Dissertations / Theses on the topic 'Personalized medicin'

Nell'ultimo decennio, uno dei messaggi chiave derivante dalle ricerche scientifiche sul cancro è la necessità di comprendere meglio il metabolismo delle cellule tumorali per lo sviluppo di una terapia personalizzata migliore e più efficace. Le cellule tumorali attuano un riarrangiamento metabolico che coinvolge diversi processi per supportare la loro natura proliferativa ed invasiva. Per meglio comprendere l’entità del cambiamento metabolico, in questo studio abbiamo utilizzato un approccio systems level impiegando la metabolomica untargeted e la flussomica mediante isotopi stabili del carbonio (13C) in cellule tumorali esprimenti un K-Ras oncogenico. Abbiamo testato gli effetti di farmaci inibitori del metabolismo di glucosio e glutammina per indagare eventuali vie metaboliche alternative attivate per la sopravvivenza delle cellule tumorali. Inoltre abbiamo investigato il ruolo del metabolismo cellulare nello sviluppo della resistenza alla terapia endocrina nel carcinoma mammario ERα positivo. I dati ottenuti hanno permesso di identificare specifici meccanismi metabolici di utilizzo della glutammina in cellule resistenti alla terapia, suggerendo l’utilizzo del farmaco metformina come adiuvante nel trattamento dei tumori resistenti alla terapia ormonale. Infine, abbiamo contribuito alla comprensione del metabolismo delle cellule tumorali nel guidare la crescita e la proliferazione, esplorando il ruolo della glutammina oltre la nota funzione di fonte di carbonio e azoto; infatti sostituendo la glutammina con fonti alternative di carbonio e azoto, si osserva un fenotipo reverse Warburg. I risultati di questa tesi aprono strade di ricerca per l'identificazione di nuovi potenziali obiettivi terapeutici e ci portano verso la progettazione di una strategia terapeutica migliore e più efficace per il trattamento dei pazienti oncologici.
In the recent decade, one of the important keynote message derived through the summation of our global efforts against cancer is the need to better understand cancer cell metabolism for the development of better and efficacious personalized therapy. Cancer cells undertake a multifaceted rewiring of metabolic pathways in order to support their proliferative and invasive nature, which requires a systems level investigation to fully comprehend the scale of metabolic deregulation. In this study, we systematically investigated the metabolic differences using untargeted metabolomics and 13C flux omics approach in oncogenic K-Ras driven tumours. We tested the effects of drug inhibitors targeting glucose and glutamine metabolism to unravel the alternative metabolic pathways required for cancer cell survival. We further expanded our research towards understanding the role of cellular metabolism in driving resistance to endocrine therapeutic drugs in ERα positive breast cancer. We identified specific metabolic mechanisms of utilization of glutamine in resistant cells while also providing further basis for the use of metformin as an adjuvant in the treatment of endocrine therapyresistant cancers. Finally, we contributed to current understanding about cancer cell metabolism by exploring the role of glutamine beyond its role as a carbon and nitrogen source in driving growth and proliferation of cancer cells. Upon substitution of glutamine with appropriateiv nitrogen and carbon sources, cancer cells exhibited reverse Warburg phenotype. The findings from this thesis open up new avenues of research through the identification of new putative targets and bring us one step closer towards designing much better and efficacious therapeutic strategy for the treatment of cancer patients.

The aim of this project was to present several therapies and possible applications of these in the field of personalized medicine along with the production techniques and workflows surrounding them. There are two main categories; cell therapies and non-cell therapies. Cell therapies utilize the body's own T cells and immune system, and non-cell therapies are mostly based on proteins and nucleotides. All of these applications face different challenges that need to be overcome to be considered effective treatments and they all have a high production cost. The report also presents differences and similarities of manufacturing models that are specifically used in the production of cell therapies. It could be argued that these manufacturing models can be adjusted and work for both cell therapies and non-cell therapies. Three different workflows for three different personalized medicines, antibody drug conjugates (ADCs), tumor infiltrating lymphocytes (TILs) and mRNA vaccines, are presented in this report. Technologies and processes valuable to the manufacturing process were also presented, including bioreactors, interleukin 2 media and cell dissociation technologies. In conclusion, there are methods and techniques that are frequently used in production that are, or possibly could be useful for manufacturing personalized drug components. Production of products used in personalized medicine is possible if the right resources are available. Personalized therapies are presently most commonly applied to cancer diseases but there are developments for these therapies that could benefit several other diseases. To fully apply personalized therapies to these diseases further studies on suitable biomarkers and targets in drugs are needed. Overall, personalized medicine has promising possibilities in treatments for many types of complex diseases. This project was assigned by Cytiva which is a global life science company and the product order can be seen in the appendix.

