Comparison of response surface methodology and artificial neural network to optimize novel ophthalmic flexible nano-liposomes: Characterization, evaluation, in vivo pharmacokinetics and molecular dynamics simulation.

To improve the topical delivery of pilocarpine hydrochloride (PN) to treat glaucoma, flexible nano-liposomes containing PN (PN-FLs) were prepared, optimized and characterized. Artificial neural network (ANN) and response surface methodology (RSM) were used to optimize the procedure and to obtain an optimal formulation. The properties of PN-FLs were investigated, including particle size, zeta potential, morphology, fourier transform infra-red (FT-IR) spectroscopy and entrapment efficiency (EE). The drug release study indicated that PN-FLs had a substantial sustained release effect. The modified Draize test and pathological section studies indicated no potential ophthalmic irritation. Non-invasive fluorescence imaging showed that PN-FLs significantly prolonged the pre-ocular residence time of PN, which was 1.81 times than that of PN solution. In pharmacokinetic studies, the AUC of PN-FLs was 4.55 times than that of the control. Molecular dynamics (MD) simulation, a new method to design and improve formulations, was also applied to evaluate formulations in this study. All data indicated that PN-FLs has great potential for ocular administration and can be used as an ocular delivery system for PN. Moreover, MD simulation provides insight that complements experimental research programs and plays an increasing role in designing and improving formulations.