A Phase I Pharmacokinetic and Pharmacodynamic Study of Intravenous Calcitriol in Combination with Oral Gefitinib in Patients with Advanced Solid Tumors
Bibliographic record
Abstract
PURPOSE: In preclinical models, calcitriol and the tyrosine kinase inhibitor gefitinib are synergistic and modulate extracellular signal-regulated kinase (Erk) and Akt pathways. Therefore, we conducted a phase I study of calcitriol and gefitinib to determine the maximum tolerated dose (MTD) of this combination. EXPERIMENTAL DESIGN: Calcitriol was given i.v. over 1 h on weeks 1, 3, and weekly thereafter. Gefitinib was given at a fixed oral daily dose of 250 mg starting at week 2 (day 8). Escalation occurred in cohorts of three patients until the MTD was defined. Pharmacokinetic studies were done for calcitriol and gefitinib. Serial skin biopsies were done to investigate epidermal growth factor receptor (EGFR) pathway pharmacodynamic interactions. RESULTS: Thirty-two patients were treated. Dose-limiting hypercalcemia was noted in two of four patients receiving 96 mug/wk of calcitriol. One of seven patients developed dose-limiting hypercalcemia at the MTD 74 mug/wk calcitriol dose level. The relationship between calcitriol dose and peak serum calcitriol (C(max)) and systemic exposure (AUC) was linear. Mean (+/-SD) serum calcitriol C(max) at the MTD was 6.68 +/- 1.42 ng/mL. Gefitinib treatment inhibited EGFR, Akt, and Erk phosphorylation in the skin. Calcitriol did not have consistent effects on skin EGFR or its downstream elements. The combination of gefitinib and calcitriol did not modulate tumor EGFR pathway in patients with serial tumor biopsies. CONCLUSIONS: High doses of weekly i.v. calcitriol can be administered safely in combination with gefitinib. Calcitriol concentrations achieved at the MTD 74 mug calcitriol exceed in vivo concentrations associated with antitumor activity in preclinical models.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".