INDUCE-2: A Phase I/II, open-label, two-part study of feladilimab in combination with tremelimumab in patients with advanced solid tumors
Bibliographic record
Abstract
Combining immunotherapies with distinct mechanisms of action has the potential to overcome treatment resistance and improve outcomes. The inducible T-cell co-stimulator (ICOS) agonist feladilimab is directed at enhancing T-cell activation and function, thereby promoting an antitumor response. INDUCE-2 (NCT03693612) was a Phase I/II, open-label, two-part study evaluating the anti-ICOS agonist feladilimab in combination with the anti-CTLA-4 antibody tremelimumab in patients with select advanced solid tumors. Objectives of Part 1 were to determine the safety, tolerability, and recommended phase 2 dose (RP2D) of feladilimab in combination with tremelimumab. In Part 2, the antitumor activity of the combination (administered at the RP2D determined in Part 1) was to be assessed in patients with relapsed/refractory head and neck squamous cell carcinoma. Primary endpoints included the rates of dose-limiting toxicities (DLTs), adverse events (AEs), AEs of special interest, and serious AEs. Secondary endpoints included overall response rate, while biomarker assessment was exploratory. A total of 26 patients were enrolled, 18 (69%) of whom had completed the study at end date. One patient, in the highest dose group (24/225 mg feladilimab/tremelimumab), experienced a DLT 18 days after the first dose of study treatment. All patients experienced at least one AE; AEs led to treatment discontinuation in four (15%) patients. Partial response was observed in one patient. Feladilimab in combination with tremelimumab was well-tolerated but showed limited efficacy. Based on the totality of data from Part 1, it was decided not to continue with Part 2.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".