A cheap and open HIV viral load technique applicable in routine analysis in a resource limited setting with a wide HIV genetic diversity

Abstract

Background: HIV infection in Cameroon is characterized by a great viral diversity with all HIV-1 groups (M, N, O, and P) and HIV-2 in circulation. HIV group determination is very important if tailored viral load analysis and treatments are to be applied. In our laboratory, HIV viral load is carried out using two platforms; Biocentric and Abbott depending on the HIV group identified. Biocentric which quantifies HIV-1 group M is a cheap and open system useful in resource limited settings. The objective of this study was to compare the viral load analyses of serologically group-indeterminate HIV samples using the two platforms with the view of reducing cost. Methods: Consecutive samples received between March and May 2014, and between August and September 2014 in our laboratory for HIV viral load analysis were included. All these samples were analyzed for their HIV groups using an in-house ELISA serotyping test. All HIV-1 group M samples were quantified using the Biocentric test while all other known atypical samples (HIV-1 groups N, O and P) were analyzed using the Abbott technique. HIV group-indeterminate samples (by serotyping) were quantified with both techniques. Results: Among the 6355 plasma samples received, HIV-1 group M was identified in 6026 (94.82%) cases; HIV-1 group O, in 20 (0.31%); HIV-1 group M + O, in 3 (0.05%) and HIV-2, in 3 (0.05%) case. HIV-group indeterminate samples represented about 4.76% (303/6355) and only 231 of them were available for analysis by Abbott Real-Time HIV-1 and Generic HIV Viral Load techniques. Results showed that 188 (81.39%) samples had undetectable viral load in both techniques. All the detectable samples showed high viral load, with a mean of 4.5 log copies/ml (range 2.1-6.5) for Abbott Real-Time and 4.5 log copies/ml (range 2-6.4) for Generic HIV Viral Load. The mean viral load difference between the two techniques was 0.03 log10 copies/ml and a good correlation was obtained (r 2 = 0.89; P < 0.001). Conclusion: Our results suggest that cheaper and open techniques such as Biocentric could be useful alternatives for HIV viral load follow-up quantification in resource limited settings like Cameroon; even with its high viral diversity.