1
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Rosa LS, Argolo CO, Nascimento CM, Pimentel AS. Identifying Substructures That Facilitate Compounds to Penetrate the Blood-Brain Barrier via Passive Transport Using Machine Learning Explainer Models. ACS Chem Neurosci 2024; 15:2144-2159. [PMID: 38723285 PMCID: PMC11157485 DOI: 10.1021/acschemneuro.3c00840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 06/06/2024] Open
Abstract
The local interpretable model-agnostic explanation (LIME) method was used to interpret two machine learning models of compounds penetrating the blood-brain barrier. The classification models, Random Forest, ExtraTrees, and Deep Residual Network, were trained and validated using the blood-brain barrier penetration dataset, which shows the penetrability of compounds in the blood-brain barrier. LIME was able to create explanations for such penetrability, highlighting the most important substructures of molecules that affect drug penetration in the barrier. The simple and intuitive outputs prove the applicability of this explainable model to interpreting the permeability of compounds across the blood-brain barrier in terms of molecular features. LIME explanations were filtered with a weight equal to or greater than 0.1 to obtain only the most relevant explanations. The results showed several structures that are important for blood-brain barrier penetration. In general, it was found that some compounds with nitrogenous substructures are more likely to permeate the blood-brain barrier. The application of these structural explanations may help the pharmaceutical industry and potential drug synthesis research groups to synthesize active molecules more rationally.
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Affiliation(s)
- Lucca
Caiaffa Santos Rosa
- Departamento de Química, Pontifícia Universidade Católica do
Rio de Janeiro, Rio de
Janeiro, RJ 22453-900, Brazil
| | - Caio Oliveira Argolo
- Departamento de Química, Pontifícia Universidade Católica do
Rio de Janeiro, Rio de
Janeiro, RJ 22453-900, Brazil
| | | | - Andre Silva Pimentel
- Departamento de Química, Pontifícia Universidade Católica do
Rio de Janeiro, Rio de
Janeiro, RJ 22453-900, Brazil
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2
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Shkil DO, Muhamedzhanova AA, Petrov PI, Skorb EV, Aliev TA, Steshin IS, Tumanov AV, Kislinskiy AS, Fedorov MV. Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation. Molecules 2024; 29:1826. [PMID: 38675645 PMCID: PMC11055041 DOI: 10.3390/molecules29081826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 04/11/2024] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
In the realm of predictive toxicology for small molecules, the applicability domain of QSAR models is often limited by the coverage of the chemical space in the training set. Consequently, classical models fail to provide reliable predictions for wide classes of molecules. However, the emergence of innovative data collection methods such as intensive hackathons have promise to quickly expand the available chemical space for model construction. Combined with algorithmic refinement methods, these tools can address the challenges of toxicity prediction, enhancing both the robustness and applicability of the corresponding models. This study aimed to investigate the roles of gradient boosting and strategic data aggregation in enhancing the predictivity ability of models for the toxicity of small organic molecules. We focused on evaluating the impact of incorporating fragment features and expanding the chemical space, facilitated by a comprehensive dataset procured in an open hackathon. We used gradient boosting techniques, accounting for critical features such as the structural fragments or functional groups often associated with manifestations of toxicity.
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Affiliation(s)
- Dmitrii O. Shkil
- Syntelly LLC, Moscow 121205, Russia; (A.A.M.); (I.S.S.); (A.V.T.); (A.S.K.)
- Moscow Institute of Physics and Technology, Moscow 141700, Russia
| | | | | | - Ekaterina V. Skorb
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg 191002, Russia; (E.V.S.); (T.A.A.)
| | - Timur A. Aliev
- Infochemistry Scientific Center, ITMO University, Saint-Petersburg 191002, Russia; (E.V.S.); (T.A.A.)
| | - Ilya S. Steshin
- Syntelly LLC, Moscow 121205, Russia; (A.A.M.); (I.S.S.); (A.V.T.); (A.S.K.)
| | | | | | - Maxim V. Fedorov
- Kharkevich Institute for Information Transmission Problems of Russian Academy of Sciences, Moscow 127994, Russia
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3
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Chen Z, Zhang L, Zhang P, Guo H, Zhang R, Li L, Li X. Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition. J Chem Inf Model 2024; 64:2528-2538. [PMID: 37864562 DOI: 10.1021/acs.jcim.3c01396] [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: 10/23/2023]
Abstract
Cytochrome P450 (CYP) is a family of enzymes that are responsible for about 75% of all metabolic reactions. Among them, CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4 participate in the metabolism of most drugs and mediate many adverse drug reactions. Therefore, it is necessary to estimate the chemical inhibition of Cytochrome P450 enzymes in drug discovery and the food industry. In the past few decades, many computational models have been reported, and some provided good performance. However, there are still several issues that should be resolved for these models, such as single isoform, models with unbalanced performance, lack of structural characteristics analysis, and poor availability. In the present study, the deep learning models based on python using the Keras framework and TensorFlow were developed for the chemical inhibition of each CYP isoform. These models were established based on a large data set containing 85715 compounds extracted from the PubChem bioassay database. On external validation, the models provided good AUC values with 0.97, 0.94, 0.94, 0.96, and 0.94 for CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, respectively. The models can be freely accessed on the Web server named CYPi-DNNpredictor (cypi.sapredictor.cn), and the codes for the model were made open source in the Supporting Information. In addition, we also analyzed the structural characteristics of chemicals with CYP450 inhibition and detected the structural alerts (SAs), which should be responsible for the inhibition. The SAs were also made available online, named CYPi-SAdetector (cypisa.sapredictor.cn). The models can be used as a powerful tool for the prediction of CYP450 inhibitors, and the SAs should provide useful information for the mechanisms of Cytochrome P450 inhibition.
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Affiliation(s)
- Zhaoyang Chen
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Le Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Pei Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Ling Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
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4
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Hoogstraten CA, Koenderink JB, van Straaten CE, Scheer-Weijers T, Smeitink JAM, Schirris TJJ, Russel FGM. Pyruvate dehydrogenase is a potential mitochondrial off-target for gentamicin based on in silico predictions and in vitro inhibition studies. Toxicol In Vitro 2024; 95:105740. [PMID: 38036072 DOI: 10.1016/j.tiv.2023.105740] [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: 07/10/2023] [Revised: 11/08/2023] [Accepted: 11/22/2023] [Indexed: 12/02/2023]
Abstract
During the drug development process, organ toxicity leads to an estimated failure of one-third of novel chemical entities. Drug-induced toxicity is increasingly associated with mitochondrial dysfunction, but identifying the underlying molecular mechanisms remains a challenge. Computational modeling techniques have proven to be a good tool in searching for drug off-targets. Here, we aimed to identify mitochondrial off-targets of the nephrotoxic drugs tenofovir and gentamicin using different in silico approaches (KRIPO, ProBis and PDID). Dihydroorotate dehydrogenase (DHODH) and pyruvate dehydrogenase (PDH) were predicted as potential novel off-target sites for tenofovir and gentamicin, respectively. The predicted targets were evaluated in vitro, using (colorimetric) enzymatic activity measurements. Tenofovir did not inhibit DHODH activity, while gentamicin potently reduced PDH activity. In conclusion, the use of in silico methods appeared a valuable approach in predicting PDH as a mitochondrial off-target of gentamicin. Further research is required to investigate the contribution of PDH inhibition to overall renal toxicity of gentamicin.
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Affiliation(s)
- Charlotte A Hoogstraten
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Jan B Koenderink
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Carolijn E van Straaten
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Tom Scheer-Weijers
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Jan A M Smeitink
- Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Department of Pediatrics, Amalia Children's Hospital, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Khondrion BV, Nijmegen 6525 EX, the Netherlands
| | - Tom J J Schirris
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands
| | - Frans G M Russel
- Division of Pharmacology and Toxicology, Department of Pharmacy, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands; Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen 6500 HB, the Netherlands.
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5
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Wu G, Zhu F, Zhang X, Ren H, Wang Y, Geng J, Liu H. PBT assessment of chemicals detected in effluent of wastewater treatment plants by suspected screening analysis. ENVIRONMENTAL RESEARCH 2023; 237:116892. [PMID: 37598848 DOI: 10.1016/j.envres.2023.116892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/10/2023] [Accepted: 08/13/2023] [Indexed: 08/22/2023]
Abstract
Wastewater treatment plants (WWTPs) are the major sources of contaminants discharged into downstream water bodies. Profiling the contaminants in effluent of WWTPs is crucial to assess the potential eco-risks toward downstream organisms. To this end, this study investigated the contaminants in effluent of 10 WWTPs locating in 10 cities of Yangtze River delta region of China by suspected screening analysis. Further, the persistence, bioaccumulation, toxicity (PBT) and the characteristics sub-structures of PBT-like chemicals were analyzed. Totally, 704 chemicals including 155 chemical products, 31 food additives, 52 natural substances, 112 personal care products, 123 pesticides, 192 pharmaceuticals, 17 hormones and 22 others were found. The results of PBT analysis suggested that 42 chemicals (5.97% among the detected chemicals in WWTPs) were with PBT property. Among them, 31 contaminants were not reported previously. 9 characteristics sub-structures (N-methyleneisobutylamine, 1-naphthaldehyde, 2,3,3-trimethylcyclohexene, cyclohexanol, N-sec-butyl-n-propylamine, (5E)-2,6-dimethylocta-1,5-diene, 2-ethylphenol, pentadecane and 6-methoxyhexane) were found for PBT-like chemicals. The sub-structures of highly linear alkyl partially explained the significantly higher PBT score for personal care products. Present study provides fundamental information on PBT properties of contaminants in effluent of WWTPs, which will benefit to prioritize contaminants with high concerns in effluent of WWTPs.
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Affiliation(s)
- Gang Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, PR China
| | - Feng Zhu
- Jiangsu Province Center for Disease Control and Prevention, Nanjing, Jiangsu, 210009, PR China
| | - Xuxiang Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, PR China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, PR China
| | - Yanru Wang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, PR China
| | - Jinju Geng
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, PR China; Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment, Ministry of Education, Chongqing University, Chongqing, 400044, PR China.
| | - Hualiang Liu
- Jiangsu Province Center for Disease Control and Prevention, Nanjing, Jiangsu, 210009, PR China.
