Controlled Human Infection Model for Hepatitis C Virus Vaccine Development: Is It Time to Be Real?
Bibliographic record
Abstract
Hepatitis C virus (HCV) elimination has been a goal of the public health and scientific communities since its identification in 1989. Despite the advance and availability of highly effective antiviral regimens to treat HCV infection, it is unlikely that global HCV elimination can be achieved without an effective preventive vaccine. Because of the lack of appropriate animal models for preclinical testing and the perception that highly effective treatments would suffice in addressing the global burden of HCV, HCV vaccine development has not been a high priority. To accelerate vaccine development, the concept of a controlled human infection model (CHIM) for HCV has been proposed, in which healthy human volunteers would be deliberately infected with HCV for the purpose of testing the efficacy of candidate vaccines. This concept of challenge studies is not new and has been applied to more than 25 infectious agents, including the cholera vaccine, for which all efficacy data that served as the basis for regulatory approval were obtained from CHIM studies. Without a CHIM strategy, HCV vaccine development may not be feasible. In a recent phase 2 randomized controlled trial, over 900 people who inject drugs were screened to enroll 548 individuals to receive a T-cell–based HCV vaccine candidate or placebo. After a monumental effort that took over 6 years to complete, the trial documented 75 incident HCV infections overall, with only 45 included in the final analysis, and showed no protection against initial infection or progression to chronicity with the vaccine. While this study demonstrated that vaccine testing in the at-risk population can be done, future testing of additional HCV vaccine candidates using a similar pathway may be impractical in light of the duration and cost. With the ability to cure those who do not clear the virus, HCV CHIM is now a conceivable approach that could be a critically important, if not necessary, intermediate stage of vaccine development. HCV CHIM could de-risk vaccine candidates, accelerate development, reduce cost, and allow the selection of more promising candidates for further testing in larger clinical trials. CHIM may conceivably be used for larger phase 2/3 efficacy trials as a basis for clinical approval. In developing such a model, many challenges and questions must be addressed. In this Supplement issue, we, the editors of this Supplement commissioned various articles written by respective subject matter experts who provide their perspectives and opinions on these challenges and questions, ranging from the source of viral inoculum and the safety of transient HCV infection to the perspective of potential volunteers and the ethics of CHIM. While we strive to be data-driven and evidence-based, some of the opinions represent the views of the authors of each article and may be controversial. Ultimately, we hope these perspectives will enable fulsome discussion about the feasibility of HCV CHIM and lead to the development of well-designed clinical protocols. Supplement sponsorship. This article appears as part of the supplement “Controlled Human Infection Model for HCV Vaccine Development,” sponsored by Toronto General Research Institute, United States National Institutes of Health, Johns Hopkins University, the Canadian Institutes of Health Research (CIHR), and the Canadian Network on Hepatitis C (CanHepC). CanHepC is funded by a joint initiative of CIHR (HPC-178912) and the Public Health Agency of Canada.
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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 | 0.021 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".