A statistical approach to enhance the productivity of Streptomyces baarensis MH-133 for bioactive compounds.

The goal of this study was to use statistical optimization to change the nutritional and environmental conditions so that MH-133 could make more active metabolites. Twelve trials were used to screen for critical variables influencing productivity using the Placket-Burman Design method. MH-133 is significantly influenced by elicitation, yeast extract, inoculum size, and incubation period in terms of antibacterial activity. A total of 27 experimental trials with various combinations of these factors were used to carry out the response surface technique using the Box-Behnken design. The analyses revealed that the model was highly significant (p < 0.001), with a lack-of-fit of 0.212 and a coefficient determination (R2) of 0.9224. Additionally, the model predicted that the response as inhibition zone diameter would reach a value of 27 mm. Under optimal conditions, MH-133 produced 18.0 g of crude extract to each 35L and was purified with column chromatography. The active fraction exhibiting antibacterial activity was characterized using spectroscopic analysis. The MIC and MBC values varied between 37.5 and 300 μg/ml and 75 and 300 μg/ml, respectively. In conclusion, the biostatistical optimization of the active fraction critical variables, including environmental and nutritional conditions, enhances the production of bioactive molecules by species.