| Literature DB >> 29929556 |
Rob Lambkin-Williams1, Nicolas Noulin2, Alex Mann2, Andrew Catchpole2, Anthony S Gilbert2.
Abstract
The Human Viral Challenge (HVC) model has, for many decades, helped in the understanding of respiratory viruses and their role in disease pathogenesis. In a controlled setting using small numbers of volunteers removed from community exposure to other infections, this experimental model enables proof of concept work to be undertaken on novel therapeutics, including vaccines, immunomodulators and antivirals, as well as new diagnostics.Crucially, unlike conventional phase 1 studies, challenge studies include evaluable efficacy endpoints that then guide decisions on how to optimise subsequent field studies, as recommended by the FDA and thus licensing studies that follow. Such a strategy optimises the benefit of the studies and identifies possible threats early on, minimising the risk to subsequent volunteers but also maximising the benefit of scarce resources available to the research group investing in the research. Inspired by the principles of the 3Rs (Replacement, Reduction and Refinement) now commonly applied in the preclinical phase, HVC studies allow refinement and reduction of the subsequent development phase, accelerating progress towards further statistically powered phase 2b studies. The breadth of data generated from challenge studies allows for exploration of a wide range of variables and endpoints that can then be taken through to pivotal phase 3 studies.We describe the disease burden for acute respiratory viral infections for which current conventional development strategies have failed to produce therapeutics that meet clinical need. The Authors describe the HVC model's utility in increasing scientific understanding and in progressing promising therapeutics through development.The contribution of the model to the elucidation of the virus-host interaction, both regarding viral pathogenicity and the body's immunological response is discussed, along with its utility to assist in the development of novel diagnostics.Future applications of the model are also explored.Entities:
Keywords: Acute respiratory infections; Respiratory medicine; Controlled clinical trial; Diagnostic; Flu; Gene switching; Human boca virus (HBoV). Therapeutics; Human rhinovirus (HRV); Immune response; Pathogenicity; Pragmatic clinical trials; Research and development; Respiratory syncytial virus (RSV); Virus-host interactions; influenza
Mesh:
Substances:
Year: 2018 PMID: 29929556 PMCID: PMC6013893 DOI: 10.1186/s12931-018-0784-1
Source DB: PubMed Journal: Respir Res ISSN: 1465-9921
A comparison of two publications separated by over a decade and the incidence of ARI due to viral infection
| Heikkinen et al. 2003 [ | Taylor et al. 2017 [ | |
|---|---|---|
| Rhinovirus | 30-50% | 41.50% |
| Coronavirus | 10-15% | 5.60% |
| Influenza viruses | 5-15% | 15.80% |
| Respiratory syncytial virus | 5% | 9.70% |
| Parainfluenza | 5% | 9.70% |
| Adenoviruses | <5% | 9.80% |
| Enterovirus | <5% | - |
| Metapneumovirus | Unknown | 5.50% |
| Unknown | 20-30% | 0 |
| HBov | NA | 2% |
NB: The two papers summarised are separated by over a decade therefore different diagnostic methodology were used and are described in each paper. Taylor et al. [8] include Enteroviruses with the rhinoviruses. Hbov was discovered in 2005 and therefore not included by Heikkinen et al. [7]
Fig. 1The Human Viral Challenge Model. The study typically consists of inputs, such as the volunteers, their selection criteria, isolation in quarantine and exposure to a GMP virus. There are two treatment options; a vaccination/prophylaxis with an antiviral or b treatment with an antiviral. Outputs from the study, summarised on the right, such as virus symptoms, virus shedding etc. X is the number of days before virus exposure vaccination may occur. Y is the number of days post virus exposure that a volunteer may be followed for
Fig. 2The role of the HVC model in the clinical development pathway. Short duration proof of concept studies, which incorporate the HVC model, typically include small numbers of subjects. The resulting safety and, particularly, efficacy data can more accurately guide decisions on whether to expose a larger number of subjects to promising candidate therapeutics in field studies than conventional phase 1 safety data alone otherwise might