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Hyperglycemia and Phosphatidylinositol 3-Kinase/Protein Kinase B/Mammalian Target of Rapamycin (PI3K/AKT/mTOR) Inhibitors in Phase I Trials: Incidence, Predictive Factors, and Management

2016· article· en· W2345838610 on OpenAlexfundno aff
Khurum Khan, Mabel Wong, Karim Rihawi, Shankar Bodla, Daniel L. Morganstein, Udai Banerji, L Rhoda Molife

Bibliographic record

VenueThe Oncologist · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsnot available
FundersInstitute of Cancer ResearchNational Institute for Health and Care ResearchCancer Research UK
KeywordsPI3K/AKT/mTOR pathwayMedicineProtein kinase BPhosphatidylinositolKinaseRPTORIncidence (geometry)SirolimusCancer researchInternal medicineSignal transductionCell biologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Dysregulation of the phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) pathway is implicated in human cancer growth and progression. Agents targeting this pathway are associated with hyperglycemia due to interaction with the insulin-glucose regulatory axis. Identifying the predictive factors for hyperglycemia in patients treated with these agents may help direct future management. MATERIALS AND METHODS: Clinical characteristics and outcomes of patients treated consecutively with PI3K, AKT, or mTOR inhibitors in the Drug Development Unit, The Royal Marsden (RM) National Health Service (NHS) Foundation Trust, between 2007 and 2012 were recorded. Baseline variables and their association with grade 3 hyperglycemia (Common Terminology Criteria for Adverse Events, version 3.0) were analyzed by using the chi-square test and Fisher exact test for categorical variables and binary logistic regression for continuous variables. RESULTS: A total of 341 patients were treated in 12 phase I trials of PI3K/AKT/mTOR inhibitors, and 298 patients (87.4%) developed hyperglycemia. Hyperglycemia was grade 1 in 217 (72.8%) and grade 2 in 61 (20.5%) patients, respectively. Grade ≥3 hyperglycemia was seen in 6.7% of patients (n = 20). According to the chi-square test, age <65 years (p = .03), history of diabetes (p = .003), and treatment with AKT and dual PI3K/mTOR inhibitors (p < .0005) predicted the occurrence of grade 3 hyperglycemia. Of 24 patients requiring intervention, 20 received metformin, 2 dietary advice, 1 insulin, and 1 both metformin and insulin. One patient required dose reduction. There were no permanent drug discontinuations, and no hyperglycemia-related dose-limiting toxicities were observed; thus, the recommended phase II dose was not affected by the hyperglycemia observed in our cohort. CONCLUSION: Hyperglycemia is common in patients treated with PI3K/AKT/mTOR inhibitors; however, it is manageable with conventional treatment. Predictive factors of age, history of diabetes, and administration of AKT and dual PI3K/mTOR inhibitors warrant prospective validation. IMPLICATIONS FOR PRACTICE: This study reviewed the clinical data of 341 patients treated in 12 phase I trials of agents targeting phosphatidylinositol3-kinase (PI3), protein kinase B (AKT), and mammalian target of rapamycin (mTOR), as well as dual inhibitors. Hyperglycemia was evident in 87.4% of patients but was ≥grade 3 in just 6.7%. Age <65 years, history of diabetes, and treatment with AKT and dual PI3K/mTOR inhibitors were each associated with grade 3 hyperglycemia. Management of patients was uncomplicated, and no permanent drug discontinuations were necessary. Despite the small study size, these findings support continued caution about enrolling patients with a history of diabetes into such trials. However, clinicians may be reassured, pending prospective validation of these results, that significant hyperglycemia is not frequent and, when it occurs, is manageable.

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)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.305
Teacher spread0.282 · 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 designObservational
Domainnot available
GenreEmpirical

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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Citations66
Published2016
Admission routes1
Has abstractyes

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