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Daily Archives: May 29, 2026

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Multiple Emulsion Mediated Delivery Of Azilsartan Medoxomil For Improved Solubility And Bioavailability

Authors: Gourani Shruthi, K. Someshwar

Abstract: This study focuses on the formulation and characterization of water-in-oil-in-water (W/O/W) multiple emulsions of Azilsartan Medoxomil to enhance its solubility, stability, and oral drug delivery performance. A total of nine formulations (AZL1–AZL9) were developed using Span 20, Span 40, and Span 80 at varying concentrations as primary emulsifiers, while Tween 80 was employed as the secondary emulsifier. The prepared multiple emulsions were evaluated for various physicochemical and morphological parameters including visual appearance, organoleptic properties, microscopic examination, globule size, polydispersity index (PDI), zeta potential, viscosity, pH, conductivity, drug content, entrapment efficiency, in vitro drug release, and stability studies. Among all formulations, AZL6 exhibited superior characteristics with a mean globule size of 2.6 μm, uniform droplet distribution, high entrapment efficiency of 98.2 ± 1.1%, optimum viscosity, and cumulative drug release of 94.3% over 12 hours. The formulation also demonstrated good colloidal stability with a zeta potential value of −29.5 mV. Drug release kinetic studies revealed that the optimized formulation followed the Higuchi diffusion model and Super Case II transport mechanism, indicating both diffusion- and erosion-controlled release behavior. Furthermore, post-formulation stability studies, including centrifugation stress testing, confirmed the physical stability of the emulsion system. The findings of this investigation suggest that multiple emulsions represent a promising and effective delivery approach for poorly water-soluble drugs such as Azilsartan Medoxomil, with potential to improve oral bioavailability and therapeutic efficacy.

DOI: http://doi.org/10.5281/zenodo.20441639

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Project Management Strategies For The Development And Approval Of Generic Drugs In The U.S. Market

Authors: Sherla Prasanna, B. Swathi

Abstract: By offering reasonably priced substitutes for name-brand drugs, the generic drug market in the United States plays a vital part in guaranteeing accessible healthcare. The U.S. Food and Drug Administration (FDA), through the Abbreviated New Drug Application (ANDA) procedure, is the primary regulatory body that oversees the process of bringing a generic medication to market. In order to help pharmaceutical businesses effectively negotiate the challenging development and approval process for generic pharmaceuticals, this thesis examines project management techniques. In order to provide an organized strategy for cost management, time efficiency, risk reduction, and regulatory compliance, the study incorporates concepts from the Project Management Body of Knowledge (PMBOK), pharmaceutical R&D, regulatory science, and quality systems. Important topics like bioequivalency research, intellectual property issues, risk-based quality management, and cross-functional team communication frameworks are emphasized. The effect of strategic project management on cutting time-to-market without sacrificing quality or compliance is illustrated through real-world case studies and industry best practices.

DOI: http://doi.org/10.5281/zenodo.20441301

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Nutritional Evaluation And Amino Acid Profile Of Guizotia Abyssinica: Addressing Protein-Energy Malnutrition In Nigerian Populations

Authors: Anpe Fut Micheal, Prof. Kagoro L. A Dayil, Prof. Dahak A Dayil, Prof. Adelakun A. Esther

Abstract: In a world grappling with malnutrition and food insecurity, Guizotia abyssinica, or niger, emerges as a beacon of hope, offering substantial nutritional benefits. This study meticulously evaluates the nutritional composition and amino acid profile of G. abyssinica across various growth stages, aiming to address protein-energy malnutrition prevalent in Nigerian populations. Conducted in the Benue State region, the research involved comprehensive analyses of chemical composition, digestibility, and fatty acid profiles in niger seeds harvested at different developmental phases. Findings revealed a significant decline in crude protein from 163 g/kg at the early vegetative stage to 86 g/kg at the grain fill stage, alongside a notable increase in fiber content, indicating the complex interplay between growth stage and nutritional quality. The fatty acid profile predominantly featured essential fatty acids such as α-linolenic acid (C18:3 n-3) and linoleic acid (C18:2 n-6), underscoring the oil's potential health benefits. The study advocates for the strategic use of G. abyssinica in dietary interventions to combat malnutrition, emphasizing its role in enhancing food security and promoting sustainable agricultural practices. Overall, the research contributes vital insights into the nutritional value of niger seeds, positioning them as a sustainable solution for addressing dietary deficiencies in vulnerable populations.

DOI: https://doi.org/10.5281/zenodo.20440373

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USFDA Guidelines: Regulatory Requirements For Combination Products Involving Drugs, Devices, And Biologics

Authors: Kavade Shirisha, K. Someshwar

Abstract: Combination products, which are a new class of treatments that present difficult regulatory issues, are made up of a combination of medications, devices, and/or biological products. The United States Food and Drug Administration's (USFDA) regulatory framework for the categorization, approval, and supervision of combination products is examined in this thesis. Key provisions under 21 CFR Part 3 are highlighted, along with the Office of Combination Products' (OCP) function, the principal mode of action (PMOA) determination procedure, and premarket submission paths such as NDA, BLA, and PMA/510(k). To demonstrate how recommendations are applied in practical situations, case studies and regulatory precedents are examined. In order to expedite product development and guarantee compliance, the study emphasizes the significance of interdisciplinary cooperation and early regulatory engagement.

DOI: http://doi.org/10.5281/zenodo.20440187

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SSA-Tuned MLP Network for Malignant Tissue Segmentation and Classification in Medical Images

Authors: E. Priyadharshini

Abstract: Medical image analysis plays a significant role in the early detection and diagnosis of cancer. Accurate segmentation and classification of malignant tissues are essential for improving clinical decision-making and patient outcomes. In recent years, Artificial Intelligence (AI) and Machine Learning (ML) techniques, particularly neural networks, have demonstrated remarkable success in biomedical image processing applications. However, the performance of conventional Multi-Layer Perceptron (MLP) networks is highly dependent on optimal parameter tuning, which remains a challenging task due to the complexity and high dimensionality of medical image data. This paper proposes an optimized MLP model using the Salp Swarm Algorithm (SSA) for malignant tissue segmentation and classification in biomedical images. SSA is a nature-inspired metaheuristic optimization technique modeled on the swarming behavior of salps in ocean environments. The algorithm offers strong global search capability, faster convergence, and improved avoidance of local optima compared with traditional optimization methods. By integrating SSA with the MLP network, the proposed model enhances feature selection, weight optimization, and classification accuracy. The proposed SSA-MLP framework is evaluated using publicly available biomedical image datasets. Performance assessment is carried out using standard evaluation metrics including Accuracy, Sensitivity, Specificity, Precision, F1-Score, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Experimental results demonstrate that the SSA-tuned MLP model achieves superior performance when compared with conventional machine learning and neural network approaches. The model shows improved segmentation quality, enhanced classification capability, and greater robustness in detecting malignant tissues. This study contributes to the advancement of intelligent medical imaging systems by presenting a reliable and efficient optimization-based neural network model for cancer diagnosis. The findings indicate that SSA can significantly improve neural network performance in medical image analysis, thereby supporting accurate diagnosis and effective clinical decision support systems.curve Receiver Operating Characteristic (AUC-ROC)

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