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6
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Alves VM, Yasgar A, Wellnitz J, Rai G, Rath M, Braga RC, Capuzzi SJ, Simeonov A, Muratov EN, Zakharov AV, Tropsha A. Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds. J Med Chem 2023; 66:12828-12839. [PMID: 37677128 DOI: 10.1021/acs.jmedchem.3c00482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Adam Yasgar
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - James Wellnitz
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Ganesha Rai
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Marielle Rath
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | | | - Stephen J Capuzzi
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Anton Simeonov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059, Brazil
| | - Alexey V Zakharov
- National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Rockville, Maryland 20850, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, United States
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7
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Zhang R, Chen Z, Wang B, Li Y, Mu Y, Li X. Modeling and Insights into the Structural Characteristics of Chemical Mitochondrial Toxicity. ACS OMEGA 2023; 8:31675-31682. [PMID: 37692239 PMCID: PMC10483523 DOI: 10.1021/acsomega.3c01725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023]
Abstract
Mitochondria are the energy metabolism center of cells and are involved in a number of other processes, such as cell differentiation and apoptosis, signal transduction, and regulation of cell cycle and cell proliferation. It is of great significance to evaluate the mitochondrial toxicity of drugs and other chemicals. In the present study, we aimed to propose easily available artificial intelligence (AI) models for the prediction of chemical mitochondrial toxicity and investigate the structural characteristics with the analysis of molecular properties and structural alerts. The consensus model achieved good predictive results with high total accuracy at 87.21% for validation sets. The models can be accessed freely via https://ochem.eu/article/158582. Besides, several commonly used chemical properties were significantly different between chemicals with and without mitochondrial toxicity. We also detected the structural alerts (SAs) responsible for mitochondrial toxicity and integrated them into the web-server SApredictor (www.sapredictor.cn). The study may provide useful tools for in silico estimation of mitochondrial toxicity and be helpful to understand the mechanisms of mitochondrial toxicity.
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Affiliation(s)
- Ruiqiu Zhang
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Zhaoyang Chen
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Baobao Wang
- Department
of Nephrology, The First Affiliated Hospital
of Shandong First Medical University & Shandong Provincial Qianfoshan
Hospital, Jinan 250014, China
| | - Yan Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Yan Mu
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
| | - Xiao Li
- Department
of Clinical Pharmacy, The First Affiliated Hospital of Shandong First
Medical University & Shandong Provincial Qianfoshan Hospital,
Shandong Engineering and Technology Research Center for Pediatric
Drug Development, Shandong Medicine and
Health Key Laboratory of Clinical Pharmacy, Jinan 250014, China
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8
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Chakravarti S. Augmenting Expert Knowledge-Based Toxicity Alerts by Statistically Mined Molecular Fragments. Chem Res Toxicol 2023. [PMID: 37207298 DOI: 10.1021/acs.chemrestox.2c00368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Structural alerts are molecular substructures assumed to be associated with molecular initiating events in various toxic effects and an integral part of in silico toxicology. However, alerts derived using the knowledge of human experts often suffer from a lack of predictivity, specificity, and satisfactory coverage. In this work, we present a method to build hybrid QSAR models by combining expert knowledge-based alerts and statistically mined molecular fragments. Our objective was to find out if the combination is better than the individual systems. Lasso regularization-based variable selection was applied on combined sets of knowledge-based alerts and molecular fragments, but the variable elimination was only allowed to happen on the molecular fragments. We tested the concept on three toxicity end points, i.e., skin sensitization, acute Daphnia toxicity, and Ames mutagenicity, which covered both classification and regression problems. Results showed the predictive performance of such hybrid models is, indeed, better than the models based solely on expert alerts or statistically mined fragments alone. The method also enables the discovery of activating and mitigating/deactivating features for toxicity alerts and the identification of new alerts, thereby reducing false positive and false negative outcomes commonly associated with generic alerts and alerts with poor coverage, respectively.
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Affiliation(s)
- Suman Chakravarti
- MultiCASE Inc., 23811 Chagrin Blvd, Suite 305, Beachwood, Ohio 44122, United States
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9
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Golden E, Ukaegbu DC, Ranslow P, Brown RH, Hartung T, Maertens A. The Good, The Bad, and The Perplexing: Structural Alerts and Read-Across for Predicting Skin Sensitization Using Human Data. Chem Res Toxicol 2023; 36:734-746. [PMID: 37126467 DOI: 10.1021/acs.chemrestox.2c00383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In our earlier work (Golden et al., 2021), we showed 70-80% accuracies for several skin sensitization computational tools using human data. Here, we expanded the data set using the NICEATM human skin sensitization database to create a final data set of 1355 discrete chemicals (largely negative, ∼70%). Using this expanded data set, we analyzed model performance and evaluated mispredictions using Toxtree (v 3.1.0), OECD QSAR Toolbox (v 4.5), VEGA's (1.2.0 BETA) CAESAR (v 2.1.7), and a k-nearest-neighbor (kNN) classification approach. We show that the accuracy on this data set was lower than previous estimates, with balanced accuracies being 63% and 65% for Toxtree and OECD QSAR Toolbox, respectively, 46% for VEGA, and 59% for a kNN approach, with the lower accuracy likely due to the higher percentage of nonsensitizing chemicals. Two hundred eighty seven chemicals were mispredicted by both Toxtree and OECD QSAR Toolbox, which was approximately 20% of the entire data set, and 84% of these were false positives. The absence or presence of metabolic simulation in OECD QSAR Toolbox made no overall difference. While Toxtree is known for overpredicting, 60% of the chemicals in the data set had no alert for skin sensitization, and a substantial number of these chemicals were in fact sensitizers, pointing to sensitization mechanisms not recognized by Toxtree. Interestingly, we observed that chemicals with more than one Toxtree alert were more likely to be nonsensitizers. Finally, a kNN approach tended to mispredict different chemicals than either OECD QSAR Toolbox or Toxtree, suggesting that there was additional information to be garnered from a kNN approach. Overall, the results demonstrate that while there is merit in structural alerts as well as QSAR or read-across approaches (perhaps even more so in their combination), additional improvement will require a more nuanced understanding of mechanisms of skin sensitization.
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Affiliation(s)
- Emily Golden
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Daniel C Ukaegbu
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Peter Ranslow
- Consortium for Environmental Risk Management (CERM), Hallowell, Maine 04347, United States
| | - Robert H Brown
- School of Medicine, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
- CAAT-Europe, University of Konstanz, 78464, Konstanz, Germany
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
- Consortium for Environmental Risk Management (CERM), Hallowell, Maine 04347, United States
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10
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Li J, Zhou Y, Eelen G, Zhou QT, Feng WB, Labroska V, Ma FF, Lu HP, Dewerchin M, Carmeliet P, Wang MW, Yang DH. A high-throughput screening campaign against PFKFB3 identified potential inhibitors with novel scaffolds. Acta Pharmacol Sin 2023; 44:680-692. [PMID: 36114272 PMCID: PMC9958033 DOI: 10.1038/s41401-022-00989-1] [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: 04/02/2022] [Accepted: 08/23/2022] [Indexed: 11/08/2022] Open
Abstract
The growth of solid tumors depends on tumor vascularization and the endothelial cells (ECs) that line the lumen of blood vessels. ECs generate a large fraction of ATP through glycolysis, and elevation of their glycolytic activity is associated with angiogenic behavior in solid tumors. 6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) positively regulates glycolysis via fructose-2/6-bisphosphate, the product of its kinase activity. Partial inhibition of glycolysis in tumor ECs by targeting PFKFB3 normalizes the otherwise abnormal tumor vessels, thereby reducing metastasis and improving the outcome of chemotherapy. Although a limited number of tool compounds exist, orally available PFKFB3 inhibitors are unavailable. In this study we conducted a high-throughput screening campaign against the kinase activity of PFKFB3, involving 250,240 chemical compounds. A total of 507 initial hits showing >50% inhibition at 20 µM were identified, 66 of them plus 1 analog from a similarity search consistently displayed low IC50 values (<10 µM). In vitro experiments yielded 22 nontoxic hits that suppressed the tube formation of primary human umbilical vein ECs at 10 µM. Of them, 15 exhibited binding affinity to PFKFB3 in surface plasmon resonance assays, including 3 (WNN0403-E003, WNN1352-H007 and WNN1542-F004) that passed the pan-assay interference compounds screening without warning flags. This study provides potential leads to the development of new PFKFB3 inhibitors.
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Affiliation(s)
- Jie Li
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
| | - Yan Zhou
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
| | - Guy Eelen
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven and Center for Cancer Biology, VIB-KU Leuven, Leuven, 3000, Belgium
| | - Qing-Tong Zhou
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
| | - Wen-Bo Feng
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China
| | - Viktorija Labroska
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Fen-Fen Ma
- Department of Pharmacy, Pudong Hospital, Fudan University, Shanghai, 201300, China
| | - Hui-Ping Lu
- Department of Pharmacy, Pudong Hospital, Fudan University, Shanghai, 201300, China
| | - Mieke Dewerchin
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven and Center for Cancer Biology, VIB-KU Leuven, Leuven, 3000, Belgium
| | - Peter Carmeliet
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven and Center for Cancer Biology, VIB-KU Leuven, Leuven, 3000, Belgium
| | - Ming-Wei Wang
- Department of Pharmacology, School of Basic Medical Sciences, Fudan University, Shanghai, 200032, China.
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Research Center for Deepsea Bioresources, Sanya, 572025, China.
- Department of Chemistry, School of Science, The University of Tokyo, Tokyo, 113-0033, Japan.
| | - De-Hua Yang
- The National Center for Drug Screening, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Research Center for Deepsea Bioresources, Sanya, 572025, China.
