Inhalable nanocomposite microparticles: preparation, characterization and factors affecting formulation. . ???aop.label???. doi: 10.1517/17425247.2016.1102224

Sumatriptan succinate (SS) is a 5-HT1 receptor agonist used in the treatment of migraine having poor bioavailability (15%) due to its extensive first-pass effect. The aim of this work was to prepare SS sublingual fast dissolving thin films (SFDTFs) allowing the drug to directly enter the systemic circulation and bypassing the first-pass metabolism. Plain thin films were prepared using solvent casting technique adopting 2(3) × 3 factorial design to study the effect of polymer and plasticizer type and concentration on mechanical properties and in vitro disintegration time of the plain prepared films using Design-Expert®. Medicated films were prepared after addition of 35 mg SS to each of the two selected plain formulae (F6 and F7) having desirability values above 0.9 showing the values of: 0.038, 0.039 kgf/mm(2) and 156.24, 164.16% and 0.0248, 0.0240 kgf/mm(2) as tensile strength, percent elongation and elastic modulus, respectively. PVP K30 was efficient as crystallization inhibitor in retarding SS crystallization. Pharmacokinetic study of the optimum formula F7 (PVP K30:SS (1:1 w/w)) in healthy human volunteers using LC/MS/MS revealed a shorter tmax (0.25 h) compared to Imitrex® tablet 25 mg (2 h) which is considered promising especially for the rapid relief of acute migraine attacks.

Objectives: This study aims to illustrate the applicability of combined mixture-process variable (MPV) design and modeling for optimization of nanovesicular systems.Methods: The D-optimal experimental plan studied the influence of three mixture components (MCs) and two process variables (PVs). The MCs were phosphatidylcholine (A), sodium glycocholate (B) and lercanidipine hydrochloride (C), while the PVs were glycerol amount in the hydration mixture (D) and sonication time (E). The studied responses were Y1: particle size, Y2: zeta potential and Y3: entrapment efficiency percent (EE%). Polynomial equations were used to study the influence of MCs and PVs on each response. Response surface methodology and multiple response optimization were applied to optimize the formulation with the goals of minimizing Y1 and maximizing Y2 and Y3.Results: The obtained polynomial models had prediction R2 values of 0.645, 0.947 and 0.795 for Y1, Y2 and Y3, respectively. Contour, Piepel’s response trace, perturbation...