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Wang H, Huang Z, Lou S, Li W, Liu G, Tang Y. In Silico Prediction of Skin Sensitization for Compounds via Flexible Evidence Combination Based on Machine Learning and Dempster-Shafer Theory. Chem Res Toxicol 2024; 37:894-909. [PMID: 38753056 DOI: 10.1021/acs.chemrestox.3c00396] [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: 06/18/2024]
Abstract
Skin sensitization is increasingly becoming a significant concern in the development of drugs and cosmetics due to consumer safety and occupational health problems. In silico methods have emerged as alternatives to traditional in vivo animal testing due to ethical and economic considerations. In this study, machine learning methods were used to build quantitative structure-activity relationship (QSAR) models on five skin sensitization data sets (GPMT, LLNA, DPRA, KeratinoSens, and h-CLAT), achieving effective predictive accuracies (correct classification rates of 0.688-0.764 on test sets). To address the complex mechanisms of human skin sensitization, the Dempster-Shafer theory was applied to merge multiple QSAR models, resulting in an evidence-based integrated decision model. Various evidence combinations and combination rules were explored, with the self-defined Q3 rule showing superior balance. The combination of evidence such as GPMT and KeratinoSens and h-CLAT achieved a correct classification rate (CCR) of 0.880 and coverage of 0.893 while maintaining the competitiveness of other combinations. Additionally, the Shapley additive explanations (SHAP) method was used to interpret important features and substructures related to skin sensitization. A comparative analysis of an external human test set demonstrated the superior performance of the proposed method. Finally, to enhance accessibility, the workflow was implemented into a user-friendly software named HSkinSensDS.
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Affiliation(s)
- Haoqiang Wang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zejun Huang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Shang Lou
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yun Tang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Guezane-Lakoud S, Ferrah M, Merabet-Khelassi M, Touil N, Toffano M, Aribi-Zouioueche L. 2-Hydroxymethyl-18-crown-6 as an efficient organocatalyst for α -aminophosphonates synthesized under eco-friendly conditions, DFT, molecular docking and ADME/T studies. J Biomol Struct Dyn 2024; 42:3332-3348. [PMID: 37184142 DOI: 10.1080/07391102.2023.2213336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 05/04/2023] [Indexed: 05/16/2023]
Abstract
Eco-friendly and simple procedure has been developed for the synthesis of α-aminophosphonates that act as topoisomerase II α-inhibiting anticancer agent, using 2-hydroxymethyl-18-crown-6 as an unexpected homogeneous organocatalyst in multicomponents reaction of aromatic aldehyde, aniline and diethylphosphite in one pot via Kabachnik-Fields reaction. This efficient method proceeds with catalytic amount, transition metal-free, at room temperature within short reaction time, giving the α-aminophosphonates derivatives (4a-r) in high chemical yields (up to 80%). Theoretical DFT calculations of three compounds (4p, 4q and 4r) were carried out in a gas phase at CAM-B3LYP 6-31G (d,p) basis set to predict the molecular geometries and chemical reactivity descriptors. The frontier orbital energies (HOMO/LUMO) were described the charge transfer and used to predict structure-activity relationship study. Molecular electrostatic potential (MEP) has also been analyzed. Molecular docking studies are implemented to analyze the binding energy and compared with Adriamycin against 1ZXM receptor which to be considered as antitumor candidates. In silico pharmacological ADMET properties as Drug likeness and oral activity have been carried out based on Lipinski's rule of five.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Samia Guezane-Lakoud
- Ecocompatible Asymmetric Catalysis Laboratory (LCAE) Badji Mokhtar Annaba-University, Annaba, Algeria
| | - Meriem Ferrah
- Ecocompatible Asymmetric Catalysis Laboratory (LCAE) Badji Mokhtar Annaba-University, Annaba, Algeria
| | - Mounia Merabet-Khelassi
- Ecocompatible Asymmetric Catalysis Laboratory (LCAE) Badji Mokhtar Annaba-University, Annaba, Algeria
| | - Nourhane Touil
- Ecocompatible Asymmetric Catalysis Laboratory (LCAE) Badji Mokhtar Annaba-University, Annaba, Algeria
| | - Martial Toffano
- Equipe de Catalyse Moléculaire-ICMMO Bât 420. Université Paris-Saclay, Paris, France
| | - Louisa Aribi-Zouioueche
- Ecocompatible Asymmetric Catalysis Laboratory (LCAE) Badji Mokhtar Annaba-University, Annaba, Algeria
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Siswina T, Rustama MM, Sumiarsa D, Apriyanti E, Dohi H, Kurnia D. Antifungal Constituents of Piper crocatum and Their Activities as Ergosterol Biosynthesis Inhibitors Discovered via In Silico Study Using ADMET and Drug-Likeness Analysis. Molecules 2023; 28:7705. [PMID: 38067436 PMCID: PMC10708292 DOI: 10.3390/molecules28237705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 11/14/2023] [Accepted: 11/16/2023] [Indexed: 12/18/2023] Open
Abstract
Along with the increasing resistance of Candida spp. to some antibiotics, it is necessary to find new antifungal drugs, one of which is from the medicinal plant Red Betel (Piper crocatum). The purpose of this research is to isolate antifungal constituents from P. crocatum and evaluate their activities as ergosterol biosynthesis inhibitors via an in silico study of ADMET and drug-likeness analysis. Two new active compounds 1 and 2 and a known compound 3 were isolated, and their structures were determined using spectroscopic methods, while their bioactivities were evaluated via in vitro and in silico studies, respectively. Antifungal compound 3 was the most active compared to 1 and 2 with zone inhibition values of 14.5, 11.9, and 13.0 mm, respectively, at a concentration of 10% w/v, together with MIC/MFC at 0.31/1.2% w/v. Further in silico study demonstrated that compound 3 had a stronger ΔG than the positive control and compounds 1 and 2 with -11.14, -12.78, -12.00, and -6.89 Kcal/mol against ERG1, ERG2, ERG11, and ERG24, respectively, and also that 3 had the best Ki with 6.8 × 10-3, 4 × 10-4, 1.6 × 10-3, and 8.88 μM. On the other hand, an ADMET analysis of 1-3 met five parameters, while 1 had one violation of Ro5. Based on the research data, the promising antifungal constituents of P. crocatum allow P. crocatum to be proposed as a new antifungal candidate to treat and cure infections due to C. albicans.
