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Lee SB, Gupta H, Min BH, Ganesan R, Sharma SP, Won SM, Jeong JJ, Cha MG, Kwon GH, Jeong MK, Hyun JY, Eom JA, Park HJ, Yoon SJ, Lee SY, Choi MR, Kim DJ, Oh KK, Suk KT. A consortium of Hordeum vulgare and gut microbiota against non-alcoholic fatty liver disease via data-driven analysis. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2024; 52:250-260. [PMID: 38687561 DOI: 10.1080/21691401.2024.2347380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
Despite many recent studies on non-alcoholic fatty liver disease (NAFLD) therapeutics, the optimal treatment has yet to be determined. In this unfinished project, we combined secondary metabolites (SMs) from the gut microbiota (GM) and Hordeum vulgare (HV) to investigate their combinatorial effects via network pharmacology (NP). Additionally, we analyzed GM or barley - signalling pathways - targets - metabolites (GBSTMs) in combinatorial perspectives (HV, and GM). A total of 31 key targets were analysed via a protein-protein interaction (PPI) network, and JUN was identified as the uppermost target in NAFLD. On a bubble plot, we revealed that apelin signalling pathway, which had the lowest enrichment factor antagonize NAFLD. Holistically, we scrutinized GBSTM to identify key components (GM, signalling pathways, targets, and metabolites) associated with the Apelin signalling pathway. Consequently, we found that the primary GMs (Eubacterium limosum, Eggerthella sp. SDG-2, Alistipes indistinctus YIT 12060, Odoribacter laneus YIT 12061, Paraprevotella clara YIT 11840, Paraprevotella xylaniphila YIT 11841) to ameliorate NAFLD. The molecular docking test (MDT) suggested that tryptanthrin-JUN is an agonist, conversely, dihydroglycitein-HDAC5, 1,3-diphenylpropan-2-ol-NOS1, and (10[(Acetyloxy)methyl]-9-anthryl)methyl acetate-NOS2, which are antagonistic conformers in the apelin signalling pathway. Overall, these results suggest that combination therapy could be an effective strategy for treating NAFLD.
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Affiliation(s)
- Su-Been Lee
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Haripriya Gupta
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Byeong-Hyun Min
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Raja Ganesan
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Satya Priya Sharma
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Sung-Min Won
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Jin-Ju Jeong
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Min-Gi Cha
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Min-Kyo Jeong
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Ji-Ye Hyun
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Jung-A Eom
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Hee-Jin Park
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Sang-Jun Yoon
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Sang Youn Lee
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Mi-Ran Choi
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Dong Joon Kim
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Ki-Kwang Oh
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
| | - Ki-Tae Suk
- Institute for Liver and Digestive Diseases, College of Medicine, Hallym University, Chuncheon, Korea
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Chung E, Wen X, Jia X, Ciallella HL, Aleksunes LM, Zhu H. Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity. JOURNAL OF HAZARDOUS MATERIALS 2024; 471:134297. [PMID: 38677119 DOI: 10.1016/j.jhazmat.2024.134297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024]
Abstract
Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we aimed to create a hybrid model to predict hepatotoxicants. We first curated a reference dataset of 869 compounds for hepatotoxicity modeling. Then, we profiled them against PubChem for existing in vitro toxicity data. Of the 2560 resulting assays, we selected the mitochondrial membrane potential (MMP) assay, a high-throughput screening (HTS) tool that can test chemical disruptors for mitochondrial function. Machine learning was applied to develop quantitative structure-activity relationship (QSAR) models with 2536 compounds tested in the MMP assay for screening new compounds. The MMP assay results, including QSAR model outputs, yielded hepatotoxicity predictions for reference set compounds with a Correct Classification Ratio (CCR) of 0.59. The predictivity improved by including 37 structural alerts (CCR = 0.8). We validated our model by testing 37 reference set compounds in human HepG2 hepatoma cells, and reliably predicting them for hepatotoxicity (CCR = 0.79). This study introduces a novel AOP modeling strategy that combines public HTS data, computational modeling, and experimental testing to predict chemical hepatotoxicity.
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Affiliation(s)
- Elena Chung
- Department of Chemistry and Biochemistry, Rowan University, NJ, USA; Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Xia Wen
- Department of Pharmacology and Toxicology, Rutgers University, Piscataway, NJ, USA
| | - Xuelian Jia
- Department of Chemistry and Biochemistry, Rowan University, NJ, USA; Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA
| | - Heather L Ciallella
- Department of Toxicology, Cuyahoga County Medical Examiner's Office, Cleveland, OH, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Rutgers University, Piscataway, NJ, USA
| | - Hao Zhu
- Department of Chemistry and Biochemistry, Rowan University, NJ, USA; Center for Biomedical Informatics and Genomics, Tulane University, New Orleans, LA, USA.
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3
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Vlasveld M, Callegaro G, Fisher C, Eakins J, Walker P, Lok S, van Oost S, de Jong B, Pellegrino-Coppola D, Burger G, Wink S, van de Water B. The integrated stress response-related expression of CHOP due to mitochondrial toxicity is a warning sign for DILI liability. Liver Int 2024; 44:760-775. [PMID: 38217387 DOI: 10.1111/liv.15822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 11/29/2023] [Accepted: 12/07/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND AND AIMS Drug-induced liver injury (DILI) is one of the most frequent reasons for failure of drugs in clinical trials or market withdrawal. Early assessment of DILI risk remains a major challenge during drug development. Here, we present a mechanism-based weight-of-evidence approach able to identify certain candidate compounds with DILI liabilities due to mitochondrial toxicity. METHODS A total of 1587 FDA-approved drugs and 378 kinase inhibitors were screened for cellular stress response activation associated with DILI using an imaging-based HepG2 BAC-GFP reporter platform including the integrated stress response (CHOP), DNA damage response (P21) and oxidative stress response (SRXN1). RESULTS In total 389, 219 and 104 drugs were able to induce CHOP-GFP, P21-GFP and SRXN1-GFP expression at 50 μM respectively. Concentration response analysis identified 154 FDA-approved drugs as critical CHOP-GFP inducers. Based on predicted and observed (pre-)clinical DILI liabilities of these drugs, nine antimycotic drugs (e.g. butoconazole, miconazole, tioconazole) and 13 central nervous system (CNS) agents (e.g. duloxetine, fluoxetine) were selected for transcriptomic evaluation using whole-genome RNA-sequencing of primary human hepatocytes. Gene network analysis uncovered mitochondrial processes, NRF2 signalling and xenobiotic metabolism as most affected by the antimycotic drugs and CNS agents. Both the selected antimycotics and CNS agents caused impairment of mitochondrial oxygen consumption in both HepG2 and primary human hepatocytes. CONCLUSIONS Together, the results suggest that early pre-clinical screening for CHOP expression could indicate liability of mitochondrial toxicity in the context of DILI, and, therefore, could serve as an important warning signal to consider during decision-making in drug development.
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Affiliation(s)
- Matthijs Vlasveld
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Giulia Callegaro
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | | | | | | | - Samantha Lok
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Siddh van Oost
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Brechtje de Jong
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Damiano Pellegrino-Coppola
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Gerhard Burger
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Steven Wink
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
| | - Bob van de Water
- Division of Drug Discovery and Safety, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Leiden, The Netherlands
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Kaito S, Takeshita JI, Iwata M, Sasaki T, Hosaka T, Shizu R, Yoshinari K. Utility of human cytochrome P450 inhibition data in the assessment of drug-induced liver injury. Xenobiotica 2024:1-30. [PMID: 38315106 DOI: 10.1080/00498254.2024.2312505] [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: 12/13/2023] [Accepted: 01/28/2024] [Indexed: 02/07/2024]
Abstract
1. Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.2. The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.3. Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.4. These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.
