The development of a next generation NK cell engager platform by integrating a potency-reduced IL-15 variant to enhance antitumor activity
Bibliographic record
Abstract
Abstract Background Natural killer cell engager (NKCE) has gained attention recently. Conventional NKCEs exhibit mild anti-tumor efficacy despite acceptable safety profiles. Therefore, next-generation NKCE development is essential to enhance efficacy. IL-15, a key NK cell activator, is explored in NKCE design. However, wild-type IL-15 shows significant toxicity in clinical trials. In this study, we present the development of a novel tetravalent NKCE platform (IL15v-NKCE) by incorporating a potency-reduced IL-15 element (IL15v) into our proprietary anti-NKp46 based NKCEs. Methods The activity of the IL15v moiety was assessed by quantifying pSTAT5 induction in primary immune cells and evaluating STAT5 activation in an IL-15 reporter cell line. The in vitro activity of IL15v-NKCE was determined using co-culture assays with NK and tumor cells. The in vivo anti-tumor efficacy and safety profile of IL15v-NKCE were evaluated in tumor-bearing mouse models. Results In vitro, IL15v selectively activates NK cells without affecting T cells, enhances NKCE cytotoxicity, prevents NK apoptosis, and promotes NK proliferation. In vivo, IL15v-NKCE shows good tolerability and superior anti-tumor efficacy compared to conventional NKCE. All four components (anti-NKp46, Fc, IL15v, anti–tumor-associated antigen) of IL15v-NKCE are essential for maximal activity, and IL15v-NKCE is more potent than the conventional NKCE when combined with anti-PD-1 in preclinical models. Conclusions By integrating IL15v into our anti-NKp46 based NKCEs, IL15v-NKCE has exhibited enhanced anti-tumor efficacy while maintaining an acceptable safety profile, thereby positioning it as a promising next-generation therapeutic modality for NK cell-based therapy.
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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".