Management of Metabolic Effects Associated With Anticancer Agents Targeting the PI3K-Akt-mTOR Pathway
Bibliographic record
Abstract
Agents inhibiting the phosphoinositide 3-kinase-Akt-mammalian target of rapamycin (PAM) pathway are currently in various stages of clinical development in oncology, ranging from some in early-phase evaluations to others that have already received regulatory approval for treatment in advanced cancers. The administration of PAM pathway inhibitors has been associated with metabolic toxicities of hyperlipidemia and hyperglycemia. The PAM Task Force of the National Cancer Institute Investigational Drug Steering Committee convened an interdisciplinary expert panel to review the pathophysiology of hyperlipidemia and hyperglycemia induced by PAM pathway inhibitors, summarize the incidence of these metabolic toxicities induced by such agents in the current literature, advise on clinical trial screening and monitoring criteria, and provide management guidance and therapeutic goals on occurrence of these toxicities. The overarching aim of this consensus report is to raise awareness of these metabolic adverse events to enable their early recognition, regular monitoring, and timely intervention in clinical trials. Hyperglycemia and hyperlipidemia are generally not acutely toxic and most often reversible with therapeutic intervention. Dose modifications or discontinuation of PAM pathway inhibitors should only be considered in situations of severe events or if progressive metabolic derangement persists after therapeutic interventions have been attempted for a sufficient duration. Specialty consultation should be sought to aid clinical trial planning and the management of these metabolic adverse events.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".