants were screened by combining screening analysis and target analysis. A total of 93 OMPs were detected, with concentration summation ranging from 157 to 1.7 × 104 ng/L among different sampling sites. A total of 17 OMPs (12 agrochemicals, 3 pharmaceuticals and personal care products, and 2 other compounds) were detected in over 80% of the sampling sites. Especially, imidacloprid, tebuconazole, and atrazine-desethyl were found in all the sampling sites. Computational toxicology models were adopted to predict the missing toxicity threshold values of the identified chemicals. Noncarcinogenic risks were estimated to be negligible among all the sampling sites, while carcinogenic risks at six sites were above 10-6 but below 10-4, indicating non-negligible risks. Griseofulvin contributed the most to the carcinogenic risk. This study offers valuable insights that can guide future initiatives to safeguard tap water safety.
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Affiliation(s)
- Yuantian Cai
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Tian Tian
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yang Huang
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Hongye Yao
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xiaojuan Qi
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jun Fan
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yidan Kuang
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Jingwen Chen
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xuehua Li
- Key Laboratory of Industrial Ecology and Environmental Engineering, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Kiwao Kadokami
- Institute of Environmental Science and Technology, University of Kitakyushu, Kitakyushu, Fukuoka 808-0135, Japan
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Oh KK, Choi I, Gupta H, Raja G, Sharma SP, Won SM, Jeong JJ, Lee SB, Cha MG, Kwon GH, Jeong MK, Min BH, Hyun JY, Eom JA, Park HJ, Yoon SJ, Choi MR, Kim DJ, Suk KT. New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:1-12. [PMID: 36562095 DOI: 10.1080/21691401.2022.2155661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.
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Affiliation(s)
- Ki-Kwang Oh
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ickwon Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Haripriya Gupta
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ganesan Raja
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Satya Priya Sharma
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sung-Min Won
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jin-Ju Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Su-Been Lee
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Gi Cha
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Kyo Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Byeong-Hyun Min
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ji-Ye Hyun
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jung-A Eom
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Hee-Jin Park
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sang-Jun Yoon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Mi-Ran Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Dong Joon Kim
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ki-Tae Suk
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
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Wekesa EN, Kimani NM, Kituyi SN, Omosa LK, Santos CBR. Therapeutic Potential of the Genus Zanthoxylum Phytochemicals: A Theoretical ADME/Tox Analysis. SOUTH AFRICAN JOURNAL OF BOTANY : OFFICIAL JOURNAL OF THE SOUTH AFRICAN ASSOCIATION OF BOTANISTS = SUID-AFRIKAANSE TYDSKRIF VIR PLANTKUNDE : AMPTELIKE TYDSKRIF VAN DIE SUID-AFRIKAANSE GENOOTSKAP VAN PLANTKUNDIGES 2023; 162:129-141. [PMID: 37840557 PMCID: PMC10569136 DOI: 10.1016/j.sajb.2023.09.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Natural products (NPs) are essential in the search for new drugs to treat a wide range of diseases, including infectious and malignant disorders. However, despite the discovery of many bioactive NPs, they often do not make it to market as drugs due to toxicity and other challenges. The development of NPs into drugs is a long and expensive process, and many promising compounds are abandoned along the way. These molecules require in silico ADMET profiling in order to speed up their development into drugs lower costs, and the high attrition rate. The objective of this work was to produce thorough ADMET profiles of secondary metabolites from several classes that were isolated from Zanthoxylum species. The genus has a long history of therapeutic use, including treating tumours, hypertension, gonorrhoea, coughs, bilharzia, chest pains, and toothaches. The study used a dataset of 406 compounds from the genus for theoretical ADMET analysis. The findings revealed that 81% of the compounds met Lipinski's rule of five, indicating good oral bioavailability. The drug-likeness criteria were taken into account, with percentages ranging from 66.2 to 88.1 percent. Additionally, 9.2% of the compounds were predicted to be lead-like, demonstrating their potential as promising drug development candidates. Interestingly, none of the compounds inhibited hERG I, while 33% inhibited hERG II, potentially having cardiac implications. Additionally, 30% of the compounds exhibited AMES toxicity inhibition, while 23.6% were identified as hepatotoxic and 22.2% would cause skin sensitivity. Moreover, 81.8% of the compounds demonstrated high intestinal absorption, making them desirable for oral drugs. In conclusion, these findings highlight the diverse properties of the investigated compounds and their potential for drug development.
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Affiliation(s)
| | | | - Sarah N. Kituyi
- Department of Biological Sciences, University of Embu, Kenya
- The Fogarty International center of the National Institutes of Health- 31 Center Dr, Bethesda, MD 20892, United States
| | | | - Cleydson B. R. Santos
- Graduate Program in Medicinal Chemistry and Molecular Modeling, Health Science Institute, Federal University of Pará, Belém 66075-110, PA, Brazil
- Laboratory of Modeling and Computational Chemistry, Department of Biological and Health Sciences, Federal University of Amapá, Macapá 68902-280, AP, Brazil
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Pandey P, MacKerell AD. Combining SILCS and Artificial Intelligence for High-Throughput Prediction of the Passive Permeability of Drug Molecules. J Chem Inf Model 2023; 63:5903-5915. [PMID: 37682640 PMCID: PMC10603762 DOI: 10.1021/acs.jcim.3c00514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Membrane permeability of drug molecules plays a significant role in the development of new therapeutic agents. Accordingly, methods to predict the passive permeability of drug candidates during a medicinal chemistry campaign offer the potential to accelerate the drug design process. In this work, we combine the physics-based site identification by ligand competitive saturation (SILCS) method and data-driven artificial intelligence (AI) to create a high-throughput predictive model for the passive permeability of druglike molecules. In this study, we present a comparative analysis of four regression models to predict membrane permeabilities of small druglike molecules; of the tested models, Random Forest was the most predictive yielding an R2 of 0.81 for the independent data set. The input feature vector used to train the developed prediction model includes absolute free energy profiles of ligands through a POPC-cholesterol bilayer based on ligand grid free energy (LGFE) profiles obtained from the SILCS approach. The use of the membrane free energy profiles from SILCS offers information on the physical forces contributing to ligand permeability, while the use of AI yields a more predictive model trained on experimental PAMPA permeability data for a collection of 229 molecules. This combination allows for rapid estimations of ligand permeability at a level of accuracy beyond currently available predictive models while offering insights into the contributions of the functional groups in the ligands to the permeability barrier, thereby offering quantitative information to facilitate rational ligand design.