[ES] El cáncer es la segunda causa de muerte en el mundo y se caracteriza principalmente por la proliferación descontrolada de las células que forman el tumor. Aunque el desarrollo de un tumor es posible debido a ciertos procesos comunes desencadenados por la desregulación del equilibrio existente entre los componentes moleculares de una célula y sus elementos de control, existe una gran heterogeneidad en los mecanismos a través de los cuales ocurre dicha desregulación. Gracias al desarrollo de nuevas tecnologías de secuenciación ha sido posible observar como esta heterogeneidad no solo se observa entre los distintos tipos de tumores sino entre las propias células de un mismo tumor. La caracterización de la heterogeneidad tumoral ha tenido un gran impacto en la comprensión de la enfermedad y el desarrollo de nuevas terapias dirigidas. Por este motivo, con el fin de mejorar la caracterización de alteraciones en los distintos mecanismos regulatorios, en esta tesis se han desarrollado dos metodologías con gran potencial para su aplicación en la medicina personalizada y que permiten estudiar la heterogeneidad inter e intratumoral de los estados de activación de elementos reguladores. En primer lugar, se desarrolló una metodología que permite determinar en una muestra el estado de activación de los factores de transcripción (FTs) a partir de la expresión de los genes a los que regula. Se aplicó la metodología para realizar un análisis sistemático de varios cánceres (conocido como estudios pan-cáncer) en el que se caracterizó por primera vez el escenario regulatorio de 52 FTs en 11 tipos de cáncer distintos. Además, al poder obtener valores de activación individuales para cada muestra, fue posible observar correlaciones entre la activación de algunos FTs con la supervivencia, sugiriendo así su uso como marcadores pronósticos. En segundo lugar, se desarrolló otra metodología en la que se emplea un modelo mecanístico para determinar el estado de activación de alrededor de 1000 circuitos de señalización a partir de datos de experimentos transcriptómicos de células únicas (scRNAseq). El uso de este modelo mecanístico en datos de scRNAseq de 4 pacientes de glioblastoma, además de mostrar la heterogeneidad intratumoral presente en las muestras, ha permitido realizar intervenciones in silico para simular el efecto de distintas drogas sobre las células. De esta manera, ha sido posible describir posibles mecanismos mediante los cuales un grupo de células pueden evitar el efecto de una terapia dirigida. Las metodologías desarrolladas en esta tesis, así como los resultados obtenidos tras su aplicación supone una valiosa fuente de información para el desarrollo de marcadores de diagnóstico, pronóstico y respuesta que ayuden a entender mejor los distintos niveles de heterogeneidad presentes en cáncer, y así, poder aumentar la eficacia de las terapias dirigidas.
[CA] El càncer és la segona causa de mort al món i es caracteritza principalment per la proliferació descontrolada de les cèl·lules que formen el tumor. Encara que el desenvolupament d'un tumor és possible a causa de certs processos comuns desencadenats per la desregulació de l'equilibri existent entre els components moleculars d'una cèl·lula i els seus elements de control, hi ha una gran heterogeneïtat en els mecanismes a través dels quals s'aconseguix aquesta desregulació. Gràcies a el desenvolupament de noves tecnologies de seqüenciació ha sigut possible observar com aquesta heterogeneïtat no només s'observa entre els diferents tipus de tumors sinó entre les pròpies cèl·lules d'un mateix tumor. La caracterització de l'heterogeneïtat tumoral ha tingut un gran impacte en la comprensió de la malaltia i el desenvolupament de noves teràpies dirigides. Per aquest motiu, per tal de millorar la caracterització d'alteracions en els diferents mecanismes reguladors, en aquesta tesi s'han desenvolupat dues metodologies amb gran potencial per a la seua aplicació en la medicina personalitzada i que permeten estudiar l'heterogeneïtat inter i intratumoral dels estats de activació d'elements reguladors. En primer lloc es va desenvolupar una metodologia que permet determinar en una mostra l'estat d'activació dels factors de transcripció (FTs) a partir de l'expressió dels gens als que regula. Es va aplicar la metodologia per a realitzar una anàlisi de pan-cancer en el qual es va caracteritzar per primera vegada l'escenari regulatori de 52 FTs a 11 tipus de càncer diferents. A més, al poder obtenir valors d'activació individuals per a cada mostra, va ser possible observar correlacions entre l'activació d'alguns FTs amb la supervivència, suggerint així el seu ús com a marcadors pronòstics. En segon lloc, es va desenvolupar una altra metodologia en la qual s'empra un model mecanístic per determinar l'estat d'activació d'al voltant de 1000 circuits de senyalització a partir d'experiments transcriptòmics de cèl·lules úniques (scRNAseq). L'ús d'aquest model mecanístic en dades de scRNAseq de 4 pacients de glioblastoma, a més de mostrar l'heterogeneïtat intratumoral present en les mostres, ha permès realitzar intervencions in silico per simular l'efecte de diferents drogues sobre les cèl·lules. D'aquesta manera, ha estat possible descriure possibles mecanismes mitjançant els quals un grup de cèl·lules poden evitar l'efecte d'una teràpia dirigida. Les metodologies desenvolupades en aquesta tesi, així com els resultats obtinguts després de la seva aplicació suposa una valuosa font d'informació per al desenvolupament de marcadors de diagnòstic, pronòstic i resposta que ajudin a entendre millor els diferents nivells d'heterogeneïtat presents en càncer, i així, poder augmentar l'eficàcia de les teràpies dirigides.
[EN] Cancer is the second leading cause of death in the world and is characterized mainly by the uncontrolled proliferation of the cells that make up the tumor. Although the development of a tumor is possible due to certain common processes triggered by the dysregulation of the existing balance between the molecular components of a cell and its control elements, there is great heterogeneity in the mechanisms through which this dysregulation is achieved. Thanks to the development of new sequencing technologies, it has been possible to observe how this heterogeneity is not only observed between the different types of tumors but also between the cells of the same tumor. The characterization of tumor heterogeneity has had a great impact on the understanding of the disease and the development of new targeted therapies. For this reason, in order to improve the characterization of alterations in the different regulatory mechanisms, in this thesis two methodologies have been developed that allow studying the inter- and intratumoral heterogeneity of the activation states of regulatory elements and with great potential for their application in personalized medicine. In the first place, a methodology that allows determining in a sample the activation state of the transcription factors (FTs) from the expression of the genes that it regulates was developed. The methodology was applied to perform a pan-cancer analysis in which the regulatory scenario of 52 FTs was characterized for the first time in 11 different types of cancer. Furthermore, by being able to obtain individual activation values for each sample, it was possible to observe correlations between the activation of some FTs with survival, thus suggesting their use as prognostic markers. Second, another methodology was developed using a mechanistic model to determine the activation state of around 1000 signaling circuits in single cell transcriptomic experiments (scRNAseq). The use of this mechanistic model in scRNAseq data from 4 glioblastoma patients, in addition to showing the intratumoral heterogeneity present in the samples, has allowed in silico interventions to simulate the effect of different drugs on cells. In this way, it has been possible to describe possible mechanisms by which a group of cells can avoid the effect of a targeted therapy. The methodologies developed in this thesis, as well as the results obtained after its application, is a valuable source of information for the development of diagnostic, prognostic and response markers that help to better understand the different levels of heterogeneity present in cancer, and thus, be able increase the effectiveness of targeted therapies.
Marín Falco, M. (2021). Estudio de la heterogeneidad regulatoria en cáncer y sus implicaciones en la medicina personalizada [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/165413
TESIS

