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Expert Review of Molecular Diagnostics Volume 6, 2006 - Issue 2
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Review

Bioinformatics approach to predicting HIV drug resistance

Frank Cordes Konrad-Zuse-Zentrum, Division Scientific Computing, Department Numerical Analysis & Modeling, Takustr. 7, D-14195 Berlin-Dahlem, Germany. [email protected]
,
Rolf Kaiser University of Cologne, Institute of Virology, Fürst-Pückler-Str. 56, D-50935 Cologne, Germany. [email protected]
&
Joachim Selbig University of Potsdam, Institute of Biochemistry & Biology, c/o Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, D-14476 Potsdam-Golm, Germany. [email protected]
Pages 207-215 | Published online: 09 Jan 2014

References

  • DeGruttola V, Dix L, D’Aquila R et al. The relation between baseline HIV drug resistance and response to antiretroviraltherapy: re-analysis of retrospective and prospective studies using a standardized data analysis plan. Antivir. Ther. 5, 41–48 (2000).
  • Clavel F, Hance AJ. HIV drug resistance. N. Engl. J. Med. 350, 1023–1035 (2004).
  • Beerenwinkel N, Sing T, Lengauer T et al. Computational methods for the design of effective therapies against drug resistant HIV strains. Bioinformatics 21, 3943–3950 (2005).
  • Jordan R, Gold L, Cummins C, Hyde C. Systematic review and meta-analysis of evidence for increasing numbers of drugs in antiretroviralcombination therapy. Br. Med. J. 324, 1–10 (2002).
  • Perrin L, Telenti A. HIV treatment failure: testing for HIV resistance in clinical practice. Science 280, 1871–1873 (1998).
  • Kellam P, Larder B. Recombinant virus assay: a rapid, phenotypic assay for assessment of drug susceptibility of human immunodeficiency virus type 1 isolates. Antimicrob. Agents Chemother. 38, 23–30 (1994).
  • Walter H, Schmidt B, Korn K, Vandamme AM, Harrer T, Überla K. Rapid, phenotypic HIV-1 drug sensitivity assay for protease and reverse transcriptase inhibitors. J. Clin. Virol. 13, 71–80 (1999).
  • Beerenwinkel N, Schmidt B, Walter H et al. Diversity and complexity of HIV-1 drug resistance: a bioinformatics approach to predicting phenotype from genotype. Proc. Natl Acad. Sci. USA 99, 8271–8276 (2002).
  • Schneider TD, Stephens RM. Sequence logos: a new way to display consensus sequences. Nucleic Acids Res. 18, 6097–6100 (1990).
  • Draghici S, Potter R. Predicting HIV drug resistance with neural networks. Bioinformatics 19, 98–107 (2003).
  • Wang D, Larder B. Enhanced prediction of lopinavir resistance from genotype by use of artificial neural networks. J. Infect. Dis. 188, 653–660 (2003).
  • Sevin AD, DeGruttola V, Nijhuis M 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. J. Infect. Dis. 182, 59–67 (2000).
  • Wang K, Jenwitheesuk E, Samudrala R, Mittler JE. Simple linear model provides highly accurate genotypic predictions of HIV-1 drug resistance. Antivir. Ther. 9, 343–352 (2004).
  • Foulkes AS, DeGruttola V. Characterizing the relationship between HIV-1 genotype and phenotype: prediction based classification. Biometrics 58, 145–156 (2002).
  • DiRienzo AG, DeGruttola V, Larder B, Hertogs K. Nonparametric methods to predict HIV drug susceptibility phenotype from genotype. Stat. Med. 22, 2785–2798 (2003).
  • Beerenwinkel N, Schmidt B, Walter Het al. Geno2pheno: interpreting genotypic HIV drug resistance tests. IEEE Intelligent Systems 16, 35–41 (2001).
  • Quinlan J. C4.5: Programs for Machine Learning. Morgan Kaufman Publishers, CA, USA (1993).
  • Johnson VA, Brun-Vézinet F, Clotet B et al. Update of the drug resistance mutations in HIV-1: 2004. Top. HIV Med. 12, 119–124 (2004).
  • Wolf K, Walter H, Beerenwinkel N et al. Tenofovir resistance and resensitization. Antimicrob. Agents Chemother. 47, 3478–3484 (2003).
  • Vapnik V. The Nature of Statistical Learning Theory. John Wiley & Sons, NY, USA (1996).
  • Jenwitheesuk E, Wang K, Mittler JE, Samudrala R. PIRSpred: a web server for reliable HIV-1 protein-inhibitor resistance/susceptibility prediction. Trends Microbiol. 13, 150–151 (2005).
  • Jenwitheesuk E, Samudrala R. Prediction of HIV-1 protease inhibitor resistance using a flexible docking approach. Antivir. Ther. 10, 157–166 (2005).
  • Durant J, Clevenbergh P, Halfon P et al. The Viradapt study. Lancet 353, 2195–21998 (1999).
