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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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.

General attrition rates in drug development pipeline have been recognized as a necessity to shift gears towards new methodologies that allow earlier and correct decisions, and the optimal use of all information accrued throughout the process. The quantitative science of pharmacometrics using pharmacokinetic-pharmacodynamic models was identified as one of the strategies core to this renaissance. Coupled with Optimal Design (OD), they constitute together an attractive toolkit to usher more rapidly and successfully new agents to marketing approval. The general aim of this thesis was to investigate how the use of novel pharmacometric methodologies can improve the design and analysis of clinical trials within drug development. The implementation of a Monte-Carlo Mapped power method permitted to rapidly generate multiple hypotheses and to adequately compute the corresponding sample size within 1% of the time usually necessary in more traditional model-based power assessment. Allowing statistical inference across all data available and the integration of mechanistic interpretation of the models, the performance of this new methodology in proof-of-concept and dose-finding trials highlighted the possibility to reduce drastically the number of healthy volunteers and patients exposed to experimental drugs. This thesis furthermore addressed the benefits of OD in planning trials with bio analytical limits and toxicity constraints, through the development of novel optimality criteria that foremost pinpoint information and safety aspects. The use of these methodologies showed better estimation properties and robustness for the ensuing data analysis and reduced the number of patients exposed to severe toxicity by 7-fold.  Finally, predictive tools for maximum tolerated dose selection in Phase I oncology trials were explored for a combination therapy characterized by main dose-limiting hematological toxicity. In this example, Bayesian and model-based approaches provided the incentive to a paradigm change away from the traditional rule-based “3+3” design algorithm. Throughout this thesis several examples have shown the possibility of streamlining clinical trials with more model-based design and analysis supports. Ultimately, efficient use of the data can elevate the probability of a successful trial and increase paramount ethical conduct.

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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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.

L’apprentissage automatique propose des outils pour faire face aux problématiques d’aujourd’hui et de demain. Les récentes percées en sciences computationnelles et l’émergence du phénomène des mégadonnées ont permis à l’apprentissage automatique d’être mis à l’avant plan tant dans le monde académique que dans la société. Les récentes réalisations de l’apprentissage automatique dans le domaine du langage naturel, de la vision et en médecine parlent d’eux-mêmes. La liste des sciences et domaines qui bénéficient des techniques de l’apprentissage automatique est longue. Cependant, les tentatives de coopération avec la pharmacométrie et les sciences connexes sont timides et peu nombreuses. L’objectif de ce projet de maitrise est d’explorer le potentiel de l’apprentissage automatique en sciences pharmaceutiques. Cela a été réalisé par l’application de techniques et des méthodes d’apprentissage automatique à des situations de pharmacologie clinique et de pharmacométrie. Le projet a été divisé en trois parties. La première partie propose un algorithme pour renforcer la fiabilité de l’étape de présélection des covariables d’un modèle de pharmacocinétique de population. Une forêt aléatoire et l’XGBoost ont été utilisés pour soutenir la présélection des covariables. Les indicateurs d’importance relative des variables pour la forêt aléatoire et pour l’XGBoost ont bien identifié l’importance de toutes les covariables qui avaient un effet sur les différents paramètres du modèle PK de référence. La seconde partie confirme qu’il est possible d’estimer des concentrations plasmatiques avec des méthodes différentes de celles actuellement utilisés en pharmacocinétique. Les mêmes algorithmes ont été sélectionnés et leur ajustement pour la tâche était appréciable. La troisième partie confirme la possibilité de faire usage des méthodes d'apprentissage automatique pour la prédiction de relations complexes et typiques à la pharmacologie clinique. Encore une fois, la forêt aléatoire et l’XGBoost ont donné lieu à un ajustement appréciable. / Machine learning offers tools to deal with current problematics. Recent breakthroughs in computational sciences and the emergence of the big data phenomenon have brought machine learning to the forefront in both academia and society. The recent achievements of machine learning in natural language, computational vision and medicine speak for themselves. The list of sciences and fields that benefit from machine learning techniques is long. However, attempts to cooperate with pharmacometrics and related sciences are timid and limited. The aim of this Master thesis is to explore the potential of machine learning in pharmaceutical sciences. This has been done through the application of machine learning techniques and methods to situations of clinical pharmacology and pharmacometrics. The project was divided into three parts. The first part proposes an algorithm to enhance the reliability of the covariate pre-selection step of a population pharmacokinetic model. Random forest and XGBoost were used to support the screening of covariates. The indicators of the relative importance of the variables for the random forest and for XGBoost recognized the importance of all the covariates that influenced the various parameters of the PK model of reference. The second part exemplifies the estimation of plasma concentrations using machine learning methods. The same algorithms were selected and their fit for the task was appreciable. The third part confirms the possibility to apply machine learning methods in the prediction of complex relationships, as some typical clinical pharmacology relationships. Again, random forest and XGBoost got a nice adjustment.

