Isolation and characterization of monoclonal antibodies against human carbonic anhydrase-IX
Bibliographic record
Abstract
The architectural complexity and heterogeneity of the tumor microenvironment (TME) remains a substantial obstacle in the successful treatment of cancer. Hypoxia, caused by insufficient oxygen supply, and acidosis, resulting from the expulsion of acidic metabolites, are prominent features of the TME. To mitigate the consequences of the hostile TME, cancer cells metabolically rewire themselves and express a series of specific transporters and enzymes instrumental to this adaptation. One of these proteins is carbonic anhydrase (CA)IX, a zinc-containing extracellular membrane bound enzyme that has been shown to play a critical role in the maintenance of a neutral intracellular pH (pHi), allowing tumor cells to survive and thrive in these harsh conditions. Although CAIX has been considered a promising cancer target, only two antibody-based therapeutics have been clinically tested so far. To fill this gap, we generated a series of novel monoclonal antibodies (mAbs) that specifically recognize the extracellular domain (ECD) of human CAIX. Here we describe the biophysical and functional properties of a set of antibodies against the CAIX ECD domain and their applicability as: 1) suitable for development as an antibody-drug-conjugate, 2) an inhibitor of CAIX enzyme activity, or 3) an imaging/detection antibody. The results presented here demonstrate the potential of these specific hCAIX mAbs for further development as novel cancer therapeutic and/or diagnostic tools.
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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) | 0.000 | 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) | 0.001 | 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".