Antibody mutations favoring <scp>pH</scp>‐dependent binding in solid tumor microenvironments: Insights from large‐scale structure‐based calculations
Bibliographic record
Abstract
Antibody-based therapeutics for treatment of various tumors have grown rapidly in recent years. Unfortunately, safety issues, attributed to off-tumor effects and cytotoxicity, are still a significant concern with the standard of care. Improvements to ensure targeted delivery of antitumor pharmaceuticals are desperately needed. We previously demonstrated that incorporating histidyl pH-switches in an anti-HER2 antibody induced selective antigen binding under acidic pH conditions (MAbs 2020;12:1682866). This led to an improved safety profile due to preferential targeting of the oncoprotein in the acidic solid tumor microenvironment. Following this success, we expanded this approach to a set of over 400 antibody structures complexed with over 100 different human oncoproteins, associated with solid tumors. Calculations suggested that mutations to His of certain residue types, namely Trp, Arg, and Tyr, could be significantly more successful for inducing pH-dependent binding under acidic conditions. Furthermore, 10 positions within the complementarity-determining region were also predicted to exhibit greater successes. Combined, these two accessible metrics could serve as the basis for a sequence-based engineering of pH-selective binding. This approach could be applied to most anticancer antibodies, which lack detailed structural characterization.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".