Reporting of Serious Adverse Drug Reactions of Targeted Anticancer Agents in Pivotal Phase III Clinical Trials
Bibliographic record
Abstract
PURPOSE: Oncologists prescribe anticancer drugs based on results of phase III randomized clinical trials (RCTs), but some safety concerns appear only later in updated drug labels. Here, we analyze adverse drug reactions (ADRs) of targeted anticancer agents from updated drug labels and their reporting in corresponding pivotal RCTs. METHODS: We searched the US Food and Drug Administration (FDA) Web site for approved targeted anticancer drugs with updates of their labels related to safety in 2008 and 2009 and at least one RCT referenced in the updated drug label. For each drug, serious ADRs, including potentially fatal ADRs, were identified from the updated label. Published reports of RCTs referenced in the label were searched to determine whether they described these ADRs. RESULTS: We identified 12 eligible targeted anticancer agents with 36 corresponding RCTs referenced in updated drug labels. There were 76 serious ADRs reported in updated drug labels, and 50% (n = 38) were potentially fatal. Of these, 39% (n = 30) of all serious ADRs and 39% (n = 15) of potentially fatal ADRs were not described in any published report of RCTs, whereas 49% and 58%, respectively, were not described in initial drug labels. After a median 4.3 years between initial approval and update of drug labels, 42% (n = 5) of targeted cancer agents acquired one or more boxed warnings (the highest level of FDA alert). CONCLUSION: Published reports of pivotal RCTs and initial drug labels contain limited information about serious ADRs of targeted anticancer agents. Rare but serious ADRs may be important causes of morbidity and mortality in general oncologic practice.
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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 | 0.161 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".