Comparative In Vitro and In Vivo Evaluation of Anti-CCR8 Full-Sized IgG and Its Fab Fragments in Murine Colorectal Cancer Models
Bibliographic record
Abstract
CCR8 chemokine receptor is a selective marker of tumor-infiltrating regulatory T cells (ti-Tregs) which interfere with the efficacy of checkpoint inhibitor immunotherapy (ICI) in many types of cancer. Eliminating CCR8+ ti-Tregs dramatically improves the results of subsequent ICI. We have recently reported using 225Actinium-labeled anti-CCR8 IgG for killing CCR8+ ti-Tregs in murine colorectal tumors which synergized with subsequent anti-CTLA4 ICI. Here, we aimed to compare the in vivo behavior of anti-CCR8 full-sized IgG and its Fab fragments to select the best antibody format for the pre-clinical development of this combination modality. Anti-CCR8 Fab fragments were generated by papain digest of the whole IgG. The whole IgG and Fab were conjugated to bifunctional chelating agent DOTA and labeled with 111Indium (111In). MC8 and CT6 murine colorectal tumor-bearing C57Bl6 and Balb/c mice, respectively, were administered 111In-DOTA-IgG or 111In-DOTA-Fab and imaged with microSPECT/CT at 2–72 h post-injection. A biodistribution was performed after the last imaging time point. Both 111In-DOTA-IgG and 111In-DOTA-Fab demonstrated high tumor uptake in both MC38 and CT26 tumors, with 111In-DOTA-IgG uptake being significantly higher from the 24 h time point and onwards. 111In-DOTA-Fab also exhibited pronounced kidney uptake which persisted even at 72 h. The kidney clearance and retention of 111In-DOTA-Fab might represent a problem during therapy employing 225Actimium or other long-lived therapeutic radionuclides by potentially causing a dose-limiting kidney toxicity. This imaging/biodistribution evaluation not only determined that full-size anti-CCR8 IgG is the optimal antibody format for pre-clinical development but also informed on the timing of immunotherapy administration in future radioimmunotherapy and immunotherapy combination studies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".