Yingxu Peng

Application of artificial neural networks in the design of controlled release drug delivery systems

Controlled release drug delivery systems offer great advantages over the conventional dosage forms. However, there are great challenges to efficiently develop controlled release drug delivery systems due to the complexity of these delivery systems. Traditional statistic response surface methodology (RSM) is one of the techniques that has been employed to develop and formulate controlled release dosage forms. However, there are some limitations to the RSM technique. Hence, another technique called artificial neural networks (ANN) has recently gained wide popularity in the development of controlled release dosage forms. In this review, the basic ANN structure, the development of the ANN model and an explanation of how to use ANN to design and develop controlled release drug delivery systems are discussed. In addition, the applications of ANN in the design and development of controlled release dosage forms are also summarized in this review.

Controlled release of oxytetracycline in sheep.

A novel biodegradable injectable formulation of oxytetracycline (OTC) was administered subcutaneously to sheep at a dose of 40 mg/kg. Blood samples were collected from the jugular vein at predetermined time intervals. The concentration of OTC in plasma was analyzed by an HPLC method. The concentrations of OTC in plasma were maintained at or above 0.5 microg/ml (minimum inhibitory concentration) for approximately 6 days. The pharmacokinetic parameters of OTC in sheep were also determined by monitoring the plasma concentration of OTC after a single intravenous injection of a commercially available OTC formulation at 10 mg/kg body weight. The in vivo release profiles of OTC from the biodegradable injectable formulations in sheep were determined from the plasma concentration time profiles by the deconvolution method using PCDCON software. The in vitro release of OTC from the biodegradable injectable formulation was tested in phosphate buffer (pH 7.4), containing 0.686% w/v of sodium sulfite as antioxidant. The correlation between the in vitro and in vivo release of OTC from the injectable formulation was also evaluated. The results of the in vivo evaluation of the formulation in sheep indicated that a controlled release biodegradable injectable dosage form of OTC for food animals is feasible.

Prediction of Dissolution Profiles of Acetaminophen Beads Using Artificial Neural Networks

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.