Analysis of the impact of pluronic acid on the thermal stability and infectivity of AAV6.2FF
Bibliographic record
Abstract
BACKGROUND: The advancement of AAV vectors into clinical testing has accelerated rapidly over the past two decades. While many of the AAV vectors being utilized in clinical trials are derived from natural serotypes, engineered serotypes are progressing toward clinical translation due to their enhanced tissue tropism and immune evasive properties. However, novel AAV vectors require formulation and stability testing to determine optimal storage conditions prior to their use in a clinical setting. RESULTS: Here, we evaluated the thermal stability of AAV6.2FF, a rationally engineered capsid with strong tropism for lung and muscle, in two different buffer formulations; phosphate buffered saline (PBS), or PBS supplemented with 0.001% non-ionic surfactant Pluronic F68 (PF-68). Aliquots of AAV6.2FF vector encoding the firefly luciferase reporter gene (AAV6.2FF-ffLuc) were incubated at temperatures ranging from -20°C to 55°C for varying periods of time and the impact on infectivity and particle integrity evaluated. Additionally, the impact of several rounds of freeze-thaw treatments on the infectivity of AAV6.2FF was investigated. Vector infectivity was measured by quantifying firefly luciferase expression in HEK 293 cells and AAV particle integrity was measured by qPCR quantification of encapsidated viral DNA. CONCLUSIONS: Our data demonstrate that formulating AAV6.2FF in PBS containing 0.001% PF-68 leads to increased stability and particle integrity at temperatures between -20℃ to 21℃ and protection against the destructive effects of freeze-thaw. Finally, AAV6.2FF-GFP formulated in PBS supplemented with 0.001% PF-68 displayed higher transduction efficiency in vivo in murine lung epithelial cells following intranasal administration than vector buffered in PBS alone further demonstrating the beneficial properties of PF-68.
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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) | 0.001 | 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".