The Cancer Drug Fraction of Metabolism Database
This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate‐depletion and...
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2019-07-01
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Series: | CPT: Pharmacometrics & Systems Pharmacology |
Online Access: | https://doi.org/10.1002/psp4.12417 |
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doaj-a86951d6d4b14878afc932feff3a28b12020-11-25T03:15:49ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062019-07-018751151910.1002/psp4.12417The Cancer Drug Fraction of Metabolism DatabaseLiyan Hua0Chien‐Wei Chiang1Wang Cong2Jin Li3Xueying Wang4Lijun Cheng5Weixing Feng6Sara K. Quinney7Lei Wang8Lang Li9College of Automation Harbin Engineering University Harbin ChinaDepartment of Biomedical Informatics College of Medicine The Ohio State University Columbus Ohio USACollege of Automation Harbin Engineering University Harbin ChinaCollege of Automation Harbin Engineering University Harbin ChinaCollege of Automation Harbin Engineering University Harbin ChinaDepartment of Biomedical Informatics College of Medicine The Ohio State University Columbus Ohio USACollege of Automation Harbin Engineering University Harbin ChinaThe Center for Computational Biology and Bioinformatics School of Medicine Indiana University Indianapolis Indiana USACollege of Automation Harbin Engineering University Harbin ChinaDepartment of Biomedical Informatics College of Medicine The Ohio State University Columbus Ohio USAThis study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate‐depletion and metabolite‐formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug–drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug–drug interaction pairs.https://doi.org/10.1002/psp4.12417 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liyan Hua Chien‐Wei Chiang Wang Cong Jin Li Xueying Wang Lijun Cheng Weixing Feng Sara K. Quinney Lei Wang Lang Li |
spellingShingle |
Liyan Hua Chien‐Wei Chiang Wang Cong Jin Li Xueying Wang Lijun Cheng Weixing Feng Sara K. Quinney Lei Wang Lang Li The Cancer Drug Fraction of Metabolism Database CPT: Pharmacometrics & Systems Pharmacology |
author_facet |
Liyan Hua Chien‐Wei Chiang Wang Cong Jin Li Xueying Wang Lijun Cheng Weixing Feng Sara K. Quinney Lei Wang Lang Li |
author_sort |
Liyan Hua |
title |
The Cancer Drug Fraction of Metabolism Database |
title_short |
The Cancer Drug Fraction of Metabolism Database |
title_full |
The Cancer Drug Fraction of Metabolism Database |
title_fullStr |
The Cancer Drug Fraction of Metabolism Database |
title_full_unstemmed |
The Cancer Drug Fraction of Metabolism Database |
title_sort |
cancer drug fraction of metabolism database |
publisher |
Wiley |
series |
CPT: Pharmacometrics & Systems Pharmacology |
issn |
2163-8306 |
publishDate |
2019-07-01 |
description |
This study aims to create a database for quantifying the fraction of metabolism of cytochrome P450 isozymes for cancer drugs approved by the US Food and Drug Administration. A reproducible data collection protocol was developed to extract essential information, including both substrate‐depletion and metabolite‐formation data from publicly available in vitro selective cytochrome P450 enzyme inhibition studies. We estimated the fraction of metabolism from the curated data. To demonstrate the utility of this database, we conducted an in vitro drug interaction prediction for the 42 cancer drugs. In the drug–drug interaction prediction, we identified 31 drug pairs with at least one cancer drug in each pair that had predicted area under concentration ratios > 2. We further found clinical drug interaction pieces of evidence in the literature to support 20 of these 31 drug–drug interaction pairs. |
url |
https://doi.org/10.1002/psp4.12417 |
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