Type 2 diabetes is a complex chronic metabolic disorder characterized by hyperglycemia associated with a relative deficiency of insulin secretion and a reduced response of target tissues to insulin. Considerable efforts have been put into the development of models describing the glucose-insulin system. The best known is Bergman’s “minimal” model for glucose, which is estimating glucose concentrations using fixed insulin concentrations as input. However, due to the involved feedback mechanisms, simultaneous modeling of both entities would be advantageous. This is particularly relevant if the model is intended to be used as a predictive tool. The mechanism-based glucose-insulin model presented in this thesis is able to simultaneously describe glucose and insulin profiles following a wide variety of clinical provocation experiments, such as intravenous and oral glucose tolerance tests, clamp studies and sequential meal tests over 24 hours. It consists of sub-models for glucose, labeled glucose and insulin kinetics. It also incorporates control mechanisms for the regulation of glucose production, insulin secretion, and glucose uptake. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Even if this model is a crude representation of a complex physiological system, its ability to represent the main processes of this system was established by identifying: 1) the difference in insulin secretion and insulin sensitivity between healthy volunteers and type 2 diabetics, 2) the action of incretin hormones after oral administration of glucose, 3) the circadian variation of insulin secretion and 4) the correct mechanism of action of a glucokinase activator, a new oral antidiabetic compound acting on both the pancreas and the liver. These promising results represent a proof of concept of a mechanistic drug-disease model that could play an important role in the clinical development of anti-diabetic drugs.

