title: Methods for optimizing antiviral combination therapies.
abstract: BACKGROUND
  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.
authors.affiliation: Max Planck Institute for Informatics, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany. beerenwinkel@mpi-sb.mpg.de
authors.forename: Niko
authors.initials: N
authors.lastname: Beerenwinkel
authors.forename: Thomas
authors.initials: T
authors.lastname: Lengauer
authors.forename: Martin
authors.initials: M
authors.lastname: Däumer
authors.forename: Rolf
authors.initials: R
authors.lastname: Kaiser
authors.forename: Hauke
authors.initials: H
authors.lastname: Walter
authors.forename: Klaus
authors.initials: K
authors.lastname: Korn
authors.forename: Daniel
authors.initials: D
authors.lastname: Hoffmann
authors.forename: Joachim
authors.initials: J
authors.lastname: Selbig
mesh_terms: D000465:Algorithms; D019380:Anti-HIV Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003205:Computing Methodologies; D004279:DNA, Viral / Q000032:analysis / Q000235:genetics; D004338:Drug Combinations; D024882:Drug Resistance, Viral / Q000235:genetics*; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D016679:Genome, Viral; D006678:HIV / Q000145:classification / Q000187:drug effects* / Q000235:genetics*; D015658:HIV Infections / Q000188:drug therapy / Q000235:genetics / Q000821:virology; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D010641:Phenotype; D011110:Polymorphism, Genetic; D011786:Quality Control; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: D019380:Anti-HIV Agents; D004279:DNA, Viral; D004338:Drug Combinations