| 1. |
- Eriksson, Ulf G, et al.
(author)
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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
- 2003
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In: Clinical Pharmacokinetics. - 0312-5963 .- 1179-1926. ; 42:7, s. 687-701
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Research review (peer-reviewed)abstract
- OBJECTIVE: 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.
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| 2. |
- Aoki, Yasunori, 1982-, et al.
(author)
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Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection
- 2017
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In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 44:6, s. 581-597
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Journal article (peer-reviewed)abstract
- Population model-based (pharmacometric) approaches are widely used for the analyses of phase IIb clinical trial data to increase the accuracy of the dose selection for phase III clinical trials. On the other hand, if the analysis is based on one selected model, model selection bias can potentially spoil the accuracy of the dose selection process. In this paper, four methods that assume a number of pre-defined model structure candidates, for example a set of dose-response shape functions, and then combine or select those candidate models are introduced. The key hypothesis is that by combining both model structure uncertainty and model parameter uncertainty using these methodologies, we can make a more robust model based dose selection decision at the end of a phase IIb clinical trial. These methods are investigated using realistic simulation studies based on the study protocol of an actual phase IIb trial for an oral asthma drug candidate (AZD1981). Based on the simulation study, it is demonstrated that a bootstrap model selection method properly avoids model selection bias and in most cases increases the accuracy of the end of phase IIb decision. Thus, we recommend using this bootstrap model selection method when conducting population model-based decision-making at the end of phase IIb clinical trials.
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| 3. |
- Bååthe, Sofie, et al.
(author)
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Population pharmacokinetics of melagatran, the active form of the oral direct thrombin inhibitor ximelagatran, in atrial fibrillation patients receiving long-term anticoagulation therapy
- 2006
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In: Clinical Pharmacokinetics. - : Springer Science and Business Media LLC. - 0312-5963 .- 1179-1926. ; 45:8, s. 803-819
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Journal article (peer-reviewed)abstract
- Background: Ximelagatran 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. Objective: To characterise the pharmacokinetics of melagatran in patients with nonvalvular atrial fibrillation (NVAF) receiving long-term treatment for prevention of stroke and systemic embolic events. Methods: A population pharmacokinetic model was developed based on data from three phase 11 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. Results: Melagatran 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. Conclusion: The 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.
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| 4. |
- Gao, H., et al.
(author)
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Estrogen attenuates vascular expression of inflammation associated genes and adhesion of monocytes to endothelial cells
- 2006
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In: Inflammation Research. - : Springer Science and Business Media LLC. - 1420-908X .- 1023-3830. ; 55:8, s. 349-353
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Journal article (peer-reviewed)abstract
- Objective: Investigate effects of estrogen at gene expression and functional levels in vascular wall cells treated with bacterial lipopolysaccharide (LPS). Materials and methods: Aortic segments from ovariectomized mice were treated with LPS for 24 h in the absence or presence of 17 beta-estradiol (E-2). Gene activity was determined by Affymetrix microarray analysis and real-time RTPCR. Adhesion of [H-3]-thymidine labelled human THP-1 monocytes to mouse bEnd.3 endothelial cells was determined by measuring radioactivity of DNA from co-culture homogenates. Results: Analysis of global gene expression profiles revealed that 10 nM E-2 attenuates LPS-induced (10 ng/ml) expression of genes coding for well-known acute-phase proteins, such as alpha-trypsin inhibitor heavy chain 4, serum amyloid A3 and lipocalin 2. The E-2-induced down-regulation of these three genes observed by microarray was confirmed by realtime RT-PCR. Treatment with 500ng/ml LPS increased adhesion of monocytes to endothelial cells more than two fold. Importantly, LPS-induced monocyte adhesion was fully prevented by 50nM E-2. Conclusion: Estrogen reduces expression of acute-phase protein genes and inhibits LPS-induced moncocyte adhesion to endothelial cells, suggesting that estrogen might have a vasculoprotective effect via this mechanism.
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| 5. |
- Hamrén, Bengt, et al.
(author)
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Mechanistic modelling of tesaglitazar pharmacokinetic data in subjects with various degrees of renal function : evidence of interconversion
- 2008
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In: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 65:6, s. 855-863
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Journal article (peer-reviewed)abstract
- 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 mu mol l(-1) and Km = 0.04 mmol 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.
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| 6. |
- Hamrén, Bengt, et al.
(author)
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Models for plasma glucose, HbA1c, and hemoglobin interrelationships in patients with type 2 diabetes following tesaglitazar treatment
- 2008
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In: Clinical Pharmacology and Therapeutics. - : Springer Science and Business Media LLC. - 0009-9236 .- 1532-6535. ; 84:2, s. 228-235
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Journal article (peer-reviewed)abstract
- Pharmacokinetic (PK) pharmacodynamic (PD) modeling was applied to understand and quantitate the interplay between tesaglitazar (a peroxisome proliferator-activated receptor alpha/gamma 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 similar to 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.
