A size exclusion chromatography- and ultrafiltration-based two-step process for purifying phage for therapeutic applications
Bibliographic record
Abstract
The rise in the number of antibiotic-resistant bacteria has created the need for alternative therapeutic approaches, including bacteriophage therapy. Bacteriophages are viruses that infect and lyse bacteria and therefore offer a highly targeted and adaptable solution to combat multidrug-resistant pathogens. However, the scalable purification of bacteriophages, while achieving high yield of product and efficient removal of impurities such as endotoxins, remains a significant barrier to their therapeutic adoption. This study presents a two-step purification process combining size-exclusion chromatography (SEC) and ultrafiltration as an efficient and scalable method for bacteriophage purification. T7 was used as the model bacteriophage. Preparative SEC using a cuboid device served as the first step for removing endotoxin and other impurities from bacterial culture filtrate. The SEC-purified phage sample was then processed using a carrier phase ultrafiltration and backflow recovery technique to further purify and enrich the bacteriophage. Phage titer, endotoxin level, and protein concentration were quantified at each stage to evaluate purification performance. The endotoxin level was substantially reduced, decreasing from 1.44 EU/mL in the initial culture filtrate to 0.49 EU/mL in the final purified phage sample, the FDA's acceptable limit for products that directly or indirectly contact the cardiovascular and lymphatic systems being 0.50 EU/mL. The phage titer was enhanced from 5.33×10⁹ PFU/mL (in the cell culture filtrate) to 9.33×10⁹ PFU/mL in the final product. The overall protein removal achieved was 95.8 % while the overall endotoxin removal was 92.5 %. These findings suggest that the proposed two-step purification process could be developed further and adopted for scalable production of high-purity bacteriophage preparations suitable for therapeutic applications.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".