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INTERNATIONAL JOURNAL OF PHARMACEUTICAL RESEARCH

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IJPR included in UGC-Approved List of Journals - Ref. No. is SL. No. 4812 & J. No. 63703

Published by : Advanced Scientific Research
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0975-2366
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IJPR 9[3] July - September 2017 Special Issue

July - September 9[3] 2017

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Biosimulation studies-based optimization of fungal derived Lovastatin

Author: R.S.UPENDRA , SANTHARAM S KATTA
Abstract: The preset study aimed to optimize downstream process (DSP) conditions of fungal derived secondary metabolite named lovastatin for the submerged state fermentation (SmF) cultures of strain Aspergillus terreus-11045. A five factorial central composite design methodology (CCD) of Response Surface design Methodology (RSM) employed to enhance impactful factors of fungal derived lovastatin biosynthesis, i.e Ethyl acetate (200–1000 mL) pH (2.0–10.0), Temperature (30–38oC), Agitation (120–200 rpm), Incubation Time (0.5–2.5 h). The optimized trail of RSM design was validated with artificial neural network (ANN). RSM trail of 750 mL of ethyl acetate, pH 2.0, temperature 38 oC, agitation speed-160 rpm and incubation time-2 h, has conferred higher yield (3.453 mg/g dry matter). ANN validated yield (3.447 mg/g dry mass) was in congruence with the experimental values (3.453 mg/g dry mass) and more than the predicted value-CCD-RSM (3.406 mg/g dry mass). The standardized conditions of the present study reported the lovastatin yield (3.453 mg/g dry mass) approximately by 3.5 times compared to that of (952.7?mg/L) lovastatin reported earlier, similarly the lovastatin yield is 3.5 times higher than the lovastatin yield (0.997 mg/g dry matter) of suboptimal SmF DSP of Aspergillus terreus MTCC-11045.
Keyword: Aspergillus terreus; Lovastatin; Downstream process; Response Surface Methodology; Artificial Neural Network.
DOI: https://doi.org/10.31838/ijpr/2021.13.01.061
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