A fibronectin scaffold approach to bispecific inhibitors of epidermal growth factor receptor and insulin-like growth factor-I receptor
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Bibliographic record
Abstract
Engineered domains of human fibronectin (Adnectins™) were used to generate a bispecific Adnectin targeting epidermal growth factor receptor (EGFR) and insulin-like growth factor-I receptor (IGF-IR), two transmembrane receptors that mediate proliferative and survival cell signaling in cancer. Single-domain Adnectins that specifically bind EGFR or IGF-IR were generated using mRNA display with a library containing as many as 10 ( 13) Adnectin variants. mRNA display was also used to optimize lead Adnectin affinities, resulting in clones that inhibited EGFR phosphorylation at 7 to 38 nM compared to 2.6 μM for the parental clone. Individual, optimized, Adnectins specific for blocking either EGFR or IGF-IR signaling were engineered into a single protein (EI-Tandem Adnectin). The EI-Tandems inhibited phosphorylation of EGFR and IGF-IR, induced receptor degradation, and inhibited down-stream cell signaling and proliferation of human cancer cell lines (A431, H292, BxPC3 and RH41) with IC 50 values ranging from 0.1 to 113 nM. Although Adnectins bound to EGFR at a site distinct from those of anti-EGFR antibodies cetuximab, panitumumab and nimotuzumab, like the antibodies, the anti-EGFR Adnectins blocked the binding of EGF to EGFR. PEGylated EI-Tandem inhibited the growth of both EGFR and IGF-IR driven human tumor xenografts, induced degradation of EGFR, and reduced EGFR phosphorylation in tumors. These results demonstrate efficient engineering of bispecific Adnectins with high potency and desired specificity. The bispecificity may improve biological activity compared to monospecific biologics as tumor growth is driven by multiple growth factors. Our results illustrate a technological advancement for constructing multi-specific biologics in cancer therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it