Mathematical modeling of herpes simplex virus-2 suppression with pritelivir predicts trial outcomes

A mathematical model for antiviral clinical trials improves dose selection and understanding of drug-virus interaction. Dosing HSV Choosing the correct drug dose can be the difference between the success or the failure of a clinical trial, yet this decision is frequently stochastic. Current models aim to improve this process by predicting the pharmacokinetics and pharmacodynamics of an antiviral agent. Schiffer et al. expand these models to include viral and immunological factors that influence the course of infection. They model herpes simplex virus shedding during dose trials of pritelivir, a DNA helicase-primase inhibitor. They find that not only does pritelivir decrease viral shedding by inhibiting replication in epithelial cells at appropriate doses but also it limits downstream viral spread to additional sites. Their model successfully predicts outcome in a subsequent trial and may serve as a tool to improve dosing decisions in the clinic. Pharmacokinetic and pharmacodynamic models estimate the potency of antiviral agents but do not capture viral and immunologic factors that drive the natural dynamics of infection. We designed a mathematical model that synthesizes pharmacokinetics, pharmacodynamics, and viral pathogenesis concepts to simulate the activity of pritelivir, a DNA helicase-primase inhibitor that targets herpes simplex virus. Our simulations recapitulate detailed viral kinetic shedding features in five dosage arms of a phase 2 clinical trial. We identify that in vitro estimates of median effective concentration (EC50) are lower than in vivo values for the drug. Nevertheless, pritelivir potently decreases shedding at appropriate doses based on its mode of action and long half-life. Although pritelivir directly inhibits replication in epithelial cells, our model indicates that pritelivir also indirectly limits downstream viral spread from neurons to genital keratinocytes, within genital ulcers, and from ulcer to new mucosal sites of infection. We validate our model based on its ability to predict outcomes in a subsequent trial with a higher dose. The model can therefore be used to optimize dose selection in clinical practice.