TY  - JOUR
A1  - Beerenwinkel, Niko
A1  - Sing, Tobias
A1  - Lengauer, Thomas
A1  - Rahnenfuhrer, Joerg
A1  - Roomp, Kirsten
A1  - Savenkov, Igor
A1  - Fischer, Roman
A1  - Hoffmann, Daniel
A1  - Selbig, Joachim
A1  - Korn, Klaus
A1  - Walter, Hauke
A1  - Berg, Thomas
A1  - Braun, Patrick
A1  - Faetkenheuer, Gerd
A1  - Oette, Mark
A1  - Rockstroh, Juergen
A1  - Kupfer, Bernd
A1  - Kaiser, Rolf
A1  - Daeumer, Martin
T1  - Computational methods for the design of effective therapies against drug resistant HIV strains
N2  - The development of drug resistance is a major obstacle to successful treatment of HIV infection. The extraordinary replication dynamics of HIV facilitates its escape from selective pressure exerted by the human immune system and by combination drug therapy. We have developed several computational methods whose combined use can support the design of optimal antiretroviral therapies based on viral genomic data
Y1  - 2005
ER  - 
TY  - JOUR
A1  - Gopalakrishnan, Sathej
A1  - Montazeri, Hesam
A1  - Menz, Stephan
A1  - Beerenwinkel, Niko
A1  - Huisinga, Wilhelm
T1  - Estimating HIV-1 fitness characteristics from cross-sectional genotype data
JF  - PLoS Computational Biology : a new community journal
N2  - Despite the success of highly active antiretroviral therapy (HAART) in the management of human immunodeficiency virus (HIV)-1 infection, virological failure due to drug resistance development remains a major challenge. Resistant mutants display reduced drug susceptibilities, but in the absence of drug, they generally have a lower fitness than the wild type, owing to a mutation-incurred cost. The interaction between these fitness costs and drug resistance dictates the appearance of mutants and influences viral suppression and therapeutic success. Assessing in vivo viral fitness is a challenging task and yet one that has significant clinical relevance. Here, we present a new computational modelling approach for estimating viral fitness that relies on common sparse cross-sectional clinical data by combining statistical approaches to learn drug-specific mutational pathways and resistance factors with viral dynamics models to represent the host-virus interaction and actions of drug mechanistically. We estimate in vivo fitness characteristics of mutant genotypes for two antiretroviral drugs, the reverse transcriptase inhibitor zidovudine (ZDV) and the protease inhibitor indinavir (IDV). Well-known features of HIV-1 fitness landscapes are recovered, both in the absence and presence of drugs. We quantify the complex interplay between fitness costs and resistance by computing selective advantages for different mutants. Our approach extends naturally to multiple drugs and we illustrate this by simulating a dual therapy with ZDV and IDV to assess therapy failure. The combined statistical and dynamical modelling approach may help in dissecting the effects of fitness costs and resistance with the ultimate aim of assisting the choice of salvage therapies after treatment failure.
Y1  - 2014
U6  - https://doi.org/10.1371/journal.pcbi.1003886
SN  - 1553-734X
SN  - 1553-7358
VL  - 10
IS  - 11
PB  - PLoS
CY  - San Fransisco
ER  -