Prediction of Dissolution Profiles of Acetaminophen Beads Using Artificial Neural Networks

Abstract

Immediate release acetaminophen (APAP) beads with 40% drug loading were prepared using the extrusion-spheronization process. Eighteen batches of beads were prepared based on a full factorial design by varying process variables such as extruder type, extruder screw speed, spheronization speed, and spheronization time. An in vitro dissolution test was carried out using the USP 27 Apparatus II (paddle) method. Artificial Neural Network (ANN) models were developed based on the aforementioned process variables and dissolution data. The trained ANN models were used to predict the dissolution profiles of APAP from the beads, which were prepared with various processing conditions. For training the ANN models, process variables were used as inputs, and percent drug released from APAP beads was used as the output. The dissolution data from one out of 18 batches of APAP beads was selected as the validation data set. The dissolution data of other 17 batches were used to train the ANN models using the ANN software (AI Trilogy®) with two different training strategies, namely, neural and genetic. The validation results showed that the ANN model trained with the genetic strategy had better predictability than the one trained with the neural strategy. The ANN model trained with the genetic strategy was then used to predict the drug release profiles of two new batches of APAP beads, which were prepared with process variables that were not used during the ANN model training process. However, the process variables used to prepare the two new batches of APAP beads were within the confines of the process variables used to prepare the 18 batches. The actual drug release profile of these two batches of APAP beads was similar to the ones predicted by the trained and validated ANN model, as indicated by the high f2 values. Furthermore, the ANN model trained with genetic strategy was also used to optimize process variables to achieve the desired dissolution profiles. These batches of APAP beads were then actually prepared using the process variables predicted by the trained and validated ANN model. The dissolution results showed that the actual dissolution profiles of the APAP beads prepared from the predicted process variables were similar to the desired dissolution profiles.

Keywords:

• extrusion-spheronization
• full factorial design
• artificial neural network
• genetic algorithm
• dissolution
• prediction