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Affiliation(s)
- Shunnosuke Kaito
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Jun-Ichi Takeshita
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
- Research Institute of Science for Safety and Sustainability, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan
| | - Misaki Iwata
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Takamitsu Sasaki
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Takuomi Hosaka
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Ryota Shizu
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Kouichi Yoshinari
- School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
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5
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Souza MAD, Rodrigues LG, Rocha JE, de Freitas TS, Bandeira PN, Marinho MM, Nunes da Rocha M, Marinho ES, Honorato Barreto AC, Coutinho HDM, Silva LMA, Julião MSDS, Marques Canuto K, Marques da Fonseca A, Teixeira AMR, Dos Santos HS. Synthesis, structural, characterization, antibacterial and antibiotic modifying activity, ADMET study, molecular docking and dynamics of chalcone ( E)-1-(4-aminophenyl)-3-(4-nitrophenyl)prop-2-en-1-one in strains of Staphylococcus aureus carrying NorA and MepA efflux pumps. J Biomol Struct Dyn 2024; 42:1670-1691. [PMID: 37222682 DOI: 10.1080/07391102.2023.2213777] [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: 11/15/2022] [Accepted: 04/05/2023] [Indexed: 05/25/2023]
Abstract
Chalcones have an open chain flavonoid structure that can be obtained from natural sources or by synthesis and are widely distributed in fruits, vegetables, and tea. They have a simple and easy to handle structure due to the α-β-unsaturated bridge responsible for most biological activities. The facility to synthesize chalcones combined with its efficient in combating serious bacterial infections make these compounds important agents in the fight against microorganisms. In this work, the chalcone (E)-1-(4-aminophenyl)-3-(4-nitrophenyl)prop-2-en-1-one (HDZPNB) was characterized by spectroscopy and electronic methods. In addition, microbiological tests were performed to investigate the modulator potential and efflux pump inhibition on S. aureus multi-resistant strains. The modulating effect of HDZPNB chalcone in association with the antibiotic norfloxacin, on the resistance of the S. aureus 1199 strain, resulted in increase the MIC. In addition, when HDZPNB was associated with ethidium bromide (EB), it caused an increase in the MIC value, thus not inhibiting the efflux pump. For the strain of S. aureus 1199B, carrying the NorA pump, the HDZPNB associated with norfloxacin showed no modulatory, and when the chalcone was used in association with EB, it had no inhibitory effect on the efflux pump. For the tested strain of S. aureus K2068, which carries the MepA pump, it can be observed that the chalcone together the antibiotic resulted in an increase the MIC. On the other hand, when chalcone was used in association with EB, it caused a decrease in bromide MIC, equal to the reduction caused by standard inhibitors. Thus, these results indicate that the HDZPNB could also act as an inhibitor of the S. aureus gene overexpressing pump MepA. The molecular docking reveals that chalcone has a good binding energies -7.9 for HDZPNB/MepA complexes, molecular dynamics simulations showed that Chalcone/MetA complexes showed good stability of the structure in an aqueous solution, and ADMET study showed that the chalcone has a good oral bioavailability, high passive permeability, low risk of efflux, low clearance rate and low toxic risk by ingestion. The microbiological tests show that the chalcone can be used as a possible inhibitor of the Mep A efflux pump.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Mikael Amaro de Souza
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | - Leilane Gomes Rodrigues
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
| | - Janaina Esmeraldo Rocha
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | - Thiago Sampaio de Freitas
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | - Paulo Nogueira Bandeira
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
| | - Márcia Machado Marinho
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
| | | | | | | | - Henrique Douglas Melo Coutinho
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
| | | | - Murilo Sergio da Silva Julião
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
- Graduate Program in Natural Science, State University of Ceará, Fortaleza, CE, Brazil
| | - Kirley Marques Canuto
- Multiusuary Laboratory of Natural Products Chemistry, Embrapa Tropical Agroindustry, Fortaleza, CE, Brazil
| | - Aluísio Marques da Fonseca
- Academic Master's Degree in Sociobiodiversity and Sustainable Technologies - MASTS, Institute of Engineering and Development Sustainable, University of International Integration of Afro-Brazilian Lusofonia, Acarape, CE, Brazil
| | - Alexandre Magno Rodrigues Teixeira
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
- Graduate Program in Natural Science, State University of Ceará, Fortaleza, CE, Brazil
| | - Hélcio Silva Dos Santos
- Graduate Program in Biological Chemistry, Department of Biological Chemistry, Regional University of Cariri, Crato, CE, Brazil
- Science and Technology Centre, Course of Chemistry, State University Vale do Acaraú, Sobral, CE, Brazil
- Graduate Program in Natural Science, State University of Ceará, Fortaleza, CE, Brazil
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Yang Q, Zhang S, Li Y. Deep Learning Algorithm Based on Molecular Fingerprint for Prediction of Drug-Induced Liver Injury. Toxicology 2024; 502:153736. [PMID: 38307192 DOI: 10.1016/j.tox.2024.153736] [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: 11/23/2023] [Revised: 01/02/2024] [Accepted: 01/23/2024] [Indexed: 02/04/2024]
Abstract
Drug-induced liver injury (DILI) is one the rare adverse drug reaction (ADR) and multifactorial endpoints. Current preclinical animal models struggle to anticipate it, and in silico methods have emerged as a way with significant potential for doing so. In this study, a high-quality dataset of 1573 compounds was assembled. The 48 classification models, which depended on six different molecular fingerprints, were built via deep neural network (DNN) and seven machine learning algorithms. Comparing the results of the DNN and machine learning models, the optional performing model was found as the one developed based on the DNN with ECFP_6 as input, which achieved the area under the receiver operating characteristic curve (AUC) of 0.713, balanced accuracy (BA) of 0.680, and F1 of 0.753. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the models, identified the crucial structural fragments related to DILI risk, and selected the top ten substructures with the highest contribution rankings to serve as warning indicators for subsequent drug hepatotoxicity screening studies. The study demonstrates that the DNN models developed based on molecular fingerprints can be a trustworthy and efficient tool for determining the risk of DILI during the pre-development of novel medications.
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Affiliation(s)
- Qiong Yang
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, Dalian, Liaoning 116024, China
| | - Shuwei Zhang
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, Dalian, Liaoning 116024, China.
| | - Yan Li
- State Key Laboratory of Fine Chemicals, Dalian University of Technology, Dalian, Liaoning 116024, China.
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7
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Avdović EH, Milanović Ž, Simijonović D, Antonijević M, Milutinović M, Nikodijević D, Filipović N, Marković Z, Vojinović R. An Effective, Green Synthesis Procedure for Obtaining Coumarin-Hydroxybenzohydrazide Derivatives and Assessment of Their Antioxidant Activity and Redox Status. Antioxidants (Basel) 2023; 12:2070. [PMID: 38136190 PMCID: PMC10740980 DOI: 10.3390/antiox12122070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 11/21/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
In this study, green synthesis of two derivatives of coumarin-hydroxybenzohydrazide, (E)-2,4-dioxo-3-(1-(2-(2,3,4-trihydroxybenzoyl)hydrazyl)ethylidene)-chroman-7-yl acetate (C-HB1), and (E)-2,4-dioxo-3-(1-(2-(3,4,5-trihydroxybenzoyl)hydrazyl)ethylidene)chroman-7-yl acetate (C-HB2) is reported. Using vinegar and ethanol as a catalyst and solvent, the reactions were carried out between 3-acetyl-4-hydroxy-coumarin acetate and corresponding trihydroxybenzoyl hydrazide. The antioxidant potential of these compounds was investigated using the DPPH and ABTS assays, as well as the FRAP test. The obtained results reveal that even at very low concentrations, these compounds show excellent radical scavenging potential. The IC50 values for C-HB1 and C-HB2 in relation to the DPPH radical are 6.4 and 2.5 μM, respectively, while they are 4.5 and 2.0 μM in relation to the ABTS radical. These compounds have antioxidant activity that is comparable to well-known antioxidants such as gallic acid, NDGA, and trolox. These results are in good correlation with theoretical parameters describing these reactions. Moreover, it was found that inhibition of DPPH● follows HAT, while inactivation of ABTS+● follows SET-PT and HAT mechanisms. Additionally, coumarin-hydroxybenzohydrazide derivatives induced moderate cytotoxic activity and show significant potential to modulate redox status in HCT-116 colorectal cancer cells. The cytotoxicity was achieved via their prooxidative activity and ability to induce oxidative stress in cancer cells by increasing O2˙- concentrations, indicated by increased MDA and GSH levels. Thus, ROS manipulation can be a potential target for cancer therapies by coumarins, as cancer cells possess an altered redox balance in comparison to normal cells. According to the ADMET analysis, the compounds investigated show good pharmacokinetic and toxicological profiles similar to vitamin C and gallic acid, which makes them good candidates for application in various fields of industry and medicine.
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Affiliation(s)
- Edina H. Avdović
- Department of Science, Institute for Information Technologies, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac, Serbia; (Ž.M.); (D.S.); (M.A.); (Z.M.)
| | - Žiko Milanović
- Department of Science, Institute for Information Technologies, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac, Serbia; (Ž.M.); (D.S.); (M.A.); (Z.M.)
| | - Dušica Simijonović
- Department of Science, Institute for Information Technologies, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac, Serbia; (Ž.M.); (D.S.); (M.A.); (Z.M.)
| | - Marko Antonijević
- Department of Science, Institute for Information Technologies, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac, Serbia; (Ž.M.); (D.S.); (M.A.); (Z.M.)
| | - Milena Milutinović
- Department of Biology and Ecology, Faculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, Serbia; (M.M.); (D.N.)
| | - Danijela Nikodijević
- Department of Biology and Ecology, Faculty of Science, University of Kragujevac, Radoja Domanovića 12, 34000 Kragujevac, Serbia; (M.M.); (D.N.)
| | - Nenad Filipović
- Faculty of Engineering, University of Kragujevac, Sestre Janjic 6, 34000 Kragujevac, Serbia
| | - Zoran Marković
- Department of Science, Institute for Information Technologies, University of Kragujevac, Jovana Cvijića bb, 34000 Kragujevac, Serbia; (Ž.M.); (D.S.); (M.A.); (Z.M.)
- Department of Natural Science and Mathematics, State University of Novi Pazar, 36300 Novi Pazar, Serbia
| | - Radiša Vojinović
- Faculty of Medical Sciences, University of Kragujevac, Svetozara Markovića 69, 34000 Kragujevac, Serbia;
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8
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Oh KK, Gupta H, Ganesan R, Sharma SP, Won SM, Jeong JJ, Lee SB, Cha MG, Kwon GH, Jeong MK, Min BH, Hyun JY, Eom JA, Park HJ, Yoon SJ, Choi MR, Kim DJ, Suk KT. The seamless integration of dietary plant-derived natural flavonoids and gut microbiota may ameliorate non-alcoholic fatty liver disease: a network pharmacology analysis. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:217-232. [PMID: 37129458 DOI: 10.1080/21691401.2023.2203734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
We comprised metabolites of gut microbiota (GM; endogenous species) and dietary plant-derived natural flavonoids (DPDNFs; exogenous species) were known as potent effectors against non-alcoholic fatty liver disease (NAFLD) via network pharmacology (NP). The crucial targets against NAFLD were identified via GM and DPDNFs. The protein interaction (PPI), bubble chart and networks of GM or natural products- metabolites-targets-key signalling (GNMTK) pathway were described via R Package. Furthermore, the molecular docking test (MDT) to verify the affinity was performed between metabolite(s) and target(s) on a key signalling pathway. On the networks of GNMTK, Enterococcus sp. 45, Escherichia sp.12, Escherichia sp.33 and Bacterium MRG-PMF-1 as key microbiota; flavonoid-rich products as key natural resources; luteolin and myricetin as key metabolites (or dietary flavonoids); AKT Serine/Threonine Kinase 1 (AKT1), CF Transmembrane conductance Regulator (CFTR) and PhosphoInositide-3-Kinase, Regulatory subunit 1 (PIK3R1) as key targets are promising components to treat NAFLD, by suppressing cyclic Adenosine MonoPhosphate (cAMP) signalling pathway. This study shows that components (microbiota, metabolites, targets and a key signalling pathway) and DPDNFs can exert combinatorial pharmacological effects against NAFLD. Overall, the integrated pharmacological approach sheds light on the relationships between GM and DPDNFs.
