title: How Can Viral Dynamics Models Inform Endpoint Measures in Clinical Trials of Therapies for Acute Viral Infections?
abstract: Acute viral infections pose many practical challenges for the accurate assessment of the impact of novel therapies on viral growth and decay. Using the example of influenza A, we illustrate how the measurement of infection-related quantities that determine the dynamics of viral load within the human host, can inform investigators on the course and severity of infection and the efficacy of a novel treatment. We estimated the values of key infection-related quantities that determine the course of natural infection from viral load data, using Markov Chain Monte Carlo methods. The data were placebo group viral load measurements collected during volunteer challenge studies, conducted by Roche, as part of the oseltamivir trials. We calculated the values of the quantities for each patient and the correlations between the quantities, symptom severity and body temperature. The greatest variation among individuals occurred in the viral load peak and area under the viral load curve. Total symptom severity correlated positively with the basic reproductive number. The most sensitive endpoint for therapeutic trials with the goal to cure patients is the duration of infection. We suggest laboratory experiments to obtain more precise estimates of virological quantities that can supplement clinical endpoint measurements.
authors.affiliation: Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Carolin
authors.initials: C
authors.lastname: Vegvari
authors.affiliation: Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Christoforos
authors.initials: C
authors.lastname: Hadjichrysanthou
authors.affiliation: Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Emilie
authors.initials: E
authors.lastname: Cauët
authors.affiliation: Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Emma
authors.initials: E
authors.lastname: Lawrence
authors.affiliation: Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Anne
authors.initials: A
authors.lastname: Cori
authors.affiliation: Janssen Prevention Center, Leiden, The Netherlands.
authors.forename: Frank
authors.initials: F
authors.lastname: de Wolf
authors.affiliation: Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
authors.forename: Roy M
authors.initials: RM
authors.lastname: Anderson
mesh_terms: D000208:Acute Disease; D000998:Antiviral Agents / Q000494:pharmacology / Q000627:therapeutic use*; D002986:Clinical Trials as Topic; D023381:Endpoint Determination; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D019562:Viral Load / Q000187:drug effects; D014777:Virus Diseases / Q000188:drug therapy*
chemical_list: D000998:Antiviral Agents