Simple pharmacometric tools for oral anti-diabetic drug development: competitive landscape for oral non-insulin therapies in type 2 diabetes

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

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

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.

Evaluation of non-linear-mixed-effect modeling to reduce the sample sizes of pediatric trials in type 2 diabetes mellitus

Recruitment for pediatric trials in Type II Diabetes Mellitus (T2DM) is very challenging, necessitating the exploration of new approaches for reducing the sample sizes of pediatric trials. This work aimed at assessing if a longitudinal Non-Linear-Mixed-Effect (NLME) analysis of T2DM trial could be more powerful and thus require fewer patients than two standard statistical analyses commonly used as primary or sensitivity efficacy analysis: Last-Observation-Carried-Forward (LOCF) followed by (co)variance (AN(C)OVA) analysis at the evaluation time-point, and Mixed-effects Model Repeated Measures (MMRM) analysis. Standard T2DM efficacy studies were simulated, with glycated hemoglobin (HbA1c) as the main endpoint, 24 weeks' study duration, 2 arms, assuming a placebo and a treatment effect, exploring three different scenarios for the evolution of HbA1c, and accounting for a dropout phenomenon. 1000 trials were simulated, then analyzed using the 3 analyses, whose powers were compared. As expected, the longitudinal modeling MMRM analysis was found to be more powerful than the LOCF + ANOVA analysis at week 24. The NLME analysis gave slightly more accurate drug-effect estimations than the two other methods, however it tended to slightly overestimate the magnitude of the drug effect, and it was more powerful than the MMRM analysis only in some scenarios of slow HbA1c decrease. The gain in power afforded by NLME was more apparent when two additional assessments enriched the design; however, the gain was not systematic for all scenarios. Finally, this work showed that NLME analyses may help to reduce significantly the required sample sizes in T2DM pediatric studies, but only for enriched designs and slow HbA1c decrease.

Differential Effects of Thiazolidinediones and Dipeptidyl Peptidase-4 Inhibitors on Insulin Resistance and β-Cell Function in Type 2 Diabetes Mellitus: A Propensity Score-Matched Analysis

INTRODUCTION

Comparisons of the glycemic durability between thiazolidinediones (TZDs) and dipeptidyl peptidase-4 (DPP-4) inhibitors remain insufficient. This study aimed to find clues for the differences in glycemic durability between TZDs and DPP-4 inhibitors by comparing the insulin resistance and β-cell function among patients using these agents.

METHODS

A total of 241 patients with type 2 diabetes mellitus (T2DM) treated with either pioglitazone (a TZD) or DPP-4 inhibitors as combination therapy with metformin for at least 1 year were analyzed. A propensity score based on the patients' baseline characteristics and glycated hemoglobin (HbA1c) was used to match them. Indices for insulin resistance and secretory function of β-cells, namely the homeostasis model assessment of insulin resistance (HOMA-IR) or β-cells (HOMA-β), were calculated and compared. Multiple regression analysis was performed to find the independent variables correlated with β-cell function or insulin resistance.

RESULTS

Evaluation of the data from 168 matched patients with T2DM showed that TZD users had significantly better insulin sensitivity compared with DPP-4 inhibitor users (HOMA-IR 2.3 ± 1.9 vs. 3.5 ± 3.2, p = 0.003). Conversely, DPP-4 inhibitor users secreted more insulin than TZD users (HOMA-β 45.7 ± 31.6 vs. 61.4 ± 49.5, p = 0.016). Multiple linear regression analysis showed that these agents were independently associated with both insulin resistance and β-cell function.

CONCLUSION

TZD users showed significantly better insulin sensitivity, whereas DPP-4 inhibitor users secreted more insulin from β-cells under similar glycemic control.

