Optimization, Production, Purification and Characterization of HIV-1 GAG-Based Virus-like Particles Functionalized with SARS-CoV-2
Bibliographic record
Abstract
Virus-like particles (VLPs) constitute a promising approach to recombinant vaccine development. They are robust, safe, versatile and highly immunogenic supra-molecular structures that closely mimic the native conformation of viruses without carrying their genetic material. HIV-1 Gag VLPs share similar characteristics with wild-type severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making them a suitable platform for the expression of its spike membrane protein to generate a potential vaccine candidate for COVID-19. This work proposes a methodology for the generation of SARS-CoV-2 VLPs by their co-expression with HIV-1 Gag protein. We achieved VLP functionalization with coronavirus spike protein, optimized its expression using a design of experiments (DoE). We also performed the bioprocess at a bioreactor scale followed by a scalable downstream purification process consisting of two clarifications, an ion exchange and size-exclusion chromatography. The whole production process is conceived to enhance its transferability at current good manufacturing practice (cGMP) industrial scale manufacturing. Moreover, the approach proposed could be expanded to produce additional Gag-based VLPs against different diseases or COVID-19 variants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".