Prediction of cytomegalovirus load and resistance patterns after antiviral chemotherapy

Abstract

Cytomegalovirus (CMV) replicates rapidly in its human host with a doubling time of approximately 1 day. Using simple mathematical models we show that the efficacy of the anti-CMV drug ganciclovir (GCV) against wild-type strains is 91.5% [95% confidence interval (CI) 89-94%] when given i.v. (5 mg/kg twice a day) but only 46.5% (95% CI 45-47.5%) when given orally (1 g three times a day) whereas the corresponding figures for a typical GCV-resistant virus are 62% (95% CI 57-66%) and 35% (95% CI 33-37%), respectively. During prolonged periods of GCV therapy we show that the apparent sudden appearance of GCV resistance is explained by the combination of two exponentially increasing populations (wild type and mutant) at doses of GCV that do not completely inhibit CMV replication. Cell culture methods to assess CMV drug resistance in vivo will underestimate its prevalence because of the fitness loss of resistant virus in the absence of therapy. The parameters determined from these models then were used to predict the likely viral load and resistance patterns in patients on prolonged therapy with GCV. The modeled and experimental data showed excellent agreement over extended time periods (up to 270 days of therapy) and provide a framework to predict the virologic course of patients at therapeutic initiation.

Figure 1

Proportion of UL97 single-site mutant within the population after the initiation of GCV therapy. Therapy lasts for 21 days and the fitness difference (s; Eq. 1) of mutant to wild type after cessation of therapy is the same as the fitness gain of mutant over wild type during therapy (5.6%). Three different starting concentrations of mutant within the population are modeled (0.015%, 0.15%, and 1.5%).

Figure 2

Proportion of single-site (A) or double-site (B) mutant in the population after in vitro culture in the absence of GCV. The data were generated by using Eq. 2 (see Materials and Methods), and curves are shown for four different wild type/mutant ratios.

Figure 3

Computation of the efficacy of i.v. GCV induction therapy (5 mg/kg b.i.d.). The linear regression line is shown (Lin Reg) superimposed on viral load decay curves generated for different efficacy of GCV together with the experimental data.

Figure 4

Computation of the efficacy of oral GCV (1 g t.i.d.) against wild-type (A) and single-site UL97 mutant viruses (B). The horizontal dotted line represents the level of sensitivity of viral load detection in the PCR assay.

Figure 5

Simulation of the viral load pattern of wild-type and single-site UL97 mutant CMV strains after initiation of oral GCV therapy. The total CMV load is shown and the proportion of mutant virus in the population at specific time points is superimposed as ⧫.

Figure 6

Simulation of the CMV load profile of a patient who develops an L595F mutation after GCV therapy. The alterations in therapeutic dose are shown together with the experimental CMV loads from quantitative competitive-PCR analyses.

Figure 7

Simulation of the CMV load profile of a heart transplant recipient during GCV induction therapy and oral GCV therapy. The experimentally determined CMV loads from ref. are superimposed.