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Affiliation(s)
- Tessa Siswina
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia; (T.S.); (D.S.); (E.A.)
- Department of Midwifery, Poltekkes Kemenkes Pontianak, Pontianak 78124, Indonesia
| | - Mia Miranti Rustama
- Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia;
| | - Dadan Sumiarsa
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia; (T.S.); (D.S.); (E.A.)
| | - Eti Apriyanti
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia; (T.S.); (D.S.); (E.A.)
| | - Hirofumi Dohi
- Graduate School of Horticulture, Chiba University, 1-33 Yayoi, Inage-ku, Chiba 263-8522, Japan;
| | - Dikdik Kurnia
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Sumedang 45363, Indonesia; (T.S.); (D.S.); (E.A.)
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4
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Alves VM, Yasgar A, Wellnitz J, Rai G, Rath M, Braga RC, Capuzzi SJ, Simeonov A, Muratov EN, Zakharov AV, Tropsha A. Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds. J Med Chem 2023; 66:12828-12839. [PMID: 37677128 DOI: 10.1021/acs.jmedchem.3c00482] [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: 09/09/2023]
Abstract
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.
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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
| | - Adam Yasgar
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - James Wellnitz
- 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
| | - Ganesha Rai
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Marielle Rath
- 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
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, 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
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059, Brazil
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, 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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Banerjee A, Roy K. Prediction-Inspired Intelligent Training for the Development of Classification Read-across Structure-Activity Relationship (c-RASAR) Models for Organic Skin Sensitizers: Assessment of Classification Error Rate from Novel Similarity Coefficients. Chem Res Toxicol 2023; 36:1518-1531. [PMID: 37584642 DOI: 10.1021/acs.chemrestox.3c00155] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
The advancements in the field of cheminformatics have led to a reduction in animal testing to estimate the activity, property, and toxicity of query chemicals. Read-across structure-activity relationship (RASAR) is an emerging concept that utilizes various similarity functions derived from chemical information to develop highly predictive models. Unlike quantitative structure-activity relationship (QSAR) models, RASAR descriptors of a query compound are computed from its close congeners instead of the compound itself, thus targeting predictions in the model training phase. The objective of the present study is not to propose new QSAR models for skin sensitization but to demonstrate the enhancement in the quality of predictions of the skin-sensitizing potential of organic compounds by developing classification-based RASAR (c-RASAR) models. A diverse, previously curated data set was collected from the literature for which 2D descriptors were computed. The extracted essential features were then used to develop a classification-based linear discriminant analysis (LDA) QSAR model. Furthermore, from the read-across-based predictions, RASAR descriptors were calculated using the basic settings of the hyperparameters for the Laplacian Kernel-based optimum similarity measure. After feature selection, an LDA c-RASAR model was developed, which superseded the prediction quality of the LDA-QSAR model. Various other combinations of RASAR descriptors were also taken to develop additional c-RASAR models, all showing better prediction quality than the LDA QSAR model while using a lower number of descriptors. Various other machine learning c-RASAR models were also developed for comparison purposes. In this work, we have proposed and analyzed three new similarity metrics: gm_class, sm1, and sm2. The first one is an indicator variable used to generate a simple univariate c-RASAR model with good prediction ability, while the remaining two are similarity indices used to analyze possible activity cliffs in the training and test sets and are believed to play an important role in the modelability analysis of data sets.
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Affiliation(s)
- Arkaprava Banerjee
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
| | - Kunal Roy
- Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700 032, India
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6
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Tran T, Ekenna C. Molecular Descriptors Property Prediction Using Transformer-Based Approach. Int J Mol Sci 2023; 24:11948. [PMID: 37569322 PMCID: PMC10419034 DOI: 10.3390/ijms241511948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/21/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
In this study, we introduce semi-supervised machine learning models designed to predict molecular properties. Our model employs a two-stage approach, involving pre-training and fine-tuning. Particularly, our model leverages a substantial amount of labeled and unlabeled data consisting of SMILES strings, a text representation system for molecules. During the pre-training stage, our model capitalizes on the Masked Language Model, which is widely used in natural language processing, for learning molecular chemical space representations. During the fine-tuning stage, our model is trained on a smaller labeled dataset to tackle specific downstream tasks, such as classification or regression. Preliminary results indicate that our model demonstrates comparable performance to state-of-the-art models on the chosen downstream tasks from MoleculeNet. Additionally, to reduce the computational overhead, we propose a new approach taking advantage of 3D compound structures for calculating the attention score used in the end-to-end transformer model to predict anti-malaria drug candidates. The results show that using the proposed attention score, our end-to-end model is able to have comparable performance with pre-trained models.
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7
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Danieli A, Colombo E, Raitano G, Lombardo A, Roncaglioni A, Manganaro A, Sommovigo A, Carnesecchi E, Dorne JLCM, Benfenati E. The VEGA Tool to Check the Applicability Domain Gives Greater Confidence in the Prediction of In Silico Models. Int J Mol Sci 2023; 24:9894. [PMID: 37373049 PMCID: PMC10298077 DOI: 10.3390/ijms24129894] [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: 04/28/2023] [Revised: 05/31/2023] [Accepted: 06/03/2023] [Indexed: 06/29/2023] Open
Abstract
A sound assessment of in silico models and their applicability domain can support the use of new approach methodologies (NAMs) in chemical risk assessment and requires increasing the users' confidence in this approach. Several approaches have been proposed to evaluate the applicability domain of such models, but their prediction power still needs a thorough assessment. In this context, the VEGA tool capable of assessing the applicability domain of in silico models is examined for a range of toxicological endpoints. The VEGA tool evaluates chemical structures and other features related to the predicted endpoints and is efficient in measuring applicability domain, enabling the user to identify less accurate predictions. This is demonstrated with many models addressing different endpoints, towards toxicity of relevance to human health, ecotoxicological endpoints, environmental fate, physicochemical and toxicokinetic properties, for both regression models and classifiers.
