Recombinant and Natural Human Interferons: Analysis of the Incidence and Clinical Impact of Neutralizing Antibodies
Bibliographic record
Abstract
This review summarizes and analyzes the clinical outcomes following treatment of a wide range of diseases with recombinant interferons (r-IFNs) and/or natural interferons (n-IFNs). The investigation focuses on the frequency of neutralizing antibodies (NABs) directed against IFN, which are formed during treatment and their clinical impact. r-IFNs (α-2a, α-2b, β-1a, and β-1b) induced seroconversion with generation of NABs in 17.2% of patients studied. The highest incidence of NABs occurred in macular degeneration (61.4%) with the lowest in multiple sclerosis (14.7%). The incidence of antibodies induced against n-IFNs was very low (<0.2%) and was significantly less than that seen for r-IFNs (P<0.0001). Overall, the fraction of relapsed and refractory patients is statistically greater in NAB positive patients compared to NAB negative patients (<0.0001), whereas the percentage of responding patients is higher in the NAB negative cohort (P<0.001). Finally, we also analyzed relapsed and refractory NAB positive patients who switched treatment to n-IFN, such as leukocyte derived Alferon N Injection® (α-n3) or Wellferon® (α-n1). Overall, in 33/40 (82%) of these relapsed or refractory patients, switching to n-IFNs restored the clinical response. This result is consistent with serology studies showing that the NABs directed against r-IFNs do not effectively cross-react with n-IFNs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 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".