The wealth of medical-related information available today gives rise to a multidimensional source of knowledge. Research discoveries published in prestigious venues, electronic-health records data, discharge summaries, clinical notes, etc., all represent important medical information that can assist in the medical decision-making process. The challenge that comes with accessing and using such vast and diverse sources of data stands in the ability to distil and extract reliable and relevant information. Computer-based tools that use natural language processing and machine learning techniques have proven to help address such challenges. This current work proposes automatic reliable solutions for solving tasks that can help achieve a personalized-medicine, a medical practice that brings together general medical knowledge and case-specific medical information. Phenotypic medical observations, along with data coming from test results, are not enough when assessing and treating a medical case. Genetic, life-style, background and environmental data also need to be taken into account in the medical decision process. This thesis’s goal is to prove that natural language processing and machine learning techniques represent reliable solutions for solving important medical-related problems. From the numerous research problems that need to be answered when implementing personalized medicine, the scope of this thesis is restricted to four, as follows: 1. Automatic identification of obesity-related diseases by using only textual clinical data; 2. Automatic identification of relevant abstracts of published research to be used for building systematic reviews; 3. Automatic identification of gene functions based on textual data of published medical abstracts; 4. Automatic identification and classification of important medical relations between medical concepts in clinical and technical data. This thesis investigation on finding automatic solutions for achieving a personalized medicine through information identification and extraction focused on individual specific problems that can be later linked in a puzzle-building manner. A diverse representation technique that follows a divide-and-conquer methodological approach shows to be the most reliable solution for building automatic models that solve the above mentioned tasks. The methodologies that I propose are supported by in-depth research experiments and thorough discussions and conclusions.

The following thesis tries to answer the question of wheter RNA processing alterations are informative for the clinical management of cancer patients. For this, the work is focused on solve two main issues: the development of computational tools to study RNA profiles from multiple tumors and the identification of RNA-related signatures that may be predictive of prognosis and therapy. The thesis is divided in three chapters: First, the development a new tool for fast quantification of differential splicing: SUPPA2. Second, a new methodology for elucidating the prognostic potential of alternative transcript isoforms across human tumours. And third, a new method for the detection of aberrant tumor splicing junctions and their antigenic evaluation.
La presente tesis intenta arrojar luz sobre la pregunta de si las alteraciones del ARN son informativas para el tratamiento clínico de pacientes con cancer. Para ello, este trabajo se centra en resolver dos cuestiones principales: el desarrollo de metodos computaciones para el estudio de perfiles de ARN en multiples tumores y la identificación de marcadores que puedan ser predictivos de prognosis y terapia. La tesis está dividida en tres capítulos: Primero, el desarollo de un nuevo método para la cuantificación rápida de splicing diferencial: SUPPA2. Segundo, una nueva metodologia para dilucidar el potencial prognóstico de tránscritos alternativos en tumores humanos. Y tercero, un nuevo método para la detección de junctions aberrantes tumorales y su evaluacion antigénica

Urban Bioethics
M.A.
Personalized medicine is the idea that every patient can be treated in a unique manner, tailored specifically to his or her individual needs. Traditionally the field of personalized medicine has focused on using genetic information to determine medical treatment. However, humans are not only the sum of their genetic parts. All people exist within the context of their environment, their experiences, and their relationships. While the connection between this greater context and medical treatment may not be immediately obvious, it exists. If we are to truly tailor medical care, it must occur in a holistic manner, combining both genetics and social context. A thorough understanding of the way that they interact, as well as the individual limitations of both, is the best way to offer individualized care to all patients.
Temple University--Theses

Modern Electronic Health Record (EHR) systems are now integral to healthcare. Having evolved from hospital billing and laboratory systems in the 80's, EHR systems have grown considerably as we learn to represent more and more aspects of patient encounter, diagnosis and treatment digitally. EHR user interfaces, however, lag considerably behind their consumer-electronics counterparts in usability, most notably with respect to customizability. This limitation is especially evident in the implementation of audible alerts that are coupled to sensors or timing devices in intensive-care settings. The most current standard, (ISO/IEC 60601-1-8) has been designed for alerts that are intended to signal situations of varying priorities: however, it is not universally implemented, and has been criticized for the difficulty that healthcare providers have in discriminating between individual alarms, and for the failure to incorporate prior research with respect to "sense of urgency" as it applies to alarm efficacy. In the present work, however, we consider that there are more effective means to allow a user to identify an alarm correctly than "sense of urgency" response. This thesis focuses on the problem of correct identification of alerts: what happens when a human subject is allowed to create or designate (i.e., personalize) one's own alerts? Given the ubiquity, low costs and commoditization of consumer-electronics devices, we believe that it is just a matter of time before such devices become the norm in critical care and replace existing, special-purpose devices for information delivery at the point of patient care. We built a tool, PASA (Personalized Alert Study Application), that would allow us to capture and edit sounds and orchestrate studies that would contrast any two types of sounds. PASA facilitated a study where study participant's responses to "personalized" sounds were contrasted with sounds that meet the ISO/IEC 60601-1-8:2012 standard. We performed two sub-studies that contrasted responses to two banks of 6-alerts and 10-alerts. The 6-alert study was repeated with the same subjects after two weeks without training to measure recall. We observed that accuracy, reaction time, and retention were significantly improved with the personalized sounds. For example, the median errors for the 6-alert baseline study were 4 for personalized vs. 27 for standard alerts. For the 6-alert repeat study it was 7 vs. 43. The median for the 10-alert study was 1 for personalized vs. 55 for standard alerts. Accuracy for recognition, while remaining constant for personalized alerts, degraded considerably for standardized alerts as the number of alerts increased from 6 to 10. We conclude that personalization of alerts may improve information delivery and reduce cognitive overload on the health care provider. This potential positive effect at the point of patient care merits further studies in a clinical or simulated clinical setting.

Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Comparative Media Studies, 2013.
Vita. Cataloged from PDF version of thesis.
Includes bibliographical references (pages 27-30).
The next era of medicine will be one of personalization, scientists and physicians promise. Personalized medicine is a refined clinical approach in which clinicians will utilize your genomic information to help you prevent disease, and tailor targeted therapies for you when you fall ill. This is the future science has slowly been approaching. However, the human genome is not enough, not unless we can decipher its language. One ambitious study to this effect is the Personal Genome Project, led by Dr. George Church at Harvard Medical School. This project will eventually recruit 100,000 volunteers to donate their genomes and a full body of information concerning their biological health. With this data, Church hopes others can cross-analyze these profiles and better determine the role in disease of each gene of the human genome. However, the Personal Genome Project is as much a study in the ethical, legal and social aspects of genomic studies as it is an effort toward personalized medicine. Church envisions a future where privacy cannot be guaranteed. Society is becoming more open and technology is more invasive than ever. Considering this, Church has informed his participants that their information will likely not remain anonymous. With their fully informed consent, he has in turn made all this data public, to promote open science. This ethical approach raises several important questions about expansive genomic studies. The scientific community will have to decide on an approach that will eventually deliver personalized medicine. On one end of the spectrum, there is Church's open approach, and the other, more security, more firewalls and more legislation. In order for personalized medicine to become a reality, society will have to prepare itself for our ever-changing ethical, technological and scientific landscape.
by Abdul-Kareem H. Ahmed.
S.M.in Science Writing

