Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance.
BACKGROUND
Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been developed to assist the HIV specialist in the choice of the best patient-tailored therapy.


METHODS
Experts' rules and statistical/machine learning algorithms for interpreting HIV drug resistance, along with their program implementations, were retrieved from PubMed and other on-line resources to be critically reviewed in terms of technical approach, performance, usability, update, and evolution (i.e. inclusion of novel drugs or expansion to other viral agents).


RESULTS
Several drug resistance prediction algorithms for the nucleotide/nucleoside/non-nucleoside reverse transcriptase, protease and integrase inhibitors as well as coreceptor antagonists are currently available, routinely used, and have been validated thoroughly in independent studies. Computer tools that combine single-drug genotypic/phenotypic resistance interpretation and optimize combination antiretroviral therapy have been also developed and implemented as web applications. Most of the systems have been updated timely to incorporate new drugs and few have recently been expanded to meet a similar need in the Hepatitis C area. Prototype systems aiming at predicting virological response from both virus and patient indicators have been recently developed but they are not yet being routinely used.


CONCLUSIONS
Computing HIV genotype to predict drug susceptibility in vitro or response to combination antiretroviral therapy in vivo is a continuous and productive research field, translating into successful treatment decision support tools, an essential component of the management of HIV patients.
Maurizio
M
Zazzi
Alessandro
A
Cozzi-Lepri
Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, US. m.prosperi@ufl.edu.
Mattia C F
MC
Prosperi
D000465:Algorithms; D019380:Anti-HIV Agents / Q000494:pharmacology / Q000627:therapeutic use*; D044966:Anti-Retroviral Agents / Q000494:pharmacology / Q000627:therapeutic use*; D024882:Drug Resistance, Viral; D005103:Expert Systems; D005838:Genotype; D015658:HIV Infections / Q000188:drug therapy*; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics; D006801:Humans
D019380:Anti-HIV Agents; D044966:Anti-Retroviral Agents