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Affiliation(s)
- Alberto Danieli
- Department of Biotechnology and Life Science, University of Insubria, Via Dunant 3, 21100 Varese, Italy;
| | - Erika Colombo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Giuseppa Raitano
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Anna Lombardo
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | - Alessandra Roncaglioni
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
| | | | | | - Edoardo Carnesecchi
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Jean-Lou C. M. Dorne
- European Food Safety Authority (EFSA), Via Carlo Magno 1A, 43126 Parma, Italy; (E.C.); (J.-L.C.M.D.)
| | - Emilio Benfenati
- Laboratory of Environmental Toxicology and Chemistry, Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCSS, 20156 Milano, Italy; (E.C.); (G.R.); (A.L.); (E.B.)
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8
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In silico study of novel niclosamide derivatives, SARS-CoV-2 nonstructural proteins catalytic residue-targeting small molecules drug candidates. ARAB J CHEM 2023; 16:104654. [PMID: 36777994 PMCID: PMC9904858 DOI: 10.1016/j.arabjc.2023.104654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 01/31/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2)-mediated coronavirus disease 2019 (COVID-19) infection remains a global pandemic and health emergency with overwhelming social and economic impacts throughout the world. Therapeutics for COVID-19 are limited to only remdesivir; therefore, there is a need for combined, multidisciplinary efforts to develop new therapeutic molecules and explore the effectiveness of existing drugs against SARS-CoV-2. In the present study, we reported eight (SCOV-L-02, SCOV-L-09, SCOV-L-10, SCOV-L-11, SCOV-L-15, SCOV-L-18, SCOV-L-22, and SCOV-L-23) novel structurally related small-molecule derivatives of niclosamide (SCOV-L series) for their targeting potential against angiotensin-converting enzyme-2 (ACE2), type II transmembrane serine protease (TMPRSS2), and SARS-COV-2 nonstructural proteins (NSPs) including NSP5 (3CLpro), NSP3 (PLpro), and RdRp. Our correlation analysis suggested that ACE2 and TMPRSS2 modulate host immune response via regulation of immune-infiltrating cells at the site of tissue/organs entries. In addition, we identified some TMPRSS2 and ACE2 microRNAs target regulatory networks in SARS-CoV-2 infection and thus open up a new window for microRNAs-based therapy for the treatment of SARS-CoV-2 infection. Our in vitro study revealed that with the exception of SCOV-L-11 and SCOV-L-23 which were non-active, the SCOV-L series exhibited strict antiproliferative activities and non-cytotoxic effects against ACE2- and TMPRSS2-expressing cells. Our molecular docking for the analysis of receptor-ligand interactions revealed that SCOV-L series demonstrated high ligand binding efficacies (at higher levels than clinical drugs) against the ACE2, TMPRSS2, and SARS-COV-2 NSPs. SCOV-L-18, SCOV-L-15, and SCOV-L-09 were particularly found to exhibit strong binding affinities with three key SARS-CoV-2's proteins: 3CLpro, PLpro, and RdRp. These compounds bind to the several catalytic residues of the proteins, and satisfied the criteria of drug-like candidates, having good adsorption, distribution, metabolism, excretion, and toxicity (ADMET) pharmacokinetic profile. Altogether, the present study suggests the therapeutic potential of SCOV-L series for preventing and managing SARs-COV-2 infection and are currently under detailed investigation in our lab.
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Oh KK, Gupta H, Min BH, Ganesan R, Sharma SP, Won SM, Jeong JJ, Lee SB, Cha MG, Kwon GH, Jeong MK, Hyun JY, Eom JA, Park HJ, Yoon SJ, Choi MR, Kim DJ, Suk KT. The identification of metabolites from gut microbiota in NAFLD via network pharmacology. Sci Rep 2023; 13:724. [PMID: 36639568 PMCID: PMC9839744 DOI: 10.1038/s41598-023-27885-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 01/10/2023] [Indexed: 01/15/2023] Open
Abstract
The metabolites of gut microbiota show favorable therapeutic effects on nonalcoholic fatty liver disease (NAFLD), but the active metabolites and mechanisms against NAFLD have not been documented. The aim of the study was to investigate the active metabolites and mechanisms of gut microbiota against NAFLD by network pharmacology. We obtained a total of 208 metabolites from the gutMgene database and retrieved 1256 targets from similarity ensemble approach (SEA) and 947 targets from the SwissTargetPrediction (STP) database. In the SEA and STP databases, we identified 668 overlapping targets and obtained 237 targets for NAFLD. Thirty-eight targets were identified out of those 237 and 223 targets retrieved from the gutMgene database, and were considered the final NAFLD targets of metabolites from the microbiome. The results of molecular docking tests suggest that, of the 38 targets, mitogen-activated protein kinase 8-compound K and glycogen synthase kinase-3 beta-myricetin complexes might inhibit the Wnt signaling pathway. The microbiota-signaling pathways-targets-metabolites network analysis reveals that Firmicutes, Fusobacteria, the Toll-like receptor signaling pathway, mitogen-activated protein kinase 1, and phenylacetylglutamine are notable components of NAFLD and therefore to understanding its processes and possible therapeutic approaches. The key components and potential mechanisms of metabolites from gut microbiota against NAFLD were explored utilizing network pharmacology analyses. This study provides scientific evidence to support the therapeutic efficacy of metabolites for NAFLD and suggests holistic insights on which to base further research.
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Affiliation(s)
- Ki-Kwang Oh
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Haripriya Gupta
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Byeong Hyun Min
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Raja Ganesan
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Satya Priya Sharma
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Sung Min Won
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Jin Ju Jeong
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Su Been Lee
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Min Gi Cha
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Goo Hyun Kwon
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Min Kyo Jeong
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Ji Ye Hyun
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Jung A Eom
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Hee Jin Park
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Sang Jun Yoon
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Mi Ran Choi
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Dong Joon Kim
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea
| | - Ki Tae Suk
- Center for Microbiome, Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, 24252, South Korea.
