Despite a decade of public and private efforts to promote patient centricity in healthcare, there is still considerable ambiguity and skepticism regarding the concept and its business impact in the pharmaceutical industry. In this research, a novel methodology is developed to quantify firms’ strategic orientation using public 10-K reports. The Strategic Orientation Ratio (SOR) was developed and first validated to examine customer centricity for 9 non-pharmaceutical companies. The SOR is then extended from customer centricity to patient centricity, and it was applied to measure the extent of patient centricity in 10 multinational pharmaceutical firms. The method was successfully validated by identifying the strategic orientation of non-pharma firms such as Walmart, Apple, and Amazon. Next, by the same method, the extent of patient centricity is quantified in 10 big pharmaceutical companies for 2005-2015. This revealed the extent to which patient centricity exists in pharmaceutical companies, and how this has changed over time. The combination of an expressed patient-centric strategic orientation, personalized medicine (measured by oncology products), and patient access (measured by sales) is shown empirically to have a significant positive effect on firm performance. This implies that not only is patient centricity “the right thing to do,” it can also be a viable model for pharmaceutical firm competitiveness. / Business Administration/Strategic Management

Les maladies cardiovasculaires (MCV) sont d’une étiologie complexe et elles sont soumises à de nombreux facteurs environnementaux ainsi que génétiques. Malgré les succès obtenus, pendant la dernière décennie, et pour réduire la mortalité CV il est nécessaire l’identification de nouveaux biomarqueurs en utilisant des approches différentes. Cette thèse propose une approche intégrative pour découvrir de nouvelles associations génétiques associés avec les MCV. Nous avons d’abord réuni les résultats existants grâce à des GWAS précédents, puis nous avons recherché la pléiotropie de ces gènes et nous avons dirigé nos efforts vers une possible traduction des résultats obtenus dans l’application clinique. Nous avons détecté les effets pléiotropiques de différent gènes (IL-6R et ABO) avec différents phénotypes lipidiques et inflammatoires. Par ailleurs, nous avons trouvé quelques associations gène-genre intéressantes pour certains gènes étudiés (ABO et GNB3). Concernant l’implémentation clinique des connaissances obtenues par cette thèse, une SNP dans le gène TREM-1, pourrait être utilisé comme un marqueur de risque pour différentes maladies, et nous avons déposé un brevet Européen et nous envisageons de mener des essais cliniques de chez les patients. D’autre part, nous avons détecté une haplotype du gène IL6R qui pourrait être utilisés dans la médecine personnalisée. Nos résultats aident à mieux comprendre comment les gènes étudiés exercent leurs effets au niveau moléculaire, en influant finalement sur l’état des patients souffrant de MCV. Nous espérons que nos résultats vont être pris en compte pour faire progresser la médecine personnalisée / Cardiovascular diseases (CVD) are complex diseases where many environmental and genetic factors are involved. Although the genetic aetiology of the CVD has been extensively investigated the last two decades, alternative approaches are needed in order to keep advancing in the pathophysiology of CVD. In this thesis, we propose an integrative approach to discover new genetic associations potentially involved in CVD. We chose previous GWAS hits and we centred our efforts in studying the pleiotropic and gene-gender interaction effects. Finally, we focused on the implementation of personalized genome-based therapy of the results obtained. New pleiotropic effects were discovered in the IL-6R and ABO genes relating them with different inflammatory and lipid phenotypes. In addition, we studied the gene-gender interaction effects, finding some sex-specific associations in two of the genes studied (ABO and GNB3). Further, we centered our efforts in implementing the results obtained during the thesis at the clinical level. One SNP within the TREM-1 gene was associated with increased levels of its protein and could be used as a predictor or risk biomarker for different diseases. Due to the high potential of this SNP, we applied a European patent and we are planning to start clinical trials in patients. Also, one haplotype in the IL-6R gene could be used in the treatment of personalized medicine. During this thesis, we discovered new gene-phenotype associations involved in CVD and other diseases. Our results help to better understand how the studied genes are exerting their effects at the molecular level. Our results will hopefully be taken into account in future personalized treatments

