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James BD, Medvedev AV, Makarov SS, Nelson RK, Reddy CM, Hahn ME. Moldable Plastics (Polycaprolactone) can be Acutely Toxic to Developing Zebrafish and Activate Nuclear Receptors in Mammalian Cells. ACS Biomater Sci Eng 2024. [PMID: 38981095 DOI: 10.1021/acsbiomaterials.4c00693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
Popularized on social media, hand-moldable plastics are formed by consumers into tools, trinkets, and dental prosthetics. Despite the anticipated dermal and oral contact, manufacturers share little information with consumers about these materials, which are typically sold as microplastic-sized resin pellets. Inherent to their function, moldable plastics pose a risk of dermal and oral exposure to unknown leachable substances. We analyzed 12 moldable plastics advertised for modeling and dental applications and determined them to be polycaprolactone (PCL) or thermoplastic polyurethane (TPU). The bioactivities of the most popular brands advertised for modeling applications of each type of polymer were evaluated using a zebrafish embryo bioassay. While water-borne exposure to the TPU pellets did not affect the targeted developmental end points at any concentration tested, the PCL pellets were acutely toxic above 1 pellet/mL. The aqueous leachates of the PCL pellets demonstrated similar toxicity. Methanolic extracts from the PCL pellets were assayed for their bioactivity using the Attagene FACTORIAL platform. Of the 69 measured end points, the extracts activated nuclear receptors and transcription factors for xenobiotic metabolism (pregnane X receptor, PXR), lipid metabolism (peroxisome proliferator-activated receptor γ, PPARγ), and oxidative stress (nuclear factor erythroid 2-related factor 2, NRF2). By nontargeted high-resolution comprehensive two-dimensional gas chromatography (GC × GC-HRT), we tentatively identified several compounds in the methanolic extracts, including PCL oligomers, a phenolic antioxidant, and residues of suspected antihydrolysis and cross-linking additives. In a follow-up zebrafish embryo bioassay, because of its stated high purity, biomedical grade PCL was tested to mitigate any confounding effects due to chemical additives in the PCL pellets; it elicited comparable acute toxicity. From these orthogonal and complementary experiments, we suggest that the toxicity was due to oligomers and nanoplastics released from the PCL rather than chemical additives. These results challenge the perceived and assumed inertness of plastics and highlight their multiple sources of toxicity.
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Affiliation(s)
- Bryan D James
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
- Department of Biology, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
| | - Alexander V Medvedev
- Attagene, Research Triangle Park, Morrisville, North Carolina 27709, United States
| | - Sergei S Makarov
- Attagene, Research Triangle Park, Morrisville, North Carolina 27709, United States
| | - Robert K Nelson
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
| | - Christopher M Reddy
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
| | - Mark E Hahn
- Department of Biology, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, United States
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2
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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [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: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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3
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Schäfer AM, Rysz MA, Schädeli J, Hübscher M, Khosravi H, Fehr M, Seibert I, Potterat O, Smieško M, Meyer Zu Schwabedissen HE. St. John's Wort Formulations Induce Rat CYP3A23-3A1 Independent of Their Hyperforin Content. Mol Pharmacol 2023; 105:14-22. [PMID: 37863663 DOI: 10.1124/molpharm.123.000725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/08/2023] [Accepted: 09/21/2023] [Indexed: 10/22/2023] Open
Abstract
The pregnane X receptor (PXR) is a ligand-activated regulator of cytochrome P450 (CYP)3A enzymes. Among the ligands of human PXR is hyperforin, a constituent of St John's wort (SJW) extracts and potent inducer of human CYP3A4. It was the aim of this study to compare the effect of hyperforin and SJW formulations controlled for its content on CYP3A23-3A1 in rats. Hyperiplant was used as it contains a high hyperforin content and Rebalance because it is controlled for a low hyperforin content. In silico analysis revealed a weak hyperforin-rPXR binding affinity, which was further supported in cell-based reporter gene assays showing no hyperforin-mediated reporter activation in presence of rPXR. However, cellular exposure to Hyperiplant and Rebalance transactivated the CYP3A reporter 3.8-fold and 2.8-fold, respectively, and they induced Cyp3a23-3a1 mRNA expression in rat hepatoma cells compared with control 48-fold and 18-fold, respectively. In Wistar rats treated for 10 days with 400 mg/kg of Hyperiplant, we observed 1.8 times the Cyp3a23-3a1 mRNA expression, a 2.6-fold higher CYP3A23-3A1 protein amount, and a 1.6-fold increase in activity compared with controls. For Rebalance we only observed a 1.8-fold hepatic increase of CYP3A23-3A1 protein compared with control animals. Even though there are differing effects on rCyp3a23-3a1/CYP3A23-3A1 in rat liver reflecting the hyperforin content of the SJW extracts, the modulation is most likely not linked to an interaction of hyperforin with rPXR. SIGNIFICANCE STATEMENT: Treatment with St John's wort (SJW) has been reported to affect CYP3A expression and activity in rats. Our comparative study further supports this finding but shows that the pregnane X receptor-ligand hyperforin is not the driving force for changes in rat CYP3A23-3A1 expression and function in vivo and in vitro. Importantly, CYP3A induction mimics findings in humans, but our results suggest that another so far unknown constituent of SJW is responsible for the expression- and function-modifying effects in rat liver.
