The development and assessment of high‐throughput mass spectrometry‐based methods for the quantification of a nanoparticle drug delivery agent in cellular lysate
Bibliographic record
Abstract
The safe use of lipid-based drug delivery agents requires fast and sensitive qualitative and quantitative assessment of their cellular interactions. Many mass spectrometry (MS) based analytical platforms can achieve such task with varying capabilities. Therefore, four novel high-throughput MS-based quantitative methods were evaluated for the analysis of a small organic gene delivery agent: N,N-bis(dimethylhexadecyl)-1,3-propane-diammonium dibromide (G16-3). Analysis utilized MS instruments that detect analytes using low-resolution tandem MS (MS/MS) analysis (i.e. QTRAP or linear ion trap in this work) or high-resolution MS analysis (i.e. time of flight (ToF) or Orbitrap). Our results indicate that the validated fast chromatography (FC)-QTRAP-MS/MS, FC- LTQ-Orbitrap-MS, desorption electrospray ionization-collision-induced dissociation (CID)-MS/MS and matrix assisted laser desorption ionization-ToF/ToF-MS MS methods were superior in the area of method development and sample analysis time to a previously developed liquid chromatography (LC)-CID-MS/MS. To our knowledge, this is the first evaluation of the abilities of five MS-based quantitative methods that target a single pharmaceutical analyte. Our findings indicate that, in comparison to conventional LC-CID-MS/MS, the new MS-based methods resulted in a (1) substantial reduction in the analysis time, (2) reduction in the time required for method development and (3) production of either superior or comparable quantitative data. The four new high-throughput MS methods, therefore, were faster, more efficient and less expensive than a conventional LC-CID-MS/MS for the quantification of the G16-3 analyte within tissue culture. When applied to cellular lysate, no significant change in the concentration of G16-3 gemini surfactant within PAM212 cells was observed between 5 and 53 h, suggesting the absence of any metabolism/excretion from PAM212 cells.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".