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Dose-Dependent Mutation Rates Determine Optimum Erlotinib Dosing Strategies for EGFR Mutant Non-Small Cell Lung Cancer Patients.

Liu LL, Li F, Pao W, Michor F - PLoS ONE (2015)

Bottom Line: Unfortunately, however, resistance often emerges and renders these agents ineffective after a variable amount of time.The relationship between the serum drug concentration and the rate at which resistance mutations arise can lead to non-intuitive results about the best dose administration strategies to prevent or delay the emergence of resistance.For new and existing targeted drugs, our methodology can be employed to compare the effectiveness of different dose administration schedules and investigate the influence of changing mutation rates on outcomes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, United States of America.

ABSTRACT

Background: The advent of targeted therapy for cancer treatment has brought about a paradigm shift in the clinical management of human malignancies. Agents such as erlotinib used for EGFR-mutant non-small cell lung cancer or imatinib for chronic myeloid leukemia, for instance, lead to rapid tumor responses. Unfortunately, however, resistance often emerges and renders these agents ineffective after a variable amount of time. The FDA-approved dosing schedules for these drugs were not designed to optimally prevent the emergence of resistance. To this end, we have previously utilized evolutionary mathematical modeling of treatment responses to elucidate the dosing schedules best able to prevent or delay the onset of resistance. Here we expand on our approaches by taking into account dose-dependent mutation rates at which resistant cells emerge. The relationship between the serum drug concentration and the rate at which resistance mutations arise can lead to non-intuitive results about the best dose administration strategies to prevent or delay the emergence of resistance.

Methods: We used mathematical modeling, available clinical trial data, and different considerations of the relationship between mutation rate and drug concentration to predict the effectiveness of different dosing strategies.

Results: We designed several distinct measures to interrogate the effects of different treatment dosing strategies and found that a low-dose continuous strategy coupled with high-dose pulses leads to the maximal delay until clinically observable resistance. Furthermore, the response to treatment is robust against different assumptions of the mutation rate as a function of drug concentration.

Conclusions: For new and existing targeted drugs, our methodology can be employed to compare the effectiveness of different dose administration schedules and investigate the influence of changing mutation rates on outcomes.

No MeSH data available.


Related in: MedlinePlus

Effects of varying dosing regimes on the evolution of resistance.Here we consider β = 10−10. (A) The dosing regimes with no loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (B) The dosing regimes with loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (C) Mutation rate as a function of treatment concentration under different assumptions: blue: independent with treatment concentration; black: increasing with treatment concentration; green: decreasing with treatment concentration. (D)-(F) Without pre-existing resistance, the probability of resistance monitored up to one month under (D) constant mutation rate, (E) mutation rate increasing with the drug concentration, and (F) mutation rate decreasing with the drug concentration. (G)-(I) With pre-existing resistance, the expected number of resistant cells monitored up to one month under (G) constant mutation rate, (H) mutation rate increasing with the drug concentration, and (I) mutation rate decreasing with the drug concentration. Dotted line: with loading dose; solid line: without loading dose. Values for birth and death rates: μXs(t) ≈ 0.005 hour−1, μXr(t) ≈ 0.002 hour−1, λXs(t) ≈ exp(−4.4 ⋅ C(t)−3.17) hour−1, and λXr(t) ≈ −0.001 ⋅ C(t) + 0.03 hour−1
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pone.0141665.g005: Effects of varying dosing regimes on the evolution of resistance.Here we consider β = 10−10. (A) The dosing regimes with no loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (B) The dosing regimes with loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (C) Mutation rate as a function of treatment concentration under different assumptions: blue: independent with treatment concentration; black: increasing with treatment concentration; green: decreasing with treatment concentration. (D)-(F) Without pre-existing resistance, the probability of resistance monitored up to one month under (D) constant mutation rate, (E) mutation rate increasing with the drug concentration, and (F) mutation rate decreasing with the drug concentration. (G)-(I) With pre-existing resistance, the expected number of resistant cells monitored up to one month under (G) constant mutation rate, (H) mutation rate increasing with the drug concentration, and (I) mutation rate decreasing with the drug concentration. Dotted line: with loading dose; solid line: without loading dose. Values for birth and death rates: μXs(t) ≈ 0.005 hour−1, μXr(t) ≈ 0.002 hour−1, λXs(t) ≈ exp(−4.4 ⋅ C(t)−3.17) hour−1, and λXr(t) ≈ −0.001 ⋅ C(t) + 0.03 hour−1

Mentions: We then compared the performance of different dosing regimes under the three different types of dynamic mutation rate assumptions as described above. These dosing regimes include: 100mg/day, 1600mg/week, and 1600mg/week combined with 100mg/day, together with loading and no loading doses. The dosing regimes as a function of time t are shown in Fig 5A and 5B. The mutation rates over time under different assumptions are displayed in Fig 5C. Overall, when comparing across different mutation rate assumptions, without loading doses, we found that 150mg/day and the combined strategy of 1600mg/week + 100mg/day lead to the lowest probability of resistance without pre-existing resistant clones (Fig 5D–5F). When a loading dose is given, the relative performance of different strategies is altered, especially between the 150mg/day and combined strategies, where 150mg/day outperformed the combined strategy under all mutation rates assumptions. When there are pre-existing resistant clones, however, the presence of a loading dose did not significantly alter the dynamics (Fig 5G–5I). Finally, the different mutation rates assumptions also did not have an effect of the relative performance of different dosing schedules. Fig 5 displays results where β = 10−10, and similar results are shown in S2 Fig for β = 10−9 to corroborate our conclusion: within the scenarios tested, different mutation rates do not alter the relative performance of different dosing schedules.


Dose-Dependent Mutation Rates Determine Optimum Erlotinib Dosing Strategies for EGFR Mutant Non-Small Cell Lung Cancer Patients.