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10
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Aldeghi M, Graff DE, Frey N, Morrone JA, Pyzer-Knapp EO, Jordan KE, Coley CW. Roughness of Molecular Property Landscapes and Its Impact on Modellability. J Chem Inf Model 2022; 62:4660-4671. [DOI: 10.1021/acs.jcim.2c00903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Matteo Aldeghi
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - David E. Graff
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Nathan Frey
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, Massachusetts 02421, United States
| | - Joseph A. Morrone
- IBM Thomas J. Watson Research Center, Yorktown Heights, New York 10598, United States
| | | | - Kirk E. Jordan
- IBM Thomas J. Watson Research Center, Cambridge, Massachusetts 02142, United States
| | - Connor W. Coley
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, Massachusetts 02138, United States
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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11
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Lim S, Lee S, Piao Y, Choi M, Bang D, Gu J, Kim S. On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Comput Struct Biotechnol J 2022; 20:4288-4304. [PMID: 36051875 PMCID: PMC9399946 DOI: 10.1016/j.csbj.2022.07.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Revised: 07/29/2022] [Accepted: 07/29/2022] [Indexed: 11/22/2022] Open
Abstract
A large number of chemical compounds are available in databases such as PubChem and ZINC. However, currently known compounds, though large, represent only a fraction of possible compounds, which is known as chemical space. Many of these compounds in the databases are annotated with properties and assay data that can be used for drug discovery efforts. For this goal, a number of machine learning algorithms have been developed and recent deep learning technologies can be effectively used to navigate chemical space, especially for unknown chemical compounds, in terms of drug-related tasks. In this article, we survey how deep learning technologies can model and utilize chemical compound information in a task-oriented way by exploiting annotated properties and assay data in the chemical compounds databases. We first compile what kind of tasks are trying to be accomplished by machine learning methods. Then, we survey deep learning technologies to show their modeling power and current applications for accomplishing drug related tasks. Next, we survey deep learning techniques to address the insufficiency issue of annotated data for more effective navigation of chemical space. Chemical compound information alone may not be powerful enough for drug related tasks, thus we survey what kind of information, such as assay and gene expression data, can be used to improve the prediction power of deep learning models. Finally, we conclude this survey with four important newly developed technologies that are yet to be fully incorporated into computational analysis of chemical information.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sangseon Lee
- Institute of Computer Technology, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Yinhua Piao
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - MinGyu Choi
- Department of Chemistry, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
- MOGAM Institute for Biomedical Research, Yong-in 16924, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Gwanak-gu, Seoul 08826, South Korea
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12
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Chayawan, Selvestrel G, Baderna D, Toma C, Caballero Alfonso AY, Gamba A, Benfenati E. Skin sensitization quantitative QSAR models based on mechanistic structural alerts. Toxicology 2022; 468:153111. [DOI: 10.1016/j.tox.2022.153111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/05/2022] [Accepted: 01/26/2022] [Indexed: 10/19/2022]
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13
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Kim JY, Kim KB, Lee BM. Validation of Quantitative Structure-Activity Relationship (QSAR) and Quantitative Structure-Property Relationship (QSPR) approaches as alternatives to skin sensitization risk assessment. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH. PART A 2021; 84:945-959. [PMID: 34338166 DOI: 10.1080/15287394.2021.1956660] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
The aim of this study was conducted to validate the physicochemical properties of a total of 362 chemicals [305 skin sensitizers (212 in the previous study + 93 additional new chemicals), 57 non-skin sensitizers (38 in the previous study + 19 additional new chemicals)] for skin sensitization risk assessment using quantitative structure-activity relationship (QSAR)/quantitative structure-property relationship (QSPR) approaches. The average melting point (MP), surface tension (ST), and density (DS) of the 305 skin sensitizers and 57 non-sensitizers were used to determine the cutoff values distinguishing positive and negative sensitization, and correlation coefficients were employed to derive effective 3-fold concentration (EC3 (%)) values. QSAR models were also utilized to assess skin sensitization. The sensitivity, specificity, and accuracy were 80, 15, and 70%, respectively, for the Toxtree QSAR model; 88, 46, and 81%, respectively, for Vega; and 56, 61, and 56%, respectively, for Danish EPA QSAR. Surprisingly, the sensitivity, specificity, and accuracy were 60, 80, and 64%, respectively, when MP, ST, and DS (MP+ST+DS) were used in this study. Further, MP+ST+DS exhibited a sensitivity of 77%, specificity 57%, and accuracy 73% when the derived EC3 values were classified into local lymph node assay (LLNA) skin sensitizer and non-sensitizer categories. Thus, MP, ST, and DS may prove useful in predicting EC3 values as not only an alternative approach to animal testing but also for skin sensitization risk assessment.
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Affiliation(s)
- Ji Yun Kim
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
| | - Kyu-Bong Kim
- College of Pharmacy, Dankook University Dandae-ro, Cheonan, Chungnam, South Korea
| | - Byung-Mu Lee
- Division of Toxicology, College of Pharmacy, Sungkyunkwan University, Suwon, Gyeonggi-do, South Korea
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14
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Obu QS, Louis H, Odey JO, Eko IJ, Abdullahi S, Ntui TN, Offiong OE. Synthesis, spectra (FT-IR, NMR) investigations, DFT study, in silico ADMET and Molecular docking analysis of 2-amino-4-(4-aminophenyl)thiophene-3-carbonitrile as a potential anti-tubercular agent. J Mol Struct 2021. [DOI: 10.1016/j.molstruc.2021.130880] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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15
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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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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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17
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Anwar MF, Khalid R, Hasanain A, Naeem S, Zarina S, Abidi SH, Ali S. Integrated Cheminformatics-Molecular Docking Approach to Drug Discovery Against Viruses. Infect Disord Drug Targets 2020; 20:150-159. [PMID: 30345931 DOI: 10.2174/1871526518666181019162359] [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: 01/30/2018] [Revised: 08/03/2018] [Accepted: 10/11/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND In the current study, we present an integrated in silico cheminformaticsmolecular docking approach to screen and test potential therapeutic compounds against viruses. Fluoroquinolones have been shown to inhibit HCV replication by targeting HCV NS3-helicase. Based on this observation, we hypothesized that natural analogs of fluoroquinolones will have similar or superior inhibitory potential while having potentially fewer adverse effects. METHODS To screen for natural analogs of fluoroquinolones, we devised an integrated in silico Cheminformatics-Molecular Docking approach. We used 17 fluoroquinolones as bait reference, to screen large databases of natural analogs. 10399 natural compounds and their derivatives were retrieved from the databases. From these compounds, molecules bearing physicochemical similarities with fluoroquinolones were analyzed using a cheminformatics-docking approach. RESULTS From the 10399 compounds screened using our cheminformatics approach, only 20 compounds were found to share physicochemical similarities with fluoroquinolones, while the remaining 10379 compounds were physiochemically different from fluoroquinolones. Molecular docking analysis showed 32 amino acids in the HCV NS3 active site that were most frequently targeted by fluoroquinolones and their natural analogues, indicating a functional similarity between the two groups of compounds. CONCLUSION This study describes a speedy and inexpensive approach to complement drug discovery and design against viral agents. The in silico analyses we used here can be employed to shortlist promising compounds/putative drugs that can be further tested in wet-lab.
