在机器学习辅助闭环药物管理中对患者进行药物的个人3d打印-可行性研究的设计和初步结果

Claudia Langebrake , Karl Gottfried , Adrin Dadkhah , Jan Eggert , Tobias Gutowski , Moritz Rosch , Nils Schönbeck , Christopher Gundler , Sylvia Nürnberg , Frank Ückert , Michael Baehr
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引用次数: 1

摘要

药物的3D打印是一种创新的制造方法,其特点是高度数字化和自动化,能够实现针对患者的护理。目前,它与常规医疗流程的集成失败,主要是由于数字环境的要求。我们的医院是欧洲第一家在2011年引入全面患者记录并实现药品供应流程数字化和自动化的医院。我们研究的目的是评估机器学习辅助的药物3D打印与医院现有的全数字化药物流程(闭环药物管理,CLMM)的集成。在此,介绍了本可行性研究的设计和子项目的初步结果。首先,一个跨学科专家小组使用定义的评估标准,在考虑到半乳糖、临床和机器学习方面的情况下,通过多步骤方法确定了一种合适的临床相关活性成分(左旋多巴/卡比多巴)。在下一步中,将开发一种使用合适的印刷技术的半乳糖制剂,用于根据不同剂量的药物质量标准生产药物,并根据《欧洲药典》评估其是否符合质量标准。此外,还开发了一个IT概念,并根据医院当前的IT基础设施进行了调整。同样,将开发一种机器学习算法,使用智能可穿戴设备的数据来确定每个患者的最佳剂量。为此,建立了一项临床试验,作为使用可穿戴设备检测和分级帕金森病临床症状的原理验证研究。最后,考虑到监管要求,该流程将与医院的数字用药流程相连接。因此,这项跨学科的可行性研究将为将针对患者的药物3D打印融入医院日常临床实践的可能性提供重要见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patient-individual 3D-printing of drugs within a machine-learning-assisted closed-loop medication management – Design and first results of a feasibility study

3D-printing of medicines is an innovative manufacturing method that is characterised by a high degree of digitalisation and automation and enables patient-specific care. Its integration into routine healthcare processes currently fails mainly due to the requirements of a digital environment. Our hospital was the first hospital in Europe to introduce a fully comprehensive patient record in 2011 and to digitalise and automate the drug supply process.

The aim of our study is to evaluate the integration of a machine-learning assisted 3D printing of medicines into the already existing, fully digital medication process of the hospital (closed-loop medication management, CLMM). Here, the design of this feasibility study and first results of subprojects are presented.

First, a suitable and clinically relevant active ingredient (levodopa/carbidopa) was identified in a multi-step approach by an interdisciplinary panel of experts using defined evaluation criteria, taking into account galenic, clinical and machine learning aspects. In the next step, a galenic formulation using a suitable printing technology for manufacturing a drug according to pharmaceutical quality criteria in different dosages is to be developed and to be evaluated for compliance with quality criteria according to the European Pharmacopoeia. Furthermore, an IT concept was developed and adapted to the hospital's current IT infrastructure. Likewise, a machine learning algorithm is to be developed to determine the optimal dose for each individual patient using data from smart wearable devices. For this purpose, a clinical trial was set up as a proof-of-principle study for the use of wearables to detect and grade clinical symptoms from Parkinson’s Disease. Finally, the process is to be connected to the digital medication process of the hospital taking into account regulatory requirements.

Thus, this interdisciplinary feasibility study will provide important insights into the possibilities of integrating patient-specific 3D printing of medicines into everyday clinical practice in the hospital.

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