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| 7. |
- Hamrén, Bengt, et al.
(author)
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Pharmacokinetic-Pharmacodynamic Assessment of the Interrelationships Between Tesaglitazar Exposure and Renal Function in Patients With Type 2 Diabetes Mellitus
- 2012
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In: Journal of clinical pharmacology. - : Wiley. - 0091-2700 .- 1552-4604. ; 52:9, s. 1317-1327
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Journal article (peer-reviewed)abstract
- The effects of tesaglitazar on renal function (assessed as urinary clearance of 125I-sodium iothalamate or estimated by the modification of diet in renal disease formula) were studied in a 24-week open-label trial in type 2 diabetes mellitus patients randomized to daily doses of either tesaglitazar 2 mg or pioglitazone 45 mg. The aim of the analysis was to develop a population pharmacokinetic-pharmacodynamic model that could simultaneously describe the interrelationship between tesaglitazar exposure and reduction in renal function over time in patients with type 2 diabetes mellitus. The pharmacokinetic-pharmacodynamic model could adequately describe the interplay between tesaglitazar and glomerular filtration rate. A one-compartment model in which the apparent clearance was influenced by glomerular filtration rate characterized the pharmacokinetics of tesaglitazar. An indirect-response model was used for the slow time course of change in glomerular filtration rate, which decreased from 100 to 78 mL/min/1.73m2 after 12 weeks of treatment. All tesaglitazar-treated patients had a reduction in glomerular filtration rate, and available demographic variables could not explain differences in response. Patients treated with an angiotensin converting enzyme inhibitor were more sensitive to tesaglitazar and had larger glomerular filtration rate decrease compared to nontreated patients. Approximately 8 weeks after discontinuing treatment, mean glomerular filtration rate had returned towards baseline. The model and data give valuable insights into the dynamic changes in glomerular filtration rate over time.
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| 8. |
- Hamrén, Bengt, 1971-
(author)
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Safety and Efficacy Modelling in Anti-Diabetic Drug Development
- 2008
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Doctoral thesis (other academic/artistic)abstract
- A central aim in drug development is to ensure that the new drug is efficacious and safe in the intended patient population. Mathematical models describing the pharmacokinetic-pharmacodynamic (PK-PD) properties of a drug are valuable to increase the knowledge about drug effects and disease and can be used to inform decisions. The aim of this thesis was to develop mechanism-based PK-PD-disease models for important safety and efficacy biomarkers used in anti-diabetic drug development. Population PK, PK-PD and disease models were developed, based on data from clinical studies in subjects with varying degrees of renal function, non-diabetic subjects with insulin resistance and patients with type 2 diabetes mellitus (T2DM), receiving a peroxisome proliferator-activated receptor (PPAR) α/γ agonist, tesaglitazar. The PK model showed that a decreased renal elimination of the metabolite in renally impaired subjects leads to increased levels of metabolite undergoing interconversion and subsequent accumulation of tesaglitazar. Tesaglitazar negatively affects the glomerular filtration rate (GFR), and since renal function affects tesaglitazar exposure, a PK-PD model was developed to simultaneously describe this interrelationship. The model and data showed that all patients had decreases in GFR, which were reversible when discontinuing treatment. The PK-PD model described the interplay between fasting plasma glucose (FPG), glycosylated haemoglobin (HbA1c) and haemoglobin in T2DM patients. It provided a mechanistically plausible description of the release and aging of red blood cells (RBC), and the glucose dependent glycosylation of RBC to HbA1c. The PK-PD model for FPG and fasting insulin, incorporating components for β-cell mass, insulin sensitivity and impact of disease and drug treatment, realistically described the complex glucose homeostasis in the heterogeneous patient population. The mechanism-based PK, PK-PD and disease models increase the understanding about T2DM and important biomarkers, and can be used to improve decision making in the development of future anti-diabetic drugs.
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| 9. |
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| 10. |
- Nyman, Elin, et al.
(author)
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Requirements for multi-level systems pharmacology models to reach end-usage : the case of type 2 diabetes
- 2016
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In: Interface Focus. - London, UK : The Royal Society. - 2042-8898 .- 2042-8901. ; 6:2
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Research review (peer-reviewed)abstract
- 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, today's 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 decisionsupport systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.
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| 11. |
- Parkinson, Joanna, et al.