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Affiliation(s)
- Poonam Pandey
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St., HSF II-633, Baltimore, Maryland 21201, United States
| | - Alexander D MacKerell
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn St., HSF II-633, Baltimore, Maryland 21201, United States
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Neto MFA, Campos JM, Cerqueira APM, de Lima LR, Da Costa GV, Ramos RDS, Junior JTM, Santos CBR, Leite FHA. Hierarchical Virtual Screening and Binding Free Energy Prediction of Potential Modulators of Aedes Aegypti Odorant-Binding Protein 1. Molecules 2022; 27:molecules27206777. [PMID: 36296371 PMCID: PMC9612181 DOI: 10.3390/molecules27206777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/27/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
The Aedes aegypti mosquito is the main hematophagous vector responsible for arbovirus transmission in Brazil. The disruption of A. aegypti hematophagy remains one of the most efficient and least toxic methods against these diseases and, therefore, efforts in the research of new chemical entities with repellent activity have advanced due to the elucidation of the functionality of the olfactory receptors and the behavior of mosquitoes. With the growing interest of the pharmaceutical and cosmetic industries in the development of chemical entities with repellent activity, computational studies (e.g., virtual screening and molecular modeling) are a way to prioritize potential modulators with stereoelectronic characteristics (e.g., pharmacophore models) and binding affinity to the AaegOBP1 binding site (e.g., molecular docking) at a lower computational cost. Thus, pharmacophore- and docking-based virtual screening was employed to prioritize compounds from Sigma-Aldrich® (n = 126,851) and biogenic databases (n = 8766). In addition, molecular dynamics (MD) was performed to prioritize the most potential potent compounds compared to DEET according to free binding energy calculations. Two compounds showed adequate stereoelectronic requirements (QFIT > 81.53), AaegOBP1 binding site score (Score > 42.0), volatility and non-toxic properties and better binding free energy value (∆G < −24.13 kcal/mol) compared to DEET ((N,N-diethyl-meta-toluamide)) (∆G = −24.13 kcal/mol).
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Affiliation(s)
- Moysés F. A. Neto
- Laboratório de Quimioinformática e Avaliação Biológica, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, Brazil
| | - Joaquín M. Campos
- Departamento de Química Farmacéutica y Orgánica, Universidad de Granada, 18071 Granada, Spain
- Biosanitary Institute of Granada (ibs.GRANADA), SAS-University of Granada, 18071 Granada, Spain
| | - Amanda P. M. Cerqueira
- Laboratório de Quimioinformática e Avaliação Biológica, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, Brazil
| | - Lucio R. de Lima
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e da Saúde, Universidade Federal do Amapá, Macapá 68902-280, Brazil
| | - Glauber V. Da Costa
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e da Saúde, Universidade Federal do Amapá, Macapá 68902-280, Brazil
| | - Ryan Da S. Ramos
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e da Saúde, Universidade Federal do Amapá, Macapá 68902-280, Brazil
| | - Jairo T. Magalhães Junior
- Centro Multidisciplinar, Departamento de Saúde, Universidade Federal do Oeste da Bahia, Barreiras 47100-000, Brazil
| | - Cleydson B. R. Santos
- Laboratório de Modelagem e Química Computacional, Departamento de Ciências Biológicas e da Saúde, Universidade Federal do Amapá, Macapá 68902-280, Brazil
- Correspondence: (C.B.R.S.); (F.H.A.L.)
| | - Franco H. A. Leite
- Laboratório de Quimioinformática e Avaliação Biológica, Departamento de Saúde, Universidade Estadual de Feira de Santana, Feira de Santana 44036-900, Brazil
- Correspondence: (C.B.R.S.); (F.H.A.L.)
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Waters LJ, Quah XL. Predicting skin permeability using HuskinDB. Sci Data 2022; 9:584. [PMID: 36151144 PMCID: PMC9508232 DOI: 10.1038/s41597-022-01698-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
A freely accessible database has recently been released that provides measurements available in the literature on human skin permeation data, known as the ‘Human Skin Database – HuskinDB’. Although this database is extremely useful for sourcing permeation data to help with toxicity and efficacy determination, it cannot be beneficial when wishing to consider unlisted, or novel compounds. This study undertakes analysis of the data from within HuskinDB to create a model that predicts permeation for any compound (within the range of properties used to create the model). Using permeability coefficient (Kp) data from within this resource, several models were established for Kp values for compounds of interest by varying the experimental parameters chosen and using standard physicochemical data. Multiple regression analysis facilitated creation of one particularly successful model to predict Kp through human skin based only on three chemical properties. The model transforms the dataset from simply a resource of information to a more beneficial model that can be used to replace permeation testing for a wide range of compounds.
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Affiliation(s)
- Laura J Waters
- School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK.
| | - Xin Ling Quah
- School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK
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Borba JV, Alves VM, Braga RC, Korn DR, Overdahl K, Silva AC, Hall SU, Overdahl E, Kleinstreuer N, Strickland J, Allen D, Andrade CH, Muratov EN, Tropsha A. STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:27012. [PMID: 35192406 PMCID: PMC8863177 DOI: 10.1289/ehp9341] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 05/22/2023]
Abstract
BACKGROUND Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points. METHODS We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets. RESULTS In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays. CONCLUSIONS We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.
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Affiliation(s)
- Joyce V.B. Borba
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Daniel R. Korn
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kirsten Overdahl
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - Arthur C. Silva
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Steven U.S. Hall
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Erik Overdahl
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - David Allen
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, Paraiba, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
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Fernandes PO, Martins DM, de Souza Bozzi A, Martins JPA, de Moraes AH, Maltarollo VG. Molecular insights on ABL kinase activation using tree-based machine learning models and molecular docking. Mol Divers 2021; 25:1301-1314. [PMID: 34191245 PMCID: PMC8241884 DOI: 10.1007/s11030-021-10261-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/18/2021] [Indexed: 12/14/2022]
Abstract
Abelson kinase (c-Abl) is a non-receptor tyrosine kinase involved in several biological processes essential for cell differentiation, migration, proliferation, and survival. This enzyme's activation might be an alternative strategy for treating diseases such as neutropenia induced by chemotherapy, prostate, and breast cancer. Recently, a series of compounds that promote the activation of c-Abl has been identified, opening a promising ground for c-Abl drug development. Structure-based drug design (SBDD) and ligand-based drug design (LBDD) methodologies have significantly impacted recent drug development initiatives. Here, we combined SBDD and LBDD approaches to characterize critical chemical properties and interactions of identified c-Abl's activators. We used molecular docking simulations combined with tree-based machine learning models-decision tree, AdaBoost, and random forest to understand the c-Abl activators' structural features required for binding to myristoyl pocket, and consequently, to promote enzyme and cellular activation. We obtained predictive and robust models with Matthews correlation coefficient values higher than 0.4 for all endpoints and identified characteristics that led to constructing a structure-activity relationship model (SAR).
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Affiliation(s)
- Philipe Oliveira Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Diego Magno Martins
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Aline de Souza Bozzi
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - João Paulo A Martins
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Adolfo Henrique de Moraes
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil
| | - Vinícius Gonçalves Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte, MG, Brazil.
