Advantages of atmospheric pressure photoionization mass spectrometry in support of drug discovery
Bibliographic record
Abstract
The performance of the atmospheric pressure photoionization (APPI) technique was evaluated against five sets of standards and drug-like compounds and compared to atmospheric pressure chemical ionization (APCI) and electrospray ionization (ESI). The APPI technique was first used to analyze a set of 86 drug standards with diverse structures and polarities with a 100% detection rate. More detailed studies were then performed for another three sets of both drug standards and proprietary drug candidates. All 60 test compounds in these three sets were detected by APPI with an overall higher ionization efficiency than either APCI or ESI. Most of the non-polar compounds in these three sets were not ionized by APCI or ESI. Analysis of a final set of 201 Wyeth proprietary drug candidates by APPI, APCI and ESI provided an additional comparison of the ionization techniques. The detection rates in positive ion mode were 94% for APPI, 84% for APCI, and 84% for ESI. Combining positive and negative ion mode detection, APPI detected 98% of the compounds, while APCI and ESI detected 91%, respectively. This analysis shows that APPI is a valuable tool for day-to-day usage in a pharmaceutical company setting because it is able to successfully ionize more compounds, with greater structural diversity, than the other two ionization techniques. Consequently, APPI could be considered a more universal ionization method, and therefore has great potential in high-throughput drug discovery especially for open access liquid chromatography/mass spectrometry (LC/MS) applications.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".