Abstract

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.

Highlights

  • We described intracellular evolution stochastically and extracellular dynamics deterministically, gaining accuracy without escalating computational costs

  • Direct acting antiviral agents (DAAs) have revolutionized the treatment of chronic hepatitis C virus (HCV) infection, eliciting nearly 100% cure rates in clinical trials with oral treatments often lasting as short as 8 weeks [1]

  • The frequencies with which resistance-associated amino acid variants (RAVs) are likely to exist in individuals before treatment are important to the identification of optimal DAA combinations; DAAs must effectively block the growth of these pre-existing drug resistant strains during treatment [10,11,12]

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Summary

Introduction

Direct acting antiviral agents (DAAs) have revolutionized the treatment of chronic hepatitis C virus (HCV) infection, eliciting nearly 100% cure rates in clinical trials with oral treatments often lasting as short as 8 weeks [1]. Efforts are focused on identifying DAA combinations that prevent the development of drug resistance more effectively and can reduce treatment durations further [2,3,4,5,6,7,8]. The frequencies with which RAVs are likely to exist in individuals before treatment are important to the identification of optimal DAA combinations; DAAs must effectively block the growth of these pre-existing drug resistant strains during treatment [10,11,12]. Current assays are inadequately equipped to estimate the frequencies of minority strains. Mathematical modelling may provide an alternative route to estimating the frequencies of such minority variants and aid the identification of optimal DAA combinations

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