Glycogen is the sole carbohydrate storage molecule found in mammalian cells and plays an important role in cellular metabolism in nearly all tissues, including the brain. Defects in glycogen metabolism underlie the glycogen storage diseases (GSDs), genetic disorders with variable clinical phenotypes depending on the mutation type and affected gene(s). Lafora disease (LD) is a fatal form of progressive myoclonus epilepsy and a non-classical GSD. LD typically manifests in adolescence with tonic-clonic seizures, myoclonus, and a rapid, insidious progression. Patients experience increasingly severe and frequent epileptic episodes, loss of speech and muscular control, disinhibited dementia, and severe cognitive decline; death usually ensues in the second decade of life. LD, like one- third of all epilepsy disorders, is intractable and resistant to antiseizure drugs. A hallmark of LD is the accumulation of intracellular, insoluble carbohydrate aggregates known as Lafora bodies (LBs) in brain, muscle, and other tissues. LBs are a type of polyglucosan body, an insoluble aggregate of aberrant glycogen found in some GSDs and neurodegenerative disorders. Like most GSDs, LD is an autosomal recessive genetic disorder. Approximately 50% of LD patients carry mutations in the epilepsy, progressive myoclonus 2A (EPM2A) gene encoding laforin, a glycogen phosphatase. Remaining patients carry mutations in EPM2B, the gene that encodes malin, an E3 ubiquitin ligase. Laforin and malin play important roles in glycogen metabolism. In the absence of either enzyme, glycogen transforms into an insoluble, hyperphosphorylated and aberrantly branched polysaccharide reminiscent of plant starch. This abnormal polysaccharide precipitates to form LBs and has pathological consequences in the brain. Since a definitive LD diagnosis requires genetic testing, whole exome sequencing has been increasingly used to diagnose LD. As a result, numerous cases of more slowly progressing or late-onset LD have been discovered that are associated with missense mutations in EPM2A or EPM2B. Over 50 EPM2A missense mutations have been described. These mutations map to many regions of the laforin X-ray crystal structure, suggesting they produce a spectrum of effects on laforin function. In the present work, a biochemical pipeline was developed to characterize laforin patient mutations. The mutations fall into distinct classes with mild, moderate or severe effects on laforin function, providing a biochemical explanation for less severe forms of LD. LBs drive LD pathology. As a result, LBs and glycogen metabolism have become therapeutic targets. Since LBs are starch-like, and starch is degraded by amylases, these enzymes are potential therapeutics for reducing LB loads in vivo. However, amylases are normally secreted enzymes. Degradation of intracellular LBs requires a cell-penetrating delivery platform. Herein, an antibody-enzyme fusion (AEF) technology was developed to degrade LBs in vitro, in situ in cell culture, and in vivo in LD mouse models. AEFs are a now putative precision therapy for LD, potentially the first therapeutic to provide a significant clinical benefit. Prior to this work, LD was considered a homogenous disorder and treatments were only palliative. The data herein support a spectrum of clinical progression, a potential therapy for LD, and mechanistic insights into LD pathophysiology. This work illustrates how personalized medicine, both in diagnosis and treatment, can be achieved through basic biochemical approaches to human disease.

Translational Biomedical Informatics (TBMI) is an emerging discipline expanding beyond traditional bioinformatics, with a focus on developing computational technologies for real-world biomedical practice. The goal of my Ph.D. research is to address a few key challenges in TBI, including: (1) the high quality and reproducibility required by medical applications when processing high throughput data, (2) the need for knowledge management solutions that allow molecular data to be handled and evaluated by researchers, regulators, and doctors collectively, (3) the need for near real-time, efficient access to decision-oriented visualizations of integrated data and data processing results, and (4) the need for an integrated solution that can evolve as medical consensus evolves, without requiring retraining, overhaul or replacement. This dissertation resulted in the development and adoption of concrete web-based application deliverables in regular use by bioinformaticians, clinicians, biologists and nanotechnologists. These include: the Chip Artifact Correction (caCORRECT) web site and grid services, the ArrayWiki community microarray repository, and the SimpleVisGrid visualization grid services (including eGOMiner, nanoDRIVE, PathwayVis and SphingoVisGrid).

