Concentration-dependent Early Antivascular and Antitumor Effects of Itraconazole in Non–Small Cell Lung Cancer
Bibliographic record
Abstract
Abstract Purpose: Itraconazole has been repurposed as an anticancer therapeutic agent for multiple malignancies. In preclinical models, itraconazole has antiangiogenic properties and inhibits Hedgehog pathway activity. We performed a window-of-opportunity trial to determine the biologic effects of itraconazole in human patients. Experimental Design: Patients with non–small cell lung cancer (NSCLC) who had planned for surgical resection were administered with itraconazole 300 mg orally twice daily for 10–14 days. Patients underwent dynamic contrast-enhanced MRI and plasma collection for pharmacokinetic and pharmacodynamic analyses. Tissues from pretreatment biopsy, surgical resection, and skin biopsies were analyzed for itraconazole and hydroxyitraconazole concentration, and vascular and Hedgehog pathway biomarkers. Results: Thirteen patients were enrolled in this study. Itraconazole was well-tolerated. Steady-state plasma concentrations of itraconazole and hydroxyitraconazole demonstrated a 6-fold difference across patients. Tumor itraconazole concentrations trended with and exceeded those of plasma. Greater itraconazole levels were significantly and meaningfully associated with reduction in tumor volume (Spearman correlation, −0.71; P = 0.05) and tumor perfusion (Ktrans; Spearman correlation, −0.71; P = 0.01), decrease in the proangiogenic cytokines IL1b (Spearman correlation, −0.73; P = 0.01) and GM-CSF (Spearman correlation, −1.00; P < 0.001), and reduction in tumor microvessel density (Spearman correlation, −0.69; P = 0.03). Itraconazole-treated tumors also demonstrated distinct metabolic profiles. Itraconazole treatment did not alter transcription of GLI1 and PTCH1 mRNA. Patient size, renal function, and hepatic function did not predict itraconazole concentrations. Conclusions: Itraconazole demonstrates concentration-dependent early antivascular, metabolic, and antitumor effects in patients with NSCLC. As the number of fixed dose cancer therapies increases, attention to interpatient pharmacokinetics and pharmacodynamics differences may be warranted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".