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• W4366998425 abstract "Abstract A deeper understanding of HIV-1 transmission and drug resistance mechanisms can lead to improvement in current treatment policies. However, the rates at which HIV-1 drug resistance mutations (DRMs) are acquired and at which transmitted DRMs persist are multi-factorial and vary considerably between different mutations. We develop a method for estimation of drug resistance acquisition and transmission patterns, which refines the method we described in Mourad et al. AIDS 2015. The method uses maximum likelihood ancestral character reconstruction informed by treatment roll-out dates and allows for analysis of very large data sets. We apply our method to transmission trees reconstructed on the data obtained from the UK HIV drug resistance database to make predictions for known DRMs. Our results show important differences between DRMs, in particular between polymorphic and non-polymorphic DRMs, and between the B and C subtypes. Our estimates of reversion times, based on a very large number of sequences, are compatible but more accurate than those already available in the litterature, with narrower confidence intervals. We consistently find that large resistance clusters are associated with polymorphic DRMs and DRMs with long loss time, which require special surveillance. As in other high-income countries (e.g. Switzerland), the prevalence of sequences with DRMs is decreasing, but among these, the fraction of transmitted resistance is clearly increasing compared to the fraction of acquired resistance mutations. All this indicates that efforts to monitor these mutations and the emergence of resistance clusters in the population must be maintained in the long term." @default.
• W4366998425 created "2023-04-27" @default.
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• W4366998425 date "2023-04-25" @default.
• W4366998425 modified "2023-10-02" @default.
• W4366998425 title "Modelling drug resistance emergence and transmission in HIV-1 in the UK" @default.
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• W4366998425 doi "https://doi.org/10.1101/2023.04.24.23288580" @default.
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