Introduction and Aim: Pituitary adenomas are common neoplasms, with a reported prevalence of about one case in 1000 subjects. Patients with pituitary adenomas show significant morbidity due to pituitary hormone hypersecretion or deficiencies, mass effects and infiltration of the surrounding tissues. Although trans-sphenoidal surgery and radiotherapy are largely used to treat patients with pituitary adenomas, the overall long-term remission rate is not complete, beside side effects of surgery or brain irradiation. Therefore, medical treatments with pituitary-directed drugs are increasingly used in patients with secreting pituitary adenomas, especially when surgery fails or is not indicated, or awaiting for effects of radiotherapy. Somatostatin analogues (SSA) have been the mainstay of the medical treatment of GH-secreting adenomas, and nowadays are also used to treat ACTH-secreting pituitary adenomas, since these tumours express several types of somatostatin receptors (SSTR), with the prevalence of SSTR type 2 in the GH-secreting PA and of SSTR type 5 in the ACTH-secreting. Regrettably, 50% of patients with GH- secreting and 60% with ACTH- secreting pituitary adenomas do not respond to medical treatment with pituitary-directed drugs, or present only a partial hormonal reduction. Receptor desensitization, internalization and intra-cellular trafficking of SSTR could explain at least partially the lack of response, hence more data and knowledge about these cellular processes are urgently needed. Moreover, pituitary adenomas are not always benignant: some aggressive cases (up to 15-20% in all series) are characterized by rapid regrowth after first surgery, invasion of the surrounding structure, resistance to medical therapy, therefore the term Pituitary Neuroendocrine Tumor (PitNET) should be actually used. The aims of this PhD project are to describe the role of medical treatment in patients with PitNET, in order to study the efficacy of available compounds; applicate the combination of medical treatment in clinical practice; analyse the differential effects (if existing) of medical treatment compared to surgery (considered the best curative treatment). Materials and methods: Among our cohort of patients (120 with GH-, 134 with ACTH-, 171 with PRL-, 6 with TSH- secreting PitNET, 150 with non-secreting PitNET), we retrospectively and prospectively analysed clinical, radiological and pathological features of patient. Considering the treatment of aggressive PitNET or patients with Cushing’s Syndrome, we focused our attention to everolimus, temozolomide (TMZ) and metyrapone (MET) treatment. In some case, primary cell culture were used to study the effect of medical treatment. Results: Regarding medical treatment, we considered the use of everolimus, TMZ, cabergoline and MET. 1. In a patient with tuberous sclerosis complex (TSC) and silent gonadotroph PitNET we tested the efficacy of everolimus, observing a reduction of cell viability after an in vitro treatment of PitNET’s derived primary cells. TSC analysis retrieved no disease-associated variants with the exception of the heterozygous intronic variant c.4006-71C>T found in TSC2: the computational tools predicted a gain of a new splice site with consequent intron retention, not confirmed by an in-vitro analysis of patient’s lymphocyte derived RNA. 2. Regarding TMZ in aggressive PitNET, we conducted an Italian survey on 31 patients: 11 patients (35.5%) had reduction of the tumor during TMZ treatment, while 6 patients (19.4%) had progression of disease. Median follow-up after start of TMZ was 18 months. Seven patients presented disease progression. The 2-yr recurrence-free survival was 62% (95% C.I., 34 -99%). Seven patients died of progressive disease. The 2-yr and 4-yr survival rates were 90% (95% C.I., 77-100%) and 56% (95% C.I., 26-85%). Moreover, we treated a patient with a combined cabergoline+TMZ treatment, achieving excellent results. 3. Considering MET in patients with Cushing’s Syndrome, patients were treated with a median dose of 1000 mg for 9 months. UFC and LNSC decreased quickly after the first month of treatment (-67% and -57% from baseline), with sustained UFC normalization up to 12 and 24 months (in 13 and 6 patients, respectively). UFC and LNSC normalized later (after 3-6 months) in patients with severe hypercortisolism (>5-fold baseline UFC). Regarding last visit, 70% and 37% of patients normalized UFC and LNSC, respectively. Body weight reduction (-4kg) was observed after UFC normalization. Severe side-effects were not reported, half female patients complained hirsutism, and blood pressure was not increased. 4. In patients with acromegaly, a significant proportion of patients developed Central Adrenal Insufficiency (CA) over time: while primary or secondary medical treatment did not contribute to the risk of CAI, repeated surgery or radiotherapy affected pituitary-adrenal axis. CAI was diagnosed in 18% of patients (10/57) after surgery, and in 53% (9/17) after radiotherapy (p=0.01). Considering those aspects related to predict the effects of medical treatment with SSA in acromegaly, we studied the role of AIP-AHR and GIPR pathway. Considering AIP-AHR axis, involved in the detoxification of endocrine disruptors and chemical pollutants, we observed that acromegaly is more biochemically severe and resistant to SSA treatment in patients living in highly polluted areas, especially if they also carry specific AHR and/or AIP gene variants. Moreover, we found a stimulatory effect of IGF-1 on GIP promoter support in GIPR-expressing somatotropinomas, suggesting a novel molecular pathway able to induce GH-secreting PitNET. Conclusions: In this complex scenario, understanding the physio-pathology of PitNET is the beginning of personalized treatment. In clinical practice, a multidisciplinary team for the management of patients is fundamental, to suggest the correct treatment plan, tailored to the patient.
Introduzione e scopo: Gli adenomi ipofisari sono neoplasie frequenti, con una prevalenza di un caso ogni 1000 soggetti. I pazienti con adenoma ipofisario possono presentare segni e sintomi in correlazione alla secrezione autonoma (o deficitaria) di ormoni ipofisari, oppure possono presentarsi come “effetto massa” dovuto alla lesione occupante spazio in loggia ipofisaria. Sebbene la chirurgia e la radioterapia siano state molto utilizzate in passato, il controllo a lungo termine non è completo, sia in termini di secrezione che di lesione adenomatosa, esponendo comunque il paziente agli effetti collaterali dell’intervento o dell’irradiazione. Pertanto, la terapia medica è sempre più utilizzata, non solo nelle recidive post-chirurgiche, ma anche quando ulteriori interventi sono inefficaci, o in attesa degli effetti della radioterapia. Gli analoghi della somatostatina (SSA) sono stati per anni la principale terapia degli adenoma GH-secernenti, e al giorno d’oggi vengono utilizzati anche in quelli ACTH-secernenti, dato il loro effetto differenziale sui recettori della somatostatina (SSTR), soprattutto il tipo 2 nei GH-secernenti e il tipo 5 negli ACTH-secernenti. Purtroppo, fino al 50% dei pazienti non risponde in maniera soddisfacente alle terapie mediche, pertanto una maggior conoscenza della biologia cellulare ipofisaria è necessaria, per capire quale sia la strategia migliore per il paziente. Inoltre, in alcuni casi gli adenomi non sono sempre benigni (circa il 15-20% delle principali serie descritte in letteratura), caratterizzandosi per la resistenza alle terapie convenzionali, l’invasione dei tessuti locali o la rapida crescita. In tali casi, il termine Tumore Neuroendocrino Ipofisario (PitNET) viene recentemente proposto in letteratura. Lo scopo di questa tesi di dottorato è di studiare gli effetti delle terapie mediche in pazienti con PitNET; per sviluppare nuove strategie terapeutiche, per capire l’efficacia dei farmaci disponibili e per testare la loro combinazione. Materiali e metodi: I pazienti che sono seguiti presso l’ambulatorio ipofisi dell’Unità Operativa di Endocrinologia dell’Azienda Ospedaliero-Universitaria di Padova (120 con PitNET GH-secernenti, 134 ACTH-secernenti, 171 PRL-secernenti, 6 TSH- secernenti e 150 PitNET non funzionanti) sono stati seguiti in uno studio retrospettivo e prospettico. I dati clinici, bioumorali, di terapia, radiologici e patologici sono stati raccolti e analizzati. Tra le varie terapie mediche, maggior risalto è stato dato all’everolimus e alla temozolomide (TMZ) nei PitNET aggressivi e al metirapone (MET) in pazienti con Sindrome di Cushing. In casi selezionati sono state allestite linee cellulari derivanti dall’adenoma del pazienti (primarie). Risultati: in termini di terapia medica abbiamo analizzato 1. In un paziente con sclerosi tuberosa e PitNET silente abbiamo testato l’efficacia dell’everolimus in colture primarie, osservando una generale riduzione della vitalità cellulare. Abbiamo poi riscontrato una nuova variante del gene TSC2, gli studi in silico predicono la ritenzione di un introne con perdita di un sito di splicing, che andrà confermato in ulteriori studi funzionali. 2. Considerando la terapia con TMZ in PitNET aggressivi abbiamo raccolto i dati di 31 pazienti provenienti da uno studio multicentrico italiano. 11 casi hanno presentato una riduzione del PitNET, con una mediana di terapia di 18 mesi. Il 90% e il 60% dei pazienti erano liberi da malattia a 2 e 4 anni dalla terapia con TMZ. Abbiamo poi trattato un paziente con TMZ e cabergolina, ottenendo ottimi risultati. 3. 31 pazienti con Sindrome di Cushing sono stati trattati per 9 mesi con 1000 mg di MET. I parametri ormonali (cortisoluria e cortisolo salivare notturno) si sono ridotti rapidamente già dopo un solo mese di terapia, normalizzando la secrezione di cortisolo fino a 12 e 24 mesi. I pazienti con ipercorticismo severo (>5 volte i valori normali al baseline) hanno raggiunto il controllo biochimico di malattia più lentamente, tuttavia il 70% dei pazienti normalizzava la cortisoluria all’ultima visita, con una riduzione media di peso di 4kg. In generale il MET era ben tollerato, senza importanti effetti collaterali. 4. Nei pazienti con acromegalia, lo sviluppo di insufficienza surrenalica centrale (CAI) non è trascurabile nel follow-up. Mentre la terapia medica non aumenta il rischio di CAI, il 18% dei pazienti (10/57) svilippa iposurrenalismo dopo la chirurgia, mentre il 53% (9/17) lo sviluppa dopo la radioterapia. Analizzando in vitro gli aspetti che potrebbero predire la efficacia della terapia con SSA nei pazienti con acromegalia, abbiamo studiato i pathway molecolari di AIP-AHR e del GIPR. L’asse AIP-AHR, coinvolto nella detossificazione di varie molecole interferenti endocrine e inquinanti chimici, si trova maggiormente mutato in pazienti acromegalici con malattia più severa e con minor risposta agli SSA, soprattutto se vivono in zone molto inquinate. Abbiamo inoltre scoperto un ruolo promuovente del recettore dell’IGF-1 nel recettore del GIP, coinvolto nella tumorogenesi ipofisaria e quindi nuovo aspetto da studiare nei PitNET GH-secernenti. Conclusioni: Comprendere a fondo la fisiopatologia dei PitNET è l’inizio della personalizzazione della terapia medica, sempre più usata oggigiorno. Nella pratica clinica quotidiana, pertanto, un team multidisciplinare è fondamentale per proporre al paziente il corretto piano terapeutico, personalizzato secondo le proprie caratteristiche biologiche.