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11
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Structure optimization of new tumor-selective Passerini α-acyloxy carboxamides as Caspase-3/7 activators. Sci Rep 2022; 12:22390. [PMID: 36575196 PMCID: PMC9794698 DOI: 10.1038/s41598-022-26469-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 12/15/2022] [Indexed: 12/28/2022] Open
Abstract
Selective elimination of tumors has always been the mainstay of oncology research. The on-going research underlying the cellular apoptotic mechanisms reveal caspases activation, especially the key effector caspase-3, as a personalized tumor-selective therapeutic strategy. Our continued research protocol has exploited new optimized Passerini α-acyloxy carboxamides as efficient apoptotic inducers via caspase-3/7 dependent mechanism with highly selective anticancer profiles. The adopted design rationale relied on excluding structural alerts of previous leads, while merging various pharmacophoric motifs of natural and synthetic caspase activators via optimized one-pot Passerini reaction conditions. The prepared compounds resulting from Passerini reaction were screened for their cytotoxic activities against colorectal Caco-2 and liver HepG-2 cancer cells compared to normal fibroblasts utilizing MTT assay. Notably, all compounds exhibited promising low-range submicromolar IC50 against the studied cancer cell lines, with outstanding tumor selectivity (SI values up to 266). Hence, they were superior to 5-fluorouracil. Notably, 7a, 7g, and 7j conferred the highest potencies against Caco-2 and HepG-2 cells and were selected for further mechanistic studies. Caspas-3/7 activation assay of the hit compounds and flow cytometric analysis of the treated apoptotic cancer cells demonstrated their significant caspase activation potential (up to 4.2 folds) and apoptotic induction capacities (up to 58.7%). Further assessment of Bcl2 expression was performed being a physiological caspase-3 substrate. Herein, the three studied Passerini adducts were able to downregulate Bcl2 in the treated Caco-2 cells. Importantly, the mechanistic studies results of the three hits echoed their preliminary MTT antiproliferative potencies data highlighting their caspase-3 dependent apoptotic induction. Finally, the in silico predicted physicochemical and pharmacokinetic profiles, as well as ligand efficiency metrics were drug-like.
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12
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Naidoo D, Pošta M, Kar P, Roy A, Anandraj A, Tloušťová E, Beier P, Van Staden J. Design and synthesis of thiophenone and furanthione butenolide bioisosteres with inhibitory activity towards acetylcholinesterase. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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13
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Peets P, Wang WC, MacLeod M, Breitholtz M, Martin JW, Kruve A. MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15508-15517. [PMID: 36269851 PMCID: PMC9670854 DOI: 10.1021/acs.est.2c02536] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/07/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log KOW.
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Affiliation(s)
- Pilleriin Peets
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, SE-106
91 Stockholm, Sweden
| | - Wei-Chieh Wang
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, SE-106
91 Stockholm, Sweden
| | - Matthew MacLeod
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
| | - Magnus Breitholtz
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
| | - Jonathan W. Martin
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, SE-106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
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14
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Alves VM, Borba JVB, Braga RC, Korn DR, Kleinstreuer N, Causey K, Tropsha A, Rua D, Muratov EN. PreS/MD: Predictor of Sensitization hazard for chemical substances released from Medical Devices. Toxicol Sci 2022; 189:250-259. [PMID: 35916740 PMCID: PMC9516038 DOI: 10.1093/toxsci/kfac078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
In the United States, a pre-market regulatory submission for any medical device that comes into contact with either a patient or the clinical practitioner must include an adequate toxicity evaluation of chemical substances that can be released from the device during its intended use. These substances, also referred to as extractables and leachables, must be evaluated for their potential to induce sensitization/allergenicity, which traditionally has been done in animal assays such as the guinea pig maximization test (GPMT). However, advances in basic and applied science are continuously presenting opportunities to employ New Approach Methodologies (NAMs), including computational methods which, when qualified, could replace animal testing methods to support regulatory submissions. Herein, we developed a new computational tool for rapid and accurate prediction of the GPMT outcome that we have named PreS/MD (Predictor of Sensitization for Medical Devices). To enable model development, we (i) collected, curated, and integrated the largest publicly available dataset for GPMT results; (ii) succeeded in developing externally predictive (balanced accuracy of 70-74% as evaluated by both 5-fold external cross-validation and testing of novel compounds) Quantitative Structure-Activity Relationships (QSAR) models for GPMT using machine learning algorithms, including Deep Learning; and (iii) developed a publicly accessible web portal integrating PreS/MD models that can predict GPMT outcomes for any molecule of interest. We expect that PreS/MD will be used by both industry and regulatory scientists in medical device safety assessments and help replace, reduce, or refine the use of animals in toxicity testing. PreS/MD is freely available at https://presmd.mml.unc.edu/.
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Affiliation(s)
- Vinicius M Alves
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Joyce V B Borba
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | | | - Daniel R Korn
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Nicole Kleinstreuer
- National Toxicology Program, Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, NC, USA
| | - Kevin Causey
- Predictive, LLC, Research Triangle Park, NC, USA
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA.,Predictive, LLC, Research Triangle Park, NC, USA
| | - Diego Rua
- Division of Biology, Chemistry, and Materials Science, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, 20993, USA
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA
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15
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Hua Y, Cui X, Liu B, Shi Y, Guo H, Zhang R, Li X. SApredictor: An Expert System for Screening Chemicals Against Structural Alerts. Front Chem 2022; 10:916614. [PMID: 35910729 PMCID: PMC9326022 DOI: 10.3389/fchem.2022.916614] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.
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Affiliation(s)
- Yuqing Hua
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xueyan Cui
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Bo Liu
- Institute of Materia Medica, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, China
| | - Yinping Shi
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Huizhu Guo
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Ruiqiu Zhang
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
| | - Xiao Li
- Department of Clinical Pharmacy, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Engineering and Technology Research Center for Pediatric Drug Development, Shandong Medicine and Health Key Laboratory of Clinical Pharmacy, Jinan, China
- Department of Clinical Pharmacy, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China
- *Correspondence: Xiao Li, , , orcid.org/0000-0002-1148-9898
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16
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Kim H, Park M, Lee I, Nam H. BayeshERG: a robust, reliable and interpretable deep learning model for predicting hERG channel blockers. Brief Bioinform 2022; 23:6609519. [PMID: 35709752 DOI: 10.1093/bib/bbac211] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 04/19/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
Unintended inhibition of the human ether-à-go-go-related gene (hERG) ion channel by small molecules leads to severe cardiotoxicity. Thus, hERG channel blockage is a significant concern in the development of new drugs. Several computational models have been developed to predict hERG channel blockage, including deep learning models; however, they lack robustness, reliability and interpretability. Here, we developed a graph-based Bayesian deep learning model for hERG channel blocker prediction, named BayeshERG, which has robust predictive power, high reliability and high resolution of interpretability. First, we applied transfer learning with 300 000 large data in initial pre-training to increase the predictive performance. Second, we implemented a Bayesian neural network with Monte Carlo dropout to calibrate the uncertainty of the prediction. Third, we utilized global multihead attentive pooling to augment the high resolution of structural interpretability for the hERG channel blockers and nonblockers. We conducted both internal and external validations for stringent evaluation; in particular, we benchmarked most of the publicly available hERG channel blocker prediction models. We showed that our proposed model outperformed predictive performance and uncertainty calibration performance. Furthermore, we found that our model learned to focus on the essential substructures of hERG channel blockers via an attention mechanism. Finally, we validated the prediction results of our model by conducting in vitro experiments and confirmed its high validity. In summary, BayeshERG could serve as a versatile tool for discovering hERG channel blockers and helping maximize the possibility of successful drug discovery. The data and source code are available at our GitHub repository (https://github.com/GIST-CSBL/BayeshERG).
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Affiliation(s)
- Hyunho Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
| | - Minsu Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
| | - Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju, 61005, Republic of Korea
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17
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Goel R, Reilly SM, Valerio LG. A Computational Approach for Respiratory Hazard Identification of Flavor Chemicals in Tobacco Products. Chem Res Toxicol 2022; 35:450-458. [PMID: 35239324 DOI: 10.1021/acs.chemrestox.1c00361] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Flavor chemicals contribute to the appeal and toxicity of tobacco products, including electronic nicotine delivery systems (ENDS). The assortment of flavor chemicals available for use in tobacco products is extensive. In this study, a chemistry-driven computational approach was used to evaluate flavor chemicals based on intrinsic hazardous structures and reactivity of chemicals. A large library of 3012 unique flavor chemicals was compiled from publicly available information. Next, information was computed and collated based on their (1) physicochemical properties, (2) global harmonization system (GHS) health hazard classification, (3) structural alerts linked to the chemical's reactivity, instability, or toxicity, and (4) common substructure shared with FDA's harmful and potentially harmful constituents (HPHCs) flavor chemicals that are respiratory toxicants. Computational analysis of the constructed flavor library flagged 638 chemicals with GHS classified respiratory health hazards, 1079 chemicals with at least one structural alert, and 2297 chemicals with substructural similarity to FDA's established and proposed list of HPHCs. A subsequent analysis was performed on a subset of 173 chemicals in the flavor library that are respiratory health hazards, contain structural alerts as well as flavor HPHC substructures. Four general toxicophore structures with an increased potential for respiratory toxicity were then identified. In summary, computational methods are efficient tools for hazard identification and understanding structure-toxicity relationship. With appropriate context of use and interpretation, in silico methods may provide scientific evidence to support toxicological evaluations of chemicals in or emitted from tobacco products.
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18
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Choo MZY, Chai CLL. Promoting GAINs (Give Attention to Limitations in Assays) over PAINs Alerts: no PAINS, more GAINs. ChemMedChem 2022; 17:e202100710. [PMID: 35146933 DOI: 10.1002/cmdc.202100710] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/14/2022] [Indexed: 11/09/2022]
Abstract
Many concepts and guidelines in medicinal chemistry have been introduced to aid in successful drug discovery and development. An example is the concept of Pan-Assay Interference Compounds (PAINS) and the elimination of such nuisance compounds from high-throughput screening (HTS) libraries. PAINs, along with other guidelines in medicinal chemistry, are like double-edged swords. If used appropriately, they may be beneficial for drug discovery and development. However, rigid and blind use of such concepts can hinder productivity. In this perspective, we introduce GAINS (give attention to limitations in assays) and highlight its relevance for successful drug discovery.
