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MELILLO, NICOLA. "Uncertainty and sensitivity analysis for mechanistic models in pharmacometrics." Doctoral thesis, Università degli studi di Pavia, 2020. http://hdl.handle.net/11571/1315928.
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Björnsson, Marcus. "Pharmacometric Models in Anesthesia and Analgesia." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-205580.
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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.
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Evbjer, Ellen. "Non-linear mixed effect models for the relationship between fasting plasma glucose and weight loss." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-205712.
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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.
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Hall, Adam J. "Structural and statistical aspects in joint modelling of artesunate pharmacometrics and malarial parasite lifecycle." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/81913/.
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Malaria is a parasite with a complex lifecycle, and commonly used antimalarial agents from the artemisinin family have varied effectiveness over different stages of this lifecycle. The pharmacokinetic profile of the artemisinins is also strongly influenced by the parasite burden and lifecycle stage. This work introduces a new pharmacokinetic and pharmacodynamic model incorporating these interdependent drug and lifecycle features, for orally administered artesunate and its principal metabolite dihydroartemisinin. This model, like the underlying system whose features it attempts to capture, is quite complex and cannot be solved analytically like standard linear first-order compartmental models previously used for pharmacokinetic modelling of these drugs. Therefore, understanding, inference and validity are explored through use of the modern statistical technique of a Sequential Monte Carlo sampler. Structural, numerical and practical identifiability are important concepts for all models, the latter two especially so in this case as the model structure does not admit an algebraic structural identifiability analysis. Motivated by this, the above identifiability concepts are also investigated in connection with the Sequential Monte Carlo technique. Sequential Monte Carlo is demonstrated to be a useful tool for gaining insight into models whose structural identifiability is not known, just as it is also shown to have significant advantages in parameter inference over the classical approach. The coupled parasite lifecycle and artemisinin-derivative model is built in stages, starting with an in vitro submodel capturing the dynamics of uptake of artemisinins into parasitised and non-parasitised red blood cells. Next, the parasite lifecycle, or ‘ageing’ model, is introduced, which uses a new concept of shadow compartments to achieve its aims of describing ageing in continuous time and to exhibit sufficient control over the parasite population. Finally, these models are integrated together into the full coupled pharmacokinetic and pharmacodynamic model. More work is needed to fully assess the resultant model on clinical datasets, but the building blocks upon which it was constructed appear to fulfil their aims reasonably well.
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Hamberg, Anna-Karin. "Pharmacometric Models for Individualisation of Warfarin in Adults and Children." Doctoral thesis, Uppsala universitet, Klinisk farmakogenomik och osteoporos, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-197599.
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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.
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Ghadzi, Siti Maisharah Sheikh. "Pharmacometrics Modelling in Type 2 Diabetes Mellitus : Implications on Study Design and Diabetes Disease Progression." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-317040.
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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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LESTINI, GIULIA. "MODEL-BASED OPTIMAL DESIGN IN PHARMACOMETRICS USING ROBUST AND ADAPTIVE APPROACHES WITH APPLICATION IN ONCOLOGY." Doctoral thesis, Università degli studi di Pavia, 2016. http://hdl.handle.net/11571/1218159.
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Quartino, Angelica L. "Pharmacometric Models for Improved Prediction of Myelosuppression and Treatment Response in Oncology." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-150431.
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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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Røge, Rikke Meldgaard. "Pharmacometric Models of Glucose Homeostasis in Healthy Subjects and Diabetes Patients." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-274239.
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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.
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CARRARA, LETIZIA. "Does the modelling strategy make the difference in pharmacometrics? Some examples in oncology and infectious diseases." Doctoral thesis, Università degli studi di Pavia, 2018. http://hdl.handle.net/11571/1214809.
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The use of mathematical models to describe and predict the pharmacokinetics (PK), i.e., what the body does to the drug, and the phar- macodynamics (PD), i.e., what the drug does to the body, is fundamental across all the phases of the drug development process. Among the other things, these models allow to identify the most promising candidates during the preclinical studies, lead the dose selection for the First-In-Human (FIH) clinical trials, and enable to evaluate the effectiveness of a treatment and to simulate in silico different administration protocols.
Nowadays, there are several modeling tools, each of which characterized by different features and specific applicability fields. There are models with a strong mechanistic base, such as the Physiologically Based Pharmacokinetic (PBPK) models, which integrate the information on organism anatomy and physiology with the physicochemical drug properties. There are also models with a poor mechanistic base, such as the standard pharmacokinetics/pharmacodynamics (PK/PD) models. Finally, there are models in which the pharmacokinetics is not explicitely modeled (K/PD).
The scientific question of this thesis is whether an “optimal” modeling
strategy exists for a given problem. In this perspective, the sentence of George Box: “All models are wrong, some are useful” should be kept in mind.
Via some case studies, this thesis aimed to investigate two aspects: i) the suitability of a certain modeling strategy for a given problem in terms of model structure, available/required data, working hypotheses and the robustness of the results with respect to the assumptions made; ii) the dependency of conclusions from the adopted modeling approach.
In Chapter 1, to set the scene, a brief introduction on both the drug discovery and development process and the importance of mathematical modeling throughout all the phases of this process was given. The features of the modeling strategies considered in this work to describe the pharmacokinetics and the pharmacodynamics were outlined in details. Subsequently, the scientific question underlying this work of thesis was discussed together with the methodology used to address it.
In Chapter 2, the predictive performance of the Whole-Body (WB) PBPK models were investigated. To this aim, six "what-if" scenarios, in which data were added progressively into model development, starting from in vitro and animal experiments, up to human clinical trials, were created. Via these scenarios, the accuracy of the exposure predictions in dependence of the available data was evaluated. Ethambutol (EMB), one of the first-line antibiotics used for the treatment of pul- monary tuberculosis, was used as paradigm drug.
When the physiological characterization of the subject with the dis- ease is not sufficient or not available, as in the oncology fields, less mechanistic approaches, i.e., the PK/PD and the K/PD models, were used to draw conclusions on the effectiveness of candidates.
In Chapter 3 the most important models currently used for cancer drug discovery were surveyed.
In Chapter 4 the dependency on the results from the specific mod- eling strategy was investigated using as a case study the predicted effect of two anticancer drug combination (Sunitinib and Irinotecan) in xenograft mice.
In Chapter 5 in the attempt to be more mechanistic, additional details on drug behavior were added by considering drug concentration profiles not only in plasma but also into tumor tissue.
In Chapter 6 overall conclusions were reported.
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Edenståhl, Selma. "Enterprise Search for Pharmacometric Documents : A Feature and Performance Evaluation." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-417033.
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Information retrieval within a company can be referred to as enterprise search. With the use of enterprise search, employees can find the information they need in company internal data. If a business can take advantage of the knowledge within the organization, it can save time and effort, and be a source for innovation and development within the company. In this project, two open source search engines, Recoll and Apache Solr, are selected, set up, and evaluated based on requirements and needs at the pharmacometric consulting company Pharmetheus AB. A requirement analysis is performed to collect system requirements at the company. Through a literature survey, two candidate search engines are selected. Lastly, a Proof of Concept is performed to demonstrate the feasibility of the search engines at the company. The search tools are evaluated on criteria including indexing performance, search functionality and configurability. This thesis presents assessment questions to be used when evaluating a search tool. It is shown that the indexing time for both Recoll and Apache Solr appears to scale linearly for less than one hundred thousand pdf documents. The benefit of an index is demonstrated when search times for both search engines greatly outperforms the Linux command-line tools grep and find. It is also explained how the strict folder structure and naming conventions at the company can be used in Recoll to only index specific documents and sub-parts of a file share. Furthermore, I demonstrate how the Recoll web GUI can be modified to include functionality for filtering on document type. The results show that Recoll meets most of the company’s system requirements and for that reason it could serve as an enterprise search engine at the company. However, the search engine lacks support for authentication, something that has to be further investigated and implemented before the system can be put into production.
