title: The development of artificial neural networks to predict virological response to combination HIV therapy.
abstract: BACKGROUND
  When used in combination, antiretroviral drugs are highly effective for suppressing HIV replication. Nevertheless, treatment failure commonly occurs and is generally associated with viral drug resistance. The choice of an alternative regimen may be guided by a drug-resistance test. However, interpretation of resistance from genotypic data poses a major challenge.
  
  
  METHODS
  As an alternative to current interpretation systems, we have developed artificial neural network (ANN) models to predict virological response to combination therapy from HIV genotype and other clinical information.
  
  
  RESULTS
  ANN models trained with genotype, baseline viral load and time to follow-up viral load (1154 treatment change episodes from multiple clinics), produced predictions of virological response that were highly significantly correlated with actual responses (r2 = 0.53; P < 0.00001) using independent test data from clinics that contributed training data. Augmented models, trained with the additional variables of baseline CD4+ T-cell count and four treatment history variables, were more accurate, explaining 69% of the variance in virological response. Models trained with the full input dataset, but only those data involving highly active antiretroviral therapy (three or more full-dose antiretroviral drugs in combination), performed at an intermediate level, explaining 61% of the variance. The augmented models performed less well when tested with data from unfamiliar clinics that had not contributed data to the training dataset, explaining 46% of the variance in response.
  
  
  CONCLUSIONS
  These data indicate that ANN models can be quite accurate predictors of virological response to HIV therapy even for patients from unfamiliar clinics. ANN models therefore warrant further development as a potential tool to aid treatment selection.
authors.affiliation: The HIV Resistance Response Database Initiative, London, UK. brendanlarder@hivrdi.org
authors.forename: Brendan
authors.initials: B
authors.lastname: Larder
authors.forename: Dechao
authors.initials: D
authors.lastname: Wang
authors.forename: Andrew
authors.initials: A
authors.lastname: Revell
authors.forename: Julio
authors.initials: J
authors.lastname: Montaner
authors.forename: Richard
authors.initials: R
authors.lastname: Harrigan
authors.forename: Frank
authors.initials: F
authors.lastname: De Wolf
authors.forename: Joep
authors.initials: J
authors.lastname: Lange
authors.forename: Scott
authors.initials: S
authors.lastname: Wegner
authors.forename: Lidia
authors.initials: L
authors.lastname: Ruiz
authors.forename: Maria Jésus
authors.initials: MJ
authors.lastname: Pérez-Elías
authors.forename: Sean
authors.initials: S
authors.lastname: Emery
authors.forename: Jose
authors.initials: J
authors.lastname: Gatell
authors.forename: Antonella D'Arminio
authors.initials: AD
authors.lastname: Monforte
authors.forename: Carlo
authors.initials: C
authors.lastname: Torti
authors.forename: Maurizio
authors.initials: M
authors.lastname: Zazzi
authors.forename: Clifford
authors.initials: C
authors.lastname: Lane
mesh_terms: D019380:Anti-HIV Agents / Q000627:therapeutic use*; D023241:Antiretroviral Therapy, Highly Active; D001315:Australia; D018791:CD4 Lymphocyte Count; D003198:Computer Simulation; D024882:Drug Resistance, Viral / Q000235:genetics*; D005060:Europe; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy* / Q000235:genetics / Q000276:immunology / Q000821:virology; D006801:Humans; D008499:Medical Records; D016571:Neural Networks, Computer; D018579:Patient Selection; D011237:Predictive Value of Tests; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States; D019562:Viral Load
chemical_list: D019380:Anti-HIV Agents