Diabetes is one of the most common diseases in modern time. Its connection to overweight and obesity is well established, and diet and exercise are therefore important parameters in the treatment. A commonly used biomarker to diagnose and follow disease progression in diabetics is via measurements of fasting plasma glucose, FPG. In this study, the relationship between weight loss and FPG in overweight diabetics was studied. Competing hypothesis regarding the connection between weight loss and reduced FPG was investigated by using nonlinear mixed effects modeling based on data gathered from a meta-analysis by Anderson et al (1). The hypotheses suggested that either [1] weight effected FPG directly by an intermediate effector, or [2] both weight and FPG were affected by an unknown underlying mechanism. The intermediate effector was presumed to be insulin sensitivity and the underlying mechanism the blood concentration of free fatty acids.  The data was gathered from 8 different studies, all examining the results of very low energy diets (330-909 kcal/day) in overweight type 2 diabetics. Frequent measurements of weight and FPG were provided in each study with a range of 91-321 mg/dl for baseline FPG and 93-118 kg for baseline weight. The summarized studies consisted of 13 arms with 6-62 subjects in each arm. Both hypotheses were modeled by using NONMEM 7.2. A stepwise effect was used for both weight and FPG. For hypothesis [1], an inhibitory effect affected the weight input which then affected the output for insulin sensitivity by a relative change in weight or the input for the insulin sensitivity by an absolute weight change. For hypothesis [2] the same inhibitory effect affected weight input and the input for insulin sensitivity. For both models the FPG drop was then proportional to the increase in insulin sensitivity. Hypothesis [2] had a significantly lower objective function value (OFV) than hypothesis [1] and had also better results from goodness of fit plots and VPCs. It was therefore concluded that hypothesis [2] indicated the more accurate explanation of the connection between FPG and weight loss. Moreover, a strong correlation between the caloric content of the diet and the rate of weight change was seen as a result of stepwise covariate modeling. An impact from baseline BMI on rate of change for insulin sensitivity was also seen.

Modeling is a valuable tool in drug development, to support decision making, improving study design, and aid in regulatory approval and labeling. This thesis describes the development of pharmacometric models for drugs used in anesthesia and analgesia. Models describing the effects on anesthetic depth, measured by the bispectral index (BIS), for a commonly used anesthetic, propofol, and for a novel anesthetic, AZD3043, were developed. The propofol model consisted of two effect-site compartments, and could describe the effects of propofol when the rate of infusion is changed during treatment. AZD3043 had a high clearance and a low volume of distribution, leading to a short half-life. The distribution to the effect site was fast, and together with the short plasma half-life leading to a fast onset and offset of effects. It was also shown that BIS after AZD3043 treatment is related to the probability of unconsciousness similar to propofol. In analgesia studies dropout due to lack of efficacy is common. This dropout is not at random and needs to be taken into consideration in order to avoid bias. A model was developed describing the PK, pain intensity and dropout hazard for placebo, naproxen and a novel analgesic compound, naproxcinod, after removal of a wisdom tooth. The model provides an opportunity to describe the effects of other doses or formulations. Visual predictive checks created by simultaneous simulations of PI and dropout provided a good way of assessing the goodness of fit when there is informative dropout. The performance of non-linear mixed effects models in the presence of informative dropout, with and without including models that describe such informative dropout was investigated by simulations and re-estimations. When a dropout model was not included there was in general more bias. The bias increased with decreasing number of observations per subject, increasing placebo effect and increasing dropout rate. Bias was relatively unaffected by the number of subjects in the study. The bias had, in general, little effect on simulations of the underlying efficacy score, but a dropout model would still be needed in order to make realistic simulations.

