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. 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.