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Hung TNK, Le NQK, Le NH, Tuan LV, Nguyen TP, Thi C, Kang JH. An AI-based prediction model for drug-drug interactions in osteoporosis and Paget's diseases from SMILES. Mol Inform 2022; 41:e2100264. [PMID: 34989149 DOI: 10.1002/minf.202100264] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/05/2022] [Indexed: 11/06/2022]
Abstract
Referring to common skeletal-related diseases, osteoporosis and Paget's are two of the most frequently found diseases in the elderly. Nowadays, the combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which contains various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, furthermore, to mitigate cost and time in implementing the best combination of medications in clinical practice. Our dataset was collected from the DrugBank database, and we then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms have been implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74% in predicting DDIs. It was superior to individual models and previous methods in most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.
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Affiliation(s)
| | | | | | | | | | - Cao Thi
- University of Medicine and Pharmacy at Ho Chi Minh City, VIET NAM
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2
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Raju B, Choudhary S, Narendra G, Verma H, Silakari O. Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review. Drug Metab Rev 2021; 53:45-75. [PMID: 33535824 DOI: 10.1080/03602532.2021.1874406] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.
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Affiliation(s)
- Baddipadige Raju
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
| | - Shalki Choudhary
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
| | - Gera Narendra
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
| | - Himanshu Verma
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
| | - Om Silakari
- Molecular Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug Research, Punjabi University, Patiala, India
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Zheng Z, Arp HPH, Peters G, Andersson PL. Combining In Silico Tools with Multicriteria Analysis for Alternatives Assessment of Hazardous Chemicals: Accounting for the Transformation Products of decaBDE and Its Alternatives. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:1088-1098. [PMID: 33381962 PMCID: PMC7871322 DOI: 10.1021/acs.est.0c02593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 12/14/2020] [Accepted: 12/15/2020] [Indexed: 06/12/2023]
Abstract
Transformation products ought to be an important consideration in chemical alternatives assessment. In this study, a recently established hazard ranking tool for alternatives assessment based on in silico data and multicriteria decision analysis (MCDA) methods was further developed to include chemical transformation products. Decabromodiphenyl ether (decaBDE) and five proposed alternatives were selected as case chemicals; biotic and abiotic transformation reactions were considered using five in silico tools. A workflow was developed to select transformation products with the highest occurrence potential. The most probable transformation products of the alternative chemicals were often similarly persistent but more mobile in aquatic environments, which implies an increasing exposure potential. When persistence (P), bioaccumulation (B), mobility in the aquatic environment (M), and toxicity (T) are considered (via PBT, PMT, or PBMT composite scoring), all six flame retardants have at least one transformation product that can be considered more hazardous, across diverse MCDA. Even when considering transformation products, the considered alternatives remain less hazardous than decaBDE, though the range of hazard of the five alternatives was reduced. The least hazardous of the considered alternatives were melamine and bis(2-ethylhexyl)-tetrabromophthalate. This developed tool could be integrated within holistic alternatives assessments considering use and life cycle impacts or additionally prioritizing transformation products within (bio)monitoring screening studies.
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Affiliation(s)
- Ziye Zheng
- Department
of Chemistry, Umeå University, SE-901 87 Umeå, Sweden
| | - Hans Peter H. Arp
- Department
of Environmental Engineering, Norwegian
Geotechnical Institute, Ullevaal
Stadion NO-0806, Oslo, Norway
- Department
of Chemistry, Norwegian University of Science
and Technology (NTNU), NO-7491 Trondheim, Norway
| | - Gregory Peters
- Division
of Environmental Systems Analysis, Chalmers
University of Technology, SE-412 96 Göteborg, Sweden
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4
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Paini A, Leonard J, Joossens E, Bessems J, Desalegn A, Dorne J, Gosling J, Heringa M, Klaric M, Kliment T, Kramer N, Loizou G, Louisse J, Lumen A, Madden J, Patterson E, Proença S, Punt A, Setzer R, Suciu N, Troutman J, Yoon M, Worth A, Tan Y. Next generation physiologically based kinetic (NG-PBK) models in support of regulatory decision making. COMPUTATIONAL TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2019; 9:61-72. [PMID: 31008414 PMCID: PMC6472623 DOI: 10.1016/j.comtox.2018.11.002] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 11/02/2018] [Accepted: 11/08/2018] [Indexed: 02/06/2023]
Abstract
The fields of toxicology and chemical risk assessment seek to reduce, and eventually replace, the use of animals for the prediction of toxicity in humans. In this context, physiologically based kinetic (PBK) modelling based on in vitro and in silico kinetic data has the potential to a play significant role in reducing animal testing, by providing a methodology capable of incorporating in vitro human data to facilitate the development of in vitro to in vivo extrapolation of hazard information. In the present article, we discuss the challenges in: 1) applying PBK modelling to support regulatory decision making under the toxicology and risk-assessment paradigm shift towards animal replacement; 2) constructing PBK models without in vivo animal kinetic data, while relying solely on in vitro or in silico methods for model parameterization; and 3) assessing the validity and credibility of PBK models built largely using non-animal data. The strengths, uncertainties, and limitations of PBK models developed using in vitro or in silico data are discussed in an effort to establish a higher degree of confidence in the application of such models in a regulatory context. The article summarises the outcome of an expert workshop hosted by the European Commission Joint Research Centre (EC-JRC) - European Union Reference Laboratory for Alternatives to Animal Testing (EURL ECVAM), on "Physiologically-Based Kinetic modelling in risk assessment - reaching a whole new level in regulatory decision-making" held in Ispra, Italy, in November 2016, along with results from an international survey conducted in 2017 and recently reported activities occurring within the PBK modelling field. The discussions presented herein highlight the potential applications of next generation (NG)-PBK modelling, based on new data streams.
