Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

Watanabe, Reiko; Kawata, Toshio; Ueda, Shinya; Shinbo, Takumi; Higashimori, Mitsuo; Natsume-Kitatani, Yayoi; Mizuguchi, Kenji

doi:https://doi.org/10.1021/acs.molpharmaceut.2c00698

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

Watanabe, Reiko; Kawata, Toshio; Ueda, Shinya et al.

Molecular Pharmaceutics, 2022, 20(1), 419-426

アクセス数:30件（2025-03-23 00:29 集計）

固定URL: https://hdl.handle.net/11094/97678

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タイトル	Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information
著者	NRID 1000030727326 1000030727326 researchmap permalink ozkrk ozkrk 著者名 Watanabe, Reiko
	著者名 Kawata, Toshio
	著者名 Ueda, Shinya
	著者名 Shinbo, Takumi
	著者名 Higashimori, Mitsuo
	著者名 Natsume-Kitatani, Yayoi
	NRID 1000050450896 1000050450896 ORCID 0000-0003-3021-7078 0000-0003-3021-7078 researchmap permalink kenji_mizuguchi kenji_mizuguchi Scopus Author ID 57191477452 57191477452 著者名 Mizuguchi, Kenji
キーワード等	CYP3A4
	drug-drug interaction
	fm
	in silico prediction
	pharmacokinetics
内容	Watanabe R., Kawata T., Ueda S., et al. Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information. Molecular Pharmaceutics 20, 419 (2022); https://doi.org/10.1021/acs.molpharmaceut.2c00698.
抄録	The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug-drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.
出版者	American Chemical Society
収録物名	Molecular Pharmaceutics
巻(号)	20(1)
ページ	419-426
刊行年月	2022-12-20
言語	英語
ハンドルURL	https://hdl.handle.net/11094/97678
PISSN	15438384
eISSN	15438392
関連情報 (isIdenticalTo)	DOI (出版社版) https://doi.org/10.1021/acs.molpharmaceut.2c00698
アクセス権	open access
権利情報	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
出版タイプ	Version of Record
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Molecular Pharmaceutics. 2022, 20(1), p. 419-426
DC.title	Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information
DC.creator	Watanabe, Reiko
	Kawata, Toshio
	Ueda, Shinya
	Shinbo, Takumi
	Higashimori, Mitsuo
	Natsume-Kitatani, Yayoi
	Mizuguchi, Kenji
DC.publisher	American Chemical Society
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2022-12-20
DC.identifier	https://doi.org/10.1021/acs.molpharmaceut.2c00698
DC.subject	CYP3A4
	drug-drug interaction
	fm
	in silico prediction
	pharmacokinetics
DC.description	Watanabe R., Kawata T., Ueda S., et al. Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information. Molecular Pharmaceutics 20, 419 (2022); https://doi.org/10.1021/acs.molpharmaceut.2c00698.
DCTERMS.abstract	The contribution ratio of metabolic enzymes such as cytochrome P450 to in vivo clearance (fraction metabolized: fm) is a pharmacokinetic index that is particularly important for the quantitative evaluation of drug-drug interactions. Since obtaining experimental in vivo fm values is challenging, those derived from in vitro experiments have often been used alternatively. This study aimed to explore the possibility of constructing machine learning models for predicting in vivo fm using chemical structure information alone. We collected in vivo fm values and chemical structures of 319 compounds from a public database with careful manual curation and constructed predictive models using several machine learning methods. The results showed that in vivo fm values can be obtained from structural information alone with a performance comparable to that based on in vitro experimental values and that the prediction accuracy for the compounds involved in CYP induction or inhibition is significantly higher than that by using in vitro values. Our new approach to predicting in vivo fm values in the early stages of drug discovery should help improve the efficiency of the drug optimization process.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
DC.format	97678
citation_title	Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information
citation_author	Watanabe, Reiko
	Kawata, Toshio
	Ueda, Shinya
	Shinbo, Takumi
	Higashimori, Mitsuo
	Natsume-Kitatani, Yayoi
	Mizuguchi, Kenji
citation_publisher	American Chemical Society
citation_language	英語
citation_date	2022-12-20
citation_journal_title	Molecular Pharmaceutics
citation_volume	20
citation_issue	1
citation_firstpage	419
citation_lastpage	426
citation_doi	https://doi.org/10.1021/acs.molpharmaceut.2c00698
citation_public_url	https://hdl.handle.net/11094/97678

Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information

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