Novel Supercritical Carbon Dioxide Impregnation Technique for the Production of Amorphous Solid Drug Dispersions: A Comparison to Hot Melt Extrusion

The formulation of BCS Class II drugs as amorphous solid dispersions has been shown to provide advantages with respect to improving the aqueous solubility of these compounds. While hot melt extrusion (HME) and spray drying (SD) are among the most common methods for the production of amorphous solid dispersions (ASDs), the high temperatures often required for HME can restrict the processing of thermally labile drugs, while the use of toxic organic solvents during SD can impact on end-product toxicity. In this study, we investigated the potential of supercritical fluid impregnation (SFI) using carbon dioxide as an alternative process for ASD production of a model poorly water-soluble drug, indomethacin (INM). In doing so, we produced ASDs without the use of organic solvents and at temperatures considerably lower than those required for HME. Previous studies have concentrated on the characterization of ASDs produced using HME or SFI but have not considered both processes together. Dispersions were manufactured using two different polymers, Soluplus and polyvinylpyrrolidone K15 using both SFI and HME and characterized for drug morphology, homogeneity, presence of drug-polymer interactions, glass transition temperature, amorphous stability of the drug within the formulation, and nonsink drug release to measure the ability of each formulation to create a supersaturated drug solution. Fully amorphous dispersions were successfully produced at 50% w/w drug loading using HME and 30% w/w drug loading using SFI. For both polymers, formulations containing 50% w/w INM, manufactured via SFI, contained the drug in the γ-crystalline form. Interestingly, there were lower levels of crystallinity in PVP dispersions relative to SOL. FTIR was used to probe for the presence of drug-polymer interactions within both polymer systems. For PVP systems, the nature of these interactions depended upon processing method; however, for Soluplus formulations this was not the case. The area under the dissolution curve (AUC) was used as a measure of the time during which a supersaturated concentration could be maintained, and for all systems, SFI formulations performed better than similar HME formulations.

Characterisation and modelling of the thermorheological properties of pharmaceutical polymers and their blends using capillary rheometry: Implications for hot melt processing of dosage forms.

Given the growing interest in thermal processing methods, this study describes the use of an advanced rheological technique, capillary rheometry, to accurately determine the thermorheological properties of two pharmaceutical polymers, Eudragit E100 (E100) and hydroxypropylcellulose JF (HPC) and their blends, both in the presence and absence of a model therapeutic agent (quinine, as the base and hydrochloride salt). Furthermore, the glass transition temperatures (Tg) of the cooled extrudates produced using capillary rheometry were characterised using Dynamic Mechanical Thermal Analysis (DMTA) thereby enabling correlations to be drawn between the information derived from capillary rheometry and the glass transition properties of the extrudates. The shear viscosities of E100 and HPC (and their blends) decreased as functions of increasing temperature and shear rates, with the shear viscosity of E100 being significantly greater than that of HPC at all temperatures and shear rates. All platforms were readily processed at shear rates relevant to extrusion (approximately 200–300 s−1) and injection moulding (approximately 900 s−1). Quinine base was observed to lower the shear viscosities of E100 and E100/HPC blends during processing and the Tg of extrudates, indicative of plasticisation at processing temperatures and when cooled (i.e. in the solid state). Quinine hydrochloride (20% w/w) increased the shear viscosities of E100 and HPC and their blends during processing and did not affect the Tg of the parent polymer. However, the shear viscosities of these systems were not prohibitive to processing at shear rates relevant to extrusion and injection moulding. As the ratio of E100:HPC increased within the polymer blends the effects of quinine base on the lowering of both shear viscosity and Tg of the polymer blends increased, reflecting the greater solubility of quinine within E100. In conclusion, this study has highlighted the importance of capillary rheometry in identifying processing conditions, polymer miscibility and plasticisation phenomena.

An Evaluation of 1D Loss Model Collections for the Off-Design Performance Prediction of an Automotive Turbocharger Compressor

Abstract. Single-zone modelling is used to assess different collections of impeller 1D loss models. Three collections of loss models have been identified in literature, and the background to each of these collections is discussed. Each collection is evaluated using three modern automotive turbocharger style centrifugal compressors; comparisons of performance for each of the collections are made. An empirical data set taken from standard hot gas stand tests for each turbocharger is used as a baseline for comparison. Compressor range is predicted in this study; impeller diffusion ratio is shown to be a useful method of predicting compressor surge in 1D, and choke is predicted using basic compressible flow theory. The compressor designer can use this as a guide to identify the most compatible collection of losses for turbocharger compressor design applications. The analysis indicates the most appropriate collection for the design of automotive turbocharger centrifugal compressors.

