Paediatric Strategy Forum for medicinal product development of agents targeting GD2 ganglioside in children and adolescents with cancer
Bibliographic record
Abstract
GD2 is a ganglioside expressed on the cell surface of a wide range of paediatric cancers. Expression is most consistently seen at a high level in neuroblastoma, though sarcomas and central nervous system (CNS) cancers may express variable levels of GD2. GD2 has been successfully leveraged therapeutically for patients with high-risk neuroblastoma, for which GD2 monoclonal antibodies have regulatory approvals in the post-consolidation frontline and relapsed neuroblastoma settings. Not all patients benefit, and first-generation antibodies are associated with dose-limiting on-target / off-tumour neuropathic pain. More recently, anti-GD2 antibodies have been combined with chemotherapy for neuroblastoma, though none of these combinations has regulatory approval to date. The potential for targeting GD2 in paediatric cancers beyond neuroblastoma remains relatively unexplored. The 14th ACCELERATE multi-stakeholder Paediatric Strategy Forum was convened to define a strategy for further development of these antibodies, but also for emerging novel approaches leveraging GD2 as a tumour-associated antigen, including antibody-drug conjugates (ADC), radiopharmaceuticals, chimeric antigen receptor engineered T-cells (CAR-T), bispecific T-cell engagers, and vaccines. Seven products being developed by industry were reviewed along with GD2-directed CAR-Ts being developed by academia. Key conclusions included 1) the critical importance of standardisation in quantifying GD2 tumour expression; 2) need for ongoing innovation and comparative effectiveness research with monoclonal antibodies already used in the neuroblastoma frontline setting; 3) urgent need to rapidly screen compounds that may improve the efficacy of chemoimmunotherapy; 4) importance of integrating frontline therapy for neuroblastoma and other tumour types in overall development plans for novel products; 5) mitigation of neuropathic pain and other off-tumour toxicities remains a critical need; and 6) the value of early patient advocate and regulatory interactions during development.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".