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Eiji Ueyama - One of the best experts on this subject based on the ideXlab platform.
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Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium–Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus
Diabetes Therapy, 2020Co-Authors: Masako Saito, Atsunori Kaibara, Takeshi Kadokura, Junko Toyoshima, Satoshi Yoshida, Kenichi Kazuta, Eiji UeyamaAbstract:Introduction Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological Effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term Effects of ipragliflozin on clinical outcomes in patients with T2DM. Methods The time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the Maximum Drug Effect ( E _max) model and disease progression model. As a predictive factor of Drug Effect, estimated ΔUGE was introduced into the E _max model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category. Results The developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant Effects on the glucose-lowering Effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study. Conclusions Empirical models established based on the Effect of UGE well predicted the renal function-dependent long-term glucose-lowering Effects of ipragliflozin in patients with T2DM.
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Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus.
Diabetes therapy : research treatment and education of diabetes and related disorders, 2020Co-Authors: Masako Saito, Atsunori Kaibara, Takeshi Kadokura, Junko Toyoshima, Satoshi Yoshida, Kenichi Kazuta, Eiji UeyamaAbstract:Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological Effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term Effects of ipragliflozin on clinical outcomes in patients with T2DM. The time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the Maximum Drug Effect (Emax) model and disease progression model. As a predictive factor of Drug Effect, estimated ΔUGE was introduced into the Emax model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category. The developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant Effects on the glucose-lowering Effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study. Empirical models established based on the Effect of UGE well predicted the renal function-dependent long-term glucose-lowering Effects of ipragliflozin in patients with T2DM.
Johannes H Proost - One of the best experts on this subject based on the ideXlab platform.
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Pharmacokinetic-pharmacodynamic modelling of antipsychotic Drugs in patients with schizophrenia: part II: the use of subscales of the PANSS score.
Schizophrenia research, 2013Co-Authors: Venkatesh Pilla Reddy, Meindert Danhof, Magdalena Kozielska, Ahmed Abbas Suleiman, Martin Johnson, An Vermeulen, Jing Liu, Rik De Greef, Geny M M Groothuis, Johannes H ProostAbstract:The superiority of atypical antipsychotics (also known as second-generation antipsychotics (SGAs)) over typical antipsychotics (first generation antipsychotics (FGAs)) for negative symptom control in schizophrenic patients is widely debated. The objective of this study was to characterize the time course of the scores of the 3 subscales (positive, negative, general) of the Positive and Negative Syndrome Scale (PANSS) after treatment of patients with antipsychotics, and to compare the control of negative symptom by SGAs versus a FGA (haloperidol) using pharmacokinetic and pharmacodynamic (PKPD) modelling. In addition, to obtain insight in the relationship between the clinical efficacy and the in vitro and in vivo receptor pharmacology profiles, the D2 and 5-HT2A receptor occupancy levels of antipsychotics were related to the Effective concentrations. The PKPD model structure developed earlier (part I) was used to quantify the Drug Effect using the 3 PANSS subscales. The Maximum Drug Effect sizes (Emax) of oral SGAs (risperidone, olanzapine, ziprasidone, and paliperidone) across PANSS subscales were compared with that of haloperidol, while accounting for the placebo Effect. Using the estimates of PKPD model parameters, the Effective concentrations (Ceff) needed to achieve 30% reduction in the PANSS subscales were computed. Calculated Effective concentrations were then correlated with receptor pharmacology profiles. Positive symptoms of schizophrenia responded well to all antipsychotics. Olanzapine showed a better Effect towards negative symptoms than the other SGAs and haloperidol. Dropout modelling results showed that the probability of a patient dropping out from a trial was associated with all subscales, but was more strongly correlated with the positive subscale than with the negative or the general subscales. Our results suggest that different levels of D2 or 5-HT2A receptor occupancy are required to achieve improvement in PANSS subscales. This PKPD modelling approach can be helpful to differentiate the Effect of antipsychotics across the different symptom domains of schizophrenia. Our analysis revealed that olanzapine seems to be superior in treating the negative symptoms compared to other non-clozapine SGAs. The relationship between receptor pharmacology profiles of the antipsychotics and their clinical efficacy is not yet fully understood. Copyright © 2013 Elsevier B.V. All rights reserved.
