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Fröhlich E. Animals in Respiratory Research. Int J Mol Sci 2024; 25:2903. [PMID: 38474149 DOI: 10.3390/ijms25052903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 02/20/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024] Open
Abstract
The respiratory barrier, a thin epithelial barrier that separates the interior of the human body from the environment, is easily damaged by toxicants, and chronic respiratory diseases are common. It also allows the permeation of drugs for topical treatment. Animal experimentation is used to train medical technicians, evaluate toxicants, and develop inhaled formulations. Species differences in the architecture of the respiratory tract explain why some species are better at predicting human toxicity than others. Some species are useful as disease models. This review describes the anatomical differences between the human and mammalian lungs and lists the characteristics of currently used mammalian models for the most relevant chronic respiratory diseases (asthma, chronic obstructive pulmonary disease, cystic fibrosis, pulmonary hypertension, pulmonary fibrosis, and tuberculosis). The generation of animal models is not easy because they do not develop these diseases spontaneously. Mouse models are common, but other species are more appropriate for some diseases. Zebrafish and fruit flies can help study immunological aspects. It is expected that combinations of in silico, in vitro, and in vivo (mammalian and invertebrate) models will be used in the future for drug development.
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Affiliation(s)
- Eleonore Fröhlich
- Center for Medical Research, Medical University of Graz, 8010 Graz, Austria
- Research Center Pharmaceutical Engineering GmbH, 8010 Graz, Austria
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Velloso JPL, Kovacs AS, Pires DEV, Ascher DB. AI-driven GPCR analysis, engineering, and targeting. Curr Opin Pharmacol 2024; 74:102427. [PMID: 38219398 DOI: 10.1016/j.coph.2023.102427] [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: 10/26/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024]
Abstract
This article investigates the role of recent advances in Artificial Intelligence (AI) to revolutionise the study of G protein-coupled receptors (GPCRs). AI has been applied to many areas of GPCR research, including the application of machine learning (ML) in GPCR classification, prediction of GPCR activation levels, modelling GPCR 3D structures and interactions, understanding G-protein selectivity, aiding elucidation of GPCRs structures, and drug design. Despite progress, challenges in predicting GPCR structures and addressing the complex nature of GPCRs remain, providing avenues for future research and development.
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Affiliation(s)
- João P L Velloso
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia; Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - Aaron S Kovacs
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia
| | - Douglas E V Pires
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia; Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia.
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Victoria, Australia; Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, Victoria, Australia; Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia; School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia.
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