Simple algorithm derived from a geno-/phenotypic database to predict HIV-1 protease inhibitor resistance
Resistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult, particularly for PI, because of the unknown contributions of several mutations to resistance and cross-resistance. Development of an algorit...
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| Published in: | AIDS (London) Vol. 14; no. 12; p. 1731 |
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| Main Authors: | , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
England
18.08.2000
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| ISSN: | 0269-9370 |
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| Abstract | Resistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult, particularly for PI, because of the unknown contributions of several mutations to resistance and cross-resistance.
Development of an algorithm to predict PI phenotype from genotypic data.
Recombinant viruses containing patient-derived protease genes were analysed for sensitivity to indinavir, saquinavir, ritonavir and nelfinavir. Drug resistance-associated mutations were determined by direct sequencing. geno- and phenotypic data were compared for 119 samples from 97 HIV-1 infected patients.
Samples with one or two mutations in the gene for the protease were phenotypically sensitive in 74.3%, whereas 83.6% of samples with five or more mutations were resistant against all PI tested. Some mutations (361, 63P, 71V/T, 771) were frequent both in sensitive and resistant samples, whereas others (241, 30N, 461/L, 48V, 54V, 82A/F/T/S, 84V, 90M) were predominantly present in resistant samples. Therefore, the presence or absence of a single drug resistance-associated mutation predicted phenotypic PI resistance with high sensitivity (96.5-100%) but low specificity (13.3-57.4%). A more specific algorithm was obtained by taking into account the total number of drug resistance-associated mutations in the gene for the protease and restricting these to certain key positions for the PI. The algorithm was subsequently validated by analysis of 72 independent samples.
With an optimized algorithm, phenotypic PI resistance can be predicted by viral genotype with good sensitivity (89.1-93.0%) and specificity (82.6-93.3%). The reliability and relevance of this algorithm should be further evaluated in clinical practice. |
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| AbstractList | Resistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult, particularly for PI, because of the unknown contributions of several mutations to resistance and cross-resistance.BACKGROUNDResistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult, particularly for PI, because of the unknown contributions of several mutations to resistance and cross-resistance.Development of an algorithm to predict PI phenotype from genotypic data.OBJECTIVEDevelopment of an algorithm to predict PI phenotype from genotypic data.Recombinant viruses containing patient-derived protease genes were analysed for sensitivity to indinavir, saquinavir, ritonavir and nelfinavir. Drug resistance-associated mutations were determined by direct sequencing. geno- and phenotypic data were compared for 119 samples from 97 HIV-1 infected patients.METHODSRecombinant viruses containing patient-derived protease genes were analysed for sensitivity to indinavir, saquinavir, ritonavir and nelfinavir. Drug resistance-associated mutations were determined by direct sequencing. geno- and phenotypic data were compared for 119 samples from 97 HIV-1 infected patients.Samples with one or two mutations in the gene for the protease were phenotypically sensitive in 74.3%, whereas 83.6% of samples with five or more mutations were resistant against all PI tested. Some mutations (361, 63P, 71V/T, 771) were frequent both in sensitive and resistant samples, whereas others (241, 30N, 461/L, 48V, 54V, 82A/F/T/S, 84V, 90M) were predominantly present in resistant samples. Therefore, the presence or absence of a single drug resistance-associated mutation predicted phenotypic PI resistance with high sensitivity (96.5-100%) but low specificity (13.3-57.4%). A more specific algorithm was obtained by taking into account the total number of drug resistance-associated mutations in the gene for the protease and restricting these to certain key positions for the PI. The algorithm was subsequently validated by analysis of 72 independent samples.RESULTSSamples with one or two mutations in the gene for the protease were phenotypically sensitive in 74.3%, whereas 83.6% of samples with five or more mutations were resistant against all PI tested. Some mutations (361, 63P, 71V/T, 771) were frequent both in sensitive and resistant samples, whereas others (241, 30N, 461/L, 48V, 54V, 82A/F/T/S, 84V, 90M) were predominantly present in resistant samples. Therefore, the presence or absence of a single drug resistance-associated mutation predicted phenotypic PI resistance with high sensitivity (96.5-100%) but low specificity (13.3-57.4%). A more specific algorithm was obtained by taking into account the total number of drug resistance-associated mutations in the gene for the protease and restricting these to certain key positions for the PI. The algorithm was subsequently validated by analysis of 72 independent samples.With an optimized algorithm, phenotypic PI resistance can be predicted by viral genotype with good sensitivity (89.1-93.0%) and specificity (82.6-93.3%). The reliability and relevance of this algorithm should be further evaluated in clinical practice.CONCLUSIONWith an optimized algorithm, phenotypic PI resistance can be predicted by viral genotype with good sensitivity (89.1-93.0%) and specificity (82.6-93.3%). The reliability and relevance of this algorithm should be further evaluated in clinical practice. Resistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult, particularly for PI, because of the unknown contributions of several mutations to resistance and cross-resistance. Development of an algorithm to predict PI phenotype from genotypic data. Recombinant viruses containing patient-derived protease genes were analysed for sensitivity to indinavir, saquinavir, ritonavir and nelfinavir. Drug resistance-associated mutations were determined by direct sequencing. geno- and phenotypic data were compared for 119 samples from 97 HIV-1 infected patients. Samples with one or two mutations in the gene for the protease were phenotypically sensitive in 74.3%, whereas 83.6% of samples with five or more mutations were resistant against all PI tested. Some mutations (361, 63P, 71V/T, 771) were frequent both in sensitive and resistant samples, whereas others (241, 30N, 461/L, 48V, 54V, 82A/F/T/S, 84V, 90M) were predominantly present in resistant samples. Therefore, the presence or absence of a single drug resistance-associated mutation predicted phenotypic PI resistance with high sensitivity (96.5-100%) but low specificity (13.3-57.4%). A more specific algorithm was obtained by taking into account the total number of drug resistance-associated mutations in the gene for the protease and restricting these to certain key positions for the PI. The algorithm was subsequently validated by analysis of 72 independent samples. With an optimized algorithm, phenotypic PI resistance can be predicted by viral genotype with good sensitivity (89.1-93.0%) and specificity (82.6-93.3%). The reliability and relevance of this algorithm should be further evaluated in clinical practice. |
| Author | Uberla, K Schmidt, B Korn, K Moschik, B Paatz, C Vandamme, A M Schmitt, M Harrer, T van Vaerenbergh, K Walter, H |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/10985309$$D View this record in MEDLINE/PubMed |
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| Snippet | Resistance against protease inhibitors (PI) can either be analysed genotypically or phenotypically. However, the interpretation of genotypic data is difficult,... |
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| SubjectTerms | Acquired Immunodeficiency Syndrome - drug therapy Acquired Immunodeficiency Syndrome - virology Algorithms Databases, Factual Drug Resistance, Microbial - genetics Genotype HIV Protease Inhibitors - pharmacology HIV Protease Inhibitors - therapeutic use HIV-1 - drug effects HIV-1 - genetics Humans Molecular Sequence Data Phenotype Point Mutation Sensitivity and Specificity |
| Title | Simple algorithm derived from a geno-/phenotypic database to predict HIV-1 protease inhibitor resistance |
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