Overview of authorized drug products for subcutaneous administration: Pharmaceutical, therapeutic, and physicochemical properties
Bibliographic record
Abstract
There is a growing body of research about subcutaneously administered biologics, emphasizing the need for optimized bioavailability predictions. It is important to inform both translational and in silico models with properties of the drug products and compounds. However, the pharmaceutical, therapeutic and physicochemical properties of market authorized drug products for subcutaneous administration are currently not collated in the public domain. We provide an overview of subcutaneous administered drug products for humans and animals market authorized in EU, Canada, and the US. Data on the drug products were collected from the respective authorities, i.e. European Medicines Agency, Health Canada, and U.S. Food and Drug Administration. Physicochemical properties of active substances were gathered from DrugBank. Human drug products were often indicated for treatment of diabetes and anemia. EU veterinary drug products were often immunologicals. Canadian and US veterinary drug products often acted as antiinfectives for systemic use, on the genito-urinary system or as sex hormones. The final dataset with >1700 subcutaneous drug products is provided. In EU drug products, the majority of active substances were biologics. In the US, drug products most often contained small molecules. Solutions, emulsions and suspensions were the most common dosage forms. A minority of subcutaneous drug products were also registered for intramuscular or intravenous administration. The analysis presented here could aid further research, exploring formulation properties, prescription or sales of market authorized SC drug products and development of inclusive in silico models.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".