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10
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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: 10] [Impact Index Per Article: 3.3] [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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11
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Machado BHB, Frame J, Zhang J, Najlah M. Comparative Study on the Outcome of Periorbital Wrinkles Treated with Laser-Assisted Delivery of Vitamin C or Vitamin C Plus Growth Factors: A Randomized, Double-blind, Clinical Trial. Aesthetic Plast Surg 2021; 45:1020-1032. [PMID: 33326047 PMCID: PMC8144134 DOI: 10.1007/s00266-020-02035-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 10/24/2020] [Indexed: 12/01/2022]
Abstract
Background Despite promising results, laser-assisted drug delivery (LADD) is not yet considered as standard therapies and published data rely mainly on laboratory tests, animal experiments or cadaver skin. Objectives This double-blind, prospective, randomized clinical trial investigates the impact in topical application of vitamin C and a cosmeceutical containing growth factors (GFs) on periorbital wrinkles primarily treated with laser skin resurfacing. Material and Methods In total, 149 female patients with periorbital wrinkles were consented and randomized into two study groups, R-C (receiving vitamin C only) and R-CGF (receiving vitamin C and a cosmeceutical containing growth factors). The statistical analysis evaluated the efficacy of each treatment regimen using software readouts provided by a three-dimensional stereophotogrammetry system prior to treatment and three months after the procedure. Results were compared to confirm if there was a significant change in the skin roughness and the average depth of the wrinkles between the two groups after treatment. Results There was a significant reduction in both skin roughness and average depth of the wrinkles in the group treated with vitamin C and growth factors (p <0.01) than those treated with LADD followed by topical application of vitamin C alone. There were no cutaneous reactions or adverse systemic reactions observed in this study related to LADD with vitamin C and GFs. Conclusion Controlled laser application might have a great potential to facilitate the absorption of exogenous macromolecules by the skin. Periorbital wrinkles were reduced in both groups, but LADD using vitamin C and GFs provided significantly better results. Level of Evidence II This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.
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12
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Correlation between the structure and skin permeability of compounds. Sci Rep 2021; 11:10076. [PMID: 33980965 PMCID: PMC8115152 DOI: 10.1038/s41598-021-89587-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/28/2021] [Indexed: 12/02/2022] Open
Abstract
A three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.
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13
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Development and evaluation of two-parameter linear free energy models for the prediction of human skin permeability coefficient of neutral organic chemicals. J Cheminform 2021; 13:25. [PMID: 33741067 PMCID: PMC7980659 DOI: 10.1186/s13321-021-00503-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Accepted: 03/10/2021] [Indexed: 01/13/2023] Open
Abstract
The experimental values of skin permeability coefficients, required for dermal exposure assessment, are not readily available for many chemicals. The existing estimation approaches are either less accurate or require many parameters that are not readily available. Furthermore, current estimation methods are not easy to apply to complex environmental mixtures. We present two models to estimate the skin permeability coefficients of neutral organic chemicals. The first model, referred to here as the 2-parameter partitioning model (PPM), exploits a linear free energy relationship (LFER) of skin permeability coefficient with a linear combination of partition coefficients for octanol–water and air–water systems. The second model is based on the retention time information of nonpolar analytes on comprehensive two-dimensional gas chromatography (GC × GC). The PPM successfully explained variability in the skin permeability data (n = 175) with R2 = 0.82 and root mean square error (RMSE) = 0.47 log unit. In comparison, the US-EPA’s model DERMWIN™ exhibited an RMSE of 0.78 log unit. The Zhang model—a 5-parameter LFER equation based on experimental Abraham solute descriptors (ASDs)—performed slightly better with an RMSE value of 0.44 log unit. However, the Zhang model is limited by the scarcity of experimental ASDs. The GC × GC model successfully explained the variance in skin permeability data of nonpolar chemicals (n = 79) with R2 = 0.90 and RMSE = 0.23 log unit. The PPM can easily be implemented in US-EPA’s Estimation Program Interface Suite (EPI Suite™). The GC × GC model can be applied to the complex mixtures of nonpolar chemicals. ![]()
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Sebastia-Saez D, Burbidge A, Engmann J, Ramaioli M. New trends in mechanistic transdermal drug delivery modelling: Towards an accurate geometric description of the skin microstructure. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106976] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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15
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Cheruvu HS, Liu X, Grice JE, Roberts MS. Modeling percutaneous absorption for successful drug discovery and development. Expert Opin Drug Discov 2020; 15:1181-1198. [DOI: 10.1080/17460441.2020.1781085] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Hanumanth Srikanth Cheruvu
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
| | - Xin Liu
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
| | - Jeffrey E. Grice
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
| | - Michael S. Roberts
- Therapeutics Research Centre, The University of Queensland Diamantina Institute, The University of Queensland, Woolloongabba, Australia
- University of South Australia School of Pharmacy and Medical Sciences, The Queen Elizabeth Hospital, Adelaide, Australia
- Therapeutics Research Centre, Basil Hetzel Institute for Translational Health Research, The Queen Elizabeth Hospital, Adelaide, Australia
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16
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Almeida ML, Viana DCF, da Costa VCM, Dos Santos FA, Pereira MC, Pitta MGR, de Melo Rêgo MJB, Pitta IR, Pitta MGR. Synthesis, Antitumor Activity and Molecular Docking Studies on Seven Novel Thiazacridine Derivatives. Comb Chem High Throughput Screen 2020; 23:359-368. [PMID: 32189590 DOI: 10.2174/1386207323666200319105239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 02/06/2020] [Accepted: 02/19/2020] [Indexed: 11/22/2022]
Abstract
AIM AND OBJECTIVE In the last decades, cancer has become a major problem in public health all around the globe. Chimeric chemical structures have been established as an important trend on medicinal chemistry in the last years. Thiazacridines are hybrid molecules composed of a thiazolidine and acridine nucleus, both pharmacophores that act on important biological targets for cancer. By the fact it is a serious disease, seven new 3-acridin-9-ylmethyl-thiazolidine-2,4-dione derivatives were synthesized, characterized, analyzed by computer simulation and tested in tumor cells. In order to find out if the compounds have therapeutic potential. MATERIALS AND METHODS Seven new 3-acridin-9-ylmethyl-thiazolidine-2,4-dione derivatives were synthesized through Michael addition and Knoevenagel condensation strategies. Characterization was performed by NMR and Infrared spectroscopy techniques. Regarding biological activity, thiazacridines were tested against solid and hematopoietic tumoral cell lines, namely Jurkat (acute T-cell leukemia); HL-60 (acute promyelocytic leukemia); DU 145 (prostate cancer); MOLT-4 (acute lymphoblastic leukemia); RAJI (Burkitt's lymphoma); K562 (chronic myelogenous leukemia) and normal cells PBMC (healthy volunteers). Molecular docking analysis was also performed in order to assess major targets of these new compounds. Cell cycle and clonogenic assay were also performed. RESULTS Compound LPSF/AA-62 (9f) exhibited the most potent anticancer activity against HL-60 (IC50 3,7±1,7 μM), MOLT-4 (IC50 5,7±1,1 μM), Jurkat (IC50 18,6 μM), Du-145 (IC50 20±5 μM) and Raji (IC50 52,3±9,2 μM). While the compound LPSF/AA-57 (9b) exhibited anticancer activity against the K562 cell line (IC50 51,8±7,8 μM). Derivative LPSF/AA-62 (9f) did not interfere in the cell cycle phases of the Molt-4 lineage. However, the LPSF/AA-62 (9f) derivative significantly reduced the formation of prostate cancer cell clones. The compound LPSF/AA-62 (9f) has shown strong anchorage stability with enzymes topoisomerases 1 and 2, in particular due the presence of chlorine favored hydrogen bonds with topoisomerase 1. CONCLUSION The 3-(acridin-9-ylmethyl)-5-((10-chloroanthracen-9-yl)methylene)thiazolidine-2,4-dione (LPSF/AA-62) presented the most promising results, showing anti-tumor activity in 5 of the 6 cell types tested, especially inhibiting the formation of colonies of prostate tumor cells (DU-145).
