Purification of phage for therapeutic applications using high throughput anion exchange membrane chromatography
Bibliographic record
Abstract
As cases of multidrug resistant bacterial infections increase, scientists and clinicians around the world are increasingly turning to bacteriophages as alternatives to antibiotics. Even though our understanding of phage has increased significantly since the early days of its discovery, over a century ago, the currently used tools and technologies for phage purification for therapeutic applications are severely limited. Bacteriophages are produced by bacterial cultures, and impurities such as endotoxins must therefore be removed before clinical use. We present an anion exchange bind-and-elute membrane chromatographic method for purifying T7 bacteriophage from Escherichia coli culture supernatant that removes undesirable impurities, while ensuring a high viable phage count in the purified product. Our method does not involve the use of chemicals such as organic solvents and caesium chloride that could typically leave residual toxicity in the final product. It also does not require expensive equipment, such as an ultracentrifuge. Using our method, that is based on an in-house designed membrane module, 65% of viable T7 phage was recovered, and up to 94% endotoxins could be removed. The method, which took approximately 15 min, is rapid and scalable, and produces quite pure bacteriophage samples in a single step. It therefore potentially represents a major improvement over the status quo, and shows the way ahead for streamlining phage manufacturing for therapeutic use.
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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".