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Lala, Mallika. "Application of Pharmacometric Methods to Improve Pediatric Drug Development." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/2467.
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Pharmacometrics is a quantitative science that is rapidly changing the landscape of drug development, and particularly so for the pediatric population. The motivation behind the research underlying this dissertation is to contribute towards the improvement of pediatric drug development by the astute application of pharmacometric methods. Two distinct research areas have been focused upon: 1- improving pediatric pharmacokinetic (PK) trial design and 2- improving pediatric dosing of warfarin by using a genetics-based dosing regimen. The first project examined in detail the feasibility of and simulation-based methodology for implementing a recent regulatory PK quality standard. The focus was on designing pediatric PK trials that employ sparse sampling and population analysis methods, using a simulation-estimation platform. The research provided clarity on the impact of various trial design elements, such as PK sampling, adult data inclusion, PK variability and analysis method on sample size adequacy to honor the standard. The PK quality standard was found to be practically feasible in terms of sample size adequacy. Informative sampling schedule for a given number of PK samples per subject is assumed during trial design. Recommendations are made to: 1- use prior adult or pediatric data for trial design and analysis, wherever possible and 2 - use one-stage population analysis methods and biologically feasible covariate models for designing pediatric PK studies. The second project involved derivation of the first ever pediatric warfarin dosing regimen, including starting dose and titration scheme, based on pharmacogenetics (Cyp2c9 *1/*2/*3 and VKORc1 -1629 G>A polymorphisms). While extensive research and several dosing models for warfarin use in adults exist, there is paucity of data in pediatrics. A validated adult warfarin population PKPD model was bridged using physiological principles and limited pediatric data to arrive at a pediatric PKPD model and dosing regimen. Pediatric data (n=26) from an observational study conducted at the Children’s Hospital Los Angeles (CHLA) was used to qualify the pediatric model. A 2-step pediatric starting dose based on body weight (<20 kg and ≥20 kg) for each of 18 (6 Cyp2c9 x 3 VKORC1) genotype categories is proposed. The titration scheme involves percentage changes relative to previous dose, based on latest patient INR. The dosing regimen targets a major (≥ 60%) proportion of INRs within therapeutic range of 2.0-3.0, by the second week into warfarin therapy. Simulataneously, bleeding and thromboembolic risks are minimized via minimal proportions (≤ 10% and ≤ 20%) of INRs > 3.5 and INRs < 2.0, respectively. In simulations, the proposed dosing regimen performed better on target INR outcomes than the standard-of-care dosing used in the CHLA patients. Given the challeneges in and low likelihood of conducting pediatric warfarin clinical studies, the proposed dosing regimen is believed to be an important advance in pediatric warfarin therapy. Prospective warfarin studies in pediatrics using the proposed dosing regimen are recommended to refine and validate the suggested dosing strategy.
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Soeny, Kabir. "Pharmacometrically driven optimisation of dose regimens in clinical trials." Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/25822.
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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.
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Leding, Albin. "Optimized design recommendation for first pharmacokinetic in vivo experiments for new tuberculosis drugs using pharmacometrics modelling and simulation." Thesis, Uppsala universitet, Institutionen för farmaci, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447311.
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Tuberculosis, the leading cause of death by a single infection disease caused by bacteria, requires long treatments and the bacteria are prone to develop drug resistance. Therefore, new efficient treatment regiments needs developing, which requires new tools for drug development. A major reason for discontinuance of a drug under development is undesired pharmacokinetic properties. Therefore, it is important to have early information of this, preferably the first time the drug is tested in animals. The first in vivo pharmacokinetic experiment is often done in mice and the only information present at this stage are often in vitro values and physicochemical properties. Physiological-based pharmacokinetic modelling can be used to extrapolate from in vitro to in vivo values. From this, the first in vivo pharmacokinetic experiment can be designed, often with the goal of reducing the amount of mice. This goal is one of the three R.s and it is called Reduction. To explore the Reduction of an experiment population pharmacokinetic modelling can be utilized via exploration of the imprecision, bias and probability of an informative experiment to evaluate if a design meets the goal of Reduction. In this report a recommendation of the first in vivo pharmacokinetic experiment is presented. This is based on in vitro values and physicochemical properties that are common in anti-tuberculosis drugs. If the probability of an informative experiment is critical, a terminal sampling of 40 mice is recommended. If imprecision and bias are necessary, zipper sampling of 10 mice is recommended.
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Strömberg, Eric. "Faster Optimal Design Calculations for Practical Applications." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-150802.
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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.
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Lindbom, Lars. "Development, Application and Evaluation of Statistical Tools in Pharmacometric Data Analysis." Doctoral thesis, Uppsala : Acta Universitatis Upaliensis : Universitetsbiblioteket [distributör], 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6825.
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Ueckert, Sebastian. "Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-216537.
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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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Chen, Chunli. "Pharmacokinetic-Pharmacodynamic Evaluations and Experimental Design Recommendations for Preclinical Studies of Anti-tuberculosis Drugs." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-318845.
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Tuberculosis is an ancient infectious disease and a leading cause of death globally. Preclinical research is important for defining drugs and regimens which should be carried forward to human studies. This thesis aims to characterize the population pharmacokinetics and exposure-response relationships of anti-tubercular drugs alone and in combinations, and to suggest experimental designs for preclinical settings. The population pharmacokinetics of rifampicin, isoniazid, ethambutol and pyrazinamide were described for the first time in two mouse models. This allowed for linking the population pharmacokinetic model to the Multistate Tuberculosis Pharmacometric (MTP) model for biomarker response, which was used to characterize exposure-response relationships in monotherapy. Pharmacodynamic interactions in combination therapies were quantitatively described by linking the MTP model to the General Pharmacodynamic Interaction (GPDI) model, which provided estimates of single drug effects together with a quantitative model-based evaluation framework for evaluation of pharmacodynamic interactions among drugs in combinations. Synergism (more than expected additivity) was characterized between rifampicin and ethambutol, while antagonism (less than expected additivity) was characterized between rifampicin and isoniazid in combination therapies. The new single-dose pharmacokinetic design with enrichened individual sampling was more informative than the original design, in which only one sample was taken from each mouse in the pharmacokinetic studies. The new oral zipper design allows for informative pharmacokinetic sampling in a multiple-dose administration scenario for characterizing pharmacokinetic-pharmacodynamic relationships, with similar or lower bias and imprecision in parameter estimates and with a decreased total number of animals required by up to 7-fold compared to the original design. The optimized design for assessing pharmacodynamic interactions in the combination therapies, which was based on EC20, EC50 and EC80 of the single drug, provided lower bias and imprecision than a conventional reduced four-by-four microdilution checkerboard design at the same total number of samples required, which followed the 3Rs of animal welfare. In summary, in this thesis the population pharmacokinetic-pharmacodynamic models of first-line drugs in mice were characterized through linking each population pharmacokinetic model to the MTP model. Pharmacodynamic interactions were quantitatively illustrated by the MTP-GPDI model. Lastly, experimental designs were optimized and recommended to both pharmacokinetic and pharmacodynamic studies for preclinical settings.
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Dosne, Anne-Gaëlle. "Improved Methods for Pharmacometric Model-Based Decision-Making in Clinical Drug Development." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-305697.
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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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Nielsen, Elisabet I. "Pharmacometric Models for Antibacterial Agents to Improve Dosing Strategies." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-144909.
