Comparison between Rules‐Based Human Immunodeficiency Virus Type 1 Genotype Interpretations and Real or Virtual Phenotype: Concordance Analysis and Correlation with Clinical Outcome in Heavily Treated Patients

Torti, Carlo; Quiros-Roldan, Eugenia; Keulen, Wilco; Scudeller, Luigia; Caputo, Sergio Lo; Boucher, Charles A.; Castelli, Francesco; Mazzotta, Francesco; Pierotti, Piera; Been-Tiktak, Anne Mieke; Buccoliero, Giovanni Battista; Gennaro, Michele De; Carosi, Giampiero; Tinelli, Carmine

doi:10.1086/376512

Cited by 46 publications

(41 citation statements)

References 20 publications

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“…4 However, different systems provide different interpretations and it is difficult to relate the results for individual drugs to the likely responses to potential drug combinations. [5][6][7][8][9] Indeed, genotypic sensitivity score (derived by allocating scores for each drug in a regimen according to whether the genotype interpretation predicts the virus will be sensitive, intermediate or resistant to the drug) have been shown to be relatively weak predictors of virological response. 10,11 The development of computational models to obtain, directly from the genotype and other clinical information, a quantitative prediction of virological response to any combination of drugs, rather than to individual agents, may offer a potential clinical advantage.…”

Section: Introductionmentioning

confidence: 99%

The use of computational models to predict response to HIV therapy for clinical cases in Romania

Revell¹,

Ene²,

Duiculescu³

et al. 2012

GERMS

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Introduction A major challenge in Romania is the optimisation of antiretroviral therapy for the many HIV-infected adults with, on average, a decade of treatment experience. The RDI has developed computational models that predict virological response to therapy but these require a genotype, which is not routinely available in Romania. Moreover the models, which were trained without any Romanian data, have proved most accurate for patients from the healthcare settings that contributed the training data. Here we develop and test a novel model that does not require a genotype, with test data from Romania.Methods A random forest (RF) model was developed to predict the probability of the HIV viral load (VL) being reduced to <50 copies/ml following therapy change. The input variables were baseline VL, CD4 count, treatment history and time to follow-up. The model was developed with 3188 treatment changes episodes (TCEs) from North America, Western Europe and Australia. The model's predictions for 100 independent TCEs from the RDI database were compared to those of a model trained with the same data plus genotypes and then tested using 39 TCEs from Romania in terms of the area under the ROC curve (AUC).Results When tested with the 100 independent RDI TCEs, the AUC values for the models with and without genotypes were 0.88 and 0.86 respectively. For the 39 Romanian TCEs the AUC was 0.60. However, when 14 cases with viral loads that may have been between 50 and 400 copies were removed, the AUC increased to 0.83. Discussion Despite having been trained without data from Romania, the model predicted treatment responses in treatment-experienced Romanian patients with clade F virus accurately without the need for a genotype. The results suggest that this approach might be generalisable and useful in helping design optimal salvage regimens for treatment-experienced patients in countries with limited resources where genotyping is not always available.

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Section: Introductionmentioning

confidence: 99%

The use of computational models to predict response to HIV therapy for clinical cases in Romania

Revell¹,

Ene²,

Duiculescu³

et al. 2012

GERMS

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“…As new drugs are developed for the care of HIV-positive patients, novel mutations are recognized, which prompt the International AIDS Society of USA [Johnson et al, 2005], whose authority is recognized worldwide, to update regularly the spectrum of HIV-1 resistance-associated mutations. However, even though an exhaustive IAS mutation list is accessible to clinicians and many free-of-charge websites [Beerenwinkel et al, 2003;Kuiken et al, 2003;Rhee et al, 2003] furnish a computer-assisted interpretation of mutational profiles, some discrepancies continue to exist between these resistance profiles and response to therapy [Kijak et al, 2003;Ravela et al, 2003;Sturmer et al, 2003;Torti et al, 2003;De Luca et al, 2004]. Although good compliance with treatment regimens [Paterson et al, 2000;Cingolani et al, 2002], optimal antiviral potency [Daar, 2003;Gathe, 2003], and adequate drug concentrations [Yasuda et al, 2004] are major concerns for obtaining a sustained control of viral replication, the major obstacle to realizing a successful regimen capable of providing a sustained control of viral replication regards some unresolved questions related to HIV-1 resistance.…”

Section: Introductionmentioning

confidence: 99%

Impact of unreported HIV‐1 reverse transcriptase mutations on phenotypic resistance to nucleoside and non‐nucleoside inhibitors