La vancomycine est l'un des antibiotiques les plus prescrits, principalement utilisé pour les infections suspectées et confirmées à Staphylococcus aureus résistant à la méthicilline (SARM). Les infections par des souches de SARM font peser une charge importante sur le système de santé, à laquelle s'ajoute l'incertitude qui demeure quant à la posologie optimale de la vancomycine. Les récentes lignes directrices révisées sur le suivi thérapeutique de la vancomycine, publiées en 2020, avalisent principalement l'estimation directe de l'aire sous la courbe de concentration en fonction du temps (AUC) par l'utilisation d'équations bayésiennes ou pharmacocinétiques (PK) de premier ordre pour le suivi thérapeutique. Pour mieux informer la posologie de la vancomycine, nous avons d'abord mis à jour une revue précédente des analyses pharmacocinétiques de population (PopPK) de la vancomycine publiées chez les adultes et les enfants. Pour ce faire, nous avons déterminé les caractéristiques des modèles pharmacocinétiques rapportés et identifié les diverses sources potentielles de variabilité observées dans différentes sous-populations particulières. Motivés par la controverse existante autour des nouvelles directives de surveillance thérapeutique de la vancomycine et par l'absence d'une étude approfondie des méthodes recommandées, nous avons recueilli des données hospitalières et construit un cadre de modélisation qui nous a permis d'évaluer les recommandations des directives sur les méthodes de surveillance, tout en considérant une variété de scénarios et d'hypothèses cliniques réalistes. Notre analyse a confirmé que la surveillance bayésienne est la méthode la plus rapide et la plus fiable, à condition qu'elle soit correctement mise en œuvre, la plus importante condition pour cela étant l'utilisation de modèles bayésiens a priori appropriés. De plus, nous avons montré que le suivi bayésien ne nécessite pas nécessairement des niveaux de concentration de types creux ou pic et peut en fait être réalisé en utilisant un niveau aléatoire. Aussi, nous avons démontré que l'utilisation correcte des équations pharmacocinétiques de premier ordre exigerait au moins deux mesures de concentration à l'état d'équilibre. L’utilisation de la méthode creux-seulement de la vancomycine à l'état d'équilibre peut être tout aussi efficace dans certaines situations que nous avons explorées ici. En considérant la larges étendue et la grande variabilité des populations traitées à la vancomycine en termes d'âge, de gravité de l'infection et de scénarios cliniques, cette thèse adopte un regard objectif pour évaluer quantitativement le gain potentiel de chaque méthode de surveillance de la vancomycine, en explorant leur adéquation en termes d'effort nécessaire, de disponibilité des ressources et de gain potentiel. Compte tenu des lignes directrices sur la vancomycine récemment publiées et de la controverse qui persiste, nous pensons que cette thèse a permis de démêler la variété et la complexité de l'utilisation de la vancomycine et a apporté un éclairage supplémentaire plus objectifvement informé vers un suivi thérapeutique optimal de la vancomycine. / Vancomycin is among the most prescribed antibiotics, mainly used for suspected and confirmed methicillin-resistant Staphylococcus aureus (MRSA) infections. Infections by MRSA strains carry a substantial burden on the health care system, supplemented by the uncertainty that remains regarding vancomycin optimal dosing. The recent revised vancomycin therapeutic monitoring guidelines published in 2020, endorsed primarily the direct estimation of area under the concentration-time curve (AUC) through the use of Bayesian or first-order pharmacokinetic (PK) equations monitoring. To better inform vancomycin dosing, we first updated a previous review of published vancomycin population pharmacokinetic (PopPK) analysis in both adults and children. This was accomplished by determining the characteristics of the reported pharmacokinetic models and identifying the potential various sources of variability observed in different special subpopulations. Motivated by the existing controversy around the new vancomycin therapeutic monitoring guidelines and the lack of a thorough investigation of the recommended methods, we collected hospital data and built a modeling framework that allowed us to assess the guideline recommendations of monitoring methods while considering a variety of realistic clinical scenarios and assumptions. Our analysis affirmed that Bayesian monitoring is the fastest and most reliable method, conditional on its proper implementation, the most important being the use of proper Bayesian priors. Moreover, we showed that Bayesian monitoring does not necessarily require trough or peak concentration levels and can in fact be performed using a random level. Proper use of first-order PK equations required at least two steady-state concentration measurements. Alternatively, simpler trough-only vancomycin monitoring near steady-state can be as effective in certain cases that we explored here. By considering the wide ranges and the high variability in populations treated with vancomycin in terms of age, the severity of infection, and clinical scenarios, this thesis takes an objective look to quantitatively assess the potential gain of each vancomycin drug monitoring method, by investigating their suitability in terms of the effort needed, the availability of resources and the resulting gain. Considering the recently released vancomycin guidelines and the ensuing controversies between well-established clinical teams, we believe that this dissertation helped untangle the variety and complexity of vancomycin use and brought additional insights towards a more objective and optimal vancomycin therapeutic monitoring.

