Mutational pathway maps and founder effects define the within-host spectrum of hepatitis C virus mutants resistant to drugs

Knowledge of the within-host frequencies of resistance-associated amino acid variants (RAVs) is important to the identification of optimal drug combinations for the treatment of hepatitis C virus (HCV) infection. Multiple RAVs may exist in infected individuals, often below detection limits, at any resistance locus, defining the diversity of accessible resistance pathways. We developed a multiscale mathematical model to estimate the pre-treatment frequencies of the entire spectrum of mutants at chosen loci. Using a codon-level description of amino acids, we performed stochastic simulations of intracellular dynamics with every possible nucleotide variant as the infecting strain and estimated the relative infectivity of each variant and the resulting distribution of variants produced. We employed these quantities in a deterministic multi-strain model of extracellular dynamics and estimated mutant frequencies. Our predictions captured database frequencies of the RAV R155K, resistant to NS3/4A protease inhibitors, presenting a successful test of our formalism. We found that mutational pathway maps, interconnecting all viable mutants, and strong founder effects determined the mutant spectrum. The spectra were vastly different for HCV genotypes 1a and 1b, underlying their differential responses to drugs. Using a fitness landscape determined recently, we estimated that 13 amino acid variants, encoded by 44 codons, exist at the residue 93 of the NS5A protein, illustrating the massive diversity of accessible resistance pathways at specific loci. Accounting for this diversity, which our model enables, would help optimize drug combinations. Our model may be applied to describe the within-host evolution of other flaviviruses and inform vaccine design strategies. Author summary The spectrum of viral mutants that exists in infected individuals defines the diversity of drug resistance pathways accessible to any virus. Drug combinations that block these pathways the most effectively are likely to elicit the best responses. The mutants may lie below detection, rendering treatment optimization difficult. We constructed a multiscale mathematical model to estimate the pre-treatment frequencies of the entire spectrum of hepatitis C virus mutants at specific resistance loci. We described intracellular evolution stochastically and extracellular dynamics deterministically, gaining accuracy without escalating computational costs. Model predictions quantitatively captured experimental observations, explained confounding inter-subtype differences, and unraveled the massive diversity of accessible resistance pathways. Our study would help describe viral evolution more accurately, optimize drug treatments and design vaccines