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Antibiotics are among the most commonly prescribed drugs. Although the majority of these drugs were developed several decades ago, optimal dosage (dose, dosing interval and treatment duration) have still not been well defined. This thesis focuses on the development and evaluation of pharmacometric models that can be used as tools in the establishment of improved dosing strategies for novel and already clinically available antibacterial drugs. Infectious diseases are common causes of death in preterm and term newborn infants. A population pharmacokinetic (PK) model for gentamicin was developed based on data from a prospective study. Body-weight and age (gestational and post-natal age) were found to be major factors contributing to variability in gentamicin clearance and therefore important patient characteristics to consider for improved dosing regimens. A semi-mechanistic pharmacokinetic-pharmacodynamic (PKPD) model was also developed, to characterize in vitro bacterial growth and killing kinetics following exposure to six antibacterial drugs, representing a broad selection of mechanisms of action and PK as well as PD characteristics. The model performed well in describing a wide range of static and dynamic drug exposures and was easily applied to other bacterial strains and antibiotics. It is, therefore, likely to find application in early drug development programs. Dosing of antibiotics is usually based on summary endpoints such as the PK/PD indices. Predictions based on the PKPD model showed that the commonly used PK/PD indices were well identified for all investigated drugs, supporting that models based on in vitro data can be predictive of antibacterial effects observed in vivo. However, the PK/PD indices were sensitive to the study conditions and were not always consistent between patient populations. The PK/PD indices may therefore extrapolate poorly across sub-populations. A semi-mechanistic modeling approach, utilizing the type of models described here, may thus have higher predictive value in a dose optimization tailored to specific patient populations.
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Plan, Elodie L. "Pharmacometric Methods and Novel Models for Discrete Data." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-150929.
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Pharmacodynamic processes and disease progression are increasingly characterized with pharmacometric models. However, modelling options for discrete-type responses remain limited, although these response variables are commonly encountered clinical endpoints. Types of data defined as discrete data are generally ordinal, e.g. symptom severity, count, i.e. event frequency, and time-to-event, i.e. event occurrence. Underlying assumptions accompanying discrete data models need investigation and possibly adaptations in order to expand their use. Moreover, because these models are highly non-linear, estimation with linearization-based maximum likelihood methods may be biased. The aim of this thesis was to explore pharmacometric methods and novel models for discrete data through (i) the investigation of benefits of treating discrete data with different modelling approaches, (ii) evaluations of the performance of several estimation methods for discrete models, and (iii) the development of novel models for the handling of complex discrete data recorded during (pre-)clinical studies. A simulation study indicated that approaches such as a truncated Poisson model and a logit-transformed continuous model were adequate for treating ordinal data ranked on a 0-10 scale. Features that handled serial correlation and underdispersion were developed for the models to subsequently fit real pain scores. The performance of nine estimation methods was studied for dose-response continuous models. Other types of serially correlated count models were studied for the analysis of overdispersed data represented by the number of epilepsy seizures per day. For these types of models, the commonly used Laplace estimation method presented a bias, whereas the adaptive Gaussian quadrature method did not. Count models were also compared to repeated time-to-event models when the exact time of gastroesophageal symptom occurrence was known. Two new model structures handling repeated time-to-categorical events, i.e. events with an ordinal severity aspect, were introduced. Laplace and two expectation-maximisation estimation methods were found to be performing well for frequent repeated time-to-event models. In conclusion, this thesis presents approaches, estimation methods, and diagnostics adapted for treating discrete data. Novel models and diagnostics were developed when lacking and applied to biological observations.
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Karvanen, Matti. "Optimization of Colistin Dosage in the Treatment of Multiresistant Gram-negative Infections." Doctoral thesis, Uppsala universitet, Infektionssjukdomar, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-197724.
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As multidrug resistance in Gram-negative bacilli increases, the old antibiotic colistin has rapidly gained attention as one of few last line treatment options in the form of colistin methanesulfonate (CMS), which is hydrolyzed to colistin both in vitro and in vivo. There is a dearth of knowledge on fundamental aspects of colistin, including pharmacokinetics and optimal dosing regimens. The aim of this thesis was to improve the basis for optimal colistin therapy. To be able to study colistin, an LC-MS/MS assay method was developed which is sensitive, specific and useful in both in vivo and in vitro studies. Using this method we detected a significant loss of colistin during standard laboratory procedures. This loss was characterized and quantified, the hypothesis being that the loss is mainly caused by adsorption to labware. The pharmacokinetics of colistin was studied in two populations of critically ill patients, one with normal renal function and one with renal replacement therapy. Plasma concentrations were assayed with the method above, and population modeling was employed to describe the data. The results include a previously unseen, long elimination half-life of colistin. The data from the population on renal replacement therapy was described without modeling, and showed that both CMS and colistin are cleared by hemodiafiltration. Combination therapy is an approach that is often used when treating patients infected with multidrug-resistant pathogens. The thesis discusses how the joint effect of antibiotics can be measured using colistin and meropenem as a model, and proposes a method for testing antibiotic combinations. Furthermore, a PKPD model was adapted to describe the pharmacodynamics of the combination. In conclusion, a specific and sensitive method for analysis of colistin was developed and the adsorption of colistin to materials was described. The assay method has been well accepted internationally. The pharmacokinetics of colistin and CMS was described in two important patient populations, partly with surprising results that have influenced dosages of colistin worldwide. The pharmacodynamics of combination therapy was investigated and quantified, and the methods applied could be further developed into clinically useful tools for selection of antibiotic combinations.
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Khan, David D. "Pharmacokinetic-Pharmacodynamic modeling and prediction of antibiotic effects." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-282604.
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Problems of emerging antibiotic resistance are becoming a serious threat worldwide, and at the same time, the interest to develop new antimicrobials has declined. There is consequently a need for efficient methods to develop new treatments that minimize the risk of resistance development and that are effective on infections caused by resistant strains. Based on in silico mathematical models, describing the time course of exposure (Pharmacokinetics, PK) and effect (Pharmacodynamics, PD) of a drug, information can be collected and the outcome of various exposures may be predicted. A general model structure, that characterizes the most important features of the system, has advantages as it can be used for different situations. The aim of this thesis was to develop Pharmacokinetic-Pharmacodynamic (PKPD) models describing the bacterial growth and killing after mono- and combination exposures to antibiotics and to explore the predictive ability of PKPD-models across preclinical experimental systems. Models were evaluated on data from other experimental settings, including prediction into animals. A PKPD model characterizing the growth and killing for a range of E. coli bacteria strains, with different MICs, as well as emergence of resistance, was developed. The PKPD model was able to predict results from different experimental conditions including high start inoculum experiments, a range of laboratory and clinical strains as well as experiments where wild-type and mutant bacteria are competing at different drug concentrations. A PKPD model, developed based on in vitro data, was also illustrated to have the capability to replicate the data from an in vivo study. This thesis illustrates the potential of PKPD models to characterize in vitro data and their usage for predictions of different types of experiments. The thesis supports the use of PKPD models to facilitate development of new drugs and to improve the use of existing antibiotics.
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Norgren, Karin. "Evaluation of Robust Model Building Tools to Improve the Efficiency of Non-linear Mixed Effect Model Building Workflows." Thesis, Uppsala universitet, Institutionen för farmaci, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-451801.
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Population PK models aim to describe the change in drug concentration over time for a specific population. The populations in population PK modelling often refer to subjects in a clinical trial of a potential drug candidate. Population PK models are frequently described by non-linear mixed effect (NLME) models, that including both random and fixed effect components. The fixed effect components 𝜽 (THETA) portray typical parameter values in the population while the random effects components 𝜼 (ETA) allow for the incorporation of inter-individual variability (IIV) on the typical population value. The IIVs are therefore an important element of NLME models, but the estimation of the IIVs can be time consuming and become a limiting factor for more complex models. Linear approximation of the IIV’s has been suggested as a way to reduce the estimation time whilst maintaining robustness. The aim of this project was to evaluate and compare the estimation time and robustness of the IIVs for the linear approximation of parameter estimation errors in NLME models compared to those estimated in non-linear models. Population PK NLME models were developed for two datasets of phenobarbital and moxonidine. The datasets contained different levels of complexity such as number of subjects, datapoints and route of administration. The models were developed within R-studio using the assembler and Pharmpy packages and evaluated in NONMEM 7.5. Based on the objective function values (OFVs), obtained in the model building processes, selected models were linearised using Pearl speaks NONMEM (PsN). The estimated 𝜀′𝑠 and run-time of the linearised models were compared to their non-linearized counterparts. For all the models a reduction in run-time could be observed but with a slight variation in the estimations between the linearised and non-linearised models. The biggest run time reduction was seen in the oral transit compartment models for moxonidine with a 3100-fold reduction in estimation time. The estimation time reduction displayed could more quickly provide valuable information regarding the chosen error models of more complex models and while parameters estimated may not be identical to the non-linearised models, they should be sufficient during the model building phase.
