Statistical Optimization of Process Parameters and Purification of Uricase Using Isolate Pseudomonas mosselii DSS002

Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. Thi...

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Published inIndian journal of microbiology Vol. 65; no. 2; pp. 1089 - 1104
Main Authors Dudala, Sai Sushma, Venkateswarulu, T. C., Venkata Narayana, A., John Babu, D.
Format Journal Article
LanguageEnglish
Published New Delhi Springer India 01.06.2025
Springer Nature B.V
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Online AccessGet full text
ISSN0046-8991
0973-7715
DOI10.1007/s12088-025-01467-y

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Abstract Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. This study focuses on optimizing process parameters for uricase production using advanced statistical methods, namely Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). Seven key process parameters were investigated: temperature, pH, medium volume, incubation time, inoculum size, inoculum age, and rpm. Conformational experimental studies at the ANN-GA predicted optimal conditions revealed a significant uricase activity of 63.92 ± 0.06 U/mL. The purified uricase exhibited specific activity of 92.48 U/mg and a molecular weight of approximately 32 kDa. Itdemonstrated remarkable stability, withstanding a wide pH range (6.0 to 10.0) and temperatures up to 50 °C, with an optimum pH of 9.0 and temperature of 30 °C. This broad pH and temperature tolerance of the purified uricase from Pseudomonas mosselii DSS002 underscores its potential as a valuable source for industrial-scale production, catering to various pharmaceutical applications. This study’s findings pave the way for efficient and scalable uricase production, offering promising implications for the pharmaceutical industry.
AbstractList Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. This study focuses on optimizing process parameters for uricase production using advanced statistical methods, namely Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). Seven key process parameters were investigated: temperature, pH, medium volume, incubation time, inoculum size, inoculum age, and rpm. Conformational experimental studies at the ANN-GA predicted optimal conditions revealed a significant uricase activity of 63.92 ± 0.06 U/mL. The purified uricase exhibited specific activity of 92.48 U/mg and a molecular weight of approximately 32 kDa. Itdemonstrated remarkable stability, withstanding a wide pH range (6.0 to 10.0) and temperatures up to 50 °C, with an optimum pH of 9.0 and temperature of 30 °C. This broad pH and temperature tolerance of the purified uricase from DSS002 underscores its potential as a valuable source for industrial-scale production, catering to various pharmaceutical applications. This study's findings pave the way for efficient and scalable uricase production, offering promising implications for the pharmaceutical industry.
Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. This study focuses on optimizing process parameters for uricase production using advanced statistical methods, namely Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). Seven key process parameters were investigated: temperature, pH, medium volume, incubation time, inoculum size, inoculum age, and rpm. Conformational experimental studies at the ANN-GA predicted optimal conditions revealed a significant uricase activity of 63.92 ± 0.06 U/mL. The purified uricase exhibited specific activity of 92.48 U/mg and a molecular weight of approximately 32 kDa. Itdemonstrated remarkable stability, withstanding a wide pH range (6.0 to 10.0) and temperatures up to 50 °C, with an optimum pH of 9.0 and temperature of 30 °C. This broad pH and temperature tolerance of the purified uricase from Pseudomonas mosselii DSS002 underscores its potential as a valuable source for industrial-scale production, catering to various pharmaceutical applications. This study's findings pave the way for efficient and scalable uricase production, offering promising implications for the pharmaceutical industry.Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. This study focuses on optimizing process parameters for uricase production using advanced statistical methods, namely Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). Seven key process parameters were investigated: temperature, pH, medium volume, incubation time, inoculum size, inoculum age, and rpm. Conformational experimental studies at the ANN-GA predicted optimal conditions revealed a significant uricase activity of 63.92 ± 0.06 U/mL. The purified uricase exhibited specific activity of 92.48 U/mg and a molecular weight of approximately 32 kDa. Itdemonstrated remarkable stability, withstanding a wide pH range (6.0 to 10.0) and temperatures up to 50 °C, with an optimum pH of 9.0 and temperature of 30 °C. This broad pH and temperature tolerance of the purified uricase from Pseudomonas mosselii DSS002 underscores its potential as a valuable source for industrial-scale production, catering to various pharmaceutical applications. This study's findings pave the way for efficient and scalable uricase production, offering promising implications for the pharmaceutical industry.
Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. This study focuses on optimizing process parameters for uricase production using advanced statistical methods, namely Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). Seven key process parameters were investigated: temperature, pH, medium volume, incubation time, inoculum size, inoculum age, and rpm. Conformational experimental studies at the ANN-GA predicted optimal conditions revealed a significant uricase activity of 63.92 ± 0.06 U/mL. The purified uricase exhibited specific activity of 92.48 U/mg and a molecular weight of approximately 32 kDa. Itdemonstrated remarkable stability, withstanding a wide pH range (6.0 to 10.0) and temperatures up to 50 °C, with an optimum pH of 9.0 and temperature of 30 °C. This broad pH and temperature tolerance of the purified uricase from Pseudomonas mosselii DSS002 underscores its potential as a valuable source for industrial-scale production, catering to various pharmaceutical applications. This study’s findings pave the way for efficient and scalable uricase production, offering promising implications for the pharmaceutical industry.
Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic enzyme to detect uric acid in biological fluids. Enhancing uricase production is crucial to meet the demands of large-scale applications. This study focuses on optimizing process parameters for uricase production using advanced statistical methods, namely Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). Seven key process parameters were investigated: temperature, pH, medium volume, incubation time, inoculum size, inoculum age, and rpm. Conformational experimental studies at the ANN-GA predicted optimal conditions revealed a significant uricase activity of 63.92 ± 0.06 U/mL. The purified uricase exhibited specific activity of 92.48 U/mg and a molecular weight of approximately 32 kDa. Itdemonstrated remarkable stability, withstanding a wide pH range (6.0 to 10.0) and temperatures up to 50 °C, with an optimum pH of 9.0 and temperature of 30 °C. This broad pH and temperature tolerance of the purified uricase from Pseudomonas mosselii DSS002 underscores its potential as a valuable source for industrial-scale production, catering to various pharmaceutical applications. This study’s findings pave the way for efficient and scalable uricase production, offering promising implications for the pharmaceutical industry.
Author John Babu, D.
Dudala, Sai Sushma
Venkata Narayana, A.
Venkateswarulu, T. C.
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Issue 2
Keywords Artificial neural network
Uric acid
Box-Behnken design
Uricase
Genetic algorithm
Pseudomonas
Language English
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Snippet Uricase holds significant pharmaceutical applications, particularly in treating diseases associated with elevated uric acid levels and serving as a diagnostic...
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SubjectTerms Algorithms
Artificial neural networks
Back propagation
Biomedical and Life Sciences
Enzymes
Genetic algorithms
Inoculum
Life Sciences
Medical Microbiology
Medical treatment
Microbiology
Molecular weight
Mutation
Neural networks
Neurons
Optimization
Optimization techniques
Original Research Article
pH effects
Pharmaceutical industry
Pharmaceuticals
Process parameters
Pseudomonas
Pseudomonas mosselii
Response surface methodology
Rheumatism
Statistical methods
Statistics
Temperature tolerance
Urate oxidase
Uric acid
Variance analysis
Title Statistical Optimization of Process Parameters and Purification of Uricase Using Isolate Pseudomonas mosselii DSS002
URI https://link.springer.com/article/10.1007/s12088-025-01467-y
https://www.ncbi.nlm.nih.gov/pubmed/40655368
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https://www.proquest.com/docview/3229905267
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