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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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Loss of efficacy after high nasal B:9–23 immunization frequency is predicted to be attributed to lower induction of aTreg in the NALT. A: Mechanisms driving predicted efficacy differ at low and high doses. Nasal insulin B:9-23 peptide therapy was simulated and protection from diabetes assessed using the von Herrath protocol over a dose range with various pharmacokinetic effects (PK) turned off. Only results with differences from control (all effects on) are shown. B and C: Increased immunization frequency is associated with lower induction of aTreg in the NALT. Time of administration for each protocol is indicated by colored dots. Model readouts for NALT aTreg (B) and nTreg (C) cells were assessed for each protocol (black, untreated; red, Daniel protocol; blue, Kobayashi protocol; green, von Herrath protocol). A single representative VM is shown.
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Figure 2: Loss of efficacy after high nasal B:9–23 immunization frequency is predicted to be attributed to lower induction of aTreg in the NALT. A: Mechanisms driving predicted efficacy differ at low and high doses. Nasal insulin B:9-23 peptide therapy was simulated and protection from diabetes assessed using the von Herrath protocol over a dose range with various pharmacokinetic effects (PK) turned off. Only results with differences from control (all effects on) are shown. B and C: Increased immunization frequency is associated with lower induction of aTreg in the NALT. Time of administration for each protocol is indicated by colored dots. Model readouts for NALT aTreg (B) and nTreg (C) cells were assessed for each protocol (black, untreated; red, Daniel protocol; blue, Kobayashi protocol; green, von Herrath protocol). A single representative VM is shown.

Mentions: Three major cell populations contributing to disease pathogenesis are represented in the PhysioLab platform: Th1, Th2 cells, and Treg (supplementary Table 2). Both adaptive (aTreg) and natural (nTreg) are represented, with aTreg comprising characteristics of both Tr1 and Th3 cells in the model. To gain a better understanding of the mechanisms underlying the efficacy of nasal B:9-23 therapy, we selectively turned off various of those mechanisms and reassessed the VM over a specified dose range after B:9-23 nasal immunization following the von Herrath protocol (Fig. 2A). At effective low doses, induction of B:9-23–specific Treg and Th2 cells were predicted to confer protection (blue line), whereas peripheral T-cell deletion (green line) was less important. Within both characterized Treg populations, nTreg induction did not seem to be important in conferring protection after nasal B:9-23 immunization, and it was induced by all three protocols in the NALT (Fig. 2B). In contrast, ineffective doses were predicted to be the result of aTreg deletion in the NALT because turning off this mechanism resulted in protection of all the VM (orange line). Closer looks at Treg dynamics in the model revealed that aTreg, however, was induced to a much greater extent by the effective Daniel protocol (Fig. 2C). More importantly, induction of aTreg by the ineffective protocols appeared to be actually hampered by the frequent, weekly immunizations, suggesting that too frequent immunizations might lead to aTreg deletion.


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)

Loss of efficacy after high nasal B:9–23 immunization frequency is predicted to be attributed to lower induction of aTreg in the NALT. A: Mechanisms driving predicted efficacy differ at low and high doses. Nasal insulin B:9-23 peptide therapy was simulated and protection from diabetes assessed using the von Herrath protocol over a dose range with various pharmacokinetic effects (PK) turned off. Only results with differences from control (all effects on) are shown. B and C: Increased immunization frequency is associated with lower induction of aTreg in the NALT. Time of administration for each protocol is indicated by colored dots. Model readouts for NALT aTreg (B) and nTreg (C) cells were assessed for each protocol (black, untreated; red, Daniel protocol; blue, Kobayashi protocol; green, von Herrath protocol). A single representative VM is shown.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 2: Loss of efficacy after high nasal B:9–23 immunization frequency is predicted to be attributed to lower induction of aTreg in the NALT. A: Mechanisms driving predicted efficacy differ at low and high doses. Nasal insulin B:9-23 peptide therapy was simulated and protection from diabetes assessed using the von Herrath protocol over a dose range with various pharmacokinetic effects (PK) turned off. Only results with differences from control (all effects on) are shown. B and C: Increased immunization frequency is associated with lower induction of aTreg in the NALT. Time of administration for each protocol is indicated by colored dots. Model readouts for NALT aTreg (B) and nTreg (C) cells were assessed for each protocol (black, untreated; red, Daniel protocol; blue, Kobayashi protocol; green, von Herrath protocol). A single representative VM is shown.
Mentions: Three major cell populations contributing to disease pathogenesis are represented in the PhysioLab platform: Th1, Th2 cells, and Treg (supplementary Table 2). Both adaptive (aTreg) and natural (nTreg) are represented, with aTreg comprising characteristics of both Tr1 and Th3 cells in the model. To gain a better understanding of the mechanisms underlying the efficacy of nasal B:9-23 therapy, we selectively turned off various of those mechanisms and reassessed the VM over a specified dose range after B:9-23 nasal immunization following the von Herrath protocol (Fig. 2A). At effective low doses, induction of B:9-23–specific Treg and Th2 cells were predicted to confer protection (blue line), whereas peripheral T-cell deletion (green line) was less important. Within both characterized Treg populations, nTreg induction did not seem to be important in conferring protection after nasal B:9-23 immunization, and it was induced by all three protocols in the NALT (Fig. 2B). In contrast, ineffective doses were predicted to be the result of aTreg deletion in the NALT because turning off this mechanism resulted in protection of all the VM (orange line). Closer looks at Treg dynamics in the model revealed that aTreg, however, was induced to a much greater extent by the effective Daniel protocol (Fig. 2C). More importantly, induction of aTreg by the ineffective protocols appeared to be actually hampered by the frequent, weekly immunizations, suggesting that too frequent immunizations might lead to aTreg deletion.

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