Mathematical modeling for bioprocess optimization of a protein drug, uricase, production by Aspergillus welwitschiae strain 1–4

Abstract

Microbial uricase is effective protein drug used to treat hyperuricemia and its complications, including chronic gout, also in prophylaxis and treatment of tumor lysis and organ transplants hyperuricemia. Uricase is commonly used as diagnostic reagent in clinical analysis for quantification of uric acid in blood and other biological fluids. Also, it can be used as an additive in formulations of hair coloring agents. A newly isolated strain, Aspergillus sp. 1–4, was able to produce extracellular uricase on a medium containing uric acid as inducer. Phylogenetic analysis based on ITS region sequence analysis and phenotypic characteristics showed that Aspergillus sp. strain 1–4 is closely related to Aspergillus welwitschiae and its nucleotide sequence was deposited in the GenBank database and assigned sequence accession number MG323529. Statistical screening using Plackett-Burman design with 20 runs was applied to screen fifteen factors for their significance on uricase production by Aspergillus welwitschiae. Results of statistical analysis indicated that incubation time has the most significant positive effect on uricase production followed by yeast extract and inoculum size with the highest effect values of 13.48, 5.26 and 4.75; respectively. The interaction effects and optimal levels of these factors were evaluated using central composite design. The maximum uricase production was achieved at incubation time (5 days), yeast extract (2 g/L) and inoculum size (4 mL/50 mL medium) are the optimum levels for maximum uricase production (60.03 U/mL). After optimization, uricase production increased by 3.02-folds as compared with that obtained from the unoptimized medium (19.87 U/mL).

Bioprocess development for L-asparaginase production by Streptomyces rochei, purification and in-vitro efficacy against various human carcinoma cell lines

“…The strain's ability to inhibit the growth of two yeasts "(Candida albicans, Sacchromyces cerevisiae), five strains of fungi (Fusarium oxysporum, Aspergillus niger, Bipolaris oryzae, Alternaria solani, Rhizoctonia solani) and five strains of bacteria (Klebsiella, Pseudomonas aeruginosa, Staphylococcus aureus, Bacillus subtilis, Escherichia coli)" was determined. The chitosanase 78 , asparaginase 73 and uricase 79 activities were also assessed.…”

Statistical modeling of methylene blue degradation by yeast-bacteria consortium; optimization via agro-industrial waste, immobilization and application in real effluents

“…DOE as a collection of statistical and mathematical inference conducted to identify the perfect conditions for a multivariable system, construct and explore an approximate functional interaction between a response variable and a set of design independent variables techniques [ 74 ]. This approach was used to optimize environmental, incubation and nutritional conditions for improving and maximizing the performance of microbial activities without increasing the process cost [ 41 ].…”

“…Moreover, it is a time consuming, laborious practice and cost-effective. Recently, DOS has been utilized in media engineering to procure the optimal operating conditions for several purposes such as heavy metal removal [ 16 ], enzyme production [ 75 ] protein-drug production [ 74 ] and dye biosorption [ 63 ]. Initially PBD was utilized to screen the most significant factors, based on p-value, which were MB concentration, pH, molasses concentration, inoculum size and NaNO 3 concentration.…”

High Cell Density Culture of Dairy Propionibacterium sp. and Acidipropionibacterium sp.: A Review for Food Industry Applications

“…Mathematical modeling is an important tool for improving the performance of complex fermentation systems (Luo et al, 2021 ). Modeling takes into count the effect of multiple variables and their interactions upon process outputs, making it possible to predict systems behavior in otherwise difficult experimental approaches and can be successfully applied to scaling up (El-Naggar et al, 2019 ). Thus, relevant modeling approaches for improving dairy PAB bioprocess were reviewed in the following subsections.…”

“…Data-driven models based on statistical design of experiments (DoE) are frequently applied for optimization of dairy PAB cultures (Assis et al, 2020 ; Chen et al, 2013 ; Hedayati et al, 2020 ). These experimental designs significantly reduce the number of experiments, costs, and time needed in the bioprocess development, being flexible and describing complex systems in relatively simple ways (El-Naggar et al, 2019 ; Luo et al, 2021 ). On the other hand, they do not extrapolate outcomes beyond defined levels and do not explain the mechanisms of biological phenomena (Hedayati et al, 2020 ; Luo et al, 2021 ).…”