Bengt Hamrén

Models for Plasma Glucose, HbA1c, and Hemoglobin Interrelationships in Patients with Type 2 Diabetes Following Tesaglitazar Treatment

Pharmacokinetic (PK) pharmacodynamic (PD) modeling was applied to understand and quantitate the interplay between tesaglitazar (a peroxisome proliferator–activated receptor α/γ agonist) exposure, fasting plasma glucose (FPG), hemoglobin (Hb), and glycosylated hemoglobin (HbA1c) in type 2 diabetic patients. Data originated from a 12‐week dose‐ranging study with tesaglitazar. The primary objective was to develop a mechanism‐based PD model for the FPG–HbA1c relationship. The secondary objective was to investigate possible mechanisms for the tesaglitazar effect on Hb. Following initiation of tesaglitazar therapy, time to new FPG steady state was ~9 weeks, and tesaglitazar potency in females was twice that in males. The model included aging of red blood cells (RBCs) using a transit compartment approach. The RBC life span was estimated to 135 days. The transformation from RBC to HbA1c was modeled as an FPG‐dependent process. The model indicated that the tesaglitazar effect on Hb was caused by hemodilution of RBCs.

Pharmacokinetics of melagatran and the effect on ex vivo coagulation time in orthopaedic surgery patients receiving subcutaneous melagatran and oral ximelagatran: a population model analysis.

AbstractObjective: Ximelagatran, an oral direct thrombin inhibitor, is rapidly bioconverted to melagatran, its active form. The objective of this population analysis was to characterise the pharmacokinetics of melagatran and its effect on activated partial thromboplastin time (APTT), an ex vivo measure of coagulation time, in orthopaedic surgery patients sequentially receiving subcutaneous melagatran and oral ximelagatran as prophylaxis for venous thromboembolism. To support the design of a pivotal dose-finding study, the impact of individualised dosage based on bodyweight and calculated Creatinine clearance was examined. Design and methods: Pooled data obtained in three small dose-guiding studies were analysed. The patients received twice-daily administration, with either subcutaneous melagatran alone or a sequential regimen of subcutaneous melagatran followed by oral ximelagatran, for 8–11 days starting just before initiation of surgery. Nonlinear mixed-effects modelling was used to evaluate rich data of melagatran pharmacokinetics (3326 observations) and the pharmacodynamic effect on APTT (2319 observations) in samples from 216 patients collected in the three dose-guiding trials. The pharmacokinetic and pharmacodynamic models were validated using sparse data collected in a subgroup of 319 patients enrolled in the pivotal dose-finding trial. The impact of individualised dosage on pharmacokinetic and pharmacodynamic variability was evaluated by simulations of the pharmacokinetic-pharmacodynamic model. Results: The pharmacokinetics of melagatran were well described by a one-compartment model with first-order absorption after both subcutaneous melagatran and oral ximelagatran. Melagatran clearance was correlated with renal function, assessed as calculated Creatinine clearance. The median population clearance (creatinine clearance 70 mL/min) was 5.3 and 22.9 L/h for the subcutaneous and oral formulations, respectively. The bioavailability of melagatran after oral ximelagatran relative to subcutaneous melagatran was 23%. The volume of distribution was influenced by bodyweight. For a patient with a bodyweight of 75kg, the median population estimates were 15.5 and 159L for the subcutaneous and oral formulations, respectively. The relationship between APTT and melagatran plasma concentration was well described by a power function, with a steeper slope during and early after surgery but no influence by any covariates. Simulations demonstrated that individualised dosage based on Creatinine clearance or bodyweight had no clinically relevant impact on the variability in melagatran pharmacokinetics or on the effect on APTT. Conclusions: The relatively low impact of individualised dosage on the pharmacokinetic and pharmacodynamic variability of melagatran supported the use of a fixed-dose regimen in the studied population of orthopaedic surgery patients, including those with mild to moderate renal impairment.