The chapters of this thesis focus two different issues that arise in clinical trials and propose novel methods to address them. The first issue arises in the analysis of data with non-ignorable missing observations. The second issue concerns the development of methods that provide physicians better tools to understand and treat diseases efficiently by using each patient's characteristics and personal biomedical profile. Inherent to most clinical trials is the issue of missing data, specially those that arise when patients drop out the study without further measurements. Proper handling of missing data is crucial in all statistical analyses because disregarding missing observations can lead to biased results. In the first two chapters of this thesis, we deal with the "worst-rank score" missing data imputation technique in pretest-posttest clinical trials. Subjects are randomly assigned to two treatments and the response is recorded at baseline prior to treatment (pretest response), and after a pre-specified follow-up period (posttest response). The treatment effect is then assessed on the change in response from baseline to the end of follow-up time. Subjects with missing response at the end of follow-up are assign values that are worse than any observed response (worst-rank score). Data analysis is then conducted using Wilcoxon-Mann-Whitney test. In the first chapter, we derive explicit closed-form formulas for power and sample size calculations using both tied and untied worst-rank score imputation, where the worst-rank scores are either a fixed value (tied score) or depend on the time of withdrawal (untied score). We use simulations to demonstrate the validity of these formulas. In addition, we examine and compare four different simplification approaches to estimate sample sizes. These approaches depend on whether data from the literature or a pilot study are available. In second chapter, we introduce the weighted Wilcoxon-Mann-Whitney test on un-tied worst-rank score (composite) outcome. First, we demonstrate that the weighted test is exactly the ordinary Wilcoxon-Mann-Whitney test when the weights are equal. Then, we derive optimal weights that maximize the power of the corresponding weighted Wilcoxon-Mann-Whitney test. We prove, using simulations, that the weighted test is more powerful than the ordinary test. Furthermore, we propose two different step-wise procedures to analyze data using the weighted test and assess their performances through simulation studies. Finally, we illustrate the new approach using data from a recent randomized clinical trial of normobaric oxygen therapy on patients with acute ischemic stroke. The third and last chapter of this thesis concerns the development of robust methods for treatment groups identification in personalized medicine. As we know, physicians often have to use a trial-and-error approach to find the most effective medication for their patients. Personalized medicine methods aim at tailoring strategies for disease prevention, detection or treatment by using each individual subject's personal characteristics and medical profile. This would result to (1) better diagnosis and earlier interventions, (2) maximum therapeutic benefits and reduced adverse events, (3) more effective therapy, and (4) more efficient drug development. Novel methods have been proposed to identify subgroup of patients who would benefit from a given treatment. In the last chapter of this thesis, we develop a robust method for treatment assignment for future patients based on the expected total outcome. In addition, we provide a method to assess the incremental value of new covariate(s) in improving treatment assignment. We evaluate the accuracy of our methods through simulation studies and illustrate them with two examples using data from two HIV/AIDS clinical trials.

Understanding variability of heath status is highly likely to be an important component of personalized medicine to predict health status of individuals and to promote personal health. Evidences of Genome Wide Association Study and gene expression study indicating that genetic factors affect the risk susceptibility of individuals have suggested adding genetic factors as a component of health status measurements. In order to validate or to predict health risk status with collected personal data such as clinical measurements or genomic data, it is important to have a well-established profile of diseases. The primary effort of this work was to find genomic evidence relevant to coronary artery disease. Two major methods of genomic analysis, gene expression profiling and GWAS on gene expression, were performed to dissect transcriptional and genotypic fingerprints of coronary artery disease. Blood-informative transcriptional Axes that can be described by 10 covariating transcripts per each Axis were utilized as a crucial measure of gene expression analysis. This study of the relationship between gene expression variation and various measurements of coronary artery disease delivered compelling results showing strong association between two transcriptional Axes and incident of myocardial infarction. 244 transcripts closely correlated with death by cardiovascular disease related events were also showing clear association with those two transcriptional Axes. These results suggest potential transcripts for use in risk prediction for the advent of myocardial infarction and cardiac death.

