| 1. |
- Gustafsson, Mattias, 1972-
(författare)
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Energy efficiency measures in the built environment - some aspects to consider in Sweden
- 2018
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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.
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| 2. |
- Clewe, Oskar, 1986-
(författare)
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Novel Pharmacometric Methods for Informed Tuberculosis Drug Development
- 2016
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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.
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| 3. |
- Svensson, Elin M, 1985-
(författare)
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Pharmacometric Models to Improve Treatment of Tuberculosis
- 2016
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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.
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| 4. |
- 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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| 5. |
- Gomes, João, 1979-
(författare)
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Development of Concentrating Photovoltaic-Thermal Solar Collectors
- 2021
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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.
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| 6. |
- Hansson, Emma K., 1980-
(författare)
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Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy
- 2012
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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.
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| 7. |
- Johansson, Åsa M., 1983-
(författare)
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Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
- 2014
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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.
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| 8. |
- Schindler, Emilie
(författare)
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Pharmacometrics to improve clinical benefit assessment in oncology
- 2018
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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.
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| 9. |
- Svensson, Robin J., 1989-
(författare)
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Pharmacometric Models to Improve the Treatment and Development of Drugs against Tuberculosis
- 2019
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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.
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| 10. |
- Bergstrand, Martin, 1977-
(författare)
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Application of Mixed-Effect Modeling to Improve Mechanistic Understanding and Predictability of Oral Absorption
- 2011
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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.
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| 11. |
- Cullberg, Marie, 1958-
(författare)
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Direct Thrombin Inhibitors in Treatment and Prevention of Venous Thromboembolism: Dose – Concentration – Response Relationships
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- For prevention and treatment of thrombotic diseases with an anticoagulant drug it is important that an adequate dose is given to avoid occurrence or recurrence of thrombosis, without increasing the risk of bleeding and other adverse events to unacceptable levels. The aim of this thesis was to develop mathematical models that describe the dose-concentration (pharmacokinetic) and concentration-response (pharmacodynamic) relationships of direct thrombin inhibitors, in order to estimate optimal dosages for treatment and long-term secondary prevention of venous thromboembolism (VTE).Population pharmacokinetic-pharmacodynamic models were developed, based on data from clinical investigations in healthy volunteers and patients receiving intravenous inogatran, subcutaneous melagatran and/or its oral prodrug ximelagatran. The benefit-risk profiles of different ximelagatran dosages were estimated using clinical utility functions. These functions were based on the probabilities and fatal consequences of thrombosis, bleeding and elevation of the hepatic enzyme alanine aminotransferase (ALAT).The studies demonstrate that the pharmacokinetics of melagatran and ximelagatran were predictable and well correlated to renal function. The coagulation marker, activated partial thromboplastin time (APTT), increased non-linearly with increasing thrombin inhibitor plasma concentration. Overall, the systemic melagatran exposure (AUC) and APTT were similarly predictive of thrombosis and bleedings. The identified relationship between the risk of ALAT-elevation and melagatran AUC suggests that the incidence approaches a maximum at high exposures. The estimated clinical utility was favourable compared to placebo in the overall study population and in special subgroups of patients following fixed dosing of ximelagatran for long-term secondary prevention of VTE. Individualized dosing was predicted to add limited clinical benefit in this indication.The models developed can be used to support the studied dosage and for selection of alternative dosing strategies that may improve the clinical outcome of ximelagatran treatment. In addition, the models may be extrapolated to aid the dose selection in clinical trials with other direct thrombin inhibitors.
