Abstract 7330: MDNA113: A tumor targeting and conditionally activated anti-PD1-IL2SK to enhance the therapeutic index
Bibliographic record
Abstract
Abstract Background: To improve systemic tolerability of potent cytokines including anti-PD1/IL2 immunocytokines, conditional activation using tumor associated proteases have been gaining attention. MDNA113 (masked anti-PD1-IL2SK), a Bifunctional SuperKine for Immunotherapy (BiSKIT), offers key distinguishing features including (i) next-generation IL2 Superkine (IL2SK) with ‘β-enhanced not-α’ receptor selectivity, and (ii) a masking domain that also functions as a tumor targeting domain. MDNA113 incorporates an IL-13Rα2 selective IL13 superkine (IL13SK) that facilitates tumor targeting and promotes durable accumulation within IL-13Rα2 expressing tumors thereby amplifying activation and therapeutic activity at the tumor site. In addition, the IL13SK partially masks the IL2SK, sterically hindering its systemic activity to improve tolerability while maximizing localization and activity of anti-PD1-IL2SK within the tumor microenvironment (TME) which promotes synergy between IL2 receptor (IL-2R) agonism and immune checkpoint blockade by cis binding. Methods: IL-2R signaling and PD1/PD-L1 blockade were evaluated using in vitro cell based reporter assays and human PBMCs. Tolerability, pharmacodynamics, efficacy and mechanistic studies were conducted in mouse syngeneic tumor models. Results: MDNA113 demonstrated reduced IL-2R agonism while maintaining PD1/PD-L1 blockade compared to non-masked anti-PD1-IL2SK in cell-based assays. This was reflected in reduced p-STAT5 signaling in human CD8+T cells, accompanied by decreased PBMC proliferation. Upon in vitro cleavage by proteases, full IL-2R agonism was restored as intended. In mice, MDNA113 exhibited better tolerability than naked anti-PD1-IL2SK, correlating with reduced peripheral lymphocyte expansion. Systemic administration of MDNA113 showed comparable efficacy to anti-PD1-IL2SK in mouse syngeneic tumor models, consistent with the designed protease mediated activation of MDNA113 through release of the IL13SK mask within TME. Furthermore, MDNA113 achieved complete tumor regression in mice harboring IL-13Rα2 expressing solid tumors. These mice were also resistant to tumor growth when rechallenged with the same tumor antigen in the absence of any additional treatment, indicative of an underlying tumor specific memory response. Tumors from mice treated with a single dose of MDNA113 showed an elevated influx of CD8+GrzB+ T cells, consistent with synergy between IL-2R agonism and immune checkpoint blockade. In addition, MDNA113 demonstrated superior efficacy compared to single-agent anti-PD1 when tested in a neo-adjuvant setting in an orthotopic 4T1.2 triple negative breast tumor model. Conclusion: MDNA113 is conditionally active anti-PD1-IL2SK designed by leveraging IL13SK as a dual tumor targeting and masking domain for enhanced tolerability and robust efficacy in both therapeutic and neoadjuvant settings. Citation Format: Scott Rowlinson, Minh D. To, Qian Liu, Rosemina Merchant, Fahar Merchant, Aanchal Sharma. MDNA113: A tumor targeting and conditionally activated anti-PD1-IL2SK to enhance the therapeutic index [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7330.
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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) | 0.002 | 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".