Optimized Production of the Allylamine Antifungal “Terbinafine” by Lysinibacillus Isolate MK212927 Using Response Surface Methodology
Bibliographic record
Abstract
Sayed E El-Sayed,1 Ghadir S El-Housseiny,2 Neveen A Abdelaziz,1 Mona R El-Ansary,3 Khaled M Aboshanab2 1Department of Microbiology and Immunology, Faculty of Pharmacy, Ahram Canadian University, Giza, Egypt; 2Department of Microbiology and Immunology, Faculty of Pharmacy, Ain Shams University, Cairo 11566, Egypt; 3Department of Biochemistry, Modern University for Technology and Information (MTI), Cairo, EgyptCorrespondence: Khaled M Aboshanab Department of Microbiology and Immunology, Faculty of PharmacyAin Shams University, Organization of African Unity St., Abbassia, Cairo 11566, EgyptTel +011201001547800Fax +01120224051107Email aboshanab2012@pharma.asu.edu.egPurpose: We aimed to optimize the factors affecting the production of the allylamine antifungal, terbinafine, by Lysinibacillus isolate MK212927, a natural producer of this broad-spectrum fungicidal compound.Methods: We employed a central composite design to optimize the five most important variables influencing the production of terbinafine which were carbon source, nitrogen source, temperature, pH and agitation.Results: The optimum conditions were found to be starch 5 g/L, ammonium chloride 5 g/L, temperature 32°C, agitation 150 rpm and pH 7. The actual response (inhibition zone diameter) was highly comparable to the value predicted by the model, indicating a valid model. Using the standard calibration curve of terbinafine, the optimized conditions resulted in an increase in the antifungal metabolite production (terbinafine) by about 1.6-fold (1814.662 μg/mL compared to 1165.550 μg/mL under standardized conditions).Conclusion: This is the first report, to the best of our knowledge, on optimized production of terbinafine by Lysinibacillus species. Hence, these findings may be useful as baseline data for scaling up the production of terbinafine from a natural microbial source.Keywords: fungicidal, optimization, central composite design, terbinafine
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".