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Abstract IA026: Are we ready to accelerate the development of new safe and effective anticancer medicines for children and adolescents?

2024· article· en· W4402267146 on OpenAlexaboutno aff
Gilles Vassal

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

Abstract The US and European regulatory frameworks are changing for more patient-centric and scientifically -driven pediatric developments of anticancer medicinal products. Programs such ITCC P41 in the EU and NCI PIVOT2 in the US provide relevant comprehensive preclinical data to support decision making: should a drug/a combo be introduced in pediatric development? Continuous efforts should further improve relevance of pediatric cancer preclinical models, especially for immune oncology drugs. The development of pediatric precision oncology programs have installed individual patients’ tumor sequencing as a routine to best orientate therapeutic options and best learn from the early phase trials. There is a need to go beyond tumor sequencing. Early phase platform trials, such as AcSé ESMART3 and NCI COG Pediatric Match trial4 are aiming at accelerating drug development and facilitating patients’ access to innovation. The introduction of new adaptive designs and mixed criteria is essential to address the use of safety and efficacy to accelerate drug development. When starting an early phase trial, early interactions with regulatory authorities are essential to agree on a full development plan towards a potential market authorization filing. Randomized clinical trials (RCT) remain the gold standard for practice changing trials in newly diagnosed pediatric cancers. Efforts should be made to introduce alternative designs when RCT are not feasible or ethically unacceptable. The pediatric oncology community should make major investments in exploiting high quality real world data for indirect comparisons with single arm trials. The cooperative groups are essential stakeholders for the design and implementation of such programs. In conclusion, over the last 10 years, major efforts have been made by the pediatric oncology community in partnership with parents and advocates to increase capability and to create platforms and programs to accelerate the development of targeted and immune oncology drugs. ACCELERATE, the international multistakeholder (academia, advocacy, industry, regulatory networks) initiative, demonstrated the feasibility and high value of working together5. The regularly framework will better address pediatric patients’ needs when considering anticancer agents developed for adult. However, biology of pediatric malignancies is different from that of adult cancers and there is a need to invest in the development of specific pediatric anticancer assets that will target specific pediatric biological alterations. Childhood cancers will remain rare and ultra-rare with low, if any, return on investment. There is an urgent need to implement new business models to make the development of specific pediatric oncology drugs feasible and to support the appropriate evaluation of adult anticancer drugs in children6.1 https://itccp4.com; 2 https://ctep.cancer.gov/MajorInitiatives/Pediatric_PIVOT_Program.htm/; 3Geoerger B et al. Eur J Cancer 2024; 4Parsons DW et al. J Clin Oncol 2022; 5 https://www.accelerate-platform.org; 6Daems S et al. Nat Rev Drug Discov. 2023 Citation Format: Gilles Vassal. Are we ready to accelerate the development of new safe and effective anticancer medicines for children and adolescents [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA026.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.047
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0470.016

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.

Opus teacher head0.227
GPT teacher head0.534
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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