In drug development clinical trials are designed to investigate whether a new treatment is safe and has the desired effect on the disease in the target patient population. Categorical endpoints, for example different ranking scales or grading of adverse events, are commonly used to measure effects in the trials. Pharmacokinetic/Pharmacodynamic (PK/PD) models are used to describe the plasma concentration of a drug over time and its relationship to the effect studied. The models are utilized both in drug development and in discussions with drug regulating authorities. Methods for incorporation of ordered categorical data in PK/PD models were studied using a non-linear mixed effects modelling approach as implemented in the software NONMEM. The traditionally used proportional odds model was used for analysis of a 6-grade sedation scale in acute stroke patients and for analysis of a T-cell receptor expression in patients with Multiple Sclerosis, where the results also were compared with an analysis of the data on a continuous scale. Modifications of the proportional odds model were developed to enable analysis of a spontaneously reported side-effect and to analyze situations where the scale used is heterogeneous or where the drug affects the different scores in the scale in a non-proportional way. The new models were compared with the proportional odds model and were shown to give better predictive performances in the analyzed situations. The results in this thesis show that categorical data obtained in clinical trials with different design and different categorical endpoints successfully can be incorporated in PK/PD models. The models developed can also be applied to analyses of other ordered categorical scales than those presented.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Ce travail de thèse porte sur l’application de la pharmacocinétique de population dans le but d’optimiser l’utilisation de certains médicaments chez les enfants immunosupprimés et subissant une greffe. Parmi les différents médicaments utilisés chez les enfants immunosupprimés, l’utilisation du busulfan, du tacrolimus et du voriconazole reste problématique, notamment à cause d’une très grande variabilité interindividuelle de leur pharmacocinétique rendant nécessaire l’individualisation des doses par le suivi thérapeutique pharmacologique. De plus, ces médicaments n’ont pas fait l’objet d’études chez les enfants et les doses sont adaptées à partir des adultes. Cette dernière pratique ne prend pas en compte les particularités pharmacologiques qui caractérisent l’enfant tout au long de son développement et rend illusoire l’extrapolation aux enfants des données acquises chez les adultes. Les travaux effectués dans le cadre de cette thèse ont étudié successivement la pharmacocinétique du busulfan, du voriconazole et du tacrolimus par une approche de population en une étape (modèles non-linéaires à effets mixtes). Ces modèles ont permis d’identifier les principales sources de variabilités interindividuelles sur les paramètres pharmacocinétiques. Les covariables identifiées sont la surface corporelle et le poids. Ces résultats confirment l’importance de tenir en compte l’effet de la croissance en pédiatrie. Ces paramètres ont été inclus de façon allométrique dans les modèles. Cette approche permet de séparer l’effet de la mesure anthropométrique d’autres covariables et permet la comparaison des paramètres pharmacocinétiques en pédiatrie avec ceux des adultes. La prise en compte de ces covariables explicatives devrait permettre d’améliorer la prise en charge a priori des patients. Ces modèles développés ont été évalués pour confirmer leur stabilité, leur performance de simulation et leur capacité à répondre aux objectifs initiaux de la modélisation. Dans le cas du busulfan, le modèle validé a été utilisé pour proposer par simulation une posologie qui améliorerait l’atteinte de l’exposition cible, diminuerait l’échec thérapeutique et les risques de toxicité. Le modèle développé pour le voriconazole, a permis de confirmer la grande variabilité interindividuelle dans sa pharmacocinétique chez les enfants immunosupprimés. Le nombre limité de patients n’a pas permis d’identifier des covariables expliquant cette variabilité. Sur la base du modèle de pharmacocinétique de population du tacrolimus, un estimateur Bayesien a été mis au point, qui est le premier dans cette population de transplantés hépatiques pédiatriques. Cet estimateur permet de prédire les paramètres pharmacocinétiques et l’exposition individuelle au tacrolimus sur la base d’un nombre limité de prélèvements. En conclusion, les travaux de cette thèse ont permis d’appliquer la pharmacocinétique de population en pédiatrie pour explorer les caractéristiques propres à cette population, de décrire la variabilité pharmacocinétique des médicaments utilisés chez les enfants immunosupprimés, en vue de l’individualisation du traitement. Les outils pharmacocinétiques développés s’inscrivent dans une démarche visant à diminuer le taux d'échec thérapeutique et l’incidence des effets indésirables ou toxiques chez les enfants immunosupprimés suite à une transplantation. / This thesis deals with the application of population pharmacokinetics in order to optimize the use of certain medications in immunocompromised children undergoing transplantation. Among the various drugs used in immunocompromised children, the use of busulfan, tacrolimus and voriconazole remains problematic, particularly because of high interindividual variability in their pharmacokinetics necessitating individualized doses based on therapeutic drug monitoring. In addition, these drugs have not been studied in children and the doses are adapted from adults. This practice does not take into account the pharmacological characteristics of pediatrics throughout their development and makes illusory the extrapolation of data acquired in adults to children. The work done in this thesis studied sequentially the pharmacokinetics of busulfan, voriconazole and tacrolimus by a population approach (non-linear mixed effects models). The developed models have identified the main sources of interindividual variability in the pharmacokinetic parameters of these drugs. The identified covariates are body surface area and weight. These results confirm the importance of taking into account the effect of growth in children. These parameters were allometrically included in the models. This approach allows separating the effect of size from other covariates and enables the comparison of pediatric pharmacokinetic parameters with those of adults. The inclusion of these explanatory covariates should improve the management a priori of patients. The developed models were evaluated to confirm their stability, performance, and their ability to answer the original objectives of modeling. In the case of busulfan, the validated model was used to simulate dosing regimens that improve reaching the target exposure, reduce treatment failure and toxicity episodes. The developed population pharmacokinetic model for voriconazole confirmed the large variability in its pharmacokinetics in immunocompromised children. The limited data did not allow identification of covariates explaining this variability. Based on the population pharmacokinetic model of tacrolimus, a Bayesian estimator was developed, which is the first in this population of pediatric liver transplant recipients. This estimator can predict pharmacokinetic parameters and individual exposure to tacrolimus based on a limited number of samples. In conclusion, this thesis allowed applying the population pharmacokinetics approach in pediatrics to explore the characteristics of this population and describe the pharmacokinetic variability of drugs used in immunocompromised children, for the individualization of treatment. Pharmacokinetic tools developed are part of efforts to decrease the rate of treatment failure and the incidence of adverse and toxic events in immunocompromised and transplanted pediatrics.

