Nuclear Magnetic Resonance Spectroscopy as a Quantitative Tool To Determine the Concentrations of Biologically Produced Metabolites: Implications in Metabolites in Safety Testing
Bibliographic record
Abstract
Nuclear magnetic resonance (NMR) spectroscopy has traditionally been considered as an indispensable tool in elucidating structures of metabolites. With the advent of Fourier transform (FT) spectrometers, along with improvements in software and hardware (such as high-field magnets, cryoprobes, versatile pulse sequences, and solvent suppression techniques), NMR is increasingly being considered as a critical quantitative tool, despite its lower sensitivity as compared to mass spectrometry. A specific quantitative application of NMR is in determining the concentrations of biologically isolated metabolites, which could potentially be used as reference standards for further quantitative work by liquid chromatography/mass spectrometry. With the recent demands from regulatory agencies on quantitative information on metabolites, it is proposed that NMR will play a significant role in strategies aimed at addressing metabolite coverage in toxicological species. Traditionally, biologically isolated metabolites have not been considered as a way of generating "reference standards" for further quantitative work. However, because of the recent FDA guidance on safety testing of metabolites, one has to consider means of authenticating and quantitating biologically or nonbiologically generated metabolites. 1H NMR is being proposed as the method of choice, as it is able to be used as both a qualitative and a quantitative tool, hence allowing structure determination, purity check, and quantitative measurement of the isolated metabolite. In this publication, the application of NMR as a powerful and robust analytical technique in determining the concentrations of in vitro or in vivo isolated metabolites is discussed. Furthermore, to demonstrate the reliability and accuracy of metabolite concentrations determined by NMR, validation and cross-validation with gravimetric and mass spectrometric methods were conducted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".