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For long-term treatment, conventional drug formulations are required to be administered in multiple doses, which have several disadvantages [1]. Design of controlled release (CR) formulations that release drug over an extended period of time is desirable in chronic disease conditions. CR formulations offer many advantages such as low dose, reduced or no fluctuation of drug concentration in the blood, minimal side effects, improved patient compliance and cost effectiveness [2,3]. Matrix technologies have proven to be popular among oral controlled drug delivery technologies because of their ease of manufacturing and simplicity of formulation, high degree of reproducibility, stability of the excipients and dosage form, and ease of technology transfer during scale-up and process validation [4]. Hydrophilic matrix systems are widely used for providing CR from solid oral dosage forms. Hydroxypropyl methylcellulose (HPMC), a semisynthetic polymer derived from cellulose, shows minimal interaction problems when used in basic, acidic or other electrolytic systems due to its nonionic nature. HPMC is enzyme resistant, chemically stable over a wide pH range, has consistently high quality and regulatory approval, making it an excellent carrier material for a matrix system [5]. In matrix tablets prepared with HPMC, the polymer quickly hydrates to form a gelatinous layer when it comes in contact with water. As the outer gel layer fully hydrates and dissolves, a newer inner layer forms gel like structure to retard the water influx and control drug diffusion [6]. Hence, in the current investigation, HPMC was used to prepare CR hydrophilic matrix tablets.

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 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.