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Pharmacokinetic-pharmacodynamic modelling of antipsychotic Drugs in patients with schizophrenia : Part II: The use of subscales of the PANSS score
Schizophrenia Research, 2013Co-Authors: Venkatesh Pilla Reddy, Meindert Danhof, Magdalena Kozielska, Ahmed Abbas Suleiman, Martin Johnson, An Vermeulen, Jing Liu, Rik De Greef, Geny M M Groothuis, Johannes H ProostAbstract:Background and objectives: The superiority of atypical antipsychotics (also known as second-generation antipsychotics (SGAs)) over typical antipsychotics (first generation antipsychotics (FGAs)) for negative symptom control in schizophrenic patients is widely debated. The objective of this study was to characterize the time course of the scores of the 3 subscales (positive, negative, general) of the Positive and Negative Syndrome Scale (PANSS) after treatment of patients with antipsychotics, and to compare the control of negative symptom by SGAs versus a FGA (haloperidol) using pharmacokinetic and pharmacodynamic (PKPD) modelling. In addition, to obtain insight in the relationship between the clinical efficacy and the in vitro and in vivo receptor pharmacology profiles, the D-2 and 5-HT2A receptor occupancy levels of antipsychotics were related to the Effective concentrations. Methods: The PKPD model structure developed earlier (part I) was used to quantify the Drug Effect using the 3 PANSS subscales. The Maximum Drug Effect sizes (E-max) of oral SGAs (risperidone, olanzapine, ziprasidone, and paliperidone) across PANSS subscales were compared with that of haloperidol, while accounting for the placebo Effect. Using the estimates of PKPD model parameters, the Effective concentrations (C-eff) needed to achieve 30% reduction in the PANSS subscales were computed. Calculated Effective concentrations were then correlated with receptor pharmacology profiles. Results: Positive symptoms of schizophrenia responded well to all antipsychotics. Olanzapine showed a better Effect towards negative symptoms than the other SGAs and haloperidol. Dropout modelling results showed that the probability of a patient dropping out from a trial was associated with all subscales, but was more strongly correlated with the positive subscale than with the negative or the general subscales. Our results suggest that different levels of D-2 or 5-HT2A receptor occupancy are required to achieve improvement in PANSS subscales. Conclusions: This PKPD modelling approach can be helpful to differentiate the Effect of antipsychotics across the different symptom domains of schizophrenia. Our analysis revealed that olanzapine seems to be superior in treating the negative symptoms compared to other non-clozapine SGAs. The relationship between receptor pharmacology profiles of the antipsychotics and their clinical efficacy is not yet fully understood. (C) 2013 Elsevier B.V. All rights reserved.
Masako Saito - One of the best experts on this subject based on the ideXlab platform.
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Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium–Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus
Diabetes Therapy, 2020Co-Authors: Masako Saito, Atsunori Kaibara, Takeshi Kadokura, Junko Toyoshima, Satoshi Yoshida, Kenichi Kazuta, Eiji UeyamaAbstract:Introduction Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological Effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term Effects of ipragliflozin on clinical outcomes in patients with T2DM. Methods The time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the Maximum Drug Effect ( E _max) model and disease progression model. As a predictive factor of Drug Effect, estimated ΔUGE was introduced into the E _max model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category. Results The developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant Effects on the glucose-lowering Effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study. Conclusions Empirical models established based on the Effect of UGE well predicted the renal function-dependent long-term glucose-lowering Effects of ipragliflozin in patients with T2DM.
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Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus.
Diabetes therapy : research treatment and education of diabetes and related disorders, 2020Co-Authors: Masako Saito, Atsunori Kaibara, Takeshi Kadokura, Junko Toyoshima, Satoshi Yoshida, Kenichi Kazuta, Eiji UeyamaAbstract:Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological Effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term Effects of ipragliflozin on clinical outcomes in patients with T2DM. The time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the Maximum Drug Effect (Emax) model and disease progression model. As a predictive factor of Drug Effect, estimated ΔUGE was introduced into the Emax model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category. The developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant Effects on the glucose-lowering Effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study. Empirical models established based on the Effect of UGE well predicted the renal function-dependent long-term glucose-lowering Effects of ipragliflozin in patients with T2DM.
Meindert Danhof - One of the best experts on this subject based on the ideXlab platform.