Ce travail de thèse porte sur l’application de la pharmacocinétique de population dans le but d’optimiser l’utilisation de certains médicaments chez les enfants immunosupprimés et subissant une greffe. Parmi les différents médicaments utilisés chez les enfants immunosupprimés, l’utilisation du busulfan, du tacrolimus et du voriconazole reste problématique, notamment à cause d’une très grande variabilité interindividuelle de leur pharmacocinétique rendant nécessaire l’individualisation des doses par le suivi thérapeutique pharmacologique. De plus, ces médicaments n’ont pas fait l’objet d’études chez les enfants et les doses sont adaptées à partir des adultes. Cette dernière pratique ne prend pas en compte les particularités pharmacologiques qui caractérisent l’enfant tout au long de son développement et rend illusoire l’extrapolation aux enfants des données acquises chez les adultes. Les travaux effectués dans le cadre de cette thèse ont étudié successivement la pharmacocinétique du busulfan, du voriconazole et du tacrolimus par une approche de population en une étape (modèles non-linéaires à effets mixtes). Ces modèles ont permis d’identifier les principales sources de variabilités interindividuelles sur les paramètres pharmacocinétiques. Les covariables identifiées sont la surface corporelle et le poids. Ces résultats confirment l’importance de tenir en compte l’effet de la croissance en pédiatrie. Ces paramètres ont été inclus de façon allométrique dans les modèles. Cette approche permet de séparer l’effet de la mesure anthropométrique d’autres covariables et permet la comparaison des paramètres pharmacocinétiques en pédiatrie avec ceux des adultes. La prise en compte de ces covariables explicatives devrait permettre d’améliorer la prise en charge a priori des patients. Ces modèles développés ont été évalués pour confirmer leur stabilité, leur performance de simulation et leur capacité à répondre aux objectifs initiaux de la modélisation. Dans le cas du busulfan, le modèle validé a été utilisé pour proposer par simulation une posologie qui améliorerait l’atteinte de l’exposition cible, diminuerait l’échec thérapeutique et les risques de toxicité. Le modèle développé pour le voriconazole, a permis de confirmer la grande variabilité interindividuelle dans sa pharmacocinétique chez les enfants immunosupprimés. Le nombre limité de patients n’a pas permis d’identifier des covariables expliquant cette variabilité. Sur la base du modèle de pharmacocinétique de population du tacrolimus, un estimateur Bayesien a été mis au point, qui est le premier dans cette population de transplantés hépatiques pédiatriques. Cet estimateur permet de prédire les paramètres pharmacocinétiques et l’exposition individuelle au tacrolimus sur la base d’un nombre limité de prélèvements. En conclusion, les travaux de cette thèse ont permis d’appliquer la pharmacocinétique de population en pédiatrie pour explorer les caractéristiques propres à cette population, de décrire la variabilité pharmacocinétique des médicaments utilisés chez les enfants immunosupprimés, en vue de l’individualisation du traitement. Les outils pharmacocinétiques développés s’inscrivent dans une démarche visant à diminuer le taux d'échec thérapeutique et l’incidence des effets indésirables ou toxiques chez les enfants immunosupprimés suite à une transplantation. / This thesis deals with the application of population pharmacokinetics in order to optimize the use of certain medications in immunocompromised children undergoing transplantation. Among the various drugs used in immunocompromised children, the use of busulfan, tacrolimus and voriconazole remains problematic, particularly because of high interindividual variability in their pharmacokinetics necessitating individualized doses based on therapeutic drug monitoring. In addition, these drugs have not been studied in children and the doses are adapted from adults. This practice does not take into account the pharmacological characteristics of pediatrics throughout their development and makes illusory the extrapolation of data acquired in adults to children. The work done in this thesis studied sequentially the pharmacokinetics of busulfan, voriconazole and tacrolimus by a population approach (non-linear mixed effects models). The developed models have identified the main sources of interindividual variability in the pharmacokinetic parameters of these drugs. The identified covariates are body surface area and weight. These results confirm the importance of taking into account the effect of growth in children. These parameters were allometrically included in the models. This approach allows separating the effect of size from other covariates and enables the comparison of pediatric pharmacokinetic parameters with those of adults. The inclusion of these explanatory covariates should improve the management a priori of patients. The developed models were evaluated to confirm their stability, performance, and their ability to answer the original objectives of modeling. In the case of busulfan, the validated model was used to simulate dosing regimens that improve reaching the target exposure, reduce treatment failure and toxicity episodes. The developed population pharmacokinetic model for voriconazole confirmed the large variability in its pharmacokinetics in immunocompromised children. The limited data did not allow identification of covariates explaining this variability. Based on the population pharmacokinetic model of tacrolimus, a Bayesian estimator was developed, which is the first in this population of pediatric liver transplant recipients. This estimator can predict pharmacokinetic parameters and individual exposure to tacrolimus based on a limited number of samples. In conclusion, this thesis allowed applying the population pharmacokinetics approach in pediatrics to explore the characteristics of this population and describe the pharmacokinetic variability of drugs used in immunocompromised children, for the individualization of treatment. Pharmacokinetic tools developed are part of efforts to decrease the rate of treatment failure and the incidence of adverse and toxic events in immunocompromised and transplanted pediatrics.

