Physicochemical characterization of nimodipine-polyethylene glycol solid dispersion systems.

This study investigates the solid-solid interactions between nimodipine (NIM) and polyethylene glycol (PEG) of different mean molecular weights (PEG 2000, 4000 and 6000), in solid dispersion systems, applying differential scanning calorimetry (DSC), Fourier-Transform infrared spectroscopy, powder X-ray diffraction (PXRD), hot stage microscopy (HSM) and theoretical modeling by the Flory-Huggins (FH) solution theory. Phase diagrams constructed with the aid of DSC and FH solution theory showed sensitivity on the estimated values of the FH interaction parameter (χ). When χ is considered a constant number (χ = α, α ≠ 0), formation of a eutectic mixture is predicted in the 70-80% w/w PEG concentration region, while when χ was considered as a function of concentration and temperature (χ = f(φ,Τ)), the model predicts the formation of monotectic systems.

Solid dispersions in the development of a nimodipine floating tablet formulation and optimization by artificial neural networks and genetic programming.

The present study investigates the use of nimodipine-polyethylene glycol solid dispersions for the development of effervescent controlled release floating tablet formulations. The physical state of the dispersed nimodipine in the polymer matrix was characterized by differential scanning calorimetry, powder X-ray diffraction, FT-IR spectroscopy and polarized light microscopy, and the mixture proportions of polyethylene glycol (PEG), polyvinyl-pyrrolidone (PVP), hydroxypropylmethylcellulose (HPMC), effervescent agents (EFF) and nimodipine were optimized in relation to drug release (% release at 60 min, and time at which the 90% of the drug was dissolved) and floating properties (tablet's floating strength and duration), employing a 25-run D-optimal mixture design combined with artificial neural networks (ANNs) and genetic programming (GP). It was found that nimodipine exists as mod I microcrystals in the solid dispersions and is stable for at least a three-month period.

Artificial neural networks in the optimization of a nimodipine controlled release tablet formulation.

Artificial neural networks (ANNs) were employed in the optimization of a nimodipine zero-order release matrix tablet formulation, and their efficiency was compared to that of multiple linear regression (MLR) on an external validation set. The amounts of PEG-4000, PVP K30, HPMC K100 and HPMC E50LV were used as independent variables following a statistical experimental design, and three dissolution parameters (time at which the 90% of the drug was dissolved, t(90%), percentage of nimodipine released in 2 and 8h, Y(2h), and Y(8h), respectively) were chosen as response variables. It was found that a feed-forward back-propagation ANN with eight hidden units showed better fit for all responses (R(2) of 0.

Developing and optimizing a validated isocratic reversed-phase high-performance liquid chromatography separation of nimodipine and impurities in tablets using experimental design methodology.

In the present study an isocratic reversed-phase high-performance liquid chromatography was investigated for the separation of nimodipine and impurities (A, B and C) using statistical experimental design. Initially, a full factorial design was used in order to screen five independent factors: type of the organic modifier - methanol or acetonitrile - and concentration, column temperature, mobile phase flow rate and pH. Except pH, the rest examined factors were identified as significant, using ANOVA analysis.