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Ernest, II Charles. "Benefits of Non-Linear Mixed Effect Modeling and Optimal Design : Pre-Clinical and Clinical Study Applications." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-209247.
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Despite the growing promise of pharmaceutical research, inferior experimentation or interpretation of data can inhibit breakthrough molecules from finding their way out of research institutions and reaching patients. This thesis provides evidence that better characterization of pre-clinical and clinical data can be accomplished using non-linear mixed effect modeling (NLMEM) and more effective experiments can be conducted using optimal design (OD). To demonstrate applicability of NLMEM and OD in pre-clinical applications, in vitro ligand binding studies were examined. NLMEMs were used to evaluate precision and accuracy of ligand binding parameter estimation from different ligand binding experiments using sequential (NLR) and simultaneous non-linear regression (SNLR). SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR. OD of these ligand binding experiments for one and two binding site systems including commonly encountered experimental errors was performed. OD was employed using D- and ED-optimality. OD demonstrated that reducing the number of samples, measurement times, and separate ligand concentrations provides robust parameter estimation and more efficient and cost effective experimentation. To demonstrate applicability of NLMEM and OD in clinical applications, a phase advanced sleep study formed the basis of this investigation. A mixed-effect Markov-chain model based on transition probabilities as multinomial logistic functions using polysomnography data in phase advanced subjects was developed and compared the sleep architecture between this population and insomniac patients. The NLMEM was sufficiently robust for describing the data characteristics in phase advanced subjects, and in contrast to aggregated clinical endpoints, which provide an overall assessment of sleep behavior over the night, described the dynamic behavior of the sleep process. OD of a dichotomous, non-homogeneous, Markov-chain phase advanced sleep NLMEM was performed using D-optimality by computing the Fisher Information Matrix for each Markov component. The D-optimal designs improved the precision of parameter estimates leading to more efficient designs by optimizing the doses and the number of subjects in each dose group. This thesis provides examples how studies in drug development can be optimized using NLMEM and OD. This provides a tool than can lower the cost and increase the overall efficiency of drug development.My name should be listed as "Charles Steven Ernest II" on cover.
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Nyberg, Joakim. "Practical Optimal Experimental Design in Drug Development and Drug Treatment using Nonlinear Mixed Effects Models." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-160481.
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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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Karlsson, Kristin E. "Benefits of Pharmacometric Model-Based Design and Analysis of Clinical Trials." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-133104.
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Quantitative pharmacokinetic-pharmacodynamic and disease progression models are the core of the science of pharmacometrics which has been identified as one of the strategies that can make drug development more effective. To adequately develop and utilize these models one needs to carefully consider the nature of the data, choice of appropriate estimation methods, model evaluation strategies, and, most importantly, the intended use of the model. The general aim of this thesis was to investigate how the use of pharmacometric models can improve the design and analysis of clinical trials within drug development. The development of pharmacometric models for clinical assessment scales in stroke and graded severity events, in this thesis, show the benefit of describing data as close to its true nature as possible, as it increases the predictive abilities and allows for mechanistic interpretations of the models. Performance of three estimation methods implemented in the mixed-effects modeling software NONMEM; 1) Laplace, 2) SAEM, and 3) Importance sampling, applied when modeling repeated time-to-event data, was investigated. The two latter methods are to be preferred if less than approximately half of the individuals experience events. In addition, predictive performance of two validation procedures, internal and external validation, was explored, with internal validation being preferred in most cases. Model-based analysis was compared to conventional methods by the use of clinical trial simulations and the power to detect a drug effect was improved with a pharmacometric design and analysis. Throughout this thesis several examples have shown the possibility of significantly reducing sample sizes in clinical trials with a pharmacometric model-based analysis. This approach will reduce time and costs spent in the development of new drug therapies, but foremost reduce the number of healthy volunteers and patients exposed to experimental drugs.
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Buatois, Simon. "Novel pharmacometric methods to improve clinical drug development in progressive diseases." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC133.
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Suite aux progrès techniques et méthodologiques dans le secteur de la modélisation, l’apport de ces approches est désormais reconnu par l’ensemble des acteurs de la recherche clinique et pourrait avoir un rôle clé dans la recherche sur les maladies progressives. Parmi celles-ci les études pharmacométriques (PMX) sont rarement utilisées pour répondre aux hypothèses posées dans le cadre d’études dites de confirmation. Parmi les raisons évoquées, les analyses PMX traditionnelles ignorent l'incertitude associée à la structure du modèle lors de la génération d'inférence statistique. Or, ignorer l’étape de sélection du modèle peut aboutir à des intervalles de confiance trop optimistes et à une inflation de l’erreur de type I. Pour y remédier, nous avons étudié l’apport d’approches PMX innovantes dans les études de choix de dose. Le « model averaging » couplée à un test du rapport de « vraisemblance combiné » a montré des résultats prometteurs et tend à promouvoir l’utilisation de la PMX dans les études de choix de dose. Pour les études dites d’apprentissage, les approches de modélisation sont utilisées pour accroitre les connaissances associées aux médicaments, aux mécanismes et aux maladies. Dans cette thèse, les mérites de l’analyse PMX ont été évalués dans le cadre de la maladie de Parkinson. En combinant la théorie des réponses aux items à un modèle longitudinal, l’analyse PMX a permis de caractériser adéquatement la progression de la maladie tout en tenant compte de la nature composite du biomarqueur. Pour conclure, cette thèse propose des méthodes d’analyses PMX innovantes pour faciliter le développement des médicaments et/ou les décisions des autorités réglementairesIn the mid-1990, model-based approaches were mainly used as supporting tools for drug development. Restricted to the “rescue mode” in situations of drug development failure, the impact of model-based approaches was relatively limited. Nowadays, the merits of these approaches are widely recognised by stakeholders in healthcare and have a crucial role in drug development for progressive diseases. Despite their numerous advantages, model-based approaches present important drawbacks limiting their use in confirmatory trials. Traditional pharmacometric (PMX) analyses relies on model selection, and consequently ignores model structure uncertainty when generating statistical inference. The problem of model selection is potentially leading to over-optimistic confidence intervals and resulting in a type I error inflation. Two projects of this thesis aimed at investigating the value of innovative PMX approaches to address part of these shortcomings in a hypothetical dose-finding study for a progressive disorder. The model averaging approach coupled to a combined likelihood ratio test showed promising results and represents an additional step towards the use of PMX for primary analysis in dose-finding studies. In the learning phase, PMX is a key discipline with applications at every stage of drug development to gain insight into drug, mechanism and disease characteristics with the ultimate goal to aid efficient drug development. In this thesis, the merits of PMX analysis were evaluated, in the context of Parkinson’s disease. An item-response theory longitudinal model was successfully developed to precisely describe the disease progression of Parkinson’s disease patients while acknowledging the composite nature of a patient-reported outcome. To conclude, this thesis enhances the use of PMX to aid efficient drug development and/or regulatory decisions in drug development
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Strömberg, Eric. "Applied Adaptive Optimal Design and Novel Optimization Algorithms for Practical Use." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-308452.
