Inkjet printing of a thermolabile model drug onto FDM-printed substrates: formulation and evaluation
Bibliographic record
Abstract
Objective The inkjet printing (IP) and fused deposition modeling (FDM) technologies have emerged in the pharmaceutical field as novel and personalized formulation approaches. Specific manufacturing factors must be considered in each adopted methodology, i.e. the development of suitable substrates for IP and the incorporation of highly thermostable active pharmaceutical compounds (APIs) for FDM. In this study, IP and FDM printing technologies were investigated for the fabrication of hydroxypropyl methylcellulose-based mucoadhesive films for the buccal delivery of a thermolabile model drug.Significance: This proof-of-concept approach was expected to provide an alternative formulation methodology for personalized mucoadhesive buccal films.Methods Mucoadhesive substrates were prepared by FDM and were subjected to sequential IP of an ibuprofen-loaded liquid ink. The interactions between these processes and the performance of the films were evaluated by various analytical and spectroscopic techniques, as well as by in vitro and ex vivo studies.Results The model drug was efficiently deposited by sequential IP passes onto the FDM-printed substrates. Significant variations were revealed on the morphological, physicochemical and mechanical properties of the prepared films, and linked to the number of IP passes. The mechanism of drug release, the mucoadhesion and the permeation of the drug through the buccal epithelium were evaluated, in view of the extent of ink deposition onto the buccal films, as well as the distribution of the API.Conclusions The presented methodology provided a proof-of-concept formulation approach for the development of personalized mucoadhesive films.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".