Model helps decide drug dose for clinical testing

Such a tool could help improve patient outcome and reduce the high costs, time, and failure rate associated with drug development, the researchers say. Therapies often fail to move beyond late-stage clinical testing, in part because choosing the proper dose needed for a drug to be effective is often an imprecise science.

Current methods for estimating antiviral drug dosing test the drug's potency against a virus in a plate of cells. However, in vitro or cell experiments neglect the complex immune response against the virus that occurs during human infection. In search of a better approach, Joshua Schiffer and colleagues designed a mathematical model that captures the interplay between the virus, the immune response, and drug.

They used their model to determine the optimal dose for pritelivir, an experimental drug that targets herpes simplex virus-2 (HSV-2), the leading cause of genital herpes. Currently, herpes drugs like pritelivir only partially suppress the release of the virus in the genital tract. The model the researchers developed accurately reproduced results from a phase 2 clinical trial of 150 patients treated with pritelivir at four different doses. Suppressing viral shedding by 50% required a greater drug concentration, the researchers found, than published in vitro studies on pritelivir dose selection would suggest.

Model simulations revealed that at increasing doses, the drug not only blocks viral replication, but also indirectly curbs the spread of HSV-2 within genital ulcers and from ulcers to new infection sites.

The model also predicted outcomes of a separate trial of pritelivir, validating the model's approach. The researchers say that by harnessing data from phase 2 clinical studies of antiviral drugs, their model could help optimize dose selection for late-stage trials.