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The costs of developing new pharmaceuticals have increased dramatically during the past decades. Contributing to these increased expenses are the increasingly extensive and more complex clinical trials required to generate sufficient evidence regarding the safety and efficacy of the drugs. It is therefore of great importance to improve the effectiveness of the clinical phases by increasing the information gained throughout the process so the correct decision may be made as early as possible. Optimal Design (OD) methodology using the Fisher Information Matrix (FIM) based on Nonlinear Mixed Effect Models (NLMEM) has been proven to serve as a useful tool for making more informed decisions throughout the clinical investigation. The calculation of the FIM for NLMEM does however lack an analytic solution and is commonly approximated by linearization of the NLMEM. Furthermore, two structural assumptions of the FIM is available; a full FIM and a block-diagonal FIM which assumes that the fixed effects are independent of the random effects in the NLMEM. Once the FIM has been derived, it can be transformed into a scalar optimality criterion for comparing designs. The optimality criterion may be considered local, if the criterion is based on singe point values of the parameters or global (robust), where the criterion is formed for a prior distribution of the parameters. Regardless of design criterion, FIM approximation or structural assumption, the design will be based on the prior information regarding the model and parameters, and is thus sensitive to misspecification in the design stage. Model based adaptive optimal design (MBAOD) has however been shown to be less sensitive to misspecification in the design stage. The aim of this thesis is to further the understanding and practicality when performing standard and MBAOD. This is to be achieved by: (i) investigating how two common FIM approximations and the structural assumptions may affect the optimized design, (ii) reducing runtimes complex design optimization by implementing a low level parallelization of the FIM calculation, (iii) further develop and demonstrate a framework for performing MBAOD, (vi) and investigate the potential advantages of using a global optimality criterion in the already robust MBAOD.
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Leding, Albin. "Recommendation for first pharmacokinetic in vivo experiment design with a pharmacometric informed approach." Thesis, Uppsala universitet, Institutionen för farmaci, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447311.
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Tuberculosis, the leading cause of death by a single infection disease caused by bacteria, requires long treatments and the bacteria are prone to develop drug resistance. Therefore, new efficient treatment regiments needs developing, which requires new tools for drug development. A major reason for discontinuance of a drug under development is undesired pharmacokinetic properties. Therefore, it is important to have early information of this, preferably the first time the drug is tested in animals. The first in vivo pharmacokinetic experiment is often done in mice and the only information present at this stage are often in vitro values and physicochemical properties. Physiological-based pharmacokinetic modelling can be used to extrapolate from in vitro to in vivo values. From this, the first in vivo pharmacokinetic experiment can be designed, often with the goal of reducing the amount of mice. This goal is one of the three R.s and it is called Reduction. To explore the Reduction of an experiment population pharmacokinetic modelling can be utilized via exploration of the imprecision, bias and probability of an informative experiment to evaluate if a design meets the goal of Reduction. In this report a recommendation of the first in vivo pharmacokinetic experiment is presented. This is based on in vitro values and physicochemical properties that are common in anti-tuberculosis drugs. If the probability of an informative experiment is critical, a terminal sampling of 40 mice is recommended. If imprecision and bias are necessary, zipper sampling of 10 mice is recommended.
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Syed, Mohamed Ami Fazlin. "Pharmacokinetic and Pharmacodynamic Modeling of Antibiotics and Bacterial Drug Resistance." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-188306.
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Exposure to antibiotics is an important factor influencing the development of bacterial resistance. In an era where very few new antibiotics are being developed, a strategy for the development of optimal dosing regimen and combination treatment that reduces the rate of resistance development and overcome existing resistance is of utmost importance. In addition, the optimal dosing in subpopulations is often not fully elucidated. The aim of this thesis was to develop pharmacokinetic (PK) and pharmacokinetic-pharmacodynamic (PKPD) models that characterize the interaction of antibiotics with bacterial growth, killing and resistance over time, and can be applied to guide optimization of dosing regimens that enhance the efficacy of mono- and combination antibiotic therapy. A mechanism-based PKPD model that incorporates the growth, killing kinetics and adaptive resistance development in Escherichia coli against gentamicin was developed based on in vitro time-kill curve data. After some adaptations, the model was successfully applied for similar data on colistin and meropenem alone, and in combination, on one wild type and one meropenem-resistant strain of Pseudomonas aeruginosa. The developed population PK model for colistin and its prodrug colistin methanesulfonate (CMS) in combination with the PKPD model showed the benefits for applying a loading dose for this drug. Simulations predicted the variability in bacteria kill to be larger between dosing occasions than between patients. A flat-fixed loading dose followed by an 8 or 12 hourly maintenance dose with infusion duration of up to 2 hours was shown to result in satisfactory bacterial kill under these conditions. Pharmacometric models that characterize the time-course of drug concentrations, bacterial growth, antibacterial killing and resistance development were successfully developed. Predictions illustrated how PKPD models based on in vitro data can be utilized to guide development of antibiotic dosing, with examples advocating regimens that (i) promote bacterial killing and reduce risk for toxicity in preterm and term newborn infants receiving gentamicin, (ii) achieve a fast initial bacterial killing and reduced resistance development of colistin in critically ill patients by application of a loading dose, and (iii) overcome existing meropenem resistance by combining colistin and meropenem
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Savic, Radojka. "Improved pharmacometric model building techniques." Doctoral thesis, Uppsala University, Department of Pharmaceutical Biosciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9272.
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Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy.
The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics.
As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified.
In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community.
Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.
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Lacroix, Brigitte. "Pharmacometric Modeling in Rheumatoid Arthritis." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-247917.
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Biologic therapies have revolutionized the treatment of rheumatoid arthritis, a common chronic inflammatory disease, mainly characterized by the chronic inflammation of the joints. The activity and progression of the disease are highly variable, both between subjects and between the successive assessments for the same subject. Standardized assessments of clinical variables have been developed to reflect the disease activity and evaluate new therapies. Pharmacokinetics-pharmacodynamic (PKPD) models and methods for analyzing the generated time-course data are needed to improve the interpretation of the clinical trials’ outcomes, and to describe the variability between subjects, including patients characteristics, disease factors and the use of concomitant treatments that may affect the response to treatment. In addition, good simulation properties are also desirable for predicting clinical responses for various populations or for different dosing schedules. The aim of this thesis was to develop methods and models for analyzing pharmacokinetic and pharmacokinetic-pharmacodynamic (PKPD) data from rheumatoid arthritis patients, illustrated by treatment with a new anti-TNFα biologic drug under clinical development, certolizumab pegol. Two models were developed that characterized the relationship between the exposure to the drug and the efficacy ACR variables that represent improvement of the disease; a logistic-type Markov model for 20% improvement (ACR20) and a continuous-type Markov model for simultaneous analysis of 20% (ACR20), 50% (ACR50) and 70% (ACR70) improvement. Both models accounted for the within-subjects correlation in the successive clinical assessments and were able to capture the observed ACR responses over time. Simulations from these models of the ACR20 response rate supported dosing regimens of 400 mg at weeks 0, 2 and 4 to achieve a rapid onset of response to the treatment, followed by 200 mg every 2 weeks, or alternative maintenance regimen of 400 mg every 4 weeks. The immunogenicity induced by the biologic drug was characterized by a time to event model describing the time to appearance of antibodies directed against the drug. The immunogenicity was predicted to appear mainly during the first 3 months following the start of the treatment and to be reduced at higher trough concentrations of CZP, as well as with concomitant administration of MTX. The full time-course of sequential events, such as dose-exposure-efficacy relations, is most accurately described by a simultaneous analysis of all data. However, due to the complexity and runtime limitations of such an analysis, alternatives are often used. In this thesis, a method, IPPSE, was developed and compared to the reference simultaneous method and to existing alternative methods. The IPPSE method was shown to provide accuracy and precision of estimates similar to the simultaneous method, but with easier implementation and shorter run times. In conclusion, two PKPD models and one immunogenicity model were developed for evaluation of the response of a biologic drug against rheumatoid arthritis that allowed accurate analysis and simulation of clinical trial data, as well as serving as examples for how a model-informed basis for decisions about biological drugs can be created.
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Savić, Radojka. "Improved pharmacometric model building techniques /." Uppsala : Acta Universitatis Upsaliensis Acta Universitatis Upsaliensis, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-9272.
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Wallin, Johan. "Dose Adaptation Based on Pharmacometric Models." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-100569.
