Formulation optimization of aprepitant microemulsion-loaded silicated corn fiber gum particles for enhanced bioavailability

A Novel Semi-Solid Self-Emulsifying Formulation of Aprepitant for Oral Delivery: An In Vitro Evaluation

Aprepitant is the first member of a relatively new antiemetic drug class called NK₁ receptor antagonists. It is commonly prescribed to prevent chemotherapy-induced nausea and vomiting. Although it is included in many treatment guidelines, its poor solubility causes bioavailability issues. A particle size reduction technique was used in the commercial formulation to overcome low bioavailability. Production with this method consists of many successive steps that cause the cost of the drug to increase. This study aims to develop an alternative, cost-effective formulation to the existing nanocrystal form. We designed a self-emulsifying formulation that can be filled into capsules in a melted state and then solidified at room temperature. Solidification was achieved by using surfactants with a melting temperature above room temperature. Various polymers have also been tested to maintain the supersaturated state of the drug. The optimized formulation consists of Capryol^TM 90, Kolliphor^® CS20, Transcutol^® P, and Soluplus^®; it was characterized by DLS, FTIR, DSC, and XRPD techniques. A lipolysis test was conducted to predict the digestion performance of formulations in the gastrointestinal system. Dissolution studies showed an increased dissolution rate of the drug. Finally, the cytotoxicity of the formulation was tested in the Caco-2 cell line. According to the results, a formulation with improved solubility and low toxicity was obtained.

Oral Drug Delivery Systems Based on Ordered Mesoporous Silica Nanoparticles for Modulating the Release of Aprepitant

Two different types of ordered mesoporous nanoparticles, namely MCM-41 and MCM-48, with similar pore sizes but different pore connectivity, were loaded with aprepitant via a passive diffusion method. The percentage of the loaded active agent, along with the encapsulation efficiency, was evaluated using High-performance Liquid Chromatography (HPLC) analysis complemented by Thermogravimetric Analysis (TGA). The determination of the pore properties of the mesoporous particles before and after the drug loading revealed the presence of confined aprepitant in the pore structure of the particles, while Powder X-ray Diffractometry(pXRD), Differential Scanning Calorimetry (DSC), and FTIR experiments indicated that the drug is in an amorphous state. The release profiles of the drug from the two different mesoporous materials were studied in various release media and revealed an aprepitant release up to 45% when sink conditions are applied. The cytocompatibility of the silica nanoparticles was assessed in Caco-2 cell monolayers, in the presence and absence of the active agent, suggesting that they can be used as carriers of aprepitant without presenting any toxicity in vitro.

Development of a New Aprepitant Liquisolid Formulation with the Aid of Artificial Neural Networks and Genetic Programming

In the present study, liquisolid formulations were developed for improving dissolution profile of aprepitant (APT) in a solid dosage form. Experimental studies were complemented with artificial neural networks and genetic programming. Specifically, the type and concentration of liquid vehicle was evaluated through saturation-solubility studies, while the effect of the amount of viscosity increasing agent (HPMC), the type of wetting (Soluplus® vs. PVP) and solubilizing (Poloxamer®407 vs. Kolliphor®ELP) agents, and the ratio of solid coating (microcrystalline cellulose) to carrier (colloidal silicon dioxide) were evaluated based on in vitro drug release studies. The optimum liquisolid formulation exhibited improved dissolution characteristics compared to the marketed product Emend®. X-ray diffraction (XRD), scanning electron microscopy (SEM) and a novel method combining particle size analysis by dynamic light scattering (DLS) and HPLC, revealed that the increase in dissolution rate of APT in the optimum liquisolid formulation was due to the formation of stable APT nanocrystals. Differential scanning calorimetry (DSC) and attenuated total reflection FTIR spectroscopy (ATR-FTIR) revealed the presence of intermolecular interactions between APT and liquisolid formulation excipients. Multilinear regression analysis (MLR), artificial neural networks (ANNs), and genetic programming (GP) were used to correlate several formulation variables with dissolution profile parameters (Y _15min and Y _30min) using a full factorial experimental design. Results showed increased correlation efficacy for ANNs and GP (RMSE of 0.151 and 0.273, respectively) compared to MLR (RMSE = 0.413).