Pharmacokinetic-pharmacodynamic (dipeptidyl peptidase-4 inhibition) model to support dose rationale in diabetes patients, including those with renal impairment, for once-weekly administered omarigliptin.

AIMS: To characterize the population pharmacokinetics (PK) and pharmacodynamics (PD) of the once-weekly dipeptidyl peptidase-4 (DPP-4) inhibitor omarigliptin in healthy subjects and patients with type 2 diabetes mellitus, and use these models to support the dosing recommendation for patient labelling including patients with renal impairment. METHODS: PK and PD were assessed from a total of 9827 omarigliptin concentrations collected from 1387 healthy subjects and patients participating in Phase 1, 2 and 3 studies examining single- or multiple-dose weekly administration of omarigliptin at doses ranging from 0.25 to 400 mg. Population PK and PD analyses were performed using nonlinear mixed effect modelling. RESULTS: A semi-mechanistic 2-compartment model with linear unbound clearance and concentration-dependent binding of omarigliptin to the DPP-4 enzyme in both the central and peripheral compartments adequately described omarigliptin PK. Key covariates on omarigliptin PK included reduced unbound clearance with renal impairment. A direct effect sigmoid maximum inhibitory efficacy model adequately described the relationship between omarigliptin plasma concentrations and DPP-4 inhibition. These models supported the current Japan label instructions that the approved omarigliptin 25-mg once-weekly dose be halved in patients with severe renal impairment and in those with end-stage renal disease. Also, if patients missed a dose, the next dose of omarigliptin should be taken as soon as remembered up to and including the day before the next scheduled dose. No other clinically important covariates were identified. CONCLUSION: The models in the present analysis adequately described PK and PD characteristics of omarigliptin and supported the dosing and administration section of the omarigliptin label.

Leveraging model-informed approaches for drug discovery and development in the cardiovascular space.

Cardiovascular disease remains a significant global health burden, and development of cardiovascular drugs in the current regulatory environment often demands large and expensive cardiovascular outcome trials. Thus, the use of quantitative pharmacometric approaches which can help enable early Go/No Go decision making, ensure appropriate dose selection, and increase the likelihood of successful clinical trials, have become increasingly important to help reduce the risk of failed cardiovascular outcomes studies. In addition, cardiovascular safety is an important consideration for many drug development programs, whether or not the drug is designed to treat cardiovascular disease; modeling and simulation approaches also have utility in assessing risk in this area. Herein, examples of modeling and simulation applied at various stages of drug development, spanning from the discovery stage through late-stage clinical development, for cardiovascular programs are presented. Examples of how modeling approaches have been utilized in early development programs across various therapeutic areas to help inform strategies to mitigate the risk of cardiovascular-related adverse events, such as QTc prolongation and changes in blood pressure, are also presented. These examples demonstrate how more informed drug development decisions can be enabled by modeling and simulation approaches in the cardiovascular area.

Translating QT interval prolongation from conscious dogs to humans.

AIM: In spite of screening procedures in early drug development, uncertainty remains about the propensity of new chemical entities (NCEs) to prolong the QT/QTc interval. The evaluation of proarrhythmic activity using a comprehensive in vitro proarrhythmia assay does not fully account for pharmacokinetic-pharmacodynamic (PKPD) differences in vivo. In the present study, we evaluated the correlation between drug-specific parameters describing QT interval prolongation in dogs and in humans. METHODS: Using estimates of the drug-specific parameter, data on the slopes of the PKPD relationships of nine compounds with varying QT-prolonging effects (cisapride, sotalol, moxifloxacin, carabersat, GSK945237, SB237376 and GSK618334, and two anonymized NCEs) were analysed. Mean slope estimates varied between -0.98 ms µM-1 and 6.1 ms µM-1 in dogs and -10 ms µM-1 and 90 ms µM-1 in humans, indicating a wide range of effects on the QT interval. Linear regression techniques were then applied to characterize the correlation between the parameter estimates across species. RESULTS: For compounds without a mixed ion channel block, a correlation was observed between the drug-specific parameter in dogs and humans (y = -1.709 + 11.6x; R2  = 0.989). These results show that per unit concentration, the drug effect on the QT interval in humans is 11.6-fold larger than in dogs. CONCLUSIONS: Together with information about the expected therapeutic exposure, the evidence of a correlation between the compound-specific parameter in dogs and in humans represents an opportunity for translating preclinical safety data before progression into the clinic. Whereas further investigation is required to establish the generalizability of our findings, this approach can be used with clinical trial simulations to predict the probability of QT prolongation in humans.

