Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation
Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-med...
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Online Access: | https://doi.org/10.3390/molecules24213955 https://doaj.org/article/e1e91bccf6e24c32be87ddd2d9fc62c7 |
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ftdoajarticles:oai:doaj.org/article:e1e91bccf6e24c32be87ddd2d9fc62c7 2023-05-15T17:53:34+02:00 Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation Alexander Dmitriev Dmitry Filimonov Alexey Lagunin Dmitry Karasev Pavel Pogodin Anastasiya Rudik Vladimir Poroikov 2019-10-01T00:00:00Z https://doi.org/10.3390/molecules24213955 https://doaj.org/article/e1e91bccf6e24c32be87ddd2d9fc62c7 EN eng MDPI AG https://www.mdpi.com/1420-3049/24/21/3955 https://doaj.org/toc/1420-3049 1420-3049 doi:10.3390/molecules24213955 https://doaj.org/article/e1e91bccf6e24c32be87ddd2d9fc62c7 Molecules, Vol 24, Iss 21, p 3955 (2019) drug interactions ddis adverse drug reaction adr Organic chemistry QD241-441 article 2019 ftdoajarticles https://doi.org/10.3390/molecules24213955 2022-12-31T02:21:29Z Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75. Article in Journal/Newspaper Orca Directory of Open Access Journals: DOAJ Articles Molecules 24 21 3955 |
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Directory of Open Access Journals: DOAJ Articles |
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English |
topic |
drug interactions ddis adverse drug reaction adr Organic chemistry QD241-441 |
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drug interactions ddis adverse drug reaction adr Organic chemistry QD241-441 Alexander Dmitriev Dmitry Filimonov Alexey Lagunin Dmitry Karasev Pavel Pogodin Anastasiya Rudik Vladimir Poroikov Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation |
topic_facet |
drug interactions ddis adverse drug reaction adr Organic chemistry QD241-441 |
description |
Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians. This work is a development of our previous investigations and aimed to create a model for the severity of DDIs prediction. PASS program and PoSMNA descriptors were implemented for prediction of all five classes of DDIs severity according to OpeRational ClassificAtion (ORCA) system: contraindicated (class 1), provisionally contraindicated (class 2), conditional (class 3), minimal risk (class 4), no interaction (class 5). Prediction can be carried out both for known drugs and for new, not yet synthesized substances using only their structural formulas. Created model provides an assessment of DDIs severity by prediction of different ORCA classes from the first most dangerous class to the fifth class when DDIs do not take place in the human organism. The average accuracy of DDIs class prediction is about 0.75. |
format |
Article in Journal/Newspaper |
author |
Alexander Dmitriev Dmitry Filimonov Alexey Lagunin Dmitry Karasev Pavel Pogodin Anastasiya Rudik Vladimir Poroikov |
author_facet |
Alexander Dmitriev Dmitry Filimonov Alexey Lagunin Dmitry Karasev Pavel Pogodin Anastasiya Rudik Vladimir Poroikov |
author_sort |
Alexander Dmitriev |
title |
Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation |
title_short |
Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation |
title_full |
Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation |
title_fullStr |
Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation |
title_full_unstemmed |
Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation |
title_sort |
prediction of severity of drug-drug interactions caused by enzyme inhibition and activation |
publisher |
MDPI AG |
publishDate |
2019 |
url |
https://doi.org/10.3390/molecules24213955 https://doaj.org/article/e1e91bccf6e24c32be87ddd2d9fc62c7 |
genre |
Orca |
genre_facet |
Orca |
op_source |
Molecules, Vol 24, Iss 21, p 3955 (2019) |
op_relation |
https://www.mdpi.com/1420-3049/24/21/3955 https://doaj.org/toc/1420-3049 1420-3049 doi:10.3390/molecules24213955 https://doaj.org/article/e1e91bccf6e24c32be87ddd2d9fc62c7 |
op_doi |
https://doi.org/10.3390/molecules24213955 |
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Molecules |
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24 |
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21 |
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3955 |
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1766161278089297920 |