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Affiliation(s)
- Ki-Kwang Oh
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Haripriya Gupta
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Raja Ganesan
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Satya Priya Sharma
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Sung-Min Won
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Jin-Ju Jeong
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Su-Been Lee
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Min-Gi Cha
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Min-Kyo Jeong
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Byeong-Hyun Min
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Ji-Ye Hyun
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Jung-A Eom
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Hee-Jin Park
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Sang-Jun Yoon
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Mi-Ran Choi
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Dong Joon Kim
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
| | - Ki-Tae Suk
- Center for Microbiome, Institute for Liver and Digestive Diseases, Hallym University Medical Center, Chuncheon, Korea
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9
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Oh KK, Choi I, Gupta H, Raja G, Sharma SP, Won SM, Jeong JJ, Lee SB, Cha MG, Kwon GH, Jeong MK, Min BH, Hyun JY, Eom JA, Park HJ, Yoon SJ, Choi MR, Kim DJ, Suk KT. New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study. ARTIFICIAL CELLS, NANOMEDICINE, AND BIOTECHNOLOGY 2023; 51:1-12. [PMID: 36562095 DOI: 10.1080/21691401.2022.2155661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.
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Affiliation(s)
- Ki-Kwang Oh
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ickwon Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Haripriya Gupta
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ganesan Raja
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Satya Priya Sharma
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sung-Min Won
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jin-Ju Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Su-Been Lee
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Gi Cha
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Goo-Hyun Kwon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Min-Kyo Jeong
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Byeong-Hyun Min
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ji-Ye Hyun
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Jung-A Eom
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Hee-Jin Park
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Sang-Jun Yoon
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Mi-Ran Choi
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Dong Joon Kim
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
| | - Ki-Tae Suk
- Hallym University College of Medicine, Institute for Liver and Digestive Diseases, Chuncheon, Korea
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10
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Wu W, Qian J, Liang C, Yang J, Ge G, Zhou Q, Guan X. GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation. Chem Res Toxicol 2023; 36:1717-1730. [PMID: 37839069 DOI: 10.1021/acs.chemrestox.3c00199] [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: 10/17/2023]
Abstract
Drug-induced liver injury (DILI) is a significant cause of drug failure and withdrawal due to liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug development. Several DILI prediction models have been published, but they are built on different data sets, making it difficult to compare model performance. Moreover, most existing models are based on molecular fingerprints or descriptors, neglecting molecular geometric properties and lacking interpretability. To address these limitations, we developed GeoDILI, an interpretable graph neural network that uses a molecular geometric representation. First, we utilized a geometry-based pretrained molecular representation and optimized it on the DILI data set to improve predictive performance. Second, we leveraged gradient information to obtain high-precision atomic-level weights and deduce the dominant substructure. We benchmarked GeoDILI against recently published DILI prediction models, as well as popular GNN models and fingerprint-based machine learning models using the same data set, showing superior predictive performance of our proposed model. We applied the interpretable method in the DILI data set and derived seven precise and mechanistically elucidated structural alerts. Overall, GeoDILI provides a promising approach for accurate and interpretable DILI prediction with potential applications in drug discovery and safety assessment. The data and source code are available at GitHub repository (https://github.com/CSU-QJY/GeoDILI).
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Affiliation(s)
- Wenxuan Wu
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jiayu Qian
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Changjie Liang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Jingya Yang
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Guangbo Ge
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Qingping Zhou
- School of Mathematics and Statistics, Central South University, Changsha, Hunan 410083, China
| | - Xiaoqing Guan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
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11
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Lejal V, Cerisier N, Rouquié D, Taboureau O. Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis. Chem Res Toxicol 2023; 36:1456-1470. [PMID: 37652439 PMCID: PMC10523580 DOI: 10.1021/acs.chemrestox.2c00381] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Indexed: 09/02/2023]
Abstract
Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.
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Affiliation(s)
- Vanille Lejal
- Université
Paris Cité, Inserm U1133, CNRS
UMR 8251, 75013, Paris, France
| | - Natacha Cerisier
- Université
Paris Cité, Inserm U1133, CNRS
UMR 8251, 75013, Paris, France
| | - David Rouquié
- Bayer
SAS, Bayer Crop Science, 355 rue Dostoïevski, CS 90153, 06906 Valbonne, Sophia-Antipolis, France
- Université
Côte d’Azur 3IA Interdisciplinary Institute in Artificial Intelligence, 06103 Nice Cedex, France
| | - Olivier Taboureau
- Université
Paris Cité, Inserm U1133, CNRS
UMR 8251, 75013, Paris, France
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12
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Shin HK, Huang R, Chen M. In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review. Food Chem Toxicol 2023; 179:113948. [PMID: 37460037 PMCID: PMC10640386 DOI: 10.1016/j.fct.2023.113948] [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] [Received: 05/19/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 07/25/2023]
Abstract
New approach methods (NAMs) have been developed to predict a wide range of toxicities through innovative technologies. Liver injury is one of the most extensively studied endpoints due to its severity and frequency, occurring among populations that consume drugs or dietary supplements. In this review, we focus on recent developments of in silico modeling for liver injury prediction using deep learning and in vitro data based on adverse outcome pathways (AOPs). Despite these models being mainly developed using datasets generated from drug-like molecules, they were also applied to the prediction of hepatotoxicity caused by herbal products. As deep learning has achieved great success in many different fields, advanced machine learning algorithms have been actively applied to improve the accuracy of in silico models. Additionally, the development of liver AOPs, combined with big data in toxicology, has been valuable in developing in silico models with enhanced predictive performance and interpretability. Specifically, one approach involves developing structure-based models for predicting molecular initiating events of liver AOPs, while others use in vitro data with structure information as model inputs for making predictions. Even though liver injury remains a difficult endpoint to predict, advancements in machine learning algorithms and the expansion of in vitro databases with relevant biological knowledge have made a huge impact on improving in silico modeling for drug-induced liver injury prediction.
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Affiliation(s)
- Hyun Kil Shin
- Department of Predictive Toxicology, Korea Institute of Toxicology (KIT), 34114, Daejeon, Republic of Korea
| | - Ruili Huang
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Rockville, MD, 20850, USA.
| | - Minjun Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR, 72079, USA.
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13
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Russo D, Aleksunes LM, Goyak K, Qian H, Zhu H. Integrating Concentration-Dependent Toxicity Data and Toxicokinetics To Inform Hepatotoxicity Response Pathways. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:12291-12301. [PMID: 37566783 PMCID: PMC10448720 DOI: 10.1021/acs.est.3c02792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/30/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023]
Abstract
Failure of animal models to predict hepatotoxicity in humans has created a push to develop biological pathway-based alternatives, such as those that use in vitro assays. Public screening programs (e.g., ToxCast/Tox21 programs) have tested thousands of chemicals using in vitro high-throughput screening (HTS) assays. Developing pathway-based models for simple biological pathways, such as endocrine disruption, has proven successful, but development remains a challenge for complex toxicities like hepatotoxicity, due to the many biological events involved. To this goal, we aimed to develop a computational strategy for developing pathway-based models for complex toxicities. Using a database of 2171 chemicals with human hepatotoxicity classifications, we identified 157 out of 1600+ ToxCast/Tox21 HTS assays to be associated with human hepatotoxicity. Then, a computational framework was used to group these assays by biological target or mechanisms into 52 key event (KE) models of hepatotoxicity. KE model output is a KE score summarizing chemical potency against a hepatotoxicity-relevant biological target or mechanism. Grouping hepatotoxic chemicals based on the chemical structure revealed chemical classes with high KE scores plausibly informing their hepatotoxicity mechanisms. Using KE scores and supervised learning to predict in vivo hepatotoxicity, including toxicokinetic information, improved the predictive performance. This new approach can be a universal computational toxicology strategy for various chemical toxicity evaluations.
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Affiliation(s)
- Daniel
P. Russo
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
| | - Lauren M. Aleksunes
- Department
of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Katy Goyak
- ExxonMobil
Biomedical Sciences, Inc., Annandale, New Jersey 08801, United States
| | - Hua Qian
- ExxonMobil
Biomedical Sciences, Inc., Annandale, New Jersey 08801, United States
| | - Hao Zhu
- Department
of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States
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14
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Wu S, Daston G, Rose J, Blackburn K, Fisher J, Reis A, Selman B, Naciff J. Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across. Curr Res Toxicol 2023; 5:100108. [PMID: 37363741 PMCID: PMC10285556 DOI: 10.1016/j.crtox.2023.100108] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 06/28/2023] Open
Abstract
The liver is the most common target organ in toxicology studies. The development of chemical structural alerts for identifying hepatotoxicity will play an important role in in silico model prediction and help strengthen the identification of analogs used in structure activity relationship (SAR)- based read-across. The aim of the current study is development of an SAR-based expert-system decision tree for screening of hepatotoxicants across a wide range of chemistry space and proposed modes of action for clustering of chemicals using defined core chemical categories based on receptor-binding or bioactivation. The decision tree is based on ∼ 1180 different chemicals that were reviewed for hepatotoxicity information. Knowledge of chemical receptor binding, metabolism and mechanistic information were used to group these chemicals into 16 different categories and 102 subcategories: four categories describe binders to 9 different receptors, 11 categories are associated with possible reactive metabolites (RMs) and there is one miscellaneous category. Each chemical subcategory has been associated with possible modes of action (MOAs) or similar key structural features. This decision tree can help to screen potential liver toxicants associated with core structural alerts of receptor binding and/or RMs and be used as a component of weight of evidence decisions based on SAR read-across, and to fill data gaps.
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15
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Azimi S, Mohammadi B, Babadoust S, Vessally E. In Silico study and design of some new potent threonine tyrosine kinase inhibitors using molecular docking simulation. MOLECULAR SIMULATION 2023. [DOI: 10.1080/08927022.2023.2172192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Saeid Azimi
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
| | - Bagher Mohammadi
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
| | - Shahram Babadoust
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
| | - Esmail Vessally
- Department of Chemistry, Payame Noor University, P. O. BOX 19395-3697, Tehran, Iran
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16
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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: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [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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17
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Artificial neural networks in contemporary toxicology research. Chem Biol Interact 2023; 369:110269. [PMID: 36402212 DOI: 10.1016/j.cbi.2022.110269] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/04/2022] [Accepted: 11/14/2022] [Indexed: 11/18/2022]
Abstract
Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be used to predict toxicity of various chemical compounds or classify the compounds based on their toxic effects. Today, numerous ANN models have been developed, some of which may be used to detect and possibly explain complex chemico-biological interactions. Fully connected multilayer perceptrons may in some circumstances have high classification accuracy and discriminatory power in separating damaged from intact cells after exposure to a toxic substance. Regularized and not fully connected convolutional neural networks can detect and identify discrete changes in patterns of two-dimensional data associated with toxicity. Bayesian neural networks with weight marginalization sometimes may have better prediction performance when compared to traditional approaches. With the further development of artificial intelligence, it is expected that ANNs will in the future become important parts of various accurate and affordable biosensors for detection of various toxic substances and evaluation of their biochemical properties. In this concise review article, we discuss the recent research focused on the scientific value of ANNs in evaluation and prediction of toxicity of chemical compounds.
