| 1. |
- Ghadzi, Siti Maisharah Sheikh
(författare)
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Pharmacometrics Modelling in Type 2 Diabetes Mellitus : Implications on Study Design and Diabetes Disease Progression
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Pharmacometric modelling is widely used in many aspects related to type 2 diabetes mellitus (T2DM), for instance in the anti-diabetes drug development, and in quantifying the disease progression of T2DM.The aim of this thesis were to improve the design of early phase anti-diabetes drug development studies with the focus on the power to identify mechanism of drug action (MoA), and to characterize and quantify the progression from prediabetes to overt diabetes, both the natural progression and the progression with diet and exercise interventions, using pharmacometrics modelling.The appropriateness of a study design depends on the MoAs of the anti-hyperglycaemic drug. Depending on if the focus is power to identify drug effect or accuracy and precision of drug effect, the best design will be different. Using insulin measurements on top of glucose has increase the power to identify a correct drug effect, distinguish a correct MoA from the incorrect, and to identify a secondary MoA in most cases. The accuracy and precision of drug parameter estimates, however, was not affected by insulin. A natural diabetes disease progression model was successfully added in a previously developed model to describe parameter changes of glucose and insulin regulation among impaired glucose tolerance (IGT) subjects, with the quantification of the lifestyle intervention. In this model, the assessment of multiple short-term provocations was combined to predict the long-term disease progression, and offers apart from the assessment of the onset of T2DM also the framework for how to perform similar analysis. Another previously published model was further developed to characterize the weight change in driving the changes in glucose homeostasis in subjects with IGT. This model includes the complex relationship between dropout from study and weight and glucose changes.This thesis has provided a first written guidance in designing a study for pharmacometrics analysis when characterizing drug effects, for early phase anti-diabetes drug development. The characterisation of the progression from prediabetes to overt diabetes using pharmacometrics modelling was successfully performed. Both the natural progression and the progression with diet and exercise interventions were quantified in this thesis.
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| 2. |
- Wallin, Johan E., et al.
(författare)
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A tool for neutrophil guided dose adaptation in chemotherapy
- 2009
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier BV. - 0169-2607 .- 1872-7565. ; 93:3, s. 283-291
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Tidskriftsartikel (refereegranskat)abstract
- Chemotherapy dosing in anticancer treatment is a balancing act between achieving concentrations that are effective towards the malignancy and that result in acceptable side-effects. Neutropenia is one major side-effect of many antitumor agents, and is related to an increased risk of infection. A model capable of describing the time-course of myelosuppression from administered drug could be used in individual dose selection. In this paper we describe the transfer of a previously developed semi-mechanistic model for myelosuppression from NONMEM to a dosing tool in MS Excel, with etoposide as an example. The tool proved capable to solve a differential equation system describing the pharmacokinetics and pharmacodynamics, with estimation performance comparable to NONMEM. In the dosing tool the user provides neutrophil measures from a previous treatment course and request for the dose that results in a desired nadir in the upcoming course through a Bayesian estimation procedure.
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| 3. |
- Holford, N, et al.
(författare)
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Time for quantitative clinical pharmacology : a proposal for a pharmacometrics curriculum
- 2007
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Ingår i: Clinical Pharmacology and Therapeutics. - : Springer Science and Business Media LLC. - 0009-9236 .- 1532-6535. ; 82:1, s. 103-105
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Tidskriftsartikel (refereegranskat)abstract
- A formal training program in pharmacometrics is essential to train clinical pharmacology scientists. A proposal is made for a pharmacometrics curriculum. The curriculum has components at the undergraduate, graduate and postgraduate levels.
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| 4. |
- Baverel, Paul
(författare)
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Development and Evaluation of Nonparametric Mixed Effects Models
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- A nonparametric population approach is now accessible to a more comprehensive network of modelers given its recent implementation into the popular NONMEM application, previously limited in scope by standard parametric approaches for the analysis of pharmacokinetic and pharmacodynamic data. The aim of this thesis was to assess the relative merits and downsides of nonparametric models in a nonlinear mixed effects framework in comparison with a set of parametric models developed in NONMEM based on real datasets and when applied to simple experimental settings, and to develop new diagnostic tools adapted to nonparametric models. Nonparametric models as implemented in NONMEM VI showed better overall simulation properties and predictive performance than standard parametric models, with significantly less bias and imprecision in outcomes of numerical predictive check (NPC) from 25 real data designs. This evaluation was carried on by a simulation study comparing the relative predictive performance of nonparametric and parametric models across three different validation procedures assessed by NPC. The usefulness of a nonparametric estimation step in diagnosing distributional assumption of parameters was then demonstrated through the development and the application of two bootstrapping techniques aiming to estimate imprecision of nonparametric parameter distributions. Finally, a novel covariate modeling approach intended for nonparametric models was developed with good statistical properties for identification of predictive covariates. In conclusion, by relaxing the classical normality assumption in the distribution of model parameters and given the set of diagnostic tools developed, the nonparametric approach in NONMEM constitutes an attractive alternative to the routinely used parametric approach and an improvement for efficient data analysis.