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Affiliation(s)
- Marcel L Almeida
- Laboratory of Design and Drug Synthesis (LPSF), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Douglas C F Viana
- Laboratory of Design and Drug Synthesis (LPSF), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Valécia C M da Costa
- Laboratory of Immunomodulation and New Therapeutic Approaches (LINAT), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Flaviana A Dos Santos
- Laboratory of Immunomodulation and New Therapeutic Approaches (LINAT), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Michelly C Pereira
- Laboratory of Immunomodulation and New Therapeutic Approaches (LINAT), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Maira G R Pitta
- Laboratory of Immunomodulation and New Therapeutic Approaches (LINAT), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Moacyr J B de Melo Rêgo
- Laboratory of Immunomodulation and New Therapeutic Approaches (LINAT), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Ivan R Pitta
- Laboratory of Design and Drug Synthesis (LPSF), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
| | - Marina G R Pitta
- Laboratory of Design and Drug Synthesis (LPSF), Nucleus of Research in Therapeutical Innovation Suely Galdino (NUPIT SG), Biosciences Center, Federal University of Pernambuco, Recife, Brazil
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17
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Neves BJ, Braga RC, Alves VM, Lima MNN, Cassiano GC, Muratov EN, Costa FTM, Andrade CH. Deep Learning-driven research for drug discovery: Tackling Malaria. PLoS Comput Biol 2020; 16:e1007025. [PMID: 32069285 PMCID: PMC7048302 DOI: 10.1371/journal.pcbi.1007025] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 02/28/2020] [Accepted: 04/15/2019] [Indexed: 11/18/2022] Open
Abstract
Malaria is an infectious disease that affects over 216 million people worldwide, killing over 445,000 patients annually. Due to the constant emergence of parasitic resistance to the current antimalarial drugs, the discovery of new drug candidates is a major global health priority. Aiming to make the drug discovery processes faster and less expensive, we developed binary and continuous Quantitative Structure-Activity Relationships (QSAR) models implementing deep learning for predicting antiplasmodial activity and cytotoxicity of untested compounds. Then, we applied the best models for a virtual screening of a large database of chemical compounds. The top computational predictions were evaluated experimentally against asexual blood stages of both sensitive and multi-drug-resistant Plasmodium falciparum strains. Among them, two compounds, LabMol-149 and LabMol-152, showed potent antiplasmodial activity at low nanomolar concentrations (EC50 <500 nM) and low cytotoxicity in mammalian cells. Therefore, the computational approach employing deep learning developed here allowed us to discover two new families of potential next generation antimalarial agents, which are in compliance with the guidelines and criteria for antimalarial target candidates.
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Affiliation(s)
- Bruno J. Neves
- Laboratory of Cheminformatics, University Center of Anápolis – UniEVANGÉLICA, Anápolis, Goiás, Brazil
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | | | - Vinicius M. Alves
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Marília N. N. Lima
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
| | - Gustavo C. Cassiano
- Laboratory of Tropical Diseases–Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
- Global Health and Tropical Medicine (GHTM), Instituto de Higiene e Medicina Tropical (IHMT), Universidade Nova de Lisboa (UNL), Lisboa, Portugal
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, Ukraine
| | - Fabio T. M. Costa
- Laboratory of Tropical Diseases–Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
| | - Carolina Horta Andrade
- LabMol – Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, Goiás, Brazil
- Laboratory of Tropical Diseases–Prof. Dr. Luiz Jacintho da Silva, Department of Genetics, Evolution, Microbiology and Immunology, Institute of Biology, University of Campinas, Campinas, São Paulo, Brazil
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18
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Efficient identification of novel anti-glioma lead compounds by machine learning models. Eur J Med Chem 2019; 189:111981. [PMID: 31978780 DOI: 10.1016/j.ejmech.2019.111981] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 11/18/2019] [Accepted: 12/16/2019] [Indexed: 11/22/2022]
Abstract
Glioblastoma multiforme (GBM) is the most devastating and widespread primary central nervous system tumor. Pharmacological treatment of this malignance is limited by the selective permeability of the blood-brain barrier (BBB) and relies on a single drug, temozolomide (TMZ), thus making the discovery of new compounds challenging and urgent. Therefore, aiming to discover new anti-glioma drugs, we developed robust machine learning models for predicting anti-glioma activity and BBB penetration ability of new compounds. Using these models, we prioritized 41 compounds from our in-house library of compounds, for further in vitro testing against three glioma cell lines and astrocytes. Subsequently, the most potent and selective compounds were resynthesized and tested in vivo using an orthotopic glioma model. This approach revealed two lead candidates, 4m and 4n, which efficiently decreased malignant glioma development in mice, probably by inhibiting thioredoxin reductase activity, as shown by our enzymological assays. Moreover, these two compounds did not promote body weight reduction, death of animals, or altered hematological and toxicological markers, making then good candidates for lead optimization as anti-glioma drug candidates.
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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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Fourches D, Ash J. 4D- quantitative structure-activity relationship modeling: making a comeback. Expert Opin Drug Discov 2019; 14:1227-1235. [PMID: 31513441 DOI: 10.1080/17460441.2019.1664467] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Introduction: Predictive Quantitative Structure-Activity Relationship (QSAR) modeling has become an essential methodology for rapidly assessing various properties of chemicals. The vast majority of these QSAR models utilize numerical descriptors derived from the two- and/or three-dimensional structures of molecules. However, the conformation-dependent characteristics of flexible molecules and their dynamic interactions with biological target(s) is/are not encoded by these descriptors, leading to limited prediction performances and reduced interpretability. 2D/3D QSAR models are successful for virtual screening, but typically suffer at lead optimization stages. That is why conformation-dependent 4D-QSAR modeling methods were developed two decades ago. However, these methods have always suffered from the associated computational cost. Recently, 4D-QSAR has been experiencing a significant come-back due to rapid advances in GPU-accelerated molecular dynamic simulations and modern machine learning techniques. Areas covered: Herein, the authors briefly review the literature regarding 4D-QSAR modeling and describe its modern workflow called MD-QSAR. Challenges and current limitations are also highlighted. Expert opinion: The development of hyper-predictive MD-QSAR models could represent a disruptive technology for analyzing, understanding, and optimizing dynamic protein-ligand interactions with countless applications for drug discovery and chemical toxicity assessment. Therefore, there has never been a better time and relevance for molecular modeling teams to engage in hyper-predictive MD-QSAR modeling.
