Immunogenicity assessment during the development of protein therapeutics
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
OBJECTIVE: Here we provide a critical review of the state of the art with respect to non-clinical assessments of immunogenicity for therapeutic proteins. KEY FINDINGS: The number of studies on immunogenicity published annually has more than doubled in the last 5 years. The science and technology, which have reached a critical mass, provide multiple of non-clinical approaches (computational, in vitro, ex vivo and animal models) to first predict and then to modify or eliminate T-cell or B-cell epitopes via de-immunization strategies. We discuss how these may be used in the context of drug development in assigning the immunogenicity risk of new and marketed therapeutic proteins. SUMMARY: Protein therapeutics represents a large share of the pharma market and provide medical interventions for some of the most complex and intractable diseases. Immunogenicity (the development of antibodies to therapeutic proteins) is an important concern for both the safety and efficacy of protein therapeutics as immune responses may neutralize the activity of life-saving and highly effective protein therapeutics and induce hypersensitivity responses including anaphylaxis. The non-clinical computational tools and experimental technologies that offer a comprehensive and increasingly accurate estimation of immunogenic potential are surveyed here. This critical review also discusses technologies which are promising but are not as yet ready for routine use.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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