Projeto de Pós-Graduação/Dissertação apresentado à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Mestre em Ciências Farmacêuticas
Diferenças inter-individuais na eficácia e toxicidade da medicação são comuns entre os pacientes. Estima-se que a genética possa explicar entre 20 a 95 por cento da variabilidade na resposta aos fármacos. Porém associados à genética, existem fatores tais como, a idade, sexo, doenças secundárias e outros fatores ambientais que afetam a absorção, distribuição, metabolização e excreção dos fármacos podendo levar, habitualmente, à ocorrência de reações adversas. Estas reações são responsáveis por milhões de hospitalizações e milhares de mortes por ano apenas nos Estados Unidos. Identificação e caracterização de um grande número de polimorfismos genéticos (biomarcadores) nas enzimas metabolizadoras e transportadoras dos fármacos pode fornecer um conhecimento aprofundado sobre os mecanismos de diferenças inter- individuais na resposta à droga. O uso de novas tecnologias moleculares, nomeadamente de sequenciação do genoma, e de diagnóstico para avaliar o perfil genético e os biomarcadores das doenças abrem novos caminhos para permitir a cada doente o esquema de tratamento que lhe pode trazer os melhores resultados. Este esquema de tratamento, além de diminuir o número de reações adversas evita a exposição do paciente a um longo período de terapia baseado na tentativa e erro. Para os doentes, a medicina personalizada detém a promessa de terapêuticas mais eficazes e com menos efeitos secundários, podendo assim, ser poupados à perspetiva de um tratamento com efeitos adversos significativos e pouco ou nenhum efeito terapêutico Farmacogenética e farmacogenómica são duas áreas que emergiram para investigar a variabilidade individual na resposta aos fármacos. A indústria farmacêutica utiliza, cada vez mais, técnicas relacionadas com a farmacogenómica e com a farmacogenética e a informação que delas resulta para o processo de desenvolvimento de novos fármacos, promovendo a prescrição de um medicamento apropriado na dose certa para cada paciente. Inter-individual differences in the efficacy and toxicity of medication are common among patients. It is estimated that genetic factors can account for 20 to 95 percent of variability in drug responses. However associated with genetic, there are factors such as age, sex, secondary diseases and other environmental factors that affect the absorption, distribution, metabolism and excretion of drugs and may lead to adverse reactions. These reactions are responsible for millions of hospitalizations and thousands of deaths each year in the United States. Identification and characterization of a large number of genetic polymorphisms (biomarkers) in metabolizing enzymes and transporters of drugs can provide a thorough understanding of the mechanisms of inter-individual differences in drug response. The use of new molecular technologies, in particular genome sequencing, diagnosis for assessing the genetic profile and assessing biomarkers of disease open new paths to individualize each patient's treatment plan, optimizing it’s results. This treatment schedule, while decreasing the number of adverse reactions avoids exposing the patient to a long-term therapy based on trial and error. For patients, personalized medicine holds the promise of more effective therapies with fewer side effects, and may thus spare the prospect of a treatment with significant adverse effects and little or no therapeutic effect Pharmacogenetics and pharmacogenomics are two areas that have emerged to investigate the individual variability in response to drugs. The pharmaceutical industry uses increasingly techniques related to pharmacogenetics and pharmacogenomics and the information that follows them to the process of drug development, promoting the prescription of appropriate medication in the right dose for each patient.

