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Induction of the multixenobiotic/multidrug resistance system in various cell lines in response to perfluorinated carboxylic acids

100%
Rusiecka I., Składanowski A.
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2008
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vol. 55
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issue 2
329-337
EN
The multixenobiotic resistance (closely related to multidrug resistance) system controls transport across the plasma membrane as a defense against toxic molecules. Multixenobiotic resistance system consists of an efflux pump, ABCB1 (also named P-glycoprotein, P-gp), and/or a molecule of the ABCC family (also named multiple resistance associated protein, MRP). ABCB1 is able to increase efflux of many low-molecular foreign molecules. Measuring system induction may be used as a biomarker of cell/organism exposure to foreign substances. Various established cell lines were tested for constitutive and induced multixenobiotic resistance proteins by Western blotting immunodetection. The pumping function was indirectly assayed with Rhodamine B by visualization of cell fluorescence in the presence of verapamil. Changes in ABC proteins were measured by flow cytometry after exposition to various perfluorinated carboxylic acids. MCF7 and HeLa cells were found to contain the highest constitutive level of both ABCB1 and ABCC1. HEK293 exhibited much less ABCB1 and no activity of pumping out Rhodamine B. The pumping activity was found to be related to the amount of the cell-type specific 170 kDa ABCB1 protein. An 8-day exposure to 10-4 M perfluorononanoic acid resulted in about 2-2.5-fold increase of ABCB1 level. That was confirmed also for short times by flow cytometry of cells exposed to perfluorinated acids and its natural congeners. Both ABCB1- and ABCC1-related fluorescence increased along with the carbon chain in acids from C6 up to C9 and decreased for C10. Measuring of multixenobiotic resistance changes in vitro induced by chemicals may be a convenient test for screening for their potential toxicity.
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Induction of the multixenobiotic/multidrug resistance system in HeLa cells in response to imidazolium ionic liquids

100%
Rusiecka I., Składanowski A.
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2011
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vol. 58
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issue 2
187-192
EN
The multixenobiotic/multidrug resistance (MXR/MDR) system controls transport of foreign molecules across the plasma membrane as a preventive measure before toxicity becomes apparent. The system consists of an efflux pump, ABCB1, and/or a member of the ABCC family. Ionic liquids are broadly used solvents with several unique properties such as wide liquid range, negligible vapor pressure, good thermal and chemical stability and extraordinary dissolution properties for organic and inorganic compounds. Ionic liquids containing imidazolium ring are frequently used as solvents in drug synthesis. Constitutive and induced amounts of ABCB1 and ABCC1 proteins were estimated here by Western blotting and quantified by flow cytometry in HeLa cells exposed to three homologous 1-alkyl-3-methylimidazolium and one benzyl ring substituted salts. Aliphatic substituents in position 1 of the salts caused a weak toxicity but 1-benzyl ring was strongly toxic. An 8-day long treatment with 10-4 M 1-hexyl-3-methylimidazolium chloride resulted in an about 1.5-fold increase of ABCB1 level and over 2-fold increase of ABCC1 level. The amounts of both investigated ABC-proteins were linearly dependent on the length of the imidazolium ring side chain. Such distinctive changes of the amount of MXR/MDR proteins measured in cultured cells may be a useful marker when screening for potential toxicity of various chemicals.
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Deep learning in pharmacology: opportunities and threats

71%
Kocić I., Kocić M., Rusiecka I., Kocić A., Kocić E.
European Journal of Translational and Clinical Medicine
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2021
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vol. 5
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issue 2
88-94
EN
Introduction This review aims to present briefly the new horizon opened to pharmacology by the deep learning (DL) technology, but also to underline the most important threats and limitations of this method. Material and Methods We searched multiple databases for articles published before May 2021 according to the preferred reported item related to deep learning and drug research. Out of the 267 articles retrieved, we included 50 in the final review. Results DL and other different types of artificial intelligence have recently entered all spheres of science, taking an increasingly central position in the decision-making processes, also in pharmacology. Hence, there is a need for better understanding of these technologies. The basic differences between AI (artificial intelligence), DL and ML (machine learning) are explained. Additionally, the authors try to highlight the role of deep learning methods in drug research and development as well as in improving the safety of pharmacotherapy. Finally, future directions of DL in pharmacology were outlined as well as possible misuses of it. Conclusions DL is a promising and powerful tool for comprehensive analysis of big data related to all fields of pharmacology, however it has to be used carefully.
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