Aluminum Phosphate Vaccine Adjuvant: Analysis of Composition and Size Using Off-Line and In-Line Tools
Bibliographic record
Abstract
Abstract Purpose Aluminum-based adjuvants including aluminum phosphate (AlPO 4 ) are commonly used in many human vaccines to enhance immune response. The interaction between the antigen and adjuvant, including the physical adsorption of antigen, may play a role in vaccine immunogenicity and is a useful marker of vaccine product quality and consistency. Thus, it is important to study the physicochemical properties of AlPO 4 , such as particle size and chemical composition. Control of the vaccine adjuvant throughout the manufacturing process, including raw materials and the intermediate and final product stages, can be effectively achieved through monitoring of such key product attributes to help ensure product quality. Methods This study focuses on the compositional analysis of AlPO 4 adjuvant at the intermediate and final manufacturing stages using the off-line methods Fourier-Transform Infrared (FTIR) and Raman spectroscopy, X-ray Photoelectron Spectroscopy (XPS), and the in-line method Attenuated Total Reflectance (ATR). Particle size distribution of AlPO 4 was measured off-line using Laser diffraction (LD) and in-line using Focused Beam Reflectance Measurement (FBRM®). Results There was no observable difference in size distribution between the intermediate and final stage AlPO 4 by off-line and in-line analysis, in both small- or large-scale production samples. Consistent peak shifts were observed in off-line and in-line infrared (IR) spectroscopy as well as off-line XPS for both small- and large-scale AlPO 4 manufacturing runs. Additionally, IR spectroscopy and FBRM® for size distribution were used as in-line process analytical technology (PAT) to monitor reaction progress in real-time during small-scale AlPO 4 manufacturing from raw materials. The small-scale adsorption process of a model protein antigen (Tetanus toxoid) to AlPO 4 adjuvant was also monitored by in-line ReactIR probe. Conclusion This study demonstrated that in-line PAT can be used to monitor particle size and chemical composition for the various stages of adjuvant manufacturing from raw materials through intermediate to final adjuvant product stage. Similar approaches can be utilized to help assess lot-to-lot consistency during adjuvant manufacturing and vaccine product development. Moreover, the use of in-line PAT is highly conductive to advanced manufacturing strategies such as real-time product release testing and automated processes of the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".