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18
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Lim S, Kim Y, Gu J, Lee S, Shin W, Kim S. Supervised chemical graph mining improves drug-induced liver injury prediction. iScience 2022; 26:105677. [PMID: 36654861 PMCID: PMC9840932 DOI: 10.1016/j.isci.2022.105677] [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: 09/08/2022] [Revised: 11/11/2022] [Accepted: 11/23/2022] [Indexed: 12/27/2022] Open
Abstract
Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs' ATC code.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Gwanak-ro 1, Seoul 08826, South Korea
| | - Youngkuk Kim
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Seoul 08826, South Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Seoul 08826, South Korea
| | - Sunho Lee
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Seoul 08826, South Korea
| | - Wonseok Shin
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Seoul 08826, South Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National University, Gwanak-ro 1, Seoul 08826, South Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Gwanak-ro 1, Seoul 08826, South Korea
- AIGENDRUG Co., Ltd., Gwanak-ro 1, Seoul 08826, South Korea
- Corresponding author
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19
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Basnet S, Marahatha R, Shrestha A, Bhattarai S, Katuwal S, Sharma KR, Marasini BP, Dahal SR, Basnyat RC, Patching SG, Parajuli N. In Vitro and In Silico Studies for the Identification of Potent Metabolites of Some High-Altitude Medicinal Plants from Nepal Inhibiting SARS-CoV-2 Spike Protein. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27248957. [PMID: 36558090 PMCID: PMC9786757 DOI: 10.3390/molecules27248957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/01/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Despite ongoing vaccination programs against COVID-19 around the world, cases of infection are still rising with new variants. This infers that an effective antiviral drug against COVID-19 is crucial along with vaccinations to decrease cases. A potential target of such antivirals could be the membrane components of the causative pathogen, SARS-CoV-2, for instance spike (S) protein. In our research, we have deployed in vitro screening of crude extracts of seven ethnomedicinal plants against the spike receptor-binding domain (S1-RBD) of SARS-CoV-2 using an enzyme-linked immunosorbent assay (ELISA). Following encouraging in vitro results for Tinospora cordifolia, in silico studies were conducted for the 14 reported antiviral secondary metabolites isolated from T. cordifolia-a species widely cultivated and used as an antiviral drug in the Himalayan country of Nepal-using Genetic Optimization for Ligand Docking (GOLD), Molecular Operating Environment (MOE), and BIOVIA Discovery Studio. The molecular docking and binding energy study revealed that cordifolioside-A had a higher binding affinity and was the most effective in binding to the competitive site of the spike protein. Molecular dynamics (MD) simulation studies using GROMACS 5.4.1 further assayed the interaction between the potent compound and binding sites of the spike protein. It revealed that cordifolioside-A demonstrated better binding affinity and stability, and resulted in a conformational change in S1-RBD, hence hindering the activities of the protein. In addition, ADMET analysis of the secondary metabolites from T. cordifolia revealed promising pharmacokinetic properties. Our study thus recommends that certain secondary metabolites of T. cordifolia are possible medicinal candidates against SARS-CoV-2.
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Affiliation(s)
- Saroj Basnet
- Center for Drug Design and Molecular Simulation Division, Kathmandu 44600, Nepal
| | - Rishab Marahatha
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
- Department of Chemistry, Oklahoma State University, Still Water, OK 74078, USA
| | - Asmita Shrestha
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | - Salyan Bhattarai
- Paraza Pharma, Inc., 2525 Avenue Marie-Curie, Montreal, QC H4S 2E1, Canada
| | - Saurav Katuwal
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | - Khaga Raj Sharma
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | | | - Salik Ram Dahal
- Department of Chemistry, Oklahoma State University, Still Water, OK 74078, USA
- Oakridge National Laboratory, Bethel Valley Rd, Oak Ridge, TN 37830, USA
| | - Ram Chandra Basnyat
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
| | - Simon G. Patching
- Independent Researcher, Leeds LS2 9JT, UK
- Correspondence: (S.G.P.); (N.P.)
| | - Niranjan Parajuli
- Central Department of Chemistry, Tribhuvan University, Kathmandu 44618, Nepal
- Correspondence: (S.G.P.); (N.P.)
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20
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Shah I, Bundy J, Chambers B, Everett LJ, Haggard D, Harrill J, Judson RS, Nyffeler J, Patlewicz G. Navigating Transcriptomic Connectivity Mapping Workflows to Link Chemicals with Bioactivities. Chem Res Toxicol 2022; 35:1929-1949. [PMID: 36301716 PMCID: PMC10483698 DOI: 10.1021/acs.chemrestox.2c00245] [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] [Indexed: 01/09/2023]
Abstract
Screening new compounds for potential bioactivities against cellular targets is vital for drug discovery and chemical safety. Transcriptomics offers an efficient approach for assessing global gene expression changes, but interpreting chemical mechanisms from these data is often challenging. Connectivity mapping is a potential data-driven avenue for linking chemicals to mechanisms based on the observation that many biological processes are associated with unique gene expression signatures (gene signatures). However, mining the effects of a chemical on gene signatures for biological mechanisms is challenging because transcriptomic data contain thousands of noisy genes. New connectivity mapping approaches seeking to distinguish signal from noise continue to be developed, spurred by the promise of discovering chemical mechanisms, new drugs, and disease targets from burgeoning transcriptomic data. Here, we analyze these approaches in terms of diverse transcriptomic technologies, public databases, gene signatures, pattern-matching algorithms, and statistical evaluation criteria. To navigate the complexity of connectivity mapping, we propose a harmonized scheme to coherently organize and compare published workflows. We first standardize concepts underlying transcriptomic profiles and gene signatures based on various transcriptomic technologies such as microarrays, RNA-Seq, and L1000 and discuss the widely used data sources such as Gene Expression Omnibus, ArrayExpress, and MSigDB. Next, we generalize connectivity mapping as a pattern-matching task for finding similarity between a query (e.g., transcriptomic profile for new chemical) and a reference (e.g., gene signature of known target). Published pattern-matching approaches fall into two main categories: vector-based use metrics like correlation, Jaccard index, etc., and aggregation-based use parametric and nonparametric statistics (e.g., gene set enrichment analysis). The statistical methods for evaluating the performance of different approaches are described, along with comparisons reported in the literature on benchmark transcriptomic data sets. Lastly, we review connectivity mapping applications in toxicology and offer guidance on evaluating chemical-induced toxicity with concentration-response transcriptomic data. In addition to serving as a high-level guide and tutorial for understanding and implementing connectivity mapping workflows, we hope this review will stimulate new algorithms for evaluating chemical safety and drug discovery using transcriptomic data.
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Affiliation(s)
- Imran Shah
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joseph Bundy
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Bryant Chambers
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Logan J. Everett
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Derik Haggard
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Joshua Harrill
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Richard S. Judson
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
| | - Johanna Nyffeler
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
- Oak Ridge Institute for Science and Education (ORISE) Postdoctoral Fellow, Oak Ridge, Tennessee, 37831, US
| | - Grace Patlewicz
- Center for Computational Toxicology and Exposure, Office of Research and Development, US. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA
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21
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de Sá AGC, Long Y, Portelli S, Pires DEV, Ascher DB. toxCSM: comprehensive prediction of small molecule toxicity profiles. Brief Bioinform 2022; 23:6673851. [PMID: 35998885 DOI: 10.1093/bib/bbac337] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/17/2022] [Accepted: 07/23/2022] [Indexed: 01/29/2023] Open
Abstract
Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.
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Affiliation(s)
- Alex G C de Sá
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Yangyang Long
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Stephanie Portelli
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, 3052, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, 3010, Australia
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22
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Hong G, Li W, Mao L, Wang J, Liu T. Synthesis and antibacterial activity evaluation of N (7) position-modified balofloxacins. Front Chem 2022; 10:963442. [PMID: 36059868 PMCID: PMC9437215 DOI: 10.3389/fchem.2022.963442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
A series of small-molecule fluoroquinolones were synthesized, characterized by HRMS and NMR spectroscopy, and screened for their antibacterial activity against MRSA, P. aeruginosa, and E. coli as model G+/G− pathogens. Compounds 2-e, 3-e, and 4-e were more potent than the reference drug balofloxacin against MRSA and P. aeruginosa (MIC values of 0.0195 and 0.039 μg/ml for 2-e, 0.039 and 0.078 μg/ml for each of 3-e and 4-e, respectively). Analysis of the time-dependent antibacterial effect of compound 2-e toward MRSA showed that in the early logarithmic growth phase, bactericidal effects occurred, while in the late logarithmic growth phase, bacterial inhibition occurred because of concentration effects and possibly the development of drug resistance. Compound 2-e exhibited low toxicity toward normal mammalian cell lines 3T3 and L-02 and tumor cell lines A549, H520, BEL-7402, and MCF-7. The compound was not hemolytic. Atomic force microscopy (AFM) revealed that compound 2-e could effectively destroy the membrane and wall of MRSA cells, resulting in the outflow of the cellular contents. Docking studies indicated the good binding profile of these compounds toward DNA gyrase and topoisomerase IV. ADMET’s prediction showed that most of the synthesized compounds followed Lipinski’s “rule of five” and possessed good drug-like properties. Our data suggested that compound 2-e exhibited potent anti-MRSA activity and is worthy of further investigation.