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Affiliation(s)
- Malcolm Z Y Choo
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore, Singapore, 117543, Singapore
| | - Christina L L Chai
- Department of Pharmacy, National University of Singapore, 18 Science Drive 4, Singapore, Singapore, 117543, Singapore
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19
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Borba JV, Alves VM, Braga RC, Korn DR, Overdahl K, Silva AC, Hall SU, Overdahl E, Kleinstreuer N, Strickland J, Allen D, Andrade CH, Muratov EN, Tropsha A. STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:27012. [PMID: 35192406 PMCID: PMC8863177 DOI: 10.1289/ehp9341] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 01/21/2022] [Accepted: 01/24/2022] [Indexed: 05/22/2023]
Abstract
BACKGROUND Modern chemical toxicology is facing a growing need to Reduce, Refine, and Replace animal tests (Russell 1959) for hazard identification. The most common type of animal assays for acute toxicity assessment of chemicals used as pesticides, pharmaceuticals, or in cosmetic products is known as a "6-pack" battery of tests, including three topical (skin sensitization, skin irritation and corrosion, and eye irritation and corrosion) and three systemic (acute oral toxicity, acute inhalation toxicity, and acute dermal toxicity) end points. METHODS We compiled, curated, and integrated, to the best of our knowledge, the largest publicly available data sets and developed an ensemble of quantitative structure-activity relationship (QSAR) models for all six end points. All models were validated according to the Organisation for Economic Co-operation and Development (OECD) QSAR principles, using data on compounds not included in the training sets. RESULTS In addition to high internal accuracy assessed by cross-validation, all models demonstrated an external correct classification rate ranging from 70% to 77%. We established a publicly accessible Systemic and Topical chemical Toxicity (STopTox) web portal (https://stoptox.mml.unc.edu/) integrating all developed models for 6-pack assays. CONCLUSIONS We developed STopTox, a comprehensive collection of computational models that can be used as an alternative to in vivo 6-pack tests for predicting the toxicity hazard of small organic molecules. Models were established following the best practices for the development and validation of QSAR models. Scientists and regulators can use the STopTox portal to identify putative toxicants or nontoxicants in chemical libraries of interest. https://doi.org/10.1289/EHP9341.
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Affiliation(s)
- Joyce V.B. Borba
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Vinicius M. Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | | | - Daniel R. Korn
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Kirsten Overdahl
- Nicholas School of the Environment, Duke University, Durham, North Carolina, USA
| | - Arthur C. Silva
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Steven U.S. Hall
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Erik Overdahl
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods (NICEATM), National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, USA
| | - Judy Strickland
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - David Allen
- Integrated Laboratory Systems, LLC, Research Triangle Park, North Carolina, USA
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Federal University of Goias, Goiania, Goias, Brazil
| | - Eugene N. Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, Paraiba, Brazil
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina, USA
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20
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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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21
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Ebert A, Goss KU. Screening of 6000 Compounds for Uncoupling Activity: A Comparison Between a Mechanistic Biophysical Model and the Structural Alert Profiler Mitotox. Toxicol Sci 2021; 185:208-219. [PMID: 34865177 DOI: 10.1093/toxsci/kfab139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes, or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in vitro uncoupling activity, using a biophysical model based on ab initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.
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Affiliation(s)
- Andrea Ebert
- Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research-UFZ, D-04318 Leipzig, Germany
| | - Kai-Uwe Goss
- Analytical Environmental Chemistry, Helmholtz Centre for Environmental Research-UFZ, D-04318 Leipzig, Germany.,Institute of Chemistry, Martin Luther University, D-06120 Halle, Germany
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22
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Mao J, Akhtar J, Zhang X, Sun L, Guan S, Li X, Chen G, Liu J, Jeon HN, Kim MS, No KT, Wang G. Comprehensive strategies of machine-learning-based quantitative structure-activity relationship models. iScience 2021; 24:103052. [PMID: 34553136 PMCID: PMC8441174 DOI: 10.1016/j.isci.2021.103052] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Early quantitative structure-activity relationship (QSAR) technologies have unsatisfactory versatility and accuracy in fields such as drug discovery because they are based on traditional machine learning and interpretive expert features. The development of Big Data and deep learning technologies significantly improve the processing of unstructured data and unleash the great potential of QSAR. Here we discuss the integration of wet experiments (which provide experimental data and reliable verification), molecular dynamics simulation (which provides mechanistic interpretation at the atomic/molecular levels), and machine learning (including deep learning) techniques to improve QSAR models. We first review the history of traditional QSAR and point out its problems. We then propose a better QSAR model characterized by a new iterative framework to integrate machine learning with disparate data input. Finally, we discuss the application of QSAR and machine learning to many practical research fields, including drug development and clinical trials.
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Affiliation(s)
- Jiashun Mao
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen, Guangdong 518055 China
| | - Javed Akhtar
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen, Guangdong 518055, China
| | - Xiao Zhang
- Shanghai Rural Commercial Bank Co., Ltd, Shanghai 200002, China
| | - Liang Sun
- Department of Physics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong, China
| | - Shenghui Guan
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen, Guangdong 518055 China
| | - Xinyu Li
- School of Life and Health Sciences and Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Guangming Chen
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen, Guangdong 518055, China
| | - Jiaxin Liu
- Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Hyeon-Nae Jeon
- Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Min Sung Kim
- Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Kyoung Tai No
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea
| | - Guanyu Wang
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, 1088 Xueyuan Avenue, Shenzhen, Guangdong 518055, China
- Guangdong Provincial Key Laboratory of Computational Science and Material Design, Shenzhen, Guangdong 518055 China
- Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen, Guangdong 518055, China
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23
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Wu Y, Zhu J, Fu P, Tong W, Hong H, Chen M. Machine Learning for Predicting Risk of Drug-Induced Autoimmune Diseases by Structural Alerts and Daily Dose. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18137139. [PMID: 34281077 PMCID: PMC8296890 DOI: 10.3390/ijerph18137139] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/20/2021] [Accepted: 06/25/2021] [Indexed: 12/28/2022]
Abstract
An effective approach for assessing a drug’s potential to induce autoimmune diseases (ADs) is needed in drug development. Here, we aim to develop a workflow to examine the association between structural alerts and drugs-induced ADs to improve toxicological prescreening tools. Considering reactive metabolite (RM) formation as a well-documented mechanism for drug-induced ADs, we investigated whether the presence of certain RM-related structural alerts was predictive for the risk of drug-induced AD. We constructed a database containing 171 RM-related structural alerts, generated a dataset of 407 AD- and non-AD-associated drugs, and performed statistical analysis. The nitrogen-containing benzene substituent alerts were found to be significantly associated with the risk of drug-induced ADs (odds ratio = 2.95, p = 0.0036). Furthermore, we developed a machine-learning-based predictive model by using daily dose and nitrogen-containing benzene substituent alerts as the top inputs and achieved the predictive performance of area under curve (AUC) of 70%. Additionally, we confirmed the reactivity of the nitrogen-containing benzene substituent aniline and related metabolites using quantum chemistry analysis and explored the underlying mechanisms. These identified structural alerts could be helpful in identifying drug candidates that carry a potential risk of drug-induced ADs to improve their safety profiles.
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Affiliation(s)
- Yue Wu
- National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (Y.W.); (J.Z.); (W.T.); (H.H.)
| | - Jieqiang Zhu
- National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (Y.W.); (J.Z.); (W.T.); (H.H.)
| | - Peter Fu
- National Center for Toxicological Research, Division of Biochemical Toxicology, U.S. Food and Drug Administration, Jefferson, AR 72079, USA;
| | - Weida Tong
- National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (Y.W.); (J.Z.); (W.T.); (H.H.)
| | - Huixiao Hong
- National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (Y.W.); (J.Z.); (W.T.); (H.H.)
| | - Minjun Chen
- National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (Y.W.); (J.Z.); (W.T.); (H.H.)
- Correspondence: ; Fax: +1-870-543-7865
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24
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Kuz’min V, Artemenko A, Ognichenko L, Hromov A, Kosinskaya A, Stelmakh S, Sessions ZL, Muratov EN. Simplex representation of molecular structure as universal QSAR/QSPR tool. Struct Chem 2021; 32:1365-1392. [PMID: 34177203 PMCID: PMC8218296 DOI: 10.1007/s11224-021-01793-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 05/07/2021] [Indexed: 10/24/2022]
Abstract
We review the development and application of the Simplex approach for the solution of various QSAR/QSPR problems. The general concept of the simplex method and its varieties are described. The advantages of utilizing this methodology, especially for the interpretation of QSAR/QSPR models, are presented in comparison to other fragmentary methods of molecular structure representation. The utility of SiRMS is demonstrated not only in the standard QSAR/QSPR applications, but also for mixtures, polymers, materials, and other complex systems. In addition to many different types of biological activity (antiviral, antimicrobial, antitumor, psychotropic, analgesic, etc.), toxicity and bioavailability, the review examines the simulation of important properties, such as water solubility, lipophilicity, as well as luminescence, and thermodynamic properties (melting and boiling temperatures, critical parameters, etc.). This review focuses on the stereochemical description of molecules within the simplex approach and details the possibilities of universal molecular stereo-analysis and stereochemical configuration description, along with stereo-isomerization mechanism and molecular fragment "topography" identification.
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Affiliation(s)
- Victor Kuz’min
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anatoly Artemenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Luidmyla Ognichenko
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Alexander Hromov
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Anna Kosinskaya
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
- Department of Medical Chemistry, Odessa National Medical University, Odessa, 65082 Ukraine
| | - Sergij Stelmakh
- Department of Molecular Structures and Chemoinformatics, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080 Ukraine
| | - Zoe L. Sessions
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
| | - Eugene N. Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27599 USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, PB 58059 Brazil
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25
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Chikowe I, Phiri AC, Mbewe KP, Matekenya D. In-silico evaluation of Malawi essential medicines and reactive metabolites for potential drug-induced toxicities. BMC Pharmacol Toxicol 2021; 22:36. [PMID: 34134770 PMCID: PMC8207713 DOI: 10.1186/s40360-021-00499-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 05/10/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Drug-induced toxicity is one of the problems that have negatively impacted on the well-being of populations throughout the world, including Malawi. It results in unnecessary hospitalizations, retarding the development of the country. This study assessed the Malawi Essential Medicines List (MEML) for structural alerts and reactive metabolites with the potential for drug-induced toxicities. METHODS This in-silico screening study used StopTox, ToxAlerts and LD-50 values toxicity models to assess the MEML drugs. A total of 296 drugs qualified for the analysis (those that had defined chemical structures) and were screened in each software programme. Each model had its own toxicity endpoints and the models were compared for consensus of their results. RESULTS In the StopTox model, 86% of the drugs had potential to cause at least one toxicity including 55% that had the potential of causing eye irritation and corrosion. In ToxAlerts, 90% of the drugs had the potential of causing at least one toxicity and 72% were found to be potentially reactive, unstable and toxic. In LD-50, 70% of the drugs were potentially toxic. Model consensus evaluation results showed that the highest consensus was observed between ToxAlerts and StopTox (80%). The overall consensus amongst the three models was 57% and statistically significant (p < 0.05). CONCLUSIONS A large number of drugs had the potential to cause various systemic toxicities. But the results need to be interpreted cautiously since the clinical translation of QSAR-based predictions depends on many factors. In addition, inconsistencies have been reported between screening results amongst different models.