La prise en charge du cancer reste dépendante de l’utilisation des agents cytotoxiques, avec les analogues nucléosidiques. Au-delà de leur similarité structurelle, certains de ces composés partagent une voie métabolique commune, où la cytidine déaminase apparaît comme enzyme majeure. L’existence d’une variabilité génétique et/ou phénotypique de la CDA nous a mené à nous intéresser aux relations entre le statut CDA et l’issue clinique des patients afin de déterminer si la CDA pouvait être considérée comme biomarqueur d’issue clinique chez les patients.Nos travaux personnels ont consisté à évaluer deux techniques permettant de mesurer l’activité de la CDA. Nous avons publié le premier cas mondial de toxicités mortelles sous azacytidine chez un patient CDA-déficient, ainsi que le premier cas de déficience en CDA et de toxicités sous cytarabine causées par la présence d’une variation génétique du gène CDA chez une patiente transplantée hépatique. L’influence du statut CDA a également été étudiée chez deux patients traités par azacytidine. Concernant la gemcitabine, nous avons démontré l’impact délétère en terme d’efficacité de l’augmentation de l’activité CDA chez les patients, ainsi que les résultats d’une étude multicentrique prospective dont le but était de déterminer si la CDA pouvait être un marqueur prédictif de l’apparition des toxicités sous gemcitabine, avec une étude pharmacocinétique en support. Les travaux préliminaires du pyroséquençage partiel de la CDA sur technologie Roche® sont présentés. L’ensemble de ces travaux de thèse confirme l’intérêt d’évaluer le statut CDA chez les patients susceptibles de recevoir une thérapie à base d’analogues nucléosidiques. / Nowadays, the management of cancer pathology remains largely dependent on the use of cytotoxic agents, including nucleoside analogs, used in a variety of settings. Beyond their structural similarity, some of these compounds also share a common metabolic pathway, wherein the cytidine deaminase (CDA) plays a pivotal role. The existence of constitutional genetic and / or phenotypic variability in CDA prompted us to study the relationships between the CDA status and clinical outcome in patients, and to determine if the constitutional CDA could be considered as a biomarker of efficacy and toxicity in patients treated with this class of drugs.Our personal work first consisted in evaluating two methods to measure the CDA enzymatic activity, in terms of robustness and cost. Then we published the first case-report of life-threatening toxicities in a CDA-deficient patient treated with azacytidine, and the first case of CDA deficiency and cytarabine-caused toxicities correlated with presence of a genetic variation in CDA gene in a liver-transplant patient. The influence of CDA status was also assessed in two patients treated with azacytidine. Regarding gemcitabine, we present the impact of an increase in CDA activity on loss of efficacy in patients, and the results of a prospective multicenter study whose purpose was to determine whether the CDA could be a predictive marker of the occurrence of gemcitabine-toxicities, with a pharmacokinetic study support. Finally, preliminary data on partial Roche®-pyrosequencing of CDA, also presented.All these thesis work confirms the interest to evaluate the CDA status in patients likely to receive a nucleosidic analogues-based therapy.

Appliqué à des problèmes actuels de recherche pharmaceutique, ce mémoire traite de la génération de signatures de biomarqueurs par une approche d'extraction de règles d'association et une Analyse Formelle de Concepts. Elle a aboutit au développement d'une méthodologie qui a été validée par six projets de recherche de signatures de biomarqueurs.Alors qu'il n'existe pas de méthode optimale pour traiter les données biomarqueurs, cette méthodologie logique s'appuie sur un scénario global d'analyse déployant quatre méthodes, chacune dépendante de procédés différents. Cette architecture qualifie une problématique centrale de manière à optimiser la qualité d'une solution aux différents problèmes scientifiques posés. Les six applications pratiques ont démontré l'intérêt de la prise en compte précoce des critères de qualité énoncés par les experts du domaine. L'interactivité est soutenue tout au long du processus de découverte et produit des résultats imprévus pour l'expert. La méthodologie s'inscrit dans la lignée des approches dédiées à la stratification systématique des individus, qui constitue le premier palier vers une médecine personnalisée. / In the framework of current intricate questions to be solved by the pharmaceutical industry, this manuscript examines the generation of biomarker signatures through an approach that combines association rules extraction and Formal Concept Analysis. It led to the development of a methodology which was validated by six research industrial projects. While there is no single optimal method to handle biomarkers datasets, this logical methodology relies on a global datamining scenario made up of four different methods. Each method utilizes different processes. This architecture qualifies global approach that helps to optimize a response to different biomarker signatures discovery problems. The six applications presented in this manuscript demonstrate the interest of an early consideration of the quality criteria are expressed by the experts in the field. The interactivity is supported throughout the process of discovery and produces unexpected results for the expert. The methodology helps the systematic stratification of individuals, which constitutes the first step towards personalized medicine.