A Model for Glucose, Insulin, and Beta-Cell Dynamics in Subjects With Insulin Resistance and Patients With Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a progressive, metabolic disorder characterized by reduced insulin sensitivity and loss of beta‐cell mass (BCM), resulting in hyperglycemia. Population pharmacokinetic‐pharmacodynamic (PKPD) modeling is a valuable method to gain insight into disease and drug action. A semi‐mechanistic PKPD model incorporating fasting plasma glucose (FPG), fasting insulin, insulin sensitivity, and BCM in patients at various disease stages was developed. Data from 3 clinical trials (phase II/III) with a peroxisome proliferator‐activated receptor agonist, tesaglitazar, were used to develop the model. In this, a modeling framework proposed by Topp et al was expanded to incorporate the effects of treatment and impact of disease, as well as variability between subjects. The model accurately described FPG and fasting insulin data over time. The model included a strong relation between insulin clearance and insulin sensitivity, predicted 40% to 60% lower BCM in T2DM patients, and realistic improvements of BCM and insulin sensitivity with treatment. The treatment response on insulin sensitivity occurs within the first weeks, whereas the positive effects on BCM arise over several months. The semi‐mechanistic PKPD model well described the heterogeneous populations, ranging from nondiabetic, insulin‐resistant subjects to long‐term treated T2DM patients. This model also allows incorporation of clinical‐experimental studies and actual observations of BCM.

Mechanistic modelling of tesaglitazar pharmacokinetic data in subjects with various degrees of renal function – evidence of interconversion

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Tesaglitazar, is predominantly metabolized (to an acyl glucuronide of the parent compound) and 20% of given dose is found unchanged in the urine. Acyl glucuronides are know to be unstable and can become hydrolysed back to parent compound, a phenomena called interconversion. WHAT THIS STUDY ADDS A likely mechanism (interconversion) for the cause of the increased exposure of tesaglitazar in subjects with impaired renal function. A possible modelling framework to evaluate interconversion without dosing of the metabolite based on the simultaneous analysis of plasma and urine data from a group of subjects with varying renal function. A mechanistic understanding of the pharmacokinetic properties of tesaglitazar and its metabolite. AIMS To develop a mechanistic pharmacokinetic (PK) model for tesaglitazar and its metabolite (an acyl glucuronide) following oral administration of tesaglitazar to subjects with varying renal function, and derive an explanation for the increased plasma exposure of tesaglitazar in subjects with impaired renal function. METHODS Data were from a 6-week study in subjects with renal insufficiency and matched controls undergoing repeated oral dosing with tesaglitazar (n = 41). Compartmental population PK modelling was employed to describe the PK of tesaglitazar and its metabolite, in plasma and urine, simultaneously. Two hypotheses were tested to investigate the increased exposure of tesaglitazar in subjects with renal functional impairment: tesaglitazar metabolism is correlated with renal function, or metabolite elimination is reduced in renal insufficiency, leading to increased hydrolysis (interconversion) to the parent compound via biliary circulation. RESULTS The hypothesis for interconversion was best supported by the data. The population PK model included first-order absorption, two-compartment disposition and separate renal (0.027 l h(-1)) and metabolic (1.9 l h(-1)) clearances for tesaglitazar. The model for the metabolite; one-compartment disposition with renal (saturable, V(max) = 0.19 micromol l(-1) and K(m) = 0.04 micromol l(-1)) and nonrenal clearances (1.2 l h(-1)), biliary secretion (12 h(-1)) to the gut, where interconversion and reabsorption (0.8 h(-1)) of tesaglitazar occurred. CONCLUSION A mechanistic population PK model for tesaglitazar and its metabolite was developed in subjects with varying degrees of renal insufficiency. The model and data give insight into the likely mechanism (interconversion) of the increased tesaglitazar exposure in renally impaired subjects, and separate elimination and interconversion processes without dosing of the metabolite.

Population Pharmacokinetics of Melagatran, the Active Form of the Oral Direct Thrombin Inhibitor Ximelagatran, in Atrial Fibrillation Patients Receiving Long-Term Anticoagulation Therapy

BackgroundXimelagatran is an oral direct thrombin inhibitor for the prevention of thromboembolic disease. After oral administration, ximelagatran is rapidly absorbed and bioconverted to its active form, melagatran.ObjectiveTo characterise the pharmacokinetics of melagatran in patients with nonvalvular atrial fibrillation (NVAF) receiving long-term treatment for prevention of stroke and systemic embolic events.MethodsA population pharmacokinetic model was developed based on data from three phase II studies (1177 plasma concentration observations in 167 patients, treated for up to 18 months) and confirmed by including data from two phase III studies (8702 plasma concentration observations in 3188 patients, treated for up to 24 months). The impact of individualised dosing on pharmacokinetic variability was evaluated by simulations of melagatran concentrations based on the pharmacokinetic model.ResultsMelagatran pharmacokinetics were consistent across the studied doses and duration of treatment, and were described by a one-compartment model with first-order absorption and elimination. Clearance of melagatran was correlated to creatinine clearance, which was the most important predictor of melagatran exposure (explained 54% of interpatient variance in clearance). Total variability (coefficient of variation) in exposure was 45%; intraindividual variability in exposure was 23%. Concomitant medication with the most common long-term used drugs in the study population had no relevant influence on melagatran pharmacokinetics. Simulations suggested that dose adjustment based on renal function or trough plasma concentration had a minor effect on overall pharmacokinetic variability and the number of patients with high melagatran exposure.ConclusionThe pharmacokinetics of melagatran in NVAF patients were predictable, and consistent with results from previously studied patient populations. Dose individualisation was predicted to have a low impact on pharmacokinetic variability, supporting the use of a fixed-dose regimen of ximelagatran for long-term anticoagulant therapy in the majority of NVAF patients.