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
| | - Jeremy Ash
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University , Raleigh , NC , USA
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21
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Lomize AL, Hage JM, Schnitzer K, Golobokov K, LaFaive MB, Forsyth AC, Pogozheva ID. PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules. J Chem Inf Model 2019; 59:3094-3099. [PMID: 31259547 DOI: 10.1021/acs.jcim.9b00225] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
The PerMM web server and database were developed for quantitative analysis and visualization of passive translocation of bioactive molecules across lipid membranes. The server is the first physics-based web tool that calculates membrane binding energies and permeability coefficients of diverse molecules through artificial and natural membranes (phospholipid bilayers, PAMPA-DS, blood-brain barrier, and Caco-2/MDCK cell membranes). It also visualizes the transmembrane translocation pathway as a sequence of translational and rotational positions of a permeant as it moves across the lipid bilayer, along with the corresponding changes in solvation energy. The server can be applied for prediction of permeability coefficients of compounds with diverse chemical scaffolds to facilitate selection and optimization of potential drug leads. The complementary PerMM database allows comparison of computationally and experimentally determined permeability coefficients for more than 500 compounds in different membrane systems. The website and database are freely accessible at https://permm.phar.umich.edu/ .
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Affiliation(s)
- Andrei L Lomize
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
| | - Jacob M Hage
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Kevin Schnitzer
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Konstantin Golobokov
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Mitchell B LaFaive
- Department of Electrical Engineering and Computer Science, College of Engineering , University of Michigan , 1221 Beal Ave , Ann Arbor , Michigan 48109-2102 , United States
| | - Alexander C Forsyth
- Department of Computer Science, College of Literature, Science, and the Arts , University of Michigan , 2260 Hayward Street , Ann Arbor , Michigan 48109-2121 , United States
| | - Irina D Pogozheva
- Department of Medicinal Chemistry, College of Pharmacy , University of Michigan , 428 Church Street , Ann Arbor , Michigan 48109-1065 , United States
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Pecoraro B, Tutone M, Hoffman E, Hutter V, Almerico AM, Traynor M. Predicting Skin Permeability by Means of Computational Approaches: Reliability and Caveats in Pharmaceutical Studies. J Chem Inf Model 2019; 59:1759-1771. [PMID: 30658035 DOI: 10.1021/acs.jcim.8b00934] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The skin is the main barrier between the internal body environment and the external one. The characteristics of this barrier and its properties are able to modify and affect drug delivery and chemical toxicity parameters. Therefore, it is not surprising that permeability of many different compounds has been measured through several in vitro and in vivo techniques. Moreover, many different in silico approaches have been used to identify the correlation between the structure of the permeants and their permeability, to reproduce the skin behavior, and to predict the ability of specific chemicals to permeate this barrier. A significant number of issues, like interlaboratory variability, experimental conditions, data set building rationales, and skin site of origin and hydration, still prevent us from obtaining a definitive predictive skin permeability model. This review wants to show the main advances and the principal approaches in computational methods used to predict this property, to enlighten the main issues that have arisen, and to address the challenges to develop in future research.
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Affiliation(s)
- Beatrice Pecoraro
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Marco Tutone
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Ewelina Hoffman
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Victoria Hutter
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
| | - Anna Maria Almerico
- Department of Biological Chemical and Pharmaceutical Sciences and Technologies , University of Palermo , 90123 Palermo , Italy
| | - Matthew Traynor
- Department of Clinical and Pharmaceutical Sciences , University of Hertfordshire , AL10 9AB Hatfield , United Kingdom
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Rusyn I, Greene N. The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives. Toxicol Sci 2019; 161:276-284. [PMID: 29378069 DOI: 10.1093/toxsci/kfx196] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
The field of experimental toxicology is rapidly advancing by incorporating novel techniques and methods that provide a much more granular view into the mechanisms of potential adverse effects of chemical exposures on human health. The data from various in vitro assays and computational models are useful not only for increasing confidence in hazard and risk decisions, but also are enabling better, faster and cheaper assessment of a greater number of compounds, mixtures, and complex products. This is of special value to the field of green chemistry where design of new materials or alternative uses of existing ones is driven, at least in part, by considerations of safety. This article reviews the state of the science and decision-making in scenarios when little to no data may be available to draw conclusions about which choice in green chemistry is "safer." It is clear that there is no "one size fits all" solution and multiple data streams need to be weighed in making a decision. Moreover, the overall level of familiarity of the decision-makers and scientists alike with new assessment methodologies, their validity, value and limitations is evolving. Thus, while the "impact" of the new developments in toxicology on the field of green chemistry is great already, it is premature to conclude that the data from new assessment methodologies have been widely accepted yet.
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Affiliation(s)
- Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas 77843
| | - Nigel Greene
- Predictive Compound Safety and ADME, AstraZeneca Pharmaceuticals LP, Waltham, Massachusetts 02451
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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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Melo-Filho CC, Braga RC, Muratov EN, Franco CH, Moraes CB, Freitas-Junior LH, Andrade CH. Discovery of new potent hits against intracellular Trypanosoma cruzi by QSAR-based virtual screening. Eur J Med Chem 2018; 163:649-659. [PMID: 30562700 DOI: 10.1016/j.ejmech.2018.11.062] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Revised: 11/21/2018] [Accepted: 11/23/2018] [Indexed: 12/17/2022]
Abstract
Chagas disease is a neglected tropical disease (NTD) caused by the protozoan parasite Trypanosoma cruzi and is primarily transmitted to humans by the feces of infected Triatominae insects during their blood meal. The disease affects 6-8 million people, mostly in Latin America countries, and kills more people in the region each year than any other parasite-born disease, including malaria. Moreover, patient numbers are currently increasing in non-endemic, developed countries, such as Australia, Japan, Canada, and the United States. The treatment is limited to one drug, benznidazole, which is only effective in the acute phase of the disease and is very toxic. Thus, there is an urgent need to develop new, safer, and effective drugs against the chronic phase of Chagas disease. Using a QSAR-based virtual screening followed by in vitro experimental evaluation, we report herein the identification of novel potent and selective hits against T. cruzi intracellular stage. We developed and validated binary QSAR models for prediction of anti-trypanosomal activity and cytotoxicity against mammalian cells using the best practices for QSAR modeling. These models were then used for virtual screening of a commercial database, leading to the identification of 39 virtual hits. Further in vitro assays showed that seven compounds were potent against intracellular T. cruzi at submicromolar concentrations (EC50 < 1 μM) and were very selective (SI > 30). Furthermore, other six compounds were also inside the hit criteria for Chagas disease, which presented activity at low micromolar concentrations (EC50 < 10 μM) against intracellular T. cruzi and were also selective (SI > 15). Moreover, we performed a multi-parameter analysis for the comparison of tested compounds regarding their balance between potency, selectivity, and predicted ADMET properties. In the next studies, the most promising compounds will be submitted to additional in vitro and in vivo assays in acute model of Chagas disease, and can be further optimized for the development of new promising drug candidates against this important yet neglected disease.
