Quantification of Binding of IGF-1 to BI 836845, a Candidate Therapeutic Antibody Against IGF-1 and IGF-2, and Effects of This Antibody on IGF-1:IGFBP-3 Complexes In Vitro and in Male C57BL/6 Mice
Bibliographic record
Abstract
IGF-1 and IGF-2 are potent mitogens acting through the IGF-1 receptor (IGF-1R). The importance of the IGF system in neoplasia has been demonstrated in several models, and IGF-1 signaling has become a target for drug development. The drug candidate BI 836845 is a fully human IgG1 ligand-neutralizing antibody that cross-reacts with IGF-1 and IGF-2. It has been shown to reduce both IGF-1R phosphorylation and cellular proliferation in preclinical studies. In rodent studies, administration of BI 836845 leads to large increases in total IGF-1 concentration in serum, despite reduced serum IGF-1 activity as measured by a kinase activation assay. Despite the fact that anti-IGF-ligand antibodies have entered clinical trials, their effect on IGF-binding proteins has not been described. In this report, we developed a novel technique to measure ligand-BI 836845 binding, and we apply it to a mouse model in various contexts. We show that although large increases in total serum IGF-1 levels are observed, the vast majority of ligand is present as a complex with BI 836845, and total serum IGF-binding protein-3 levels are decreased. Finally, we show that BI 836845 treatment induces an increase in GH levels, a finding consistent with attempted compensation at the level of the pituitary. Our results reveal complexities in the physiologic sequelae of BI 836845 administration that have implications for determination of optimal dosing regimens and for development of pharmacodynamic endpoints for clinical trials.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".