Medizinische Universität Graz - Research portal
Navigation/Search
- Researchers
- Institutions
- Projects
- Publications
- Partners
- Sponsors
- Equipment
- Knowhow
- Fields of Research
"Faces" of the day:
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Fröhlich, E; Bordoni, A; Mohsenzada, N; Mitsche, S; Schröttner, H; Zellnitz-Neugebauer, S.
Development of Co-Amorphous Systems for Inhalation Therapy-Part 1: From Model Prediction to Clinical Success.
Pharmaceutics. 2025; 17(7):
Doi: 10.3390/pharmaceutics17070922
[OPEN ACCESS]
PubMed
FullText
FullText_MUG
- Leading authors Med Uni Graz
- Fröhlich Eleonore
- Altmetrics:
- Dimensions Citations:
- Plum Analytics:
- Scite (citation analytics):
- Abstract:
- Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.