| 1. |
- Gustafsson, Mattias, 1972-
(författare)
-
Energy efficiency measures in the built environment - some aspects to consider in Sweden
- 2018
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The traditional energy system as we know it today will change in the future. There is a worldwide concern about the global warming situation and there are different actions implemented to limit the consequences from, mainly, the use of fossil fuels.In this thesis, multi-unit apartment buildings have been simulated according to how the global CO2 emissions change when different energy efficiency measures are implemented. The simulated buildings have also been used to investigate how the calculated energy efficiency of a building according to Swedish building regulations varies depending on which technology for heating is used in the building and if the building has a solar PV installation or solar thermal system. When the energy efficiency of a building is calculated accord-ing to Swedish building regulations, this thesis shows that heat pumps are a favored technology compared to district heating. Another result is that electric-ity use/production within the investigated district heating system is the most important factor to consider when minimizing global CO2 emissions.This thesis also investigates how the configuration of electric meters owned by the distribution system operator affects the monitored amount of self-consumed and produced excess electricity. Finally, four local low-voltage distri-bution networks were simulated when a future charging scenario of electric vehicles was implemented.If a single-family house installs a solar PV installation, this thesis reveals that the configuration of the electric meter is important for the monitored amount of self-consumed electricity. This thesis also shows that the investigated low-voltage distribution networks can handle future power demand from electric vehicles and a high share of solar PV installations, but rural low-volt-age distribution networks will need to be reinforced or rebuilt to manage the investigated future scenarios.
|
|
| 2. |
- Clewe, Oskar, 1986-
(författare)
-
Novel Pharmacometric Methods for Informed Tuberculosis Drug Development
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With approximately nine million new cases and the attributable cause of death of an estimated two millions people every year there is an urgent need for new and effective drugs and treatment regimens targeting tuberculosis. The tuberculosis drug development pathway is however not ideal, containing non-predictive model systems and unanswered questions that may increase the risk of failure during late-phase drug development. The aim of this thesis was hence to develop pharmacometric tools in order to optimize the development of new anti-tuberculosis drugs and treatment regimens.The General Pulmonary Distribution model was developed allowing for prediction of both rate and extent of distribution from plasma to pulmonary tissue. A distribution characterization that is of high importance as most current used anti-tuberculosis drugs were introduced into clinical use without considering the pharmacokinetic properties influencing drug distribution to the site of action. The developed optimized bronchoalveolar lavage sampling design provides a simplistic but informative approach to gathering of the data needed to allow for a model based characterization of both rate and extent of pulmonary distribution using as little as one sample per subject. The developed Multistate Tuberculosis Pharmacometric model provides predictions over time for a fast-, slow- and non-multiplying bacterial state with and without drug effect. The Multistate Tuberculosis Pharmacometric model was further used to quantify the in vitro growth of different strains of Mycobacterium tuberculosis and the exposure-response relationships of three first line anti-tuberculosis drugs. The General Pharmacodynamic Interaction model was successfully used to characterize the pharmacodynamic interactions of three first line anti-tuberculosis drugs, showing the possibility of distinguishing drug A’s interaction with drug B from drug B’s interaction with drug A. The successful separation of all three drugs effect on each other is a necessity for future work focusing on optimizing the selection of anti-tuberculosis combination regimens.With a focus on pharmacokinetics and pharmacodynamics, the work included in this thesis provides multiple new methods and approaches that individually, but maybe more important the combination of, has the potential to inform development of new but also to provide additional information of the existing anti-tuberculosis drugs and drug regimen.
