Paediatric strategy forum for medicinal product development of PI3-K, mTOR, AKT and GSK3β inhibitors in children and adolescents with cancer
Bibliographic record
Abstract
Phosphatidylinositol 3-kinase (PI3-K) signalling pathway is a crucial path in cancer for cell survival and thus represents an intriguing target for new paediatric anti-cancer drugs. However, the unique clinical toxicities of targeting this pathway (resulting in hyperglycaemia) difficulties combining with chemotherapy, rarity of mutations in childhood tumours and concomitant mutations have resulted in major barriers to clinical translation of these inhibitors in treating both adults and children. Mutations in PIK3CA predict response to PI3-K inhibitors in adult cancers. The same mutations occur in children as in adults, but they are significantly less frequent in paediatrics. In children, high-grade gliomas, especially diffuse midline gliomas (DMG), have the highest incidence of PIK3CA mutations. New mutation-specific PI3-K inhibitors reduce toxicity from on-target PI3-Kα wild-type activity. The mTOR inhibitor everolimus is approved for subependymal giant cell astrocytomas. In paediatric cancers, mTOR inhibitors have been predominantly evaluated by academia, without an overall strategy, in empiric, mutation-agnostic clinical trials with very low response rates to monotherapy. Therefore, future trials of single agent or combination strategies of mTOR inhibitors in childhood cancer should be supported by very strong biological rationale and preclinical data. Further preclinical evaluation of glycogen synthase kinase-3 beta inhibitors is required. Similarly, even where there is an AKT mutation (∼0.1 %), the role of AKT inhibitors in paediatric cancers remains unclear. Patient advocates strongly urged analysing and conserving data from every child participating in a clinical trial. A priority is to evaluate mutation-specific, central nervous system-penetrant PI3-K inhibitors in children with DMG in a rational biological combination. The choice of combination, should be based on the genomic landscape e.g. PTEN loss and resistance mechanisms supported by preclinical data. However, in view of the very rare populations involved, innovative regulatory approaches are needed to generate data for an indication.
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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 | 0.001 |
| 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) | 0.009 | 0.003 |
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".