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Timing the emergence of resistance to anti-HIV drugs with large genetic barriers.

Arora P, Dixit NM - PLoS Comput. Biol. (2009)

Bottom Line: We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments.Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately.Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.

ABSTRACT
New antiretroviral drugs that offer large genetic barriers to resistance, such as the recently approved inhibitors of HIV-1 protease, tipranavir and darunavir, present promising weapons to avert the failure of current therapies for HIV infection. Optimal treatment strategies with the new drugs, however, are yet to be established. A key limitation is the poor understanding of the process by which HIV surmounts large genetic barriers to resistance. Extant models of HIV dynamics are predicated on the predominance of deterministic forces underlying the emergence of resistant genomes. In contrast, stochastic forces may dominate, especially when the genetic barrier is large, and delay the emergence of resistant genomes. We develop a mathematical model of HIV dynamics under the influence of an antiretroviral drug to predict the waiting time for the emergence of genomes that carry the requisite mutations to overcome the genetic barrier of the drug. We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments. Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately. Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.

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Model predictions of cell and viral dynamics.The time evolution of (A) the number of uninfected cells (red), infected cells (blue), and infectious virions (green) and (B) homozygous virions carrying wild-type genomes (pink) and single (blue), double (green), triple (orange), quadruple (red), and quintuple (black) mutants, obtained by solving Eqs. (1)–(9) with the parameters T0 = 106 cells, V00 = 5×105 virions, n = 5, l = 100 nucleotides, and εm from Figure 3 with E = 0. The remaining parameters are listed in Methods. The inset in (A) shows the evolution for the first two passages.
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pcbi-1000305-g004: Model predictions of cell and viral dynamics.The time evolution of (A) the number of uninfected cells (red), infected cells (blue), and infectious virions (green) and (B) homozygous virions carrying wild-type genomes (pink) and single (blue), double (green), triple (orange), quadruple (red), and quintuple (black) mutants, obtained by solving Eqs. (1)–(9) with the parameters T0 = 106 cells, V00 = 5×105 virions, n = 5, l = 100 nucleotides, and εm from Figure 3 with E = 0. The remaining parameters are listed in Methods. The inset in (A) shows the evolution for the first two passages.

Mentions: We perform calculations for a genetic barrier n = 5, representative of ritonavir-boosted PIs [38]. We let the separation between successive resistance mutations, l = 100 nucleotides, and choose the efficacy profile shown in Figure 3 with epistasis E = 0 (also see Methods). Here, the efficacy against the wild type, ε0, and against the strain with n mutations, εn, correspond to 400 nM of tipranavir [10]. We assume that the efficacy against intermediate mutants, εm, depends on the number of mutations, m (0≤m≤n), the genomes contain. In Figure 4A, we present the evolution of populations of uninfected cells, T, infected cells, , and infectious virions, , with time following the onset of the experiment. In the first passage, T rises due to the proliferation of uninfected cells (Figure 4A, inset). At the same time, T* rises due to the infection of T, and V rises sharply due to viral production from T*. In the second passage, the higher V enhances the infection of T. Here, the loss of T due to infection dominates cell proliferation and T declines. Consequently, following an initial rise of T* due to infection of T, target cell limitation lowers the formation of new infected cells and causes T* to decline. The resulting lower viral production causes V to decline as well. This two phase behavior within a passage–an initial rise and the subsequent fall of T*–is observed in experiments [7] and is explained by models [37],[39]. The same two phase behavior repeats in ensuing passages and an oscillatory pseudo steady state is attained. Gradually, V rises marking the emergence of drug resistant genomes.


Timing the emergence of resistance to anti-HIV drugs with large genetic barriers.

