Dokument: Opportunities and Challenges for FDM 3D Printed Oral Dosage Forms for Personalized Medicine
| Titel: | Opportunities and Challenges for FDM 3D Printed Oral Dosage Forms for Personalized Medicine | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=63663 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20230922-085259-0 | |||||||
| Kollektion: | Dissertationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Abschlussarbeiten » Dissertation | |||||||
| Medientyp: | Text | |||||||
| Autor: | Mazur, Hellen [Autor] | |||||||
| Dateien: |
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| Beitragende: | Prof. Dr. Breitkreutz, Jörg [Gutachter] Prof. Dr. Seidlitz, Anne [Gutachter] | |||||||
| Stichwörter: | 3D Printing, Drug release, Drug Dosage Form Design, Dose-independent release, drug release prediction | |||||||
| Dewey Dezimal-Klassifikation: | 600 Technik, Medizin, angewandte Wissenschaften » 610 Medizin und Gesundheit | |||||||
| Beschreibung: | Each individual human possesses a unique genetic code that influences, among other things, how they respond to a particular active pharmaceutical ingredient (API), dosage form, or dose. Personalized medicine has evolved as an approach to tailor drug therapy to the specific needs of each patient, harnessing knowledge of the genetic variability of drug effects and applying this knowledge to drug development and individualized pharmacotherapy. In recent years, the traditional one-size-fits-all approach to medicine has proven less effective, leading to an increase in drug-related problems (DRPs) and even deaths. These problems are especially clustered in the older patients, pediatric patients, patients taking multiple medications, and patients undergoing therapies with highly potent APIs. In the development of individual dosage forms, it is necessary to consider the required dose, geometry of the dosage form, composition of the formulation, and release profile of the API(s). For several years, researchers have investigated Fused Deposition Modeling (FDM) three-dimensional (3D) printing for manufacturing of medicines. Due to additive manufacturing, this process enables the production of individual, small batches on demand. The low-cost equipment is easy to obtain and could be placed in any community pharmacy, hospital, or distribution center. The aim of this dissertation is to investigate the potential of FDM 3D printing to develop solid oral drug dosage forms (DDFs) for personalized therapy and to explore the limitations of the technique.
In the studies designed for this thesis, different APIs (pramipexole, levodopa, benserazide, and praziquantel) and polymers were used to produce DDFs with various release profiles. In-vitro dissolution experiments enabled analysis of the API release, and the application of different mathematical models described the release profiles. This dissertation focuses on several areas, including a correlation between the mean dissolution time (MDT) and the surface area to volume (SA/V) ratio and a correlation between mathematical equations like Peppas Sahlin and the SA/V ratio of the DDF. These correlations helped predict the corresponding release profiles. This approach was tested and maintained using a machine-learning tool to predict a possible design of DDFs and the respective SA/V ratio based on the desired release profile and API dose, for an eventual prescription process of 3D printed DDFs from the physician to the patient. For individual medical drug therapy, a concept was developed for dose-independent release to adapt the dose and drug release to the individual patient’s needs. Based on the example of Parkinson's disease therapy, a PolyPill with different polymers and APIs was designed with variable release profiles. The implementation of a traceability concept for FDM 3D printed oral dosage forms closes the gap in anti-counterfeiting caused by low-cost and easily available equipment. In these studies, a geometry model based on the SA/V ratio could be developed that can adjust the dose in small increments for the patient, up to a factor of eight, without changing the release profile. Based on the concept of dose-independent release, different floating PolyPill designs could be created with various dosages and release profiles for the treatment of Parkinson's disease. Furthermore, a prediction concept could be established, which would allow physicians and pharmacists to predict the resulting API release behavior of the DDF based on the SA/V ratio with a deviation of less than 5 min for the MDT and <4% root mean square error (RMSE) for the mathematical models. Different classification architectures were tested on an artificial neural network to predict accurate geometries for desired release profiles and dosages, which unfortunately resulted in an accuracy of only 44% in the test run. However, with a scalar-prediction architecture, the SA/V ratio can be predicted with a mean squared error (MSE) loss of 0.05. In addition, a proof-of-concept study tested a blind-watermarking process that allows bits to be incorporated into the outer wall of the DDF, thus making the printed DDFs counterfeit-proof without additional effort or equipment. These findings can be used in the future to set up a workflow, from prescription to 3D printed DDFs, and to develop personalized therapy with 3D printed oral DDFs to create customized, anti-counterfeit DDFs; reduce the risk of DRPs and falsification; and improve patient adherence. However, this process is still limited, from both technical and regulatory points of view. | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät » WE Pharmazie » Pharmazeutische Technologie und Biopharmazie | |||||||
| Dokument erstellt am: | 22.09.2023 | |||||||
| Dateien geändert am: | 22.09.2023 | |||||||
| Promotionsantrag am: | 16.02.2023 | |||||||
| Datum der Promotion: | 23.05.2023 |
