Optimization of fermentation conditions for enhanced L-arginase production by Alcaligenes aquatilis BC2 using response surface methodology

L-arginase-based enzyme therapy, which depletes L-arginine by converting it to L-ornithine and urea, selectively inhibits the growth of L-arginine-dependent cancer cells with low toxicity. This approach shows promise as a novel cancer treatment. This research used Response Surface Methodology (RSM) to enhance L-arginase production by Alcaligenes aquatilis BC2, which was isolated from an Ethiopian soda lake. The Plackett-Burman Design was used to screen eight factors that influence L-arginase production and identified arginine concentration, peptone concentration, and incubation temperature as the most significant variables. The central composite design analysis demonstrated that the optimized conditions of 1.75 % L-arginine concentration, 3 % peptone concentration, and an incubation temperature of 37.5 °C enhance L-arginase production from a baseline of 92.45 U/mL to an optimized yield of 288.79 U/mL. This represents a 3.1-fold increase under the optimized conditions. The model was developed based on 20 experimental runs, demonstrating excellent fit with R2 = 0.9974 and a significant F-value of 420.28 (p < 0.0001). Additionally, the lack-of-fit test was conducted and found to be non-significant (F-value = 4.18, p = 0.0714), further supporting the model's predictive strength. This investigation showed that applying statistical design to optimize fermentation conditions leads to increased production of L-arginase, thereby advancing enzyme-based therapeutic practices and highlighting statistical optimization as essential for bioprocess development.