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Affiliation(s)
- Muhammad Faraz Anwar
- National Center for Proteomics, University of Karachi, Karachi, Pakistan.,Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan
| | - Ramsha Khalid
- Department of Biochemistry, University of Karachi, Karachi, Pakistan
| | | | - Sadaf Naeem
- Department of Biochemistry, University of Karachi, Karachi, Pakistan
| | - Shamshad Zarina
- National Center for Proteomics, University of Karachi, Karachi, Pakistan
| | - Syed Hani Abidi
- Department of Biological and Biomedical Sciences, Aga Khan University, Karachi, Pakistan.,Nuffield Department of Clinical Medicine, University of Oxford, Oxford, United Kingdom
| | - Syed Ali
- Department of Biomedical Sciences, Nazarbayev University School of Medicine, Nazarbayev University, Astana, Kazakhstan
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18
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Matsumura K. Skin sensitizer classification using dual-input machine learning model. CHEM-BIO INFORMATICS JOURNAL 2020. [DOI: 10.1273/cbij.20.54] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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19
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Melnikov F, Geohagen BC, Gavin T, LoPachin RM, Anastas PT, Coish P, Herr DW. Application of the hard and soft, acids and bases (HSAB) theory as a method to predict cumulative neurotoxicity. Neurotoxicology 2020; 79:95-103. [PMID: 32380191 DOI: 10.1016/j.neuro.2020.04.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 04/07/2020] [Accepted: 04/22/2020] [Indexed: 12/14/2022]
Abstract
Xenobiotic electrophiles can form covalent adducts that may impair protein function, damage DNA, and may lead a range of adverse effects. Cumulative neurotoxicity is one adverse effect that has been linked to covalent protein binding as a Molecular Initiating Event (MIE). This paper describes a mechanistic in silico chemical screening approach for neurotoxicity based on Hard and Soft Acids and Bases (HSAB) theory. We evaluated the applicability of HSAB-based electrophilicity screening protocol for neurotoxicity on 19 positive and 19 negative reference chemicals. These reference chemicals were identified from the literature, using available information on mechanisms of neurotoxicity whenever possible. In silico screening was based on structural alerts for protein binding motifs and electrophilicity index in the range of known neurotoxicants. The approach demonstrated both a high positive prediction rate (82-90 %) and specificity (90 %). The overall sensitivity was relatively lower (47 %). However, when predicting the toxicity of chemicals known or suspected of acting via non-specific adduct formation mechanism, the HSAB approach identified 7/8 (sensitivity 88 %) of positive control chemicals correctly. Consequently, the HSAB-based screening is a promising approach of identifying possible neurotoxins with adduct formation molecular initiating events. While the approach must be expanded over time to capture a wider range of MIEs involved in neurotoxicity, the mechanistic nature of the screen allows users to flag chemicals for possible adduct formation MIEs. Thus, the HSAB based toxicity screening is a promising strategy for toxicity assessment and chemical prioritization in neurotoxicology and other health endpoints that involve adduct formation.
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Affiliation(s)
- Fjodor Melnikov
- School of Forestry and Environmental Studies, Yale University, New Haven, CT, 06511, United States.
| | - Brian C Geohagen
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E. 210th St, Bronx, NY, 10467, United States.
| | - Terrence Gavin
- Department of Chemistry, Iona College, 402 North Avenue, New Rochelle, NY, 10804, United States.
| | - Richard M LoPachin
- Department of Anesthesiology, Montefiore Medical Center, Albert Einstein College of Medicine, 111 E. 210th St, Bronx, NY, 10467, United States.
| | - Paul T Anastas
- School of Forestry and Environmental Science, School of Public Health, Yale University, New Haven, CT 06511, United States.
| | - Phillip Coish
- School of Forestry and Environmental Science, Yale University, New Haven, CT 06511, United States.
| | - David W Herr
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, United States.
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20
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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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21
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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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22
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Funar-Timofei S, Ilia G. QSAR Modeling of Dye Ecotoxicity. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2020. [DOI: 10.1007/978-1-0716-0150-1_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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23
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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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Application of an integrated cheminformatics-molecular docking approach for discovery for physicochemically similar analogs of fluoroquinolones as putative HCV inhibitors. Comput Biol Chem 2019; 84:107167. [PMID: 31855781 DOI: 10.1016/j.compbiolchem.2019.107167] [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: 01/24/2018] [Revised: 10/02/2019] [Accepted: 11/16/2019] [Indexed: 01/13/2023]
Abstract
BACKGROUND Hepatitis C Virus (HCV) infection is a major public health concern across the globe. At present, direct-acting antivirals are the treatment of choice. However, the long-term effect of this therapy has yet to be ascertained. Previously, fluoroquinolones have been reported to inhibit HCV replication by targeting NS3 protein. Therefore, it is logical to hypothesize that the natural analogs of fluoroquinolones will exhibit NS3 inhibitory activity with substantially lesser side effects. METHOD In this study, we tested the application of a recently devised integrated in-silico Cheminformatics-Molecular Docking approach to identify physicochemically similar natural analogs of fluoroquinolones from the available databases (Ambinter, Analyticon, Indofines, Specs, and TimTec). Molecular docking and ROC curve analyses were performed, using PatchDock and Graphpad software, respectively, to compare and analyze drug-protein interactions between active natural analogs, Fluoroquinolones, and HCV NS3 protein. RESULT In our analysis, we were able to shortlist 18 active natural analogs, out of 10,399, that shared physicochemical properties with the template drugs (fluoroquinolones). These analogs showed comparable binding efficacy with fluoroquinolones in targeting 32 amino acids in the HCV NS3 active site that are crucial for NS3 activity. Our approach had around 80 % sensitivity and 70 % specificity in identifying physicochemically similar analogs of fluoroquinolones. CONCLUSION Our current data suggest that our approach can be efficiently applied to identify putative HCV drug inhibitors that can be taken for in vitro testing. This approach can be applied to discover physicochemically similar analogs of virtually any drug, thus providing a speedy and inexpensive approach to complement drug discovery and design, which can tremendously economize on time and money spent on the screening of putative drugs.
