Abstract IA16: Accelerating innovation for children with cancer in the new regulatory environment
Bibliographic record
Abstract
Abstract Over the last 20 years, several pieces of regulation have been launched, in the US and in Europe, to mandate and incentivize the development of safe and effective medicines for children. It has been a success in several pediatric specialties such as rheumatology, infection diseases, cardiovascular diseases, allergy, and several rare pediatric diseases. In oncology, the landscape of pediatric drug development has significantly changed, but very few new anticancer medicines have been approved for the treatment of pediatric malignancies over the last 10 years as compared to the high number of anticancer drugs approved for the treatment of cancer in adults: dinutuximab for neuroblastoma, blinatumomab and tisagenlecleucel for acute lymphoblastic leukemia, and larotrectinib for NTRK positive malignancies. So far, the regulations mandated the pediatric development of any drug if the indicated disease in adults occurred in children. If not, a waiver was issued and the company did not have to study the drug in children. In oncology, malignancies in children and in adults are different, but the same drugs are used to treat both, and often the same biologic alterations (targets) are found in both adult and pediatric malignancies. Too many oncology drugs have been waived. In addition, the pediatric trials of oncology products as part of Pediatric Investigation Plans and Pediatric Study Plans started late in the life cycle of product development and often close to or after the marketing authorization. There is a crucial need to improve and accelerate new drug development for children and adolescents with cancer. The goal is to drive pediatric oncology drug developments through science (using biology, preclinical evaluation, and precision medicine), to better meet patients’ unmet medical needs and to facilitate prioritization among all compounds in development. The FDA Race for Children Act is setting a new regulatory environment that will improve the situation by asking pediatric development of oncology products if their target is “directed at a molecular target that the Secretary determines to be substantially relevant to the growth or progression of a pediatric cancer.” In addition, over the last 5 years, the value of having all stakeholders, i.e. academia, parents, industry and regulators, working together has been demonstrated by ACCELERATE, the international multistakeholder platform (www.accelerate-platform.org). Accelerating innovation for pediatric cancers is urgently needed and feasible in the new regulatory environment and requires international cooperation of all stakeholders. Citation Format: Gilles Vassal. Accelerating innovation for children with cancer in the new regulatory environment [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA16.
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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 | 0.037 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.058 | 0.017 |
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".