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Record W4388408707 · doi:10.3390/molecules28217451

Identification of the Metabolites of Both Formononetin in Rat Hepatic S9 and Ononin in Rat Urine Samples and Preliminary Network Pharmacology Evaluation of Their Main Metabolites

2023· article· en· W4388408707 on OpenAlexaff
Yuzhu Yang, Tao Wang, Qilei Chen, Hubiao Chen, Qiansong He, Yazhou Zhang

Bibliographic record

VenueMolecules · 2023
Typearticle
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Analysis
Canadian institutionsMcGill University
FundersInnovation and Technology FundGuizhou University
KeywordsFormononetinIsoflavonoidCalycosinChemistryPaeoniflorinGlucuronidationChromatographyPharmacologyTraditional medicineHigh-performance liquid chromatographyBiochemistryFlavonoidAntioxidantIn vitroMicrosomeDaidzeinMedicineGenistein

Abstract

fetched live from OpenAlex

Astragalus membranaceus is a traditional Chinese medicine derived from the roots of Astragalus membranaceus (Fisch.) Bge., which has the same medicinal and edible uses in China. It is also widely used in daily food, and its pharmacological effects mainly include antioxidant effects, vascular softening effects, etc. Currently, it is increasingly widely used in the prevention of hypertension, cerebral ischemia, and stroke in China. Formononetin and its glucopyranoside (ononin) are both important components of Astragalus membranaceuss and may play important roles in the treatment of cardiovascular diseases (CVDs). This study conducted metabolic studies using formononectin and its glucopyranoside (ononin), including a combination of the in vitro metabolism of Formonetin using rat liver S9 and the in vivo metabolism of ononin administered orally to rats. Five metabolites (Sm2, 7, 9, 10, and 12) were obtained from the solution incubated with formononetin and rat hepatic S9 fraction using chromatographic methods. The structures of the five metabolites were elucidated as (Sm2)6,7,4′-trihydroxy-isoflavonoid; (Sm7)7,4′-dihydroxy-isoflavonoid; (Sm9)7,8,4′-trihydroxy-isoflavonoid; (Sm10)7,8,-dihydroxy-4′-methoxy-isoflavonoid; and (Sm12)6,7-dihydroxy-4′-methoxy- isoflavonoid on the basis of UV, NMR, and MS data. Totally, 14 metabolites were identified via HPLC-DAD-ESI-IT-TOF-MSn analysis, from which the formononetin was incubated with rat hepatic S9 fraction, and the main metabolic pathways were hydroxylation, demethylation, and glycosylation. Then, 21 metabolites were identified via HPLC-DAD-ESI-IT-TOF-MSn analysis from the urine samples from SD rats to which ononin was orally administered, and the main metabolic pathways were glucuronidation, hydroxylation, demethylation, and sulfonation. The main difference between the in vitro metabolism of formononetin and the in vivo metabolism of ononin is that ononin undergoes deglycemic transformation into Formonetin in the rat intestine, while Formonetin is absorbed into the bloodstream for metabolism, and the metabolic products also produce combined metabolites during in vivo metabolism. The six metabolites obtained from the aforementioned separation indicate the primary forms of formononetin metabolism, and due to their higher contents of similar isoflavone metabolites, they are considered the main active compounds that are responsible for pharmacological effects. To investigate the metabolites of the active ingredients of formononetin in the rat liver S9 system, network pharmacology was used to evaluate the cardiovascular disease (CVD) activities of the six primary metabolites that were structurally identified. Additionally, the macromolecular docking results of six main components and two core targets (HSP90AA1 and SRC) related to CVD showed that formononetin and its main metabolites, Sm10 and Sm12, may have roles in CVD treatment due to their strong binding activities with the HSP90AA1 receptor, while the Sm7 metabolite may have a role in CVD treatment due to its strong binding activity with the SRC receptor.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.301
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2023
Admission routes1
Has abstractyes

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