International Journal of Pharmaceutics最新文献

Roller compaction is a key unit operation in a dry granulation line for pharmaceutical tablet manufacturing. During product development, one would like to find the roller compactor (RC) settings that are required to achieve a desired ribbon solid fraction. These settings can be determined from the compression profile of the powder mixture being compacted and a mathematical model that interprets it. However, establishing compression profiles in an RC requires relatively large amounts of powder, which are expensive and may not be available during drug development. As a cost-effective alternative to an RC, a compactor simulator (CS) can be used, which is a small-scale equipment that uses minimal amounts of powder to build the compression profile. However, since the working principles of a CS and an RC are different, the compression profiles obtained from the two devices for a given powder are also different. In this study, we propose a transfer learning approach that allows the RC compression profile of a given powder to be easily predicted from the compression profile obtained in a CS for the same powder. Based on the well-known Johanson model and on the mass correction factor theory, we examine the compaction behavior of six formulations, two of which including active ingredients, and we find that the mass correction factor does not depend significantly on the powder being compacted. We develop a simple, generalized correlation (transfer model) that allows the mass correction factor to be predicted solely as a function of the pressure at which the compaction is carried out. By using the proposed transfer model, the prediction of the RC compression profiles for the validation powders is significantly improved over the case where a constant value of the mass correction factor is used.

The Prevalence of Depressive and Psychiatric disorders is increasing globally, and despite the availability of numerous FDA-approved drugs, treatment remains challenging. Many conventional antidepressants and antipsychotic formulations face issues such as low solubility, high first-pass metabolism, poor bioavailability, inadequate blood-brain barrier penetration, and systemic side effects. These challenges lead to reduced efficacy, slower onset of action, and decreased patient adherence to treatment. To address these problems, recent studies have explored the nose-to-brain route for drug delivery. This method offers several advantages, including non-invasive drug administration, direct access to the brain, rapid onset of action, reduced systemic exposure and side effects, avoidance of first-pass metabolism, enhanced bioavailability, precision dosing, and improved patient compliance. The formulations used for this approach include lipidic nanoparticles, polymeric nanoparticles, nasal gels, cubosomes, niosomes, polymeric micelles, nanosuspensions, nanoemulsions, nanocapsules, and elastosomes. This review analyzes and summarizes the published work on the nose-to-brain delivery of FDA-approved antidepressants and antipsychotic drugs, with a focus on the preparation, characterization, pharmacokinetics, pharmacodynamics, and toxicity profiling of these nanoformulations.

An interactive tool has been developed to help design oral solid dosage form formulations. The tool enables quantitative explorations and comparisons of physical, bulk, and mechanical properties, and takes into account functional characteristics as well. In this manner, comparisons and clustering of both excipients and APIs can be carried out. These comparisons enable the generation of alternatives as well as surrogate identification, so as to spare resources and material. Multiple data sources were merged to create a "joint" data table with all relevant properties. Four main workflow activities are supported: Explore Materials, Search Similar APIs, Search Similar Excipients and Search Material Clusters. Multi-dimensional filtering can be superimposed to each functionality. Suggested visualizations are made particularly accessible by providing them as "standard plots". The underlying philosophy is to empower formulation scientists to explore options, rather than prescribe decisions on exclusively mathematical grounds. The tool described here is the first step towards a holistic optimization incorporating predictions of mixture properties. Methodology of use is illustrated through three material selection application examples.