image: 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. view more
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 this week 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 this week 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.
###
Image Caption: 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
Image Link: https://www.plos.org/wp-content/uploads/2015/04/von-kleist-30-Apr.jpg
All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://www.ploscompbiol.org/article/info:doi/10.1371/journal. pcbi.1004200
Contact: Max von Kleist
Address: Freie Universität Berlin
Mathematics and Computer Science
Arnimallee 6
Berlin, Berlin 14195
GERMANY
Phone: 00493083875257
Email: vkleist@zedat.fu-berlin.de
Citation: Duwal S, Winkelmann S, Schütte C, von Kleist M (2015) Optimal Treatment Strategies in the Context of 'Treatment for Prevention' against HIV-1 in Resource-Poor Settings. PLoS Comput Biol 11(4): e1004200. doi:10.1371/journal.pcbi.1004200
Funding: MvK and SD receive funding through the BMBF e:Bio junior research group "Systems Pharmacology & Disease Control", grant number 031A307 and through the DFG-research center MATHEON, project A21 "Modeling, Simulation and Therapy Optimization for Infectious Diseases". MvK receives funding through the Einstein Center for Mathematics Berlin, project CH4: "Optimal control of chemical reaction systems and application to drug resistance mitigating therapy". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
About PLOS Computational Biology
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLOS Computational Biology are Open Access. All content is immediately available and subject only to the condition that the original authorship and source are properly attributed. Copyright is retained. For more information follow @PLOSCompBiol on Twitter or contact ploscompbiol@plos.org.
About PLOS
PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.plos.org.
Journal
PLoS Computational Biology