Normal-Phase Automated Mass-Directed HPLC Purification of a Pyrrolobenzodiazepine Library with Vasopressin Agonist Activity
Bibliographic record
Abstract
A 23-member library of pyrrolobenzodiazepine derivatives with vasopressin agonist activity was purified on a 100-mg per injection scale using normal-phase (NP) automated mass-directed HPLC. Analytical NP APCI-LC/MS on an experimental monolith silica CN column utilizing gradients of methanol in ethoxynonafluorobutane (hexane-like solvent) was used to provide data on chromatographic purity and ionization of the solutes. The analytical data collected were used to program a preparative LC/MS instrument for "smart" fraction collection based on the protonated molecular ion of the component of interest. Preparative HPLC was carried out on a preparative cyano column with gradients of polar organic solvents in heptane containing n-propylamine as a basic additive. Flow rates twice as high as conventional ones were used for purification of library compounds. Small aliquots of the preparative flow were mixed with makeup solvent and introduced into an APCI source of a quadrupole mass spectrometer, which triggered collection of solutes. Two methods with fixed instrument parameters were used for purification. The system utilized commercially available instrumentation and software, which provided excellent recovery and purity of the library components and appeared to be useful as a fast and efficient alternative to traditional purification technologies based on reversed-phase LC/MS.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".