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Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection.

Ke R, Loverdo C, Qi H, Sun R, Lloyd-Smith JO - PLoS Comput. Biol. (2015)

Bottom Line: We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes.We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed.By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed. Using data from large-scale resistance assays, we demonstrate that the risk of resistance can be reduced substantially by applying these principles to a combination therapy of daclatasvir and asunaprevir. By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.

No MeSH data available.


Related in: MedlinePlus

There is a high-risk window early in treatment when missing doses is more likely to cause de novo resistance.(A) The changes in the risk of de novo resistance, Φm, generated by a partially resistant mutant over time. The two sets of trajectories, A and B, differ in that the value of μeff for trajectory B is smaller by a factor of 10–5 (representing one additional nucleotide mutation) than the value set for trajectory A. Each set of trajectories shows the risk when the number of doses missed (m) is 1,2 or 3. (B) Dynamics of the two time-varying quantities in Eq 4, i.e. the number of cells infected by the partially resistant mutant relative to the initial number before treatment (I(t)/I(0); blue dashed line), and the value of Θ(t), green dotted lines, as shown in Eq 4. Under effective treatment, the number of infected cells I(t) decreases exponentially, while the number of target cells rebounds to the infection-free level quickly, causing an increase in Rave,m and thus Θ(t). Together these changes cause Φm to increase initially and then to decrease exponentially at longer times (as seen in panel A).
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pcbi.1004040.g003: There is a high-risk window early in treatment when missing doses is more likely to cause de novo resistance.(A) The changes in the risk of de novo resistance, Φm, generated by a partially resistant mutant over time. The two sets of trajectories, A and B, differ in that the value of μeff for trajectory B is smaller by a factor of 10–5 (representing one additional nucleotide mutation) than the value set for trajectory A. Each set of trajectories shows the risk when the number of doses missed (m) is 1,2 or 3. (B) Dynamics of the two time-varying quantities in Eq 4, i.e. the number of cells infected by the partially resistant mutant relative to the initial number before treatment (I(t)/I(0); blue dashed line), and the value of Θ(t), green dotted lines, as shown in Eq 4. Under effective treatment, the number of infected cells I(t) decreases exponentially, while the number of target cells rebounds to the infection-free level quickly, causing an increase in Rave,m and thus Θ(t). Together these changes cause Φm to increase initially and then to decrease exponentially at longer times (as seen in panel A).

Mentions: The strong dependence of Φm on μeff predicts that designing combination therapies to increase the genetic barrier to full resistance, e.g. using DAAs with higher genetic barrier or adding an extra drug into the combination, can reduce Φm by orders of magnitude or more, thus it would lead to drastic reductions in the probability of generating full resistance (compare trajectories a and b in Fig 3A).


Rational Design and Adaptive Management of Combination Therapies for Hepatitis C Virus Infection.

Ke R, Loverdo C, Qi H, Sun R, Lloyd-Smith JO - PLoS Comput. Biol. (2015)

There is a high-risk window early in treatment when missing doses is more likely to cause de novo resistance.(A) The changes in the risk of de novo resistance, Φm, generated by a partially resistant mutant over time. The two sets of trajectories, A and B, differ in that the value of μeff for trajectory B is smaller by a factor of 10–5 (representing one additional nucleotide mutation) than the value set for trajectory A. Each set of trajectories shows the risk when the number of doses missed (m) is 1,2 or 3. (B) Dynamics of the two time-varying quantities in Eq 4, i.e. the number of cells infected by the partially resistant mutant relative to the initial number before treatment (I(t)/I(0); blue dashed line), and the value of Θ(t), green dotted lines, as shown in Eq 4. Under effective treatment, the number of infected cells I(t) decreases exponentially, while the number of target cells rebounds to the infection-free level quickly, causing an increase in Rave,m and thus Θ(t). Together these changes cause Φm to increase initially and then to decrease exponentially at longer times (as seen in panel A).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4488346&req=5

pcbi.1004040.g003: There is a high-risk window early in treatment when missing doses is more likely to cause de novo resistance.(A) The changes in the risk of de novo resistance, Φm, generated by a partially resistant mutant over time. The two sets of trajectories, A and B, differ in that the value of μeff for trajectory B is smaller by a factor of 10–5 (representing one additional nucleotide mutation) than the value set for trajectory A. Each set of trajectories shows the risk when the number of doses missed (m) is 1,2 or 3. (B) Dynamics of the two time-varying quantities in Eq 4, i.e. the number of cells infected by the partially resistant mutant relative to the initial number before treatment (I(t)/I(0); blue dashed line), and the value of Θ(t), green dotted lines, as shown in Eq 4. Under effective treatment, the number of infected cells I(t) decreases exponentially, while the number of target cells rebounds to the infection-free level quickly, causing an increase in Rave,m and thus Θ(t). Together these changes cause Φm to increase initially and then to decrease exponentially at longer times (as seen in panel A).
Mentions: The strong dependence of Φm on μeff predicts that designing combination therapies to increase the genetic barrier to full resistance, e.g. using DAAs with higher genetic barrier or adding an extra drug into the combination, can reduce Φm by orders of magnitude or more, thus it would lead to drastic reductions in the probability of generating full resistance (compare trajectories a and b in Fig 3A).

Bottom Line: We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes.We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed.By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology and Evolutionary Biology, University of California, Los Angeles, Los Angeles, California, United States of America.

ABSTRACT
Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for de novo resistance, and quantifying the number of additional doses needed to compensate when doses are missed. Using data from large-scale resistance assays, we demonstrate that the risk of resistance can be reduced substantially by applying these principles to a combination therapy of daclatasvir and asunaprevir. By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.

No MeSH data available.


Related in: MedlinePlus