Diabetes is a group of metabolic diseases characterized by hyperglycaemia resulting from defects in insulin secretion, insulin action, or both. Several models have been developed for describing the glucose-insulin system. Silber and Jauslin developed a semi-mechanistic integrated glucose insulin (IGI) model which simultaneously describe glucose and insulin profiles in either healthy subjects or type 2 diabetis mellitus (T2DM) patients. The model was developed for describing the basal system, i.e. when no drugs are present in the body. In this thesis the IGI model was extended to also include the effects of anti-diabetic drugs on glucose homeostasis. The model was extended to describe postprandial glucose and insulin excursions in T2DM patients treated with either biphasic insulin aspart or the GLP-1 receptor agonist liraglutide. These extensions make the model a useful tool in drug development as it can be used for elucidating the effects of new products as well as for clinical trial simulation. In this thesis several modelling tasks were also performed to get a more mechanistic description of the glucose-insulin system. A model was developed which describes the release of the incretin hormones glucosedependent insulinotropic polypeptide and glucagon-like peptide-1 following the ingestion of various glucose doses. The effects of these hormones on the beta cell function were incorporated in a model describing both the C-peptide and insulin concentrations in healthy subjects and T2DM patients during either an oral glucose tolerance test or an isoglycaemic intravenous glucose infusion. By including measurements of both C-peptide and insulin concentrations in the model it could also be used to characterize the hepatic extraction of insulin.

The dose regimen of a drug gives important information about the dose sizes, dose frequency and the duration of treatment. Optimisation of dose regimens is critical to ensure therapeutic success of the drug and to minimise its possible adverse effects. The central theme of this thesis is the Efficient Dosing (ED) algorithm - a computation algorithm developed by us for optimisation of dose regimens. In this thesis, we have attempted to develop a quantitative framework for measuring the efficiency of a dose regimen for specified criteria and computing the most efficient dose regimen using the ED algorithm. The criteria considered by us seek to prevent over- and under-exposure to the drug. For example, one of the criteria is to maintain the drug's concentration around a desired target level. Another criterion is to maintain the concentration within a therapeutic range or window. The ED algorithm and its various extensions are programmed in MATLAB R . Some distinguishing features of our methods are: mathematical explicitness in the optimisation process for a general objective function, creation of a theoretical base to draw comparisons among competing dose regimens, adaptability to any drug for which the PK model is known, and other computational features. We develop the algorithm further to compute the optimal ratio of two partner drugs in a fixed dose combination unit and the efficient dose regimens. In clinical trials, the parameters of the PK model followed by the drug are often unknown. We develop a methodology to apply our algorithm in an adaptive setting which enables estimation of the parameters while optimising the dose regimens for the typical subject in each cohort. A potential application of the ED algorithm for individualisation of dose regimens is discussed. We also discuss an application for computation of efficient dose regimens for obliteration of a pre-specified viral load.

Warfarin is one of the most widely used anticoagulants. Therapy is complicated by warfarin’s narrow therapeutic range and pronounced variability in individual dose requirements. Although warfarin therapy is uncommon in children, it is crucial for children with certain congenital or acquired heart diseases. Treatment in children is especially difficult due to the lack of i) a decision support tool for efficient and consistent dose adjustments, and ii) a flexible warfarin formulation for accurate and reproducible dosing. The overall aim of this thesis was to develop a PKPD-based pharmacometric model for warfarin that describes the dose-response relationship over time, and to identify important predictors that influence individual dose requirements both in adults and children. Special emphasis was placed on investigating the contribution of genetic factors to the observed variability. A clinically useful pharmacometric model for warfarin has been developed using NONMEM. The model has been successfully reformulated into a KPD-model that describes the relationship between warfarin dose and INR response, and that is applicable to both adults and children. From a clinical perspective, this is a very important change since it allows the use of information on dose and INR that is available routinely. The model incorporates both patient and clinical characteristics, such as age, weight, CYP2C9 and VKORC1 genotype, and baseline and target INR, for the prediction of an individualised starting dose. It also enables the use of information from previous doses and INR observations to further individualise the dose a posteriori using a Bayesian forecasting method. The NONMEM model has been transferred to a user-friendly, platform independent tool to aid use in clinical practice. The tool can be used for a priori and a posteriori individualisation of warfarin therapy in both adults and children. The tool should ensure consistent dose adjustment practices, and provide more efficient individualisation of warfarin dosing in all patients, irrespective of age, body weight, CYP2C9 or VKORC1 genotype, baseline or target INR. The expected outcome is improved warfarin therapy compared with empirical dosing, with patients achieving a therapeutic and stable INR faster and avoiding high INRs that increase the risk of bleeding.