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Pharmacokinetic-pharmacodynamic modelling of antipsychotic Drugs in patients with schizophrenia : Part II: The use of subscales of the PANSS score
Schizophrenia Research, 2013Co-Authors: Venkatesh Pilla Reddy, Meindert Danhof, Magdalena Kozielska, Ahmed Abbas Suleiman, Martin Johnson, An Vermeulen, Jing Liu, Rik De Greef, Geny M M Groothuis, Johannes H ProostAbstract:Background and objectives: The superiority of atypical antipsychotics (also known as second-generation antipsychotics (SGAs)) over typical antipsychotics (first generation antipsychotics (FGAs)) for negative symptom control in schizophrenic patients is widely debated. The objective of this study was to characterize the time course of the scores of the 3 subscales (positive, negative, general) of the Positive and Negative Syndrome Scale (PANSS) after treatment of patients with antipsychotics, and to compare the control of negative symptom by SGAs versus a FGA (haloperidol) using pharmacokinetic and pharmacodynamic (PKPD) modelling. In addition, to obtain insight in the relationship between the clinical efficacy and the in vitro and in vivo receptor pharmacology profiles, the D-2 and 5-HT2A receptor occupancy levels of antipsychotics were related to the Effective concentrations. Methods: The PKPD model structure developed earlier (part I) was used to quantify the Drug Effect using the 3 PANSS subscales. The Maximum Drug Effect sizes (E-max) of oral SGAs (risperidone, olanzapine, ziprasidone, and paliperidone) across PANSS subscales were compared with that of haloperidol, while accounting for the placebo Effect. Using the estimates of PKPD model parameters, the Effective concentrations (C-eff) needed to achieve 30% reduction in the PANSS subscales were computed. Calculated Effective concentrations were then correlated with receptor pharmacology profiles. Results: Positive symptoms of schizophrenia responded well to all antipsychotics. Olanzapine showed a better Effect towards negative symptoms than the other SGAs and haloperidol. Dropout modelling results showed that the probability of a patient dropping out from a trial was associated with all subscales, but was more strongly correlated with the positive subscale than with the negative or the general subscales. Our results suggest that different levels of D-2 or 5-HT2A receptor occupancy are required to achieve improvement in PANSS subscales. Conclusions: This PKPD modelling approach can be helpful to differentiate the Effect of antipsychotics across the different symptom domains of schizophrenia. Our analysis revealed that olanzapine seems to be superior in treating the negative symptoms compared to other non-clozapine SGAs. The relationship between receptor pharmacology profiles of the antipsychotics and their clinical efficacy is not yet fully understood. (C) 2013 Elsevier B.V. All rights reserved.
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Pharmacokinetic-pharmacodynamic modelling of antipsychotic Drugs in patients with schizophrenia: part II: the use of subscales of the PANSS score.
Schizophrenia research, 2013Co-Authors: Venkatesh Pilla Reddy, Meindert Danhof, Magdalena Kozielska, Ahmed Abbas Suleiman, Martin Johnson, An Vermeulen, Jing Liu, Rik De Greef, Geny M M Groothuis, Johannes H ProostAbstract:The superiority of atypical antipsychotics (also known as second-generation antipsychotics (SGAs)) over typical antipsychotics (first generation antipsychotics (FGAs)) for negative symptom control in schizophrenic patients is widely debated. The objective of this study was to characterize the time course of the scores of the 3 subscales (positive, negative, general) of the Positive and Negative Syndrome Scale (PANSS) after treatment of patients with antipsychotics, and to compare the control of negative symptom by SGAs versus a FGA (haloperidol) using pharmacokinetic and pharmacodynamic (PKPD) modelling. In addition, to obtain insight in the relationship between the clinical efficacy and the in vitro and in vivo receptor pharmacology profiles, the D2 and 5-HT2A receptor occupancy levels of antipsychotics were related to the Effective concentrations. The PKPD model structure developed earlier (part I) was used to quantify the Drug Effect using the 3 PANSS subscales. The Maximum Drug Effect sizes (Emax) of oral SGAs (risperidone, olanzapine, ziprasidone, and paliperidone) across PANSS subscales were compared with that of haloperidol, while accounting for the placebo Effect. Using the estimates of PKPD model parameters, the Effective concentrations (Ceff) needed to achieve 30% reduction in the PANSS subscales were computed. Calculated Effective concentrations were then correlated with receptor pharmacology profiles. Positive symptoms of schizophrenia responded well to all antipsychotics. Olanzapine showed a better Effect towards negative symptoms than the other SGAs and haloperidol. Dropout modelling results showed that the probability of a patient dropping out from a trial was associated with all subscales, but was more strongly correlated with the positive subscale than with the negative or the general subscales. Our results suggest that different levels of D2 or 5-HT2A receptor occupancy are required to achieve improvement in PANSS subscales. This PKPD modelling approach can be helpful to differentiate the Effect of antipsychotics across the different symptom domains of schizophrenia. Our analysis revealed that olanzapine seems to be superior in treating the negative symptoms compared to other non-clozapine SGAs. The relationship between receptor pharmacology profiles of the antipsychotics and their clinical efficacy is not yet fully understood. Copyright © 2013 Elsevier B.V. All rights reserved.
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The influence of dosage time of midazolam on its pharmacokinetics and Effects in humans.
