Accelerating Drug Development for Neuroblastoma: Consensus Statement From the Third Neuroblastoma Drug Development Strategy Forum
Bibliographic record
Abstract
High-risk neuroblastoma is a poor prognosis cancer of the sympathetic nervous system that accounts for a disproportionate number of childhood cancer deaths. Many viable biological targets have been identified, and the number of potential combinations is even larger. Several products have attained marketing authorization for treatment of patients with neuroblastoma. Patient outcomes remain poor, with approximately 50% of children with newly diagnosed high-risk neuroblastoma cured of their disease. International, multistakeholder Neuroblastoma Drug Development Strategy (NDDS) meetings were established more than a decade ago. This third NDDS meeting included academia, industry, regulatory, and patient advocacy representatives to prioritize agents and to address key challenges in drug development in this disease. Given the central role that anti-GD2 therapy plays, novel GD2-directed combinations were a key focus, including epigenetic enzymes such as EZH2 and immunologic targets such as IL15 and TIGIT as potential combination partners. GD2-directed chimeric antigen receptor (CAR)-T cells were a top priority, along with emerging CAR-T targets such as B7-H3 and GPC2. Recognizing that combination therapies are likely to be most impactful for patients and for advancing therapies to frontline, another key focus was on high priority combinations of targeted therapies, including Aurora A kinase plus BCL2 or ATR inhibitors. Additional targets and agents were prioritized or deprioritized based upon current data. Access to drugs for clinical trials was viewed as a major barrier to progress. Strategies to overcome this challenge focused on united efforts by the international scientific and advocacy community and early engagement by industry with regulatory authorities.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.039 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".