Modeling and simulation are becoming indispensable elements in drug development. Mechanistic modeling is susceptible to impacting several drug development and regulatory decisions through extrapolations beyond clinically researched scenarios by using its capacity to incorporate diverse data to provide a detailed and comprehensive understanding of a physiological and biological system. Physiologically based pharmacokinetic (PBPK) modeling is the fastest-growing Model-informed drug development subfield, and regulatory authorities currently demand or advocate its usage for specific applications. Shiny is based on the open-source software R, which is widely used in the statistics and data science communities, including finance, medicine, and pharmaceuticals. Shiny conveys complex models to non-technical audiences via interactive graphics and sensitivity analysis. The Shiny R package is a crucial development that opens up PBPK models developed in R to a wider audience. This project's main goal is to create a framework and, a user-friendly tool for PBPK model evaluations and performance comparisons during model development, customized for the Open System Pharmacology Suite. To develop the PBPK framework, a PBPK model was retrieved from the open system pharmacology repository on GitHub, and based on the model and parameter identification inputs, the shiny framework was coded in R.Principal diagnostic techniques such as visual predictive checks to match and correlate model-simulated concentration-time profiles with clinical data, the goodness of fit (GOF) analysis (e.g., residuals over time, residuals against predictions, etc.), and extensive precision and bias measures were used to assess and validate the performance robustness of the model. Various quantitative metrics such as root mean square error, mean absolute error, and concordance correlation coefficient were used to assess and evaluate the model performance. The PBPK-QSP shiny framework was developed, allowing users to do the model evaluation with minimal effort. Using frameworks such as Shiny can expedite and automate the PBPK procedure, saving a significant amount of money and time in the evaluation of model performance.

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.

The pressure from high population density leads to the creation of high-rise structures within urban areas. Consequently, the design of facilities which confront the challenges of emergency evacuation from high-rise buildings become a complex concern. This paper proposes an embedded program which combines a deterministic (GMAFLAD) and stochastic model (M/G/C/C State Dependent Queueing model) into one program, GMAF_MGCC, to solve an evacuation problem. An evacuation problem belongs to Quadratic Assignment Problem (QAP) class which will be formulated as a Quadratic Set Packing model (QSP) including the random flow out of the building and the random pairwise traffic flow among activities. The procedure starts with solving the QSP model to find all potential optimal layouts for the problem. Then, the stochastic model calculates an evacuation time of each solution which is the primary decision variable to figure the best design for the building. Here we also discuss relevant topics to the new program including the computational accuracy and the correlation between a successful rate of solving and problems’ scale. This thesis examines the relationship of independent variables including arrival rate, population and a number of stories with the dependent variable, evacuation time. Finally, the study also analyzes the probability distribution of an evacuation time for a wide range of problem scale.

The research attempts to provide a graphical theory-based approach to solve the facility layout problem. Which has generally been approached using Quadratic Assignment Problem (QAP) in the past, an algebraic method. It is a very complex problem and is part of the NP-Hard optimization class, because of the nonlinear quadratic objective function and (0,1) binary variables. The research is divided into three phases which together provide an optimal facility layout, block plan solution consisting of MHS (material handling solution) projected onto the block plan. In phase one, we solve for the position of departments in a facility based on flow and utility factor (weight for location). The position of all the departments is identified on the vertices of MPG (maximal planar graph), which maximizes the possibility of flow. We use named MPG produced in literature, throughout the research. The grouping of the department is achieved through GMAFLAD, a QSP (quadratic set packing) based optimizer. In Phase 2, the dual for the MPG’s is solved consisting of department location as per phase 1, to generate Voronoi graphs. These graphs are then, expanded by an ingenious parameter optimization formulation to achieve area fitting for individual cases. Optimization modeling software, Lingo17.0 is used for solving the parameter optimization for generating coordinates of the block plan. The plotting of coordinates for the block plan graphics is done via Autodesk inventor 2019. In phase 3, the solution for MHS is achieved using an RSMT (Rectilinear Steiner minimal tree) graph approach. The Voronoi seed coordinates produced through phase 2 results are computed by GeoSteiner package to generated the RSMT graph for projection onto the block plan (Also, done by Inventor 2019). The graphical method employed in this research, itself has complex and NP-hard problem segments in it, which have been relaxed to polynomial time complexity by fragmenting into groups and solving them in sections. Solving for MPG & RSMT are a class of NP-Hard problem, which have been restricted to N=32 here. Finally, to validate the research and its methodology a real-life case study of a shipyard building for the data set of PDVSA, Venezuela is performed and verified.

Focussing on Archaeozoological faunal analysis, this dissertation aims to investigate the animal food utilization practices of the Ndzundza Ndebele by combining archaeozoologcial methods, archaeological data, ethnographic and historic information. The Ndzundza Ndebele inhabited three different sites in the Steelpoort River Valley during c. 1700 AD – 1900 AD. They were forced to relocate from KwaMaza and Esikhunjini to KoNomtjarhelo as a result of continual fighting between themselves and contemporary Iron Age/Historic communities, the British and the Boers during this period. I aim to identify the animal species utilized by the Ndzundza Ndebele in addition to whether or not the hostile and politically unstable period had any effect on Ndzundza animal food procurement, use and discard. Additionally I investigate whether the faunal remains recovered from the three sites can be used to identify ethnic affiliations, gender roles and ritual use with regards to animals and/or animal parts. Ultimately, I aim to demonstrate that faunal remains cannot be fully understood and interpreted without incorporating relevant ethnographic and/or historic information and as comprehensive an archaeological context as possible. Copyright / Dissertation (MA)--University of Pretoria, 2009. / Anthropology and Archaeology / unrestricted

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