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Many drugs exhibit major variability in both pharmacokinetic (PK) and pharmacodynamic (PD) parameters that prevents the use of the same dose for all patients. Variability can occur both between patients (IIV) as well as within patients over the course of time (IOV). In a drug with narrow therapeutic range and substantial IIV, dose selection may require individual adaptation. Adaptation can either be made before (a priori) or after (a posteriori) first drug administration. The former implies basing the dose on prior information known to be influential, such as kidney function indicators, weight or concomitant medication, whereas a posteriori dose adaptations are based on post-treatment observations. Often individualization cannot be based on the clinical outcome itself. In such cases, drug concentrations or biomarkers may be valuable for dose individualisation. In this thesis two therapeutic areas where dosing is critical have been investigated regarding the possibilities of a priori and a posteriori dose adaptation; anticancer treatment where myelosuppression is dose limiting, and tacrolimus used for immunosuppression in paediatric transplantation. In tacrolimus previously published models were found to be of little value for dose adaptation in the early critical days post-transplantation. New PK models were developed and used to suggest new dosing regimens tailored for the paediatric population, recognizing the changing pharmacokinetics in the early time post-transplantation. For several anticancer drugs covariates were identified that partly explained IIV in myelosuppression. IOV were found to be lower than IIV which implies that individual dose adaptations a posteriori can be valuable. Dose adaptation, using Bayesian principles in order to simultaneously minimise the risk of severe toxicity or subtherapeutic levels, was evaluated using simulations. Type and amount of data needed, as well as variability parameters influential on the outcome, were evaluated. Results show drug concentrations being of little value, if neutrophil counts are available. The models discussed in this thesis have been implemented in MS Excel macros for Bayesian forecasting, to allow widespread distribution to clinical settings without necessitating access to specific statistical software.
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Baverel, Paul. "Development and Evaluation of Nonparametric Mixed Effects Models." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-144583.
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A nonparametric population approach is now accessible to a more comprehensive network of modelers given its recent implementation into the popular NONMEM application, previously limited in scope by standard parametric approaches for the analysis of pharmacokinetic and pharmacodynamic data. The aim of this thesis was to assess the relative merits and downsides of nonparametric models in a nonlinear mixed effects framework in comparison with a set of parametric models developed in NONMEM based on real datasets and when applied to simple experimental settings, and to develop new diagnostic tools adapted to nonparametric models. Nonparametric models as implemented in NONMEM VI showed better overall simulation properties and predictive performance than standard parametric models, with significantly less bias and imprecision in outcomes of numerical predictive check (NPC) from 25 real data designs. This evaluation was carried on by a simulation study comparing the relative predictive performance of nonparametric and parametric models across three different validation procedures assessed by NPC. The usefulness of a nonparametric estimation step in diagnosing distributional assumption of parameters was then demonstrated through the development and the application of two bootstrapping techniques aiming to estimate imprecision of nonparametric parameter distributions. Finally, a novel covariate modeling approach intended for nonparametric models was developed with good statistical properties for identification of predictive covariates. In conclusion, by relaxing the classical normality assumption in the distribution of model parameters and given the set of diagnostic tools developed, the nonparametric approach in NONMEM constitutes an attractive alternative to the routinely used parametric approach and an improvement for efficient data analysis.
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Lohy, Das Jesmin Permala. "Modelling and Simulation to Improve Antimalarial Therapy." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-330113.
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The introduction of artemisinin-based combination therapy (ACT) substantially reduced malaria-related mortality and morbidity during the past decade. Despite the widespread use of ACT, there is still a considerable knowledge gap with regards to safety, efficacy and pharmacokinetic properties of these drugs, particularly in vulnerable populations like children and pregnant women. In addition, there is growing evidence of widespread artemisinin-resistance across the Greater Mekong Subregion. Expedited delivery of novel antimalarial drugs with different mechanisms of action to the clinical setting is still far off; therefore, it is crucial to improve the use of existing antimalarial drugs for optimal outcome in order to prolong their therapeutic life span. This thesis focuses on utilizing pharmacometric tools to support this effort for malaria prevention and treatment. An extensive simulation framework was used to explore alternative malaria chemopreventive dosing regimens of a commonly used ACT, dihydroartemisinin-piperaquine. Different monthly and weekly dosing regimens were evaluated and this allowed an understanding of the interplay between adherence, loading dose and malaria incidence. A weekly dosing regimen substantially improved the prevention effect and was less impacted by poor adherence. This is also expected to reduce selection pressure for development of resistance to piperaquine. Population pharmacokinetics-pharmacodynamic models were developed for artesunate and the active metabolite dihydroartemisinin, effect on parasite clearance, in patients with artemisinin-resistant and -sensitive malaria infections in Southeast Asia. The modeling identified an association between parasite density and drug bioavailability. It predicted the presence of high levels of artemisinin resistant infection among patients in Cambodia and its spread into Myanmar. A nomogram to identify patients with artemisinin resistant infections was developed. Furthermore, the model was used to demonstrate the need for extended treatment duration to treat patients with artemisinin resistant infections. A population pharmacokinetic model developed from data on pregnant women in East Africa allowed further understanding of artemether-lumefantrine exposure in pregnant populations. It also suggested that the lumefantrine exposure in this population is not compromised. In summary, the results presented in this thesis demonstrate the value of pharmacometric approaches for improving antimalarial drug treatment and prevention. This ultimately contributes to overcoming the prevailing challenges to malaria control.
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Bergstrand, Martin. "Application of Mixed-Effect Modeling to Improve Mechanistic Understanding and Predictability of Oral Absorption." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-149314.
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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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Choy, Steve. "Semi-mechanistic models of glucose homeostasis and disease progression in type 2 diabetes." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273709.
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Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by consistently high blood glucose, resulting from a combination of insulin resistance and reduced capacity of β-cells to secret insulin. While the exact causes of T2DM is yet unknown, obesity is known to be a major risk factor as well as co-morbidity for T2DM. As the global prevalence of obesity continues to increase, the association between obesity and T2DM warrants further study. Traditionally, mathematical models to study T2DM were mostly empirical and thus fail to capture the dynamic relationship between glucose and insulin. More recently, mechanism-based population models to describe glucose-insulin homeostasis with a physiological basis were proposed and offered a substantial improvement over existing empirical models in terms of predictive ability. The primary objectives of this thesis are (i) examining the predictive usefulness of semi-mechanistic models in T2DM by applying an existing population model to clinical data, and (ii) exploring the relationship between obesity and T2DM and describe it mathematically in a novel semi-mechanistic model to explain changes to the glucose-insulin homeostasis and disease progression of T2DM. Through the use of non-linear mixed effects modelling, the primary mechanism of action of an antidiabetic drug has been correctly identified using the integrated glucose-insulin model, reinforcing the predictive potential of semi-mechanistic models in T2DM. A novel semi-mechanistic model has been developed that incorporated a relationship between weight change and insulin sensitivity to describe glucose, insulin and glycated hemoglobin simultaneously in a clinical setting. This model was also successfully adapted in a pre-clinical setting and was able to describe the pathogenesis of T2DM in rats, transitioning from healthy to severely diabetic. This work has shown that a previously unutilized biomarker was found to be significant in affecting glucose homeostasis and disease progression in T2DM, and that pharmacometric models accounting for the effects of obesity in T2DM would offer a more complete physiological understanding of the disease.
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Clewe, Oskar. "Novel Pharmacometric Methods for Informed Tuberculosis Drug Development." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303872.
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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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Maloney, Alan. "Optimal (Adaptive) Design and Estimation Performance in Pharmacometric Modelling." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-182284.