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Affiliation(s)
- Ibrahim Chikowe
- Pharmacy Department, College of Medicine, University of Malawi, Blantyre, Malawi.
| | | | - Kirios Patrick Mbewe
- Pharmacy Department, College of Medicine, University of Malawi, Blantyre, Malawi
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26
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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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27
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Serafini M, Cargnin S, Massarotti A, Tron GC, Pirali T, Genazzani AA. What's in a Name? Drug Nomenclature and Medicinal Chemistry Trends using INN Publications. J Med Chem 2021; 64:4410-4429. [PMID: 33847110 PMCID: PMC8154580 DOI: 10.1021/acs.jmedchem.1c00181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Indexed: 12/13/2022]
Abstract
The World Health Organization assigns international nonproprietary names (INN), also known as common names, to compounds upon request from drug developers. Structures of INNs are publicly available and represent a source, albeit underused, to understand trends in drug research and development. Here, we explain how a common drug name is composed and analyze chemical entities from 2000 to 2021. In the analysis, we describe some changes that intertwine chemical structure, newer therapeutic targets (e.g., kinases), including a significant increase in the use of fluorine and of heterocycles, and some other evolutionary modifications, such as the progressive increase in molecular weight. Alongside these, small signs of change can be spotted, such as the rise in spirocyclic scaffolds and small rings and the emergence of unconventional structural moieties that might forecast the future to come.
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Affiliation(s)
| | | | | | - Gian Cesare Tron
- Department of Pharmaceutical
Sciences, Università del Piemonte
Orientale, Largo Donegani 2, 28100 Novara, Italy
| | - Tracey Pirali
- Department of Pharmaceutical
Sciences, Università del Piemonte
Orientale, Largo Donegani 2, 28100 Novara, Italy
| | - Armando A. Genazzani
- Department of Pharmaceutical
Sciences, Università del Piemonte
Orientale, Largo Donegani 2, 28100 Novara, Italy
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28
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Mukherjee A, Su A, Rajan K. Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors. J Chem Inf Model 2021; 61:2187-2197. [PMID: 33872000 DOI: 10.1021/acs.jcim.0c01409] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
This paper aims to identify structural motifs within a molecule that contribute the most toward a chemical being an endocrine disruptor. We have developed a deep neural network-based toolkit toward this aim. The trained model can virtually assess a synthetic chemical's potential to be an endocrine disruptor using machine-readable molecular representation, simplified molecular input line entry system (SMILES). Our proposed toolkit is a multilabel or multioutput classification model that combines both convolution and long short-term memory (LSTM) architectures. The toolkit leverages the advantages of an active learning-based framework that combines multiple sources of data. Class activation maps (CAMs) generated from the feature-extraction layers can identify the structural alerts and the chemical environment that determines the specificity of the structural alerts.
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Affiliation(s)
- Arpan Mukherjee
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York 14260-1660, United States
| | - An Su
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York 14260-1660, United States
| | - Krishna Rajan
- Department of Materials Design and Innovation, University at Buffalo, Buffalo, New York 14260-1660, United States
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29
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Yang ZY, Yang ZJ, Zhao Y, Yin MZ, Lu AP, Chen X, Liu S, Hou TJ, Cao DS. PySmash: Python package and individual executable program for representative substructure generation and application. Brief Bioinform 2021; 22:6168498. [PMID: 33709154 DOI: 10.1093/bib/bbab017] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 01/06/2021] [Accepted: 01/12/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable physicochemical and biological properties. With the continuous accumulation of more experimental data, data-driven computational systems which can derive representative substructures from large chemical libraries attract more attention. Therefore, the development of an integrated and convenient tool to generate and implement representative substructures is urgently needed. RESULTS In this study, PySmash, a user-friendly and powerful tool to generate different types of representative substructures, was developed. The current version of PySmash provides both a Python package and an individual executable program, which achieves ease of operation and pipeline integration. Three types of substructure generation algorithms, including circular, path-based and functional group-based algorithms, are provided. Users can conveniently customize their own requirements for substructure size, accuracy and coverage, statistical significance and parallel computation during execution. Besides, PySmash provides the function for external data screening. CONCLUSION PySmash, a user-friendly and integrated tool for the automatic generation and implementation of representative substructures, is presented. Three screening examples, including toxicophore derivation, privileged motif detection and the integration of substructures with machine learning (ML) models, are provided to illustrate the utility of PySmash in safety profile evaluation, therapeutic activity exploration and molecular optimization, respectively. Its executable program and Python package are available at https://github.com/kotori-y/pySmash.
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Affiliation(s)
- Zi-Yi Yang
- Department of Pharmacy, Xiangya Hospital, Central South University and the Xiangya School of Pharmaceutical Sciences, Central South University, Sichuan, China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Hunan, China
| | - Yue Zhao
- Xiangya School of Pharmaceutical Sciences, Central South University (Changsha), Sichuan, China
| | - Ming-Zhu Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Hunan
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Hunan
| | - Shao Liu
- Department of Pharmacy, Xiangya Hospital, Central South University, Hunan
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, China
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30
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Benchmarking the mechanisms of frequent hitters: limitation of PAINS alerts. Drug Discov Today 2021; 26:1353-1358. [PMID: 33581116 DOI: 10.1016/j.drudis.2021.02.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/19/2021] [Accepted: 02/02/2021] [Indexed: 12/15/2022]
Abstract
In 2010, the pan-assay interference compounds (PAINS) rule was proposed to identify false-positive compounds, especially frequent hitters (FHs), in biological screening campaigns, and has rapidly become an essential component in drug design. However, the specific mechanisms remain unknown, and the result validation and follow-up processing schemes are still unclear. In this review, a large benchmark collection of >600,000 compounds sourced from databases and the literature, including six common false-positive mechanisms, was used to evaluate the detection ability of PAINS. In addition, 400 million purchasable molecules from the ZINC database were also applied to PAINS screening. The results indicate that the PAINS rule is not suitable for the screening of all types of false-positive results and needs more improvement.
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31
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Hughes TB, Flynn N, Dang NL, Swamidass SJ. Modeling the Bioactivation and Subsequent Reactivity of Drugs. Chem Res Toxicol 2021; 34:584-600. [PMID: 33496184 DOI: 10.1021/acs.chemrestox.0c00417] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Electrophilically reactive drug metabolites are implicated in many adverse drug reactions. In this mechanism-termed bioactivation-metabolic enzymes convert drugs into reactive metabolites that often conjugate to nucleophilic sites within biological macromolecules like proteins. Toxic metabolite-product adducts induce severe immune responses that can cause sometimes fatal disorders, most commonly in the form of liver injury, blood dyscrasia, or the dermatologic conditions toxic epidermal necrolysis and Stevens-Johnson syndrome. This study models four of the most common metabolic transformations that result in bioactivation: quinone formation, epoxidation, thiophene sulfur-oxidation, and nitroaromatic reduction, by synthesizing models of metabolism and reactivity. First, the metabolism models predict the formation probabilities of all possible metabolites among the pathways studied. Second, the exact structures of these metabolites are enumerated. Third, using these structures, the reactivity model predicts the reactivity of each metabolite. Finally, a feedfoward neural network converts the metabolism and reactivity predictions to a bioactivation prediction for each possible metabolite. These bioactivation predictions represent the joint probability that a metabolite forms and that this metabolite subsequently conjugates to protein or glutathione. Among molecules bioactivated by these pathways, we predicted the correct pathway with an AUC accuracy of 89.98%. Furthermore, the model predicts whether molecules will be bioactivated, distinguishing bioactivated and nonbioactivated molecules with 81.06% AUC. We applied this algorithm to withdrawn drugs. The known bioactivation pathways of alclofenac and benzbromarone were identified by the algorithm, and high probability bioactivation pathways not yet confirmed were identified for safrazine, zimelidine, and astemizole. This bioactivation model-the first of its kind that jointly considers both metabolism and reactivity-enables drug candidates to be quickly evaluated for a toxicity risk that often evades detection during preclinical trials. The XenoSite bioactivation model is available at http://swami.wustl.edu/xenosite/p/bioactivation.
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Affiliation(s)
- Tyler B Hughes
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - Noah Flynn
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - Na Le Dang
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
| | - S Joshua Swamidass
- Department of Pathology and Immunology, Washington University School of Medicine, Campus Box 8118, 660 South Euclid Avenue, St. Louis, Missouri 63110, United States
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32
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Wedlake AJ, Allen TEH, Goodman JM, Gutsell S, Kukic P, Russell PJ. Confidence in Inactive and Active Predictions from Structural Alerts. Chem Res Toxicol 2020; 33:3010-3022. [PMID: 33295767 DOI: 10.1021/acs.chemrestox.0c00332] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Having a measure of confidence in computational predictions of biological activity from in silico tools is vital when making predictions for new chemicals, for example, in chemical risk assessment. Where predictions of biological activity are used as an indicator of a potential hazard, false-negative predictions are the most concerning prediction; however, assigning confidence in inactive predictions is particularly challenging. How can one confidently identify the absence of activating features? In this study, we present methods for assigning confidence to both active and inactive predictions from structural alerts for protein-binding molecular initiating events (MIEs). Structural alerts were derived through an iterative statistical method. Confidence in the activity predictions is assigned by measuring the Tanimoto similarity between Morgan fingerprints of chemicals in the test set to relevant chemicals in the training set, and suitable cutoff values have been defined to give different confidence categories. To avoid a potential compound series bias in the test set and hence overestimate the performance of the method, we measured the biological activity of 27 compounds with 24 proteins, which gave us an additional 648 experimental measurements; many of the measurements are currently nonexistent in the literature and databases. This data set was complemented with newly measured biological activities published in ChEMBL25 and formed a combined independent validation data set. Applying the confidence categories to the computational predictions for the new data leads to the identification of chemicals for which one should be confident of either an inactive or active prediction, allowing model predictions to be used responsibly.