The aim of my master degree project of Advanced Product Design was to develop a monitoring and medication device to empower people who are suffering from asthma. Asthma is a major non-communicable disease characterized by recurrent attacks of breathlessness and wheezing. It is not possible to cure asthma, but appropriate management can control the disease and enable people to enjoy a good quality of life. Asthma varies in severity and frequency from person to person and not every asthmatic will require the same level of treatment. Also asthma conditions can vary over time and the level of airways inflammation has to be reviewed and medication has to be adjusted. Asthma diagnostic takes place at a point-of-care environment and is usually based on the pattern of symptoms, response to therapy over time and by objective parameters like lung function tests. My degree project was focused on a home used diagnostic and medication system in order to adjust the dosage of asthma medication on a more frequently basis.

Understanding of pharmacokinetic-pharmacodynamic relationship for a given drug, and factors determining this, is an important part of personalised medicine in terms of choosing the correct regimen and maximising the therapeutic benefit with minimal toxicity. In the field of cancer, diversity of disease characteristics, complexity of regimens, as well as limited understanding of PK-PD profiles of rapidly-progressing anticancer drugs, create challenges for clinicians and patients to achieve the optimal outcome. This thesis addresses areas of cancer treatment where there is a need for identification of pharmacokinetic (PK), pharmacogenetic (PG) or physiological contributors, which may explain the observed variability, pharmacokinetics or toxicity of the studied drugs. The first component of the work aimed to investigate pharmacogenetic factors determining pharmacokinetics of actinomycin D. The study involves identification of the candidate transporters through in vitro uptake assays, which then led to clinical PK-PG analysis to determine clinically-significant transporter genotype influencing actinomycin D pharmacokinetics. The second component of the work explores pharmacodynamic associations between toxicity (pyrexia: body temperature > 38C) and exposure to dabrafenib and trametinib (CombiDT) used in the treatment of patients with melanoma expressing the common BRAF V600E/K mutation. A biomarker analysis using a panel of cytokines was also conducted to investigate their role in predicting or indicating the incidence of this toxicity. The significant findings of our study include identification of involvement of Solute Carrier (SLC) transporters (OAT4 and PEPT2) in actinomycin D uptake in vitro and the potentially predictive role of cytokines (IL-1B and IL-6) in CombiDT-induced pyrexia. Some of the results, such as the role of SLC transporters in clinical pharmacokinetics of actinomycin D in paediatric cancer patients, and drug exposure-pyrexia relationship for CombiDT treatment, lacked definitive conclusions due to study limitations, and provide areas of further research. Overall, we believe that our study has made valuable contributions to the enhanced understanding of these drugs, and a step closer to personalised medicine in the treatment of cancer.

In personalized medicine, two important tasks are predicting disease risk and selecting appropriate treatments for individuals based on their baseline information. The dissertation focuses on providing improved risk prediction for ordinal outcome data and proposing score-based test to identify informative markers for treatment selection. In Chapter 1, we take up the first problem and propose a disease risk prediction model for ordinal outcomes. Traditional ordinal outcome models leave out intermediate models which may lead to suboptimal prediction performance; they also don't allow for non-linear covariate effects. To overcome these, a continuation ratio kernel machine (CRKM) model is proposed both to let the data reveal the underlying model and to capture potential non-linearity effect among predictors, so that the prediction accuracy is maximized. In Chapter 2, we seek to develop a kernel machine (KM) score test that can efficiently identify markers that are predictive of treatment difference. This new approach overcomes the shortcomings of the standard Wald test, which is scale-dependent and only take into account linear effect among predictors. To do this, we propose a model-free score test statistics and implement the KM framework. Simulations and real data applications demonstrated the advantage of our methods over the Wald test. In Chapter 3, based on the procedure proposed in Chapter 2, we further add sparsity assumption on the predictors to take into account the real world problem of sparse signal. We incorporate the generalized higher criticism (GHC) to threshold the signals in a group and maintain a high detecting power. A comprehensive comparison of the procedures in Chapter 2 and Chapter 3 demonstrated the advantages and disadvantages of difference procedures under different scenarios.
Biostatistics

