(258a) Stability Kinetic Study for Amylase and Protease Enzymes

Surfactants are one of the main ingredients in the formulation of detergents as they reduce the surface tension of water and promote the solubility of soil in the liquid medium. However, they are marketed as disposable products, entering a negative impact on the environment due to its lack of biodegradability. For this reason, the design of detergent formulations is currently focused on employing ingredients that satisfy the cleaning needs respecting the environment ¹. Nowadays enzymes such as amylases and proteases are considered as a potential alternative ingredient due to their biodegradability capability ². These proteins are able to improve the cleaning performance, reducing the cleaning time together with the fresh water and energy consumption ². In this way, more environmentally friendly cleaning process is carried out, promoting the achievement of the objectives set by United Nations in terms of sustainability and responsible consumption and production ³.

However, the stain removal performance might be altered by the stability of the enzymes ^4–9. For this reason, this work evaluates the impact of pH, temperature, hardness, bleaching agents such as peroxide and peracid-based bleaches, manganese-based bleaching catalyst, chelating and builder compounds in order to propose a kinetic mechanism and build an integrated model being able to predict the stability of the amylase and protease based on nonlinear differential equations. A preliminary statistical analysis was carried out in order to identify the most significant variation sources which affect the stability of each enzyme based on a response surface methodology, selecting which ones with a p-value lower than 0.01. Once the deactivation factors have been selected, a kinetic mechanism is proposed, formulating the kinetics terms, and including them in an integrated model based on experimental data and previous research. In addition, a kinetic model dissolution was included for spherical enzymes particles to evaluate the dissolution process of each studied enzyme. Subsequently, the parameters of the kinetic terms were fitted. The parameter estimation was done by gProms employing a nonlinear regression technique based on NLPSQP solver. The acceptance of the parameter estimation was based on their standard deviation and t-value. Finally, a validation of the mechanistic model for each enzyme was evaluated by the determination coefficient obtained from the linear regression between the estimated and measured concentration for each enzyme. This analysis was done for training and validation data set.

Statistical analysis shows a thermal decomposition of amylase led by pH – temperature interaction. Additionally, deprotonated peracid-based bleach promotes the amylase decomposition together with the protonated peroxide base bleach, being the last one, activated by the manganese bleaching catalyst. On the other hand, the protease presents an abrupt thermal decomposition from a temperature of 50 C, regardless of pH. In contrast to amylase, the protease is decomposed by the protonated and deprotonated forms of peracid and peroxide-based bleaches respectively. All the mechanistic fitted parameters present a low standard deviation together with a t-value upper than the refence t-value 95% of confidence for one tailed analysis. The validation of the mechanistic model provided a training determination coefficient of 0.84 and 0.90 for amylase and protease respectively. In addition, the validation determination coefficient is 0.90 for both enzymes, showing a large capability of the models to predict the amylase and protease stability as a function of variation sources, being not overfitted models.

References

(1) Zoller, U. Handbook of Detergents, Part E: Applications; CRC Press, 2008.

(2) Pérez Mohedano, R. Cleaning Principles in Automatic Dishwashers, University of Birmingham, 2015.

(3) United nations. Sustainable Development Goals | United Nations Development Programme. https://www.undp.org/sustainable-development-goals (accessed 2022-09-20).

(4) Rodríguez, V. B.; Alameda, E. J.; Gallegos, J. F. M.; Requena, A. R.; López, A. I. G. Thermal Deactivation of a Commercial α-Amylase from Bacillus Licheniformis Used in Detergents. Biochem. Eng. J. 2006, 27 (3), 299–304. https://doi.org/10.1016/j.bej.2005.08.018.

(5) Cavello, I. A.; Hours, R. A.; Cavalitto, S. F. Bioprocessing of “Hair Waste” by Paecilomyces Lilacinus as a Source of a Bleach-Stable, Alkaline, and Thermostable Keratinase with Potential Application as a Laundry Detergent Additive: Characterization and Wash Performance Analysis. Biotechnol. Res. Int. 2012, 2012 (December), 1–12. https://doi.org/10.1155/2012/369308.

(6) Tijskens, L. M. M.; Greiner, R.; Biekman, E. S. A.; Konietzny, U. Modeling the Effect of Temperature and PH on Activity of Enzymes: The Case of Phytases. Biotechnol. Bioeng. 2001, 72 (3), 323–330. https://doi.org/10.1002/1097-0290(20010205)72:3<323::AID-BIT9>3.0.CO;2-I.

(7) Schick, M. J.; Hubbard, A. T. Liquids Detergent; CRC Press, Ed.; 2005.

(8) Naganthran, A.; Masomian, M.; Rahman, R. N. Z. R. A.; Ali, M. S. M.; Nooh, H. M. Improving the Efficiency of New Automatic Dishwashing Detergent Formulation by Addition of Thermostable Lipase, Protease and Amylase. Molecules 2017, 22 (9), 1–18. https://doi.org/10.3390/molecules22091577.

(9) Lund, H.; Kaasgaard, S. G.; Skagerlind, P.; Jorgensen, L.; Jørgensen, C. I.; Van De Weert, M. Protease and Amylase Stability in the Presence of Chelators Used in Laundry Detergent Applications: Correlation between Chelator Properties and Enzyme Stability in Liquid Detergents. J. Surfactants Deterg. 2012, 15 (3), 265–276. https://doi.org/10.1007/s11743-011-1318-8.