Artificial Neural Network Modeling of Sustained Antihypertensive Drug Delivery using Polyelectrolyte Complex based on Carboxymethyl-kappa-carrageenan and Chitosan as Prospective Carriers

The polyelectrolyte complexes (PECs) formed by the combination of opposing charge biopolymers proves to be a widely used method for the development of excipients for pharmaceutical use. The aim of this work is the use of an artificial neural network (ANN) to modeling the prolonged release profile of an antihypertensive active agent: Valsartan from a matrix tablet formed from PEC based on carboxymethyl-kappa-carrageenan (CMKC) and chitosan (CTS). ANN is used to predict and describe the kinetic release profile and rate. Several formulas have been developed and a study of the pharmacotechnical and rheological properties of pulverulent mixtures has been carried out. Rheological tests showed satisfactory flowability and compaction. The percentage of compressibility expressed by the Carr index and the Hausner index indicates a good flowability. The tablets obtained after direct compression of the pulverulent mixtures showed acceptable levels of hardness and friability at optimum compression forces. The results of the in vitro dissolution test showed that the release kinetics of Valsartan depend essentially on the concentration of PEC in the hydrophilic matrix forming the tablets. The ANN model is optimized and a root mean square value of the order of $2.09\times 10^{-17}$ is reached. In this work, the efficacy of ANN to contribute to the development of release dosage forms has been demonstrated, it could describe the relationship that connect the formulation parameters and the drug release profile.