Optimising drug solubilisation in amorphous polymer dispersions: rational selection of hot-melt extrusion processing parameters

The aim of this article was to construct a T–ϕ phase diagram for a model drug (FD) and amorphous polymer (Eudragit® EPO) and to use this information to understand the impact of how temperature–composition coordinates influenced the final properties of the extrudate. Defining process boundaries and understanding drug solubility in polymeric carriers is of utmost importance and will help in the successful manufacture of new delivery platforms for BCS class II drugs. Physically mixed felodipine (FD)–Eudragit® EPO (EPO) binary mixtures with pre-determined weight fractions were analysed using DSC to measure the endset of melting and glass transition temperature. Extrudates of 10 wt% FD–EPO were processed using temperatures (110°C, 126°C, 140°C and 150°C) selected from the temperature–composition (T–ϕ) phase diagrams and processing screw speed of 20, 100 and 200rpm. Extrudates were characterised using powder X-ray diffraction (PXRD), optical, polarised light and Raman microscopy. To ensure formation of a binary amorphous drug dispersion (ADD) at a specific composition, HME processing temperatures should at least be equal to, or exceed, the corresponding temperature value on the liquid–solid curve in a F–H T–ϕ phase diagram. If extruded between the spinodal and liquid–solid curve, the lack of thermodynamic forces to attain complete drug amorphisation may be compensated for through the use of an increased screw speed. Constructing F–H T–ϕ phase diagrams are valuable not only in the understanding drug–polymer miscibility behaviour but also in rationalising the selection of important processing parameters for HME to ensure miscibility of drug and polymer.

Self-consistent modelling of line-driven hot-star winds with Monte Carlo radiation hydrodynamics

Radiative pressure exerted by line interactions is a prominent driver of outflows in astrophysical systems, being at work in the outflows emerging from hot stars or from the accretion discs of cataclysmic variables, massive young stars and active galactic nuclei. In this work, a new radiation hydrodynamical approach to model line-driven hot-star winds is presented. By coupling a Monte Carlo radiative transfer scheme with a finite volume fluid dynamical method, line-driven mass outflows may be modelled self-consistently, benefiting from the advantages of Monte Carlo techniques in treating multiline effects, such as multiple scatterings, and in dealing with arbitrary multidimensional configurations. In this work, we introduce our approach in detail by highlighting the key numerical techniques and verifying their operation in a number of simplified applications, specifically in a series of self-consistent, one-dimensional, Sobolev-type, hot-star wind calculations. The utility and accuracy of our approach are demonstrated by comparing the obtained results with the predictions of various formulations of the so-called CAK theory and by confronting the calculations with modern sophisticated techniques of predicting the wind structure. Using these calculations, we also point out some useful diagnostic capabilities our approach provides. Finally, we discuss some of the current limitations of our method, some possible extensions and potential future applications.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Mechanochemical Synthesis of Pharmaceutical Cocrystal Suspensions via Hot Melt Extrusion: Feasibility Studies and Physicochemical Characterisation

Engineered cocrystals offer an alternative solid drug form with tailored physicochemical properties. Interestingly, although cocrystals provide many new possibilities, they also present new challenges, particularly in regard to their design and large-scale manufacture. Current literature has primarily focused on the preparation and characterization of novel cocrystals typically containing only the drug and coformer, leaving the subsequent formulation less explored. In this paper we propose, for the first time, the use of hot melt extrusion for the mechanochemical synthesis of pharmaceutical cocrystals in the presence of a meltable binder. In this approach, we examine excipients that are amenable to hot melt extrusion, forming a suspension of cocrystal particulates embedded in a pharmaceutical matrix. Using ibuprofen and isonicotinamide as a model cocrystal reagent pair, formulations extruded with a small molecular matrix carrier (xylitol) were examined to be intimate mixtures wherein the newly formed cocrystal particulates were physically suspended in a matrix. With respect to formulations extruded using polymeric carriers (Soluplus and Eudragit EPO, respectively), however, there was no evidence within PXRD patterns of either crystalline ibuprofen or the cocrystal. Importantly, it was established in this study that an appropriate carrier for a cocrystal reagent pair during HME processing should satisfy certain criteria including limited interaction with parent reagents and cocrystal product, processing temperature sufficiently lower than the onset of cocrystal Tm, low melt viscosity, and rapid solidification upon cooling.

Modeling of Turbulent Natural Gas and Biogas Flames of the Delft Jet-in-Hot-Coflow Burner: Effects of Coflow Temperature, Fuel Temperature and Fuel Composition on the Flame Lift-Off Height

The structure of a turbulent non-premixed flame of a biogas fuel in a hot and diluted coflow mimicking moderate and intense low dilution (MILD) combustion is studied numerically. Biogas fuel is obtained by dilution of Dutch natural gas (DNG) with CO2. The results of biogas combustion are compared with those of DNG combustion in the Delft Jet-in-Hot-Coflow (DJHC) burner. New experimental measurements of lift-off height and of velocity and temperature statistics have been made to provide a database for evaluating the capability of numerical methods in predicting the flame structure. Compared to the lift-off height of the DNG flame, addition of 30 % carbon dioxide to the fuel increases the lift-off height by less than 15 %. Numerical simulations are conducted by solving the RANS equations using Reynolds stress model (RSM) as turbulence model in combination with EDC (Eddy Dissipation Concept) and transported probability density function (PDF) as turbulence-chemistry interaction models. The DRM19 reduced mechanism is used as chemical kinetics with the EDC model. A tabulated chemistry model based on the Flamelet Generated Manifold (FGM) is adopted in the PDF method. The table describes a non-adiabatic three stream mixing problem between fuel, coflow and ambient air based on igniting counterflow diffusion flamelets. The results show that the EDC/DRM19 and PDF/FGM models predict the experimentally observed decreasing trend of lift-off height with increase of the coflow temperature. Although more detailed chemistry is used with EDC, the temperature fluctuations at the coflow inlet (approximately 100K) cannot be included resulting in a significant overprediction of the flame temperature. Only the PDF modeling results with temperature fluctuations predict the correct mean temperature profiles of the biogas case and compare well with the experimental temperature distributions.