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Affiliation(s)
- Andrew J Wedlake
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom.,MRC Toxicology Unit, University of Cambridge, Gleeson Building, Tennis Court Road, Cambridge CB2 1QR, United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Predrag Kukic
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, United Kingdom
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33
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Golden E, Maertens M, Hartung T, Maertens A. Mapping Chemical Respiratory Sensitization: How Useful Are Our Current Computational Tools? Chem Res Toxicol 2020; 34:473-482. [PMID: 33320000 DOI: 10.1021/acs.chemrestox.0c00320] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Chemical respiratory sensitization is an immunological process that manifests clinically mostly as occupational asthma and is responsible for 1 in 6 cases of adult asthma, although this may be an underestimate of the prevalence, as it is under-diagnosed. Occupational asthma results in unemployment for roughly one-third of those affected due to severe health issues. Despite its high prevalence, chemical respiratory sensitization is difficult to predict, as there are currently no validated models and the mechanisms are not entirely understood, creating a significant challenge for regulatory bodies and industry alike. The Adverse Outcome Pathway (AOP) for respiratory sensitization is currently incomplete. However, some key events have been identified, and there is overlap with the comparatively well-characterized AOP for dermal sensitization. Because of this, and the fact that dermal sensitization is often assessed by in vivo, in chemico, or in silico methods, regulatory bodies are defaulting to the dermal sensitization status of chemicals as a proxy for respiratory sensitization status when evaluating chemical safety. We identified a data set of known human respiratory sensitizers, which we used to investigate the accuracy of a structural alert model, Toxtree, designed for skin sensitization and the Centre for Occupational and Environmental Health (COEH)'s model, a model developed specifically for occupational asthma. While both models had a reasonable level of accuracy, the COEH model achieved the highest balanced accuracy at 76%; when the models agreed, the overall accuracy was 87%. There were important differences between the models: Toxtree had superior performance for some structural alerts and some categories of well-characterized skin sensitizers, while the COEH model had high accuracy in identifying sensitizers that lacked identified skin sensitization reactivity domains. Overall, both models achieved respectable accuracy. However, neither model addresses potency, which, along with data quality, remains a hurdle, and the field must prioritize these issues to move forward.
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Affiliation(s)
- Emily Golden
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Mikhail Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States.,CAAT-Europe, University of Konstanz, 78464 Konstanz, Germany
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
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34
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Yang ZY, Dong J, Yang ZJ, Yin M, Jiang HL, Lu AP, Chen X, Hou TJ, Cao DS. ChemFLuo: a web-server for structure analysis and identification of fluorescent compounds. Brief Bioinform 2020; 22:5985287. [PMID: 33201188 DOI: 10.1093/bib/bbaa282] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 09/12/2020] [Accepted: 09/25/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Fluorescent detection methods are indispensable tools for chemical biology. However, the frequent appearance of potential fluorescent compound has greatly interfered with the recognition of compounds with genuine activity. Such fluorescence interference is especially difficult to identify as it is reproducible and possesses concentration-dependent characteristic. Therefore, the development of a credible screening tool to detect fluorescent compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS In this study, we developed a webserver ChemFLuo for fluorescent compound detection, based on two large and high-quality training datasets containing 4906 blue and 8632 green fluorescent compounds. These molecules were used to construct a group of prediction models based on the combination of three machine learning algorithms and seven types of molecular representations. The best blue fluorescence prediction model achieved with balanced accuracy (BA) = 0.858 and area under the receiver operating characteristic curve (AUC) = 0.931 for the validation set, and BA = 0.823 and AUC = 0.903 for the test set. The best green fluorescence prediction model achieved the prediction accuracy with BA = 0.810 and AUC = 0.887 for the validation set, and BA = 0.771 and AUC = 0.852 for the test set. Besides prediction model, 22 blue and 16 green representative fluorescent substructures were summarized for the screening of potential fluorescent compounds. The comparison with other fluorescence detection tools and theapplication to external validation sets and large molecule libraries have demonstrated the reliability of prediction model for fluorescent compound detection. CONCLUSION ChemFLuo is a public webserver to filter out compounds with undesirable fluorescent properties, which will benefit the design of high-quality chemical libraries for drug discovery. It is freely available at http://admet.scbdd.com/chemfluo/index/.
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Jie Dong
- Central South University of Forestry and Technology, Changsha, 410004, P.R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Mingzhu Yin
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Hong-Li Jiang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
| | - Xiang Chen
- Department of Dermatology, Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, P.R. China
| | - Ting-Jun Hou
- Institute of Innovative Medicine, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410003, P. R. China.,Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong SAR, P.R. China
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35
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Tinkov O, Polishchuk P, Matveieva M, Grigorev V, Grigoreva L, Porozov Y. The Influence of Structural Patterns on Acute Aquatic Toxicity of Organic Compounds. Mol Inform 2020; 40:e2000209. [PMID: 33029954 DOI: 10.1002/minf.202000209] [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: 08/14/2020] [Accepted: 10/01/2020] [Indexed: 12/28/2022]
Abstract
Investigation of the influence of molecular structure of different organic compounds on acute toxicity towards Fathead minnow, Daphnia magna, and Tetrahymena pyriformis has been carried out using 2D simplex representation of molecular structure and two modelling methods: Random Forest (RF) and Gradient Boosting Machine (GBM). Suitable QSAR (Quantitative Structure - Activity Relationships) models were obtained. The study was focused on QSAR models interpretation. The aim of the study was to develop a set of structural fragments that simultaneously consistently increase toxicity toward Fathead minnow, Daphnia magna, Tetrahymena pyriformis. The interpretation allowed to gain more details about known toxicophores and to propose new fragments. The results obtained made it possible to rank the contributions of molecular fragments to various types of toxicity to aquatic organisms. This information can be used for molecular optimization of chemicals. According to the results of structural interpretation, the most significant common mechanisms of the toxic effect of organic compounds on Fathead minnow, Daphnia magna and Tetrahymena pyriformis are reactions of nucleophilic substitution and inhibition of oxidative phosphorylation in mitochondria. In addition acetylcholinesterase and voltage-gated ion channel of Fathead minnow and Daphnia magna are important targets for toxicants. The on-line version of the OCHEM expert system (https://ochem.eu) were used for a comparative QSAR investigation. The proposed QSAR models comply with the OECD principles and can be used to reliably predict acute toxicity of organic compounds towards Fathead minnow, Daphnia magna and Tetrahymena pyriformis with allowance for applicability domain estimation.
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Affiliation(s)
- Oleg Tinkov
- Department of Computer Science, Military Institute of the Ministry of Defense, 3300, Gogol str. 2"B", Tiraspol, Transdniestria, Moldova.,Department of Pharmacology and Pharmaceutical Chemistry, Medical Faculty, Transnistrian State University, 3300, October 25 str. 128, Tiraspol, Transdniestria, Moldova
| | - Pavel Polishchuk
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Mariia Matveieva
- Institute of Molecular and Translational Medicine Faculty of Medicine and Dentistry Palacký University and University Hospital in Olomouc, Hnevotinska 5, 77900, Olomouc, Czech Republic
| | - Veniamin Grigorev
- Institute of Physiologically Active Compounds, Russian Academy of Sciences, 142432, Severniy proezd 1, Chernogolovka, Moscow region, Russia
| | - Ludmila Grigoreva
- Department of Fundamental Physical and Chemical Engineering, Moscow State University, 119991, Leninskiye Gory 1/51, Moscow, Russia
| | - Yuri Porozov
- World-Class Research Center "Digital biodesign and personalized healthcare", I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Department of Computational Biology, Sirius University of Science and Technology, 354340, Olympic Ave 1, Sochi, Russia
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36
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Gilmour N, Kern PS, Alépée N, Boislève F, Bury D, Clouet E, Hirota M, Hoffmann S, Kühnl J, Lalko JF, Mewes K, Miyazawa M, Nishida H, Osmani A, Petersohn D, Sekine S, van Vliet E, Klaric M. Development of a next generation risk assessment framework for the evaluation of skin sensitisation of cosmetic ingredients. Regul Toxicol Pharmacol 2020; 116:104721. [DOI: 10.1016/j.yrtph.2020.104721] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/16/2020] [Accepted: 06/19/2020] [Indexed: 12/17/2022]
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37
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Yang ZY, Yang ZJ, Lu AP, Hou TJ, Cao DS. Scopy: an integrated negative design python library for desirable HTS/VS database design. Brief Bioinform 2020; 22:5901981. [PMID: 32892221 DOI: 10.1093/bib/bbaa194] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/27/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND High-throughput screening (HTS) and virtual screening (VS) have been widely used to identify potential hits from large chemical libraries. However, the frequent occurrence of 'noisy compounds' in the screened libraries, such as compounds with poor drug-likeness, poor selectivity or potential toxicity, has greatly weakened the enrichment capability of HTS and VS campaigns. Therefore, the development of comprehensive and credible tools to detect noisy compounds from chemical libraries is urgently needed in early stages of drug discovery. RESULTS In this study, we developed a freely available integrated python library for negative design, called Scopy, which supports the functions of data preparation, calculation of descriptors, scaffolds and screening filters, and data visualization. The current version of Scopy can calculate 39 basic molecular properties, 3 comprehensive molecular evaluation scores, 2 types of molecular scaffolds, 6 types of substructure descriptors and 2 types of fingerprints. A number of important screening rules are also provided by Scopy, including 15 drug-likeness rules (13 drug-likeness rules and 2 building block rules), 8 frequent hitter rules (four assay interference substructure filters and four promiscuous compound substructure filters), and 11 toxicophore filters (five human-related toxicity substructure filters, three environment-related toxicity substructure filters and three comprehensive toxicity substructure filters). Moreover, this library supports four different visualization functions to help users to gain a better understanding of the screened data, including basic feature radar chart, feature-feature-related scatter diagram, functional group marker gram and cloud gram. CONCLUSION Scopy provides a comprehensive Python package to filter out compounds with undesirable properties or substructures, which will benefit the design of high-quality chemical libraries for drug design and discovery. It is freely available at https://github.com/kotori-y/Scopy.