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| 5. |
- Zandvliet, Anthe S., et al.
(författare)
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Two-stage model-based clinical trial design to optimize phase I development of novel anticancer agents
- 2010
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Ingår i: Investigational new drugs. - : Springer Science and Business Media LLC. - 0167-6997 .- 1573-0646. ; 28:1, s. 61-75
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Tidskriftsartikel (refereegranskat)abstract
- Background The phase I program of anticancer agents usually consists of multiple dose escalation studies to select a safe dose for various administration schedules. We hypothesized that pharmacokinetic and pharmacodynamic (PK-PD) modeling of an initial phase I study (stage 1) can be used for selection of an optimal starting dose for subsequent studies (stage 2) and that a post-hoc PK-PD analysis enhances the selection of a recommended dose for phase II evaluation. The aim of this analysis was to demonstrate that this two-stage model-based design, which does not interfere in the conduct of trials, is safe, efficient and effective. Methods PK and PD data of dose escalation studies were simulated for nine compounds and for five administration regimens (stage 1) for drugs with neutropenia as dose-limiting toxicity. PK-PD models were developed for each simulated study and were used to determine a starting dose for additional phase I studies (stage 2). The model-based design was compared to a conventional study design regarding safety (number of dose-limiting toxicities (DLTs)), efficiency (number of patients treated with a dose below the recommended dose) and effectiveness (precision of dose selection). Retrospective data of the investigational anticancer drug indisulam were used to show the applicability of the model-based design. Results The model-based design was as safe as the conventional design (median number of DLTs = 3) and resulted in a reduction of the number of patients who were treated with a dose below the recommended dose (-27%, power 89%). A post-hoc model-based determination of the recommended dose for future phase II studies was more precise than the conventional selection of the recommended dose (root mean squared error 8.3% versus 30%). Conclusions A two-stage model-based phase I design is safe for anticancer agents with dose-limiting myelosuppression and may enhance the efficiency of dose escalation studies by reducing the number of patients treated with a dose below the recommended dose and by increasing the precision of dose selection for phase II evaluation.
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| 6. |
- Liu, Han, et al.
(författare)
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A multistate modeling and simulation framework to learn dose–response of oncology drugs: Application to bintrafusp alfa in non‐small cell lung cancer
- 2023
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Ingår i: CPT. - 2163-8306. ; 12:11, s. 1738-1750
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- The dose/exposure-efficacy analyses are often conducted separately for oncology end points like best overall response, progression-free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose-end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition-specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF-beta and PD-L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non-small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose-specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2-months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.
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| 7. |
- Ueckert, Sebastian, 1983-, et al.
(författare)
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Challenges and potential of optimal design in late phase clinical trials through application in Alzheimer’s disease
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Optimal design is a methodology that can be a valuable tool for the planning of clinical studies. Current applications however, are largely limited to early phases of the drug development process. The increasing complexity in late phase trials is a major reason why optimal design is not applied at these stages. This work uses the example of Alzheimer's disease to investigate challenges and potential of applying optimal design in late phase clinical trials.Information from several sources was used to construct a disease progression model for Alzheimer's disease. The resulting model was used to optimize the study design of an Alzheimer's trial for three distinct metrics: maximal information, minimal number of samples and maximal power to detect a drug effect. Challenges encountered and addressed during the implementation included covariates, dropout and clinical constraints.Depending on the optimization criterion used, the optimal designs had 35% a higher efficiency, needed 33% fewer samples to obtain the same amount of information or required 70% fewer individuals to achieve 80% power compared to the reference design.Optimal design can improve the design and therefore reduce the costs of late phase trials. Several tools and techniques have been identified to address the main challenges connected to this application.
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| 8. |
- Verbeeck, Johan, et al.
