Abstract 924: PROTECT, a novel antibody platform for integrating tumor-specific immune modulation and enhancing the therapeutic window of targeted multispecific biologics
Bibliographic record
Abstract
Abstract Many novel immune-oncology biologics are limited in clinical utility by narrow therapeutic windows. One strategy to overcome this limitation relies on the engineering of ‘masks' that block antibody paratopes outside of the tumor microenvironment (TME). The PROTECT (PROgrammed Tumor Engagement & Checkpoint/Costimulation Targeting) platform is designed to employ the orthogonal mechanistic features of a multispecific design to increase the therapeutic window by limiting exposure and activity in peripheral tissues while focusing activities to the tumor. In particular, we aim to bring TME-specific activity and enhanced immune modulation in a single transferable, conditionally active design. To achieve this, we engineered the N-termini of antibody heavy and light chains with the fusion of IgV domains of commonly targeted immunomodulatory pairs, such as PD-1 and PD-L1, to sterically preclude binding of the antibody paratopes to their target tumor antigens. We demonstrate that this approach can effectively mask the antibody binding and activity for targets by 10-1000 fold, with recovery of binding and anti-tumor activity upon release of the immunomodulatory mask once proteolytically cleaved by a tumor-specific protease such as urokinase plasminogen activator (uPA). In addition, we also demonstrate that we can selectively cleave and remove one half of the immunomodulatory pair (e.g. PD-L1) from the antibody thereby creating an antibody fused to only PD-1. The resulting bispecific antibody can now co-engage both the tumor antigen (TA) and its counterpart checkpoint inhibitor (PD-L1) to confer additional antitumor activities. As a proof of concept, we showed that for an engineered anti-CD3/HER2 bispecific T-cell engager (TCE), incorporation of the PD-L1/PD-1 PROTECT design creates a conditionally activated anti-CD3/PD-L1/HER2 tri-specific TCE that has (i) masked CD3 engagement with a reduction of the EC50 by 2 orders of magnitude, and (ii) enhanced T-cell dependent cytotoxicity over the parent anti-CD3/HER2 TCE by an order of magnitude once activated by uPA. Importantly, the enhanced activity of the tri-specific TCE is greater than the combination of the parental TCE with the anti-PD-L1 monoclonal antibody atezolizumab. We have also demonstrated that the PROTECT platform can be combined with anti-TA antibody to introduce a masking effect while increasing antibody-dependent cell cytotoxicity (ADCC) activity upon co-engagement of TA and PD-L1, again synergistic relative to the combination of anti-TA antibody and atezolizumab. Taken together, the PROTECT platform represents a novel approach to integrate immune modulation while limiting off-tumor activities for the development of conditional multispecific antibodies with potentially enhanced therapeutic window and activity. Citation Format: Surjit Dixit, Florian Heinkel, Anna Von Rossum, Harsh Pratap, Sifa Arrafi, Javairia Rahim, Purva Bhojane, Liz Stangle, Leisa Stenberg, Gesa Volkers, Eric Escobar-Cabrera, Thomas Spreter. PROTECT, a novel antibody platform for integrating tumor-specific immune modulation and enhancing the therapeutic window of targeted multispecific biologics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 924.
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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) | 0.001 | 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".