Trends and Predictors of Transmitted Drug Resistance (TDR) and Clusters with TDR in a Local Belgian HIV-1 Epidemic

Abstract

We aimed to study epidemic trends and predictors for transmitted drug resistance (TDR) in our region, its clinical impact and its association with transmission clusters. We included 778 patients from the AIDS Reference Center in Leuven (Belgium) diagnosed from 1998 to 2012. Resistance testing was performed using population-based sequencing and TDR was estimated using the WHO-2009 surveillance list. Phylogenetic analysis was performed using maximum likelihood and Bayesian techniques. The cohort was predominantly Belgian (58.4%), men who have sex with men (MSM) (42.8%), and chronically infected (86.5%). The overall TDR prevalence was 9.6% (95% confidence interval (CI): 7.7–11.9), 6.5% (CI: 5.0–8.5) for nucleoside reverse transcriptase inhibitors (NRTI), 2.2% (CI: 1.4–3.5) for non-NRTI (NNRTI), and 2.2% (CI: 1.4–3.5) for protease inhibitors. A significant parabolic trend of NNRTI-TDR was found (p = 0.019). Factors significantly associated with TDR in univariate analysis were male gender, Belgian origin, MSM, recent infection, transmission clusters and subtype B, while multivariate and Bayesian network analysis singled out subtype B as the most predictive factor of TDR. Subtype B was related with transmission clusters with TDR that included 42.6% of the TDR patients. Thanks to resistance testing, 83% of the patients with TDR who started therapy had undetectable viral load whereas half of the patients would likely have received a suboptimal therapy without this test. In conclusion, TDR remained stable and a NNRTI up-and-down trend was observed. While the presence of clusters with TDR is worrying, we could not identify an independent, non-sequence based predictor for TDR or transmission clusters with TDR that could help with guidelines or public health measures.

Epidemiological trends of HIV‐1 infection in blood donors from Catalonia, Spain (2005‐2014)

“…The MSM acquisition route was a predictor of TDRMs, although the association was not significant, likely because of the small number of individuals harboring these mutations. These findings are in agreement with previous reports from both Spanish and European studies …”

“…31 The prevalence of TDRMs to antiretroviral drugs in Catalonian blood donors was 14%, which was greater than the values recently reported in Barcelona (9%), 12 in the CoRIS population (9.9%), 32 and in Madrid (8.3%). 33 It was also higher than the stable overall TDRM levels of 9.6% in Belgium and 9% in France, where the results were obtained using a study design and time frame similar to the work presented here, 34,35 and higher than the overall trend of 9.4% described in Europe between 2000 and 2013. 36 These differing results may be explained by the fact that the other studies did not include the glutamic acid to alanine codon 138 (E138A) mutation, which is associated with decreased susceptibility to rilpivirine and etravirine according to the IAS-USA 2014 21 and the Stanford HIV drug-resistance database.…”

Genetic Analysis of HIV-1 in Japan: a Comprehensive Analysis of Donated Blood

“…The dissemination of drug-resistant viruses may have been prevented by efficient anti-HIV-1 therapy. Twelve types of drug-resistance mutations were newly identified among the more recent samples with the mutations reportedly associated with the use of nelfinavir (resistance to protease inhibitors: D30N, L10I/F, and N88D), lamivudine or emtricitabine (resistance to nRTI: M184V), and nevirapine or efavirenz (resistance to NNRTI: K103N) (36,47,48). It is believed that these mutations were induced via use of these medications, given that they were approved in Japan in 2003,1999,2005,1998, and 2008, respectively (49).…”

Analyses of HTLV-1 sequences suggest interaction between ORF-I mutations and HAM/TSP outcome

“…Many different machine-learning tools have been used to predict therapy failure and conditional dependency among drug resistance mutations (Pineda-Pena et al, 2014; Prosperi and De Luca, 2012). Here, our data mining approach confirmed the association among HAM/TSP, proviral load and specific single mutations found with univariate analysis.…”