Liu LL, Li F, Pao W, Michor F - PLoS ONE (2015)

Effects of varying dosing regimes on the evolution of resistance.Here we consider β = 10−10. (A) The dosing regimes with no loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (B) The dosing regimes with loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (C) Mutation rate as a function of treatment concentration under different assumptions: blue: independent with treatment concentration; black: increasing with treatment concentration; green: decreasing with treatment concentration. (D)-(F) Without pre-existing resistance, the probability of resistance monitored up to one month under (D) constant mutation rate, (E) mutation rate increasing with the drug concentration, and (F) mutation rate decreasing with the drug concentration. (G)-(I) With pre-existing resistance, the expected number of resistant cells monitored up to one month under (G) constant mutation rate, (H) mutation rate increasing with the drug concentration, and (I) mutation rate decreasing with the drug concentration. Dotted line: with loading dose; solid line: without loading dose. Values for birth and death rates: μXs(t) ≈ 0.005 hour−1, μXr(t) ≈ 0.002 hour−1, λXs(t) ≈ exp(−4.4 ⋅ C(t)−3.17) hour−1, and λXr(t) ≈ −0.001 ⋅ C(t) + 0.03 hour−1
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4633116&req=5

pone.0141665.g005: Effects of varying dosing regimes on the evolution of resistance.Here we consider β = 10−10. (A) The dosing regimes with no loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (B) The dosing regimes with loading dose for 100mg/day, 150mg/day, 1600mg/week, 1600mg/week combined with 100mg/day during the week, and 1600mg/week combined with 75mg/day during the week. (C) Mutation rate as a function of treatment concentration under different assumptions: blue: independent with treatment concentration; black: increasing with treatment concentration; green: decreasing with treatment concentration. (D)-(F) Without pre-existing resistance, the probability of resistance monitored up to one month under (D) constant mutation rate, (E) mutation rate increasing with the drug concentration, and (F) mutation rate decreasing with the drug concentration. (G)-(I) With pre-existing resistance, the expected number of resistant cells monitored up to one month under (G) constant mutation rate, (H) mutation rate increasing with the drug concentration, and (I) mutation rate decreasing with the drug concentration. Dotted line: with loading dose; solid line: without loading dose. Values for birth and death rates: μXs(t) ≈ 0.005 hour−1, μXr(t) ≈ 0.002 hour−1, λXs(t) ≈ exp(−4.4 ⋅ C(t)−3.17) hour−1, and λXr(t) ≈ −0.001 ⋅ C(t) + 0.03 hour−1
Mentions: We then compared the performance of different dosing regimes under the three different types of dynamic mutation rate assumptions as described above. These dosing regimes include: 100mg/day, 1600mg/week, and 1600mg/week combined with 100mg/day, together with loading and no loading doses. The dosing regimes as a function of time t are shown in Fig 5A and 5B. The mutation rates over time under different assumptions are displayed in Fig 5C. Overall, when comparing across different mutation rate assumptions, without loading doses, we found that 150mg/day and the combined strategy of 1600mg/week + 100mg/day lead to the lowest probability of resistance without pre-existing resistant clones (Fig 5D–5F). When a loading dose is given, the relative performance of different strategies is altered, especially between the 150mg/day and combined strategies, where 150mg/day outperformed the combined strategy under all mutation rates assumptions. When there are pre-existing resistant clones, however, the presence of a loading dose did not significantly alter the dynamics (Fig 5G–5I). Finally, the different mutation rates assumptions also did not have an effect of the relative performance of different dosing schedules. Fig 5 displays results where β = 10−10, and similar results are shown in S2 Fig for β = 10−9 to corroborate our conclusion: within the scenarios tested, different mutation rates do not alter the relative performance of different dosing schedules.

Bottom Line: Unfortunately, however, resistance often emerges and renders these agents ineffective after a variable amount of time.The relationship between the serum drug concentration and the rate at which resistance mutations arise can lead to non-intuitive results about the best dose administration strategies to prevent or delay the emergence of resistance.For new and existing targeted drugs, our methodology can be employed to compare the effectiveness of different dose administration schedules and investigate the influence of changing mutation rates on outcomes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, United States of America.

ABSTRACT

Background: The advent of targeted therapy for cancer treatment has brought about a paradigm shift in the clinical management of human malignancies. Agents such as erlotinib used for EGFR-mutant non-small cell lung cancer or imatinib for chronic myeloid leukemia, for instance, lead to rapid tumor responses. Unfortunately, however, resistance often emerges and renders these agents ineffective after a variable amount of time. The FDA-approved dosing schedules for these drugs were not designed to optimally prevent the emergence of resistance. To this end, we have previously utilized evolutionary mathematical modeling of treatment responses to elucidate the dosing schedules best able to prevent or delay the onset of resistance. Here we expand on our approaches by taking into account dose-dependent mutation rates at which resistant cells emerge. The relationship between the serum drug concentration and the rate at which resistance mutations arise can lead to non-intuitive results about the best dose administration strategies to prevent or delay the emergence of resistance.

Methods: We used mathematical modeling, available clinical trial data, and different considerations of the relationship between mutation rate and drug concentration to predict the effectiveness of different dosing strategies.

Results: We designed several distinct measures to interrogate the effects of different treatment dosing strategies and found that a low-dose continuous strategy coupled with high-dose pulses leads to the maximal delay until clinically observable resistance. Furthermore, the response to treatment is robust against different assumptions of the mutation rate as a function of drug concentration.

Conclusions: For new and existing targeted drugs, our methodology can be employed to compare the effectiveness of different dose administration schedules and investigate the influence of changing mutation rates on outcomes.

No MeSH data available.


Related in: MedlinePlus