Developing Alfuzosin Tablet Formulation Based on Quality by Design (QbD) Approach by Using Artificial Neural Network

Abstract

The purpose of this study was to develop sustained release direct compressible alfuzosin (ALF) hydrochloride (HCl) tablet based on the concept of quality by design (QbD) approach using artificial neural network programs. At the first step of the study, the target product profile (TPP) of the formulation was defined. Subsequently, risk assessment tools were used to determine critical quality attributes (CQAs) and critical formulation parameters (CFPs). In-process control tests, assay and dissolution studies were performed. The test results were transferred to the artificial neural network (ANN) and the program was trained based on these data. The program offered new tablet formulations which have not been studied before and dissolution test results of this formulation was highly similar to the reference product's results than the other formulations. In conclusion, using the ANN programs within the scope of QbD approach for solid dosage formulation developments brings a lot of industry-wide benefits and advantages to ease scaling-up and meet the recent ICH guideline requirements.