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25
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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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Wilm A, Stork C, Bauer C, Schepky A, Kühnl J, Kirchmair J. Skin Doctor: Machine Learning Models for Skin Sensitization Prediction that Provide Estimates and Indicators of Prediction Reliability. Int J Mol Sci 2019; 20:E4833. [PMID: 31569429 PMCID: PMC6801714 DOI: 10.3390/ijms20194833] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 09/17/2019] [Accepted: 09/18/2019] [Indexed: 12/19/2022] Open
Abstract
The ability to predict the skin sensitization potential of small organic molecules is of high importance to the development and safe application of cosmetics, drugs and pesticides. One of the most widely accepted methods for predicting this hazard is the local lymph node assay (LLNA). The goal of this work was to develop in silico models for the prediction of the skin sensitization potential of small molecules that go beyond the state of the art, with larger LLNA data sets and, most importantly, a robust and intuitive definition of the applicability domain, paired with additional indicators of the reliability of predictions. We explored a large variety of molecular descriptors and fingerprints in combination with random forest and support vector machine classifiers. The most suitable models were tested on holdout data, on which they yielded competitive performance (Matthews correlation coefficients up to 0.52; accuracies up to 0.76; areas under the receiver operating characteristic curves up to 0.83). The most favorable models are available via a public web service that, in addition to predictions, provides assessments of the applicability domain and indicators of the reliability of the individual predictions.
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Affiliation(s)
- Anke Wilm
- Center for Bioinformatics, Universität Hamburg, 20146 Hamburg, Germany.
- HITeC e.V, 22527 Hamburg, Germany.
| | - Conrad Stork
- Center for Bioinformatics, Universität Hamburg, 20146 Hamburg, Germany.
| | - Christoph Bauer
- Department of Chemistry, University of Bergen, 5020 Bergen, Norway.
- Computational Biology Unit (CBU), University of Bergen, 5020 Bergen, Norway.
| | - Andreas Schepky
- Front End Innovation, Beiersdorf AG, 20253 Hamburg, Germany.
| | - Jochen Kühnl
- Front End Innovation, Beiersdorf AG, 20253 Hamburg, Germany.
| | - Johannes Kirchmair
- Center for Bioinformatics, Universität Hamburg, 20146 Hamburg, Germany.
- Department of Chemistry, University of Bergen, 5020 Bergen, Norway.
- Computational Biology Unit (CBU), University of Bergen, 5020 Bergen, Norway.
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27
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Alves VM, Hwang D, Muratov E, Sokolsky-Papkov M, Varlamova E, Vinod N, Lim C, Andrade CH, Tropsha A, Kabanov A. Cheminformatics-driven discovery of polymeric micelle formulations for poorly soluble drugs. SCIENCE ADVANCES 2019; 5:eaav9784. [PMID: 31249867 PMCID: PMC6594770 DOI: 10.1126/sciadv.aav9784] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 05/16/2019] [Indexed: 05/29/2023]
Abstract
Many drug candidates fail therapeutic development because of poor aqueous solubility. We have conceived a computer-aided strategy to enable polymeric micelle-based delivery of poorly soluble drugs. We built models predicting both drug loading efficiency (LE) and loading capacity (LC) using novel descriptors of drug-polymer complexes. These models were employed for virtual screening of drug libraries, and eight drugs predicted to have either high LE and high LC or low LE and low LC were selected. Three putative positives, as well as three putative negative hits, were confirmed experimentally (implying 75% prediction accuracy). Fortuitously, simvastatin, a putative negative hit, was found to have the desired micelle solubility. Podophyllotoxin and simvastatin (LE of 95% and 87% and LC of 43% and 41%, respectively) were among the top five polymeric micelle-soluble compounds ever studied experimentally. The success of the strategy described herein suggests its broad utility for designing drug delivery systems.
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Affiliation(s)
- Vinicius M. Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Duhyeong Hwang
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Department of Pharmaceutical Sciences, Federal University of Paraíba, Joao Pessoa, PB 58059, Brazil
| | - Marina Sokolsky-Papkov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ekaterina Varlamova
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Natasha Vinod
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- UNC/NC State Joint Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Chaemin Lim
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Carolina H. Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiania, GO 74605-170, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Alexander Kabanov
- Center for Nanotechnology in Drug Delivery, Division of Pharmacoengineering and Molecular Pharmaceutics, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599, USA
- Laboratory of Chemical Design of Bionanomaterials, Faculty of Chemistry, M.V. Lomonosov Moscow State University, Moscow 119992, Russia
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28
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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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29
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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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30
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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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31
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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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32
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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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Bogen KT, Garry MR. Risks of Allergic Contact Dermatitis Elicited by Nickel, Chromium, and Organic Sensitizers: Quantitative Models Based on Clinical Patch Test Data. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2018; 38:1036-1051. [PMID: 29023909 DOI: 10.1111/risa.12902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 06/30/2017] [Accepted: 08/14/2017] [Indexed: 06/07/2023]
Abstract
Risks of allergic contact dermatitis (ACD) from consumer products intended for extended (nonpiercing) dermal contact are regulated by E.U. Directive EN 1811 that limits released Ni to a weekly equivalent dermal load of ≤0.5 μg/cm2 . Similar approaches for thousands of known organic sensitizers are hampered by inability to quantify respective ACD-elicitation risk levels. To help address this gap, normalized values of cumulative risk for eliciting a positive ("≥+") clinical patch test response reported in 12 studies for a total of n = 625 Ni-sensitized patients were modeled in relation to observed ACD-eliciting Ni loads, yielding an approximate lognormal (LN) distribution with a geometric mean and standard deviation of GMNi = 15 μg/cm2 and GSDNi = 8.0, respectively. Such data for five sensitizers (including formaldehyde and 2-hydroxyethyl methacrylate) were also ∼LN distributed, but with a common GSD value equal to GSDNi and with heterogeneous sensitizer-specific GM values each defining a respective ACD-eliciting potency GMNi /GM relative to Ni. Such potencies were also estimated for nine (meth)acrylates by applying this general LN ACD-elicitation risk model to respective sets of fewer data. ACD-elicitation risk patterns observed for Cr(VI) (n = 417) and Cr(III) (n = 78) were fit to mixed-LN models in which ∼30% and ∼40% of the most sensitive responders, respectively, were estimated to exhibit a LN response also governed by GSDNi . The observed common LN-response shape parameter GSDNi may reflect a common underlying ACD mechanism and suggests a common interim approach to quantitative ACD-elicitation risk assessment based on available clinical data.
