Global ETD Search

31	Imaging and Quantification of Brain Serotonergic Activity using PET Lundquist, Pinelopi January 2006 (has links) This thesis investigates the potential of using positron emission tomography (PET) to study the biosynthesis and release of serotonin (5HT) at the brain serotonergic neuron. As PET requires probe compounds with specific attributes to enable imaging and quantification of biological processes, emphasis was placed on the evaluation of these attributes. The experiments established that the 5HT transporter radioligand [11C]-3-amino-4-(2-dimethylaminomethyl-phenylsulfanyl)-benzonitrile, [11C]DASB, is suitable for imaging and quantification of transporters in rats and rhesus monkeys. In addition, the binding of [11C]DASB in brain tissue is decreased when 5HT concentrations are increased by tranylcypromine administration. The sensitivity of [11C]DASB binding, under these experimental conditions, to increased endogenous 5HT concentrations demonstrates the potential of in vivo monitoring of 5HT release in rat and monkey models. The irreversible binding of 5-hydroxy-L-[β-11C]tryptophan, [11C]HTP, in the monkey brain was lower in the presence of NSD1015, which was used to inhibit the decarboxylase step in 5HT synthesis. [11C]HTP seems thus to have potential for tracking changes in the activity of this biosynthesis enzyme. In contrast, the accumulation of [11C]HTP was unaffected by clorgyline, which was used to inhibit metabolism of the probe in the brain. This appears to indicate that elimination of the main metabolite from the brain could be negligible and thus will not alter [11C]HTP quantification. The extent and distribution of the irreversible binding of a substrate for the first enzyme in 5HT formation, α-[11C]methyl-L-tryptophan, [11C]AMT, was different from those for [11C]HTP. This suggests that the two studied probe compounds provide estimates related to the enzyme activity of different steps in the 5HT biosynthesis pathway. A reference tissue version of the Patlak method for the analysis of data obtained by PET was also developed. This approach takes into account irreversible binding in the reference region and appears, therefore, to yield more reliable parameter estimates than the conventional reference Patlak analysis. The method is recommended for parameter estimation of [11C]HTP data when no metabolite-corrected plasma curve is available. Knowledge of altered 5HT synthesis and release in disease states and the consequences for effective pharmacotherapy can improve our knowledge of the aetiology of certain psychiatric and neurological diseases and enhance our ability to design more effective drugs. Pharmacokinetics/Pharmacotherapy Brain serotonin Neurotransmitter release Neurotransmitter synthesis Positron emission tomography Tracer validation Tracer modelling Farmakokinetik/Farmakoterapi
32	Mechanism-Based Modeling of the Glucose-Insulin Regulation during Clinical Provocation Experiments Jauslin-Stetina, Petra January 2008 (has links) Type 2 diabetes is a complex chronic metabolic disorder characterized by hyperglycemia associated with a relative deficiency of insulin secretion and a reduced response of target tissues to insulin. Considerable efforts have been put into the development of models describing the glucose-insulin system. The best known is Bergman’s “minimal” model for glucose, which is estimating glucose concentrations using fixed insulin concentrations as input. However, due to the involved feedback mechanisms, simultaneous modeling of both entities would be advantageous. This is particularly relevant if the model is intended to be used as a predictive tool. The mechanism-based glucose-insulin model presented in this thesis is able to simultaneously describe glucose and insulin profiles following a wide variety of clinical provocation experiments, such as intravenous and oral glucose tolerance tests, clamp studies and sequential meal tests over 24 hours. It consists of sub-models for glucose, labeled glucose and insulin kinetics. It also incorporates control mechanisms for the regulation of glucose production, insulin secretion, and glucose uptake. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Even if this model is a crude representation of a complex physiological system, its ability to represent the main processes of this system was established by identifying: 1) the difference in insulin secretion and insulin sensitivity between healthy volunteers and type 2 diabetics, 2) the action of incretin hormones after oral administration of glucose, 3) the circadian variation of insulin secretion and 4) the correct mechanism of action of a glucokinase activator, a new oral antidiabetic compound acting on both the pancreas and the liver. These promising results represent a proof of concept of a mechanistic drug-disease model that could play an important role in the clinical development of anti-diabetic drugs. Pharmacokinetics/Pharmacotherapy Glucose homeostasis Type 2 diabetes IVGTT OGTT Meal test Circadian variation Mechanism-based NONMEM Farmakokinetik/Farmakoterapi
33	Models for Ordered Categorical Pharmacodynamic Data Zingmark, Per-Henrik January 2005 (has links) In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented. Pharmacokinetics/Pharmacotherapy Pharmacodynamic Modelling Categorical data NONMEM Proportional odds model Markov model Differential drug effect model Farmakokinetik/Farmakoterapi PHARMACY FARMACI
34	Pharmacokinetics of antidepressant drugs : naturalistic and clinical trials / Reis, Margareta January 2003 (has links) (PDF) Diss. Linköping : Univ., 2003.
