Pharmacodynamic and Antineoplastic Activity of BI 836845, a Fully Human IGF Ligand-Neutralizing Antibody, and Mechanistic Rationale for Combination with Rapamycin
Why this work is in the frame
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Bibliographic record
Abstract
Insulin-like growth factor (IGF) signaling is thought to play a role in the development and progression of multiple cancer types. To date, therapeutic strategies aimed at disrupting IGF signaling have largely focused on antibodies that target the IGF-I receptor (IGF-IR). Here, we describe the pharmacologic profile of BI 836845, a fully human monoclonal antibody that utilizes an alternative approach to IGF signaling inhibition by selectively neutralizing the bioactivity of IGF ligands. Biochemical analyses of BI 836845 demonstrated high affinity to human IGF-I and IGF-II, resulting in effective inhibition of IGF-induced activation of both IGF-IR and IR-A in vitro. Cross-reactivity to rodent IGFs has enabled rigorous assessment of the pharmacologic activity of BI 836845 in preclinical models. Pharmacodynamic studies in rats showed potent reduction of serum IGF bioactivity in the absence of metabolic adverse effects, leading to growth inhibition as evidenced by reduced body weight gain and tail length. Moreover, BI 836845 reduced the proliferation of human cell lines derived from different cancer types and enhanced the antitumor efficacy of rapamycin by blocking a rapamycin-induced increase in upstream signaling in vitro as well as in human tumor xenograft models in nude mice. Our data suggest that BI 836845 represents a potentially more effective and tolerable approach to the inhibition of IGF signaling compared with agents that target the IGF-I receptor directly, with potential for rational combinations with other targeted agents in clinical studies.
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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) | 0.000 | 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) | 0.000 | 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