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34
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Non-animal skin sensitization safety assessments for cosmetic ingredients – What is possible today? CURRENT OPINION IN TOXICOLOGY 2017. [DOI: 10.1016/j.cotox.2017.08.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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35
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Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles. Toxicol Lett 2017; 275:57-66. [DOI: 10.1016/j.toxlet.2017.03.023] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 03/24/2017] [Accepted: 03/24/2017] [Indexed: 01/13/2023]
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36
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Braga RC, Alves VM, Muratov EN, Strickland J, Kleinstreuer N, Trospsha A, Andrade CH. Pred-Skin: A Fast and Reliable Web Application to Assess Skin Sensitization Effect of Chemicals. J Chem Inf Model 2017; 57:1013-1017. [DOI: 10.1021/acs.jcim.7b00194] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Rodolpho C. Braga
- Laboratory
for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-170, Brazil
| | - Vinicius M. Alves
- Laboratory
for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-170, Brazil
- 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
- Department
of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Judy Strickland
- Integrated Laboratory Systems, Inc., P.O. Box 13501, Research Triangle Park, North Carolina 27709, United States
| | - Nicole Kleinstreuer
- National
Toxicology Program Interagency Center for the Evaluation of Alternative
Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709, United States
| | - Alexander Trospsha
- 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
| | - Carolina Horta Andrade
- Laboratory
for Molecular Modeling and Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, GO 74605-170, Brazil
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Current nonclinical testing paradigms in support of safe clinical trials: An IQ Consortium DruSafe perspective. Regul Toxicol Pharmacol 2017; 87 Suppl 3:S1-S15. [PMID: 28483710 DOI: 10.1016/j.yrtph.2017.05.009] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/03/2017] [Accepted: 05/04/2017] [Indexed: 12/18/2022]
Abstract
The transition from nonclinical to First-in-Human (FIH) testing is one of the most challenging steps in drug development. In response to serious outcomes in a recent Phase 1 trial (sponsored by Bial), IQ Consortium/DruSafe member companies reviewed their nonclinical approach to progress small molecules safely to FIH trials. As a common practice, safety evaluation begins with target selection and continues through iterative in silico and in vitro screening to identify molecules with increased probability of acceptable in vivo safety profiles. High attrition routinely occurs during this phase. In vivo exploratory and pivotal FIH-enabling toxicity studies are then conducted to identify molecules with a favorable benefit-risk profile for humans. The recent serious incident has reemphasized the importance of nonclinical testing plans that are customized to the target, the molecule, and the intended clinical plan. Despite the challenges and inherent risks of transitioning from nonclinical to clinical testing, Phase 1 studies have a remarkably good safety record. Given the rapid scientific evolution of safety evaluation, testing paradigms and regulatory guidance must evolve with emerging science. The authors posit that the practices described herein, together with science-based risk assessment and management, support safe FIH trials while advancing development of important new medicines.
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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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40
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Wang CC, Lin YC, Wang SS, Shih C, Lin YH, Tung CW. SkinSensDB: a curated database for skin sensitization assays. J Cheminform 2017; 9:5. [PMID: 28194231 PMCID: PMC5285290 DOI: 10.1186/s13321-017-0194-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2016] [Accepted: 01/23/2017] [Indexed: 12/13/2022] Open
Abstract
Skin sensitization is an important toxicological endpoint for chemical hazard determination and safety assessment. Prediction of chemical skin sensitizer had traditionally relied on data from rodent models. The development of the adverse outcome pathway (AOP) and associated alternative in vitro assays have reshaped the assessment of skin sensitizers. The integration of multiple assays as key events in the AOP has been shown to have improved prediction performance. Current computational models to predict skin sensitization mainly based on in vivo assays without incorporating alternative in vitro assays. However, there are few freely available databases integrating both the in vivo and the in vitro skin sensitization assays for development of AOP-based skin sensitization prediction models. To facilitate the development of AOP-based prediction models, a skin sensitization database named SkinSensDB has been constructed by curating data from published AOP-related assays. In addition to providing datasets for developing computational models, SkinSensDB is equipped with browsing and search tools which enable the assessment of new compounds for their skin sensitization potentials based on data from structurally similar compounds. SkinSensDB is publicly available at http://cwtung.kmu.edu.tw/skinsensdb.
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Affiliation(s)
- Chia-Chi Wang
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan.,PhD Program in Toxicology, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan.,National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, 35053 Taiwan.,Institute of Environmental Engineering, National Sun Yat-sen University, Kaohsiung, 80424 Taiwan
| | - Ying-Chi Lin
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan.,PhD Program in Toxicology, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan
| | - Shan-Shan Wang
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan
| | - Chieh Shih
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan
| | - Yi-Hui Lin
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan
| | - Chun-Wei Tung
- School of Pharmacy, Kaohsiung Medical University, 100 Shih-Chuan 1st Road, Kaohsiung, 80708 Taiwan.,PhD Program in Toxicology, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan.,National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County, 35053 Taiwan.,Research Center for Environmental Medicine, Kaohsiung Medical University, Kaohsiung, 80708 Taiwan
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41
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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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43
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Consensus of classification trees for skin sensitisation hazard prediction. Toxicol In Vitro 2016; 36:197-209. [DOI: 10.1016/j.tiv.2016.07.014] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 07/08/2016] [Accepted: 07/21/2016] [Indexed: 11/20/2022]
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44
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Alves V, Muratov E, Capuzzi S, Politi R, Low Y, Braga R, Zakharov AV, Sedykh A, Mokshyna E, Farag S, Andrade C, Kuz'min V, Fourches D, Tropsha A. Alarms about structural alerts. GREEN CHEMISTRY : AN INTERNATIONAL JOURNAL AND GREEN CHEMISTRY RESOURCE : GC 2016; 18:4348-4360. [PMID: 28503093 PMCID: PMC5423727 DOI: 10.1039/c6gc01492e] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.