(författare)
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How to Analyze Continuous and Discrete Repeated Measures in Small-Sample Cross-Over Trials?
- 2023
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Ingår i: Biometrics. - : Oxford University Press. - 0006-341X .- 1541-0420. ; 79:4, s. 3998-4011
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Tidskriftsartikel (refereegranskat)abstract
- To optimize the use of data from a small number of subjects in rare disease trials, an at first sight advantageous design is the repeated measures cross-over design. However, it is unclear how these within-treatment period and within-subject clustered data are best analyzed in small-sample trials. In a real-data simulation study based upon a recent epidermolysis bullosa simplex trial using this design, we compare non-parametric marginal models, generalized pairwise comparison models, GEE-type models and parametric model averaging for both repeated binary and count data. The recommendation of which methodology to use in rare disease trials with a repeated measures cross-over design depends on the type of outcome and the number of time points the treatment has an effect on. The non-parametric marginal model testing the treatment-time-interaction effect is suitable for detecting between group differences in the shapes of the longitudinal profiles. For binary outcomes with the treatment effect on a single time point, the parametric model averaging method is recommended, while in the other cases the unmatched generalized pairwise comparison methodology is recommended. Both provide an easily interpretable effect size measure, and do not require exclusion of periods or subjects due to incompleteness.
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| 9. |
- Alskär, Oskar
(författare)
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Mechanism-Based Modelling of Clinical and Preclinical Studies of Glucose Homeostasis
- 2018
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Glucose is an important nutrient and energy source in the body. However, too high concentration in the blood is harmful and may lead to several complications developing over time. It was estimated that 5 million people in the world died from complications related to diabetes during 2015. Several hormones and physiological factors are involved in the regulation of glucose homeostasis. To evaluate different aspects of glucose homeostasis and the effect of interventions, such as pharmacological treatment, glucose tolerance tests can be performed. In a glucose tolerance test glucose is administered either orally or intravenously, blood is sampled frequently and analyzed for different biomarkers. Mechanism-based pharmacometric models is a valuable tool in drug development, which can be applied to increase the knowledge about complex systems such as glucose homeostasis, quantify the effects of drugs, generate more information from clinical trials and contribute to more efficient study design. In this thesis, a new comprehensive mechanism-based pharmacometric model was developed. The model is capable of describing the most important aspects of glucose homeostasis during glucose tolerance test in healthy individuals and patients with type 2 diabetes, over a wide range of oral and intravenous glucose doses. Moreover, it can simultaneously describe regulation of gastric emptying and glucose absorption, regulation of the incretin hormones GLP-1 and GIP, hepatic extraction of insulin and the incretin effect, regulation of glucagon synthesis and regulation of endogenous glucose production. In addition, an interspecies scaling approach was developed by scaling a previously developed clinical glucose insulin model to describe intravenous glucose tolerance tests performed in mice, rats, dogs, pigs and monkeys. In conclusion, the developed mechanism-based models in this thesis increases the knowledge about short term regulation of glucose homeostasis and can be used to investigate combination treatments, drugs with multiple effects, and translation of drug effects between species, leading to improved drug development of new antidiabetic compounds.
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| 10. |
- Lacroix, Brigitte, 1978-, et al.
(författare)
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A Pharmacodynamic Markov Mixed-Effects Model for Determining the Effect of Exposure to Certolizumab Pegol on the ACR20 Score in Patients With Rheumatoid Arthritis
- 2009
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Ingår i: Clinical Pharmacology and Therapeutics. - : Nature Publishing Group. - 0009-9236 .- 1532-6535. ; 86:4, s. 387-395
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Tidskriftsartikel (refereegranskat)abstract
- The American College of Rheumatology 20% preliminary definition of improvement of rheumatoid arthritis (ACR20) is widely used in clinical trials to assess response to treatment. The objective of this analysis was to develop an exposure-response model of ACR20 in subjects treated with certolizumab pegol, and to predict clinical outcome following various treatment schedules. At each visit, subjects were classified as being ACR20 responders, ACR20 non-responders, or having dropped out. A Markov mixed-effect model was developed to investigate the drug effect on the transitions between the 3 defined states. Increasing certolizumab pegol exposure predicted an increasing probability of becoming a responder and remaining a responder, as well as a reduced probability of dropping out of treatment. Simulations of the ACR20 response rate support dosing regimens of 400 mg at weeks 0, 2 and 4 followed by 200 mg every 2 weeks, or alternative maintenance regimen of 400 mg every 4 weeks.
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