Evaluation of Microflow Digital Imaging Particle Analysis for Sub-Visible Particles Formulated with an Opaque Vaccine Adjuvant
Bibliographic record
Abstract
Microflow digital imaging (MDI) has become a widely accepted method for assessing sub-visible particles in pharmaceutical formulations however, to date; no data have been presented on the utility of this methodology when formulations include opaque vaccine adjuvants. This study evaluates the ability of MDI to assess sub-visible particles under these conditions. A Fluid Imaging Technologies Inc. FlowCAM® instrument was used to assess a number of sub-visible particle types in solution with increasing concentrations of AddaVax™, a nanoscale squalene-based adjuvant. With the objective (10X) used and the limitations of the sensor resolution, the instrument was incapable of distinguishing between sub-visible particles and AddaVax™ droplets at particle sizes less than 5 μm. The instrument was capable of imaging all particle types assessed (polystyrene beads, borosilicate glass, cellulose, polyethylene protein aggregate mimics, and lysozyme protein aggregates) at sizes greater than 5 μm in concentrations of AddaVax™ up to 50% (vol:vol). Reduced edge gradients and a decrease in measured particle sizes were noted as adjuvant concentrations increased. No significant changes in particle counts were observed for polystyrene particle standards and lysozyme protein aggregates, however significant reductions in particle counts were observed for borosilicate (80% of original) and cellulose (92% of original) particles. This reduction in particle counts may be due to the opaque adjuvant masking translucent particles present in borosilicate and cellulose samples. Although the results suggest that the utility of MDI for assessing sub-visible particles in high concentrations of adjuvant may be highly dependent on particle morphology, we believe that further investigation of this methodology to assess sub-visible particles in challenging formulations is warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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".