a Bengal College of Pharmaceutical Sciences and Research , West Bengal University of Technology , B.R.B. Sarani , Durgapur , West Bengal , India and.
b S. Bhagwan Singh PG Institute of Biomedical Sciences and Research , Balawala , Dehradun , Uttarakhand , India.
Drug Deliv. 2016;23(3):1026-37. doi: 10.3109/10717544.2014.930759. Epub 2014 Jul 3.
Buccoadhesive wafer dosage form containing Loratadine is formulated utilizing Formulation by Design (FbD) approach incorporating sodium alginate and lactose monohydrate as independent variable employing solvent casting method.
The wafers were statistically optimized using Response Surface Methodology (RSM) and Artificial Neural Network algorithm (ANN) for predicting physicochemical and physico-mechanical properties of the wafers as responses. Morphologically wafers were tested using SEM. Quick disintegration of the samples was examined employing Optical Contact Angle (OCA).
The comparison of the predictability of RSM and ANN showed a high prognostic capacity of RSM model over ANN model in forecasting mechanical and physicochemical properties of the wafers. The in vivo assessment of the optimized buccoadhesive wafer exhibits marked increase in bioavailability justifying the administration of Loratadine through buccal route, bypassing hepatic first pass metabolism.
采用设计型配方（FbD）方法，将洛雷他定制成颊黏附片剂型，其中海藻酸钠和一水乳糖为独立变量，采用溶剂浇铸法。
使用响应面法（RSM）和人工神经网络算法（ANN）对片剂进行统计学优化，以片剂的理化和物理机械性能作为响应。使用扫描电子显微镜（SEM）对片剂的形态进行测试。采用光学接触角（OCA）法检查样品的快速崩解情况。
RSM 和 ANN 的预测能力比较表明，RSM 模型在预测片剂的机械和理化性能方面优于 ANN 模型。优化后的颊黏附片的体内评估显示生物利用度显著增加，证明通过颊黏膜途径给予氯雷他定可以绕过肝脏首过代谢。