|
|
| 3. |
- Svensson, Elin M, 1985-
(författare)
-
Pharmacometric Models to Improve Treatment of Tuberculosis
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Tuberculosis (TB) is the world’s most deadly infectious disease and causes enormous public health problems. The comorbidity with HIV and the rise of multidrug-resistant TB strains impede successful therapy through drug-drug interactions and the lack of efficient second-line treatments. The aim of this thesis was to support the improvement of anti-TB therapy through development of pharmacometric models, specifically focusing on the novel drug bedaquiline, pharmacokinetic interactions and methods for pooled population analyses.A population pharmacokinetic model of bedaquiline and its metabolite M2, linked to semi-mechanistic models of body weight and albumin concentrations, was developed and used for exposure-response analysis. Treatment response was quantified by measurements of mycobacterial load and early bedaquiline exposure was found to significantly impact the half-life of bacterial clearance. The analysis represents the first successful characterization of a concentration-effect relationship for bedaquiline.Single-dose Phase I studies investigating potential interactions between bedaquiline and efavirenz, nevirapine, ritonavir-boosted lopinavir, rifampicin and rifapentine were analyzed with a model-based approach. Substantial effects were detected in several cases and dose-adjustments mitigating the impact were suggested after simulations. The interaction effects of nevirapine and ritonavir-boosted lopinavir were also confirmed in patients with multidrug-resistant TB on long-term treatment combining the antiretrovirals and bedaquiline. Furthermore, the outcomes from model-based analysis were compared to results from conventional non-compartmental analysis in a simulation study. Non-compartmental analysis was found to consistently underpredict the interaction effect when most of the concentration-time profile was not observed, as commonly is the case for compounds with very long terminal half-life such as bedaquiline.To facilitate pooled analyses of individual patient data from multiple sources a structured development procedure was outlined and a fast diagnostic tool for extensions of the stochastic model components was developed. Pooled analyses of nevirapine and rifabutin pharmacokinetics were performed; the latter generating comprehensive dosing recommendations for combined administration of rifabutin and antiretroviral protease inhibitors.The work presented in this thesis demonstrates the usefulness of pharmacometric techniques to improve treatment of TB and especially contributes evidence to inform optimized dosing regimens of new and old anti-TB drugs in various clinical contexts.
|
|
| 4. |
- Ghadzi, Siti Maisharah Sheikh
(författare)
-
Pharmacometrics Modelling in Type 2 Diabetes Mellitus : Implications on Study Design and Diabetes Disease Progression
- 2017
-
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.
|
|
| 5. |
- Gomes, João, 1979-
(författare)
-
Development of Concentrating Photovoltaic-Thermal Solar Collectors
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Fossil fuels have greatly improved human living standards and saved countless lives. However, today, their continued use threatens human survival, as CO2 levels rise at an unprecedented pace to levels never seen during human existenceon earth.This thesis aims at gathering knowledge on solar energy in general and photovoltaic thermal (PVT) and concentrating photovoltaic thermal (C-PVT) in particular. This thesis establishes several key research questions for PVTs and C-PVT collectors and attempts to answer them.A comprehensive market study of solar thermal (ST), photovoltaic (PV) and PVT was conducted to obtain prices and performance. Simulations of the energy output around the world were conducted. A ratio between ST and PV annual output was defined to serve as a tool for comparison and plotted on a world map.A key issue for PVT collectors is how to encapsulate the solar cells in a way that, amongst other things, protects the cell from the thermal expansion of the receiver, has a high transparency, and insulates electrically while at the same time conducts the heat to the receiver. In order to be useful, this analysis must also consider the impacts on the production processes. Several prototypes were constructed, a test methodology was created, and the analysis of the results enabled several conclusions on the validity of the different silicon encapsulations methods.This thesis relies heavily on collector testing with 30 different prototypes of C-PVTs being designed and constructed. Most testing was conducted using steady state method but quasi dynamic was also carried out. From this work, several guidelines were created for the design of collectors in terms of reflector geometry, cell size, string configuration, encapsulation method and several other design aspects. These analyses were complemented with thermal simulations (COMSOL & ANSYS), string layout (LT SPICE) and evaluation of existing installations. Two novel design ideas came from this thesis work, which the author will patent in the coming year. Additionally, raytracing work has been conducted and a new reflector geometry more appropriate for C-PVTs has been found to significantly improve the annual performance. Finally, the current and future position of PVTs in the global energy market is discussed.
|
|
| 6. |
- Hansson, Emma K., 1980-
(författare)
-
Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy
- 2012
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- New approaches quantifying the effect of treatment are needed in oncology to improve the drug development process and to enable treatment optimization for existing therapies. This thesis focuses on the development of pharmacometric models for biomarkers, side effects and efficacy in order to identify predictors of clinical response in anti-cancer drug therapy. The variability in myelosuppression was characterized in six different cytotoxic anticancer treatments to evaluate a model-based dose individualization approach utilizing neutrophil counts as a biomarker. The estimated impact of inter-occasion variability was relatively low in relation to the inter-individual variability, indicating that myelosuppression is predictable from one treatment course to another. The approach may thereby be useful for dose optimization within an individual. To further study and to identify predictors for the severe side effect febrile neutropenia (FN), the relationship between the shape of the myelosuppression time-course and the probability of FN was characterized. Patients with a rapid decline in neutrophil counts and high drug sensitivity were identified to have a higher probability of developing FN compared with other patients who experience grade 4 neutropenia. Predictors of clinical response in patients receiving sunitinib for the treatment of gastro-intestinal stromal tumor (GIST) were identified by the development of an integrated modeling framework. Drug exposure, biomarkers, tumor dynamics, side effects and overall survival (OS) were linked in a unified structure, and univariate and multivariate exposure variables were tested for their predictive capacities. The soluble biomarker, sVEGFR-3 and tumor size at start of treatment were found to be promising predictors of overall survival, with decreased sVEGFR-3 levels and smaller baseline tumor size being predictive of longer OS. Also hypertension and neutropenia was identified as predictors of OS. The developed modeling framework may be useful to monitor clinical response, optimize dosing in sunitinib and to facilitate dose individualization.
