Scalable purification enables high-quality virus-like particles for therapeutic translation
Bibliographic record
Abstract
Emerging molecular therapies introduce enzymatic activity into cells by delivering genes, transcripts, or proteins. Owing to their robust cell-entry capacity, virus-like particles (VLPs) represent a technology of choice in genome editing, where low doses of heterologous proteins and nucleic acids are essential. However, clinical translation of VLP vectors is hindered by inadequate purification methods. Current approaches, relying primarily on ultracentrifugation, suffer from inconsistent product quality and poor scalability. Here, we report the development of a broadly applicable purification strategy that improves the purity and therapeutic efficacy of genome-editing VLPs. Considering the characteristic properties of murine leukemia virus-derived engineered VLPs and HIV-derived engineered nucleocytosolic vehicles for loading of programmable editors, we developed a workflow that involves single-modal and multimodal chromatographic steps, effectively removing host cell proteins and cell-culture contaminants while improving VLP integrity and biological activity. Our purified VLPs displayed superior protein composition, consistency, and enhanced functional delivery compared to VLPs partially purified by conventional ultracentrifugation methods. Mass spectrometric analysis revealed a substantial decrease in contaminants, with VLP-specific proteins comprising >90% of the final product. In vivo studies confirmed improved therapeutic outcomes when chromatographically purified VLPs were used. Our scalable purification platform addresses critical manufacturing bottlenecks and constitutes a starting point for further development of VLP therapeutics, enabling robust production of pure VLPs for diverse applications such as genome editing, vaccine development, and other uses that require intracellular protein delivery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".