Clinical pharmacology and therapeutics, 1991Co-Authors: Richard P. Koopmans, J. Dingemanse, Meindert Danhof, Gerard P M Horsten, Chris J. Van BoxtelAbstract:The influence of dosage time of midazolam on its pharmacokinetics and Effects on the central nervous system were investigated in six healthy volunteers, with pharmacokinetic-pharmacodynamic modeling. Each volunteer received single oral doses of 15 mg midazolam on four separate occasions: 8 AM, 2 PM, 8 PM, and 2 AM. An almost significant circadian variation was found in elimination half-life, shortest at 2 PM (1.26 ± 0.47 hours, mean ± SD) and longest at 2 AM (1.57 ± 0.44 hours) (p = 0.05). Drug Effects measured were α activity of the electroencepalograph and P100 latency of the visual-evoked response. The Maximum Drug Effect (Emax) model described the concentration-Effect relationship, extended with either a threshold Drug concentration or a sigmoidicity parameter. A significant circadian variation was found in baseline α activity: highest at 8 AM (109% ± 19% of the 24-hour mean) and lowest at 2 AM (80% ± 12%). For α activity the Drug concentration at half-Maximum Effect of both threshold Emax model and sigmoid Emax model showed lower values at 8 AM and 2 AM and higher values at 2 PM and 8 PM. However, these differences were either not significant (p = 0.10, threshold model) or on the verge of statistical significance (p = 0.05, sigmoid model). No circadian variation was found in the parameters describing the Effect on the visual-evoked response. We conclude that the sensitivity of the central nervous system to midazolam, as reflected in α activity, possibly shows a circadian variation. Clinical Pharmacology and Therapeutics (1991) 50, 16–24; doi:10.1038/clpt.1991.99
Atsunori Kaibara - One of the best experts on this subject based on the ideXlab platform.
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Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium–Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus
Diabetes Therapy, 2020Co-Authors: Masako Saito, Atsunori Kaibara, Takeshi Kadokura, Junko Toyoshima, Satoshi Yoshida, Kenichi Kazuta, Eiji UeyamaAbstract:Introduction Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological Effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term Effects of ipragliflozin on clinical outcomes in patients with T2DM. Methods The time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the Maximum Drug Effect ( E _max) model and disease progression model. As a predictive factor of Drug Effect, estimated ΔUGE was introduced into the E _max model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category. Results The developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant Effects on the glucose-lowering Effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study. Conclusions Empirical models established based on the Effect of UGE well predicted the renal function-dependent long-term glucose-lowering Effects of ipragliflozin in patients with T2DM.
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Model-based Prediction of the Long-term Glucose-Lowering Effects of Ipragliflozin, a Selective Sodium-Glucose Cotransporter 2 (SGLT2) Inhibitor, in Patients with Type 2 Diabetes Mellitus.
Diabetes therapy : research treatment and education of diabetes and related disorders, 2020Co-Authors: Masako Saito, Atsunori Kaibara, Takeshi Kadokura, Junko Toyoshima, Satoshi Yoshida, Kenichi Kazuta, Eiji UeyamaAbstract:Sodium-dependent glucose cotransporter 2 (SGLT2) inhibitors inhibit the reabsorption of glucose from the kidneys and increase urinary glucose excretion (UGE), thereby lowering the blood glucose concentration in people suffering from type 1 and type 2 diabetes mellitus (T2DM). In a previous study, we reported a pharmacokinetics/pharmacodynamics model to estimate individual change in UGE (ΔUGE), which is a direct pharmacological Effect of SGLT2 inhibitors. In this study, we report our enhancement of the previous model to predict the long-term Effects of ipragliflozin on clinical outcomes in patients with T2DM. The time course of fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) in patients with T2DM following ipragliflozin treatment that had been observed in earlier clinical trials was modeled using empirical models combined with the Maximum Drug Effect (Emax) model and disease progression model. As a predictive factor of Drug Effect, estimated ΔUGE was introduced into the Emax model, instead of ipragliflozin exposure. The developed models were used to simulate the time course of FPG and HbA1c following once-daily treatment with placebo or ipragliflozin at doses of 12.5, 25, 50 and 100 mg, and the changes at 52 weeks at the approved dose of 50 mg were summarized by renal function category. The developed models that included UGE as a dependent variable of response were found to well describe observed time courses in FPG and HbA1c. Baseline blood glucose level and renal function had significant Effects on the glucose-lowering Effect of ipragliflozin, and these models enabled quantification of these impacts on clinical outcomes. Simulated median changes in HbA1c in T2DM patients with mild and moderate renal impairment were 25 and 63% lower, respectively, than those in T2DM patients with normal renal function. These results are consistent with the observed clinical data from a previous renal impairment study. Empirical models established based on the Effect of UGE well predicted the renal function-dependent long-term glucose-lowering Effects of ipragliflozin in patients with T2DM.