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| 12. |
- Dosne, Anne-Gaëlle, 1986-
(författare)
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Improved Methods for Pharmacometric Model-Based Decision-Making in Clinical Drug Development
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Pharmacometric model-based analysis using nonlinear mixed-effects models (NLMEM) has to date mainly been applied to learning activities in drug development. However, such analyses can also serve as the primary analysis in confirmatory studies, which is expected to bring higher power than traditional analysis methods, among other advantages. Because of the high expertise in designing and interpreting confirmatory studies with other types of analyses and because of a number of unresolved uncertainties regarding the magnitude of potential gains and risks, pharmacometric analyses are traditionally not used as primary analysis in confirmatory trials.The aim of this thesis was to address current hurdles hampering the use of pharmacometric model-based analysis in confirmatory settings by developing strategies to increase model compliance to distributional assumptions regarding the residual error, to improve the quantification of parameter uncertainty and to enable model prespecification.A dynamic transform-both-sides approach capable of handling skewed and/or heteroscedastic residuals and a t-distribution approach allowing for symmetric heavy tails were developed and proved relevant tools to increase model compliance to distributional assumptions regarding the residual error. A diagnostic capable of assessing the appropriateness of parameter uncertainty distributions was developed, showing that currently used uncertainty methods such as bootstrap have limitations for NLMEM. A method based on sampling importance resampling (SIR) was thus proposed, which could provide parameter uncertainty in many situations where other methods fail such as with small datasets, highly nonlinear models or meta-analysis. SIR was successfully applied to predict the uncertainty in human plasma concentrations for the antibiotic colistin and its prodrug colistin methanesulfonate based on an interspecies whole-body physiologically based pharmacokinetic model. Lastly, strategies based on model-averaging were proposed to enable full model prespecification and proved to be valid alternatives to standard methodologies for studies assessing the QT prolongation potential of a drug and for phase III trials in rheumatoid arthritis.In conclusion, improved methods for handling residual error, parameter uncertainty and model uncertainty in NLMEM were successfully developed. As confirmatory trials are among the most demanding in terms of patient-participation, cost and time in drug development, allowing (some of) these trials to be analyzed with pharmacometric model-based methods will help improve the safety and efficiency of drug development.
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| 13. |
- Jönsson, Siv, et al.
(författare)
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Population Pharmacokinetics of Ethambutol in South African Tuberculosis Patients
- 2011
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Ingår i: Antimicrobial Agents and Chemotherapy. - 0066-4804 .- 1098-6596. ; 55:9, s. 4230-4237
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Tidskriftsartikel (refereegranskat)abstract
- Ethambutol, one of four drugs in the first-line antitubercular regimen, is used to protect against rifampin resistance in the event of preexisting resistance to isoniazid. The population pharmacokinetics of ethambutol in South African patients with pulmonary tuberculosis were characterized using nonlinear mixed-effects modeling. Patients from 2 centers were treated with ethambutol (800 to 1,500 mg daily) combined with standard antitubercular medication. Plasma concentrations of ethambutol were measured following multiple doses at steady state and were determined using a validated high-pressure liquid chromatography-tandem mass spectrometric method. The data comprised 189 patients (54% male, 12% HIV positive) weighing 47 kg, on average (range, 29 to 86 kg), and having a mean age of 36 years (range, 16 to 72 years). The estimated creatinine clearance was 79 ml/min (range, 23 to 150 ml/min). A two-compartment model with one transit compartment prior to first-order absorption and allometric scaling by body weight on clearance and volume terms was selected. HIV infection was associated with a 15% reduction in bioavailability. Renal function was not related to ethambutol clearance in this cohort. Interoccasion variability exceeded interindividual variability for oral clearance (coefficient of variation, 36 versus 20%). Typical oral clearance in this analysis (39.9 liters/h for a 50-kg individual) was lower than that previously reported, a finding partly explained by the differences in body weight between the studied populations. In summary, a population model describing the pharmacokinetics of ethambutol in South African tuberculosis patients was developed, but additional studies are needed to characterize the effects of renal function.
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| 14. |
- Kjellsson, Maria C., 1975-
(författare)
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Methodological Studies on Models and Methods for Mixed-Effects Categorical Data Analysis
- 2008
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Effects of drugs are in clinical trials often measured on categorical scales. These measurements are increasingly being analyzed using mixed-effects logistic regression. However, the experience with such analyzes is limited and only a few models are used. The aim of this thesis was to investigate the performance and improve the use of models and methods for mixed-effects categorical data analysis. The Laplacian method was shown to produce biased parameter estimates if (i) the data variability is large or (ii) the distribution of the responses is skewed. Two solutions are suggested; the Gaussian quadrature method and the back-step method. Two assumptions made with the proportional odds model have also been investigated. The assumption with proportional odds for all categories was shown to be unsuitable for analysis of data arising from a ranking scale of effects with several underlying causes. An alternative model, the differential odds model, was developed and shown to be an improvement, in regard to statistical significance as well as predictive performance, over the proportional odds model for such data. The appropriateness of the likelihood ratio test was investigated for an analysis where dependence between observations is ignored, i.e. performing the analysis using the proportional odds model. The type I error was found to be affected; thus assessing the actual critical value is prudent in order to verify the statistical significance level. An alternative approach is to use a Markov model, in which dependence between observations is incorporated. In the case of polychotomous data such model may involve considerable complexity and thus, a strategy for the reduction of the time-consuming model building with the Markov model and sleep data is presented. This thesis will hopefully contribute to a more confident use of models for categorical data analysis within the area of pharmacokinetic and pharmacodynamic modelling in the future.