The human genome is a source of information for researchers that study complex diseases with the perspective of a better understanding of the pathologies and the development of new therapeutic strategies. Starting from the beginning of the current century, a growing number of technologies devoted to DNA sequencing have emerged, generally referred to as Next Generation Sequencing (NGS) technologies. NGS gradually decreased the cost of sequencing a human genome to around US$1000, enabling the use of these technologies for clinical and research purposes, such as Genome-wide association studies (GWAS). GWAS studies have enlightened the presence of disease- associated loci, in particular variants that could be used to evaluate the risk of an individual to develop a disease. Unfortunately, different sources of errors are able to impair the interpretation and use of NGS data: on the one hand, we have noise related to the process of DNA sequencing and read alignment errors, which could lead to false positive calls or artifacts. On the other hand, variants could be poor predictors for the manifestation of their associated disease. Nowadays the challenge of genomic data interpretation has driven the research towards the development of methods for the analysis and interpretation of genomic variations, eventually predicting the probability of a patient to develop a definite disease. A fair evaluation of these tools is essential to understand the applicability of the presented methods in clinical practice. The Critical Assessment of Genome Interpretation (CAGI) has been developed with the aim of defining the current state of art in terms of methods for predicting the impact of genomic changes at molecular and phenotype levels. CAGI is a community-driven experiment in which different prediction methods, developed by a set of invited groups, are evaluated on a common dataset. Unfortunately, no common guidelines were given to evaluate the tools presented in CAGI experiments, this has made the comparison between different CAGI experiments cumbersome, since different mathematical indexes and scripts have been used to evaluate the involved methods. My PhD project has been focused on the development of software for the assessment of machine learning methods in regression and multiple phenotype challenges. This tool is based on state of the art assessment principles, derived from literature or previous CAGI experiments. This software is available as an R package and has been used to repeat or perform new assessments on a wide range of CAGI experiments. The knowledge acquired during the development of this project was used to evaluate two CAGI 5 challenges: Pericentriolar Material 1 (PCM1) and Intellectual Disability (ID) panel. The experience I have acquired, through the development of all previously mentioned works, has led the improvement and assessment of a machine learning method. In particular, I have developed a software for the prediction of cholesterol levels, based on genotype data. Eventually I have tested the reliability of this method. This tool was the milestone in a project founded by the Italian Ministry of Health.
Il genoma umano è una risorsa ricca di informazioni per i ricercatori che si dedicano allo studio delle patologie complesse. L’obiettivo di questo genere di ricerche è giungere ad una migliore comprensione di queste malattie e quindi sviluppare nuove strategie terapeutiche per la cura dei pazienti affetti. Dall’inizio di questo secolo, un numero crescente di tecnologie per il sequenziamento del DNA sono state sviluppate, sono conosciute come tecnologie “Next Generation Sequencing” (NGS). Le tecnologie NGS hanno gradualmente diminuito il costo del sequenziamento di un genoma umano fino a circa 1000 dollari, ciò ha consentito l’utilizzo di questi strumenti nella pratica clinica e nella ricerca, in particolare negli studi di associazione genome-wide o “Genome-wide association studies” (GWAS). Questi lavori hanno portato alla luce l’associazione di alcune varianti con alcune patologie o caratteri complessi. Queste varianti potrebbero essere utilizzate per valutare il rischio che un individuo sviluppi una particolare patologia. Sfortunatamente diverse sorgenti di errore sono in grado di ostacolare l’uso e l’interpretazione dei dati genomici: da una parte abbiamo il rumore legato al processo di sequenziamento e gli errori di allineamento delle reads. Dall’altra parte gli SNP non sempre possono essere utilizzati in modo affidabile per predire l’insorgenza della malattia a cui sono stati associati. Il Critical Assessment of Genome Interpretation è stato organizzato con l’obiettivo di definire lo stato dell’arte nei metodi che stimano l’effetto di variazioni genetiche a livello molecolare o fenotipico. Negli anni il CAGI ha dato vita a più competizioni in cui diversi gruppi di ricerca hanno testato i loro metodi di predizione su diversi dataset condivisi. L’assenza di linee generali su come condurre la valutazione delle performance dei predittori, ha reso difficile un confronto fra metodi sviluppati in edizioni diverse del CAGI. In questo contesto, il progetto di dottorato si è focalizzato nello sviluppo di un software per la valutazione di metodi di apprendimento automatici basati sulla regressione o la predizione di fenotipi multipli. Questo strumento si fonda su criteri di analisi della performance, derivanti dalla letteratura e da precedenti esperimenti del CAGI. Questo software è stato sviluppato in R ed utilizzato per ripetere o valutare ex novo la qualità dei predittori in un gran numero di esperimenti del CAGI. Le conoscenze acquisite durante lo sviluppo di questo progetto, sono state utilizzate per valutare due competizioni del CAGI 5: la Pericentriolar Material 1 (PCM1) e il Pannello per le Disabilità Intellettive (ID). L’esperienza derivante dal completamento dei lavori precedentemente elencati, ha guidato lo sviluppo e il miglioramento delle prestazioni di un metodo predittivo. In particolare è stato sviluppato un software per la predizione dei livelli di colesterolo, basato su dati genotipici, di cui è stata testata la validità con criteri matematici allo stato dell’arte. Questo strumento è stato la pietra portante di un progetto fondato dal Ministero della Salute Italiano.

Cancer is a disease of the genome. The study of tumor genomic alterations is used to guide several precision medicine strategies, some approved and a large number under clinical development. On the other hand, the study of tumor immunity is recently becoming the key for the success of other personalized strategies, named immunotherapies. Along this thesis I have made several contributions towards the advance of cancer precision medicine, based on the study of tumor “omics” data. First, I evinced the landscape of genomic-guided anti-cancer therapies. Second, I developed OncoPaD, a tool for the rational design of cost-effective cancer gene panels. Third, I contributed to the development of Cancer Genome Interpreter, a tool for the biological and therapeutic interpretation of variants found in newly sequenced tumors. Forth, I identified tumor intrinsic molecular mechanisms involved in tumor immune evasion.
El càncer és una malaltia del genoma. L'estudi de les alteracions genòmiques dels tumors s’utilitza com a guia en varies estratègies de medicina de precisió, algunes d'elles aprovades i d'altres en assajos clínics. D'altra banda, l’estudi de la immunitat tumoral està esdevenint una peça clau per l’èxit d’altres estratègies terapèutiques, anomenades immunoteràpies. Al llarg d'aquesta tesi, mitjançant l'estudi de les dades “òmiques” tumorals, he contribuït de varies maneres cap a l'avenç de la medicina de precisió pel càncer. Primer, he identificat el panorama de les teràpies anticanceroses guiades per alteracions genòmiques. Segon, he desenvolupat OncoPaD, una eina pel disseny cost-efectiu i racional de panells de seqüenciació per càncer. A més, he contribuït al desenvolupament del Cancer Genome Interpreter, una eina que ajuda a la interpretació biològica i terapèutica de les variants presents a tumors novament seqüenciats. Per últim, he identificat diversos mecanismes mitjançant els quals els tumors són capaços d'evadir l’atac del sistema immunològic.

