Anti‐HCV therapies in chimeric scid‐Alb/uPA mice parallel outcomes in human clinical application†
Bibliographic record
Abstract
Compounds with in vitro anti-hepatitis C virus (HCV) activity are often advanced directly into clinical trials with limited or no in vivo efficacy data. This limits prediction of clinical efficacy of compounds in the HCV drug pipeline, and may expose human subjects to unnecessary treatment effects. The scid-Alb-uPA mouse supports proliferation of transplanted human hepatocytes and subsequent HCV infection. Cohorts of genotype 1a HCV-infected mice were treated with interferon alpha-2b(IFN-alpha), BILN-2061 (anti-NS3 protease), or HCV371 (anti-NS5B polymerase). Mice treated with 1350 IU/g/day IFN-alpha intramuscularly for 10 to 28 days demonstrated reduced viral titers compared with controls in all five experiments (P < .05, t test); viral titers rebounded after treatment withdrawal. A more pronounced antiviral effect with IFN-alpha was seen in genotype 3a-infected mice. Pilot studies with BILN2061 confirmed exposure to 10X replicon EC50 at trough and reduced viral titer over 2 log at 4 days. In a second 7-day study, mean HCV RNA titers dropped 1.1 log in BILN2061-treated animals, 0.6 log in IFN-treated mice, and rose 0.2 log in controls (P = .013, ANOVA). Pre-existing mutants with partial resistance to BILN2061 were identified by sequencing both the human inoculum and sera from treated mice. The polymerase inhibitor HCV371 yielded a decline in HCV titers of 0.3 log relative to vehicle-treated controls (P = NS). Performance of all three antiviral regimens in the chimeric mouse model paralleled responses in humans. In conclusion, this system may help selection of lead compounds for advancement into human trials with an increased likelihood of clinical success while broadening the tools available for study of the biology of HCV infection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".