PopED is a software developed by the Pharmacometrics Research Group at the Department of Pharmaceutical Biosiences, Uppsala University written mainly in MATLAB. It uses pharmacometric population models to describe the pharmacokinetics and pharmacodynamics of a drug and then estimates an optimal design of a trial for that drug. With optimization calculations in average taking a very long time, it was desirable to increase the calculation speed of the software by parallelizing the serial calculation script. The goal of this project was to investigate different methods of parallelization and implement the method which seemed the best for the circumstances.The parallelization was implemented in C/C++ by using Open MPI and tested on the UPPMAX Kalkyl High-Performance Computation Cluster. Some alterations were made in the original MATLAB script to adapt PopED to the new parallel code. The methods which where parallelized included the Random Search and the Line Search algorithms. The testing showed a significant performance increase, with effectiveness per active core rangingfrom 55% to 89% depending on model and number of evaluated designs.

Chemotherapy plays an important role in the treatment of cancer. However, these drugs also cause death of non-malignant cells, resulting in severe side-effects. In addition, drug resistance may exist. Predictive tools for dose and drug selection are therefore warranted. In this thesis predictive pharmacometric models were developed for the main dose-limiting side-effect, neutropenia, and for treatment response following chemotherapy. Neutropenia is associated with a high risk for life-threatening infections and leads frequently to reduced dose delivery and thereby suboptimal treatment of the tumor. A better characterization of the dynamics of docetaxel induced neutropenia was obtained by simultaneous analysis of neutrophils and leukocytes. The fraction of neutrophils was shown to change over the time-course, hence leukocytes and neutrophil counts are not interchangeable biomarkers. Sometimes neutrophil count is reported as categorical severity of neutropenia (Grade 0-4). A method was developed that allowed analysis of these data closer to its true continuous nature. The main regulatory hormone of neutrophils is granulocyte colony stimulating factor (G-CSF). Although recombinant G-CSF is used as supportive therapy to prevent neutropenia, little is known of how the endogenous G-CSF concentrations vary in patients following chemotherapy. A prospective study was carried out and simultaneous analysis of endogenous G-CSF and neutrophils following chemotherapy enabled a more mechanistic model to be developed that also could verify the self-regulatory properties of the physiological system. Patient characteristics were investigated using a pharmacokinetic-myelosuppression model for docetaxel in patients with normal and impaired liver function. The model was a useful tool in evaluating different dosing strategies and a reduced dosing scheme was suggested in patients with poor liver function, thereby enabling docetaxel treatment in this patient population which has previously been excluded. Treatment of acute myeloid leukemia with daunorubicin and cytarabine is associated with drug resistance and high variability in pharmacokinetics, which was partly explained for daunorubicin by peripheral leukocyte count. An integrated model of the in vitro drug sensitivity and treatment response showed that in vitro drug sensitivity was predictive for treatment outcome in this patient population and may therefore be used for choice of drug. The developed pharmacometric models in this thesis may be useful in the optimization of treatments schedules for existing and new drugs as well as to assist in drug and dose selection to improve therapy in an individual patient. The models and methods presented may also facilitate pooled analysis of data and demonstrate principles which could be useful for the pharmacometric community.

No description available.