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Affiliation(s)
- Anima M Schäfer
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Marta A Rysz
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Julia Schädeli
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Michelle Hübscher
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Haleh Khosravi
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Michelle Fehr
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Isabell Seibert
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Olivier Potterat
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Martin Smieško
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
| | - Henriette E Meyer Zu Schwabedissen
- Biopharmacy (A.M.S., M.A.R., J.S., M.H., H.K., M.F., I.S., H.E.M.), Computational Pharmacy (M.S.), and Pharmaceutical Biology (O.P.), Department of Pharmaceutical Sciences, University of Basel, Basel, Switzerland
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4
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AbdulHameed MDM, Liu R, Wallqvist A. Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors. ACS OMEGA 2023; 8:21853-21861. [PMID: 37360478 PMCID: PMC10286257 DOI: 10.1021/acsomega.3c01583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 05/19/2023] [Indexed: 06/28/2023]
Abstract
The bile salt export pump (BSEP) is a key transporter involved in the efflux of bile salts from hepatocytes to bile canaliculi. Inhibition of BSEP leads to the accumulation of bile salts within the hepatocytes, leading to possible cholestasis and drug-induced liver injury. Screening for and identification of chemicals that inhibit this transporter aid in understanding the safety liabilities of these chemicals. Moreover, computational approaches to identify BSEP inhibitors provide an alternative to the more resource-intensive, gold standard experimental approaches. Here, we used publicly available data to develop predictive machine learning models for the identification of potential BSEP inhibitors. Specifically, we analyzed the utility of a graph convolutional neural network (GCNN)-based approach in combination with multitask learning to identify BSEP inhibitors. Our analyses showed that the developed GCNN model performed better than the variable-nearest neighbor and Bayesian machine learning approaches, with a cross-validation receiver operating characteristic area under the curve of 0.86. In addition, we compared GCNN-based single-task and multitask models and evaluated their utility in addressing data limitation challenges commonly observed in bioactivity modeling. We found that multitask models performed better than single-task models and can be utilized to identify active molecules for targets with limited data availability. Overall, our developed multitask GCNN-based BSEP model provides a useful tool for prioritizing hits during early drug discovery and in risk assessment of chemicals.
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Affiliation(s)
- Mohamed Diwan M. AbdulHameed
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
- The
Henry M. Jackson Foundation for the Advancement of Military Medicine,
Inc., Bethesda 20817, Maryland, United States
| | - Ruifeng Liu
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
- The
Henry M. Jackson Foundation for the Advancement of Military Medicine,
Inc., Bethesda 20817, Maryland, United States
| | - Anders Wallqvist
- Department
of Defense Biotechnology High Performance Computing Software Applications
Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick 21702, Maryland, United States
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5
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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6
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Hirte S, Burk O, Tahir A, Schwab M, Windshügel B, Kirchmair J. Development and Experimental Validation of Regularized Machine Learning Models Detecting New, Structurally Distinct Activators of PXR. Cells 2022; 11:cells11081253. [PMID: 35455933 PMCID: PMC9029776 DOI: 10.3390/cells11081253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 03/30/2022] [Indexed: 02/04/2023] Open
Abstract
The pregnane X receptor (PXR) regulates the metabolism of many xenobiotic and endobiotic substances. In consequence, PXR decreases the efficacy of many small-molecule drugs and induces drug-drug interactions. The prediction of PXR activators with theoretical approaches such as machine learning (ML) proves challenging due to the ligand promiscuity of PXR, which is related to its large and flexible binding pocket. In this work we demonstrate, by the example of random forest models and support vector machines, that classifiers generated following classical training procedures often fail to predict PXR activity for compounds that are dissimilar from those in the training set. We present a novel regularization technique that penalizes the gap between a model’s training and validation performance. On a challenging test set, this technique led to improvements in Matthew correlation coefficients (MCCs) by up to 0.21. Using these regularized ML models, we selected 31 compounds that are structurally distinct from known PXR ligands for experimental validation. Twelve of them were confirmed as active in the cellular PXR ligand-binding domain assembly assay and more hits were identified during follow-up studies. Comprehensive analysis of key features of PXR biology conducted for three representative hits confirmed their ability to activate the PXR.
