AZD8701, an Antisense Oligonucleotide Targeting FOXP3 mRNA, as Monotherapy and in Combination with Durvalumab: A Phase I Trial in Patients with Advanced Solid Tumors
Bibliographic record
Abstract
PURPOSE: AZD8701 uses next-generation antisense oligonucleotide (ASO) technology to selectively reduce human forkhead box P3 (FOXP3) expression in regulatory T cells, reversing their immunosuppressive function. FOXP3 ASO alone or with PD-(L)1 inhibition attenuated tumor growth in mice. We report a phase I study of AZD8701 alone or combined with durvalumab in patients with advanced solid tumors. PATIENTS AND METHODS: Eligible patients had solid tumors and received prior standard-of-care treatment, including anti-PD-(L)1 therapy. Patient cohorts were treated with AZD8701 intravenously weekly at escalating doses, either alone (60-960 mg) or combined (240-720 mg) with durvalumab 1,500 mg intravenous every 4 weeks. The primary objective was safety and tolerability, with the aim of determining the MTD. RESULTS: Forty-five patients received AZD8701 monotherapy, and 18 received AZD8701 with durvalumab. One dose-limiting toxicity (increased alanine aminotransferase) occurred with AZD8701 960 mg. The most common adverse events related to AZD8701 monotherapy were fatigue (22.2%), asthenia, pyrexia, and increased alanine aminotransferase (20% each); the safety profile was similar when combined with durvalumab. With AZD8701 monotherapy, 24.4% and 15.6% of the patients had stable disease for ≥16 and ≥24 weeks, respectively; one patient treated with AZD8701 720 mg and durvalumab had a partial response. FOXP3 mRNA changes were heterogeneous (8/13 patients showed a reduction), with no clear dose relationship. ASO accumulated in the tumor epithelium and stroma. CONCLUSIONS: This study demonstrates the clinical feasibility of ASO therapy, with generally manageable adverse events, FOXP3 knockdown, and ASO delivery to the tumor.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".