Evaluation of the Stability of Bacteriophages in Different Solutions Suitable for the Production of Magistral Preparations in Belgium
Bibliographic record
Abstract
In Belgium, the incorporation of phages into magistral preparations for human application has been permitted since 2018. The stability of such preparations is of high importance to guarantee quality and efficacy throughout treatments. We evaluated the ability to preserve infectivity of four different phages active against three different bacterial species in five different buffer and infusion solutions commonly used in medicine and biotechnological manufacturing processes, at two different concentrations (9 and 7 log pfu/mL), stored at 4 °C. DPBS without Ca2+ and Mg2+ was found to be the best option, compared to the other solutions. Suspensions with phage concentrations of 7 log pfu/mL were unsuited as their activity dropped below the effective therapeutic dose (6–9 log pfu/mL), even after one week of storage at 4 °C. Strong variability between phages was observed, with Acinetobacter baumannii phage Acibel004 being stable in four out of five different solutions. We also studied the long term storage of lyophilized staphylococcal phage ISP, and found that the titer could be preserved during a period of almost 8 years when sucrose and trehalose were used as stabilizers. After rehydration of the lyophilized ISP phage in saline, the phage solutions remained stable at 4 °C during a period of 126 days.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".