Serious Adverse Drug Reactions (sADRs) Involving Hematology and Resulting in Black Box Warnings or FDA Non-Approval: Results from the First Quarter Century of RADAR/Sonar
Bibliographic record
Abstract
Background: Two National Cancer Institute-funded pharmacovigilance programs have identified 49 serious hematology-related ADRs since 1998. The programs, called RADAR (Research on Adverse Drug events and Reports) and then SONAR (Southern Network on Adverse Reactions) involve > 50 institutions and > 75 co-investigators worldwide. The work builds on three prior studies evaluating “Davids and Goliaths” in medical oncology and in cardiology (PLOS One, eClinicalMedicine, and Journal of Scientific and Professional Integrity). Here we review the hematology focused safety investigations of RADAR/SONAR. Methods: Data were based on initial safety signals being identified by a RADAR/SONAR co-investigator. Then, case-series were derived. Collaborations with basic scientists allowed for basic science correlative studies. Results were disseminated primarily as Brief Report in the literature and Black Box warnings on the related drug. In some instances, studies identified hematologic toxicities that prevent FDA from approving a submitted drug application. Results: sADRs were frequently identified based on very small case series, including ticlopidine-and clopidogrel associated thrombotic thrombocytopenic purpura (22 and 10 patients, respectively), thalidomide- and lenalidomide-associated venous thromboembolism (9 and 5 patients, respectively), rituximab-associated progressive multi-focal leukoencephalopathy (22 patients), peginesatide-associated fatal anaphylaxis (5 patients), and COVID-19 vaccine associated immune thrombocytopenia (1 patient). Meta-analyses provided data for epoetin- and darbepoetin-associated mortality among cancer patients and lenalidomide- and thalidomide-associated venous thromboembolism. Following the onset of the COVID-19 pandemic, RADAR/SONAR investigated social media and pre-prints (COVID-19 vaccine associated cerebral vein thrombosis and immune thrombocytopenia). Time from FDA approval to sADR discovery was a median of 5 years (range, 0 months (thalidomide-associated venous thromboembolism) to 34 years (ciprofloxacin-associated neuropsychiatric toxicity). Basic science correlative studies identified ADAMTS13 autoantibodies (ticlopidine), leachates that developed in a multi-dose vial (peginesatide), anti-red blood cell antibodies (epoetin-associated pure red cell aplasia), and high-risk genes (fluoroquinolones and rituximab). Overall, RADAR/SONAR studies are estimated to have saved over 1 million lives and also resulted in overall payments to the Department of Justice of $1.5 billion (related to marketing of unsafe drugs). Discussion: RADAR/SONAR has proven to be a very important adjunct to FDA and pharmaceutical manufacturer-led safety investigations for hematology, paralleling the success in medical oncology and in cardiology. Going forward, independent centers of excellence for safety-focused investigations such as the CERSI network and the SENTINEL network (both are NIH funded) should be broadened to include a hematology-focused safety center.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".