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Affiliation(s)
- Steffen Hirte
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
| | - Oliver Burk
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (O.B.); (M.S.)
| | - Ammar Tahir
- Division of Pharmacognosy, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, University of Tübingen, 70376 Stuttgart, Germany; (O.B.); (M.S.)
- Departments of Clinical Pharmacology and Biochemistry and Pharmacy, University of Tuebingen, 72074 Tübingen, Germany
- Cluster of Excellence IFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, 72074 Tübingen, Germany
| | - Björn Windshügel
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Discovery Research Screening Port, 22525 Hamburg, Germany;
- Department of Life Sciences and Chemistry, Jacobs University Bremen, 28759 Bremen, Germany
| | - Johannes Kirchmair
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
- Correspondence: ; Tel.: +43-1-4277-55104
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7
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Hall A, Chanteux H, Ménochet K, Ledecq M, Schulze MSED. Designing Out PXR Activity on Drug Discovery Projects: A Review of Structure-Based Methods, Empirical and Computational Approaches. J Med Chem 2021; 64:6413-6522. [PMID: 34003642 DOI: 10.1021/acs.jmedchem.0c02245] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
This perspective discusses the role of pregnane xenobiotic receptor (PXR) in drug discovery and the impact of its activation on CYP3A4 induction. The use of structural biology to reduce PXR activity on drug discovery projects has become more common in recent years. Analysis of this work highlights several important molecular interactions, and the resultant structural modifications to reduce PXR activity are summarized. The computational approaches undertaken to support the design of new drugs devoid of PXR activation potential are also discussed. Finally, the SAR of empirical design strategies to reduce PXR activity is reviewed, and the key SAR transformations are discussed and summarized. In conclusion, this perspective demonstrates that PXR activity can be greatly diminished or negated on active drug discovery projects with the knowledge now available. This perspective should be useful to anyone who seeks to reduce PXR activity on a drug discovery project.
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Affiliation(s)
- Adrian Hall
- UCB, Avenue de l'Industrie, Braine-L'Alleud 1420, Belgium
| | | | | | - Marie Ledecq
- UCB, Avenue de l'Industrie, Braine-L'Alleud 1420, Belgium
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8
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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9
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Bower MJ, Aronov AM, Cleveland T, Hariparsad N, McGaughey GB, McMasters DR, Zhang X, Goldman B. Smallest Maximum Intramolecular Distance: A Novel Method to Mitigate Pregnane Xenobiotic Receptor Activation. J Chem Inf Model 2020; 60:2091-2099. [DOI: 10.1021/acs.jcim.9b00692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Michael J. Bower
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Alex M. Aronov
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Thomas Cleveland
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Niresh Hariparsad
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Georgia B. McGaughey
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Daniel R. McMasters
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Xiaodan Zhang
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
| | - Brian Goldman
- Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02210, United States
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10
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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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11
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Kato H. Computational prediction of cytochrome P450 inhibition and induction. Drug Metab Pharmacokinet 2019; 35:30-44. [PMID: 31902468 DOI: 10.1016/j.dmpk.2019.11.006] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/27/2019] [Accepted: 11/17/2019] [Indexed: 12/14/2022]
Abstract
Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.
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Affiliation(s)
- Harutoshi Kato
- DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Aoba-ku, Yokohama-shi, 227-0033, Japan.
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12
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 346] [Impact Index Per Article: 69.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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Fraser K, Bruckner DM, Dordick JS. Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies. Chem Res Toxicol 2018; 31:412-430. [PMID: 29722533 DOI: 10.1021/acs.chemrestox.8b00054] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.
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Affiliation(s)
- Keith Fraser
- Department of Chemical and Biological Engineering and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies , Rensselaer Polytechnic Institute , Troy , New York 12180 , United States
| | - Dylan M Bruckner
- Department of Chemical and Biological Engineering and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies , Rensselaer Polytechnic Institute , Troy , New York 12180 , United States
| | - Jonathan S Dordick
- Department of Chemical and Biological Engineering and Department of Biological Sciences Center for Biotechnology and Interdisciplinary Studies , Rensselaer Polytechnic Institute , Troy , New York 12180 , United States
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Villela-Ma LM, Velez-Ayal AK, Lopez-Sanc RDC, Martinez-C JA, Hernandez- JA. Advantages of Drug Selective Distribution in Cancer Treatment: Brentuximab Vedotin. INT J PHARMACOL 2017. [DOI: 10.3923/ijp.2017.785.807] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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15
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Yin C, Yang X, Wei M, Liu H. Predictive models for identifying the binding activity of structurally diverse chemicals to human pregnane X receptor. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2017; 24:20063-20071. [PMID: 28699014 DOI: 10.1007/s11356-017-9690-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2017] [Accepted: 06/30/2017] [Indexed: 06/07/2023]
Abstract
Toxic chemicals entered into human body would undergo a series of metabolism, transport and excretion, and the key roles played in there processes were metabolizing enzymes, which was regulated by the pregnane X receptor (PXR). However, some chemicals in environment could activate or antagonize human pregnane X receptor, thereby leading to a disturbance of normal physiological systems. In this study, based on a larger number of 2724 structurally diverse chemicals, we developed qualitative classification models by the k-nearest neighbor method. Moreover, the logarithm of 20 and 50% effective concentrations (log EC 20 and log EC 50) was used to establish quantitative structure-activity relationship (QSAR) models. With the classification model, two descriptors were enough to establish acceptable models, with the sensitivity, specificity, and accuracy being larger than 0.7, highlighting a high classification performance of the models. With two QSAR models, the statistics parameters with the correlation coefficient (R 2) of 0.702-0.749 and the cross-validation and external validation coefficient (Q 2) of 0.643-0.712, this indicated that the models complied with the criteria proposed in previous studies, i.e., R 2 > 0.6, Q 2 > 0.5. The small root mean square error (RMSE) of 0.254-0.414 and the good consistency between observed and predicted values proved satisfactory goodness of fit, robustness, and predictive ability of the developed QSAR models. Additionally, the applicability domains were characterized by the Euclidean distance-based approach and Williams plot, and results indicated that the current models had a wide applicability domain, which especially included a few classes of environmental contaminant, those that were not included in the previous models.
