CPE-DB: An Open Database of Chemical Penetration Enhancers

The cutaneous delivery route currently accounts for almost 10% of all administered drugs and it is becoming more common. Chemical penetration enhancers (CPEs) increase the transport of drugs across skin layers by different mechanisms that depend on the chemical nature of the penetration enhancers. I...

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Main Authors: Ekaterina P. Vasyuchenko, Philipp S. Orekhov, Grigoriy A. Armeev, Marine E. Bozdaganyan
Format: Article
Language:English
Published: MDPI AG 2021-01-01
Series:Pharmaceutics
Subjects:
Online Access:https://www.mdpi.com/1999-4923/13/1/66
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spelling doaj-de8ddb45f24949888baa7286d2c08ff62021-01-08T00:00:33ZengMDPI AGPharmaceutics1999-49232021-01-0113666610.3390/pharmaceutics13010066CPE-DB: An Open Database of Chemical Penetration EnhancersEkaterina P. Vasyuchenko0Philipp S. Orekhov1Grigoriy A. Armeev2Marine E. Bozdaganyan3School of Biology, Lomonosov Moscow State University, 119234 Moscow, RussiaSchool of Biology, Lomonosov Moscow State University, 119234 Moscow, RussiaSchool of Biology, Lomonosov Moscow State University, 119234 Moscow, RussiaSchool of Biology, Lomonosov Moscow State University, 119234 Moscow, RussiaThe cutaneous delivery route currently accounts for almost 10% of all administered drugs and it is becoming more common. Chemical penetration enhancers (CPEs) increase the transport of drugs across skin layers by different mechanisms that depend on the chemical nature of the penetration enhancers. In our work, we created a chemical penetration enhancer database (CPE-DB) that is, to the best of our knowledge, the first CPE database. We collected information about known enhancers and their derivatives in a single database, and classified and characterized their molecular diversity in terms of scaffold content, key chemical moieties, molecular descriptors, etc. CPE-DB can be used for virtual screening and similarity search to identify new potent and safe enhancers, building quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) models, and other machine-learning (ML) applications for the prediction of biological activity.https://www.mdpi.com/1999-4923/13/1/66chemical penetration enhancersdatabasestratum corneumtransdermal drug deliveryskin penetration
collection DOAJ
language English
format Article
sources DOAJ
author Ekaterina P. Vasyuchenko
Philipp S. Orekhov
Grigoriy A. Armeev
Marine E. Bozdaganyan
spellingShingle Ekaterina P. Vasyuchenko
Philipp S. Orekhov
Grigoriy A. Armeev
Marine E. Bozdaganyan
CPE-DB: An Open Database of Chemical Penetration Enhancers
Pharmaceutics
chemical penetration enhancers
database
stratum corneum
transdermal drug delivery
skin penetration
author_facet Ekaterina P. Vasyuchenko
Philipp S. Orekhov
Grigoriy A. Armeev
Marine E. Bozdaganyan
author_sort Ekaterina P. Vasyuchenko
title CPE-DB: An Open Database of Chemical Penetration Enhancers
title_short CPE-DB: An Open Database of Chemical Penetration Enhancers
title_full CPE-DB: An Open Database of Chemical Penetration Enhancers
title_fullStr CPE-DB: An Open Database of Chemical Penetration Enhancers
title_full_unstemmed CPE-DB: An Open Database of Chemical Penetration Enhancers
title_sort cpe-db: an open database of chemical penetration enhancers
publisher MDPI AG
series Pharmaceutics
issn 1999-4923
publishDate 2021-01-01
description The cutaneous delivery route currently accounts for almost 10% of all administered drugs and it is becoming more common. Chemical penetration enhancers (CPEs) increase the transport of drugs across skin layers by different mechanisms that depend on the chemical nature of the penetration enhancers. In our work, we created a chemical penetration enhancer database (CPE-DB) that is, to the best of our knowledge, the first CPE database. We collected information about known enhancers and their derivatives in a single database, and classified and characterized their molecular diversity in terms of scaffold content, key chemical moieties, molecular descriptors, etc. CPE-DB can be used for virtual screening and similarity search to identify new potent and safe enhancers, building quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) models, and other machine-learning (ML) applications for the prediction of biological activity.
topic chemical penetration enhancers
database
stratum corneum
transdermal drug delivery
skin penetration
url https://www.mdpi.com/1999-4923/13/1/66
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