Hereditary transthyretin amyloidosis (ATTR amyloidosis) is a multi-system protein folding disorder that results from >100 described mutations in the transthyretin (TTR) gene. In the disease, non-natively folded TTR, originally produced by the liver, travels throughout circulation and deposits extracellularly at downstream target organs. The multi-tissue etiology of the disease makes it difficult to study in vitro, while no mouse model accurately recapitulates disease pathology. Therefore, we utilized patient-specific induced pluripotent stem cells (iPSCs) to test the hypothesis that production of and exposure to destabilized TTRs results in distinct cellular and molecular changes. The liver’s contribution to the deposition of TTR at distal tissues is understudied. As a result, in Aim 1 we sought to assess the effects of destabilized TTR production on effector hepatic cells. To this end, we utilized gene editing to generate isogenic, patient iPSCs expressing either mutant or wild-type TTR. Combining this tool with single cell RNAseq, we identified hepatic proteostasis factors, including unfolded protein response (UPR) pathways, whose expression coincided with the production of destabilized TTR. Enhancing endoplasmic reticulum (ER) proteostasis within patient hepatic cells via exogenous activation of adaptive UPR signaling, we demonstrated preferential reduction in the secretion of pathogenic TTR. In turn, we demonstrated that production of disease-associated TTR correlates with expression of proteostasis factors capable of regulating TTR secretion and in turn downstream pathogenesis. ATTR amyloidosis patients exhibit extreme phenotypic variation (e.g. TTR fibril deposits at cardiac tissue and/or peripheral nerves). In Aim 2, we sought to define responses of target cell types to pathologically-diverse TTRs. To accomplish this, we profiled transcriptomic changes resulting from exposure to a variety of destabilized TTRs to determine 1) target cell response to TTR exposure and 2) how this response changes across diverse variants and cell types. In doing so, we found that TTR exposure elicits distinct variant- and cell type-specific transcriptional responses. Herein, we addressed our central hypothesis by profiling destabilized TTR production within hepatic cells and TTR exposure at target cell types. Collectively, these data may result in the discovery of unidentified and potentially druggable pathologically-associated pathways for ATTR amyloidosis and other systemic amyloid diseases.

abstract: Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic prostate cancer is most commonly treated with hormonal therapy. The idea behind hormonal therapy is to reduce androgen production, which prostate cancer cells require for growth. Recently, the exploration of the synergistic effects of the drugs used in hormonal therapy has begun. The aim was to build off of these recent advancements and further refine the synergistic drug model. The advancements I implement come by addressing biological shortcomings and improving the model’s internal mechanistic structure. The drug families being modeled, anti-androgens, and gonadotropin-releasing hormone analogs, interact with androgen production in a way that is not completely understood in the scientific community. Thus the models representing the drugs show progress through their ability to capture their effect on serum androgen. Prostate-specific antigen is the primary biomarker for prostate cancer and is generally how population models on the subject are validated. Fitting the model to clinical data and comparing it to other clinical models through the ability to fit and forecast prostate-specific antigen and serum androgen is how this improved model achieves validation. The improved model results further suggest that the drugs’ dynamics should be considered in adaptive therapy for prostate cancer. / Dissertation/Thesis / Masters Thesis Mathematics 2020