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.