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The pharmaceutical industry now recognises the importance of the newly defined discipline of pharmacometrics. Pharmacometrics uses mathematical models to describe and then predict the performance of new drugs in clinical development. To ensure these models are useful, the clinical studies need to be designed such that the data generated allows the model predictions to be sufficiently accurate and precise. The capability of the available software to reliably estimate the model parameters must also be well understood. This thesis investigated two important areas in pharmacometrics: optimal design and software estimation performance. The three optimal design papers progressed significant areas of optimal design research, especially relevant to phase II dose response designs. The use of exposure, rather than dose, was investigated within an optimal design framework. In addition to using both optimal design and clinical trial simulation, this work employed a wide range of metrics for assessing design performance, and was illustrative of how optimal designs for exposure response models may yield dose selections quite different to those based on standard dose response models. The investigation of the optimal designs for Poisson dose response models demonstrated a novel mathematical approach to the necessary matrix calculations for non-linear mixed effects models. Finally, the enormous potential of using optimal adaptive designs over fixed optimal designs was demonstrated. The results showed how the adaptive designs were robust to initial parameter misspecification, with the capability to "learn" the true dose response using the accruing subject data. The two estimation performance papers investigated the relative performance of a number of different algorithms and software programs for two complex pharmacometric models. In conclusion these papers, in combination, cover a wide spectrum of study designs for non-linear dose/exposure response models, covering: normal/non-normal data, fixed/mixed effect models, single/multiple design criteria metrics, optimal design/clinical trial simulation, and adaptive/fixed designs.
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Ungphakorn, Wanchana. "Pharmacometric models of oral ciprofloxacin for children with malnutrition." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=18680.
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Children with severe malnutrition typically suffer from numerous associated complications. Among these, septicaemia, especially with Gram-negative organisms, remains the major concern because it is associated with a high mortality rate. The World Health Organization (WHO) has been releasing standard guidelines for the treatment of bacterial infections for many years; however, it has been found that the mortality rate remains high even if these guidelines are followed. Ciprofloxacin is a fluoroquinolone antimicrobial agent that has been considered as alternative treatment option. However, to date, data around the pharmacokinetics (PK) of ciprofloxacin, as well as other drugs, are limited in malnourished children. The aim of this thesis was to develop pharmacokinetic models for describing and predicting the PK of drugs in such children. A population analysis was performed by using ciprofloxacin concentration-time data obtained from 52 malnourished children. It was found that a one-compartment model, with first-order absorption and a lag, adequately described the data. The final population model included the effect of body weight, high mortality risk and serum sodium concentration on clearance (CL), and the effect of body weight and sodium concentration on volume of distribution (V). Inclusion of these factors reduced inter-individual variability in CL from 50% to 38%, and in V from 49% to 43%. Absorption rate (ka) was poorly estimated and highly variable. Internal validation techniques, including nonparametric bootstrap, a visual predictive check, normalised prediction distribution error and a jackknife analysis, were used to assess the stability and robustness of the final population model. The results of these analyses indicated that the model was stable and had a favourable predictive performance for CL and V. To develop new dosage regimens, the population model was used to perform a 10,000-patient Monte Carlo simulation. The probabilities of achieving the therapeutic target AUC0-24/MIC ratio and the expected population response were then iv determined. The results showed that PK-PD breakpoints were 0.06-0.125 mg/L and 0.25-0.5 mg/L for Gram-negative and Gram-positive organisms, respectively. The overall response with the 30 mg/kg/day dose was 80% for Escherichia coli, Klebsiella pneumoniae and Salmonella species, but <60% for Pseudomonas aeruginosa and Streptococcus pneumoniae. The results suggested that an oral dose of ciprofloxacin 10 mg/kg three times daily (30 mg/kg/day) may be appropriate for the management of septicaemia in severely malnourished children. Discrepancies of susceptibility breakpoints between reference sources were also found, i.e., PK-PD, CLSI and EUCAST, and these discrepancies were most pronounced for P. aeruginosa and S. pneumoniae. The population model was alsmpartment model, with first-order absorption and a lag, adequately described the data. The final population model included the effect of body weight, high mortality risk and serum sodium concentration on clearance (CL), and the effect of body weight and sodium concentration on volume of distribution (V). Inclusion of these factors reduced inter-individual variability in CL from 50% to 38%, and in V from 49% to 43%. Absorption rate (ka) was poorly estimated and highly variable. Internal validation techniques, including nonparametric bootstrap, a visual predictive check, normalised prediction distribution error and a jackknife analysis, were used to assess the stability and robustness of the final population model. The results of these analyses indicated that the model was stable and had a favourable predictive performance for CL and V. o used to determine optimal design for future population PK studies. A number of design options and design variables were examined. The results suggest that the optimal number of groups was three and two for three- and four-sample designs, respectively. When using two groups, it was possible to vary the number of individuals in each group. If permission was given to obtain up to five samples from each patient, one group of participants would be adequate. Only samples taken after the first dose gave sufficient information. The expected coefficient of variation (CV) of all parameters was under 10% with sample sizes of 25 and 40 for five- and four-sample designs, respectively. For three samples, the CV for ka remained above 20%, although the sample size was increased to 100. It was also found that the optimal designs were highly dependent on the prior information, so prior knowledge of drug concentration-time profiles should be used with optimal design methods when designing population PKt model, with first-order absorption and a lag, adequately described the data. The final population model included the effect of body weight, high mortality risk and serum sodium concentration on clearance (CL), and the effect of body weight and sodium concentration on volume of distribution (V). Inclusion of these factors reduced inter-individual variability in CL from 50% to 38%, and in V from 49% to 43%. Absorption rate (ka) was poorly estimated and highly variable. Internal validation techniques, including nonparametric bootstrap, a visual predictive check, normalised prediction distribution error and a jackknife analysis, were used to assess the stability and robustness of the final population model. The results of these analyses indicated that the model was stable and had a favourable predictive performance for CL and V. studies. In order to predict the disposition of other drugs in a malnourished population, whole body physiologically based pharmacokinetic (WBPBPK) models were developed by using ciprofloxacine as a model drug. The WBPBPK model was initially developed for healthy adults and then scaled to healthy and malnourished children. Kp values were calculated using the Poulin method, the Rodgers method and empirical method. The results showed that, for healthy adults and children, the predicted versus observed concentration-time profiles were well described with intravenous (IV bolus v and short infusion) models. Oral predictions were also in good agreement with the data from the literature, but peak concentrations were more rapidly achieved with a higher dose. Unlike the Poulin method, the concentration-time profiles predicted using Kp from the Rodgers method and the empirical methods were similar, and closely resembled the observed data. When models were scaled for malnutrition, inter-individual variability was higher, especially during the absorption phase. However, PK profiles were still adequately described. The models developed in this thesis are useful tools for describing and predicting drug PK in malnourished children. However, due to the scarcity of data, further studies to characterise the alteration of drug kinetics, particularly during the absorption process, might improve the performance of the models. Application of these models to other drugs and data is also required to substantiate the predictive performance of the model.
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Johansson, Åsa M. "Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-224098.
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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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Kloprogge, Frank Lodewijk. "Pharmacokinetics and pharmacodynamics of antimalarial drugs in pregnant women." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:79ce1a37-3ba2-45e4-9f80-0692a66837f1.
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Malaria is the most important parasitic disease in man and it kills approximately 2,000 people each day. Pregnant women are especially vulnerable to malaria with increased incidence and mortality rates. There are indications that pregnancy alters the pharmacokinetic properties of many antimalarial drugs. This is worrisome as lower drug exposures might result in lowered efficacy and lower drug exposures can also accelerate the development and spread of resistant parasites. The aim of this research was to study the pharmacokinetics and pharmacodynamics of the most commonly used drugs for the treatment of uncomplicated Plasmodium falciparum malaria during the second and third trimester of pregnancy using a pharmacometric approach. This thesis presents a number of important findings that increase the current knowledge of antimalarial drug pharmacology and that may have an impact in terms of drug efficacy and resistance. (1) Lower lumefantrine plasma concentrations at day 7 were evident in pregnant women compared to that in non-pregnant patients. Subsequent in-silico simulations with the final pharmacokinetic-pharmacodynamic lumefantrine/desbutyl-lumefantrine model showed a decreased treatment failure rate after a proposed extended artemether-lumefantrine treatment. (2) Dihydroartemisinin exposure (after intravenous and oral administration of artesunate) was lower during pregnancy compared to that in women 3 months post-partum (same women without malaria). Consecutive in-silico simulations with the final model showed that the underexposure of dihydroartemisinin during pregnancy could be compensated by a 25% dose increase. (3) Artemether/dihydroartemisinin exposure in pregnant women was also lower compared to literature values in non-pregnant patients. This further supports the urgent need for a study in pregnant women with a non-pregnant control group. (4) Quinine pharmacokinetics was not affected by pregnancy trimester within the study population and a study with a non-pregnant control group is needed to evaluate the absolute effects of pregnancy. (5) Finally, a data-dependent power calculation methodology using the log likelihood ratio test was successfully used for sample size calculations of mixed pharmacokinetic study designs (i.e. sparsely and densely sampled patients). Such sample size calculations can contribute to a better design of future pharmacokinetic studies. In conclusion, this thesis showed lower exposures for drugs used to treat uncomplicated Plasmodium falciparum malaria during the second and third trimester of pregnancy. More pharmacokinetic studies in pregnant women with a non-pregnant control group are urgently needed to confirm the current findings and to enable an evidence-based dose optimisation. The data-dependent power calculation methodology using the log likelihood ratio test can contribute to an effective design of these future pharmacokinetic studies.