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23
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Jia X, Wen X, Russo DP, Aleksunes LM, Zhu H. Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay. JOURNAL OF HAZARDOUS MATERIALS 2022; 436:129193. [PMID: 35739723 PMCID: PMC9262097 DOI: 10.1016/j.jhazmat.2022.129193] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 05/13/2022] [Accepted: 05/17/2022] [Indexed: 05/20/2023]
Abstract
Traditional experimental approaches to evaluate hepatotoxicity are expensive and time-consuming. As an advanced framework of risk assessment, adverse outcome pathways (AOPs) describe the sequence of molecular and cellular events underlying chemical toxicities. We aimed to develop an AOP that can be used to predict hepatotoxicity by leveraging computational modeling and in vitro assays. We curated 869 compounds with known hepatotoxicity classifications as a modeling set and extracted assay data from PubChem. The antioxidant response element (ARE) assay, which quantifies transcriptional responses to oxidative stress, showed a high correlation to hepatotoxicity (PPV=0.82). Next, we developed quantitative structure-activity relationship (QSAR) models to predict ARE activation for compounds lacking testing results. Potential toxicity alerts were identified and used to construct a mechanistic hepatotoxicity model. For experimental validation, 16 compounds in the modeling set and 12 new compounds were selected and tested using an in-house ARE-luciferase assay in HepG2-C8 cells. The mechanistic model showed good hepatotoxicity predictivity (accuracy = 0.82) for these compounds. Potential false positive hepatotoxicity predictions by only using ARE results can be corrected by incorporating structural alerts and vice versa. This mechanistic model illustrates a potential toxicity pathway for hepatotoxicity, and this strategy can be expanded to develop predictive models for other complex toxicities.
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Affiliation(s)
- Xuelian Jia
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Xia Wen
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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24
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Silva J, Esmeraldo Rocha J, da Cunha Xavier J, Sampaio de Freitas T, Douglas Melo Coutinho H, Nogueira Bandeira P, Rodrigues de Oliveira M, Nunes da Rocha M, Machado Marinho E, de Kassio Vieira Monteiro N, Ribeiro LR, Róseo Paula Pessoa Bezerra de Menezes R, Machado Marinho M, Magno Rodrigues Teixeira A, Silva dos Santos H, Silva Marinho E. Antibacterial and antibiotic modifying activity of chalcone (2E)-1-(4′-aminophenyl)-3-(4-methoxyphenyl)-prop-2-en-1-one in strains of Staphylococcus aureus carrying NorA and MepA efflux pumps: In vitro and in silico approaches. Microb Pathog 2022; 169:105664. [DOI: 10.1016/j.micpath.2022.105664] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 06/19/2022] [Accepted: 06/28/2022] [Indexed: 01/11/2023]
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25
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Chen Z, Jiang Y, Zhang X, Zheng R, Qiu R, Sun Y, Zhao C, Shang H. The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications. Brief Bioinform 2022; 23:6598880. [PMID: 35656709 DOI: 10.1093/bib/bbac196] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/12/2022] [Accepted: 04/27/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
In the previous study, we developed the generalized drug-induced liver injury (DILI) prediction model—ResNet18DNN to predict DILI based on multi-source combined DILI dataset and achieved better performance than that of previously published described DILI prediction models. Recently, we were honored to receive the invitation from the editor to response the Letter to Editor by Liu Zhichao, et al. We were glad that our research has attracted the attention of Liu’s team and they has put forward their opinions on our research. In this response to Letter to the Editor, we will respond to these comments.
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Affiliation(s)
- Zhao Chen
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yin Jiang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Xiaoyu Zhang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Ruijin Qiu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
| | - Chen Zhao
- Institute of Basic Research in Clinical Medicine , China Academy of Chinese Medical Sciences, Beijing, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education , Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China
- College of Integrated Traditional Chinese and Western Medicine , Hunan University of Chinese Medicine, Changsha, Hunan 410208 , China
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26
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A novel dual three and five-component reactions between dimedone, aryl aldehydes, and 1-naphthylamine: synthesis and computational studies. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.132569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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27
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An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning. Molecules 2022; 27:molecules27103112. [PMID: 35630587 PMCID: PMC9147181 DOI: 10.3390/molecules27103112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022] Open
Abstract
In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.
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28
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Ellison C, Hewitt M, Przybylak K. In Silico Models for Hepatotoxicity. Methods Mol Biol 2022; 2425:355-392. [PMID: 35188639 DOI: 10.1007/978-1-0716-1960-5_14] [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] [Indexed: 06/14/2023]
Abstract
In this chapter, we review the state of the art of predicting human hepatotoxicity using in silico techniques. There has been significant progress in this area over the past 20 years but there are still some challenges ahead. Principally, these challenges are our partial understanding of a very complex biochemical system and our ability to emulate that in a predictive capacity. Here, we provide an overview of the published modeling approaches in this area to date and discuss their design, strengths and weaknesses. It is interesting to note the diversity in modeling approaches, whether they be statistical algorithms or evidenced-based approaches including structural alerts and pharmacophore models. Irrespective of modeling approach, it appears a common theme of access to appropriate, relevant, and high-quality data is a limitation to all and is likely to continue to be the focus of future research.
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Affiliation(s)
- Claire Ellison
- Human and Natural Sciences Directorate, School of Science, Engineering and Environment, University of Salford, Manchester, UK
| | - Mark Hewitt
- School of Pharmacy, Faculty of Science and Engineering, University of Wolverhampton, Wolverhampton, UK.
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29
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Joint Decision-Making Model Based on Consensus Modeling Technology for the Prediction of Drug-Induced Liver Injury. J CHEM-NY 2021. [DOI: 10.1155/2021/2293871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Drug-induced liver injury (DILI) is the major cause of clinical trial failure and postmarketing withdrawals of approved drugs. It is very expensive and time-consuming to evaluate hepatotoxicity using animal or cell-based experiments in the early stage of drug development. In this study, an in silico model based on the joint decision-making strategy was developed for DILI assessment using a relatively large dataset of 2608 compounds. Five consensus models were developed with PaDEL descriptors and PubChem, Substructure, Estate, and Klekota–Roth fingerprints, respectively. Submodels for each consensus model were obtained through joint optimization. The parameters and features of each submodel were optimized jointly based on the hybrid quantum particle swarm optimization (HQPSO) algorithm. The application domain (AD) based on the frequency-weighted and distance (FWD)-based method and Tanimoto similarity index showed the wide AD of the qualified consensus models. A joint decision-making model was integrated by the qualified consensus models, and the overwhelming majority principle was used to improve the performance of consensus models. The application scope narrowing caused by the overwhelming majority principle was successfully solved by joint decision-making. The proposed model successfully predicted 99.2% of the compounds in the test set, with an accuracy of 80.0%, a sensitivity of 83.9, and a specificity of 73.3%. For an external validation set containing 390 compounds collected from DILIrank, 98.2% of the compounds were successfully predicted with an accuracy of 79.9%, a sensitivity of 97.1%, and a specificity of 66.0%. Furthermore, 25 privileged substructures responsible for DILI were identified from Substructure, PubChem, and Klekota–Roth fingerprints. These privileged substructures can be regarded as structural alerts in hepatotoxicity evaluation. Compared with the main published studies, our method exhibits certain advantage in data size, transparency, and standardization of the modeling process and accuracy and credibility of prediction results. It is a promising tool for virtual screening in the early stage of drug development.
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30
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Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space. Molecules 2021; 26:molecules26247548. [PMID: 34946636 PMCID: PMC8707960 DOI: 10.3390/molecules26247548] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/09/2021] [Accepted: 12/09/2021] [Indexed: 01/22/2023] Open
Abstract
Drug-induced liver injury (DILI) is a major concern for drug developers, regulators, and clinicians. However, there is no adequate model system to assess drug-associated DILI risk in humans. In the big data era, computational models are expected to play a revolutionary role in this field. This study aimed to develop a deep neural network (DNN)-based model using extended connectivity fingerprints of diameter 4 (ECFP4) to predict DILI risk. Each data set for the predictive model was retrieved and curated from DILIrank, LiverTox, and other literature. The best model was constructed through ten iterations of stratified 10-fold cross-validation, and the applicability domain was defined based on integer ECFP4 bits of the training set which represented substructures. For the robustness test, we employed the concept of the endurance level. The best model showed an accuracy of 0.731, a sensitivity of 0.714, and a specificity of 0.750 on the validation data set in the complete applicability domain. The model was further evaluated with four external data sets and attained an accuracy of 0.867 on 15 drugs with DILI cases reported since 2019. Overall, the results suggested that the ECFP4-based DNN model represents a new tool to identify DILI risk for the evaluation of drug safety.
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31
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Chen Z, Jiang Y, Zhang X, Zheng R, Qiu R, Sun Y, Zhao C, Shang H. ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. Brief Bioinform 2021; 23:6457162. [PMID: 34882224 DOI: 10.1093/bib/bbab503] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/27/2021] [Accepted: 11/02/2021] [Indexed: 01/22/2023] Open
Abstract
Drug-induced liver injury (DILI) has always been the focus of clinicians and drug researchers. How to improve the performance of the DILI prediction model to accurately predict liver injury was an urgent problem for researchers in the field of medical research. In order to solve this scientific problem, this research collected a comprehensive and accurate dataset of DILI with high recognition and high quality based on clinically confirmed DILI compound datasets, including 1446 chemical compounds. Then, the residual neural network with 18-layer by using more 5-layer blocks (ResNet18) with deep neural network (ResNet18DNN) model was proposed to predict DILI, which was an improved model for DILI prediction through vectorization of compound structure image. In predicting DILI, the ResNet18DNN learned greatly and outperformed the existing state-of-the-art DILI predictors. The results of DILI prediction model based on ResNet18DNN showed that the AUC (area under the curve), accuracy, recall, precision, F1-score and specificity of the training set were 0.973, 0.992, 0.995, 0.994, 0.995 and 0.975; those of test set were, respectively, 0.958, 0.976, 0.935, 0.947, 0.926 and 0.913, which were better than the performance of previously published described DILI prediction models. This method adopted ResNet18 embedding method to vectorize molecular structure images and the evaluation indicators of Resnet18DNN were obtained after 10 000 iterations. This prediction approach will greatly improve the performance of the predictive model of DILI and provide an accurate and precise early warning method for DILI in drug development and clinical medication.