35	Covariate Model Building in Nonlinear Mixed Effects Models Ribbing, Jakob January 2007 (has links) <p>Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass.</p><p>The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design.</p><p>A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature.</p> Pharmacokinetics/Pharmacotherapy Pharmacokinetics Pharmacodynamics Modeling Covariate selection Stepwise selection Covariate analysis Methodology Model validation Model evaluation Type-2 diabetes Beta-cell function Meta analysis Cross-validation Pharmacometrics ED optimization Farmakokinetik/Farmakoterapi
36	Covariate Model Building in Nonlinear Mixed Effects Models Ribbing, Jakob January 2007 (has links) Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass. The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design. A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature. Pharmacokinetics/Pharmacotherapy Pharmacokinetics Pharmacodynamics Modeling Covariate selection Stepwise selection Covariate analysis Methodology Model validation Model evaluation Type-2 diabetes Beta-cell function Meta analysis Cross-validation Pharmacometrics ED optimization Farmakokinetik/Farmakoterapi
37	Quantification of Pharmaceuticals at the sub-cellular level using the NanoSIMS Dost, Maryam January 2024 (has links) Mass spectroscopy imaging (MSI) has become a vital tool in modern research due to its ability to visualize the spatial distribution of molecules within tissue samples. The collaboration between researchers at AZ, the University of Gothenburg, and Chalmers University of Technology using the NanoSIMS instrument and MSI-SIMS technology has opened up new avenues of exploration in pharmaceutical development, particularly in examining drugs and metabolites at sub-cellular levels. This groundbreaking research has the potential to significantly improve the efficacy and safety of future pharmaceutical products. NanoSIMS possesses a unique imaging and processing technique that enables high-resolution imaging of cellular structures and subcellular compartments. This powerful tool allows for the visualization and measurement of elements and isotopes at the subcellular level. The technique involves bombarding a sample with a focused primary ion beam, which causes the emission of secondary ions. These secondary ions are then analyzed to determine the elemental and isotopic composition of the sample. NanoSIMS is particularly useful for analyzing biomolecules since traditional Mass spectrometry methods cannot provide information about how molecules behave at the cellular level. Given that many of the drugs used today have intra-cellular targets, hence understanding the drug's cellular pathways is extremely important, especially in cases where the risk for organ toxicity is high due to the high dosage of the drugs. Our data from the image analysis indicated the presence of amiodarone inside the lysosomes; however, the lack of enrichment from the 13C portion of the dual-labeled molecule made it difficult to reach a variation below the LOD. Since our LOD is relatively high when working with 13C12C, we focused on the fact that accuracy, precision, and sensitivity would be the most crucial factors in our study. After adjusting these parameters, we obtained an image that made the measurement possible. This project aims to utilize a dual-labeled drug (13C and 127I) to bridge the absolute quantification ability of the 13C labeling scheme to the more sensitive labeling scheme. The focus of this study lies therefore on optimization and the relationship between Spatial resolution, Sensitivity, Mass Resolution, Accuracy, and Precision. This technique is extremely promising, but the limit of detection is relatively high mainly due to the high percentage of carbon in the sample. Despite this fact, we were able to present some valuable data. Our analysis showed that the sensitivity of the 127I is much better than 13C, however, we produced an image where the ratio between the labels was above the detection limit. Using this data, a Relative sensitivity factor (RSF) value was measured, and the concentration of the drug could be estimated by applying the quantification equation. Mass spectroscopy imaging AstraZeneca Nanosims Therapeutics pharmacokinetics pharmacodynamics Quantitative Visualization calibration curve RSF pharmacology Data Analysis Chemistry Quantification isotopes Pharmaceuticals subcellular drug drug target nucleotide biodistribution Bioanalytical electrochemistry spectroscopy mass spectrometry analytical separations Analytical Chemistry ROI Bioanalytical Chemistry Masspektroskopi avbildning AstraZeneca Nanosims Terapeutik farmakokinetik farmakodynamik Kvantitativ visualisering kalibreringskurva RSF farmakologi Dataanalys Kemi Kvantifiering isotoper Läkemedel subcellulär läkemedel läkemedelsmål bioskopisk elektrofördelning nukleotid bioskopisk bioskopisk distribution masspektrometri analytiska separationer analytisk kemi bioanalytisk kemi Analytical Chemistry Analytisk kemi

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