Identifying the translational gap in the evaluation of drug-induced QTc interval prolongation.

AIMS: Given the similarities in QTc response between dogs and humans, dogs are used in pre-clinical cardiovascular safety studies. The objective of our investigation was to characterize the PKPD relationships and identify translational gaps across species following the administration of three compounds known to cause QTc interval prolongation, namely cisapride, d, l-sotalol and moxifloxacin. METHODS: Pharmacokinetic and pharmacodynamic data from experiments in conscious dogs and clinical trials were included in this analysis. First, pharmacokinetic modelling and deconvolution methods were applied to derive drug concentrations at the time of each QT measurement. A Bayesian PKPD model was then used to describe QT prolongation, allowing discrimination of drug-specific effects from other physiological factors known to alter QT interval duration. A threshold of ≥10 ms was used to explore the probability of prolongation after drug administration. RESULTS: A linear relationship was found to best describe the pro-arrhythmic effects of cisapride, d,l-sotalol and moxifloxacin both in dogs and in humans. The drug-specific parameter (slope) in dogs was statistically significantly different from humans. Despite such differences, our results show that the probability of QTc prolongation ≥10 ms in dogs nears 100% for all three compounds at the therapeutic exposure range in humans. CONCLUSIONS: Our findings indicate that the slope of PKPD relationship in conscious dogs may be used as the basis for the prediction of drug-induced QTc prolongation in humans. Furthermore, the risk of QTc prolongation can be expressed in terms of the probability associated with an increase ≥10 ms, allowing direct inferences about the clinical relevance of the pro-arrhythmic potential of a molecule.

Not-in-trial simulation I: Bridging cardiovascular risk from clinical trials to real-life conditions.

AIMS: The assessment of heart rate-corrected QT (QTc) interval prolongation relies on the evidence of drug effects in healthy subjects. This study demonstrates the relevance of pharmacokinetic-pharmacodynamic (PKPD) relationships to characterize drug-induced QTc interval prolongation and explore the discrepancies between clinical trials and real-life conditions. METHODS: d,l-Sotalol data from healthy subjects and from the Rotterdam Study cohort were used to assess treatment response in a phase I setting and in a real-life conditions, respectively. Using modelling and simulation, drug effects at therapeutic doses were predicted in both populations. RESULTS: Inclusion criteria were shown to restrict the representativeness of the trial population in comparison to real-life conditions. A significant part of the typical patient population was excluded from trials due to weight and baseline QTc interval criteria. Relative risk was significantly different between sotalol users with and without heart failure, hypertension, diabetes and myocardial infarction (P < 0.01). Although drug effects do cause an increase in the relative risk of QTc interval prolongation, the presence of diabetes represented an increase from 4.0 [95% confidence interval (CI) 2.7-5.8] to 6.5 (95% CI 1.6-27.1), whilst for myocardial infarction it increased from 3.4 (95% CI 2.3-5.13) to 15.5 (95% CI 4.9-49.3). CONCLUSIONS: Our findings show that drug effects on QTc interval do not explain the observed QTc values in the population. The prevalence of high QTc values in the real-life population can be assigned to co-morbidities and concomitant medications. These findings substantiate the need to account for these factors when evaluating the cardiovascular risk of medicinal products.