Methods for optimizing antiviral combination therapies

MOTIVATION Despite some progress with antiretroviral combination therapies, therapeutic success in the management of HIV-infected patients is limited. The evolution of drug-resistant genetic variants in response to therapy plays a key role in treatment failure and finding a new potent drug combination after therapy failure is considered challenging. RESULTS To estimate the activity of a drug combination against a particular viral strain, we develop a scoring function whose independent variables describe a set of antiviral agents and viral DNA sequences coding for the molecular targets of the respective drugs. The construction of this activity score involves (1) predicting phenotypic drug resistance from genotypes for each drug individually, (2) probabilistic modeling of predicted resistance values and integration into a score for drug combinations, and (3) searching through the mutational neighborhood of the considered strain in order to estimate activity on nearby mutants. For a clinical data set, we determine the optimal search depth and show that the scoring scheme is predictive of therapeutic outcome. Properties of the activity score and applications are discussed.

[1]  G. Bocharov,et al.  Recombination: Multiply infected spleen cells in HIV patients , 2002, Nature.

[2]  Chris Hyde,et al.  Systematic review and meta-analysis of evidence for increasing numbers of drugs in antiretroviral combination therapy , 2002, BMJ : British Medical Journal.

[3]  A. Telenti,et al.  HIV treatment failure: testing for HIV resistance in clinical practice. , 1998, Science.

[4]  Steven Skiena,et al.  Deconvolving Sequence Variation in Mixed DNA Populations , 2003, J. Comput. Biol..

[5]  Richard H. Lathrop,et al.  Knowledge-Based Avoidance of Drug-Resistant HIV Mutants , 1998, AI Mag..

[6]  B. Schmidt,et al.  Rapid, phenotypic HIV-1 drug sensitivity assay for protease and reverse transcriptase inhibitors. , 1999, Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology.

[7]  E. De Clercq,et al.  Managing resistance to anti-HIV drugs: an important consideration for effective disease management. , 1999, Drugs.

[8]  Richard H. Lathrop,et al.  Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses , 1999, J. Comb. Optim..

[9]  J. Holland,et al.  RNA virus populations as quasispecies. , 1992, Current topics in microbiology and immunology.

[10]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[11]  Thomas Lengauer,et al.  Quantitative phenotype prediction by support vector machines , 2002 .

[12]  Thomas Lengauer,et al.  Geno2pheno: Interpreting Genotypic HIV Drug Resistance Tests , 2001, IEEE Intell. Syst..

[13]  A. Jetzt,et al.  High Rate of Recombination throughout the Human Immunodeficiency Virus Type 1 Genome , 2000, Journal of Virology.

[14]  Rami Kantor,et al.  The Genetic Basis of HIV-1 Resistance to Reverse Transcriptase and Protease Inhibitors. , 2000, AIDS reviews.

[15]  R. Laufs,et al.  Primary genotypic resistance of HIV-1 to the fusion inhibitor T-20 in long-term infected patients. , 2001, AIDS.

[16]  S. Hammer,et al.  The Relation between Baseline HIV Drug Resistance and Response to Antiretroviral Therapy: Re-Analysis of Retrospective and Prospective Studies Using a Standardized Data Analysis Plan , 2000, Antiviral therapy.

[17]  Thomas Lengauer,et al.  Diversity and complexity of HIV-1 drug resistance: A bioinformatics approach to predicting phenotype from genotype , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Thomas Lengauer,et al.  Geno2pheno is predictive of short-term virological response , 2002 .

[19]  J. Schapiro,et al.  Methods for investigation of the relationship between drug-susceptibility phenotype and human immunodeficiency virus type 1 genotype with applications to AIDS clinical trials group 333. , 2000, The Journal of infectious diseases.