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Affiliation(s)
- Zhao Chen
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yin Jiang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Xiaoyu Zhang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Rui Zheng
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Ruijin Qiu
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Yang Sun
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China
| | - Chen Zhao
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongcai Shang
- Key Laboratory of Chinese Internal Medicine of Ministry of Education, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.,College of Integrated Traditional Chinese and Western Medicine, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China
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32
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Bassan A, Alves VM, Amberg A, Anger LT, Auerbach S, Beilke L, Bender A, Cronin MT, Cross KP, Hsieh JH, Greene N, Kemper R, Kim MT, Mumtaz M, Noeske T, Pavan M, Pletz J, Russo DP, Sabnis Y, Schaefer M, Szabo DT, Valentin JP, Wichard J, Williams D, Woolley D, Zwickl C, Myatt GJ. In silico approaches in organ toxicity hazard assessment: current status and future needs in predicting liver toxicity. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2021; 20:100187. [PMID: 35340402 PMCID: PMC8955833 DOI: 10.1016/j.comtox.2021.100187] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
Hepatotoxicity is one of the most frequently observed adverse effects resulting from exposure to a xenobiotic. For example, in pharmaceutical research and development it is one of the major reasons for drug withdrawals, clinical failures, and discontinuation of drug candidates. The development of faster and cheaper methods to assess hepatotoxicity that are both more sustainable and more informative is critically needed. The biological mechanisms and processes underpinning hepatotoxicity are summarized and experimental approaches to support the prediction of hepatotoxicity are described, including toxicokinetic considerations. The paper describes the increasingly important role of in silico approaches and highlights challenges to the adoption of these methods including the lack of a commonly agreed upon protocol for performing such an assessment and the need for in silico solutions that take dose into consideration. A proposed framework for the integration of in silico and experimental information is provided along with a case study describing how computational methods have been used to successfully respond to a regulatory question concerning non-genotoxic impurities in chemically synthesized pharmaceuticals.
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Affiliation(s)
- Arianna Bassan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Vinicius M. Alves
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Alexander Amberg
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | - Scott Auerbach
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Lisa Beilke
- Toxicology Solutions Inc., San Diego, CA, USA
| | - Andreas Bender
- AI and Data Analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW
| | - Mark T.D. Cronin
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | | | - Jui-Hua Hsieh
- The National Institute of Environmental Health Sciences, Division of the National Toxicology, Program, Research Triangle Park, NC 27709, USA
| | - Nigel Greene
- Data Science and AI, DSM, IMED Biotech Unit, AstraZeneca, Boston, USA
| | - Raymond Kemper
- Nuvalent, One Broadway, 14th floor, Cambridge, MA, 02142, USA
| | - Marlene T. Kim
- US Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD, 20993, USA
| | - Moiz Mumtaz
- Office of the Associate Director for Science (OADS), Agency for Toxic Substances and Disease, Registry, US Department of Health and Human Services, Atlanta, GA, USA
| | - Tobias Noeske
- Imaging and Data Analytics, Clinical Pharmacology & Safety Sciences, R&D, AstraZeneca, Gothenburg, Sweden
| | - Manuela Pavan
- Innovatune srl, Via Giulio Zanon 130/D, 35129 Padova, Italy
| | - Julia Pletz
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK
| | - Daniel P. Russo
- Department of Chemistry, Rutgers University, Camden, NJ 08102, USA
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Yogesh Sabnis
- UCB Biopharma SRL, Chemin du Foriest – B-1420 Braine-l’Alleud, Belgium
| | - Markus Schaefer
- Sanofi, R&D Preclinical Safety Frankfurt, Industriepark Hoechst, D-65926 Frankfurt am Main, Germany
| | | | | | - Joerg Wichard
- Bayer AG, Genetic Toxicology, Müllerstr. 178, 13353 Berlin, Germany
| | - Dominic Williams
- Functional & Mechanistic Safety, Clinical Pharmacology & Safety Sciences, AstraZeneca, Darwin Building 310, Cambridge Science Park, Milton Rd, Cambridge CB4 0FZ, UK
| | - David Woolley
- ForthTox Limited, PO Box 13550, Linlithgow, EH49 7YU, UK
| | - Craig Zwickl
- Transendix LLC, 1407 Moores Manor, Indianapolis, IN 46229, USA
| | - Glenn J. Myatt
- Instem, 1393 Dublin Road, Columbus, OH 43215. USA
- Corresponding author. (G.J. Myatt)
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33
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Ogunyemi OM, Gyebi GA, Ibrahim IM, Olaiya CO, Ocheje JO, Fabusiwa MM, Adebayo JO. Dietary stigmastane-type saponins as promising dual-target directed inhibitors of SARS-CoV-2 proteases: a structure-based screening. RSC Adv 2021; 11:33380-33398. [PMID: 35497510 PMCID: PMC9042289 DOI: 10.1039/d1ra05976a] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 10/01/2021] [Indexed: 12/15/2022] Open
Abstract
Despite the development of COVID-19 vaccines, at present, there is still no approved antiviral drug against the pandemic. The SARS-CoV-2 3-chymotrypsin-like proteases (S-3CLpro) and papain-like protease (S-PLpro) are essential for the viral proliferation cycle, hence attractive drug targets. Plant-based dietary components that have been extensively reported for antiviral activities may serve as cheap sources of preventive nutraceuticals and/or antiviral drugs. A custom-made library of 176 phytochemicals from five West African antiviral culinary herbs was screened for potential dual-target-directed inhibitors of S-3CLpro and S-PLpro in silico. The docking analysis revealed fifteen steroidal saponins (FSS) from Vernonia amygdalina with the highest binding tendency for the active sites of S-3CLpro and S-PLpro. In an optimized docking analysis, the FSS were further docked against four equilibrated conformers of the S-3CLpro and S-PLpro. Three stigmastane-type steroidal saponins (vernonioside A2, vernonioside A4 and vernonioside D2) were revealed as the lead compounds. These compounds interacted with the catalytic residues of both S-3CLpro and S-PLpro, thereby exhibiting dual inhibitory potential against these SARS-CoV-2 cysteine proteases. The binding free energy calculations further corroborated the static and optimized docking analysis. The complexed proteases with these promising phytochemicals were stable during a full atomistic MD simulation while the phytochemicals exhibited favourable physicochemical and ADMET properties, hence, recommended as promising inhibitors of SARS-CoV-2 cysteine proteases.
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Affiliation(s)
- Oludare M Ogunyemi
- Human Nutraceuticals and Bioinformatics Research Unit, Department of Biochemistry, Salem University Lokoja Nigeria
- Nutritional and Industrial Biochemistry Unit, Department of Biochemistry, University of Ibadan Nigeria
| | - Gideon A Gyebi
- Department of Biochemistry, Faculty of Science and Technology, Bingham University P.M.B 005, Karu Nasarawa Nigeria +234-7063983652
| | - Ibrahim M Ibrahim
- Department of Biophysics, Faculty of Sciences, Cairo University Giza Egypt
| | - Charles O Olaiya
- Nutritional and Industrial Biochemistry Unit, Department of Biochemistry, University of Ibadan Nigeria
| | - Joshua O Ocheje
- Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University Akwa Nigeria
| | - Modupe M Fabusiwa
- Human Nutraceuticals and Bioinformatics Research Unit, Department of Biochemistry, Salem University Lokoja Nigeria
| | - Joseph O Adebayo
- Department of Biochemistry, Faculty of Life Sciences, University of Ilorin Ilorin Nigeria
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Jaganathan K, Tayara H, Chong KT. Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets. Int J Mol Sci 2021; 22:8073. [PMID: 34360838 PMCID: PMC8348336 DOI: 10.3390/ijms22158073] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/18/2021] [Accepted: 07/23/2021] [Indexed: 02/05/2023] Open
Abstract
Drug-induced liver toxicity is one of the significant safety challenges for the patient's health and the pharmaceutical industry. It causes termination of drug candidates in clinical trials and also the retractions of approved drugs from the market. Thus, it is essential to identify hepatotoxic compounds in the initial stages of drug development process. The purpose of this study is to construct quantitative structure activity relationship models using machine learning algorithms and systematical feature selection methods for molecular descriptor sets. The models were built from a large and diverse set of 1253 drug compounds and were validated internally with 10-fold cross-validation. In this study, we applied a variety of feature selection techniques to extract the optimal subset of descriptors as modeling features to improve the prediction performance. Experimental results suggested that the support vector machine-based classifier had achieved a better classification accuracy with reduced molecular descriptors. The final optimal model provides an accuracy of 0.811, a sensitivity of 0.840, a specificity of 0.783 and Mathew's correlation coefficient of 0.623 with an internal validation set. Furthermore, this model outperformed the prior studies while evaluated in both the internal and external test sets. The utilization of distinct optimal molecular descriptors as modeling features produce an in silico model with a superior performance.
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Affiliation(s)
- Keerthana Jaganathan
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Korea;
- Advanced Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Korea
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35
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Kang W, Podtelezhnikov AA, Tanis KQ, Pacchione S, Su M, Bleicher KB, Wang Z, Laws GM, Griffiths TG, Kuhls MC, Chen Q, Knemeyer I, Marsh DJ, Mitra K, Lebron J, Sistare FD. Development and Application of a Transcriptomic Signature of Bioactivation in an Advanced In Vitro Liver Model to Reduce Drug-induced Liver Injury Risk Early in the Pharmaceutical Pipeline. Toxicol Sci 2021; 177:121-139. [PMID: 32559289 DOI: 10.1093/toxsci/kfaa094] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
Early risk assessment of drug-induced liver injury (DILI) potential for drug candidates remains a major challenge for pharmaceutical development. We have previously developed a set of rat liver transcriptional biomarkers in short-term toxicity studies to inform the potential of drug candidates to generate a high burden of chemically reactive metabolites that presents higher risk for human DILI. Here, we describe translation of those NRF1-/NRF2-mediated liver tissue biomarkers to an in vitro assay using an advanced micropatterned coculture system (HEPATOPAC) with primary hepatocytes from male Wistar Han rats. A 9-day, resource-sparing and higher throughput approach designed to identify new chemical entities with lower reactive metabolite-forming potential was qualified for internal decision making using 93 DILI-positive and -negative drugs. This assay provides 81% sensitivity and 90% specificity in detecting hepatotoxicants when a positive test outcome is defined as the bioactivation signature score of a test drug exceeding the threshold value at an in vitro test concentration that falls within 3-fold of the estimated maximum drug concentration at the human liver inlet following highest recommended clinical dose administrations. Using paired examples of compounds from distinct chemical series and close structural analogs, we demonstrate that this assay can differentiate drugs with lower DILI risk. The utility of this in vitro transcriptomic approach was also examined using human HEPATOPAC from a single donor, yielding 68% sensitivity and 86% specificity when the aforementioned criteria are applied to the same 93-drug test set. Routine use of the rat model has been adopted with deployment of the human model as warranted on a case-by-case basis. This in vitro transcriptomic signature-based strategy can be used early in drug discovery to derisk DILI potential from chemically reactive metabolites by guiding structure-activity relationship hypotheses and candidate selection.