(author)
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Application of the integrated glucose–insulin model for cross-study characterization of T2DM patients on metformin background treatment
- 2016
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In: British Journal of Clinical Pharmacology. - : Wiley. - 0306-5251 .- 1365-2125. ; 82, s. 1613-1624
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Journal article (peer-reviewed)abstract
- © 2016 The British Pharmacological Society Aim: The integrated glucose–insulin (IGI) model is a semi-mechanistic physiological model which can describe the glucose–insulin homeostasis system following various glucose challenge settings. The aim of the present work was to apply the model to a large and diverse population of metformin-only-treated type 2 diabetes mellitus (T2DM) patients and identify patient-specific covariates. Methods: Data from four clinical studies were pooled, including glucose and insulin concentration–time profiles from T2DM patients on stable treatment with metformin alone following mixed-meal tolerance tests. The data were collected from a wide range of patients with respect to the duration of diabetes and level of glycaemic control. Results: The IGI model was expanded by four patient-specific covariates. The level of glycaemic control, represented by baseline glycosylated haemoglobin was identified as a significant covariate for steady-state glucose, insulin-dependent glucose clearance and the magnitude of the incretin effect, while baseline body mass index was a significant covariate for steady-state insulin levels. In addition, glucose dose was found to have an impact on glucose absorption rate. The developed model was used to simulate glucose and insulin profiles in different groups of T2DM patients, across a range of glycaemic control, and it was found accurately to characterize their response to the standard oral glucose challenge. Conclusions: The IGI model was successfully applied to characterize differences between T2DM patients across a wide range of glycaemic control. The addition of patient-specific covariates in the IGI model might be valuable for the future development of antidiabetic treatment and for the design and simulation of clinical studies.
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| 12. |
- Ribbing, Jakob, et al.
(author)
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A model for glucose, insulin, and beta-cell dynamics in subjects with insulin resistance and patients with type 2 diabetes.
- 2010
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In: Journal of clinical pharmacology. - : Wiley. - 1552-4604 .- 0091-2700. ; 50:8, s. 861-72
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Journal article (peer-reviewed)abstract
- 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.
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| 13. |
- Wellhagen, Gustaf J., et al.
(author)
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Dose-Response Mixed Models for Repeated Measures – a New Method for Assessment of Dose-Response
- 2020
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In: Pharmaceutical research. - : SPRINGER/PLENUM PUBLISHERS. - 0724-8741 .- 1573-904X. ; 37:8
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Journal article (peer-reviewed)abstract
- Purpose In this paper we investigated a new method for dose-response analysis of longitudinal data in terms of precision and accuracy using simulations. Methods The new method, called Dose-Response Mixed Models for Repeated Measures (DR-MMRM), combines conventional Mixed Models for Repeated Measures (MMRM) and dose-response modeling. Conventional MMRM can be applied for highly variable repeated measure data and is a way to estimate the drug effect at each visit and dose, however without any assumptions regarding the dose-response shape. Dose-response modeling, on the other hand, utilizes information across dose arms and describes the drug effect as a function of dose. Drug development in chronic kidney disease (CKD) is complicated by many factors, primarily by the slow progression of the disease and lack of predictive biomarkers. Recently, new approaches and biomarkers are being explored to improve efficiency in CKD drug development. Proteinuria, i.e. urinary albumin-to-creatinine ratio (UACR) is increasingly used in dose finding trials in patients with CKD. We use proteinuria to illustrate the benefits of DR-MMRM. Results The DR-MMRM had higher precision than conventional MMRM and less bias than a dose-response model on UACR change from baseline to end-of-study (DR-EOS). Conclusions DR-MMRM is a promising method for dose-response analysis.
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| 14. |
- Wellhagen, Gustaf, 1988-
(author)
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Pharmacometric Investigations of Prediction Precision and Advances of Models for Composite Scale Data
- 2020
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Doctoral thesis (other academic/artistic)abstract
- Clinical trials are needed to evaluate new treatments. In late-stage clinical trials, failures are mostly due to lack of efficacy. Fit-for-purpose analysis methods will likely increase the success rates and advance drug development by providing higher precision to support decisions such as go/no-go, dose selection, or sample size. This thesis presents new methods for analysis of composite scale data, and comparisons of prediction precision of new and standard analysis methods. Composite scale data arise from questions/items rated with integers. A total score can be derived, which is discrete and bounded. Item response theory (IRT) models are the natural choice for such data, since they use the item-level information. However, when only the total score is available they cannot be used. The bounded integer (BI) model is a new method for discrete, bounded outcomes. With composite scale total score data, it had superior fit compared to standard methods, because it respects the nature of the data. Further, a new method, formally linking IRT models to models for total score, was developed. The expected mean and variance, given an IRT model, was implemented in BI and continuous variable models. This improved fit, allowed estimation of IRT parameters, and allowed comparison of different model types.The prediction precision of both outcome and parameters were investigated with different methods, ranging from t-test to mechanistic pharmacometric models, for composite scale and continuous data. The most suitable method depended on the purpose, for example mechanistic models are superior at establishing a drug’s site of action.In conclusion, the choice of method should be based on the primary question, and also the data collected. The method should not be more complex than necessary, and the nature of the data respected. This thesis will help modellers select the most appropriate analysis method for a problem at hand.
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