|
|
| 7. |
- Johansson, Åsa M., 1983-
(författare)
-
Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
- 2014
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- To obtain a better understanding of the pharmacokinetic and/or pharmacodynamic characteristics of an investigated treatment, clinical data is often analysed with nonlinear mixed effects modelling. The developed models can be used to design future clinical trials or to guide individualised drug treatment. Missing data is a frequently encountered problem in analyses of clinical data, and to not venture the predictability of the developed model, it is of great importance that the method chosen to handle the missing data is adequate for its purpose. The overall aim of this thesis was to develop methods for handling missing data in the context of nonlinear mixed effects models and to compare strategies for handling missing data in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.In accordance with missing data theory, all missing data can be divided into three categories; missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). When data are MCAR, the underlying missing data mechanism does not depend on any observed or unobserved data; when data are MAR, the underlying missing data mechanism depends on observed data but not on unobserved data; when data are MNAR, the underlying missing data mechanism depends on the unobserved data itself.Strategies and methods for handling missing observation data and missing covariate data were evaluated. These evaluations showed that the most frequently used estimation algorithm in nonlinear mixed effects modelling (first-order conditional estimation), resulted in biased parameter estimates independent on missing data mechanism. However, expectation maximization (EM) algorithms (e.g. importance sampling) resulted in unbiased and precise parameter estimates as long as data were MCAR or MAR. When the observation data are MNAR, a proper method for handling the missing data has to be applied to obtain unbiased and precise parameter estimates, independent on estimation algorithm.The evaluation of different methods for handling missing covariate data showed that a correctly implemented multiple imputations method and full maximum likelihood modelling methods resulted in unbiased and precise parameter estimates when covariate data were MCAR or MAR. When the covariate data were MNAR, the only method resulting in unbiased and precise parameter estimates was a full maximum likelihood modelling method where an extra parameter was estimated, correcting for the unknown missing data mechanism's dependence on the missing data.This thesis presents new insight to the dynamics of missing data in nonlinear mixed effects modelling. Strategies for handling different types of missing data have been developed and compared in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.
|
|
| 8. |
- Schindler, Emilie
(författare)
-
Pharmacometrics to improve clinical benefit assessment in oncology
- 2018
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The high attrition rate in oncology drug development calls for new approaches that would increase the understanding of drugs’ efficacy and safety profiles. This thesis focuses on the development of pharmacometric models to characterize and quantify the relationships between drug exposure, circulating and imaging biomarkers, adverse effects, overall survival (OS), and patient-reported outcomes (PROs).In axitinib-treated metastatic renal cell carcinoma patients, exposure-driven changes in soluble VEGF receptor 3 were linked to tumor size dynamics, which could in turn predict OS better than biomarker- or hypertension-related predictors. In sunitinib-treated gastro-intestinal stromal tumor (GIST) patients, the tumor metabolic response was sensitive to sunitinib dosing schedule and a substantial inter-lesion variability was quantified. A more pronounced decrease in tumor metabolism for the lesion that best responds to treatment after one week was predictive of longer OS. In imatinib-treated GIST patients, tumor volume better detected size changes of liver metastases and were slightly more predictive of OS than conventional tumor diameters, while tumor density had no predictive value.A new modeling approach, the minimal continuous-time Markov model (mCTMM), was developed to facilitate the analysis of ordered categorical scores with Markovian features, e.g. fatigue or hand-foot syndrome grades. The mCTMM is applicable when existing approaches are not appropriate (non-uniform assessment intervals) or not easily implemented (variables with large number of categories).An item response theory pharmacometric framework was established to describe longitudinal item-level data of a PRO questionnaire, the Functional Assessment of Cancer Therapy-Breast (FACT-B). Four correlated latent well-being variables characterized the multi-dimensional nature of FACT-B. When applied to data from breast cancer patients, the progression of physical well-being was typically better in patients treated with ado-trastuzumab emtansine (T-DM1) than with capecitabine-plus-lapatinib-treated patients. No relationship was identified between T-DM1 exposure and any of the latent variables.In summary, the developed models advance the use of pharmacometrics in assessing the clinical benefit of anti-cancer therapies. They provide a quantitative understanding of the desired and adverse responses to drugs, and their relationships to exposure and long-term clinical outcome. Such frameworks may help to early assess response to therapy and optimize dosing strategies for investigational or existing therapies.