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Affiliation(s)
- Cleber C Melo-Filho
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmacia, Universidade Federal de Goiás - UFG, Rua 240, Qd.87, Goiania, GO, 74605-510, Brazil
| | - Rodolpho C Braga
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmacia, Universidade Federal de Goiás - UFG, Rua 240, Qd.87, Goiania, GO, 74605-510, 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, 1. Shevchenko Ave., Odessa, 65000, Ukraine
| | - Caio Haddad Franco
- National Laboratory of Biosciences (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, SP, 13083-970, Brazil
| | - Carolina B Moraes
- National Laboratory of Biosciences (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, SP, 13083-970, Brazil; Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil
| | - Lucio H Freitas-Junior
- National Laboratory of Biosciences (LNBio), Centro Nacional de Pesquisa em Energia e Materiais (CNPEM), Campinas, SP, 13083-970, Brazil; Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, SP, 05508-900, Brazil
| | - Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmacia, Universidade Federal de Goiás - UFG, Rua 240, Qd.87, Goiania, GO, 74605-510, Brazil.
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26
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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: 31] [Impact Index Per Article: 5.2] [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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Toropova AP, Toropov AA. The index of ideality of correlation: A criterion of predictability of QSAR models for skin permeability? THE SCIENCE OF THE TOTAL ENVIRONMENT 2017; 586:466-472. [PMID: 28196626 DOI: 10.1016/j.scitotenv.2017.01.198] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Revised: 01/24/2017] [Accepted: 01/29/2017] [Indexed: 06/06/2023]
Abstract
New criterion of the predictive potential of quantitative structure-property/activity relationships (QSPRs/QSARs) is suggested. This criterion is calculated with utilization of the correlation coefficient between experimental and calculated values of endpoint for the calibration set, with taking into account the positive and negative dispersions between experimental and calculated values. The utilization of this criterion improves the predictive potential of QSAR models of dermal permeability coefficient, logKp (cm/h).
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Affiliation(s)
- Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy.
| | - Andrey A Toropov
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milan, Italy
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29
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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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Capuzzi SJ, Muratov EN, Tropsha A. Phantom PAINS: Problems with the Utility of Alerts for Pan-Assay INterference CompoundS. J Chem Inf Model 2017; 57:417-427. [PMID: 28165734 PMCID: PMC5411023 DOI: 10.1021/acs.jcim.6b00465] [Citation(s) in RCA: 164] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The use of substructural alerts to identify Pan-Assay INterference compoundS (PAINS) has become a common component of the triage process in biological screening campaigns. These alerts, however, were originally derived from a proprietary library tested in just six assays measuring protein-protein interaction (PPI) inhibition using the AlphaScreen detection technology only; moreover, 68% (328 out of the 480 alerts) were derived from four or fewer compounds. In an effort to assess the reliability of these alerts as indicators of pan-assay interference, we performed a large-scale analysis of the impact of PAINS alerts on compound promiscuity in bioassays using publicly available data in PubChem. We found that the majority (97%) of all compounds containing PAINS alerts were actually infrequent hitters in AlphaScreen assays measuring PPI inhibition. We also found that the presence of PAINS alerts, contrary to expectations, did not reflect any heightened assay activity trends across all assays in PubChem including AlphaScreen, luciferase, beta-lactamase, or fluorescence-based assays. In addition, 109 PAINS alerts were present in 3570 extensively assayed, but consistently inactive compounds called Dark Chemical Matter. Finally, we observed that 87 small molecule FDA-approved drugs contained PAINS alerts and profiled their bioassay activity. Based on this detailed analysis of PAINS alerts in nonproprietary compound libraries, we caution against the blind use of PAINS filters to detect and triage compounds with possible PAINS liabilities and recommend that such conclusions should be drawn only by conducting orthogonal experiments.
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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 , Chapel Hill, North Carolina 27599, 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 , Chapel Hill, North Carolina 27599, United States
| | - 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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Capuzzi SJ, Kim ISJ, Lam WI, Thornton TE, Muratov EN, Pozefsky D, Tropsha A. Chembench: A Publicly Accessible, Integrated Cheminformatics Portal. J Chem Inf Model 2017; 57:105-108. [PMID: 28045544 DOI: 10.1021/acs.jcim.6b00462] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The enormous increase in the amount of publicly available chemical genomics data and the growing emphasis on data sharing and open science mandates that cheminformaticians also make their models publicly available for broad use by the scientific community. Chembench is one of the first publicly accessible, integrated cheminformatics Web portals. It has been extensively used by researchers from different fields for curation, visualization, analysis, and modeling of chemogenomics data. Since its launch in 2008, Chembench has been accessed more than 1 million times by more than 5000 users from a total of 98 countries. We report on the recent updates and improvements that increase the simplicity of use, computational efficiency, accuracy, and accessibility of a broad range of tools and services for computer-assisted drug design and computational toxicology available on Chembench. Chembench remains freely accessible at https://chembench.mml.unc.edu.
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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, and ‡Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Ian Sang-June Kim
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, and ‡Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Wai In Lam
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, and ‡Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Thomas E Thornton
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, and ‡Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, and ‡Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Diane Pozefsky
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, and ‡Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, and ‡Department of Computer Science, University of North Carolina , Chapel Hill, North Carolina 27599, United States
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Alves VM, Capuzzi SJ, Muratov E, Braga RC, Thornton T, Fourches D, Strickland J, Kleinstreuer N, Andrade CH, Tropsha A. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. GREEN CHEMISTRY : AN INTERNATIONAL JOURNAL AND GREEN CHEMISTRY RESOURCE : GC 2016; 18:6501-6515. [PMID: 28630595 PMCID: PMC5473635 DOI: 10.1039/c6gc01836j] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Skin sensitization is a major environmental and occupational health hazard. Although many chemicals have been evaluated in humans, there have been no efforts to model these data to date. We have compiled, curated, analyzed, and compared the available human and LLNA data. Using these data, we have developed reliable computational models and applied them for virtual screening of chemical libraries to identify putative skin sensitizers. The overall concordance between murine LLNA and human skin sensitization responses for a set of 135 unique chemicals was low (R = 28-43%), although several chemical classes had high concordance. We have succeeded to develop predictive QSAR models of all available human data with the external correct classification rate of 71%. A consensus model integrating concordant QSAR predictions and LLNA results afforded a higher CCR of 82% but at the expense of the reduced external dataset coverage (52%). We used the developed QSAR models for virtual screening of CosIng database and identified 1061 putative skin sensitizers; for seventeen of these compounds, we found published evidence of their skin sensitization effects. Models reported herein provide more accurate alternative to LLNA testing for human skin sensitization assessment across diverse chemical data. In addition, they can also be used to guide the structural optimization of toxic compounds to reduce their skin sensitization potential.
