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Standing genetic variation and the evolution of drug resistance in HIV.

Pennings PS - PLoS Comput. Biol. (2012)

Bottom Line: We use a population-dynamic and population-genetic model to understand the observations and to estimate important evolutionary parameters under the assumption that treatment failure is caused by the fixation of a single drug resistance mutation.We find that both the effective population size of the virus before treatment, and the fitness of the resistant mutant during treatment, are key-arameters which determine the probability that resistance evolves from standing genetic variation.Importantly, clinical data indicate that both of these parameters can be manipulated by the kind of treatment that is used.

View Article: PubMed Central - PubMed

Affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America. pennings@fas.harvard.edu

ABSTRACT
Drug resistance remains a major problem for the treatment of HIV. Resistance can occur due to mutations that were present before treatment starts or due to mutations that occur during treatment. The relative importance of these two sources is unknown. Resistance can also be transmitted between patients, but this process is not considered in the current study. We study three different situations in which HIV drug resistance may evolve: starting triple-drug therapy, treatment with a single dose of nevirapine and interruption of treatment. For each of these three cases good data are available from literature, which allows us to estimate the probability that resistance evolves from standing genetic variation. Depending on the treatment we find probabilities of the evolution of drug resistance due to standing genetic variation between 0 and 39%. For patients who start triple-drug combination therapy, we find that drug resistance evolves from standing genetic variation in approximately 6% of the patients. We use a population-dynamic and population-genetic model to understand the observations and to estimate important evolutionary parameters under the assumption that treatment failure is caused by the fixation of a single drug resistance mutation. We find that both the effective population size of the virus before treatment, and the fitness of the resistant mutant during treatment, are key-arameters which determine the probability that resistance evolves from standing genetic variation. Importantly, clinical data indicate that both of these parameters can be manipulated by the kind of treatment that is used.

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Probability of detecting resistance per year of treatment.The probability that resistance is detected for the first time in the first, second or third year of treatment, given that it was not detected until then. Grey bars are the estimates from the Margot et al ([36]) dataset, and the number of patients on which the estimates are based are noted at the top of the graph. The red dashed area reflects the inferred probability that resistance mutations from standing genetic variation become established. The black squares are values calculated using equations 2 and 4. The red circles are estimated from 1000 simulations. Parameters as in table 2.
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pcbi-1002527-g001: Probability of detecting resistance per year of treatment.The probability that resistance is detected for the first time in the first, second or third year of treatment, given that it was not detected until then. Grey bars are the estimates from the Margot et al ([36]) dataset, and the number of patients on which the estimates are based are noted at the top of the graph. The red dashed area reflects the inferred probability that resistance mutations from standing genetic variation become established. The black squares are values calculated using equations 2 and 4. The red circles are estimated from 1000 simulations. Parameters as in table 2.

Mentions: Given the cost, the mutation rate, and , and using the assumption that there are 200 HIV generations in a year [41], we can find the combinations of , and that are compatible with the data (shown in figure 1). Estimates for the effective population size in untreated patients range from [42] to [43]. We know that a large proportion of untreated patients carries low frequency drug resistance mutations, but not all patients, which gives us some additional information about the population size in an untreated patient (see figure 1b). If we choose a value of of , then we find that about half of the patients should carry pre-existing DRMs. This is somewhat higher than what is usually detected, but that can be due in part to the limits of detection of current tests [8]. An overview of the parameter estimates that were used in the simulations and for analytical predictions can be found in table 2.


Standing genetic variation and the evolution of drug resistance in HIV.

Pennings PS - PLoS Comput. Biol. (2012)

Probability of detecting resistance per year of treatment.The probability that resistance is detected for the first time in the first, second or third year of treatment, given that it was not detected until then. Grey bars are the estimates from the Margot et al ([36]) dataset, and the number of patients on which the estimates are based are noted at the top of the graph. The red dashed area reflects the inferred probability that resistance mutations from standing genetic variation become established. The black squares are values calculated using equations 2 and 4. The red circles are estimated from 1000 simulations. Parameters as in table 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3369920&req=5

pcbi-1002527-g001: Probability of detecting resistance per year of treatment.The probability that resistance is detected for the first time in the first, second or third year of treatment, given that it was not detected until then. Grey bars are the estimates from the Margot et al ([36]) dataset, and the number of patients on which the estimates are based are noted at the top of the graph. The red dashed area reflects the inferred probability that resistance mutations from standing genetic variation become established. The black squares are values calculated using equations 2 and 4. The red circles are estimated from 1000 simulations. Parameters as in table 2.
Mentions: Given the cost, the mutation rate, and , and using the assumption that there are 200 HIV generations in a year [41], we can find the combinations of , and that are compatible with the data (shown in figure 1). Estimates for the effective population size in untreated patients range from [42] to [43]. We know that a large proportion of untreated patients carries low frequency drug resistance mutations, but not all patients, which gives us some additional information about the population size in an untreated patient (see figure 1b). If we choose a value of of , then we find that about half of the patients should carry pre-existing DRMs. This is somewhat higher than what is usually detected, but that can be due in part to the limits of detection of current tests [8]. An overview of the parameter estimates that were used in the simulations and for analytical predictions can be found in table 2.

Bottom Line: We use a population-dynamic and population-genetic model to understand the observations and to estimate important evolutionary parameters under the assumption that treatment failure is caused by the fixation of a single drug resistance mutation.We find that both the effective population size of the virus before treatment, and the fitness of the resistant mutant during treatment, are key-arameters which determine the probability that resistance evolves from standing genetic variation.Importantly, clinical data indicate that both of these parameters can be manipulated by the kind of treatment that is used.

View Article: PubMed Central - PubMed

Affiliation: Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, United States of America. pennings@fas.harvard.edu

ABSTRACT
Drug resistance remains a major problem for the treatment of HIV. Resistance can occur due to mutations that were present before treatment starts or due to mutations that occur during treatment. The relative importance of these two sources is unknown. Resistance can also be transmitted between patients, but this process is not considered in the current study. We study three different situations in which HIV drug resistance may evolve: starting triple-drug therapy, treatment with a single dose of nevirapine and interruption of treatment. For each of these three cases good data are available from literature, which allows us to estimate the probability that resistance evolves from standing genetic variation. Depending on the treatment we find probabilities of the evolution of drug resistance due to standing genetic variation between 0 and 39%. For patients who start triple-drug combination therapy, we find that drug resistance evolves from standing genetic variation in approximately 6% of the patients. We use a population-dynamic and population-genetic model to understand the observations and to estimate important evolutionary parameters under the assumption that treatment failure is caused by the fixation of a single drug resistance mutation. We find that both the effective population size of the virus before treatment, and the fitness of the resistant mutant during treatment, are key-arameters which determine the probability that resistance evolves from standing genetic variation. Importantly, clinical data indicate that both of these parameters can be manipulated by the kind of treatment that is used.

Show MeSH
Related in: MedlinePlus