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Affiliation(s)
- Cen Yin
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094, China
| | - Xianhai Yang
- Ministry of Environmental Protection, Nanjing Institute of Environmental Sciences, Jiang-Wang-Miao Street, Nanjing, 210042, China.
| | - Mengbi Wei
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094, China
| | - Huihui Liu
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu Province, 210094, China.
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Xi L, Yao J, Wei Y, Wu X, Yao X, Liu H, Li S. The in silico identification of human bile salt export pump (ABCB11) inhibitors associated with cholestatic drug-induced liver injury. MOLECULAR BIOSYSTEMS 2017; 13:417-424. [PMID: 28092392 DOI: 10.1039/c6mb00744a] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Drug-induced liver injury (DILI) is one of the major causes of drug attrition and failure. Currently, there is increasing evidence that direct inhibition of the human bile salt export pump (BSEP/ABCB11) by drugs and/or metabolites is one of the most important mechanisms of cholestatic DILI. In the present study, we employ two in silico methods, random forest (RF) and the pharmacophore method, to recognize potential BSEP inhibitors that could cause cholestatic DILI, with the aim of mitigating the risk of cholestatic DILI to some extent. The RF model achieved the best prediction performance, producing AUC (area under receiver operating characteristic curve) values of 0.901, 0.929 and 0.996 for leave-one-out cross-validation, the test set and the external test set, respectively, indicating that the built RF model has a satisfactory identification ability. As a complement to the RF model, the pharmacophore model was also built and was proved to be reliable with good predictive performance based on the internal and external validation results. Further analysis indicates that hydrophobicity, molecular size and polarity are important factors that influence the inhibitory activity of BSEP. Furthermore, the two models are applied to screen FDA-approved small molecule drugs, among which the drugs with the potential risk of cholestatic DILI are reported. In conclusion, the RF and pharmacophore models that we present can be considered as integrated screening tools to indicate the potential risk of cholestatic DILI by inhibition of BSEP.
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Affiliation(s)
- Lili Xi
- Department of Pharmacy, The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, 730000, China
| | - Jia Yao
- Department of Science and Technology, The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, 730000, China
| | - Yuhui Wei
- Department of Pharmacy, The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, 730000, China
| | - Xin'an Wu
- Department of Pharmacy, The First Hospital of Lanzhou University, Lanzhou University, Lanzhou, 730000, China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, 730000, China.
| | - Huanxiang Liu
- School of Pharmacy, Lanzhou University, Lanzhou, 730000, China
| | - Shuyan Li
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, 730000, China.
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QSAR development and profiling of 72,524 REACH substances for PXR activation and CYP3A4 induction. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.comtox.2017.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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AbdulHameed MDM, Ippolito DL, Stallings JD, Wallqvist A. Mining kidney toxicogenomic data by using gene co-expression modules. BMC Genomics 2016; 17:790. [PMID: 27724849 PMCID: PMC5057266 DOI: 10.1186/s12864-016-3143-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2015] [Accepted: 09/29/2016] [Indexed: 12/15/2022] Open
Abstract
Background Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix—a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals—and identified gene modules associated with kidney injury to probe the molecular-level details of this disease. Results We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury. Conclusions Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3143-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mohamed Diwan M AbdulHameed
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, MD, 21702, USA
| | - Danielle L Ippolito
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702, USA
| | - Jonathan D Stallings
- U.S. Army Center for Environmental Health Research, 568 Doughten Drive, Fort Detrick, MD, 21702, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, 504 Scott Street, Fort Detrick, MD, 21702, USA.
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