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Affiliation(s)
- A. Paini
- European Commission Joint Research Centre, Ispra, Italy
| | - J.A. Leonard
- Oak Ridge Institute for Science and Education, 100 ORAU Way, Oak Ridge, TN 37830, USA
| | - E. Joossens
- European Commission Joint Research Centre, Ispra, Italy
| | - J.G.M. Bessems
- European Commission Joint Research Centre, Ispra, Italy
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - A. Desalegn
- European Commission Joint Research Centre, Ispra, Italy
| | - J.L. Dorne
- European Food Safety Authority, 1a, Via Carlo Magno, 1A, 43126 Parma PR, Italy
| | - J.P. Gosling
- School of Mathematics, University of Leeds, Leeds, UK
| | - M.B. Heringa
- RIVM - The National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | | | - T. Kliment
- European Commission Joint Research Centre, Ispra, Italy
| | - N.I. Kramer
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80177, 3508TD Utrecht, The Netherlands
| | - G. Loizou
- Health and Safety Executive, Buxton, UK
| | - J. Louisse
- Division of Toxicology, Wageningen University, Tuinlaan 5, 6703 HE Wageningen, The Netherlands
- RIKILT Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands
| | - A. Lumen
- Division of Biochemical Toxicology, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR 72079, USA
| | - J.C. Madden
- School of Pharmacy and Bimolecular Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK
| | - E.A. Patterson
- School of Engineering, University of Liverpool, Liverpool L69 3GH, UK
| | - S. Proença
- European Commission Joint Research Centre, Ispra, Italy
- Institute for Risk Assessment Sciences, Utrecht University, P.O. Box 80177, 3508TD Utrecht, The Netherlands
| | - A. Punt
- RIKILT Wageningen University and Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands
| | - R.W. Setzer
- U.S. Environmental Protection Agency, National Exposure Research Laboratory, 109 TW Alexander Drive, Research Triangle Park, NC 27709, USA
| | - N. Suciu
- DiSTAS, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - J. Troutman
- Central Product Safety, The Procter & Gamble Company, Cincinnati, OH, USA
| | - M. Yoon
- ScitoVation, 6 Davis Drive, PO Box 110566, Research Triangle Park, NC 27709, USA
- ToxStrategies, Research Triangle Park Office, 1249 Kildaire Farm Road 134, Cary, NC 27511, USA
| | - A. Worth
- European Commission Joint Research Centre, Ispra, Italy
| | - Y.M. Tan
- School of Engineering, University of Liverpool, Liverpool L69 3GH, UK
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5
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Tyzack JD, Kirchmair J. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chem Biol Drug Des 2019; 93:377-386. [PMID: 30471192 PMCID: PMC6590657 DOI: 10.1111/cbdd.13445] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2018] [Revised: 11/05/2018] [Accepted: 11/11/2018] [Indexed: 01/08/2023]
Abstract
In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.
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Affiliation(s)
| | - Johannes Kirchmair
- Department of Chemistry, University of Bergen, Bergen, Norway.,Computational Biology Unit (CBU), University of Bergen, Bergen, Norway.,Center for Bioinformatics, Universität Hamburg, Hamburg, Germany
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6
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Jeffryes JG, Seaver SMD, Faria JP, Henry CS. A pathway for every product? Tools to discover and design plant metabolism. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2018; 273:61-70. [PMID: 29907310 DOI: 10.1016/j.plantsci.2018.03.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/13/2018] [Accepted: 03/19/2018] [Indexed: 06/08/2023]
Abstract
The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.
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Affiliation(s)
- James G Jeffryes
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - Samuel M D Seaver
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - José P Faria
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - Christopher S Henry
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States.
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7
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Pedretti A, Mazzolari A, Vistoli G, Testa B. MetaQSAR: An Integrated Database Engine to Manage and Analyze Metabolic Data. J Med Chem 2018; 61:1019-1030. [DOI: 10.1021/acs.jmedchem.7b01473] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Alessandro Pedretti
- Dipartimento
di Scienze Farmaceutiche “Pietro Pratesi”, Facoltà
di Farmacia, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Angelica Mazzolari
- Dipartimento
di Scienze Farmaceutiche “Pietro Pratesi”, Facoltà
di Farmacia, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
| | - Giulio Vistoli
- Dipartimento
di Scienze Farmaceutiche “Pietro Pratesi”, Facoltà
di Farmacia, Università degli Studi di Milano, Via Luigi Mangiagalli 25, I-20133 Milano, Italy
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