Abstract 5616: Tumor-targeted immune activation via a site-specific TLR7-agonist antibody-drug conjugate
Bibliographic record
Abstract
Abstract Traditional antibody-drug conjugates (ADCs) selectively deliver cytotoxic payloads to tumor-associated antigen (TAA)-expressing tumor cells, thereby limiting healthy cell damage and toxicities associated with systemic administration. Analogously, a novel class of immune-stimulating antibody conjugates (ISACs) has recently emerged to achieve tumor-targeted activation of anti-tumor immune responses, avoiding dose-limiting immunotoxicities seen with the systemic immune agonist delivery. Most of the ADC or ISAC to date utilize random payload conjugation to native surface-exposed lysines or cysteines, and relatively labile linkage chemistries, leading to conjugation site and drug-to-antibody ratio (DAR) heterogeneity, and ADC/ISAC instability in systemic circulation. In particular, the presence of high-DAR species and linker instability can lead to off-tumor toxicity, thus limiting the therapeutic window. Here we describe the generation and preclinical characterization of a site-specific TLR7-agonist (TLR7a) ISAC targeting an undisclosed TAA1. To specify the conjugation site and DAR, a non-natural amino acid (nnAA), para-acetyl-L-phenylalanine (pAF) was genetically incorporated at defined sites within the anti-TAA1 antibody, providing an orthogonal chemical handle for covalent TLR7 agonist payload conjugation through a highly stable oxime bond. Using this site-specific, homogenous ISAC platform, we systematically screened different conjugate sites and TLR7 agonist payloads to obtain an optimized TAA1-TLR7a ISAC drug candidate. ISAC platform stability, homogeneity, and flexibility were critical, because conjugation site and payload structure dramatically influenced ISAC in vitro activity, PK profile and in vivo efficacy. Using in vitro co-culture assays with human immune cells and tumor cell lines, we showed that the optimized TAA1 ISAC can induce multiple anti-tumor immune mechanisms, including proinflammatory cytokine production, the myeloid cell activation markers induction and enhanced ADCC mediated tumor cell killing. The TAA1 ISAC is 100-fold more active than the free, unconjugated TLR7 agonist, and all activity is conditional on the presence of TAA1-expressing tumor cells. Finally, we demonstrated in vivo efficacy in both xenograft and syngeneic tumor models. In a syngeneic MC38-tumor model expressing human TAA1, ISAC treatment led to complete tumor regression and formation of immunologic memory. These results provide a strong rationale for site-specific TLR7 agonist ISAC as a next generation platform for tumor-targeted, innate immune agonist immunotherapy. Citation Format: Mingchao Kang, Sung-Ju Moon, Ji Young Kim, Andy Beck, Molly Allen, Jay Nelson, Keith Tatsukawa, Hon Tran, Manoj Pal, Michael Gray, Nick Knudsen, Lillian Skidmore, David Mills, Yingchun Lu, Ying Buechler, Sukumar Sakamuri, Shawn Zhang, Feng Tian. Tumor-targeted immune activation via a site-specific TLR7-agonist antibody-drug conjugate [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5616.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".