L’observance, qui décrit à quel degré le patient suit la prescription, est un facteur essentiel pour que le traitement réussisse. Les observances des patients varient beaucoup et l’efficacité du médicament varie parallèlement. Par conséquent, il faut avoir des paramètres sensibles et fiables pour mesurer l’observance. Dans la littérature, on trouve beaucoup de paramètres pour évaluer l’observance mais leurs avantages, limites et inconvénients, en ce qui concerne l’évaluation de l’impact de l’observance sur les effets des médicaments n’ont pas encore été étudiés en profondeur. L’évaluation de ces paramètres nécessite de les tester dans différentes situations. Comme les données disponibles sur l’observance ne concernent pas un ensemble exhaustif de situations, le recours à la simulation, en s’inspirant des cas réels ou plausibles, est très pertinent. On a ainsi réussi à développer un modèle dont les paramètres sont simples et compréhensibles et qui est pratique et flexible pour simuler les différents cas et même les cas extrêmes de l’observance. On a proposé de nouveaux paramètres pour mesurer l’impact biopharmaceutique de l’observance. Ensuite, on a comparé la performance, en termes de sensibilité et la fiabilité, des paramètres proposés et celles de paramètres déjà utilisés. En conclusion, on peut souligner qu’il n’y a pas de paramètre parfait étant donné que chacun a ses propres limites. Par exemple, pour les médicaments dont les effets sont directement liés aux leurs concentrations plasmatiques, le pourcentage des doses prises, qui est le paramètre le plus utilisé, offre la pire performance; par contre, le pourcentage des doses correctes nettes qui est un nouveau paramètre possède une bonne performance et des avantages prometteurs. / Compliance, which refers to the degree of the conformity to the prescription, is an essential factor for a successful treatment. The compliances of patients vary widely and the effectiveness of medication varies in parallel. Therefore, we need to have reliable and sensible parameters to measure it. In literature, there are many parameters to describe it, but their advantages, disadvantages and limitations regarding the assessment of the impact of compliance on drug have not yet been studied in depth. The evaluation of these parameters requires testing them in different situations. As available compliance data are not exhaustive, the use of the simulation, based on real or plausible cases, is very relevant. We succeeded to develop a model whose parameters are simple and understandable and which is convenient and flexible to simulate the different cases and even the extreme cases of compliance. We proposed new parameters for measuring the biopharmaceutical impact of compliance. Then, we compared the performance, in terms of sensibility and reliability, of these parameters and those already used to assess compliance and discussed their performances and limitations. In conclusion, we can emphasize that there is no ideal parameter since each one has its own limitations. For example, for drugs whose effects are directly related to their plasma concentrations, the percentage of taken doses, which is the most used parameter, has the worst performance, on the other hand, the percentage of the weighted correct doses, which is a new parameter, possesses a good performance and promising advantages.

L’observance, qui décrit à quel degré le patient suit la prescription, est un facteur essentiel pour que le traitement réussisse. Les observances des patients varient beaucoup et l’efficacité du médicament varie parallèlement. Par conséquent, il faut avoir des paramètres sensibles et fiables pour mesurer l’observance. Dans la littérature, on trouve beaucoup de paramètres pour évaluer l’observance mais leurs avantages, limites et inconvénients, en ce qui concerne l’évaluation de l’impact de l’observance sur les effets des médicaments n’ont pas encore été étudiés en profondeur. L’évaluation de ces paramètres nécessite de les tester dans différentes situations. Comme les données disponibles sur l’observance ne concernent pas un ensemble exhaustif de situations, le recours à la simulation, en s’inspirant des cas réels ou plausibles, est très pertinent. On a ainsi réussi à développer un modèle dont les paramètres sont simples et compréhensibles et qui est pratique et flexible pour simuler les différents cas et même les cas extrêmes de l’observance. On a proposé de nouveaux paramètres pour mesurer l’impact biopharmaceutique de l’observance. Ensuite, on a comparé la performance, en termes de sensibilité et la fiabilité, des paramètres proposés et celles de paramètres déjà utilisés. En conclusion, on peut souligner qu’il n’y a pas de paramètre parfait étant donné que chacun a ses propres limites. Par exemple, pour les médicaments dont les effets sont directement liés aux leurs concentrations plasmatiques, le pourcentage des doses prises, qui est le paramètre le plus utilisé, offre la pire performance; par contre, le pourcentage des doses correctes nettes qui est un nouveau paramètre possède une bonne performance et des avantages prometteurs. / Compliance, which refers to the degree of the conformity to the prescription, is an essential factor for a successful treatment. The compliances of patients vary widely and the effectiveness of medication varies in parallel. Therefore, we need to have reliable and sensible parameters to measure it. In literature, there are many parameters to describe it, but their advantages, disadvantages and limitations regarding the assessment of the impact of compliance on drug have not yet been studied in depth. The evaluation of these parameters requires testing them in different situations. As available compliance data are not exhaustive, the use of the simulation, based on real or plausible cases, is very relevant. We succeeded to develop a model whose parameters are simple and understandable and which is convenient and flexible to simulate the different cases and even the extreme cases of compliance. We proposed new parameters for measuring the biopharmaceutical impact of compliance. Then, we compared the performance, in terms of sensibility and reliability, of these parameters and those already used to assess compliance and discussed their performances and limitations. In conclusion, we can emphasize that there is no ideal parameter since each one has its own limitations. For example, for drugs whose effects are directly related to their plasma concentrations, the percentage of taken doses, which is the most used parameter, has the worst performance, on the other hand, the percentage of the weighted correct doses, which is a new parameter, possesses a good performance and promising advantages.