First-in-Human Pharmacokinetic and Pharmacodynamic Study of the Dual m-TORC 1/2 Inhibitor AZD2014
Bibliographic record
Abstract
PURPOSE: AZD2014 is a novel, oral, m-TORC 1/2 inhibitor that has shown in vitro and in vivo efficacy across a range of preclinical human cancer models. EXPERIMENTAL DESIGN: A rolling six-dose escalation was performed to define an MTD (part A), and at MTD a further cohort of patients was treated to further characterize toxicities and perform pre- and posttreatment biopsies (part B). AZD2014 was administered orally twice a day continuously. Flow cytometry, ELISA, and immunohistochemistry were used to quantify pharmacodynamic biomarkers. Pharmacokinetic analysis was carried out by mass spectrometry. RESULTS: A total of 56 patients were treated across a dose range of 25 to 100 mg. The MTD was 50 mg twice daily. The dose-limiting toxicities were fatigue and mucositis. At the MTD, the most common adverse events (AE) were fatigue (78%), nausea (51%), and mucositis (49%), but these were equal to or greater than grade 3 in only 5% of patients. Drug levels achieved at the MTD (AUC SS: 6686 ng·h/mL, Cmax ss 1,664 ng/mL) were consistent with activity in preclinical models. A reduction in p-S6 levels and Ki67 staining was observed in 8 of 8 and 5 of 9 evaluable paired biopsy samples. Partial responses were seen in a patient with pancreatic cancer and a patient with breast cancer, who were found to have a PDGFR and ERBB2 mutation, respectively. CONCLUSIONS: The recommended phase II dose for further evaluation of AZD2014 is 50 mg twice daily, and at this dose it has been possible to demonstrate pharmacologically relevant plasma concentrations, target inhibition in tumor, and clinical responses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".