|
|
| 9. |
- Svensson, Robin J., 1989-
(författare)
-
Pharmacometric Models to Improve the Treatment and Development of Drugs against Tuberculosis
- 2019
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With 10 million new infections yearly, tuberculosis has a major impact on the human well-being of the world. Most patients have infections susceptible to a first-line treatment with a treatment success rate of 80%, a number that can potentially be improved by optimising the first-line treatment. Besides susceptible disease, each year half a million patients are infected by tuberculosis with resistance to first-line treatment where only 50% of patients get cured. Thus, new drugs against resistant tuberculosis are desperately needed but given the inefficiency of developing new anti-tuberculosis drugs, enough new drugs will not reach patients in time. The aim of this thesis was to develop pharmacometric models to optimise the development and use of current and future drugs for treating tuberculosis.A population pharmacokinetic model for rifampicin, the most prominent first-line drug, was developed and later used for developing exposure-response models followed by clinical trial simulations. The developed exposure-response models were based on liquid culture data and were expanded to describe the relationship between liquid culture results and a new biomarker, the molecular bacterial load assay which is a quicker alternative to liquid culture and is also contamination-free.The in vitro-derived semi-mechanistic Multistate Tuberculosis Pharmacometric (MTP) model was applied to clinical rifampicin and clofazimine colony forming unit datasets. This novel application of the MTP model allowed detection of statistically significant exposure-response relationships between rifampicin and clofazimine for the specific killing of non-multiplying, persister bacteria. Furthermore, the MTP model was compared to conventional statistical analyses for detecting drug effects in Phase IIa. If designing and analysing Phase IIa using the MTP model, the required sample size for detecting drug effects can be lowered. An improved design and analysis of pre-clinical treatment outcome assessments was developed which increased the information gain compared to a conventional design yet kept the animal use at a minimum. Lastly, a therapeutic drug monitoring approach was suggested based on updated targets for rifampicin, a framework easily expandable to second-line drugs.In conclusion this thesis presents the development of pharmacometric models which will streamline both the development and use of drugs against tuberculosis.
|
|
| 10. |
- Bergstrand, Martin, 1977-
(författare)
-
Application of Mixed-Effect Modeling to Improve Mechanistic Understanding and Predictability of Oral Absorption
- 2011
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Several sophisticated techniques to study in vivo GI transit and regional absorption of pharmaceuticals are available and increasingly used. Examples of such methods are Magnetic Marker Monitoring (MMM) and local drug administration with remotely operated capsules. Another approach is the paracetamol and sulfapyridine double marker method which utilizes observed plasma concentrations of the two substances as markers for GI transit. Common for all of these methods is that they generate multiple types of observations e.g. tablet GI position, drug release and plasma concentrations of one or more substances. This thesis is based on the hypothesis that application of mechanistic nonlinear mixed-effect models could facilitate a better understanding of the interrelationship between such variables and result improved predictions of the processes involved in oral absorption. Mechanistic modeling approaches have been developed for application to data from MMM studies, paracetamol and sulfapyridine double marker studies and for linking in vitro and in vivo drug release. Models for integrating information about tablet GI transit, in vivo drug release and drug plasma concentrations measured in MMM studies was outlined and utilized to describe drug release and absorption properties along the GI tract for felodipine and the investigational drug AZD0837. A mechanistic link between in vitro and in vivo drug release was established by estimation of the mechanical stress in different regions of the GI tract in a unit equivalent to rotation speed in the in vitro experimental setup. The effect of atropine and erythromycin on gastric emptying and small intestinal transit was characterized with a semi-mechanistic model applied to double marker studies in fed and fasting dogs. The work with modeling of in vivo drug absorption has highlighted the need for, and led to, further development of mixed-effect modeling methodology with respect to model diagnostics and the handling of censored observations.
|
|
