HIV drug resistance

Drug resistance is a major factor in therapy failure of HIV infected patients. The development of resistant mutants is an evolutionary process that is characterized by the accumulation of mutations. The presence of these genetic alterations in the virus population limits therapeutic options and reduces the chance of successful treatment. Moreover, HIV displays a very high genetic variability which complicates the interpretation of genetic changes with respect to the resistance phenotype.

By integrating genomic and clinical data, we develop statistical models and efficient algorithms for analyzing the HIV genome and for selecting optimal drug combinations. Our approach emphasizes the evolutionary dynamics of HIV as a driving force of therapy failure. We have shown that the individual evolutionary potential of the HIV population within each single patient is predictive of therapy outcome. Thus, our computational methods support diagnostics and personalized treatment of HIV infections.

We have developed the web server geno2pheno, which is used by virologists and clinicians for predicting HIV drug resistance and coreceptor usage. The evolutionary modeling of HIV drug resistance has been implemented in the programs Mtreemix and MtreeHMM. We are currently investigating the use of ultra-deep sequencing in the management of HIV infection.

Selected references

• Beerenwinkel N, Drton M (2007). A mutagenetic tree hidden Markov model for longitudinal clonal HIV sequence data. Biostatistics 8:53-71.
• Altmann A, Beerenwinkel N, Sing T et al. (2007). Improved prediction of response to antiretroviral combination therapy using the genetic barrier to drug resistance. Antiviral Therapy 12:169-178.
• Beerenwinkel N, Sing T, Lengauer T et al. (2005). Computational methods for the design of effective therapies against drug resistant HIV strains. Bioinformatics 21:3943-3950.
• Beerenwinkel N, Rahnenführer J, Däumer M et al. (2005). Learning multiple evolutionary pathways from cross-sectional data. Journal of Computational Biolog 12:584-598.