Delineating the effects of hot-melt extrusion on the performance of a polymeric film using artificial neural networks and an evolutionary algorithm
| Autor: | Galit Regev, Lisa C. Rohan, Sravan Kumar Patel, Ayman Akil, DeAngelo Mckinley |
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| Rok vydání: | 2019 |
| Předmět: |
Hot Temperature
Materials science Mean squared error Polymers Chemistry Pharmaceutical Drug Compounding Evolutionary algorithm Pharmaceutical Science 02 engineering and technology 030226 pharmacology & pharmacy Article 03 medical and health sciences 0302 clinical medicine Hot melt Dissolution Drug Carriers Artificial neural network Hot Melt Extrusion Technology Process (computing) 021001 nanoscience & nanotechnology Vaginal Film Drug Liberation Extrusion Neural Networks Computer 0210 nano-technology Biological system |
| Zdroj: | Int J Pharm |
| ISSN: | 0378-5173 |
| Popis: | The aim of this study was to utilize an artificial neural network (ANN) in conjunction with an evolutionary algorithm to investigate the relationship between hot melt extrusion (HME) process parameters and vaginal film performance. Investigated HME process parameters were: barrel temperature, screw speed, and feed rate. Investigated film performance attributes were: percent dissolution at 30 min, puncture strength, and drug content. An ANN model was successfully developed and validated with a root mean squared error of 0.043 and 0.098 for training and validation, respectively. Of all three assessed process parameters, the model revealed that barrel temperature has a significant impact on film performance. An increase in barrel temperature resulted in increased dissolution and punctures strength and decreased drug content. Additionally, a successful implementation of an evolutionary algorithm was carried out in order to demonstrate the potential applicability of the developed ANN model in film formulation optimization. In this analysis, the values predicted of film performance attributes were within 1% error of the experimental data. The findings of this study provide a quantitative framework to understand the relationship between HME parameters and film performance. This quantitative framework has the potential to be used for film formulation development and optimization. |
| Databáze: | OpenAIRE |
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