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Drug release from liposomal formulations is governed by a complex interplay of kinetic (i.e., drug permeability) and thermodynamic factors (i.e., drug partitioning to the bilayer surface). Release studies under sink conditions that attempt to mimic physiological conditions are insufficient to decipher these separate contributions. The present study explores release studies performed under nonsink conditions coupled with appropriate mathematical models to describe both the release kinetics and the conditions in which equilibrium is established. Liposomal release profiles for a model anticancer agent, topotecan, under nonsink conditions provided values for both the first-order rate constant for drug release and the bilayer/water partition coefficient. These findings were validated by conducting release studies under sink conditions via dynamic dialysis at the same temperature and buffer pH. A nearly identical rate constant for drug release could be obtained from dynamic dialysis data when appropriate volume corrections were applied and a mechanism-based mathematical model was employed to account for lipid bilayer binding and dialysis membrane transport. The usefulness of the nonsink method combined with mathematical modeling was further explored by demonstrating the effects of topotecan dimerization and bilayer surface charge potential on the bilayer/water partition coefficient at varying suspension concentrations of lipid and drug.
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http://dx.doi.org/10.1021/mp400765n | DOI Listing |
Crit Rev Ther Drug Carrier Syst
January 2025
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View Article and Find Full Text PDFJ Vis Exp
August 2025
Department of Periodontology, Faculty of Dentistry, University of Çukurova;
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View Article and Find Full Text PDFIEEE J Biomed Health Inform
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Drug-target interaction (DTI) identification is of great significance in drug development in various areas, such as drug repositioning and potential drug side effects. Although a great variety of computational methods have been proposed for DTI prediction, it is still a challenge in the face of sparsely correlated drugs or targets. To address the impact of data sparsity on the model, we propose a multi-view neighborhood-enhanced graph contrastive learning approach (MneGCL), which is based on graph clustering according to the adjacency relationship in various similarity networks between drugs or targets, to fully exploit the information of drugs and targets with few corrections.
View Article and Find Full Text PDFMacromol Biosci
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IMEM-BRT Group, Departament d'Enginyeria Química, EEBE, Universitat Politècnica de Catalunya, Barcelona, Spain.
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Key Laboratory of Textile Science & Technology, College of Textiles, Ministry of Education, Donghua University, Shanghai, China.
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