Personalized medicine aims to effectively and efficiently provide customized drugs that cater to diverse populations, which is a significant yet challenging task. Recently, the integration of artificial intelligence (AI) and three-dimensional (3D) printing technology has transformed the medical field, and was expected to facilitate the efficient design and development of customized drugs through the synergy of their respective advantages. In this study, we present an innovative method that combines AI and 3D printing technology to design and fabricate customized capsules. Initially, we discretized and encoded the geometry of the capsule, simulated the dissolution process of the capsule with classical drug dissolution model, and verified it by experiments. Subsequently, we employed a genetic algorithm to explore the capsule geometric structure space and generate a complex multi-layer structure that satisfies the target drug release profiles, including stepwise release and zero-order release. Finally, Two model drugs, isoniazid and acetaminophen, were selected and fused deposition modeling (FDM) 3D printing technology was utilized to precisely print the AI-designed capsule. The reliability of the method was verified by comparing the in vitro release curve of the printed capsules with the target curve, and the f value was more than 50. Notably, accurate and autonomous design of the drug release curve was achieved mainly by changing the geometry of the capsule. This approach is expected to be applied to different drug needs and facilitate the development of customized oral dosage forms.

中文翻译：

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AI 驱动的定制 3D 打印多层胶囊设计，具有受控药物释放曲线，用于个性化医疗

个性化医疗旨在有效、高效地提供适合不同人群的定制药物，这是一项重大而又具有挑战性的任务。近年来，人工智能（AI）和三维（3D）打印技术的融合改变了医疗领域，并有望通过各自优势的协同，促进定制药物的高效设计和开发。在这项研究中，我们提出了一种结合人工智能和 3D 打印技术来设计和制造定制胶囊的创新方法。首先，我们对胶囊的几何形状进行离散化和编码，用经典的药物溶出模型模拟胶囊的溶出过程，并通过实验进行验证。随后，我们采用遗传算法探索胶囊几何结构空间，并生成满足目标药物释放曲线（包括逐步释放和零级释放）的复杂多层结构。最后，选择异烟肼和对乙酰氨基酚两种模型药物，利用熔融沉积建模（FDM）3D打印技术精确打印AI设计的胶囊。通过将打印胶囊的体外释放曲线与目标曲线进行比较，验证了该方法的可靠性，f值大于50。值得注意的是，主要通过改变几何形状实现了药物释放曲线的准确自主设计。胶囊的。这种方法有望应用于不同的药物需求，并促进定制口服剂型的开发。