Anti-Drug Antibody Response to Therapeutic Antibodies and Potential Mitigation Strategies
Bibliographic record
Abstract
The development of anti-drug antibodies (ADAs) against therapeutic monoclonal antibodies (mAbs) poses significant challenges in the efficacy and safety of these treatments. ADAs can lead to adverse immune reactions, reduced drug efficacy, and increased clearance of therapeutic antibodies. This paper reviews the formation and mechanisms of ADAs, explores factors contributing to their development, and discusses potential strategies to mitigate ADA responses. Current and emerging strategies to reduce ADA formation include in silico and in vitro prediction tools, deimmunization techniques, antibody engineering, and various drug delivery methods. Additionally, novel approaches such as tolerogenic nanoparticles, oral tolerance, and in vivo delivery of therapeutic proteins via viral vectors and synthetic mRNA or DNA are explored. These strategies have the potential to enhance clinical outcomes of mAb therapies by minimizing immunogenicity and improving patient safety. Further research and innovation in this field are critical to overcoming the ongoing challenges of ADA responses in therapeutic antibody development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".