Protein drug delivery: current dosage form profile and formulation strategies
Bibliographic record
Abstract
Protein drugs present specific challenges to the maintenance of long-term stability, which can be accomplished by altering parameters of obtention, purification, molecule structure and formulation. As we believe, commercial formulations are undervalued; therefore, this review focuses on screening, categorising and discussing all formulations of protein drugs approved and not withdrawn by regulatory agencies from United States, Canada and Europe until mid-2018. Peptides (<50 amino acids) were not included to allow a more precise evaluation of choices for larger molecules. We extracted data from the DrugBank database, cross-checked it with the FDA purple book and supplemented it with patient information leaflets and papers. We further classified and discussed the entries according to protein function, drug delivery, route of administration and types of excipient (freeze-dried forms). In addition, alternative choices of excipients were discussed. Experimental work included here relates to targeting strategies with verified pharmacokinetics or in vivo effectiveness to identify physiologically relevant options. Although no single rule can be set for efficient protein formulation, our data help to better understand and optimise the choice for excipients and pharmaceutical dosage forms. For more information, see the Supplemental Data.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".