Saracino

Monno

Scudeller

et al. 2005

Journal of Medical Virology

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An extended spectrum of HIV-1 reverse-transcriptase (RT) mutations in HAART-treated patients has been recently described. To verify the possible association of previously unreported RT mutations with a decrease of phenotypic susceptibility to nucleoside (NRTIs) and non-nucleoside (NNRTIs) RT inhibitors, the RT sequence of 328 HIV-1-positive patients (102 naïve and 226 treated with HAART participating in either the PhenGen or Genpherex study) was analyzed. All treated patients were tested at the time of therapeutic failure with both phenotypic (Antivirogram 1 , Virco) and genotypic analyses (VircoGen TM ); the frequency of RT substitutions (positions 1-240) with respect to consensus B was compared to that of naïve patients using a Chi-square test. Amino acid changes at 13 positions not included in the IAS list of resistance-associated mutations were detected more frequently in treated than in naïve subjects. The mutations involving 10 of these positions were associated with a reduced susceptibility to antiretroviral drugs; K20R, T39A, K43EQN, E203KD, H208Y, and D218E were correlated with NRTI resistance while mutations K101EQP, H221Y, K223EQ, L228HR were associated to NNRTI resistance. A correlation was found between K20R and lamivudine resistance (P ¼ 0.006) while T39A (P ¼ 0.005), K43EQN (<0.001), E203KD (P ¼ 0.010), and H208Y (P ¼ < 0.001) seemed to be associated with a previous use of zidovudine and stavudine and with the development of thymidine analog resistance. For H208Y, an association with use/resistance to abacavir (P ¼ 0.004) was also noted. D218E showed a weak association to didanosine resistance (P ¼ 0.013). The data confirm that previously unreported mutations are associated with antiretroviral drug experience and, more importantly, with a reduced susceptibility to NRTIs and NNRTIs.

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“…Studies were performed mainly on subtype B viruses, and even within this subtype, differences have been detected (6,21,29,34,35,36).…”

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confidence: 99%

“…Comparisons between these interpretation systems have already been made for subtype B strains; however, the subtype dependency of resistance assessment by these interpretations systems has not yet been determined (6,21,29,34,35,36). In this study, we investigated four frequently used interpretation systems across a large number of non-B sequences to determine whether discordance between the systems was dependent on the viral subtype.…”

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confidence: 99%

Discordances between Interpretation Algorithms for Genotypic Resistance to Protease and Reverse Transcriptase Inhibitors of Human Immunodeficiency Virus Are Subtype Dependent

Snoeck

Kantor

Shafer

et al. 2006

Antimicrob Agents Chemother

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The major limitation of drug resistance genotyping for human immunodeficiency virus remains the interpretation of the results. We evaluated the concordance in predicting therapy response between four different interpretation algorithms (Rega 6.3, HIVDB-08/04, ANRS [07/04], and VGI 8.0). Sequences were gathered through a worldwide effort to establish a database of non-B subtype sequences, and demographic and clinical information about the patients was gathered. The most concordant results were found for nonnucleoside reverse transcriptase (RT) inhibitors (93%), followed by protease inhibitors (84%) and nucleoside RT inhibitor (NRTIs) (76%). For therapy-naive patients, for nelfinavir, especially for subtypes C and G, the discordances were driven mainly by the protease (PRO) mutational pattern 82I/V ؉ 63P ؉ 36I/V for subtype C and 82I ؉ 63P ؉ 36I ؉ 20I for subtype G. Subtype F displayed more discordances for ritonavir in untreated patients due to the combined presence of PRO 20R and 10I/V. In therapy-experienced patients, subtype G displayed a lot of discordances for saquinavir and indinavir due to mutational patterns involving PRO 90 M and 82I. Subtype F had more discordance for nelfinavir attributable to the presence of PRO 88S and 82A ؉ 54V. For the NRTIs lamivudine and emtricitabine, CRF01_AE had more discordances than subtype B due to the presence of RT mutational patterns 65R ؉ 115 M and 118I ؉ 215Y, respectively. Overall, the different algorithms agreed well on the level of resistance scored, but some of the discordances could be attributed to specific (subtypedependent) combinations of mutations. It is not yet known whether therapy response is subtype dependent, but the advice given to clinicians based on a genotypic interpretation algorithm differs according to the subtype.Genotyping for the assessment of anti-human immunodeficiency virus (HIV) drug resistance is often used in the management of individual patient therapy. Currently, it is recommended in European as well as American guidelines (17,38). In several retrospective and prospective studies, genotyping proved beneficial in optimizing treatment for individual patients (5,10,16,23,25,31,37).Although genotyping is commonly used, there are still many uncertainties with respect to the value of genotype in the assignment of a new regimen. The current genotypic assays are not always able to report all drug resistance mutations among non-B subtypes (11,18,19,24). Regardless of subtype, genotyping is not sensitive to mutations that are present as a minor variant in the population (22,40). Genotyping results also differ depending on the laboratory where they are performed. Quality control studies indicate that mutations, even present as a pure variant, are often underestimated (32).However, separate from the quality and sensitivity issues, the interpretation of genotypic results is still not standardized.

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Comparison between Rules‐Based Human Immunodeficiency Virus Type 1 Genotype Interpretations and Real or Virtual Phenotype: Concordance Analysis and Correlation with Clinical Outcome in Heavily Treated Patients

Cited by 46 publications

References 20 publications

The use of computational models to predict response to HIV therapy for clinical cases in Romania

The use of computational models to predict response to HIV therapy for clinical cases in Romania

Impact of unreported HIV‐1 reverse transcriptase mutations on phenotypic resistance to nucleoside and non‐nucleoside inhibitors

Discordances between Interpretation Algorithms for Genotypic Resistance to Protease and Reverse Transcriptase Inhibitors of Human Immunodeficiency Virus Are Subtype Dependent

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