Population Pharmacokinetics and Exposure-Response (Efficacy and Safety/Tolerability) of Empagliflozin in Patients with Type 2 Diabetes

INTRODUCTION

The aim of the analysis was to characterize the population pharmacokinetics (PKs) and exposure-response (E-R) for efficacy (fasting plasma glucose, glycated hemoglobin) and safety/tolerability [hypoglycemia, genital infections, urinary tract infection (UTI), and volume depletion] of the sodium glucose cotransporter 2 inhibitor, empagliflozin, in patients with type 2 diabetes mellitus. This study extends the findings of previous analyses which described the PK and pharmacodynamics (PD) using early clinical studies of up to 12 weeks in duration.

METHODS

Population pharmacokinetic and E-R models were developed based on two Phase I, four Phase II, and four Phase III studies.

RESULTS

Variability in empagliflozin exposure was primarily affected by estimated glomerular filtration rate (eGFR) (less than twofold increase in exposure in patients with severe renal impairment). Consistent with its mode of action, the efficacy of empagliflozin was increased with elevated baseline plasma glucose levels and attenuated with decreasing renal function, but was still maintained to nearly half the maximal effect with eGFR as low as 30 mL/min/1.73 m(2). All other investigated covariates, including sex, body mass index, race, and age did not alter the PK or efficacy of empagliflozin to a clinically relevant extent. Compared with placebo, empagliflozin administration was associated with an exposure-independent increase in the incidence of genital infections and no significant change in the risk of UTI, hypoglycemia, or volume depletion.

CONCLUSION

Based on the results from the PK and E-R analysis, no dose adjustment is required for empagliflozin in the patient population for which the drug is approved.

FUNDING

Boehringer Ingelheim.

Exposure-response modelling for empagliflozin, a sodium glucose cotransporter 2 (SGLT2) inhibitor, in patients with type 2 diabetes

AIMS

To provide model-based clinical development decision support including dose selection guidance for empagliflozin, an orally administered sodium glucose cotransporter 2 inhibitor, through developed exposure-response (E-R) models for efficacy and tolerability in patients with type 2 diabetes mellitus (T2DM).

METHODS

Five randomized, placebo-controlled, multiple oral dose studies of empagliflozin in patients with T2DM (n = 974; 1-100 mg once daily, duration ≤12 weeks) were used to develop E-R models for efficacy (glycosylated haemoglobin [HbA1c ], fasting plasma glucose [FPG] and urinary glucose excretion). Two studies (n = 748, 12 weeks) were used to evaluate tolerability E-R.

RESULTS

The efficacy model predicted maximal decreases in FPG and HbA1c of 16% and 0.6%, respectively, assuming a baseline FPG concentration of 8 mm (144 mg dl(-1) ) and 10-25 mg every day empagliflozin targeted 80-90% of these maximums. Increases in exposure had no effect on incidence rates of hypoglycaemia (n = 4), urinary tract infection (n = 17) or genital/vulvovaginal-related (n = 16) events, although low prevalence rates may have precluded more accurate evaluation.

CONCLUSIONS

E-R analyses indicated that 10 and 25 mg once daily empagliflozin doses achieved near maximal glucose lowering efficacy.

Evaluation of the long-term durability and glycemic control of fasting plasma glucose and glycosylated hemoglobin for pioglitazone in Japanese patients with type 2 diabetes

BACKGROUND

This study applied a pharmacodynamic model-based approach to evaluate the long-term durability and glycemic control of pioglitazone in comparison with other oral glucose-lowering drugs in Japanese type 2 diabetes mellitus (T2DM) patients.

SUBJECTS AND METHODS

Japanese T2DM patients were enrolled in a prospective, randomized, open-label, blinded-end point study and received pioglitazone with or without other oral glucose-lowering drugs (excluding another thiazolidinedione [TZD]) (n=293) or oral glucose-lowering drugs excluding TZD (n=294). Treatment was adjusted to achieve glycosylated hemoglobin (HbA1c) <6.9%, and samples for fasting plasma glucose (FPG) and HbA1c were collected over 2.5-4 years. A simultaneous cascading indirect response model structure was applied to describe the time course of FPG and HbA1c. HbA1c levels were described using both an FPG-dependent and an FPG-independent function. To account for titration, drug effects for both treatment groups were implemented using a time-dependent Emax model.