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Hansson, Emma K. "Pharmacometric Models for Biomarkers, Side Effects and Efficacy in Anticancer Drug Therapy." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170738.
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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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Novakovic, Ana M. "Longitudinal Models for Quantifying Disease and Therapeutic Response in Multiple Sclerosis." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-316562.
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Treatment of patients with multiple sclerosis (MS) and development of new therapies have been challenging due to the disease complexity and slow progression, and the limited sensitivity of available clinical outcomes. Modeling and simulation has become an increasingly important component in drug development and in post-marketing optimization of use of medication. This thesis focuses on development of pharmacometric models for characterization and quantification of the relationships between drug exposure, biomarkers and clinical endpoints in relapse-remitting MS (RRMS) following cladribine treatment. A population pharmacokinetic model of cladribine and its main metabolite, 2-chloroadenine, was developed using plasma and urine data. The renal clearance of cladribine was close to half of total elimination, and was found to be a linear function of creatinine clearance (CRCL). Exposure-response models could quantify a clear effect of cladribine tablets on absolute lymphocyte count (ALC), burden of disease (BoD), expanded disability status scale (EDSS) and relapse rate (RR) endpoints. Moreover, they gave insight into disease progression of RRMS. This thesis further demonstrates how integrated modeling framework allows an understanding of the interplay between ALC and clinical efficacy endpoints. ALC was found to be a promising predictor of RR. Moreover, ALC and BoD were identified as predictors of EDSS time-course. This enables the understanding of the behavior of the key outcomes necessary for the successful development of long-awaited MS therapies, as well as how these outcomes correlate with each other. The item response theory (IRT) methodology, an alternative approach for analysing composite scores, enabled to quantify the information content of the individual EDSS components, which could help improve this scale. In addition, IRT also proved capable of increasing the detection power of potential drug effects in clinical trials, which may enhance drug development efficiency. The developed nonlinear mixed-effects models offer a platform for the quantitative understanding of the biomarker(s)/clinical endpoint relationship, disease progression and therapeutic response in RRMS by integrating a significant amount of knowledge and data.
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Bouchene, Salim. "Physiologically Based Pharmacometric Models for Colistin and the Immune Response to Bacterial Infection." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-280208.
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Antibiotic treatment failure might be due to bacterial resistance or suboptimal exposure at target site and there is a lack of knowledge on the interaction between antimicrobial pharmacodynamics (PD) and the immune response to bacterial infections. Therefore, it is crucial to develop tools to increase the understanding of drug disposition to better evaluate antibiotic candidates in drug development and to elucidate the role of the immune system in bacterial infections. Colistin is used as salvage therapy against multidrug resistant Gram-negative infections. In this work, a whole-body physiologically based pharmacokinetic model (WBPBPK) was developed to characterize the pharmacokinetics (PK) of colistin and its prodrug colistin methanesulfonate (CMS) in animal and human. The scalability of the model from animal to human was assessed with satisfactory predictive performance for CMS and demonstrating the need for a mechanistic understanding of colistin elimination. The WBPBPK model was applied to investigate the impact of pathophysiological changes commonly observed in critically ill patients on tissue distribution of colistin and to evaluate different dosing strategies. Model predicted concentrations in tissue were used in combination with a semi-mechanistic PKPD model to predict bacterial killing in tissue for two strains of Pseudomonas aeruginosa. Finally, a toxicokinetic (TK) model was constructed to describe the time course of E. coli endotoxin concentrations in plasma and the effect on pro-inflammatory cytokine release. The model adequately described the concentration-time profiles of endotoxin and its stimulation of IL-6 and TNF-α production using an indirect response model combined with a transit compartment chain with a tolerance component to endotoxemia. The WBPBPK model developed in this work increased the knowledge on colistin tissue exposure under various conditions and could be used in drug development process to assess antibiotic efficacy or to test new drug combinations. The model describing endotoxin TK and its effect on cytokines is a new tool to be further applied in longitudinal studies to explore the immune response cascade induced by bacterial infections. The methodology applied in this thesis contributes to the development of an integrated modeling framework including physiology, drug distribution, bacterial growth and killing as well as the immune response to infection.
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Grišić, Ana-Marija [Verfasser]. "Pharmacometric analysis of monoclonal antibodies to support clinical decision-making / Ana-Marija Grišić." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1235400395/34.
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Melin, Johanna [Verfasser]. "Pharmacometric approaches to assess hydrocortisone therapy in paediatric patients with adrenal insufficiency / Johanna Melin." Berlin : Freie Universität Berlin, 2018. http://d-nb.info/1176631810/34.
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Bender, Brendan. "Pharmacometric Models for Antibody Drug Conjugates and Taxanes in HER2+ and HER2- Breast Cancer." Doctoral thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-292617.
Full textAbstract:
In oncology, there is a need to optimize drug treatment for efficient eradication of tumors, minimization of adverse effects (AEs), and prolonging patient survival. Pharmacometric models can be developed to streamline information between drug development phases, describe and quantify response to treatment, and determine dose regimens that balance toxicity and efficacy. In this thesis, data from trastuzumab emtansine (T-DM1) and taxane drug treatment were used to develop pharmacometric models of pharmacokinetics (PK), AEs, anti-tumor response, and survival, supporting drug development. T-DM1 is an antibody-drug conjugate (ADC) for treatment of human epidermal growth factor receptor 2 (HER2)–positive breast cancer. ADCs are a relatively new class of oncologic agents, and contain multiple drug-to-antibody ratio (DAR) moieties in their dose product. The complex distribution of T-DM1 was elucidated through PK models developed using in vitro and in vivo rat and cynomolgus monkey DAR data. Mechanism–based PK/pharmacodynamic (PKPD) models were also developed for T-DM1 that described the AEs thrombocytopenia (TCP) and hepatotoxicity in patients receiving T-DM1. Variable patterns of platelet and transaminase (ALT and AST) response were quantified, including an effect of Asian ethnicity that was related to higher incidences of TCP. Model simulations, comparing dose intensities (DI) and Grade 3/4 incidences between the approved T-DM1 dose (3.6 mg/kg every three weeks) and weekly regimens, determined that 2.4 mg/kg weekly provided the highest DI. Docetaxel and paclitaxel are taxane treatment options for HER2–negative breast cancer. Tumor response data from these treatments were used to develop a mechanism–based model of tumor quiescence and drug–resistance. Subsequently, a parametric survival analysis found that tumor baseline and the model–predicted time to tumor growth (TTG) were predictors of overall survival (OS). This tumor and OS modeling approach can be applied to other anticancer treatments with similar patterns of drug–resistance. Overall, the pharmacometric models developed within this thesis present new modeling approaches and provide understanding on ADC PK and PKPD (TCP and hepatotoxicity), as well as drug–resistance tumor response. These models can inform simulation strategies and clinical study design, and be applied towards dose finding for anticancer drugs in development, especially ADCs.