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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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The relationship between the length of a treatment interruption and the probability that DRMs become established.Estimated probability that resistance mutations become established due to a single treatment interruption. Grey bars are data from seven clinical trials,  estimated standard error (see supplementary table S3 in text S2). The number of patients (and the number of interruptions per patient) are noted at the top of the graph. The red circles are estimated from 1000 simulations,  estimated standard error. The black squares are predictions using the average population size from the simulations and equation 2. Parameters as in table 2.
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pcbi-1002527-g006: The relationship between the length of a treatment interruption and the probability that DRMs become established.Estimated probability that resistance mutations become established due to a single treatment interruption. Grey bars are data from seven clinical trials, estimated standard error (see supplementary table S3 in text S2). The number of patients (and the number of interruptions per patient) are noted at the top of the graph. The red circles are estimated from 1000 simulations, estimated standard error. The black squares are predictions using the average population size from the simulations and equation 2. Parameters as in table 2.

Mentions: Using the parameter values from the last two sections, we can predict the risk that resistance mutations become established due to a treatment interruption of a certain length. We use the estimated fitness of the mutant virus during NVP therapy, and assume that the fitness of the mutant in absence of drugs is the same. With that value, we can calculate the fitness of the wildtype in the absence of drugs, because of the assumption that the cost of the resistance mutation is 5%. The wildtype fitness will determine how fast the virus grows in the simulations after treatment is interrupted, and therefore how long it takes before the population size is back at the pretreatment level. Specifically, we use . In the simulations, the population size plateaus after just 14 days, but reaches its expected value only after 60 days (figure 6).


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

Pennings PS - PLoS Comput. Biol. (2012)

The relationship between the length of a treatment interruption and the probability that DRMs become established.Estimated probability that resistance mutations become established due to a single treatment interruption. Grey bars are data from seven clinical trials,  estimated standard error (see supplementary table S3 in text S2). The number of patients (and the number of interruptions per patient) are noted at the top of the graph. The red circles are estimated from 1000 simulations,  estimated standard error. The black squares are predictions using the average population size from the simulations and equation 2. Parameters as in table 2.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002527-g006: The relationship between the length of a treatment interruption and the probability that DRMs become established.Estimated probability that resistance mutations become established due to a single treatment interruption. Grey bars are data from seven clinical trials, estimated standard error (see supplementary table S3 in text S2). The number of patients (and the number of interruptions per patient) are noted at the top of the graph. The red circles are estimated from 1000 simulations, estimated standard error. The black squares are predictions using the average population size from the simulations and equation 2. Parameters as in table 2.
Mentions: Using the parameter values from the last two sections, we can predict the risk that resistance mutations become established due to a treatment interruption of a certain length. We use the estimated fitness of the mutant virus during NVP therapy, and assume that the fitness of the mutant in absence of drugs is the same. With that value, we can calculate the fitness of the wildtype in the absence of drugs, because of the assumption that the cost of the resistance mutation is 5%. The wildtype fitness will determine how fast the virus grows in the simulations after treatment is interrupted, and therefore how long it takes before the population size is back at the pretreatment level. Specifically, we use . In the simulations, the population size plateaus after just 14 days, but reaches its expected value only after 60 days (figure 6).

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