Assessment of Optimized Process Parameters for Superior Bioactive Metabolite Production by Nonomuraea longicatena VSM-16 Using Response Surface Methodology

    Published on:November 2019
    Journal of Young Pharmacists, 2019; 11(4):377-381
    Original Article | doi:10.5530/jyp.2019.11.77
    Authors:

    Ushakiranmayi Managamuri1, Muvva Vijayalakshmi1, Venkata Siva Rama Krishna Ganduri2, Satish Babu Rajulapati3, Sudhakar Poda4*

    1Department of Botany and Microbiology, Acharya Nagarjuna University, Nagarjuna nagar, Guntur, Andhra Pradesh, INDIA.

    2Department of Biotechnology, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, INDIA.

    3Department of Biotechnology, National Institute of Technology, Warangal, Telangana, INDIA.

    4Department of Biotechnology, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, Andhra Pradesh, INDIA.

    Abstract:

    Objectives: The present work was carried out to check the capability of novel actinobacterium, Nonomuraea longicatena (VSM-16) for bioactive metabolite production and optimization of its process parameters by statistical and mathematical modeling. Methods: Response Surface Methodology (RSM) regression evaluation was done to fit the experimental data of each response with the aid of second order polynomial. Unstructured kinetic models had been developed for growth, substrate utilization and bioactive metabolite production (in terms of responses). Model based kinetic parameters were estimated and the profiles of bioactive metabolite production, substrate utilization and growth had been drawn. Results: The results have shown accurate interaction among process variables at optimized values of incubation time at 8-9 days, pH at 8-9, temperature at 30-31°C, concentrations of Mannitol at 2-2.2% and Biopeptone at 1.5-1.7% and the data have been effectively fitted into second-order polynomial models. Under these conditions, the responses (zones of inhibition) of various organisms Staphylococcus aureus, Streptococcus mutans, Xanthomonas campestris, Pseudomonas aeruginosa and Candida albicans have been also matched with experimental and predicted consequences. Conclusion: The zones of inhibition (responses) for the organisms had been also determined to be best fitted with experiment and model values.

    Key words: Bioactive metabolites, Mathematical modeling, Nonomuraea longicatena, Optimization, Regression Analysis, Response Surface Methodology.

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