Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical Success
<b>Background/Objectives</b>: 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...
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2025-07-01
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| author | Eleonore Fröhlich Aurora Bordoni Nila Mohsenzada Stefan Mitsche Hartmuth Schröttner Sarah Zellnitz-Neugebauer |
| author_facet | Eleonore Fröhlich Aurora Bordoni Nila Mohsenzada Stefan Mitsche Hartmuth Schröttner Sarah Zellnitz-Neugebauer |
| author_sort | Eleonore Fröhlich |
| collection | DOAJ |
| description | <b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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. |
| format | Article |
| id | doaj-art-3acc8bfc82a745cfa6b1fa07bd4505b5 |
| institution | DOAJ |
| issn | 1999-4923 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Pharmaceutics |
| spelling | doaj-art-3acc8bfc82a745cfa6b1fa07bd4505b52025-08-20T03:08:02ZengMDPI AGPharmaceutics1999-49232025-07-0117792210.3390/pharmaceutics17070922Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical SuccessEleonore Fröhlich0Aurora Bordoni1Nila Mohsenzada2Stefan Mitsche3Hartmuth Schröttner4Sarah Zellnitz-Neugebauer5Research Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, AustriaResearch Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, AustriaResearch Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, AustriaInstitute of Electron Microscopy and Nanoanalysis (FELMI), Graz University of Technology, Steyrergasse 17, 8010 Graz, AustriaInstitute of Electron Microscopy and Nanoanalysis (FELMI), Graz University of Technology, Steyrergasse 17, 8010 Graz, AustriaResearch Center Pharmaceutical Engineering, Inffeldgasse 13, 8010 Graz, Austria<b>Background/Objectives</b>: 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. <b>Methods</b>: 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. <b>Results</b>: 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. <b>Conclusions</b>: 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.https://www.mdpi.com/1999-4923/17/7/922co-amorphousmachine learninginhalationtuberculosisethambutolrifampicin |
| spellingShingle | Eleonore Fröhlich Aurora Bordoni Nila Mohsenzada Stefan Mitsche Hartmuth Schröttner Sarah Zellnitz-Neugebauer Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical Success Pharmaceutics co-amorphous machine learning inhalation tuberculosis ethambutol rifampicin |
| title | Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical Success |
| title_full | Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical Success |
| title_fullStr | Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical Success |
| title_full_unstemmed | Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical Success |
| title_short | Development of Co-Amorphous Systems for Inhalation Therapy—Part 1: From Model Prediction to Clinical Success |
| title_sort | development of co amorphous systems for inhalation therapy part 1 from model prediction to clinical success |
| topic | co-amorphous machine learning inhalation tuberculosis ethambutol rifampicin |
| url | https://www.mdpi.com/1999-4923/17/7/922 |
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