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Record W2969972640 · doi:10.1016/j.csbj.2019.08.003

Aluminum Phosphate Vaccine Adjuvant: Analysis of Composition and Size Using Off-Line and In-Line Tools

2019· article· en· W2969972640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComputational and Structural Biotechnology Journal · 2019
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsYork UniversitySanofi (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaYork UniversityOntario Centres of Excellence
KeywordsProcess analytical technologyMaterials scienceAttenuated total reflectionRaman spectroscopyAdjuvantParticle sizeParticle-size distributionX-ray photoelectron spectroscopyFourier transform infrared spectroscopyChemistryOpticsChemical engineeringImmunologyPhysicsMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.228
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it