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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 Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - 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, 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 Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Rodolpho C. Braga
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Thomas Thornton
- 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
| | - Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Judy Strickland
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, NC, 27709, USA
| | - Nicole Kleinstreuer
- National Institutes of Environmental Health Sciences, Research Triangle Park, NC, 27709, USA
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Design, Faculty of Pharmacy, Federal University of Goias, 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
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Chemical applicability domain of the Local Lymph Node Assay (LLNA) for skin sensitization potency. Part 1. Underlying physical organic chemistry principles and the extent to which they are represented in the LLNA validation dataset. Regul Toxicol Pharmacol 2016; 80:247-54. [DOI: 10.1016/j.yrtph.2016.07.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2016] [Revised: 07/06/2016] [Accepted: 07/11/2016] [Indexed: 01/08/2023]
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Ezendam J, Braakhuis HM, Vandebriel RJ. State of the art in non-animal approaches for skin sensitization testing: from individual test methods towards testing strategies. Arch Toxicol 2016; 90:2861-2883. [PMID: 27629427 DOI: 10.1007/s00204-016-1842-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2016] [Accepted: 08/29/2016] [Indexed: 11/28/2022]
Abstract
The hazard assessment of skin sensitizers relies mainly on animal testing, but much progress is made in the development, validation and regulatory acceptance and implementation of non-animal predictive approaches. In this review, we provide an update on the available computational tools and animal-free test methods for the prediction of skin sensitization hazard. These individual test methods address mostly one mechanistic step of the process of skin sensitization induction. The adverse outcome pathway (AOP) for skin sensitization describes the key events (KEs) that lead to skin sensitization. In our review, we have clustered the available test methods according to the KE they inform: the molecular initiating event (MIE/KE1)-protein binding, KE2-keratinocyte activation, KE3-dendritic cell activation and KE4-T cell activation and proliferation. In recent years, most progress has been made in the development and validation of in vitro assays that address KE2 and KE3. No standardized in vitro assays for T cell activation are available; thus, KE4 cannot be measured in vitro. Three non-animal test methods, addressing either the MIE, KE2 or KE3, are accepted as OECD test guidelines, and this has accelerated the development of integrated or defined approaches for testing and assessment (e.g. testing strategies). The majority of these approaches are mechanism-based, since they combine results from multiple test methods and/or computational tools that address different KEs of the AOP to estimate skin sensitization potential and sometimes potency. Other approaches are based on statistical tools. Until now, eleven different testing strategies have been published, the majority using the same individual information sources. Our review shows that some of the defined approaches to testing and assessment are able to accurately predict skin sensitization hazard, sometimes even more accurate than the currently used animal test. A few defined approaches are developed to provide an estimate of the potency sub-category of a skin sensitizer as well, but these approaches need further independent evaluation with a new dataset of chemicals. To conclude, this update shows that the field of non-animal approaches for skin sensitization has evolved greatly in recent years and that it is possible to predict skin sensitization hazard without animal testing.
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Affiliation(s)
- Janine Ezendam
- Department of Innovative Testing Strategies, Center for Health Protection, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, The Netherlands.
| | - Hedwig M Braakhuis
- Department of Innovative Testing Strategies, Center for Health Protection, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, The Netherlands
| | - Rob J Vandebriel
- Department of Innovative Testing Strategies, Center for Health Protection, National Institute for Public Health and the Environment (RIVM), PO Box 1, 3720 BA, Bilthoven, The Netherlands
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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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36
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Neves BJ, Dantas RF, Senger MR, Melo-Filho CC, Valente WCG, de Almeida ACM, Rezende-Neto JM, Lima EFC, Paveley R, Furnham N, Muratov E, Kamentsky L, Carpenter AE, Braga RC, Silva-Junior FP, Andrade CH. Discovery of New Anti-Schistosomal Hits by Integration of QSAR-Based Virtual Screening and High Content Screening. J Med Chem 2016; 59:7075-88. [PMID: 27396732 PMCID: PMC5844225 DOI: 10.1021/acs.jmedchem.5b02038] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Schistosomiasis is a debilitating neglected tropical disease, caused by flatworms of Schistosoma genus. The treatment relies on a single drug, praziquantel (PZQ), making the discovery of new compounds extremely urgent. In this work, we integrated QSAR-based virtual screening (VS) of Schistosoma mansoni thioredoxin glutathione reductase (SmTGR) inhibitors and high content screening (HCS) aiming to discover new antischistosomal agents. Initially, binary QSAR models for inhibition of SmTGR were developed and validated using the Organization for Economic Co-operation and Development (OECD) guidance. Using these models, we prioritized 29 compounds for further testing in two HCS platforms based on image analysis of assay plates. Among them, 2-[2-(3-methyl-4-nitro-5-isoxazolyl)vinyl]pyridine and 2-(benzylsulfonyl)-1,3-benzothiazole, two compounds representing new chemical scaffolds have activity against schistosomula and adult worms at low micromolar concentrations and therefore represent promising antischistosomal hits for further hit-to-lead optimization.
