Evaluation of Suitable Polymeric Matrix/Carriers during Loading of Poorly Water Soluble Drugs onto Mesoporous Silica: Physical Stability and In Vitro Supersaturation.

The application of mesoporous carriers in formulations of amorphous solid dispersions (ASDs) has been suggested to enhance the stability of amorphous drugs. However, mesoporous carriers do not demonstrate satisfactory inhibitory effects on the precipitation of active pharmaceutical ingredients (APIs), and the inclusion of an appropriate polymer within ASDs becomes imperative to maintaining drug supersaturation. The aim of this study was to evaluate ternary olanzapine (OLN) ASDs with Syloid 244FP and to find an appropriate polymeric carrier. The polymer's selection criteria were based on the physical stability of the ASDs and the release rate of the drug from the systems. The polymers investigated were hydroxypropylmethyl cellulose (HPMC) and copovidone (coPVP). The formation of ASDs was achievable in all investigated cases, as demonstrated by the complete lack of crystallinity confirmed through both powder X-ray diffraction (pXRD) analysis and differential scanning calorimetry (DSC) for all developed formulations. The solvent shift method was employed to evaluate the ability of the studied carriers to inhibit the precipitation of supersaturated OLN. coPVP emerged as a more suitable precipitation inhibitor compared with HPMC and Syloid 244 FP. Subsequently, in vitro dissolution studies under non-sink conditions revealed a higher degree of supersaturation in ternary systems where coPVP was used as a polymeric carrier, as these systems exhibited, under the examined conditions, up to a 2-fold increase in the released OLN compared with the pure crystalline drug. Moreover, stability studies conducted utilizing pXRD demonstrated that ternary formulations incorporating coPVP and Syloid 244 FP maintained stability for an extended period of 8 months. In contrast, binary systems exhibited a comparatively shorter stability duration, indicating the synergistic effect of coPVP and Syloid 244 FP on the physical stability of the amorphous API. Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) studies showed that the development of stronger molecular interactions can be provided as an explanation for this synergistic effect, as the formation of robust H-bonds may be considered responsible for inhibiting the precipitation of the supersaturated API. Therefore, the incorporation of coPVP into OLN ASDs with Syloid 244 FP is considered a highly promising technique for increasing the degree of OLN supersaturation in in vitro dissolution studies and improving the stability of systems.

Drug crystal growth in ternary amorphous solid dispersions: Effect of surfactants and polymeric matrix-carriers.

The present study evaluates the crystal growth rate of amorphous drugs when dispersed in different ternary polymeric amorphous solid dispersions (ASDs) in the presence of surfactants. Specifically, ternary ASDs of aprepitant (APT, selected as a model drug) were prepared via melt-quench cooling by evaluating three commonly used ASDs matrix/carriers, namely hydroxypropyl cellulose (HPC), poly(vinylpyrrolidone) (PVP) and the copolymer Soluplus® (SOL), and two suitable surfactants, namely d-alpha tocopheryl polyethylene glycol 1000 succinate (TPGS) and poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol) (P407). Results showed that all components were completely miscible (verified via hot stage polarized microscopy) and both surfactants were acting as plasticizers to the API. APT's crystal growth rate was increased in the presence of both P407 and TPGS, while PVP was identified as the matrix/carrier with the greatest impact API's crystal growth rate inhibition. Interestingly, TPGS presented a noticeable synergistic effect when combined with PVP resulting in a further reduction of APT's crystal growth rate. Furthermore, evaluation of APT's nucleation induction time in dissolution medium (PBS pH 6.8) revealed PVP as the most effective crystallization inhibitor, whereas the addition of TPGS showed to improve PVP's ability to inhibit APT's recrystallization. Finally, the formation of intermolecular interactions in the ternary APT-PVP-TPGS provided an explanation for the observed PVP-TPGS synergistic effects, with molecular dynamics simulations being able to unravel the type and extent of these interactions on a theoretical basis.

Determination of the physical state of a drug in amorphous solid dispersions using artificial neural networks and ATR-FTIR spectroscopy.

The objective of the present study was to evaluate the use of artificial neural networks (ANNs) in the development of a new chemometric model that will be able to simultaneously distinguish and quantify the percentage of the crystalline and the neat amorphous drug located within the drug-rich amorphous zones formed in an amorphous solid dispersion (ASD) system. Attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy was used, while Rivaroxaban (RIV, drug) and Soluplus® (SOL, matrix-carrier) were selected for the preparation of a suitable ASD model system. Adequate calibration and test sets were prepared by spiking different percentages of the crystalline and the amorphous drug in the ASDs (prepared by the melting - quench cooling approach), while a 24 full factorial experimental design was employed for the screening of ANN's structure and training parameters as well as spectra region selection and data preprocessing. Results showed increased prediction performance, measured based on the root mean squared error of prediction (RMSEp) for the test sample, for both the crystalline (RMSEp (crystal) = 0.86) and the amorphous (RMSEp (amorphous) = 2.14) drug. Comparison with traditional regression techniques, such as partial least square and principle component regressions, revealed the superiority of ANNs, indicating that in cases of high structural similarity between the investigated compounds (i.e., the crystalline and the amorphous forms of the same compound) the implementation of more powerful/sophisticated regression techniques, such as ANNs, is mandatory.