Fig 1. The impacts of suboptimal adherence on viral fitness.

(A) A schematic illustrating how a non-preexisting mutant, A’B’, fully resistant to a combination therapy involving two drugs, A and B, can be generated when adherence is suboptimal. Each black circle represents a mutant on the parameter space of resistance levels to A and B. AB, A’B and AB’ are preexisting mutants that are non-resistant, resistant to A only and resistant to B only, respectively. Colored areas denote parameter regimes where mutants are fully resistant to the therapy (red), can grow if doses are missed (pink), and do not grow (blue). Note that the pink area can grow or shrink on the parameter space depending on the number of consecutively missed doses and drug PK/PD, and mutants lying in the pink area are ‘partially resistant mutants’. (B) The dynamics of viral strains under treatment are determined by several factors: drug concentration, [D], which decreases with an increasing number of missed doses, m (upper panel); how viral replication is affected by drug (1-ε; middle panel); and the relative number of target cells, h(t) (lower panel). Upon effective treatment, h(t) increases to the infection-free level. (C) We integrate all these factors into a single fitness parameter, R _eff(t). Viral fitness increases as drug concentration drops (indicated by shades of green) and as target cell abundance rises (the blue arrow). Values of R _eff(t) can exceed 1, i.e. positive growth, if doses are missed after a period of effective treatment.