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.

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.

The gold standard for pharmacometrics modeling, along with its modeling format, is currently NONMEM. In order to use other software, there is often a manual step of converting a model from one format to another, which is both time-consuming and causes manual errors. This project aimed to solve this problem by creating a conversion- and validation tool from NONMEM to two formats: nlmixr2 and RxODE2. These are both, unlike NONMEM, freely available and integrated into R. This was done by integrating the two tools (conversion and validation) into the program Pharmpy, which can extract model information from NONMEM's model format. For conversion, the model was read into Pharmpy and then, part by part, converted to the respective model format. The associated validation compared the predictions of the respective programs to see if they differed significantly from each other. The project showed that this type of conversion is possible, but the programs showed a greater difference than expected. Part of this can be explained by a rounding of parameter values in Pharmpy, but further analysis also indicated fundamental differences in the determination of the predictions between the programs. Larger differences in predictions are for instance oftentimes equidistant from the actual observation, meaning the predictions are presumably calculated differently. While not disproving the converted model, smaller discrepancies between the programs would indicate a more confident validation. In summary, the developed tools are considered useful for models where Pharmpy supports parsing of the model. If not for complete conversion, then at least for partial conversion with manual correction, which is also an improvement over an entirely manual workflow.

Introduction: The current development strategy of population pharmacokinetic models is a complex and iterative process that is manually performed by modellers. Such a strategy is time-demanding, subjective, and dependent on the modellers’ experience. This thesis presents a novel model building tool that automates the development process of pharmacokinetic (PK) structural models. Methods: Modelsearch is a tool in Pharmpy library, an open-source package for pharmacometrics modelling, that searches for the best structural model using an exhaustive stepwise search algorithm. Given a dataset, a starting model and a pre-specified model search space of structural model features, the tool creates and fits a series of candidate models that are then ranked based on a selection criterion, leading to the selection of the best model. The Modelsearch tool was used to develop structural models for 10 clinical PK datasets (5 orally and 5 i.v. administered drugs). A starting model for each dataset was generated using the assemblerr package in R, which included a first-order (FO) absorption without any absorption delay for oral drugs, one-compartment disposition, FO elimination, a proportional residual error model, and inter-individual variability on the starting model parameters with a correlation between clearance (CL) and central volume of distribution (VC). The model search space included aspects of absorption and absorption delay (for oral drugs), distribution and elimination. In order to understand the effects of different IIV structures on structural model selection, five model search approaches were investigated that differ in the IIV structure of candidate models: 1. naïve pooling, 2. IIV on starting model parameters only, 3. additional IIV on mean delay time parameter, 4. additional diagonal IIVs on newly added parameters, and 5. full block IIVs. Additionally, the implementation of structural model selection in the workflow of the fully automatic model development was investigated. Three strategies were evaluated: SIR, SRI, and RSI depending on the development order of structural model (S), IIV model (I) and residual error model (R). Moreover, the NONMEM errors encountered when using the tool were investigated and categorized in order to be handled in the automatic model building workflow. Results: Differences in the final selected structural models for each drug were observed between the five different model search approaches. The same distribution components were selected through Approaches 1 and 2 for 6/10 drugs. Approach 2 has also identified an absorption delay component in 4/5 oral drugs, whilst the naïve pooling approach only identified an absorption delay model in 2 drugs. Compared to Approaches 1 and 2, Approaches 3, 4 and 5 tended to select more complex models and more often resulted in minimization errors during the search. For the SIR, SRI and RSI investigations, the same structural model was selected in 9/10 drugs with a significant higher run time in RSI strategy compared to the other strategies. The NONMEM errors were categorized into four categories based on the handling suggestions which is valuable to further improve the tool in its automatic error handling. Conclusions: The Modelsearch tool was able to automatically select a structural model with different strategies of setting the IIV model structure. This novel tool enables the evaluation of numerous combinations of model components, which would not be possible using a traditional manual model building strategy. Furthermore, the tool is flexible and can support multiple research investigations for how to best implement structural model selection in a fully automatic model development workflow.

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.