Intestinal permeability of N-acetylcysteine is driven by gut microbiota-dependent cysteine palmitoylation
Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform...
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Published in | Nature communications Vol. 16; no. 1; pp. 4623 - 15 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
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19.05.2025
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Abstract | Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ
P
eff
-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by
Bacteroides
. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification.
Here, based on single-pass intestinal perfusion platform, the authors establish a microbiota-based drug permeability screening framework to compare perfusion and metabolomic profiles of 32 orally administered drugs in germ-free rats, and show that increased permeability of N-Acetylcysteine is mediated by cysteine-3-ketosphinganine of
Bacteroides
. |
---|---|
AbstractList | Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ
P
eff
-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by
Bacteroides
. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification.
Here, based on single-pass intestinal perfusion platform, the authors establish a microbiota-based drug permeability screening framework to compare perfusion and metabolomic profiles of 32 orally administered drugs in germ-free rats, and show that increased permeability of N-Acetylcysteine is mediated by cysteine-3-ketosphinganine of
Bacteroides
. Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ Peff-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by Bacteroides. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification.Here, based on single-pass intestinal perfusion platform, the authors establish a microbiota-based drug permeability screening framework to compare perfusion and metabolomic profiles of 32 orally administered drugs in germ-free rats, and show that increased permeability of N-Acetylcysteine is mediated by cysteine-3-ketosphinganine of Bacteroides. Abstract Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ P eff-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by Bacteroides. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification. Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ Peff-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by Bacteroides. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification.Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ Peff-values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by Bacteroides. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification. Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains challenging due to the highly variable composition of intestinal flora among individuals. Using single-pass intestinal perfusion (SPIP) platform, we establish the microbiota-based permeability screening framework involving germ-free (GF) and specific-pathogen-free (SPF) rats to compare in-situ P -values and metabolomic profiles of 32 orally administered drugs with disputable classifications of permeability, prior to the verifications of bioorthogonal chemistry and LC-MS/MS. In contrast with SPF controls, N-Acetylcysteine (NAC) exhibits significantly increased permeability in GF rats, which is inversely related to reduced cysteine-3-ketosphinganine by Bacteroides. To further validate these microbiome features, we integrate clinical descriptors from a prospective cohort of 319 participants to optimize a 15-feature eXtreme Gradient Boosting (XGB) model, which reveal that cysteine palmitoylation by intestinal microbiota has significantly affected NAC permeability. By comparison of net reclassification improvement (NRI) index, this machine learning (ML) model of clinical prediction model encompassing intestinal microbial features outperforms other three commercial models in predicting NAC permeability. Here we have developed an intestinal microbiota-based strategy to evaluate uncharacterized NAC permeability, thus accounting for its discordant biopharmaceutics classification. |
ArticleNumber | 4623 |
Author | Wang, Wen-Yu Qi, Tian-Tian Zhou, Gan Cui, Yi-Min Xia, Man-Cheng Zhang, Yu-Hang Dai, Chen-Shu Wang, Ya-Jie |
Author_xml | – sequence: 1 givenname: Yu-Hang orcidid: 0009-0004-7119-1630 surname: Zhang fullname: Zhang, Yu-Hang email: yuhang@pkufh.cn organization: Institute of Clinical Pharmacology, Peking University First Hospital, State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University – sequence: 2 givenname: Chen-Shu surname: Dai fullname: Dai, Chen-Shu organization: Institute of Clinical Pharmacology, Peking University First Hospital, Department of Pharmacy, Wenzhou Medical University – sequence: 3 givenname: Ya-Jie surname: Wang fullname: Wang, Ya-Jie organization: Department of Gastrointestinal Surgery, Peking University First Hospital – sequence: 4 givenname: Wen-Yu orcidid: 0000-0003-3725-6539 surname: Wang fullname: Wang, Wen-Yu organization: Beijing Anzhen Hospital, Capital Medical University – sequence: 5 givenname: Tian-Tian surname: Qi fullname: Qi, Tian-Tian organization: Institute of Clinical Pharmacology, Peking University First Hospital, Department of Pharmacy, Xuzhou Medical University – sequence: 6 givenname: Man-Cheng surname: Xia fullname: Xia, Man-Cheng organization: Department of Gastrointestinal Surgery, Peking University First Hospital – sequence: 7 givenname: Gan surname: Zhou fullname: Zhou, Gan email: zhougan77@163.com organization: Department of Pharmacy, Xiangya Hospital of Central South University – sequence: 8 givenname: Yi-Min orcidid: 0000-0002-4186-1005 surname: Cui fullname: Cui, Yi-Min email: cui.pharm@pkufh.com organization: Institute of Clinical Pharmacology, Peking University First Hospital, State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University |
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SubjectTerms | 14/19 14/34 49/40 49/47 49/90 49/91 59/5 631/154/152 631/154/436/1729 64/86 Acetylcysteine Acetylcysteine - metabolism Acetylcysteine - pharmacokinetics Animals Bacteroides Classification Cysteine Cysteine - metabolism Drug interaction Female Gastrointestinal Microbiome - physiology Germ-Free Life Germfree Gut microbiota Humanities and Social Sciences Humans Intestinal Barrier Function Intestinal microflora Intestinal Mucosa - metabolism Lipoylation Machine learning Male Metabolomics Microbiomes Microbiota Microorganisms multidisciplinary Oral administration Palmitoylation Perfusion Permeability Prediction models Rats Rats, Sprague-Dawley Science Science (multidisciplinary) Specific pathogen free Specific Pathogen-Free Organisms |
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Title | Intestinal permeability of N-acetylcysteine is driven by gut microbiota-dependent cysteine palmitoylation |
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