  • Cohen CJ, Hunt S, Sension M et al. A randomized trial assessing the impact of phenotypic resistance testing on antiretroviral therapy. AIDS 16(4), 579–588 (2002).
  • Wegner SA, Wallace MR, Aronson NE et al. Long-term efficacy of routine access to antiretroviral-resistance testing in HIV type 1-infected patients: results of the clinical efficacy of resistance testing trial. Clin. Infect. Dis. 38(5), 723–730 (2004).
  • Oette M, Kaiser R, Däumer M et al. Primary HIV drug-resistance and efficacy of first-line antiretroviral therapy guided by resistance testing. AIDS (2006) (In Press).
  • Tural C, Ruiz L, Holtzer C et al. Clinical utility of HIV-1 genotyping and expert advice: the Havana trial. AIDS 16(2), 209–218 (2002).
  • Wainberg MA, Miller MD, Quan Y et al. In vitro selection and characterization of HIV-1 with reduced susceptibility to PMPA. Antivir. Ther. 4, 87–94 (1999).
  • Tisdale M, Alnadaf T, Cousens D. Combination of mutations in human immunodeficiency virus type 1 reverse transcriptase required for resistance to the carbocyclic nucleoside 1592U89. Antimicrob. Agents Chemother. 41, 1094–1098 (1997).
  • Larder BA, Kellam P, Kemp SD. Convergent combination therapy can select viable multidrug-resistant HIV-1 in vitro. Nature 365, 451–453 (1993).
  • Bazmi HZ, Hammond JL, Cavalcanti SC, Chu CK, Schinazi RF, Mellors JW. In vitro selection of mutations in the human immunodeficiency virus type 1 reverse transcriptase that decrease susceptibility to (–)-β-D-dioxolane-guanosine and suppress resistance to 3´-azido-3´-deoxythymidine. Antimicrob. Agents Chemother. 44, 1783–1788 (2000).
  • Underwood M, St Clair M, Ross L, Gerondelis P, Parkin N, Lanier R. Cross-resistance of clinical samples with K65R, L74V, and M184V Mutations. 12th CROI Boston, MA, USA, (2005) (Poster 714).
  • Mueller SM, Daeumer M, Kaiser R, Walter H, Colonno R, Korn K. Susceptibility to saquinavir and atazanavir in highly protease inhibitor (PI) resistant HIV-1 is caused by Lopinavir-induced drug resitant mutation L76V. Antivir. Ther. 9, S44 (2004).
  • Beerenwinkel N, Däumer M, Sing T et al. Estimating HIV evolutionary pathways and the genetic barrier to drug resistance. J. Infect. Dis. 191, 1953–1960 (2005).
  • Farthing C, Khanlou H, Yeh V. Early virologic failure in a pilot study evaluating the efficacy of abacavir, lamivudine and tenofovir in the treatment naive HIV-infected patients. Program and Abstracts of the 2nd IAS Conference on HIV Pathogenesis and Treatment. July 13–16, Paris, France, (2003) (Abstract 43).
  • Rosin CD, Belew RK, Morris GM, Olson AJ, Goodsel DS. Coevolutionary analysis of resistance-evading peptidomimetic inhibitors of HIV-1 protease. Proc. Natl Acad. Sci. USA 96, 1369–1374 (1999).
  • Green SM, Marshall GR. 3D QSAR : a current perpective. Trends Pharmacol. Sci. 16, 285–291 (1995).
  • Huang X, Xu L, Luo X et al. Elucidating the inhibiting mode of AHPBA derivatives against HIV-1 protease and building predictive 3D-QSAR models. J. Med.Chem. 45, 333–343 (2002).
  • Deuflhard P. From molecular dynamics to conformational dynamics in drug design. In: Trends in Nonlinear Analysis. Kirkilionis M, Krömker S, Rannacher R, Toni F (Eds), Springer-Verlag, Berlin, Germany, 269–288 (2003).
  • Sadowski J, Schwab CH, Gasteiger J. 3D structure generation and conformational searching. In: Computational Medicinal Chemistry and Drug Discovery. Bultinck P, De Winter H, Langenaeker W, Tollenaere JP (Eds), Dekker Inc., NY, USA, 151–212 (2003).
  • Halgren TA. Merck molecular force field. I-V: Basis, form, scope, parametrization, and performance of MMFF94. J. Comp. Chem. 17, 490–519 (1996).
  • Baum D. Multiple semi-flexible 3D superposition of drug-sized molecules. In: CompLife 2005. Berthold MR, Glen R, Diederichs K, Kohlbacher O, Fischer I (Eds). LNBI 3695, Springer, Berlin, Germany 198–207 (2005).
  • Müller K-R. Mika S, Rätsch G, Tsuda K, Schölkopf B. An introduction to kernel-based learning algorithms. IEEE Transactions Neural Networks 12, 181–202 (2001).
  • Perola E, Charifson PS. Conformational analysis of drug-like molecules bound to proteins: an extensive study of ligand organization upon binding. J. Med. Chem. 47, 2499–2510 (2004).

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