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Affiliation(s)
- Zi-Yi Yang
- Xiangya School of Pharmaceutical Sciences, Central South University (Changsha)
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong
| | - Ting-Jun Hou
- College of Pharmaceutical Sciences, Zhejiang University, China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, China
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38
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Borba JVB, Braga RC, Alves VM, Muratov EN, Kleinstreuer N, Tropsha A, Andrade CH. Pred-Skin: A Web Portal for Accurate Prediction of Human Skin Sensitizers. Chem Res Toxicol 2020; 34:258-267. [PMID: 32673477 DOI: 10.1021/acs.chemrestox.0c00186] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Safety assessment is an essential component of the regulatory acceptance of industrial chemicals. Previously, we have developed a model to predict the skin sensitization potential of chemicals for two assays, the human patch test and murine local lymph node assay, and implemented this model in a web portal. Here, we report on the substantially revised and expanded freely available web tool, Pred-Skin version 3.0. This up-to-date version of Pred-Skin incorporates multiple quantitative structure-activity relationship (QSAR) models developed with in vitro, in chemico, and mice and human in vivo data, integrated into a consensus naïve Bayes model that predicts human effects. Individual QSAR models were generated using skin sensitization data derived from human repeat insult patch tests, human maximization tests, and mouse local lymph node assays. In addition, data for three validated alternative methods, the direct peptide reactivity assay, KeratinoSens, and the human cell line activation test, were employed as well. Models were developed using open-source tools and rigorously validated according to the best practices of QSAR modeling. Predictions obtained from these models were then used to build a naïve Bayes model for predicting human skin sensitization with the following external prediction accuracy: correct classification rate (89%), sensitivity (94%), positive predicted value (91%), specificity (84%), and negative predicted value (89%). As an additional assessment of model performance, we identified 11 cosmetic ingredients known to cause skin sensitization but were not included in our training set, and nine of them were accurately predicted as sensitizers by our models. Pred-Skin can be used as a reliable alternative to animal tests for predicting human skin sensitization.
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Affiliation(s)
- Joyce V B Borba
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil.,Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | | | - Vinicius M Alves
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.,Department of Pharmaceutical Sciences, Federal University of Paraíba, João Pessoa, Paraíba 58059, Brazil
| | - Nicole Kleinstreuer
- National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Alexander Tropsha
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Carolina Horta Andrade
- Laboratory for Molecular Modeling and Drug Design, Faculty of Pharmacy, Universidade Federal de Goiás, Goiânia, Goiás 74605-170, Brazil
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39
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Tinkov OV, Grigorev VY, Razdolsky AN, Grigoryeva LD, Dearden JC. Effect of the structural factors of organic compounds on the acute toxicity toward Daphnia magna. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2020; 31:615-641. [PMID: 32713201 DOI: 10.1080/1062936x.2020.1791250] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 06/30/2020] [Indexed: 06/11/2023]
Abstract
The acute toxicity of organic compounds towards Daphina magna was subjected to QSAR analysis. The two-dimensional simplex representation of molecular structure (2D SiRMS) and the support vector machine (SVM), gradient boosting (GBM) methods were used to develop QSAR models. Adequate regression QSAR models were developed for incubation of 24 h. Their interpretation allowed us to quantitatively describe and rank the well-known toxicophores, to refine their molecular surroundings, and to distinguish the structural derivatives of the fragments that significantly contribute to the acute toxicity (LC50) of organic compounds towards D. magna. Based on the results of the interpretation of the regression models, a molecular design (modification) of highly toxic compounds was performed in order to reduce their hazard. In addition, acceptable classification QSAR models were developed to reliably predict the following mode of action (MOA): specific and non-specific toxicity of organic compounds towards D. magna. When interpreting these models, we were able to determine the structural fragments and the physicochemical characteristics of molecules that are responsible for the manifestation of one of the modes of action. The on-line version of the OCHEM expert system (https://ochem.eu), HYBOT descriptors, and the random forest and SVM methods were used for a comparative QSAR investigation.
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Affiliation(s)
- O V Tinkov
- Department of Computer Science, Military Institute of the Ministry of Defense , Tiraspol, Moldova
| | - V Y Grigorev
- Department of Computer-aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka, Russia
| | - A N Razdolsky
- Department of Computer-aided Molecular Design, Institute of Physiologically Active Compounds of the Russian Academy of Science , Chernogolovka, Russia
| | - L D Grigoryeva
- Department of Fundamental Physicochemical Engineering, Moscow State University , Moscow, Russia
| | - J C Dearden
- School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University , Liverpool, UK
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40
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Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov D, Poroikov V, Oprea TI, Baskin II, Varnek A, Roitberg A, Isayev O, Curtarolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis D, Cherkasov A, Tropsha A. QSAR without borders. Chem Soc Rev 2020; 49:3525-3564. [PMID: 32356548 PMCID: PMC8008490 DOI: 10.1039/d0cs00098a] [Citation(s) in RCA: 319] [Impact Index Per Article: 79.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Prediction of chemical bioactivity and physical properties has been one of the most important applications of statistical and more recently, machine learning and artificial intelligence methods in chemical sciences. This field of research, broadly known as quantitative structure-activity relationships (QSAR) modeling, has developed many important algorithms and has found a broad range of applications in physical organic and medicinal chemistry in the past 55+ years. This Perspective summarizes recent technological advances in QSAR modeling but it also highlights the applicability of algorithms, modeling methods, and validation practices developed in QSAR to a wide range of research areas outside of traditional QSAR boundaries including synthesis planning, nanotechnology, materials science, biomaterials, and clinical informatics. As modern research methods generate rapidly increasing amounts of data, the knowledge of robust data-driven modelling methods professed within the QSAR field can become essential for scientists working both within and outside of chemical research. We hope that this contribution highlighting the generalizable components of QSAR modeling will serve to address this challenge.
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Affiliation(s)
- Eugene N Muratov
- UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA.
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41
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Sharma R. Data science-driven analyses of drugs inducing hypertension as an adverse effect. Mol Divers 2020; 25:801-810. [PMID: 32415493 DOI: 10.1007/s11030-020-10059-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Accepted: 02/21/2020] [Indexed: 12/15/2022]
Abstract
The utilization of approved medication is a requisite to combat certain diseases for health; however, the undesirable adverse effects (AEs) due to medication are generally unavoidable. Hypertension is one of such AEs resulting from approved medication in which blood pressure in the arteries gets elevated and is a risk factor for several diseases including heart and kidney failure. HTs are the approved drugs that can cause hypertension as an AE. Here, the goal of the study is to investigate the structural and functional diversities of HTs. In our quest to unravel the structural parameters of the HTs, a systematic analysis of the HTs having a different number and type of ring systems was conducted. The cellular component, molecular function and biological processes adopted by the gene products were analyzed. Moreover, our systematically done analysis suggests that all the target families are active in a common pathway, that is, nerve transmission. A comparison of the selected structural and functional aspect of HTs with anti-hypertensives suggests that HTs follow certain structural and functional features in spite of many possibilities. Our study provides a promising methodology that considers the influence of structural diversity of AE causing agents on a functional perspective for precursory clinical decision making. This could be extended to explore the structural and functional trends that are adopted by agents causing certain diseases or AEs.
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Affiliation(s)
- Reetu Sharma
- CSIR-Indian Institute of Chemical Technology, Tarnaka, Hyderabad, 500007, India.
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42
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Hemmerich J, Troger F, Füzi B, F.Ecker G. Using Machine Learning Methods and Structural Alerts for Prediction of Mitochondrial Toxicity. Mol Inform 2020; 39:e2000005. [PMID: 32108997 PMCID: PMC7317375 DOI: 10.1002/minf.202000005] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 02/25/2020] [Indexed: 02/05/2023]
Abstract
Over the last few years more and more organ and idiosyncratic toxicities were linked to mitochondrial toxicity. Despite well-established assays, such as the seahorse and Glucose/Galactose assay, an in silico approach to mitochondrial toxicity is still feasible, particularly when it comes to the assessment of large compound libraries. Therefore, in silico approaches could be very beneficial to indicate hazards early in the drug development pipeline. By combining multiple endpoints, we derived the largest so far published dataset on mitochondrial toxicity. A thorough data analysis shows that molecules causing mitochondrial toxicity can be distinguished by physicochemical properties. Finally, the combination of machine learning and structural alerts highlights the suitability for in silico risk assessment of mitochondrial toxicity.
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Affiliation(s)
- Jennifer Hemmerich
- University of ViennaDepartment of Pharmaceutical ChemistryAlthanstr. 141090ViennaAustria
| | - Florentina Troger
- University of ViennaDepartment of Pharmaceutical ChemistryAlthanstr. 141090ViennaAustria
| | - Barbara Füzi
- University of ViennaDepartment of Pharmaceutical ChemistryAlthanstr. 141090ViennaAustria
| | - Gerhard F.Ecker
- University of ViennaDepartment of Pharmaceutical ChemistryAlthanstr. 141090ViennaAustria
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43
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Hemmerich J, Ecker GF. In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020; 10:e1475. [PMID: 35866138 PMCID: PMC9286356 DOI: 10.1002/wcms.1475] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 03/09/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap‐filling and guide risk minimization strategies. Techniques such as structural alerts, read‐across, quantitative structure–activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics
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Affiliation(s)
- Jennifer Hemmerich
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
| | - Gerhard F. Ecker
- Department of Pharmaceutical Chemistry University of Vienna Vienna Austria
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44
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Drakakis G, Cortés-Ciriano I, Alexander-Dann B, Bender A. Elucidating Compound Mechanism of Action and Predicting Cytotoxicity Using Machine Learning Approaches, Taking Prediction Confidence into Account. ACTA ACUST UNITED AC 2020; 11:e73. [PMID: 31483099 DOI: 10.1002/cpch.73] [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: 02/06/2023]
Abstract
The modes of action (MoAs) of drugs frequently are unknown, because many are small molecules initially identified from phenotypic screens, giving rise to the need to elucidate their MoAs. In addition, the high attrition rate for candidate drugs in preclinical studies due to intolerable toxicity has motivated the development of computational approaches to predict drug candidate (cyto)toxicity as early as possible in the drug-discovery process. Here, we provide detailed instructions for capitalizing on bioactivity predictions to elucidate the MoAs of small molecules and infer their underlying phenotypic effects. We illustrate how these predictions can be used to infer the underlying antidepressive effects of marketed drugs. We also provide the necessary functionalities to model cytotoxicity data using single and ensemble machine-learning algorithms. Finally, we give detailed instructions on how to calculate confidence intervals for individual predictions using the conformal prediction framework. © 2019 by John Wiley & Sons, Inc.
