Peng Yingxu, Geraldrajan Maria, Chen Quanmin, Sun Yichun, Johnson James R, Shukla Atul J
Department of Pharmaceutical Sciences, College of Pharmacy, University of Tennessee Health Science Center, Memphis, TN, USA.
Pharm Dev Technol. 2006;11(3):337-49. doi: 10.1080/10837450600769744.
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.
采用挤出滚圆法制备了载药量为40%的速释对乙酰氨基酚（APAP）微丸。基于全因子设计，通过改变诸如挤出机类型、挤出机螺杆速度、滚圆速度和滚圆时间等工艺变量，制备了18批微丸。使用美国药典27装置II（桨法）进行体外溶出试验。基于上述工艺变量和溶出数据建立了人工神经网络（ANN）模型。训练后的ANN模型用于预测在各种加工条件下制备的APAP微丸的溶出曲线。为了训练ANN模型，将工艺变量用作输入，将APAP微丸释放的药物百分比用作输出。从18批APAP微丸中选出一批的溶出数据作为验证数据集。其他17批的溶出数据用于使用具有两种不同训练策略（即神经策略和遗传策略）的ANN软件（AI Trilogy）训练ANN模型。验证结果表明，采用遗传策略训练的ANN模型比采用神经策略训练的模型具有更好的预测性。然后，采用遗传策略训练的ANN模型用于预测两批新的APAP微丸的药物释放曲线，这两批微丸是在ANN模型训练过程中未使用的工艺变量下制备的。然而，用于制备这两批新APAP微丸的工艺变量在用于制备18批微丸的工艺变量范围内。如高f2值所示，这两批APAP微丸的实际药物释放曲线与经过训练和验证的ANN模型预测的曲线相似。此外，采用遗传策略训练 的ANN模型还用于优化工艺变量以实现所需的溶出曲线。然后实际使用经过训练和验证的ANN模型预测的工艺变量制备这些批次的APAP微丸。溶出结果表明，由预测工艺变量制备的APAP微丸的实际溶出曲线与所需溶出曲线相似。
