Penicillin G acylase is a biocatalyst produced by bacteria as well as fungi in order to acylate/deacylate penicillin G to its constituent 6-aminopenicilanic acid and phenyl acetic acid or vice versa which is termed as hydrolysis (H) and synthesis (S) respectively, (Figure 1). Due to hydrolytic and synthetic ability of penicillin G acylase and importance of semi synthetic antibiotics, the enzyme has profound uses in related industry.

Penicillin acylase in its soluble form is unstable, cannot be separated from the reaction mixture easily which will add to the production cost of the final product thus such a commercially, industrially important biocatalyst has been immobilized by different techniques employing various supports by many investigators [1-7]. Each technique or method has its own limitation and disadvantages where immobilization method can alter kinetic properties of immobilized enzyme as compared to its soluble counterpart. The changes can be brought about by the supports or the reagents employed. Investigators are trying to employ technique and reagents which will improve the kinetic properties of the enzyme under immobilization process as compared to its soluble counterpart [7-11]. In this article, we have attempted to optimize the immobilization process of penicillin G acylase onto super para magnetite iron oxide nanoparticles by considering factors such as, enzyme concentration, weight of nanoparticle, concentration of polyethyleneimine, temperature and time of reaction using surface response methodology. Response surface methodology (RSM) is a collection of statistical and mathematical techniques useful for developing, improving, and optimizing processes. The applications of RSM are in industry, biological & chemical sciences, pharmaceutical, physical and engineering sciences. Further the most extensive applications of RSM are in the particular situations where several input variables potentially influence some performance measure or quality characteristic of the process. In RSM, the main effects and interactions between various factors, each at different levels can be simultaneously studied [12].

Table 1 shows the actual value at high and low level. Through Table 1 the experimental matrix was designed employing 2 level factorial design. The response (Y) was expressed in terms of units of enzyme adsorbed onto iron oxide nanoparticles. The responses obtained revealed that experiments no. 4, 12, 17 and 38 are yielded highest immobilization rate. It is noticed that adsorption of the enzyme onto the support has taken place at 4˚C and in experiments 6 and 21, the amount of enzyme is at minimum level, but the adsorption of the enzyme has taken place at 4˚C with almost 50% adsorption equalizing to that of high value of enzyme concentration showing enzyme concentration and reaction temperature are important factors in immobilization process of penicillin G acylase (Table 3, Figures 2 and 3).

Table 2. The experimental matrix designed by Ease State version 6, bearing the actual factors at high and low level where the response is expressed in terms of unit/ml.

Table 3. Regression analysis of variance for RSM (ANOVA).

Since response surface methodology is a statistical technique employed to optimize the processes where several factors are involved. This methodology can be employed to optimize bioprocesses and the obtained model can be validated in order to follow up the accuracy of the model [13-16]. The statistical significance of regression equation was checked by F-test, and the analysis of variance (ANOVA) for response surface quadratic polynomial model done by software (Design-Expert) is shown in Table 3. The P value of 0.0009 for response surface quadratic model shows significance of the model thus it was selected to study further. The value of the determination coefficient (R²) calculated from the quadratic regression model was 0.845, while the value of the adjusted (R²) was 0.74 that almost indicates fairly high degree of correlation between the observed and predicted values. All these results suggested that the model, as evidenced by the calculated F value (7.95) and low probability value (P < 0.0001), was adequate for the prediction of the immobilization of penicillin G acylase within the variable range employed. The lack-of-fit test measures failure of the model to represent the data in the experimental domain at points which are not included in the regression. As shown in Table 3, F value (2.93) of lack-of-fit implied that it was not significant relative to the pure error, which indicated the model equation was adequate for predicting the yield of immobilization process under any combination of values of the variables. The P value was used as a tool to check the significance of each coefficient, which in turn may indicate the pattern of the interactions among the variables. The smaller the P value, the more significant the corresponding coefficient. The whole design consisted of 50 experimental points, which included 32 factorial points, 10 axial and 8 central points. By employing multiple regression analysis on the experimental data, the response Y for the yield i.e. the amount of penicillin G acylase immobilized on to super paramagnetic iron oxide nanoparticles can be obtained by the following second-order polynomial equation.

As it can be observed from the Table 3 the P values for factors like A, D, AD, A² and D² are < 0.0001, 0.001, 0.0049 and 0.0016 respectively showing the significance of the factors involved in immobilization process of penicillin G acylase.

The quadratic model was validated by performing the immobilization process under optimal conditions and the yield of penicillin acylase was comparable to that of calculated by the model thereby the selected model was appropirate.In this way penicillin G acylase (industrially and commercially important biocatalyst) could be immobilized through optimization by surface response methodology

The corresponding author thereby acknowledges the grant given by Iran National Science Foundation: INSF to carry out this research project.