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Virtual optimization of nasal insulin therapy predicts immunization frequency to be crucial for diabetes protection.

Fousteri G, Chan JR, Zheng Y, Whiting C, Dave A, Bresson D, Croft M, von Herrath M - Diabetes (2010)

Bottom Line: The experimental aim was to evaluate the impact of dose, frequency of administration, and age at treatment on Treg induction and optimal therapeutic outcome.Here, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed.In silico modeling was able to streamline the experimental design and to identify the particular time frame at which biomarkers associated with protection in live NODs were induced.

View Article: PubMed Central - PubMed

Affiliation: Diabetes Center, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA.

ABSTRACT

Objective: Development of antigen-specific strategies to treat or prevent type 1 diabetes has been slow and difficult because of the lack of experimental tools and defined biomarkers that account for the underlying therapeutic mechanisms.

Research design and methods: The type 1 diabetes PhysioLab platform, a large-scale mathematical model of disease pathogenesis in the nonobese diabetic (NOD) mouse, was used to investigate the possible mechanisms underlying the efficacy of nasal insulin B:9-23 peptide therapy. The experimental aim was to evaluate the impact of dose, frequency of administration, and age at treatment on Treg induction and optimal therapeutic outcome.

Results: In virtual NOD mice, treatment efficacy was predicted to depend primarily on the immunization frequency and stage of the disease and to a lesser extent on the dose. Whereas low-frequency immunization protected from diabetes atrributed to Treg and interleukin (IL)-10 induction in the pancreas 1-2 weeks after treatment, high-frequency immunization failed. These predictions were confirmed with wet-lab approaches, where only low-frequency immunization started at an early disease stage in the NOD mouse resulted in significant protection from diabetes by inducing IL-10 and Treg.

Conclusions: Here, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed. In silico modeling was able to streamline the experimental design and to identify the particular time frame at which biomarkers associated with protection in live NODs were induced. These results support the development and application of humanized platforms for the design of clinical trials (i.e., for the ongoing nasal insulin prevention studies).

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Two different amounts of B:9-23 peptide cannot protect from diabetes when administered nasally in 10-week-old NOD mice. A: Graphic representation of the B:9-23 nasal immunization protocol followed. Ten-week-old NOD mice received 40 or 100 μg B:9-23 peptide nasal every other day for the first 2 weeks and once a week for the following 5 weeks. B: There was no protection from diabetes after following the protocol described in A. Efficacy of treatment was assessed by monitoring blood glucose over time (n = 12 in all groups). Mice were considered diabetic when blood glucose was >16.67 mmol/l for two consecutive weekly measurements. C: Multiphasic dose responses were predicted for the protocols presented in supplementary Table 1. Nasal B:9-23 peptide therapy was simulated for the Daniel (green solid line), Kobayashi (red solid line), and von Herrath (red dashed line) protocols across a wide range of doses (x-axis). The shaded box indicates the peptide range (10–100 μg) used to calibrate the VM. Efficacy represents the number of VM that were protected from diabetes.
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Figure 1: Two different amounts of B:9-23 peptide cannot protect from diabetes when administered nasally in 10-week-old NOD mice. A: Graphic representation of the B:9-23 nasal immunization protocol followed. Ten-week-old NOD mice received 40 or 100 μg B:9-23 peptide nasal every other day for the first 2 weeks and once a week for the following 5 weeks. B: There was no protection from diabetes after following the protocol described in A. Efficacy of treatment was assessed by monitoring blood glucose over time (n = 12 in all groups). Mice were considered diabetic when blood glucose was >16.67 mmol/l for two consecutive weekly measurements. C: Multiphasic dose responses were predicted for the protocols presented in supplementary Table 1. Nasal B:9-23 peptide therapy was simulated for the Daniel (green solid line), Kobayashi (red solid line), and von Herrath (red dashed line) protocols across a wide range of doses (x-axis). The shaded box indicates the peptide range (10–100 μg) used to calibrate the VM. Efficacy represents the number of VM that were protected from diabetes.

Mentions: Nasal B:9-23 peptide therapy has been used with mixed efficacy to induce tolerance in prediabetic NOD mice. A comparison of all published protocols shows differences in dose and frequency of administration. Daniel et al. (9) showed that 40 μg of B:9-23 given over 3 consecutive days at 4 weeks of age and then again every 4 weeks beginning at 9 weeks of age induced tolerance. By contrast, Kobayashi et al. (12) did not observe efficacy when 20 μg of the peptide was given over 5 consecutive days at 4 weeks of age and then once a week for 5 weeks thereafter. Because the impact of age at treatment initiation had not been assessed in either of these studies, and to obtain further constraints for the modeling effort, we used one additional protocol. In these experiments, nasal B:9-23 therapy failed to confer protection at two different doses (40, 100 μg/mouse) when immunizations were started in 10-week-old mice (Fig. 1A and B). The complex differences between these immunization protocols provided the rationale for computer modeling to more precisely define our experimental options.


Virtual optimization of nasal insulin therapy predicts immunization frequency to be crucial for diabetes protection.

