Abstract 1919: Activity and affinity tuning next-generation immunotoxins for targeted therapy
Bibliographic record
Abstract
Abstract Classic cancer-associated receptors, such as EGFR and Her2, are often overexpressed on malignant cells relative to healthy cells. Numerous molecules have been developed to leverage this differential expression with the goal of developing specific and safe targeted therapies, including antibody-drug-conjugates and immunotoxins. The general schematic of these molecules is to fuse toxic payloads -either small molecules or proteins - with a targeting moiety that binds to the cancer-specific receptor. Despite the general success of this approach, even low levels of these receptors on healthy tissues result in on-target/off-tumor toxicity thus limiting the dose of therapeutic molecules available to tumors. Targeted therapies with greater on-tumor efficacy and reduced toxicity are urgently needed in order to further advance therapeutic outcomes. To achieve this, we set out to exploit the unique features of bacterial toxins as highly modular protein delivery systems to design next-generation targeted therapies. We demonstrate the capacity of the immunotoxin platform to deliver various therapeutic proteins, with distinct activities from inhibiting protein synthesis to degrading oncogenic signaling pathways, such as the RAS/MAPK pathway. Further, we show how modifying the affinity, valency, and specificity of the receptor-binding moieties, as well as tuning the enzymatic activity of the toxic enzyme payloads interact to affect the on-target efficacy and off-target toxicity of the targeted molecules. Altogether, these data demonstrate the potential of immunotoxins as highly flexible and tunable platforms for the development of a new class of anti-cancer therapeutics. Citation Format: Huazhu (Peter) Liang, Greg L. Beilhartz, Shi Bo Cao, Roman A. Melnyk. Activity and affinity tuning next-generation immunotoxins for targeted therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1919.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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 teacher head, 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".