Enantioselectivity of mass spectrometry: Challenges and promises
Bibliographic record
Abstract
With the fast growing market of pure enantiomer drugs and bioactive molecules, new chiral-selective analytical tools have been instigated including the use of mass spectrometry (MS). Even though MS is one of the best analytical tools that has efficiently been used in several pharmaceutical and biological applications, traditionally MS is considered as a "chiral-blind" technique. This limitation is due to the MS inability to differentiate between two enantiomers of a chiral molecule based merely on their masses. Several approaches have been explored to assess the potential role of MS in chiral analysis. The first approach depends on the use of MS-hyphenated techniques utilizing fast and sensitive chiral separation tools such as liquid chromatography (LC), gas chromatography (GC), and capillary electrophoresis (CE) coupled to MS detector. More recently, several alternative separation techniques have been evaluated such as supercritical fluid chromatography (SFC) and capillary electrochromatography (CEC); the latter being a hybrid technique that combines the efficiency of CE with the selectivity of LC. The second approach is based on using the MS instrument solely for the chiral recognition. This method depends on the behavioral differences between enantiomers towards a foreign molecule and the ability of MS to monitor such differences. These behavioral differences can be divided into three types: (i) differences in the enantiomeric affinity for association with the chiral selector, (ii) differences of the enantiomeric exchange rate with a foreign reagent, and (iii) differences in the complex MS dissociation behaviors of the enantiomers. Most recently, ion mobility spectrometry was introduced to qualitatively and quantitatively evaluate chiral compounds. This article provides an overview of MS role in chiral analysis by discussing MS based methodologies and presenting the challenges and promises associated with each approach.
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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 | 0.039 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".