Bilateral Prediction of Formulation Parameters and Drug Release Profiles in Porous Monolithic Devices

Mathematical modeling of drug delivery systems has turned to an interesting field of steadily increasing academic and industrial attention also an enormous future potential. This is especially true with long acting controlled-release drug delivery systems where, a huge burden of the experimentations are required at formulation development stage to achieve the desired release profile and dose delivered at each time interval. Here, artificial neural networks is used to simulate porous corticosteroid releasing drug delivery devices based on monolithic dispersion of betamethasone in a silicone matrix using two leachable porogens also prediction of the optimized formulation parameters including the initial drug loading percentage (%w/w), porogen type, size and content. The network was also successfully examined in determination of the formulation parameters for a given drug release profile.

Keywords:

Silicone , Porous Monolithic Devices Dispersion , Drug Release Profile , Artificial Neural Network