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 inNature communications Vol. 16; no. 1; pp. 4623 - 15
Main Authors Zhang, Yu-Hang, Dai, Chen-Shu, Wang, Ya-Jie, Wang, Wen-Yu, Qi, Tian-Tian, Xia, Man-Cheng, Zhou, Gan, Cui, Yi-Min
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Published London Nature Publishing Group UK 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
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Snippet Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions remains...
Abstract Trillions of intestinal microbiota are essential to the permeability of orally administered drugs. However, identifying microbial-drug interactions...
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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
URI https://link.springer.com/article/10.1038/s41467-025-59916-7
https://www.ncbi.nlm.nih.gov/pubmed/40389439
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