Development and preclinical evaluation of cixutumumab drug conjugates in a model of insulin growth factor receptor I (IGF-1R) positive cancer
Bibliographic record
Abstract
Abstract Overexpression of insulin growth factor receptor type 1 (IGF-1R) is observed in many cancers. Antibody drug conjugates (ADCs) with PEGylated maytansine (PEG6-DM1) show promise in vitro. We developed PEG6-DM1 ADCs with low and high drug to antibody ratios (DAR) using an anti-IGF-1R antibody cixutumumab (IMC-A12). Conjugates with low (cixutumumab-PEG6-DM1-Low) and high (cixutumumab-PEG6-DM1-High) DAR as 3.4 and 7.2, respectively, were generated. QC was performed by UV spectrophotometry, HPLC, bioanalyzer, and biolayer-interferometry. We compared the in vitro binding and internalization rates of the ADCs in IGF-1R-positive MCF-7/Her18 cells. We radiolabeled the ADCs with 111In and used microSPECT/CT imaging and ex vivo biodistribution to understand their in vivo behavior in MCF-7/Her18 xenograft mice. The therapeutic potential of the ADC was studied in vitro and in mouse xenograft. Internalization rates of all ADCs was high and increased over 48 h and EC50 was in the low nanomolar range. MicroSPECT/CT imaging and ex vivo biodistribution showed significantly lower tumor uptake of 111In-cixutumumab-PEG6-DM1-High compared to 111In-cixutumumab-PEG6-DM1-Low and 111In-cixutumumab. Cixutumumab-PEG6-DM1-Low significantly prolonged the survival of mice bearing MCF-7/Her18 xenograft compared with cixutumumab, cixutumumab-PEG6-DM1-High, or the PBS control group. Cixutumumab-PEG6-DM1-Low ADC was more effective. The study highlights the potential utility of cixutumumab-ADCs as theranostics against IGF-1R positive cancers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".