Use of Real-world Data for New Drug Applications and Line Extensions
Bibliographic record
Abstract
PURPOSE: For this article, the authors compiled, summarized, and analyzed data from 27 cases in which real-world data (RWD) were applied in regulatory approval. The aims were to provide an overview of RWD, based on classifications per therapeutic area, age group, drivers of acceptability, utility, data sources, and timelines, and to present insights on how it has been applied in regulatory decision making to date. METHODS: Clarivate Analytics was commissioned to collect data from cases in which RWD was used for new drug applications and line extensions submitted to the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), Health Canada, and Japan's Pharmaceuticals and Medical Devices Agency. The query resulted in 27 cases in which regulatory approval was associated with RWD. The data were then categorized and elaborated with supporting information gathered from public databases and company websites. FINDINGS: There were 17 identified cases in which RWD were used for new drug applications, and 10 for line extensions, between the years 1998 and 2019. Approvals were spread across regulatory bodies: the EMA alone (6 cases), the FDA alone (4 cases), or jointly between the EMA and FDA or other regulatory bodies. The applications were also distributed across age groups and therapeutic areas but were mostly applied in oncology and metabolism. The new drug applications of all 17 products were approved, with drugs from new drug applications initially marketed as orphan drugs. In most cases, RWD were used either as primary data, when noncomparative data were available to demonstrate tolerability and efficacy, or as supportive data when validating findings. Common sources of RWD have been health or medical records (16 cases) and registries (8 cases). Review timelines in which RWD were applied were than 1 year for new drug applications and between 3 and 10 months for line extensions. IMPLICATIONS: The analysis of this study was limited in that the data were gathered from the commissioned query and may therefore have been nonexhaustive. Nonetheless, we recognize that the use of RWD has been gaining attention across the community and is expected to expand as a result of the various initiatives and efforts carried out in the sector. While the current application of RWD has been limited to specific cases, there is a potential to further explore and develop its application. Further refinements in the analytical processes, methodologies, and techniques would need to be established to achieve similar effects observed in randomized controlled trials.
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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 | 0.043 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".