Trend of Drug Approval on Hematological Malignancies in the U.S. and Japan.
Bibliographic record
Abstract
Abstract Introduction: Characteristics on hematological malignancies, e.g., many of them arise from one chromosomal abnormality and there are many molecules discriminating malignancies from normal cells, have recently played very important role on the development of novel therapeutic options. Molecular-targeted therapies, such as antibodies and signal inhibitors, are good examples. On the other hand, drug evaluation and approval methods have been suffered from the difficulties in fastening approval periods and evaluating efficacies and safeties more precisely, especially in the case of these entirely new concepts of drugs. In this study, we clarified the trends of drug approval on hematological malignancies in the U.S. and Japan. By the comparison, the trends were made more clearly. Methods: Drugs for hematological malignancies, including CMPDs, which approved by December 2004 in the US or Japan were eligible. Supportive drugs, immunomodulators, biochemical modulators, and “off-label use” were excluded. Package inserts, reviews by agencies, publications on clinical trials were examined. The geographical analysis on clinical trials of oncologic drugs was based on the previous report (Proc ASCO2003; 22:534a). Results: Forty-six drugs were approved in the U.S., and 43 were in Japan. Twenty-seven drugs were approved in both countries. Twenty-two of 27 drugs were approved earlier in the U.S., and the dates of approval were considerably earlier in the U.S. (median: 46.0 Mo, mean: 54.7 Mo). These differences have not been shorten when compared in every 10-year period. Eight drugs were approved as “Accelerated Approval”, which stated in CFRs as “Subpart H”. Seven of eight “accelerated approval” drugs were approved only in the U.S. Furthermore, around one-thirds of drugs (7/19: 36.8%) approved only in the U.S. were based on “accelerated approval”. However, one drug approved as “accelerated approval” could have shown its clinical benefit in the designated clinical trial. Among the drugs approve only in the U.S., the number of drugs for “first line”, “second line or thereafter”, and “not specified” were 2, 13, 4, respectively. The geographical comparison of clinical trials was summarized in the Table below. The ratio of non-U.S. studies was considerably low in hematological malignancies. In Japan, the data on clinical trials exclusively performed in Japan was generally stated. Five drugs approved only in Japan were approved in the US for diseases other than hematological malignancies, while no drug was approved in the reverse case. Conclusion: “Accelerated approval” is useful for fastening the period until the approval, although the problem whether “surrogate markers” leads to “survival and/or QOL benefit” has not been clarified, yet. The outstanding result that most of pivotal/supportive studies were not “non-U.S.” studies may be caused by the superiority of drug development, especially in new concepts of drugs for hematological malignancies and the ability to conduct appropriate clinical trials in the U.S. On the contrary, the expansion of the indication would be the problem in the U.S. to be considered. Geographical Location of Studies U.S. only U.S. & Canada U.S. & Europe Non-U.S. Total All oncology drugs (1986.1–2002.9) 77 (43.5%) 23 (13.0%) 35 (19.8%) 42 (23.7%) 177 studies Hematological malignancies (1986.1–2004.12) 27 (62.8%) 4 (9.3%) 9 (20.9%) 3 (7.0%) 42 studies
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".