Antiretroviral treatment outcome and molecular epidemiology of HIV-1 subtype C drug resistance in Ethiopia

Over the last decade, antiretroviral therapy (ART) has been rolled out in Ethiopia at a large scale. However, the outcome and aspects of drug resistance have been poorly studied at the national level. Also, the epidemic has been reported to be dominated by HIV-1 subtype C (HIV-1C), but this has not been verified at a nationwide level. In this thesis, I aimed at studying the outcome of ART and molecular epidemiology of HIV-1C drug resistance in Ethiopia using the first countrywide HIV-1 cohort. Data and plasma samples were collected from 874 adult and adolescent patients (age ≥ 14 years; 60% females), recruited from seven university hospitals during 2009 – 2011. In Paper I, by on-treatment (OT) and intention-to-treat (ITT) analyses we determined the rate of treatment failure at month 6 and 12. Four multivariable logistic regression models were developed to identify baseline predictors of treatment outcome. OT analysis identified treatment failure in 8% and 7%, whereas with ITT analysis the figures were 23% and 34% at month 6 and 12, respectively. Hence, our study indicated early death and lost to follow up (LTFU) as the main risk factors for poor treatment outcome. In addition to the well-known baseline factors of ART outcomes, study site was identified as a strong predictor of failure, where regional hospitals had higher proportions of treatment failure and LTFU as compared to a national tertiary level hospital in the capital city. In Paper II, we assessed surveillance drug resistance mutations (sDRM) in major and minor viral populations by population-based Sanger sequencing (PBSS) and next-generation sequencing (NGS). The short-term impact of sDRM on the outcome of ART was also assessed. NGS detected sDRM associated with reverse transcriptase inhibitors (RTIs) and protease inhibitors (PIs) more frequently than PBSS as well as major integrase strand transfer inhibitors (INSTIs) DRMs in minor viral variants. The baseline RTI-DRMs were associated with treatment failure at month 6 and 12. In Paper III, by genotypic analysis of the V3-loop of env, we described the HIV-1C molecular epidemiology and co-receptor tropism trend. The epidemic has been still found to be monophylogenetically dominated by CCR5-tropic HIV-1C even in patients with advanced immunodeficiency, although a slight increasing temporal trend was observed in CXCR4- containing strains. In Paper IV, genotypic resistance testing (GRT) of the HIV-1 pol gene described acquired DRMs at month 6 and 12 and near-full length genome (NFLG) analysis assessed amino acid changes in the gag, pol, vif, vpr, tat, vpu, and nef genes in paired baseline and month 6 samples of virologic failures. Broad major RTI- DRMs were detected in most failure patients. K65R, at a high rate, was identified only in TDF treated patients. The NFLG assay described all target regions of interest for HIVDR. In conclusion, early death and LTFU are major contributors for ART failure in Ethiopia rather than detectable viremia; the universal test and treat strategy could possibly improve the treatment outcome. There is a geographical variation in ART outcome, which calls the need for provision of more support at regional hospitals. HIV-1C still dominates monophylogenetically the epidemic in Ethiopia. PBSS reports a broad DRM, but underestimates prevalence of the transmitted drug resistance (TDR). NGS identifies an even higher rate of DRMs including in the minor viral variants. Our NFLG assay covers all relevant target genes and is an attractive cost-efficient alternative for HIVDR surveillance

Prediction of coreceptor usage by five bioinformatics tools in a large Ethiopian HIV-1 subtype C cohort.

Genotypic tropism testing (GTT) has been developed largely on HIV-1 subtype B. Although a few reports have analysed the utility of GTT in other subtypes, more studies using HIV-1 subtype C (HIV-1C) are needed, considering the huge contribution of HIV-1C to the global epidemic.Plasma was obtained from 420 treatment-naïve HIV-1C infected Ethiopians recruited 2009-2011. The V3 region was sequenced and the coreceptor usage was predicted by five tools: Geno2Pheno clinical-and clonal-models, PhenoSeq-C, C-PSSM and Raymond's algorithm. The impact of baseline tropism on antiretroviral treatment (ART) outcome was evaluated.Of 352 patients with successful baseline V3 sequences, the proportion of predicted R5 virus varied between the methods by 12.5% (78.1%-90.6%). However, only 58.2% of the predictions were concordant and only 1.7% were predicted to be X4-tropic across the five methods. Compared pairwise, the highest concordance was between C-PSSM and Geno2Pheno clonal (86.4%). In bivariate intention to treat (ITT) analysis, R5 infected patients achieved treatment success more frequently than X4 infected at month six as predicted by Geno2Pheno clinical (77.8% vs 58.7%, P = 0.004) and at month 12 by C-PSSM (61.9% vs 46.6%, P = 0.038). However, in the multivariable analysis adjusted for age, gender, baseline CD4 and viral load, only tropism as predicted by C-PSSM showed an impact on month 12 (P = 0.04, OR 2.47, 95% CI 1.06-5.79).Each of the bioinformatics models predicted R5 tropism with comparable frequency but there was a large discordance between the methods. Baseline tropism had an impact on outcome of first line ART at month 12 in multivariable ITT analysis but only based on prediction by C-PSSM which thus possibly could be used for predicting outcome of ART in HIV-1C infected Ethiopians

Multivariable analysis of the impact of baseline viral tropism on antiretroviral treatment response by five bioinformatic methods in an intention to treat analysis.

<p>Multivariable analysis of the impact of baseline viral tropism on antiretroviral treatment response by five bioinformatic methods in an intention to treat analysis.</p

Multivariable associations between baseline characteristics and treatment failure at 6 and 12 months.

<p>Multivariable associations between baseline characteristics and treatment failure at 6 and 12 months.</p

Baseline characteristics of 352 HIV-1C infected Ethiopians in relation to predicted tropism by five bioinformatics tools.

<p>Baseline characteristics of 352 HIV-1C infected Ethiopians in relation to predicted tropism by five bioinformatics tools.</p

Concordance of tropism in HIV-1C infected patients as predicted by five bioinformatics tools, before antiretroviral treatment and at virological treatment failure.

<p>Concordance of tropism in HIV-1C infected patients as predicted by five bioinformatics tools, before antiretroviral treatment and at virological treatment failure.</p

Bivariate intention to treat analysis of treatment outcome of patients at month 6 and month 12 as baseline tropism predicted by five methods.

<p>Bivariate intention to treat analysis of treatment outcome of patients at month 6 and month 12 as baseline tropism predicted by five methods.</p

Bivariate on-treatment analysis of treatment outcome of patients at month 6 and 12 as tropism predicted by five methods.

<p>Bivariate on-treatment analysis of treatment outcome of patients at month 6 and 12 as tropism predicted by five methods.</p