RESULTS

Pioglitazone was superior in both time to maximum effect and the magnitude of reduction achieved in FPG and HbA1c. A greater reduction in median FPG (-21 mg/dL vs. -9 mg/dL) was observed with pioglitazone (P<0.05). Maximum drug effect for FPG was predicted to occur earlier (11 months) for pioglitazone than for the control group (14 months). The simulated additional reduction in FPG and HbA1c achieved with pioglitazone was predicted to be maintained beyond the currently observed study duration.

CONCLUSIONS

Pioglitazone was found to result in improved glycemic control and durability compared with control treatment. This model-based approach enabled the quantification of differences in FPG and HbA1c for both treatment groups and simulation to evaluate longer-term durability on FPG and HbA1c.

Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches

The link between glucose and HbA1c at steady state has previously been described using steady-state or longitudinal relationships. We evaluated five published methods for prediction of HbA1c after 26/28 weeks using data from four clinical trials. Methods (1) and (2): steady-state regression of HbA1c on fasting plasma glucose and mean plasma glucose, respectively, (3) an indirect response model of fasting plasma glucose effects on HbA1c, (4) model of glycosylation of red blood cells, and (5) coupled indirect response model for mean plasma glucose and HbA1c. Absolute mean prediction errors were 0.61, 0.38, 0.55, 0.37, and 0.15% points, respectively, for Methods 1 through 5. This indicates that predictions improved by using mean plasma glucose instead of fasting plasma glucose, by inclusion of longitudinal glucose data and further by inclusion of longitudinal HbA1c data until 12 weeks. For prediction of trial outcome, the longitudinal models based on mean plasma glucose (Methods 4 and 5) had substantially better performance compared with the other methods.

Longitudinal Modeling of the Relationship Between Mean Plasma Glucose and HbA1c Following Antidiabetic Treatments

Late-phase clinical trials within diabetes generally have a duration of 12–24 weeks, where 12 weeks may be too short to reach steady-state glycated hemoglobin (HbA1c). The main determinant for HbA1c is blood glucose, which reaches steady state much sooner. In spite of this, few publications have used individual data to assess the time course of both glucose and HbA1c, for predicting HbA1c. In this paper, we present an approach for predicting HbA1c at end-of-trial (24–28 weeks) using glucose and HbA1c measurements up to 12 weeks. The approach was evaluated using data from 4 trials covering 12 treatment arms (oral antidiabetic drug, glucagon-like peptide-1, and insulin treatment) with measurements at 24–28 weeks to evaluate predictions vs. observations. HbA1c percentage was predicted for each arm at end-of-trial with a mean prediction error of 0.14% [0.01;0.24]. Furthermore, end points in terms of HbA1c reductions relative to comparator were accurately predicted. The proposed model provides a good basis to optimize late-stage clinical development within diabetes.

LX4211, a dual SGLT1/SGLT2 inhibitor, improved glycemic control in patients with type 2 diabetes in a randomized, placebo-controlled trial

Thirty-six patients with type 2 diabetes mellitus (T2DM) were randomized 1:1:1 to receive a once-daily oral dose of placebo or 150 or 300 mg of the dual SGLT1/SGLT2 inhibitor LX4211 for 28 days. Relative to placebo, LX4211 enhanced urinary glucose excretion by inhibiting SGLT2-mediated renal glucose reabsorption; markedly and significantly improved multiple measures of glycemic control, including fasting plasma glucose, oral glucose tolerance, and HbA(1c); and significantly lowered serum triglycerides. LX4211 also mediated trends for lower weight, lower blood pressure, and higher glucagon-like peptide-1 levels. In a follow-up single-dose study in 12 patients with T2DM, LX4211 (300 mg) significantly increased glucagon-like peptide-1 and peptide YY levels relative to pretreatment values, probably by delaying SGLT1-mediated intestinal glucose absorption. In both studies, LX4211 was well tolerated without evidence of increased gastrointestinal side effects. These data support further study of LX4211-mediated dual SGLT1/SGLT2 inhibition as a novel mechanism of action in the treatment of T2DM.