Investigational drug labeling variability
Bibliographic record
Abstract
BACKGROUND/AIMS: In comparison with commercial drugs, there are few regulations concerning the labeling of investigational drugs. This leads to variability in their content and layout. This increases the risk of errors during storage, validation, compounding, dispensing and administration. The aim of this study was to evaluate the conformity and variability of investigational drug labels. Additional exploratory aims were to evaluate the use of an automated script to describe the labels and to identify the factors associated with the ease of finding a kit number. METHODS: An 87-criterion list was developed to evaluate content, format and readability. It included eight criteria to evaluate the conformity to the Canadian Food and Drugs Regulation. A systematic cross-sectional evaluation of all investigational drug labels in our 500-bed mother-child center was performed. All active protocols during the period of 14-22 February 2018 were included. Labels from drugs that were sourced locally were excluded. Labels affixed to the outside (external) and inside (internal) containers, as well as labels from American and European sponsors, were compared with the chi-square and Student's t tests. A script was developed in Python to automatically determine key information (number of words, main colors and their proportion). A short survey was conducted with a convenience sample of pharmacists to rate the ease of finding the kit number on labels. Correlation was evaluated with different factors. RESULTS: A total of 27 protocols were included (24 internal and 34 external labels). The majority (33/34) of external labels were compliant with the Regulation. Some internal labels did not state the expiry date (9/13), the sponsor address (2/13) or storing conditions (1/13). A total of 10 criteria were different between internal and external labels, for instance, the number of languages was higher on external labels (median 3 (2-14) vs 10 (2-50); p = 0.013). Five criteria were different depending on the sponsors' location, for instance, European sponsors were more prone to use bold characters (25% vs 61%, p = 0.034). There was a mean of 146 ± 111 words and 78.3% ± 7.3% empty space per label. These were positively correlated (p < 0.001). The proportion of free space on a label was also correlated with the ease of finding the kit number (p = 0.002). CONCLUSION: We measured a high variability in the labeling of investigational drugs. Key information was missing from labels affixed to internal containers, despite the use of a high number of pages. The automation worked well and further work is needed to identify criteria that may improve readability and reduce error risk. Detailed and harmonized international guidelines are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".