An evolutionary-based approach to quantify the genetic barrier to drug resistance in fast-evolving viruses: an application to HIV-1 subtypes and integrase inhibitors

Abstract

Viral pathogens causing global disease burdens are often characterised by high rates of evolutionary changes, facilitating escape from therapeutic or immune selective pressure. Extensive viral diversity at baseline can shorten the time to resistance emergence and alter mutational pathways, but the impact of genotypic background on the genetic barrier can be difficult to capture, in particular for antivirals in experimental stages, recently approved or expanded into new settings. We developed an evolutionary-based counting method to quantify the population genetic potential to resistance and assess differences between populations. We demonstrate its applicability to HIV-1 integrase inhibitors, as their increasing use globally contrasts with limited availability of non-B subtype resistant sequences and corresponding knowledge gap on drug resistance. A large sequence dataset encompassing most prevailing subtypes and resistance mutations of first- and second-generation inhibitors were investigated. A varying genetic potential for resistance across HIV-1 subtypes was detected for 15 mutations at 12 positions, with notably 140S in subtype B, while 140C was discarded to vary across subtypes. An additional analysis for HIV-1 reverse transcriptase inhibitors identified a higher potential for 65R in subtype C, on the basis of a differential codon usage not reported before. The evolutionary interpretation of genomic differences for antiviral treatment remains challenging. Our framework advances existing counting methods with an increased sensitivity that identified novel subtype dependencies as well as rejected previous statements. Future applications include novel HIV-1 drug classes as well as other viral pathogens.