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Affiliation(s)
- Wen Kang
- Safety Assessment & Laboratory Animal Resources
| | | | | | | | - Ming Su
- Safety Assessment & Laboratory Animal Resources
| | | | - Zhibin Wang
- Safety Assessment & Laboratory Animal Resources
| | | | | | | | - Qing Chen
- Pharmacokinetics, Pharmacodynamics & Drug Metabolism, Merck & Co., Inc., West Point, Pennsylvania 19486
| | - Ian Knemeyer
- Pharmacokinetics, Pharmacodynamics & Drug Metabolism, Merck & Co., Inc., West Point, Pennsylvania 19486
| | | | | | - Jose Lebron
- Safety Assessment & Laboratory Animal Resources
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36
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Tosca EM, Bartolucci R, Magni P, Poggesi I. Modeling approaches for reducing safety-related attrition in drug discovery and development: a review on myelotoxicity, immunotoxicity, cardiovascular toxicity, and liver toxicity. Expert Opin Drug Discov 2021; 16:1365-1390. [PMID: 34181496 DOI: 10.1080/17460441.2021.1931114] [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] [Indexed: 12/29/2022]
Abstract
Introduction:Safety and tolerability is a critical area where improvements are needed to decrease the attrition rates during development of new drug candidates. Modeling approaches, when smartly implemented, can contribute to this aim.Areas covered:The focus of this review was on modeling approaches applied to four kinds of drug-induced toxicities: hematological, immunological, cardiovascular (CV) and liver toxicity. Papers, mainly published in the last 10 years, reporting models in three main methodological categories - computational models (e.g., quantitative structure-property relationships, machine learning approaches, neural networks, etc.), pharmacokinetic-pharmacodynamic (PK-PD) models, and quantitative system pharmacology (QSP) models - have been considered.Expert opinion:The picture observed in the four examined toxicity areas appears heterogeneous. Computational models are typically used in all areas as screening tools in the early stages of development for hematological, cardiovascular and liver toxicity, with accuracies in the range of 70-90%. A limited number of computational models, based on the analysis of drug protein sequence, was instead proposed for immunotoxicity. In the later stages of development, toxicities are quantitatively predicted with reasonably good accuracy using either semi-mechanistic PK-PD models (hematological and cardiovascular toxicity), or fully exploited QSP models (immuno-toxicity and liver toxicity).
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Affiliation(s)
- Elena M Tosca
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Roberta Bartolucci
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Paolo Magni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Italo Poggesi
- Clinical Pharmacology & Pharmacometrics, Janssen Research & Development, Beerse, Belgium
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Zhao L, Russo DP, Wang W, Aleksunes LM, Zhu H. Mechanism-Driven Read-Across of Chemical Hepatotoxicants Based on Chemical Structures and Biological Data. Toxicol Sci 2021; 174:178-188. [PMID: 32073637 DOI: 10.1093/toxsci/kfaa005] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Hepatotoxicity is a leading cause of attrition in the drug development process. Traditional preclinical and clinical studies to evaluate hepatotoxicity liabilities are expensive and time consuming. With the advent of critical advancements in high-throughput screening, there has been a rapid accumulation of in vitro toxicity data available to inform the risk assessment of new pharmaceuticals and chemicals. To this end, we curated and merged all available in vivo hepatotoxicity data obtained from the literature and public resources, which yielded a comprehensive database of 4089 compounds that includes hepatotoxicity classifications. After dividing the original database of chemicals into modeling and test sets, PubChem assay data were automatically extracted using an in-house data mining tool and clustered based on relationships between structural fragments and cellular responses in in vitro assays. The resultant PubChem assay clusters were further investigated. During the cross-validation procedure, the biological data obtained from several assay clusters exhibited high predictivity of hepatotoxicity and these assays were selected to evaluate the test set compounds. The read-across results indicated that if a new compound contained specific identified chemical fragments (ie, Molecular Initiating Event) and showed active responses in the relevant selected PubChem assays, there was potential for the chemical to be hepatotoxic in vivo. Furthermore, several mechanisms that might contribute to toxicity were derived from the modeling results including alterations in nuclear receptor signaling and inhibition of DNA repair. This modeling strategy can be further applied to the investigation of other complex chemical toxicity phenomena (eg, developmental and reproductive toxicities) as well as drug efficacy.
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Affiliation(s)
- Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey
| | - Daniel P Russo
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey
| | - Wenyi Wang
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey.,Department of Chemistry, Rutgers University, Camden, New Jersey
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38
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Jeon J, Kang S, Kim HU. Predicting biochemical and physiological effects of natural products from molecular structures using machine learning. Nat Prod Rep 2021; 38:1954-1966. [PMID: 34047331 DOI: 10.1039/d1np00016k] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.
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Affiliation(s)
- Junhyeok Jeon
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Seongmo Kang
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. and KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea and BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
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39
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Béquignon OJ, Pawar G, van de Water B, Cronin MT, van Westen GJ. Computational Approaches for Drug-Induced Liver Injury (DILI) Prediction: State of the Art and Challenges. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11535-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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40
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Gallic acid: Pharmacological activities and molecular mechanisms involved in inflammation-related diseases. Biomed Pharmacother 2021; 133:110985. [DOI: 10.1016/j.biopha.2020.110985] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 11/01/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
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41
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Schmidt F. Computational Toxicology. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11534-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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42
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Rathman J, Yang C, Ribeiro JV, Mostrag A, Thakkar S, Tong W, Hobocienski B, Sacher O, Magdziarz T, Bienfait B. Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment. Chem Res Toxicol 2020; 34:601-615. [PMID: 33356149 DOI: 10.1021/acs.chemrestox.0c00423] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Drug-induced liver injury (DILI) remains a challenge when translating knowledge from the preclinical stage to human use cases. Attempts to model human DILI directly based on the information from drug labels have had some success; however, the approach falls short of providing insights or addressing uncertainty due to the difficulty of decoupling the idiosyncratic nature of human DILI outcomes. Our approach in this comparative analysis is to leverage existing preclinical and clinical data as well as information on metabolism to better translate mammalian to human DILI. The human DILI knowledge base from the United States Food and Drug Administration (U.S. FDA) National Center for Toxicology Research contains 1036 pharmaceuticals from diverse therapeutic categories. A human DILI training set of 305 oral marketed drugs was prepared and a binary classification scheme applied. The second knowledge base consists of mammalian repeated dose toxicity with liver toxicity data from various regulatory sources. Within this knowledge base, we identified 278 pharmaceuticals containing 198 marketed or withdrawn oral drugs with data from the U.S. FDA new drug application and 98 active pharmaceutical ingredients from ToxCast. From this collection, a set of 225 oral drugs was prepared as the mammalian hepatotoxicity training set with particular end points of pathology findings in the liver and bile duct. Both human and mammalian data sets were processed using various learning algorithms, including artificial intelligence approaches. The external validations for both models were comparable to the training statistics. These data sets were also used to extract species-differentiating chemotypes that differentiate DILI effects on humans from mammals. A systematic workflow was devised to predict human DILI and provide mechanistic insights. For a given query molecule, both human and mammalian models are run. If the predictions are discordant, both metabolites and parents are investigated for quantitative structure-activity relationship and species-differentiating chemotypes. Their results are combined using the Dempster-Shafer decision theory to yield a final outcome prediction for human DILI with estimated uncertainty. Finally, these tools are implementable within an in silico platform for systematic evaluation.
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Affiliation(s)
- James Rathman
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany.,Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Chihae Yang
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - J Vinicius Ribeiro
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Aleksandra Mostrag
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Shraddha Thakkar
- National Center for Toxicology Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Weida Tong
- National Center for Toxicology Research, United States Food and Drug Administration, Jefferson, Arkansas 72079, United States
| | - Bryan Hobocienski
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Oliver Sacher
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Tomasz Magdziarz
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
| | - Bruno Bienfait
- Molecular Networks GmbH - Computerchemie (MN-AM), 90411 Nurnberg, Germany
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43
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Li T, Tong W, Roberts R, Liu Z, Thakkar S. Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury. Front Bioeng Biotechnol 2020; 8:562677. [PMID: 33330410 PMCID: PMC7728858 DOI: 10.3389/fbioe.2020.562677] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 11/05/2020] [Indexed: 12/14/2022] Open
Abstract
Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.
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Affiliation(s)
- Ting Li
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.,Joint Bioinformatics Program, University of Arkansas at Little Rock and University of Arkansas for Medical Sciences, Little Rock, AR, United States
| | - Weida Tong
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Ruth Roberts
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States.,ApconiX Ltd., Alderley Edge, United Kingdom.,Department of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Zhichao Liu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
| | - Shraddha Thakkar
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, United States
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44
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Synthesis, Molecular Docking, Druglikeness Analysis, and ADMET Prediction of the Chlorinated Ethanoanthracene Derivatives as Possible Antidepressant Agents. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217727] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Ethanoanthracene cycloadducts (5–7) anti, (5–7) syn, and (5–7) dec have been synthesized from the Diels–Alder (DA) reaction of diene 1,8-dichloroanthracene 2, with the dienophiles; acrylonitrile 3, 1-cynavinyl acetate 4, and phenyl vinyl sulfone 5, individually. The steric effect of dienophile substituents were more favorable toward the anti-isomer formation as deduced from 1H-NMR spectrum. The cheminformatics prediction for (5–7) anti and (5–7) syn was investigated. The in silico anticipated anti-depression activity of the (5–7) anti and (5–7) syn compounds were investigated and compared to maprotiline 9 as reference anti-depressant drug. The study showed that steric interactions play a crucial role in the binding affinity of these compounds to the representative models; 4xnx, 2QJU, and 3GWU. The pharmacokinetic and drug-like properties of (5–7) anti and (5–7) syn exhibited that these compounds could be represented as potential candidates for further development into antidepressant-like agents.
