Enhanced anti-metastatic bioactivity of an IGF-TRAP re-engineered to improve physicochemical properties
Bibliographic record
Abstract
Abstract The insulin-like growth factor (IGF) axis has been implicated in the progression of malignant disease and identified as a clinically important therapeutic target. Several IGF-1 receptor (IGF-1R) targeting drugs including humanized monoclonal antibodies have advanced to phase II/III clinical trials, but to date, have not progressed to clinical use, due, at least in part, to interference with insulin receptor signalling. We previously reported on the production of a soluble fusion protein consisting of the extracellular domain of human IGF-1R fused to the Fc portion of human IgG1 (first generation IGF-TRAP) that bound human IGF-1 and IGF-2 with a 3 log higher affinity than insulin. We showed that the IGF-TRAP had potent anti-cancer activity in several pre-clinical models of aggressive carcinomas. Here we report on the re-engineering of the IGF-TRAP with the aim of improving physicochemical properties and suitability for clinical applications. We show that cysteine-serine substitutions in the Fc hinge region of IGF-TRAP eliminated high-molecular-weight oligomerized species, while a further addition of a flexible linker, not only improved the pharmacokinetic profile, but also enhanced the therapeutic profile of the IGF-TRAP, as evaluated in an experimental colon carcinoma metastasis model. Dose-response profiles of the modified IGF-TRAPs correlated with their bio-availability profiles, as measured by the IGF kinase-receptor-activation (KIRA) assay, providing a novel, surrogate biomarker for drug efficacy. This study provides a compelling example of structure-based re-engineering of Fc-fusion-based biologics for better manufacturability that also significantly improved pharmacological parameters. It identifies the re-engineered IGF-TRAP as a potent anti-cancer therapeutic.
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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 | 0.000 |
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".