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This study aimed to formulate and evaluate hydrodynamically balanced system (HBS) capsules of Voglibose for sustained drug release. Five formulations (VC1 to VC5) were developed and characterized based on micromeritic properties, weight and drug content uniformity, in vitro drug release, and release kinetics. Micromeritic analysis revealed that VC1 exhibited superior flow properties, with the lowest Carr’s Index (6.81%) and Hausner Ratio (1.07), ensuring efficient capsule filling. All formulations passed the weight and drug content uniformity tests, with VC1 showing the most consistent results (101.01% ± 1.89). In vitro drug release profiles indicated that all formulations provided sustained drug release over 12 hours, with VC1 achieving the highest cumulative release (75%). Release kinetics analysis showed that the drug release followed the Higuchi diffusion model (R²= 0.959 forVC1), indicating a diffusion-controlled mechanism. Additionally, VC1 demonstrated a strong fit with the zero-order model (R2 = 0.936), ensuring a uniform drug release over time. The n value from the Korsmeyer-Peppas model suggested non-Fickian or anomalous diffusion. Among all formulations, VC1 emerged as the most optimized formulation, combining excellent flow properties, uniformity and efficient sustained drug release, making it a potential candidate for clinical development. [ABSTRACT FROM AUTHOR]

Introduction: Rheumatoid arthritis (RA) is a chronic, autoimmune and inflammatory disease that mostly impacts the joints. Chronotherapeutics refers to a treatment method in which in-vivo drug availability is timed to match rhythms of disease in order to optimize therapeutic outcomes and minimize side effects. Flurbiprofen is a non-steroidal anti-inflammatory drug, indicated for the relief of inflammation., Objectives: The aim of the present study was to develop & optimize the microsponges based of Flurbiprofen tablet for Chronotherapeutics for enhanced therapeutic effect., Methods: Microsponges were developed by Quasi Emulsion solvent diffusion method. Prepared microsponges were optimized in order to analyze the effects of independent variables like concentration of PVA (X1), Volume of Dichloromethane (X2) & stirring speed (X3) on the Entrapment Efficiency (Y1), Mean particle size (Y2) and Drug release at 8 hr (Y3) using box Behnken design. The optimized formulation was subjected to in vitro study and Comparison with marketed formulation. With release kinetics study., Result: The optimized formulation Batch (F-18) Show particle size of 49.12µm, entrapment efficiency of 87.46%, and drug release at 8 h 70.49%, which is under the acceptance criteria, which is more effective compared with Marketed tablet., Conclusion: The results showed that, as stirring speed increases, the particle size decreases and entrapment efficiency increases. While volume of dichloromethane increases, particle size decreases. Morphology was found to be porous and spherical. Optimized batch of Flurbiprofen microsponge was further formulated in future for invivo study and clinical trials.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial intelligence has fundamentally changed the diagnostic picture by providing effective and early means of identifying these crippling illnesses. As a subset of computational intelligence, machine-learning algorithms have become very effective tools for the analysis of large datasets that include genetic, imaging, and clinical data. Moreover, multi-modal data integration, which includes information from brain imaging (MRI, PET scans), genetic profiles, and clinical evaluations, is made easier by computational intelligence. A thorough knowledge of the course of the illness is made possible by this consolidative method, which also facilitates the creation of predictive models for early medical evaluation and outcome prediction. Furthermore, there has been a great deal of promise shown by the use of artificial intelligence to neuroimaging analysis. Sophisticated image processing methods combined with machine learning algorithms make it possible to identify functional and structural anomalies in the brain, which often act as early indicators of neurodegenerative diseases. This chapter examines how computational intelligence plays a critical role in improving the diagnosis of neurodegenerative diseases such as Parkinson's, Alzheimer's, etc. To sum up, computational intelligence provides a revolutionary approach for improving the identification of neurodegenerative illnesses. In the battle against these difficult disorders, embracing and improving these computational techniques will surely pave the path for more individualized therapy and more therapies that are successful.