Repository: Freie Universität Berlin, Math Department

Drug-Class Specific Impact of Antivirals on the Reproductive Capacity of HIV

von Kleist, M. and Menz, S. and Huisinga, W. (2010) Drug-Class Specific Impact of Antivirals on the Reproductive Capacity of HIV. Plos Computational Biology, 6 (3). e1000720.

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Predictive markers linking drug efficacy to clinical outcome are a key component in the drug discovery and development process. In HIV infection, two different measures, viral load decay and phenotypic assays, are used to assess drug efficacy in vivo and in vitro. For the newly introduced class of integrase inhibitors, a huge discrepancy between these two measures of efficacy was observed. Hence, a thorough understanding of the relation between these two measures of drug efficacy is imperative for guiding future drug discovery and development activities in HIV. In this article, we developed a novel viral dynamics model, which allows for a mechanistic integration of the mode of action of all approved drugs and drugs in late clinical trials. Subsequently, we established a link between in vivo and in vitro measures of drug efficacy, and extract important determinants of drug efficacy in vivo. The analysis is based on a new quantity—the reproductive capacity—that represents in mathematical terms the in vivo analog of the read-out of a phenotypic assay. Our results suggest a drug-class specific impact of antivirals on the total amount of viral replication. Moreover, we showed that the (drug-)target half life, dominated by immune-system related clearance processes, is a key characteristic that affects both the emergence of resistance as well as the in vitro–in vivo correlation of efficacy measures in HIV treatment. We found that protease- and maturation inhibitors, due to their target half-life, decrease the total amount of viral replication and the emergence of resistance most efficiently.

Item Type:Article
Subjects:Subjects allied to Medicine > Pharmacology > Pharmacology
Mathematical and Computer Sciences > Mathematics > Mathematical Modelling
Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Biological Sciences > Microbiology > Virology
Biological Sciences > Biology > Population Biology
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics > BioComputing Group
ID Code:940
Deposited By: Dr Max von Kleist
Deposited On:08 Sep 2010 18:07
Last Modified:03 Mar 2017 14:40

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