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| 15. |
- Nyberg, Joakim, 1978-
(författare)
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Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The cost of releasing a new drug on the market has increased rapidly in the last decade. The reasons for this increase vary with the drug, but the need to make correct decisions earlier in the drug development process and to maximize the information gained throughout the process is evident. Optimal experimental design (OD) describes the procedure of maximizing relevant information in drug development and drug treatment processes. While various optimization criteria can be considered in OD, the most common is to optimize the unknown model parameters for an upcoming study. To date, OD has mainly been used to optimize the independent variables, e.g. sample times, but it can be used for any design variable in a study. This thesis addresses the OD of multiple continuous or discrete design variables for nonlinear mixed effects models. The methodology for optimizing and the optimization of different types of models with either continuous or discrete data are presented and the benefits of OD for such models are shown. A software tool for optimizing these models in parallel is developed and three OD examples are demonstrated: 1) optimization of an intravenous glucose tolerance test resulting in a reduction in the number of samples by a third, 2) optimization of drug compound screening experiments resulting in the estimation of nonlinear kinetics and 3) an individual dose-finding study for the treatment of children with ciclosporin before kidney transplantation resulting in a reduction in the number of blood samples to ~27% of the original number and an 83% reduction in the study duration. This thesis uses examples and methodology to show that studies in drug development and drug treatment can be optimized using nonlinear mixed effects OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development and drug treatment.
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| 16. |
- Quartino, Angelica L, 1979-
(författare)
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Pharmacometric Models for Improved Prediction of Myelosuppression and Treatment Response in Oncology
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 17. |
- Silber, Hanna, 1977-
(författare)
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Integrated Modeling of Glucose and Insulin Regulation Following Provocation Experiments
- 2009
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Blood glucose is controlled by a complex system of insulin and other hormones to assure a constant supply of glucose to the tissues. Type 2 diabetes is a metabolic disorder which is characterized by progressively worsening glycemic control due to a relative deficiency of insulin secretion and a decreased response to insulin. Numerous mathematical models have been developed with the aim of describing glucose and insulin regulation. A drawback with most previously presented models is that they use an open-loop approach which simplifies the model development but at the same time limits the possible use for predictive purposes. The integrated glucose-insulin model presented in this thesis is a semi-mechanistic model which describes glucose and insulin simultaneously. The model has been used to analyze both intravenous and oral provocations and has been shown to describe and predict healthy and diabetic individuals well. Important differences between healthy and diabetic individuals were identified in insulin secretion and sensitivity. The model was used for design optimization of the intravenous glucose tolerance test and it was shown that the design could be improved and simplified by reduction of the number of samples and by change of glucose and insulin dose. Two methodological aspects which were of importance for model development were evaluated. These were (i) comparison of methods for incorporation of baseline data, and (ii) evaluation of the effects of model misspecification on hypothesis testing for covariate inclusion. Baseline information should be included in the model using either of three presented methods and normalization or subtraction of baseline should be avoided. The likelihood ratio test performed well in most cases except when serial correlation was present. In conclusion, a new model for glucose and insulin regulation has been proposed which is expected to play an important role in clinical development of anti-diabetic drugs.
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| 18. |
- Ueckert, Sebastian, 1983-
(författare)
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Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis.This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits.The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties.In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies.
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| 19. |
- Zingmark, Per-Henrik, 1972-
(författare)
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Models for Ordered Categorical Pharmacodynamic Data
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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