Anti-Drug Antibody Response to Therapeutic Antibodies and Potential Mitigation Strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it