Affinity maturation of a broadly neutralizing human monoclonal antibody that prevents acute hepatitis C virus infection in mice
Bibliographic record
Abstract
Direct-acting antivirals (DAAs) have led to a high cure rate in treated patients with chronic hepatitis C virus (HCV) infection, but this still leaves a large number of treatment failures secondary to the emergence of resistance-associated variants (RAVs). To increase the barrier to resistance, a complementary strategy is to use neutralizing human monoclonal antibodies (HMAbs) to prevent acute infection. However, earlier efforts with the selected antibodies led to RAVs in animal and clinical studies. Therefore, we identified an HMAb that is less likely to elicit RAVs for affinity maturation to increase potency and, more important, breadth of protection. Selected matured antibodies show improved affinity and neutralization against a panel of diverse HCV isolates. Structural and modeling studies reveal that the affinity-matured HMAb mediates virus neutralization, in part, by inducing conformational change to the targeted epitope, and that the maturated light chain is responsible for the improved affinity and breadth of protection. A matured HMAb protected humanized mice when challenged with an infectious HCV human serum inoculum for a prolonged period. However, a single mouse experienced breakthrough infection after 63 days when the serum HMAb concentration dropped by several logs; sequence analysis revealed no viral escape mutation. CONCLUSION: The findings suggest that a single broadly neutralizing antibody can prevent acute HCV infection without inducing RAVs and may complement DAAs to reduce the emergence of RAVs. (Hepatology 2016;64:1922-1933).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".