Cerda Jose R, Arifi Talaya, Ayyoubi Sejad, Knief Peter, Ballesteros Maria Paloma, Keeble William, Barbu Eugen, Healy Anne Marie, Lalatsa Aikaterini, Serrano Dolores R
Departament of Pharmaceutics and Food Technology and Instituto Universitario de Farmacia Industrial (IUFI), School of Pharmacy, University Complutense, Avenida Complutense, 28040 Madrid, Spain.
Biomaterials, Bio-engineering and Nanomedicine (BioN) Lab, Institute of Biomedical and Biomolecular Sciences, School of Pharmacy and Biomedical Sciences, University of Portsmouth, White Swan Road, Portsmouth PO1 2 DT, UK.
Pharmaceutics. 2020 Apr 11;12(4):345. doi: 10.3390/pharmaceutics12040345.
Although not readily accessible yet to many community and hospital pharmacists, fuse deposition modelling (FDM) is a 3D printing technique that can be used to create a 3D pharmaceutical dosage form by employing drug loaded filaments extruded via a nozzle, melted and deposited layer by layer. FDM requires printable filaments, which are commonly manufactured by hot melt extrusion, and identifying a suitable extrudable drug-excipient mixture can sometimes be challenging. We propose here the use of passive diffusion as an accessible loading method for filaments that can be printed using FDM technology to allow for the fabrication of oral personalised medicines in clinical settings. Utilising Hansen Solubility Parameters (HSP) and the concept of HSP distances (Ra) between drug, solvent, and filament, we have developed a facile pre-screening tool for the selection of the optimal combination that can provide a high drug loading (a high solvent-drug Ra, >10, and an intermediate solvent-filament Ra value, ~10). We have identified that other parameters such as surface roughness and stiffness also play a key role in enhancing passive diffusion of the drug into the filaments. A predictive model for drug loading was developed based on Support Vector Machine (SVM) regression and indicated a strong correlation between both Ra and filament stiffness and the diffusion capacity of a model BCS Class II drug, nifedipine (NFD), into the filaments. A drug loading, close to 3% w/w, was achieved. 3D printed tablets prepared using a PVA-derived filament (Hydrosupport, 3D Fuel) showed promising characteristics in terms of dissolution (with a sustained release over 24 h) and predicted chemical stability (>3 years at 25 °C/60% relative humidity), similar to commercially available NFD oral dosage forms. We believe FDM coupled with passive diffusion could be implemented easily in clinical settings for the manufacture of tailored personalised medicines, which can be stored over long periods of time (similar to industrially manufactured solid dosage forms).
尽管许多社区和医院药剂师还难以轻易接触到，但熔丝沉积建模（FDM）是一种3D打印技术，可通过使用经喷嘴挤出、熔化并逐层沉积的载药长丝来创建3D药物剂型。FDM需要可打印的长丝，这些长丝通常通过热熔挤出制造，而确定合适的可挤出药物-辅料混合物有时具有挑战性。我们在此提出将被动扩散用作一种可接触的长丝加载方法，该长丝可使用FDM技术进行打印，以便在临床环境中制造口服个性化药物。利用汉森溶解度参数（HSP）以及药物、溶剂和长丝之间的HSP距离（Ra）概念，我们开发了一种简便的预筛选工具，用于选择能够提供高载药量（高溶剂-药物Ra，>10，以及中等溶剂-长丝Ra值，~10）的最佳组合。我们已经确定，其他参数如表面粗糙度和硬度在增强药物向长丝的被动扩散中也起着关键作用。基于支持向量机（SVM）回归开发了载药量预测模型，该模型表明Ra和长丝硬度与模型BCS II类药物硝苯地平（NFD）向长丝的扩散能力之间存在很强的相关性。实现了接近3% w/w的载药量。使用源自聚乙烯醇的长丝（Hydrosupport，3D Fuel）制备的3D打印片剂在溶出度（24小时持续释放）和预测的化学稳定性（25°C/60%相对湿度下>3年）方面表现出良好的特性，类似于市售的NFD口服剂型。我们相信，FDM与被动扩散相结合可以在临床环境中轻松实现定制个性化药物的制造，这些药物可以长期储存（类似于工业制造的固体剂型）。
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