Comparative Study for Optimization of Pharmaceutical Self-Emulsifying Pre-concentrate by Design of Experiment and Artificial Neural Network

• Published in: AAPS PharmSciTech Vol. 19; no. 7; pp. 3311 - 3321
• Main Authors: Parikh, Kinjal J., Sawant, Krutika K.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-10-2018
• Subjects: Biochemistry; Biomedical and Life Sciences; Biomedicine; Biotechnology; Pharmacology/Toxicology; Pharmacy; Research Article; formulation optimization; artificial neural network; self-emulsifying pre-concentrate; multiple regression; I-optimal design
• ISSN: 1530-9932; 1530-9932
• DOI: 10.1208/s12249-018-1173-2

The present investigation aimed to optimize the critical parameters affecting the globule size of self-emulsifying drug delivery system. Based on preliminary screening, three critical parameters,
viz.
, amount of oil, surfactant, and co-surfactant were found to affect the globule size. I-optimal mixture design and Artificial Neural Network (ANN) were used to optimize the formulation with respect to minimum globule size. Comparative study was carried out to identify which optimization technique gave better predictability for the selected output parameter.
R
-value and MSE values were taken into consideration for comparison of both techniques. Using Response Surface Methodology-based I-optimal mixture design approach, the
R
2
value was found to be 0.9867, whereas with ANN technique, it was found to be 0.99548. The predicted size for the optimized batch by I-optimal design was 122.377 nm, whereas by ANN, it was 119.6783 nm against the actual obtained size of 118.2 ± 2.3 nm. This analysis indicated superior predictability of output for given input variables by ANN as compared to model-dependent DoE I-optimal design approach.