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.
Highlights
According to a 2014 report, the costs of developing a new pharmaceutical has increased by 145% since 2003[1]
The dynamics of the model are given by the following equations: dT 1⁄4 ÀbTV dt dV 1⁄4 rbTV À gV dt where T is the number of epithelial target cells susceptible to viral infection, V is the viral load, β is the infection rate of target cells, r is the virus production rate, and γ is the virus death or clearance rate which encompasses the action of specific and nondoi:10.1371/journal.pone.0158237.g001
The greatest variation among individuals occurs in peak viral load (CV = 1.26) and viral load area under the curve (AUC) (CV = 1.19) Temperature significantly correlates with viral load
Summary
According to a 2014 report, the costs of developing a new pharmaceutical has increased by 145% since 2003[1]. A major part of this cost increase is due to losses incurred on drug candidates that fail during clinical development[2]. Failure may be due to adverse events or an inability to detect beneficial effects on the target disease. The reasons why clinical trials fail to demonstrate efficacy of potential novel treatments are many and complex. Failure is often due to the choice of inadequate endpoints, or poor trial design. Good endpoints in clinical trials should fulfill the following two key criteria: i) they should reflect the action of the treatment on the underlying cause of disease, and ii) they should be reliably quantifiable
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