Preclinical evaluation of manufacturable SARS-CoV-2 spike virus-like particles produced in Chinese Hamster Ovary cells
Bibliographic record
Abstract
BACKGROUND: As the COVID-19 pandemic continues to evolve, novel vaccines need to be developed that are readily manufacturable and provide clinical efficacy against emerging SARS-CoV-2 variants. Virus-like particles (VLPs) presenting the spike antigen at their surface offer remarkable benefits over other vaccine antigen formats; however, current SARS-CoV-2 VLP vaccines candidates in clinical development suffer from challenges including low volumetric productivity, poor spike antigen density, expression platform-driven divergent protein glycosylation and complex upstream/downstream processing requirements. Despite their extensive use for therapeutic protein manufacturing and proven ability to produce enveloped VLPs, Chinese Hamster Ovary (CHO) cells are rarely used for the commercial production of VLP-based vaccines. METHODS: Using CHO cells, we aimed to produce VLPs displaying the full-length SARS-CoV-2 spike. Affinity chromatography was used to capture VLPs released in the culture medium from engineered CHO cells expressing spike. The structure, protein content, and glycosylation of spikes in VLPs were characterized by several biochemical and biophysical methods. In vivo, the generation of neutralizing antibodies and protection against SARS-CoV-2 infection was tested in mouse and hamster models. RESULTS: We demonstrate that spike overexpression in CHO cells is sufficient by itself to generate high VLP titers. These VLPs are evocative of the native virus but with at least three-fold higher spike density. In vivo, purified VLPs elicit strong humoral and cellular immunity at nanogram dose levels which grant protection against SARS-CoV-2 infection. CONCLUSIONS: Our results show that CHO cells are amenable to efficient manufacturing of high titers of a potently immunogenic spike protein-based VLP vaccine antigen.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".