Available Tools to Facilitate Early Patient Access to Medicines in the EU and the USA: Analysis of Conditional Approvals and the Implications for Personalized Medicine
Bibliographic record
Abstract
Scientific knowledge and our understanding of the human body and diseases have limited any possible treatment tailoring to each patient. The technological advances enabling the integration of various data sets (e.g. '-omics', microbiome, epigenetics and environmental exposure) have facilitated a greater understanding of the human body, the molecular basis of disease and all the factors influencing disease onset, progression and response to treatment, thereby ushering in the era of personalized medicine. We evaluate the regulatory approaches available to facilitate early patient access to efficacious and safe compounds in the EU and the USA in order to make more informed recommendations in the future as to the gaps in regulations for early patient access. An in-depth analysis of conditional approvals (EU) and accelerated approvals (USA) is performed based on the publicly available information (European public assessment reports and a summary review of products approved under both programmes). The types of product, indications, time to approval and type of evidence submitted were analysed. Between 2007 and early 2015, 17 products were conditionally approved in the EU and 25 in the USA, most of them in the area of oncology and based on evidence from phase II clinical trial data. Early approval of promising products based on data from early phases of development is already possible in the EU and the USA. Some of the improvements could entail implementing a rolling assessment of evidence in Europe and extending the scope of early dialogues.
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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 | 0.048 | 0.136 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".