Nanostructured lipid carriers (NLC) belong to youngest lipid-based nanocarrier class and they have gained increasing attention over the last ten years. NLCs are composed of a mixture of solid and liquid lipids, which solubilizes the active pharmaceutical ingredient, stabilized by a surfactant. The miscibility of the lipid excipients and structural changes (polymorphism) play an important role in the stability of the formulation and are not easily predicted in the early pharmaceutical development. Even when the excipients are macroscopically miscible, microscopic heterogeneities can result in phase separation during storage, which is only detected after several months of stability studies. In this sense, this work aimed to evaluate the miscibility and the presence of polymorphism in lipid mixtures containing synthetic (cetyl palmitate, Capryol 90®, Dhaykol 6040 LW®, Precirol ATO5® and myristyl myristate) and natural (beeswax, cocoa and shea butters, copaiba, sweet almond, sesame and coconut oils) excipients using Raman mapping and multivariate curve resolution - alternating least squares (MCR-ALS) method. The results were correlated to the macroscopic stability of the formulations. Chemical maps constructed for each excipient allowed the direct comparison among formulations, using standard deviation of the histograms and the Distributional Homogeneity Index (DHI). Lipid mixtures of cetyl palmitate/Capryol®; cetyl palmitate/Dhaykol®; myristyl myristate/Dhaykol® and myristyl myristate/coconut oil presented a single histogram distribution and were stable. The sample with Precirol®/Capryol® was not stable, although the histogram distribution was narrower than the samples with cetyl palmitate, indicating that miscibility was not the factor responsible for the instability. Structural changes before and after melting were identified for cocoa butter and shea butter, but not in the beeswax. Beeswax + copaiba oil sample was very homogenous, without polymorphism and stable over 6 months. Shea butter was also homogeneous and, in spite of the polymorphism, was stable. Formulations with cocoa butter presented a wider histogram distribution and were unstable. This paper showed that, besides the miscibility evaluation, Raman imaging could also identify the polymorphism of the lipids, two major issues in lipid-based formulation development that could help guide the developer understand the stability of the NLC formulations.
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http://dx.doi.org/10.1016/j.ejps.2019.05.002 | DOI Listing |
Bioinform Adv
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Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON K1K 4P7, Canada.
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Institute of Information Technology, Vietnam Academy of Science and Technology, Hoang Quoc Viet, Hanoi 10072, Vietnam.
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January 2025
State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, China.
In grid intelligent inspection systems, automatic registration of infrared and visible light images in power scenes is a crucial research technology. Since there are obvious differences in key attributes between visible and infrared images, direct alignment is often difficult to achieve the expected results. To overcome the high difficulty of aligning infrared and visible light images, an image alignment method is proposed in this paper.
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January 2025
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China.
With the increasing importance of securing images during network transmission, this paper introduces a novel image encryption algorithm that integrates a 3D chaotic system with V-shaped scrambling techniques. The proposed method begins by constructing a unique 3D chaotic system to generate chaotic sequences for encryption. These sequences determine a random starting point for V-shaped scrambling, which facilitates the transformation of image pixels into quaternary numbers.
View Article and Find Full Text PDFHypertens Res
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Division of Internal Medicine, Candiolo Cancer Institute, FPO-IRCCS, Turin, Italy.
Blood pressure (BP) variability (BPV) is an independent predictor of cardiovascular (CV) events. The role of BPV in defining risk of cancer therapy-related cardiovascular toxicity (CTR-CVT) is currently unknown. The aims of this study were: (i) to evaluate BPV in a population of patients with Multiple Myeloma, undergoing proteasome inhibitors therapy; (ii) to assess the predictive value of BPV for CTR-CVT; (iii) to analyze clusters of subjects based on BPV.
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