关键词: Complex Doxorubicin Hydrogels Reinforcement learning

Mesh : Doxorubicin / chemistry pharmacology Hydrogels / chemistry Chitosan / chemistry analogs & derivatives Humans Leukemia / drug therapy Molecular Docking Simulation Cadmium / chemistry Hyaluronic Acid / chemistry Metal-Organic Frameworks / chemistry Drug Carriers / chemistry Cell Line, Tumor Animals Antibiotics, Antineoplastic / chemistry pharmacology

来  源:   DOI:10.1038/s41598-024-61809-6   PDF(Pubmed)

Abstract:
A new 3D metal-organic frameworks [Cd6(L)4(bipy)3(H2O)2·H2O] (1) was gained by employing Cd(II) and organic ligand [H3L = 4,4\',4\'\'-(benzene-1,3,5-triyltris(oxy))tribenzoic acid)benzene acid; bipy = 4,4\'-bipyridine] in the solvothermal condition, which has been fully examined via single-X ray diffraction, FTIR and elemental analysis and so on. Using natural polysaccharides hyaluronic acid (HA) and carboxymethyl chitosan (CMCS) as raw materials, we successfully prepared HA/CMCS hydrogels and observed their internal micromorphology by scanning electron microscopy. Using doxorubicin (Dox) as a drug model, we synthesized a novel metal gel particle loaded with doxorubicin, and their encapsulation and release effects were studied using fluorescence spectroscopy, followed by further investigation of their components through thermogravimetric analysis. Based on this, the therapeutic effect on leukemia was evaluated. Finally, an enhanced learning method for automatically designing new ligand structures from host ligands was proposed. Through generative modeling and molecular docking simulations, the biological behavior of the host and predicted cadmium complexes was extensively studied.
摘要:
通过使用Cd(II)和有机配体[H3L=4,4\',获得了一种新的3D金属有机骨架[Cd6(L)4(bipy)3(H2O)2·H2O](1),4'\'-(苯-1,3,5-三(氧基)三苯甲酸)苯甲酸;bipy=4,4'-联吡啶]在溶剂热条件下,通过单X射线衍射进行了全面检查,FTIR和元素分析等。以天然多糖透明质酸(HA)和羧甲基壳聚糖(CMCS)为原料,我们成功制备了HA/CMCS水凝胶,并通过扫描电子显微镜观察了其内部微观形态。使用阿霉素(Dox)作为药物模型,我们合成了一种负载阿霉素的新型金属凝胶颗粒,并使用荧光光谱法研究了它们的包封和释放效果,然后通过热重分析进一步研究它们的成分。基于此,评估了对白血病的治疗效果。最后,提出了一种从主体配体自动设计新配体结构的增强学习方法。通过生成建模和分子对接模拟,对宿主和预测的镉配合物的生物学行为进行了广泛的研究。
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