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Affiliation(s)
- Vinicius Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Eugene Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
- Department of Chemical Technology, Odessa National Polytechnic University, Odessa, 65000, Ukraine
| | - Stephen Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Regina Politi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yen Low
- Netflix, San Francisco, CA 94123, USA
| | - Rodolpho Braga
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Alexey V. Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Rockville, MD 20850, USA
| | | | - Elena Mokshyna
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Sherif Farag
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Carolina Andrade
- Laboratory for Molecular Modeling and Design, Department of Pharmacy, Federal University of Goias, Goiania, GO, 74605-170, Brazil
| | - Victor Kuz'min
- Laboratory of Theoretical Chemistry, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine
| | - Denis Fourches
- Department of Chemistry and Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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Neves BJ, 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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Fourches D, Muratov E, Tropsha A. Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation. J Chem Inf Model 2016; 56:1243-52. [PMID: 27280890 PMCID: PMC5657146 DOI: 10.1021/acs.jcim.6b00129] [Citation(s) in RCA: 191] [Impact Index Per Article: 23.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
There is a growing public concern about the lack of reproducibility of experimental data published in peer-reviewed scientific literature. Herein, we review the most recent alerts regarding experimental data quality and discuss initiatives taken thus far to address this problem, especially in the area of chemical genomics. Going beyond just acknowledging the issue, we propose a chemical and biological data curation workflow that relies on existing cheminformatics approaches to flag, and when appropriate, correct possibly erroneous entries in large chemogenomics data sets. We posit that the adherence to the best practices for data curation is important for both experimental scientists who generate primary data and deposit them in chemical genomics databases and computational researchers who rely on these data for model development.
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Affiliation(s)
- Denis Fourches
- Department of Chemistry, Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27695, 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
| | - 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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47
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Integrated Computational Solution for Predicting Skin Sensitization Potential of Molecules. PLoS One 2016; 11:e0155419. [PMID: 27271321 PMCID: PMC4896476 DOI: 10.1371/journal.pone.0155419] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2015] [Accepted: 04/28/2016] [Indexed: 11/29/2022] Open
Abstract
Introduction Skin sensitization forms a major toxicological endpoint for dermatology and cosmetic products. Recent ban on animal testing for cosmetics demands for alternative methods. We developed an integrated computational solution (SkinSense) that offers a robust solution and addresses the limitations of existing computational tools i.e. high false positive rate and/or limited coverage. Results The key components of our solution include: QSAR models selected from a combinatorial set, similarity information and literature-derived sub-structure patterns of known skin protein reactive groups. Its prediction performance on a challenge set of molecules showed accuracy = 75.32%, CCR = 74.36%, sensitivity = 70.00% and specificity = 78.72%, which is better than several existing tools including VEGA (accuracy = 45.00% and CCR = 54.17% with ‘High’ reliability scoring), DEREK (accuracy = 72.73% and CCR = 71.44%) and TOPKAT (accuracy = 60.00% and CCR = 61.67%). Although, TIMES-SS showed higher predictive power (accuracy = 90.00% and CCR = 92.86%), the coverage was very low (only 10 out of 77 molecules were predicted reliably). Conclusions Owing to improved prediction performance and coverage, our solution can serve as a useful expert system towards Integrated Approaches to Testing and Assessment for skin sensitization. It would be invaluable to cosmetic/ dermatology industry for pre-screening their molecules, and reducing time, cost and animal testing.
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Neves BJ, Muratov E, Machado RB, Andrade CH, Cravo PVL. Modern approaches to accelerate discovery of new antischistosomal drugs. Expert Opin Drug Discov 2016; 11:557-67. [PMID: 27073973 PMCID: PMC6534417 DOI: 10.1080/17460441.2016.1178230] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
INTRODUCTION The almost exclusive use of only praziquantel for the treatment of schistosomiasis has raised concerns about the possible emergence of drug-resistant schistosomes. Consequently, there is an urgent need for new antischistosomal drugs. The identification of leads and the generation of high quality data are crucial steps in the early stages of schistosome drug discovery projects. AREAS COVERED Herein, the authors focus on the current developments in antischistosomal lead discovery, specifically referring to the use of automated in vitro target-based and whole-organism screens and virtual screening of chemical databases. They highlight the strengths and pitfalls of each of the above-mentioned approaches, and suggest possible roadmaps towards the integration of several strategies, which may contribute for optimizing research outputs and led to more successful and cost-effective drug discovery endeavors. EXPERT OPINION Increasing partnerships and access to funding for drug discovery have strengthened the battle against schistosomiasis in recent years. However, the authors believe this battle also includes innovative strategies to overcome scientific challenges. In this context, significant advances of in vitro screening as well as computer-aided drug discovery have contributed to increase the success rate and reduce the costs of drug discovery campaigns. Although some of these approaches were already used in current antischistosomal lead discovery pipelines, the integration of these strategies in a solid workflow should allow the production of new treatments for schistosomiasis in the near future.
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Affiliation(s)
- Bruno Junior Neves
- a LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil
| | - Eugene Muratov
- b Laboratory for Molecular Modeling, Eshelman School of Pharmacy , University of North Carolina , Chapel Hill , NC , USA
| | - Renato Beilner Machado
- c GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil
| | - Carolina Horta Andrade
- a LabMol - Laboratory for Molecular Modeling and Drug Design, Faculdade de Farmácia , Universidade Federal de Goiás , Goiânia , Brazil
| | - Pedro Vitor Lemos Cravo
- c GenoBio - Laboratory of Genomics and Biotechnology, Instituto de Patologia Tropical e Saúde Pública , Universidade Federal de Goiás , Goiânia , Brazil
- d Instituto de Higiene e Medicina Tropical , Universidade Nova de Lisboa , Lisbon , Portugal
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Tomal JH, Welch WJ, Zamar RH. Exploiting Multiple Descriptor Sets in QSAR Studies. J Chem Inf Model 2016; 56:501-9. [DOI: 10.1021/acs.jcim.5b00663] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jabed H. Tomal
- Department
of Computer and Mathematical Sciences, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada
| | - William J. Welch
- Department
of Statistics, The University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Ruben H. Zamar
- Department
of Statistics, The University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
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Classification of dermal sensitizers in pharmaceutical manufacturing. Regul Toxicol Pharmacol 2015; 72:501-5. [DOI: 10.1016/j.yrtph.2015.05.026] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2015] [Revised: 04/26/2015] [Accepted: 05/25/2015] [Indexed: 02/02/2023]
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