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45
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Tugcu G, Kırmızıbekmez H, Aydın A. The integrated use of in silico methods for the hepatotoxicity potential of Piper methysticum. Food Chem Toxicol 2020; 145:111663. [PMID: 32827561 DOI: 10.1016/j.fct.2020.111663] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 06/27/2020] [Accepted: 07/30/2020] [Indexed: 02/07/2023]
Abstract
Herbal products as supplements and therapeutic intervention have been used for centuries. However, their toxicities are not completely evaluated and the mechanisms are not clearly understood. Dried rhizome of the plant kava (Piper methysticum) is used for its anxiolytic, and sedative effects. The drug is also known for its hepatotoxicity potential. Major constituents of the plant were identified as kavalactones, alkaloids and chalcones in previous studies. Kava hepatotoxicity mechanism and the constituent that causes the toxicity have been debated for decades. In this paper, we illustrated the use of computational tools for the hepatotoxicity of kava constituents. The proposed mechanisms and major constituents that are most probably responsible for the toxicity have been scrutinized. According to the experimental and prediction results, the kava constituents play a substantial role in hepatotoxicity by some means or other via glutathione depletion, CYP inhibition, reactive metabolite formation, mitochondrial toxicity and cyclooxygenase activity. Some of the constituents, which have not been tested yet, were predicted to involve mitochondrial membrane potential, caspase-3 stimulation, and AhR activity. Since Nrf2 activation could be favorable for prevention of hepatotoxicity, we also suggest that these compounds should undergo testing given that they were predicted not to be activating Nrf2. Among the major constituents, alkaloids appear to be the least studied and the least toxic group in general. The outcomes of the study could help to appreciate the mechanisms and to prioritize the kava constituents for further testing.
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Affiliation(s)
- Gulcin Tugcu
- Yeditepe University, Faculty of Pharmacy, Department of Toxicology, 34755, Atasehir, Istanbul, Turkey
| | - Hasan Kırmızıbekmez
- Yeditepe University, Faculty of Pharmacy, Department of Pharmacognosy, 34755, Atasehir, Istanbul, Turkey
| | - Ahmet Aydın
- Yeditepe University, Faculty of Pharmacy, Department of Toxicology, 34755, Atasehir, Istanbul, Turkey.
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46
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Mora JR, Marrero-Ponce Y, García-Jacas CR, Suarez Causado A. Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches. Chem Res Toxicol 2020; 33:1855-1873. [PMID: 32406679 DOI: 10.1021/acs.chemrestox.0c00030] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Drug-induced liver injury (DILI) is a key safety issue in the drug discovery pipeline and a regulatory concern. Thus, many in silico tools have been proposed to improve the hepatotoxicity prediction of organic-type chemicals. Here, classifiers for the prediction of DILI were developed by using QuBiLS-MAS 0-2.5D molecular descriptors and shallow machine learning techniques, on a training set composed of 1075 molecules. The best ensemble model build, E13, was obtained with good statistical parameters for the learning series, namely, the following: accuracy = 0.840, sensibility = 0.890, specificity = 0.761, Matthew's correlation coefficient = 0.660, and area under the ROC curve = 0.904. The model was also satisfactorily evaluated with Y-scrambling test, and repeated k-fold cross-validation and repeated k-holdout validation. In addition, an exhaustive external validation was also carried out by using two test sets and five external test sets, with an average accuracy value equal to 0.854 (±0.062) and a coverage equal to 98.4% according to its applicability domain. A statistical comparison of the performance of the E13 model, with regard to results and tools (e.g., Padel DDPredictor Software, Deep Learning DILIserver, and Vslead) reported in the literature, was also performed. In general, E13 presented the best global performance in all experiments. The sum of the ranking differences procedure provided a very similar grouping pattern to that of the M-ANOVA statistical analysis, where E13 was identified as the best model for DILI predictions. A noncommercial and fully cross-platform software for the DILI prediction was also developed, which is freely available at http://tomocomd.com/apps/ptoxra. This software was used for the screening of seven data sets, containing natural products, leads, toxic materials, and FDA approved drugs, to assess the usefulness of the QSAR models in the DILI labeling of organic substances; it was found that 50-92% of the evaluated molecules are positive-DILI compounds. All in all, it can be stated that the E13 model is a relevant method for the prediction of DILI risk in humans, as it shows the best results among all of the methods analyzed.
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Affiliation(s)
- Jose R Mora
- Grupo de Química Computacional y Teórica (QCT-USFQ), Departamento de Ingeniería Química, Universidad San Francisco de Quito (USFQ), Quito 17-1200-841, Ecuador.,Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y Vía Interoceánica, Quito 17-1200-841, Ecuador
| | - Yovani Marrero-Ponce
- Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y Vía Interoceánica, Quito 17-1200-841, Ecuador.,Grupo de Medicina Molecular y Traslacional (MeM&T), Colegio de Ciencias de la Salud (COCSA), Escuela de Medicina, Edificio de Especialidades Médicas, and Instituto de Simulación Computacional (ISC-USFQ), Universidad San Francisco de Quito (USFQ), Diego de Robles y vía Interoceánica, Quito, Pichincha 170157, Ecuador
| | - César R García-Jacas
- Cátedras Conacyt-Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Ensenada, Baja California 22860, México
| | - Amileth Suarez Causado
- Grupo de Investigación Prometeus & Biomedicina Aplicada a las Ciencias Clínicas, Área de Bioquímica, Campus de Zaragocilla, Facultad de Medicina, Universidad de Cartagena, Cartagena de Indias 130001, Colombia
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Jin IS, Yoon MS, Park CW, Hong JT, Chung YB, Kim JS, Shin DH. Replacement techniques to reduce animal experiments in drug and nanoparticle development. JOURNAL OF PHARMACEUTICAL INVESTIGATION 2020. [DOI: 10.1007/s40005-020-00487-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Yamagata Y, Yamada H, Horii I. Current status and future perspective of computational toxicology in drug safety assessment under ontological intellection. J Toxicol Sci 2020; 44:721-735. [PMID: 31708530 DOI: 10.2131/jts.44.721] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
For the safety assessment of pharmaceuticals, initial data management requires accurate toxicological data acquisition, which is based on regulatory safety studies according to guidelines, and computational systems have been developed under the application of Good Laboratory Practice (GLP). In addition to these regulatory toxicology studies, investigative toxicological study data for the selection of lead compound and candidate compound for clinical trials are directed to estimation by computational systems such as Quantitative Structure-Activity Relationship (QSAR) and related expert systems. Furthermore, in the "Go" or "No-Go" decision of drug development, supportive utilization of a scientifically interpretable computational toxicology system is required for human safety evaluation. A pharmaceutical safety evaluator as a related toxicologist who is facing practical decision needs not only a data-driven Artificial Intelligence (AI) system that calls for the final consequence but also an explainable AI that can provide comprehensive information necessary for evaluation and can help with decision making. Through the explication and suggestion of information on the mechanism of toxic effects to safety assessment scientists, a subsidiary partnership system for risk assessment is ultimately to be a powerful tool that can indicate project-vector with data weight for the corresponding counterparts. To bridge the gaps between big data and knowledge, multi-dimensional thinking based on philosophical ontology theory is necessary for handling heterogeneous data for integration. In this review, we will explain the current state and future perspective of computational toxicology related to drug safety assessment from the viewpoint of ontology thinking.
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Affiliation(s)
- Yuki Yamagata
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Health and Nutrition
| | - Hiroshi Yamada
- Toxicogenomics Informatics Project, National Institute of Biomedical Innovation, Health and Nutrition
| | - Ikuo Horii
- Global Drug Safety Research & Development, Pfizer.,Faculty of Pharmaceutical Science, Tokyo University of Science
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Abstract
The drugs we use to cure our diseases can cause damage to the liver as it is the primary organ responsible for metabolism of environmental chemicals and drugs. To identify and eliminate potentially problematic drug candidates in the early stages of drug discovery, in silico techniques provide quick and practical solutions for toxicity determination. Deep learning has emerged as one of the solutions in recent years in the field of pharmaceutical chemistry. Generally, in the case of small data sets as used in toxicology, these data-hungry algorithms are prone to overfitting. We approach the problem from two sides. First, we use images of the three-dimensional conformations and benefit from convolutional neural networks which have fewer parameters than the standard deep neural networks with similar depth. Using images allows connecting various chemical features to the geometry of the compounds. Second, we employ the method COVER to up-sample the data set. It is used not only for increasing the size of the data set, but also for balancing the two classes, i.e., toxic and not toxic. The proof of concept is performed on the p53 end point from the Tox21 data set. The results, which are compatible with the winners of the data challenge, encouraged us to use our methods to predict liver toxicity. We use the most extensive publicly available liver toxicity data set by Mulliner et al. and obtain a sensitivity of 0.79 and a specificity of 0.52. These results demonstrate the applicability of image based toxicity prediction using deep neural networks.
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Affiliation(s)
- Ece Asilar
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, A-1090 Vienna, Austria
| | - Jennifer Hemmerich
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, A-1090 Vienna, Austria
| | - Gerhard F Ecker
- Department of Pharmaceutical Chemistry, University of Vienna, Althanstrasse 14, A-1090 Vienna, Austria
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50
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Sinha M, Dhawan A, Parthasarathi R. In Silico Approaches in Predictive Genetic Toxicology. Methods Mol Biol 2019; 2031:351-373. [PMID: 31473971 DOI: 10.1007/978-1-4939-9646-9_20] [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] [Indexed: 04/05/2023]
Abstract
Genetic toxicology testing is a weight-of-evidence approach to identify and characterize chemical substances that can cause genetic modifications in somatic and/or germ cells. Prediction of genetic toxicology using computational tools is gaining more attention and preferred by regulatory authorities as an alternate safety assessment for in vivo or in vitro approaches. Due to the cost and time associated with experimental genetic toxicity tests, it is essential to develop more robust in silico methods to predict chemical genetic toxicity. A number of in silico genotoxicity predictive tools/models are developed based on the experimental data gathered over the years. These in silico tools are divided into statistical quantitative structure-activity relationships (QSAR)-based approaches and expert-based systems. This chapter covers the state of the art in silico toxicology approaches and standardized protocols, essential for conducting genetic toxicity predictions of chemicals. This chapter also highlights various parameters for the validation of the prediction results obtained from QSAR models.
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Affiliation(s)
- Meetali Sinha
- Computational Toxicology Facility, Academy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India
| | - Alok Dhawan
- Nanomaterials Toxicology Group, CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India
| | - Ramakrishnan Parthasarathi
- Computational Toxicology Facility, Academy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Toxicology Research, Lucknow, Uttar Pradesh, India.
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