Fousteri G, Chan JR, Zheng Y, Whiting C, Dave A, Bresson D, Croft M, von Herrath M - Diabetes (2010)

Two different amounts of B:9-23 peptide cannot protect from diabetes when administered nasally in 10-week-old NOD mice. A: Graphic representation of the B:9-23 nasal immunization protocol followed. Ten-week-old NOD mice received 40 or 100 μg B:9-23 peptide nasal every other day for the first 2 weeks and once a week for the following 5 weeks. B: There was no protection from diabetes after following the protocol described in A. Efficacy of treatment was assessed by monitoring blood glucose over time (n = 12 in all groups). Mice were considered diabetic when blood glucose was >16.67 mmol/l for two consecutive weekly measurements. C: Multiphasic dose responses were predicted for the protocols presented in supplementary Table 1. Nasal B:9-23 peptide therapy was simulated for the Daniel (green solid line), Kobayashi (red solid line), and von Herrath (red dashed line) protocols across a wide range of doses (x-axis). The shaded box indicates the peptide range (10–100 μg) used to calibrate the VM. Efficacy represents the number of VM that were protected from diabetes.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2992777&req=5

Figure 1: Two different amounts of B:9-23 peptide cannot protect from diabetes when administered nasally in 10-week-old NOD mice. A: Graphic representation of the B:9-23 nasal immunization protocol followed. Ten-week-old NOD mice received 40 or 100 μg B:9-23 peptide nasal every other day for the first 2 weeks and once a week for the following 5 weeks. B: There was no protection from diabetes after following the protocol described in A. Efficacy of treatment was assessed by monitoring blood glucose over time (n = 12 in all groups). Mice were considered diabetic when blood glucose was >16.67 mmol/l for two consecutive weekly measurements. C: Multiphasic dose responses were predicted for the protocols presented in supplementary Table 1. Nasal B:9-23 peptide therapy was simulated for the Daniel (green solid line), Kobayashi (red solid line), and von Herrath (red dashed line) protocols across a wide range of doses (x-axis). The shaded box indicates the peptide range (10–100 μg) used to calibrate the VM. Efficacy represents the number of VM that were protected from diabetes.
Mentions: Nasal B:9-23 peptide therapy has been used with mixed efficacy to induce tolerance in prediabetic NOD mice. A comparison of all published protocols shows differences in dose and frequency of administration. Daniel et al. (9) showed that 40 μg of B:9-23 given over 3 consecutive days at 4 weeks of age and then again every 4 weeks beginning at 9 weeks of age induced tolerance. By contrast, Kobayashi et al. (12) did not observe efficacy when 20 μg of the peptide was given over 5 consecutive days at 4 weeks of age and then once a week for 5 weeks thereafter. Because the impact of age at treatment initiation had not been assessed in either of these studies, and to obtain further constraints for the modeling effort, we used one additional protocol. In these experiments, nasal B:9-23 therapy failed to confer protection at two different doses (40, 100 μg/mouse) when immunizations were started in 10-week-old mice (Fig. 1A and B). The complex differences between these immunization protocols provided the rationale for computer modeling to more precisely define our experimental options.

Bottom Line: The experimental aim was to evaluate the impact of dose, frequency of administration, and age at treatment on Treg induction and optimal therapeutic outcome.Here, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed.In silico modeling was able to streamline the experimental design and to identify the particular time frame at which biomarkers associated with protection in live NODs were induced.

View Article: PubMed Central - PubMed

Affiliation: Diabetes Center, La Jolla Institute for Allergy and Immunology, La Jolla, California, USA.

ABSTRACT

Objective: Development of antigen-specific strategies to treat or prevent type 1 diabetes has been slow and difficult because of the lack of experimental tools and defined biomarkers that account for the underlying therapeutic mechanisms.

Research design and methods: The type 1 diabetes PhysioLab platform, a large-scale mathematical model of disease pathogenesis in the nonobese diabetic (NOD) mouse, was used to investigate the possible mechanisms underlying the efficacy of nasal insulin B:9-23 peptide therapy. The experimental aim was to evaluate the impact of dose, frequency of administration, and age at treatment on Treg induction and optimal therapeutic outcome.

Results: In virtual NOD mice, treatment efficacy was predicted to depend primarily on the immunization frequency and stage of the disease and to a lesser extent on the dose. Whereas low-frequency immunization protected from diabetes atrributed to Treg and interleukin (IL)-10 induction in the pancreas 1-2 weeks after treatment, high-frequency immunization failed. These predictions were confirmed with wet-lab approaches, where only low-frequency immunization started at an early disease stage in the NOD mouse resulted in significant protection from diabetes by inducing IL-10 and Treg.

Conclusions: Here, the advantage of applying computer modeling in optimizing the therapeutic efficacy of nasal insulin immunotherapy was confirmed. In silico modeling was able to streamline the experimental design and to identify the particular time frame at which biomarkers associated with protection in live NODs were induced. These results support the development and application of humanized platforms for the design of clinical trials (i.e., for the ongoing nasal insulin prevention studies).

Show MeSH
Related in: MedlinePlus