Development of a new ultra sensitive real-time PCR assay (ultra sensitive RTQ-PCR) for the quantification of HBV-DNA

<p>Abstract</p> <p>Background</p> <p>Improved sensitivity of HBV-DNA tests is of critical importance for the management of HBV infection. Our aim was to develop and assess a new ultra sensitive in-house real-time PCR assay for HBV-DNA quantification (ultra sensitive RTQ-PCR).</p> <p>Results</p> <p>Previously used HBV-DNA standards were calibrated against the WHO 1^stInternational Standard for HBV-DNA (OptiQuant^®HBV-DNA Quantification Panel, Accrometrix Europe B.V.). The 95% and 50% HBV-DNA detection end-point of the assay were 22.2 and 8.4 IU/mL. According to the calibration results, 1 IU/mL equals 2.8 copies/mL. Importantly the clinical performance of the ultra sensitive real-time PCR was tested similar (67%) to the Procleix Ultrio discriminatory HBV test (dHBV) (70%) in low-titer samples from patients with occult Hepatitis B. Finally, in the comparison of ultra sensitive RTQ-PCR with the commercially available COBAS TaqMan HBV Test, the in-house assay identified 94.7% of the 94 specimens as positive versus 90.4% identified by TaqMan, while the quantitative results that were positive by both assay were strongly correlated (<it>r </it>= 0.979).</p> <p>Conclusions</p> <p>We report a new ultra sensitive real time PCR molecular beacon based assay with remarkable analytical and clinical sensitivity, calibrated against the WHO 1^stInternational standard.</p

Comparative evaluation of the performance of the Abbott RealTime HIV-1 assay for measurement of HIV-1 plasma viral load on genetically diverse samples from Greece

Abstract Background HIV-1 is characterized by increased genetic heterogeneity which tends to hinder the reliability of detection and accuracy of HIV-1 RNA quantitation assays. Methods In this study, the Abbott RealTime HIV-1 (Abbott RealTime) assay was compared to the Roche Cobas TaqMan HIV-1 (Cobas TaqMan) and the Siemens Versant HIV-1 RNA 3.0 (bDNA 3.0) assays, using clinical samples of various viral load levels and subtypes from Greece, where the recent epidemiology of HIV-1 infection has been characterized by increasing genetic diversity and a marked increase in subtype A genetic strains among newly diagnosed infections. Results A high correlation was observed between the quantitative results obtained by the Abbott RealTime and the Cobas TaqMan assays. Viral load values quantified by the Abbott RealTime were on average lower than those obtained by the Cobas TaqMan, with a mean (SD) difference of -0.206 (0.298) log10 copies/ml. The mean differences according to HIV-1 subtypes between the two techniques for samples of subtype A, B, and non-A/non-B were 0.089, -0.262, and -0.298 log10 copies/ml, respectively. Overall, differences were less than 0.5 log10 for 85% of the samples, and >1 log10 in only one subtype B sample. Similarly, Abbott RealTime and bDNA 3.0 assays yielded a very good correlation of quantitative results, whereas viral load values assessed by the Abbott RealTime were on average higher (mean (SD) difference: 0.160 (0.287) log10 copies/ml). The mean differences according to HIV-1 subtypes between the two techniques for subtype A, B and non-A/non-B samples were 0.438, 0.105 and 0.191 log10 copies/ml, respectively. Overall, the majority of samples (86%) differed by less than 0.5 log10, while none of the samples showed a deviation of more than 1.0 log10. Conclusions In an area of changing HIV-1 subtype pattern, the Abbott RealTime assay showed a high correlation and good agreement of results when compared both to the Cobas TaqMan and bDNA 3.0 assays, for all HIV-1 subtypes tested. All three assays could determine viral load from samples of different HIV-1 subtypes adequately. However, assay variation should be taken into account when viral load monitoring of the same individual is assessed by different systems.</p

Comparative evaluation of the performance of the Abbott RealTime HIV-1 assay for measurement of HIV-1 plasma viral load on genetically diverse samples from Greece

