Optimal treatment strategies in the context of 'treatment for prevention' against HIV-1. Without medical treatment (upper panel) HIV-1 infected individuals have a high viral titer, which is related to a high probability to infect a sero-discordant partner after sexual contact. In contrast, diagnostic-guided (middle panel) and pro-active treatment switching strategies (lower panel) can durably suppress the virus in an HIV-1 infected individual, thus reducing the probability that the individual spreads the infection.
Image Credit: Sulav Duwal
HIV-1 continues to spread globally. While neither a cure, nor an effective vaccine are available, recent focus has been put on 'treatment-for-prevention', which is a method by which treatment is used to reduce the contagiousness of an infected person. A study published in PLOS Computational Biology challenges current treatment paradigms in the context of 'treatment for prevention' against HIV-1.
Sulav Duwal, Max von Kleist and their collaborators develop and employ optimal control theory to compute and assess diagnostic-guided vs. pro-active treatment strategies in terms of their expected costs, treatment benefit and reduction of onwards transmission.
In the study published in PLOS Computational Biology, the authors provide a mathematical platform that can be used to compute optimal diagnostic-guided vs. pro-active treatment strategies under consideration of available resources. They apply this framework to a stochastic model of viral intra-host dynamics and drug resistance development. When applied to resource-constrained settings, they show that pro-active strategies may be worthwhile.