Nonlinear Mixed effect models are often used to describe population pharmacokinetics (PK) and Pharmacodynamics (PD) and play an important part of drug development both from regulatory and industry point of view. However, they can be time consuming and computationally expensive to develop. This thesis is a part of a larger collaboration between Uppsala University and two pharmaceutical companies, with the aim to develop a suite of software that can automate the model building process with more efficiency. One aspect that is important during the model building process is to detect how much the population parameter estimates are influenced by particular individuals. The results of this might lead to reconsideration of the model structure, as well as exclusion of these individuals from the dataset. The current tools available to detect this use case deletion diagnostics (CDD) to run the model multiple times with each subject removed from the dataset to examine whether the population estimates alter when that individual is removed. Another important aspect is whether an individual is an outlier from the population parameter predictions, which is obtained from simulating the model and evaluating the residuals (simeval). Both of these tools are computationally expensive and can take a lot of time, in particular CDD. Therefore, we developed a tool using machine learning (ML) algorithms that can predict these individuals based on other criteria, which will decrease the runtime in an automated model building procedure, whilst maintaining the robustness of the current methods described above.  To create a training database for the ML models, predictors were extracted from 27 previously published models and the CDD and simeval diagnostic tools were run on these models to obtain that true values we want the ML model to predict. The database was then used to train two artificial neural networks (ANN) which is an efficient and powerful method in ML. To enable ‘on-the-fly’ predictions, the developed ANN models were deployed using tflite into pharmpy. The resulted ANNs were able to predict outlying individuals with 79% sensitivity, 83% precision, and 99.1% specificity. While the influential individuals ANN was able to predict with 58% sensitivity, 63% precision and 99.6% specificity. Both ANNs offered a rapid assessment of influential individuals and outlying individuals and were able to make predictions in a matter of sub-seconds compared to hours using traditional methods.

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.

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.

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

No description available.

Tuberculosis is one the leading causes of death globally and was before the COVID-19 pandemic the leading cause of death from a single infectious agent. Developing active tuberculosis is life threatening and therefore is the rise of drug-resistant tuberculosis alarming as this risk causing current treatments to become ineffective. Linezolid is a promising drug for treatment of drug-resistant pulmonary tuberculosis, but the effect of linezolid treatment for pulmonary tuberculosis subjects is still not understood well enough and the World Health Organization has requested this knowledge gap to be filled. In this project we support the closing of this knowledge gap by describing the pharmacokinetics of linezolid for treatment of pulmonary tuberculosis using data collected from a phase two clinical trial in a South African population. This was done by creating a pop-PK model and resulted in the PK of linezolid in pulmonary tuberculosis patients from South Africa was best described using a one-compartment model, with first-order absorption process preceded by a series of transit compartments and saturable elimination. However, the diagnostics of the model still show that there are room for improvements and future work is necessary to further optimize the model.

PBPK modelling is used in drug development to predict how the drug isaffected by the body. These models are built on physiology and describeprocesses like absorption, distribution, metabolism and excretion.Simulations of these processes can take place in the software PK-Sim forinstance. In PK-Sim, datasets of clinical observations can be importedfor model validation. When building the models, correct data and correcttype of model has to used to prevent incorrect predictions. These errorscan be discovered by performing quality control, preferably beforeinitiating model development in order to save time. An automatic processof quality control is also a way to save time, as well as to setstandards and provide transparency. At Pharmetheus, where PBPK modellingin PK-Sim is performed, quality control is executed manually. In thisproject, an infrastructure for quality control of data in PBPK projectsin PK-Sim was developed in R. The intention was to use thisinfrastructure before initiating model development and as a complementto manual quality control. The quality control was focused on dataintegrity and data concordance. Controls that are performed includedcorrectness of values and units for the observed data as well asensuring that each data had been assigned to the corresponding model.The developed model achieved to capture all defined errors to a highdegree. However, in many scenarios rounding of values occurred which wasnot always handled as intended. More evaluation also has to be performedbefore this infrastructure can be used in production.

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.

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.

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.