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

Background: Viral pathogens causing global disease burdens are often characterised by high rates of evolutionary changes. The extensive viral diversity at baseline can shorten the time to escape from therapeutic or immune selective pressure and alter mutational pathways. The impact of genotypic background on the barrier to resistance can be difficult to capture, particularly for agents in experimental stages, recently approved or expanded into new patient populations. Setting: We developed an evolutionary-based counting method to quickly quantify the population genetic potential to resistance and assess population differences. We demonstrate its applicability to HIV-1 integrase inhibitors, as their increasing use globally contrasts with limited availability of non-B subtype resistant sequence data and corresponding knowledge gap. Methods: A large sequence dataset encompassing most prevailing HIV-1 subtypes and resistance-associated mutations of currently approved integrase inhibitors were investigated. Results: A complex interplay between codon predominance, polymorphisms and associated evolutionary costs resulted in a subtype-dependent varying genetic potential for 15 resistance mutations against integrase inhibitors. While we confirm the lower genetic barrier of subtype B for G140S, we convincingly discard a similar effect previously suggested for G140C. A supplementary analysis for HIV-1 reverse transcriptase inhibitors identified a lower genetic barrier for K65R in subtype C through differential codon usage not reported before. Conclusion: To aid evolutionary interpretations of genomic differences for antiviral strategies, we advanced existing counting methods with increased sensitivity to identify subtype dependencies of resistance emergence. Future applications include novel HIV-1 drug classes or vaccines as well as other viral pathogens.