Background: HIV-1 is characterized by increased genetic heterogeneity which tends to hinder the reliability of detection and accuracy of HIV-1 RNA quantitation assays. Methods: In this study, the Abbott RealTime HIV-1 (Abbott RealTime) assay was compared to the Roche Cobas TaqMan HIV-1 (Cobas TaqMan) and the Siemens Versant HIV-1 RNA 3.0 (bDNA 3.0) assays, using clinical samples of various viral load levels and subtypes from Greece, where the recent epidemiology of HIV-1 infection has been characterized by increasing genetic diversity and a marked increase in subtype A genetic strains among newly diagnosed infections. Results: A high correlation was observed between the quantitative results obtained by the Abbott RealTime and the Cobas TaqMan assays. Viral load values quantified by the Abbott RealTime were on average lower than those obtained by the Cobas TaqMan, with a mean (SD) difference of -0.206 (0.298) log(10) copies/ml. The mean differences according to HIV-1 subtypes between the two techniques for samples of subtype A, B, and non-A/non-B were 0.089, -0.262, and -0.298 log(10) copies/ml, respectively. Overall, differences were less than 0.5 log(10) for 85% of the samples, and &gt;1 log(10) in only one subtype B sample. Similarly, Abbott RealTime and bDNA 3.0 assays yielded a very good correlation of quantitative results, whereas viral load values assessed by the Abbott RealTime were on average higher (mean (SD) difference: 0.160 (0.287) log(10) copies/ml). The mean differences according to HIV-1 subtypes between the two techniques for subtype A, B and non-A/non-B samples were 0.438, 0.105 and 0.191 log(10) copies/ml, respectively. Overall, the majority of samples (86%) differed by less than 0.5 log(10), while none of the samples showed a deviation of more than 1.0 log(10). Conclusions: In an area of changing HIV-1 subtype pattern, the Abbott RealTime assay showed a high correlation and good agreement of results when compared both to the Cobas TaqMan and bDNA 3.0 assays, for all HIV-1 subtypes tested. All three assays could determine viral load from samples of different HIV-1 subtypes adequately. However, assay variation should be taken into account when viral load monitoring of the same individual is assessed by different systems

Scatter plot of the proportion of IDUs against the phylodynamic transmission potential ( = <i>N</i>/<i>NeT</i>) for each subtype.

<p>Scatter plot of the proportion of IDUs against the phylodynamic transmission potential ( = <i>N</i>/<i>NeT</i>) for each subtype.</p

Contour plots showing how generation time (<i>T</i>), basic reproductive number (<i>R</i>₀) and the proportion of transmitters in the population (<i>u</i>) co-vary.

<p>Gray bands highlight different values of <i>u</i>. The area between the white dashed lines represents <i>R</i>₀ values estimated by sensitivity analysis of mortality and recovery rate (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002876#pcbi.1002876.s007" target="_blank">Table S3</a>). The area between the yellow dashed lines represents the 95% confidence limits of <i>R</i>₀ values estimated assuming 40 years of infectivity and 70 years of life expectancy. The black dots show the maximum <i>T</i> value for each subtype, which is defined by empirical values for <i>u</i> and the median values of <i>R</i>₀ (see text).</p

Abbreviations and terms used throughout the manuscript.

<p>Abbreviations and terms used throughout the manuscript.</p

Estimates of transmission parameters for each HCV subtype.

1<p>The phylodynamic transmission parameter PTP = <i>N</i>/(<i>NeT</i>) has been estimated as the coefficient of the linear regression of <i>N</i> versus <i>NeT</i> without constant term. For the confidence intervals the autocorrelation structure of each variable has been taken into account according to the Newey-West correction.</p>2<p>Generation time estimated as Var(<i>Z</i>)/PTP (maximum estimate assuming that the minimum proportion of transmitters equals the proportion of IDUs in each subtype).</p>3<p>Proportion of transmitters, practically equal to the proportion of IDUs within each subtype.</p>4<p>Upper 1% of the distribution of secondary infections including transmitters and non-transmitters.</p