La dopamine est un neurotransmetteur important dans le fonctionnement des ganglions de la base, région du cerveau impliquée dans la fonction motrice et l’apprentissage. Un dérèglement de la dynamique de la dopamine peut être à l’origine de différentes pathologies neurologiques, telles que la maladie de Parkinson et le trouble de déficit de l’attention avec ou sans hyperactivité (TDAH). La lévodopa, un précurseur de la dopamine, est utilisée pour réduire les symptômes associés à la maladie de Parkinson, sans action directe sur ses causes. La lévodopa est très efficace au début de la maladie, mais la durée de son effet ainsi que son index thérapeutique diminuent avec la progression de la dénervation induite par la maladie. Ces changements compliquent considérablement l’optimisation des régimes posologiques. Le méthylphénidate, quant à lui, est administré pour réduire les symptômes du TDAH et agit entre autres en bloquant la recapture de la dopamine. Bien que les données confirment une certaine implication de la dopamine dans le TDAH, son étiologie exacte demeure inconnue. Peu d’études ont cerné l’effet de la lévodopa sur le système dopaminergique des ganglions de la base et son évolution avec la progression de la maladie. Aussi, bien que le TDAH ait suscité beaucoup d’intérêt, rares sont les études quantitatives de nature mécanistiques sur le sujet. L’approche de modélisation mathématique utilisée dans cette thèse s’inscrit dans un effort global visant l’optimisation de la lévodopa et du méthylphénidate, appuyé par l’élucidation des mécanismes impliqués dans la maladie de Parkinson et dans le TDAH. En adoptant une approche de pharmacologie quantitative des systèmes (QSP), nous avons développé un modèle intégratif du système dopaminergique des ganglions de la base, avec l’objectif d’élucider les mécanismes impliqués, d’évaluer l’impact de la dopamine chez dessujets souffrant de Parkinson ou de TDAH, et recevant ou non un traitement, et enfin de guider objectivement l’exercice d’optimisation des régimes posologiques. À notre connaissance, c’est le premier cadre unificateur de modélisation qui s’adresse à ces deux pathologies. Le modèle développé dans cette thèse est composé de trois sous-modèles : le premier décrit la pharmacocinétique du médicament concerné, soit la lévodopa ou le méthylphénidate ; le deuxième exprime mathématiquement les différents mécanismes impliqués dans la dynamique de la dopamine ; le troisième représente la complexité de la neurotransmission dans les ganglions de la base. Avec des adaptations appropriées, nous avons appliqué ce même modèle au contexte de la maladie de Parkinson et au TDAH, ainsi qu’à leurs thérapies respectives. Pour représenter physiologiquement la maladie de Parkinson, nous avons intégré dans le modèle l’évolution de la perte neuronale ainsi que les différents mécanismes de compensation qui en résultent. La fréquence de tapotement des doigts est utilisée comme mesure clinique de la bradykinésie, définie comme le ralentissement des mouvements chez les patients parkinsoniens. Le modèle développé se base sur les connaissances actuelles de la pathophysiologie et pharmacologie du Parkinson, assurant ainsi sa validité en comparaison à des observations expérimentales et cliniques. Ensuite, à l’aide de ce modèle, les relations non-linéaires entre la concentration plasmatique de lévodopa, la concentration en dopamine dans le cerveau et la réponse à une tâche motrice sont étudiées. Le rétrécissement de l’index thérapeutique de la lévodopa au cours de la progression de la maladie dû à ces non-linéarités est investigué. Enfin, pour assurer l’aspect translationnel de notre approche, nous avons développé une application web à laquelle ce modèle a été intégré. Cette application sert de preuve de concept à un outil facilitant l’optimisation et l’individualisation des régimes posologiques. Pour l’étude du TDAH, nous avons adapté le modèle du système dopaminergique en y intégrant la libération tonique et phasique de la dopamine, cette dernière se produisant durant une tâche d’apprentissage par renforcement. Des individus virtuels ont été créés avec et sans déséquilibre du ratio tonique/phasique de la dopamine. En simulant une tâche de réponse à des stimuli dans un contexte de déséquilibre de la dopamine, le modèle nous a permis d’observer des symptômes similiaires à ceux de patients réels souffrant de TDAH. Finalement, la réponse au méthylphénidate résultant de l’inhibition de la recapture de la dopamine, à travers différents scénarios d’apprentissage a aussi été étudiée. Le développement d’une métrique nous a permis de différencier les répondants des non-répondants, et ainsi de mettre en évidence l’implication possible d’un apprentissage excessif chez les nonrépondants. Une meilleure compréhension de la réponse au méthylphénidate permettrait d’éviter la surmédication chez les non-répondants et d’aider les cliniciens dans leur pratique. Malgré la complexité du système dopaminergique et des traitements associés, cette thèse est un pas en avant dans la compréhension des mécanismes sous-jacents et de leur implication dans la thérapie. Ces avancées ont été réalisées en adoptant une approche de pharmacologie quantitative des