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Affiliation(s)
- Georgios Drakakis
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Isidro Cortés-Ciriano
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Ben Alexander-Dann
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
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45
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Baderna D, Gadaleta D, Lostaglio E, Selvestrel G, Raitano G, Golbamaki A, Lombardo A, Benfenati E. New in silico models to predict in vitro micronucleus induction as marker of genotoxicity. JOURNAL OF HAZARDOUS MATERIALS 2020; 385:121638. [PMID: 31757721 DOI: 10.1016/j.jhazmat.2019.121638] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 11/03/2019] [Accepted: 11/07/2019] [Indexed: 06/10/2023]
Abstract
The evaluation of genotoxicity is a fundamental part of the safety assessment of chemicals due to the relevance of the potential health effects of genotoxicants. Among the testing methods available, the in vitro micronucleus assay with mammalian cells is one of the most used and required by regulations targeting several industrial sectors such as the cosmetic industry and food-related sectors. As an alternative to the testing methods, in recent years, lots in silico methods were developed to predict the genotoxicity of chemicals, including models for the Ames mutagenicity test, the in vitro chromosomal aberrations and the in vivo micronucleus assay. We developed several in silico models for the prediction of genotoxicity as reflected by the in vitro micronucleus assay. The resulting models include both statistical and knowledge-based models. The most promising model is the one based on fragments extracted with the SARpy platform. More than 100 structural alerts were extracted, including also fragments associated with the non-genotoxic activity. The model is characterized by high accuracy and the lowest false negative rate, making this tool suitable for chemical screening according to the regulators' needs. The SARpy model will be implemented on the VEGA platform (https://www.vegahub.eu) and will be freely available.
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Affiliation(s)
- Diego Baderna
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy.
| | - Domenico Gadaleta
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Eleonora Lostaglio
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Gianluca Selvestrel
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Giuseppa Raitano
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Azadi Golbamaki
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Anna Lombardo
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
| | - Emilio Benfenati
- Laboratory of Environmental Chemistry and Toxicology, Environmental Health Department, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milano, Italy
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Yang H, Lou C, Li W, Liu G, Tang Y. Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery. Chem Res Toxicol 2020; 33:1312-1322. [DOI: 10.1021/acs.chemrestox.0c00006] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Hongbin Yang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Chaofeng Lou
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Weihua Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Guixia Liu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Yun Tang
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
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47
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Schaduangrat N, Lampa S, Simeon S, Gleeson MP, Spjuth O, Nantasenamat C. Towards reproducible computational drug discovery. J Cheminform 2020; 12:9. [PMID: 33430992 PMCID: PMC6988305 DOI: 10.1186/s13321-020-0408-x] [Citation(s) in RCA: 78] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 01/02/2020] [Indexed: 12/11/2022] Open
Abstract
The reproducibility of experiments has been a long standing impediment for further scientific progress. Computational methods have been instrumental in drug discovery efforts owing to its multifaceted utilization for data collection, pre-processing, analysis and inference. This article provides an in-depth coverage on the reproducibility of computational drug discovery. This review explores the following topics: (1) the current state-of-the-art on reproducible research, (2) research documentation (e.g. electronic laboratory notebook, Jupyter notebook, etc.), (3) science of reproducible research (i.e. comparison and contrast with related concepts as replicability, reusability and reliability), (4) model development in computational drug discovery, (5) computational issues on model development and deployment, (6) use case scenarios for streamlining the computational drug discovery protocol. In computational disciplines, it has become common practice to share data and programming codes used for numerical calculations as to not only facilitate reproducibility, but also to foster collaborations (i.e. to drive the project further by introducing new ideas, growing the data, augmenting the code, etc.). It is therefore inevitable that the field of computational drug design would adopt an open approach towards the collection, curation and sharing of data/code.
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Affiliation(s)
- Nalini Schaduangrat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand
| | - Samuel Lampa
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden
| | - Saw Simeon
- Interdisciplinary Graduate Program in Bioscience, Faculty of Science, Kasetsart University, 10900, Bangkok, Thailand
| | - Matthew Paul Gleeson
- Department of Biomedical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang, 10520, Bangkok, Thailand.
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, 751 24, Uppsala, Sweden.
| | - Chanin Nantasenamat
- Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, 10700, Bangkok, Thailand.
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48
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Wedlake AJ, Folia M, Piechota S, Allen TEH, Goodman JM, Gutsell S, Russell PJ. Structural Alerts and Random Forest Models in a Consensus Approach for Receptor Binding Molecular Initiating Events. Chem Res Toxicol 2020; 33:388-401. [PMID: 31850746 DOI: 10.1021/acs.chemrestox.9b00325] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
A molecular initiating event (MIE) is the gateway to an adverse outcome pathway (AOP), a sequence of events ending in an adverse effect. In silico predictions of MIEs are a vital tool in a modern, mechanism-focused approach to chemical risk assessment. For 90 biological targets representing important human MIEs, structural alert-based models have been constructed with an automated procedure that uses Bayesian statistics to iteratively select substructures. These models give impressive average performance statistics (an average of 92% correct predictions across targets), significantly improving on previous models. Random Forest models have been constructed from physicochemical features for the same targets, giving similarly impressive performance statistics (93% correct predictions). A key difference between the models is interpretation of predictions-the structural alert models are transparent and easy to interpret, while Random Forest models can only identify the most important physicochemical features for making predictions. The two complementary models have been combined in a consensus model, improving performance compared to each individual model (94% correct predictions) and increasing confidence in predictions. Variation in model performance has been explained by calculating a modelability index (MODI), using Tanimoto coefficient between Morgan fingerprints to identify nearest neighbor chemicals. This work is an important step toward building confidence in the use of in silico tools for assessment of toxicity.
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Affiliation(s)
- Andrew J Wedlake
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge , CB2 1EW , United Kingdom
| | - Maria Folia
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
| | - Sam Piechota
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
| | - Timothy E H Allen
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge , CB2 1EW , United Kingdom.,MRC Toxicology Unit , University of Cambridge , Lancaster Road , Leicester LE19HN , United Kingdom
| | - Jonathan M Goodman
- Centre for Molecular Informatics, Department of Chemistry , University of Cambridge , Lensfield Road , Cambridge , CB2 1EW , United Kingdom
| | - Steve Gutsell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
| | - Paul J Russell
- Unilever Safety and Environmental Assurance Centre , Colworth Science Park , Sharnbrook , Bedfordshire , MK44 1LQ , United Kingdom
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49
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Howard AS, Choksi N. Evaluation of two in silico programs for predicting mutagenicity and carcinogenicity potential for 4-methylimidazole (4-MeI) and known metabolites. Toxicol Mech Methods 2020; 30:246-256. [PMID: 31903850 DOI: 10.1080/15376516.2019.1709237] [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: 10/25/2022]
Abstract
4-Methylimidazole (4-MeI) is a nitrogen-containing heterocyclic compound that is used in the manufacture of chemicals, dyes and pharmaceuticals and may be found in a variety of foods following formation during heating. The purpose of this study was to use two different in silico programs, CASE Ultra and Toxtree, to investigate potential structure-activity relationships in 4-MeI and its metabolites for mutagenicity and carcinogenicity, and combine that information with the available literature to draw conclusions regarding the strength of the predictions observed. Neither CASE Ultra nor Toxtree identified any structural alerts that were associated with mutagenic activity. Data for 4-MeI from a single study were used in the development of the CASE Ultra mouse and rat carcinogenicity models, but no additional similar structures were identified in the carcinogenicity model training set. One metabolite, 5-methylhydantoin, was predicted to be positive in the CASE Ultra carcinogenicity male and female mouse models; positive predictivity percentages of 60.9% and 73.7%, respectively. However, low structural similarity between 5-methylhydantoin and the compounds identified in the training set (<25%) decreases confidence in the positive prediction. Three metabolites were predicted to be positive in the CASE Ultra mouse micronucleus model, but again suffered from low structural similarity. Both limited structural similarity and inconsistent responses among the other clastogenicity models suggest that additional structurally similar compounds are needed to assess the predictive capacity of these alerts for biological activity of these compounds.
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Affiliation(s)
- Angela S Howard
- Integrated Laboratory Systems, Inc, Research Triangle Park, NC, USA
| | - Neepa Choksi
- Integrated Laboratory Systems, Inc, Research Triangle Park, NC, USA
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50
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Cavalluzzi MM, Imbrici P, Gualdani R, Stefanachi A, Mangiatordi GF, Lentini G, Nicolotti O. Human ether-à-go-go-related potassium channel: exploring SAR to improve drug design. Drug Discov Today 2019; 25:344-366. [PMID: 31756511 DOI: 10.1016/j.drudis.2019.11.005] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 10/22/2019] [Accepted: 11/12/2019] [Indexed: 12/12/2022]
Abstract
hERG is best known as a primary anti-target, the inhibition of which is responsible for serious side effects. A renewed interest in hERG as a desired target, especially in oncology, was sparked because of its role in cellular proliferation and apoptosis. In this study, we survey the most recent advances regarding hERG by focusing on SAR in the attempt to elucidate, at a molecular level, off-target and on-target actions of potential hERG binders, which are highly promiscuous and largely varying in structure. Understanding the rationale behind hERG interactions and the molecular determinants of hERG activity is a real challenge and comprehension of this is of the utmost importance to prioritize compounds in early stages of drug discovery and to minimize cardiotoxicity attrition in preclinical and clinical studies.
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Affiliation(s)
- Maria Maddalena Cavalluzzi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona, 4, 70126 Bari, Italy
| | - Paola Imbrici
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona, 4, 70126 Bari, Italy
| | - Roberta Gualdani
- Laboratory of Cell Physiology, Institute of Neuroscience, Université Catholique de Louvain, Brussels 1200, Belgium
| | - Angela Stefanachi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona, 4, 70126 Bari, Italy
| | | | - Giovanni Lentini
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona, 4, 70126 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli Studi di Bari 'Aldo Moro', Via E. Orabona, 4, 70126 Bari, Italy.
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