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Affiliation(s)
- Bruno J. Neves
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
| | - Rafael F. Dantas
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Mario R. Senger
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Cleber C. Melo-Filho
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
| | - Walter C. G. Valente
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Ana C. M. de Almeida
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - João M. Rezende-Neto
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Elid F. C. Lima
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Ross Paveley
- Department of Infection and Immunity, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom
| | - Nicholas Furnham
- Department of Infection and Immunity, London School of Hygiene and Tropical Medicine, London WC1E 7HT, United Kingdom
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill North Carolina 27955-7568, United States
| | - Lee Kamentsky
- Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, United States
| | - Anne E. Carpenter
- Imaging Platform, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, United States
| | - Rodolpho C. Braga
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
| | - Floriano P. Silva-Junior
- LaBECFar—Laboratório de Bioquímica Experimental e Computacional de Fármacos, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Av. Brasil, 4365, Rio de Janeiro 21040-900, Rio de Janeiro, Brazil
| | - Carolina Horta Andrade
- LabMol—Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia, Universidade Federal de Goiás, Rua 240, Qd.87, Setor Leste Universitário, Goiânia 74605-510, Brazil
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Fitzpatrick JM, Roberts DW, Patlewicz G. Is skin penetration a determining factor in skin sensitization potential and potency? Refuting the notion of a LogKow threshold for skin sensitization. J Appl Toxicol 2016; 37:117-127. [PMID: 27357739 DOI: 10.1002/jat.3354] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2016] [Revised: 04/21/2016] [Accepted: 05/09/2016] [Indexed: 11/09/2022]
Abstract
It is widely accepted that substances that cannot penetrate through the skin will not be sensitizers. LogKow and molecular weight (MW) have been used to set thresholds for sensitization potential. Highly hydrophilic substances e.g. LogKow ≤ 1 are expected not to penetrate effectively to induce sensitization. To investigate whether LogKow >1 is a true requirement for sensitization, a large dataset of substances that had been evaluated for their skin sensitization potential under Registration, Evaluation, Authorisation and restriction of CHemicals (REACH), together with available measured LogKow values was compiled using the OECD eChemPortal. The incidence of sensitizers relative to non-sensitizers above and below a LogKow of 1 was explored. Reaction chemistry principles were used to explain the sensitization observed for the subset of substances with a LogKow ≤0. 1482 substances were identified with skin sensitization data and measured LogKow values. 525 substances had a measured LogKow ≤ 1, 100 of those were sensitizers. There was no significant difference in the incidence of sensitizers above and below a LogKow of 1. Reaction chemistry principles that had been established for lower MW and more hydrophobic substances were found to be still valid in rationalizing the skin sensitizers with a LogKow ≤ 0. The LogKow threshold arises from the widespread misconception that the ability to efficiently penetrate the stratum corneum is a key determinant of sensitization potential and potency. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Jeremy M Fitzpatrick
- National Center for Computational Toxicology (NCCT), US Environmental Protection Agency (US EPA), USA
| | - David W Roberts
- School of Pharmacy and Chemistry, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Grace Patlewicz
- National Center for Computational Toxicology (NCCT), US Environmental Protection Agency (US EPA), USA
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38
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Jaworska JS, Natsch A, Ryan C, Strickland J, Ashikaga T, Miyazawa M. Bayesian integrated testing strategy (ITS) for skin sensitization potency assessment: a decision support system for quantitative weight of evidence and adaptive testing strategy. Arch Toxicol 2015; 89:2355-83. [PMID: 26612363 DOI: 10.1007/s00204-015-1634-2] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2015] [Accepted: 10/20/2015] [Indexed: 12/22/2022]
Abstract
The presented Bayesian network Integrated Testing Strategy (ITS-3) for skin sensitization potency assessment is a decision support system for a risk assessor that provides quantitative weight of evidence, leading to a mechanistically interpretable potency hypothesis, and formulates adaptive testing strategy for a chemical. The system was constructed with an aim to improve precision and accuracy for predicting LLNA potency beyond ITS-2 (Jaworska et al., J Appl Toxicol 33(11):1353-1364, 2013) by improving representation of chemistry and biology. Among novel elements are corrections for bioavailability both in vivo and in vitro as well as consideration of the individual assays' applicability domains in the prediction process. In ITS-3 structure, three validated alternative assays, DPRA, KeratinoSens and h-CLAT, represent first three key events of the adverse outcome pathway for skin sensitization. The skin sensitization potency prediction is provided as a probability distribution over four potency classes. The probability distribution is converted to Bayes factors to: 1) remove prediction bias introduced by the training set potency distribution and 2) express uncertainty in a quantitative manner, allowing transparent and consistent criteria to accept a prediction. The novel ITS-3 database includes 207 chemicals with a full set of in vivo and in vitro data. The accuracy for predicting LLNA outcomes on the external test set (n = 60) was as follows: hazard (two classes)-100 %, GHS potency classification (three classes)-96 %, potency (four classes)-89 %. This work demonstrates that skin sensitization potency prediction based on data from three key events, and often less, is possible, reliable over broad chemical classes and ready for practical applications.
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Affiliation(s)
| | | | - Cindy Ryan
- Procter and Gamble Company, Mason, OH, 45040, USA
| | - Judy Strickland
- ILS/Contractor Supporting NICEATM, Research Triangle Park, NC, 27709, USA
| | | | - Masaaki Miyazawa
- Kao Corporation, R&D Safety Science Research, Tochigi, 321-3497, Japan
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Selzer D, Neumann D, Schaefer UF. Mathematical models for dermal drug absorption. Expert Opin Drug Metab Toxicol 2015; 11:1567-83. [PMID: 26166490 DOI: 10.1517/17425255.2015.1063615] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Mathematical models of dermal transport offer the advantages of being much faster and less expensive than in vitro or in vivo studies. The number of methods used to create such models has been increasing rapidly, probably due to the steady rise in computational power. Although each of the various approaches has its own virtues and limitations, it may be difficult to decide which approach is best suited to address a given problem. AREAS COVERED Here we outline the basic ideas, drawbacks and advantages of compartmental and quantitative structure-activity relationship models, as well as of analytical and numerical approaches for solving the diffusion equation. Examples of special applications of the different approaches are given. EXPERT OPINION Although some models are sophisticated and might be used in future to predict transport through damaged or diseased skin, the comparatively low availability of suitable and accurate experimental data limits extensive usage of these models and their predictive accuracy. Due to the lack of experimental data, the possibility of validating mathematical models is limited.
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Affiliation(s)
- Dominik Selzer
- a 1 Saarland University, Biopharmaceutics and Pharmaceutical Technology , 66123 Saarbruecken, Germany.,b 2 Scientific Consilience GmbH, Saarland University , Bldg. 30, 66123 Saarbruecken, Germany +49 681 302 71230 ; +49 681 302 64956 ;
| | - Dirk Neumann
- a 1 Saarland University, Biopharmaceutics and Pharmaceutical Technology , 66123 Saarbruecken, Germany.,b 2 Scientific Consilience GmbH, Saarland University , Bldg. 30, 66123 Saarbruecken, Germany +49 681 302 71230 ; +49 681 302 64956 ;
| | - Ulrich F Schaefer
- c 3 Saarland University, Biopharmaceutics and Pharmaceutical Technology , 66123 Saarbruecken, Germany
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Pires DEV, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem 2015; 58:4066-72. [PMID: 25860834 PMCID: PMC4434528 DOI: 10.1021/acs.jmedchem.5b00104] [Citation(s) in RCA: 2009] [Impact Index Per Article: 223.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
![]()
Drug development has a high attrition
rate, with poor pharmacokinetic
and safety properties a significant hurdle. Computational approaches
may help minimize these risks. We have developed a novel approach
(pkCSM) which uses graph-based signatures to develop predictive models
of central ADMET properties for drug development. pkCSM performs as
well or better than current methods. A freely accessible web server
(http://structure.bioc.cam.ac.uk/pkcsm), which retains
no information submitted to it, provides an integrated platform to
rapidly evaluate pharmacokinetic and toxicity properties.
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Affiliation(s)
- Douglas E V Pires
- †Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Sanger Building, Cambridge, Cambridgshire CB2 1GA, U.K.,‡Centro de Pesquisas René Rachou, Fundação Oswaldo Cruz, Belo Horizonte 30190-002, Brazil
| | - Tom L Blundell
- †Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Sanger Building, Cambridge, Cambridgshire CB2 1GA, U.K
| | - David B Ascher
- †Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Sanger Building, Cambridge, Cambridgshire CB2 1GA, U.K
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41
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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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