systèmes, associée à une modélisation neurocomputationnelle du domaine du génie électrique, et complétée par un aspect de transfert au chevet du patient. Ce n’est qu’en transcendant ainsi les frontières disciplinaires qu’une visée aussi globale et intégrative est possible, afin de faire face aux défis multidimensionnels du système de la santé. / Dopamine is an important neurotransmitter of the basal ganglia, a region of the brain involved in motor function and learning. Disruption of dopamine dynamics can cause various neurological conditions, such as Parkinson’s disease and attention deficit hyperactivity disorder (ADHD). Levodopa, a dopamine precursor, is used to reduce the symptoms associated with Parkinson’s disease, without directly alleviating its causes. Levodopa is very effective in the early stages of the disease, but its effect duration along with its therapeutic index decrease with disease-induced denervation. These modifications further challenge determination of optimal dosing regimens of levodopa. In the case of ADHD, methylphenidate is administered to reduce its symptoms by, among other things, blocking dopamine recapture. Although evidence supports involvement of dopamine in ADHD, its exact etiology remains unknown. Few studies have investigated the effect of levodopa on the basal ganglia dopaminergic system and how it evolves with disease progression. Also, although ADHD has received a lot of interest, few quantitative studies of a mechanistic nature have been conducted on the subject. The mathematical modeling approach used in this thesis is part of an overall effort to optimize levodopa and methylphenidate, supported by the elucidation of the mechanisms involved in Parkinson’s disease and ADHD. Using a quantitative systems pharmacology (QSP) approach, we have developed an integrative model of the basal ganglia dopaminergic system, with the objective of elucidating the mechanisms involved, assessing the impact of dopamine in subjects with Parkinson’s or ADHD, with and without treatment, and objectively guiding the dosing regimens optimization. To the best of our knowledge, this is the first unifying modeling framework that addresses at the same time these two pathologies and their therapies. The model developed in this thesis includes three sub-models: the first one describes the drug pharmacokinetics, either levodopa or methylphenidate; the second one translates mathematically the different mechanisms involved in the dopamine dynamics; the third one is a computational representation of the complexity of neurotransmission in the basal ganglia. With appropriate adaptations, we have applied this same model to the context of Parkinson’s disease and ADHD, as well as to their respective pharmacotherapies. In order to physiologically represent Parkinson’s disease, we have integrated the denervation process in the model as well as the resulting compensation mechanisms. The finger tapping frequency is used as a clinical endpoint of bradykinesia, defined as the slowing of movements. The developed model is based on up-to-date knowledge of the pathophysiology and pharmacology of Parkinson’s disease, thus ensuring its validity in comparison with experimental and clinical observations. Using this model, the non-linear relationships between plasma levodopa concentration, dopamine concentration in the brain and response to a motor task were studied. The narrowing of levodopa therapeutic index during the progression of the disease due to these non-linearities was investigated. Finally, to ensure the translational aspect of our approach, we developed a web application in which this model was integrated. This application serves as a proof of concept for a tool aimed to facilitate the optimization and individualization of dosing regimens. For the study of ADHD, we adapted the developed model by integrating tonic and phasic dopamine release, the latter occurring during a reinforcement learning task. Virtual individuals were created with and without dopamine imbalance in the tonic/phasic ratio. By simulating a stimulus-response task, we observe ADHD-like symptoms among virtual patients with dopamine imbalance. Finally, the response to methylphenidate resulting from dopamine recapture inhibition, through different learning scenarios, was also studied. The development of a metric allowed us to differentiate responders from non-responders, and thus to highlight the possible implication of excessive learning in non-responders. A better understanding of methylphenidate response would help avoid overmedication in non-responders and assist clinicians in their practice. Despite the complexity of the dopaminergic system and its associated therapies, this thesis is a step forward in understanding the underlying mechanisms and their involvement in pharmacotherapy. These advances were achieved by adopting a quantitative systems pharmacology approach, combined with neurocomputational modeling borrowed from the electrical engineering field, and complemented by a translational bedside aspect. It is only by transcending disciplinary boundaries and adopting such an integrative approach that this ultimate goal of having a real impact on the multifaceted health system is possible.