Preclinical Efficacy of an Antibody–Drug Conjugate Targeting Mesothelin Correlates with Quantitative 89Zr-ImmunoPET
Bibliographic record
Abstract
Abstract Antibody–drug conjugates (ADC) use monoclonal antibodies (mAb) as vehicles to deliver potent cytotoxic drugs selectively to tumor cells expressing the target. Molecular imaging with zirconium-89 (89Zr)-labeled mAbs recapitulates similar targeting biology and might help predict the efficacy of these ADCs. An anti-mesothelin antibody (AMA, MMOT0530A) was used to make comparisons between its efficacy as an ADC and its tumor uptake as measured by 89Zr immunoPET imaging. Mesothelin-targeted tumor growth inhibition by monomethyl auristatin E (MMAE), ADC AMA-MMAE (DMOT4039A), was measured in mice bearing xenografts of ovarian cancer OVCAR-3×2.1, pancreatic cancers Capan-2, HPAC, AsPC-1, and HPAF-II, or mesothelioma MSTO-211H. Ex vivo analysis of mesothelin expression was performed using immunohistochemistry. AMA-MMAE showed the greatest growth inhibition in OVCAR-3×2.1, Capan-2, and HPAC tumors, which showed target-specific tumor uptake of 89Zr-AMA. The less responsive xenografts (AsPC-1, HPAF-II, and MSTO-211H) did not show 89Zr-AMA uptake despite confirmed mesothelin expression. ImmunoPET can demonstrate the necessary delivery, binding, and internalization of an ADC antibody in vivo and this correlates with the efficacy of mesothelin-targeted ADC in tumors vulnerable to the cytotoxic drug delivered. Mol Cancer Ther; 16(1); 134–42. ©2016 AACR.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".