Arora P, Dixit NM - PLoS Comput. Biol. (2009)

Model predictions of cell and viral dynamics.The time evolution of (A) the number of uninfected cells (red), infected cells (blue), and infectious virions (green) and (B) homozygous virions carrying wild-type genomes (pink) and single (blue), double (green), triple (orange), quadruple (red), and quintuple (black) mutants, obtained by solving Eqs. (1)–(9) with the parameters T0 = 106 cells, V00 = 5×105 virions, n = 5, l = 100 nucleotides, and εm from Figure 3 with E = 0. The remaining parameters are listed in Methods. The inset in (A) shows the evolution for the first two passages.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2643484&req=5

pcbi-1000305-g004: Model predictions of cell and viral dynamics.The time evolution of (A) the number of uninfected cells (red), infected cells (blue), and infectious virions (green) and (B) homozygous virions carrying wild-type genomes (pink) and single (blue), double (green), triple (orange), quadruple (red), and quintuple (black) mutants, obtained by solving Eqs. (1)–(9) with the parameters T0 = 106 cells, V00 = 5×105 virions, n = 5, l = 100 nucleotides, and εm from Figure 3 with E = 0. The remaining parameters are listed in Methods. The inset in (A) shows the evolution for the first two passages.
Mentions: We perform calculations for a genetic barrier n = 5, representative of ritonavir-boosted PIs [38]. We let the separation between successive resistance mutations, l = 100 nucleotides, and choose the efficacy profile shown in Figure 3 with epistasis E = 0 (also see Methods). Here, the efficacy against the wild type, ε0, and against the strain with n mutations, εn, correspond to 400 nM of tipranavir [10]. We assume that the efficacy against intermediate mutants, εm, depends on the number of mutations, m (0≤m≤n), the genomes contain. In Figure 4A, we present the evolution of populations of uninfected cells, T, infected cells, , and infectious virions, , with time following the onset of the experiment. In the first passage, T rises due to the proliferation of uninfected cells (Figure 4A, inset). At the same time, T* rises due to the infection of T, and V rises sharply due to viral production from T*. In the second passage, the higher V enhances the infection of T. Here, the loss of T due to infection dominates cell proliferation and T declines. Consequently, following an initial rise of T* due to infection of T, target cell limitation lowers the formation of new infected cells and causes T* to decline. The resulting lower viral production causes V to decline as well. This two phase behavior within a passage–an initial rise and the subsequent fall of T*–is observed in experiments [7] and is explained by models [37],[39]. The same two phase behavior repeats in ensuing passages and an oscillatory pseudo steady state is attained. Gradually, V rises marking the emergence of drug resistant genomes.

Bottom Line: We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments.Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately.Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Indian Institute of Science, Bangalore, India.

ABSTRACT
New antiretroviral drugs that offer large genetic barriers to resistance, such as the recently approved inhibitors of HIV-1 protease, tipranavir and darunavir, present promising weapons to avert the failure of current therapies for HIV infection. Optimal treatment strategies with the new drugs, however, are yet to be established. A key limitation is the poor understanding of the process by which HIV surmounts large genetic barriers to resistance. Extant models of HIV dynamics are predicated on the predominance of deterministic forces underlying the emergence of resistant genomes. In contrast, stochastic forces may dominate, especially when the genetic barrier is large, and delay the emergence of resistant genomes. We develop a mathematical model of HIV dynamics under the influence of an antiretroviral drug to predict the waiting time for the emergence of genomes that carry the requisite mutations to overcome the genetic barrier of the drug. We apply our model to describe the development of resistance to tipranavir in in vitro serial passage experiments. Model predictions of the times of emergence of different mutant genomes with increasing resistance to tipranavir are in quantitative agreement with experiments, indicating that our model captures the dynamics of the development of resistance to antiretroviral drugs accurately. Further, model predictions provide insights into the influence of underlying evolutionary processes such as recombination on the development of resistance, and suggest guidelines for drug design: drugs that offer large genetic barriers to resistance with resistance sites tightly localized on the viral genome and exhibiting positive epistatic interactions maximally inhibit the emergence of resistant genomes.

Show MeSH
Related in: MedlinePlus