Fröhlich Eleonore, Bordoni Aurora, Mohsenzada Nila, Mitsche Stefan, Schröttner Hartmuth, Zellnitz-Neugebauer Sarah
Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria.
Center for Medical Research, Medical University of Graz, Stiftingtalstr 24, 8010 Graz, Austria.
Pharmaceutics. 2025 Jul 16;17(7):922. doi: 10.3390/pharmaceutics17070922.
: The integration of machine learning (ML) and artificial intelligence (AI) has revolutionized the pharmaceutical industry by improving drug discovery, development and manufacturing processes. Based on literature data, an ML model was developed by our group to predict the formation of binary co-amorphous systems (COAMSs) for inhalation therapy. The model's ability to develop a dry powder formulation with the necessary properties for a predicted co-amorphous combination was evaluated. : An extended experimental validation of the ML model by co-milling and X-ray diffraction analysis for 18 API-API (active pharmaceutical ingredient) combinations is presented. Additionally, one COAMS of rifampicin (RIF) and ethambutol (ETH), two first-line tuberculosis (TB) drugs are developed further for inhalation therapy. : The ML model has shown an accuracy of 79% in predicting suitable combinations for 35 APIs used in inhalation therapy; experimental accuracy was demonstrated to be 72%. The study confirmed the successful development of stable COAMSs of RIF-ETH either via spray-drying or co-milling. In particular, the milled COAMSs showed better aerosolization properties (higher ED and FPF with lower standard deviation). Further, RIF-ETH COAMSs show much more reproducible results in terms of drug quantity dissolved over time. : ML has been shown to be a suitable tool to predict COAMSs that can be developed for TB treatment by inhalation to save time and cost during the experimental screening phase.
机器学习（ML）与人工智能（AI）的融合通过改进药物发现、开发和制造流程，彻底改变了制药行业。基于文献数据，我们团队开发了一个ML模型，用于预测吸入疗法用二元共无定形体系（COAMSs）的形成。评估了该模型开发具有预测共无定形组合所需特性的干粉制剂的能力。：通过共研磨和X射线衍射分析对18种活性药物成分（API）-API组合进行了ML模型的扩展实验验证。此外，还进一步开发了两种一线抗结核药物利福平（RIF）和乙胺丁醇（ETH）的一种COAMS用于吸入疗法。：ML模型在预测吸入疗法中使用的35种API的合适组合方面显示出79%的准确率；实验准确率为72%。该研究证实了通过喷雾干燥或共研磨成功开发了稳定的RIF-ETH共无定形体系。特别是，研磨后的COAMS表现出更好的雾化特性（更高的有效剂量和细颗粒分数，标准偏差更低）。此外，RIF-ETH共无定形体系在药物溶解量随时间的变化方面显示出更可重复的结果。：ML已被证明是一种合适的工具，可用于预测可开发用于吸入治疗结核病的共无定形体系，以在实验筛选阶段节省时间和成本。
