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Use of artificial neural networks to examine parameters affecting the immobilization of streptokinase in chitosan.

Modaresi SM, Faramarzi MA, Soltani A, Baharifar H, Amani A - Iran J Pharm Res (2014)

Bottom Line: The aim of this research was to establish an artificial neural networks (ANNs) model for identifying main factors influencing the loading efficiency of streptokinase, as an essential parameter determining efficacy of the enzyme.Subsequently, the experimental data were modeled and the model was validated against a set of unseen data.The developed model indicated chitosan concentration as probably the most important factor, having reverse effect on the loading efficiency.

View Article: PubMed Central - PubMed

Affiliation: Departemant of Biology, Faculty of Basic Sciences, Kharazmi University, Tehran, Iran.

ABSTRACT
Streptokinase is a potent fibrinolytic agent which is widely used in treatment of deep vein thrombosis (DVT), pulmonary embolism (PE) and acute myocardial infarction (MI). Major limitation of this enzyme is its short biological half-life in the blood stream. Our previous report showed that complexing streptokinase with chitosan could be a solution to overcome this limitation. The aim of this research was to establish an artificial neural networks (ANNs) model for identifying main factors influencing the loading efficiency of streptokinase, as an essential parameter determining efficacy of the enzyme. Three variables, namely, chitosan concentration, buffer pH and enzyme concentration were considered as input values and the loading efficiency was used as output. Subsequently, the experimental data were modeled and the model was validated against a set of unseen data. The developed model indicated chitosan concentration as probably the most important factor, having reverse effect on the loading efficiency.

No MeSH data available.


Related in: MedlinePlus

3D Plots of loading efficiency predicted by the ANNs model fixed at low, medium and high concentrations of the enzyme
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Figure 1: 3D Plots of loading efficiency predicted by the ANNs model fixed at low, medium and high concentrations of the enzyme

Mentions: To do so, we first examined the influence of chitosan concentration and pH on the level of streptokinase loading while the enzyme concentration is fixed at low, mid-range and high values. The results are shown in Figure 1. As can be seen, when chitosan concentration is medium or high (i.e. >~0.4 mg/mL), by increasing the chitosan concentration, a peak in loading efficiency is observed which represent optimum value of pH (~ 5.1). Stirring oppositely charged polyelectrolytes in a solution causes their self-assembly due to the creation of strong but reversible electrostatic interactions. Many factors have been reported to affect the formation and stability of the polyelectrolyte complexes. Some examples include charge density and distribution on the polyelectrolytes, concentration and mixing ratio of the polymers, mixing order, molecular weight of the agents as well as the temperature and pH of the interaction environment (-). It is believed that cationic and anionic interaction sites are the main cause of streptokinase loading onto the chitosan. Therefore, at pH values between the isoelectric pH values of chitosan (i.e. ~6.0) and streptokinase (i.e. 4.7), the amino groups of chitosan are protonated and interact favorably with negatively charged carboxyl groups of streptokinase (2, 14, 29). Accordingly, at an optimum pH value (i.e. ~ 5.1, in this work), the most efficient interactions may be observed. Similarly, Alsarra et al. showed that when using electrostatic interactions between chitosan and TPP solution, pH and the ionic nature are of great importance in determining the loading efficiency. They also indicated that an optimum pH value is required to maximize the loading efficiency because of a proper ratio of the cationic and anionic interaction sites (14).


Use of artificial neural networks to examine parameters affecting the immobilization of streptokinase in chitosan.

Modaresi SM, Faramarzi MA, Soltani A, Baharifar H, Amani A - Iran J Pharm Res (2014)

3D Plots of loading efficiency predicted by the ANNs model fixed at low, medium and high concentrations of the enzyme
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4232804&req=5

Figure 1: 3D Plots of loading efficiency predicted by the ANNs model fixed at low, medium and high concentrations of the enzyme
Mentions: To do so, we first examined the influence of chitosan concentration and pH on the level of streptokinase loading while the enzyme concentration is fixed at low, mid-range and high values. The results are shown in Figure 1. As can be seen, when chitosan concentration is medium or high (i.e. >~0.4 mg/mL), by increasing the chitosan concentration, a peak in loading efficiency is observed which represent optimum value of pH (~ 5.1). Stirring oppositely charged polyelectrolytes in a solution causes their self-assembly due to the creation of strong but reversible electrostatic interactions. Many factors have been reported to affect the formation and stability of the polyelectrolyte complexes. Some examples include charge density and distribution on the polyelectrolytes, concentration and mixing ratio of the polymers, mixing order, molecular weight of the agents as well as the temperature and pH of the interaction environment (-). It is believed that cationic and anionic interaction sites are the main cause of streptokinase loading onto the chitosan. Therefore, at pH values between the isoelectric pH values of chitosan (i.e. ~6.0) and streptokinase (i.e. 4.7), the amino groups of chitosan are protonated and interact favorably with negatively charged carboxyl groups of streptokinase (2, 14, 29). Accordingly, at an optimum pH value (i.e. ~ 5.1, in this work), the most efficient interactions may be observed. Similarly, Alsarra et al. showed that when using electrostatic interactions between chitosan and TPP solution, pH and the ionic nature are of great importance in determining the loading efficiency. They also indicated that an optimum pH value is required to maximize the loading efficiency because of a proper ratio of the cationic and anionic interaction sites (14).

Bottom Line: The aim of this research was to establish an artificial neural networks (ANNs) model for identifying main factors influencing the loading efficiency of streptokinase, as an essential parameter determining efficacy of the enzyme.Subsequently, the experimental data were modeled and the model was validated against a set of unseen data.The developed model indicated chitosan concentration as probably the most important factor, having reverse effect on the loading efficiency.

View Article: PubMed Central - PubMed

Affiliation: Departemant of Biology, Faculty of Basic Sciences, Kharazmi University, Tehran, Iran.

ABSTRACT
Streptokinase is a potent fibrinolytic agent which is widely used in treatment of deep vein thrombosis (DVT), pulmonary embolism (PE) and acute myocardial infarction (MI). Major limitation of this enzyme is its short biological half-life in the blood stream. Our previous report showed that complexing streptokinase with chitosan could be a solution to overcome this limitation. The aim of this research was to establish an artificial neural networks (ANNs) model for identifying main factors influencing the loading efficiency of streptokinase, as an essential parameter determining efficacy of the enzyme. Three variables, namely, chitosan concentration, buffer pH and enzyme concentration were considered as input values and the loading efficiency was used as output. Subsequently, the experimental data were modeled and the model was validated against a set of unseen data. The developed model indicated chitosan concentration as probably the most important factor, having reverse effect on the loading efficiency.

No MeSH data available.


Related in: MedlinePlus