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

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.