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology—QSP—models). However, todays multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example—type 2 diabetes—and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them ‘personalized’ (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

Exposure- response for biomarkers of anticoagulant effects by the oral direct thrombin inhibitor AZD0837 in patients with atrial fibrillation.

AIMS AZD0837 is a novel oral anticoagulant investigated in clinical studies for stroke prevention in patients with atrial fibrillation (AF). It is bioconverted to its active form, AR-H067637, a potent, specific and reversible thrombin inhibitor. The effects on coagulation biomarkers were correlated with the pharmacokinetic (PK) exposure of AR-H067637 to guide selection of the effective dose regimen for a confirmatory efficacy study in AF patients. METHODS Blood samples were obtained from 601 AF patients randomized to one of four doses of AZD0837 (blinded treatment) or dose-adjusted vitamin K antagonists (VKA, open treatment) for 3-9 months. A pharmacodynamic model was developed to describe the time course of the AR-H067637 exposure dependent effects and the effect of VKA on fibrin D-dimer. The thrombin generation measured ex vivo in venous plasma was also investigated. RESULTS The PK exposure of AR-H067637 was stable with an interindividual variability of 33% and no or minor influence of patient demographics or comedications. For AZD0837, D-dimer levels decreased with more rapid onset than for VKA. The decrease in D-dimer levels correlated with steady-state plasma concentrations (C(ss)) of AR-H067637, with a maximum decrease of baseline D-dimer levels estimated to approximately 60% for both AZD0837 and VKA therapy. The effect on thrombin generation correlated closely with the plasma concentration of AR-H067637. CONCLUSIONS The effects on thrombin generation and fibrin D-dimer levels correlated with the plasma concentration of its active form and provided comparable effects to well-controlled VKA therapy at an exposure at least corresponding to the 300 mg once daily dose of AZD0837.

Comparison of the exposure–response relationship of dapagliflozin in adult and paediatric patients with type 2 diabetes mellitus

To quantitatively compare the exposure–response relationship of dapagliflozin in adult and paediatric patients with type 2 diabetes mellitus (T2DM) and to assess the potential impact of covariate effects.

Integrating dose estimation into a decision-making framework for model-based drug development

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Exposure‐Response Analyses Supporting Ticagrelor Dosing Recommendation in Patients With Prior Myocardial Infarction

The relationships between drug exposure and the composite risk of cardiovascular (CV) death, myocardial infarction (MI), and stroke as well as the risk of TIMI major bleeding were estimated following long‐term treatment with ticagrelor 60 or 90 mg twice daily in 20,942 patients with prior MI. These analyses support the primary reported efficacy and safety evaluations by showing that there were clear separations from placebo early in treatment with both doses, regardless of ticagrelor exposure, for both endpoints. In addition, the exposure‐response analyses provided new insight into the contribution of individual exposure levels, rather than dose, as a predictor of events and accounted for differences in the baseline risk between patients. The predicted risks of CV death/MI/stroke were similar despite an increase in the median predicted ticagrelor average steady‐state concentration from 606 nmol/L with ticagrelor 60 mg to 998 nmol/L with ticagrelor 90 mg (hazard ratios vs placebo of 0.83 and 0.81, respectively). The corresponding predicted risk of TIMI major bleeding slightly increased (hazard ratios vs placebo of 2.4 and 2.6, respectively). Apart from Japanese patients, showing a lower risk of CV death/MI/stroke, the response to ticagrelor was consistent across the study population, as supported by the combination of relatively flat exposure‐response relationships in the studied exposure range, similar sensitivity to ticagrelor exposure, and small exposure differences. Consequently, the present analyses support the selection of the 60‐mg dose for all demographic subgroups of patients studied.