This dissertation, in general, is about finding stable solutions to statistical models with very large number of parameters and to analyze their asymptotic statistical properties. In particular, it is centered in the study of regularization methods based on penalized estimation. Those procedures find an estimator that is the result of an optimization problem balancing out the fitting to the data with the plausability of the estimation. The first chapter studies a smoothness regularization estimator for an infinite dimensional parameter in an exponential family model with functional predictors. We focused on the Reproducing Kernel Hilbert space approach and show that regardless the generality of the method, minimax optimal convergence rates are achieved. In order to derive the asymptotic analysis of the estimator, we developed a simultaneous diagonalization tool for two positive definite operators: the kernel operator and the operator defined by the second Frechet derivative of the expected data t functional. By using the proposed simultaneous diagonalization tool sharper bounds on the minimax rates are obtained. The second chapter studies the statistical properties of the method of regularization using Radial Basis Functions in the context of linear inverse problems. The regularization here serves two purposes, one is creating a stable solution for the inverse problem and the other is prevent the over-fitting on the nonparametric estimation of the functional target. Different degrees for the ill-posedness in the inversion of the operator A are considered: mildly and severely ill-posed. Also, we study different types for radial basis kernels classifieded by the strength of the penalization norm: Gaussian, Multiquadrics and Spline type of kernels. The third chapter deals with the problem of Individualized Treatment Rule (ITR) and analyzes the solution of it through Discriminant Analysis. In the ITR problem, the treatment assignment is done based on the particular patient's prognosis covariates in order to maximizes some reward function. Data generated from a random clinical trial is considered. Maximizing the empirical value function is an NP-hard computational problem. We consider estimating directly the decision rule by maximizing the expected value, using a surrogate function in order to make the optimization problem computationally feasible (convex programming). Necessary and sufficient conditions for Infinite Sample Consistency on the surrogate function are found for different scenarios: binary treatment selection, treatment selection with withholding and multi-treatment selection.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 165-173).
Recent advances in machine learning and optimization hold much promise for influencing real-world decision making, especially in areas such as health care where abundant data are increasingly being collected. However, imperfections in the data pose a major challenge to realizing their full potential: missing values, noisy observations, and unobserved counterfactuals all impact the performance of data-driven methods. In this thesis, with a fresh perspective from optimization, I revisit some of the well-known problems in statistics and machine learning, and develop new methods for prescriptive analytics. I show examples of how common machine learning tasks, such as missing data imputation in Chapter 2 and classication in Chapter 3, can benet from the added edge of rigorous optimization formulations and solution techniques. In particular, the proposed opt.impute algorithm improves imputation quality by 13.7% over state-of-the-art methods, as averaged over 95 real data sets, which leads to further performance gains in downstream tasks. The power of prescriptive analytics is shown in Chapter 4 by our approach to personalized diabetes management, which identifies response patterns using machine learning and individualizes treatments via optimization. These newly developed machine learning algorithms not only demonstrate improved performance in large-scale experiments, but are also applied to solve the problems in health care that motivated them. Our simulated trial for diabetic patients in Chapter 4 demonstrates a clinically relevant reduction in average hemoglobin A1c levels compared to current practice. Finally, when predicting mortality for cancer patients in Chapter 5, applying opt.impute on missing data along with the cutting-edge algorithm Optimal Classication Tree on a rich data set prepared from electronic medical records, we are able to accurately risk stratify patients, providing physicians with interpretable insights and valuable risk estimates at time of treatment decisions and end-of-life planning.
by Ying Daisy Zhuo.
Ph. D.

Inflammatory bowel disease (IBD) is a lifelong chronic inflammatory condition of the gastrointestinal tract (GIT), with incidence and prevalence increasing worldwide. It is considered a complex, multifactorial disease with no cure. Even though large progress has been made in recent years, current therapies are far from satisfactory, and show extreme variability of outcomes due to patient heterogeneity. The traditional therapy consists of anti-inflammatories, corticosteroids, antibiotics, and immunomodulatory drugs. This non-specific immunosuppression guarantees disease-control in some patients although the long-term use of these drugs is correlated with a significant number of therapy-associated complications and side-effects. A dramatic improvement in disease management was achieved by the introduction of biological agents targeting pro-inflammatory cytokines such as anti-TNF-α. Despite the revolutionary impact of these agents in IBD disease management, treatments such as anti-TNF-α do show several drawbacks – for example, up to 50% of patients do not respond at all or eventually lose response. This variability in clinical outcome is reflecting the variability of individuals due to different genetics, life style and inflammatory state. Therefore, there is a need to define the specific inflammatory state of a given patient, considering individual complications and develop new in-vitro systems and biomarkers that predict drug responsiveness and allow developing patient-specific treatment In this thesis, different in-vitro models were developed addressing different aspects and compartments of IBD pathology including the enteric nervous system, the ECM component fibrillin-1, as well as patient-derived, three dimensional short-term and long-term cultures that will bring us a step closer towards personalized medicine.
Le malattie infiammatorie croniche intestinali (MICI) sono un gruppo di patologie complesse ad eziologia multifattoriale, caratterizzate da uno stato infiammatorio cronico del tratto gastrointestinale, la cui incidenza a livello mondiale è in continuo aumento. Nonostante nel corso degli ultimi anni siano stati fatti numerosi progressi nel controllo della malattia, l’attuale approccio terapeutico rimane ancora lontano dall’essere soddisfacente e l’esito clinico che ne deriva è estremamente variabile a causa della vasta eterogeneità tra i pazienti. La terapia tradizionale, che consiste nella somministrazione di farmaci antinfiammatori, corticosteroidi, antibiotici o farmaci immunomodulatori, garantisce il controllo della malattia in alcuni pazienti, ma, a causa della non-specificità e del lungo periodo di utilizzo, è correlata all’insorgenza di numerose complicanze ed effetti collaterali. Un significativo miglioramento nella gestione della malattia è stato raggiunto attraverso l’introduzione di farmaci biologici, il cui target è rappresentato principalmente dalle citochine pro-infiammatorie implicate nella patologia, come ad esempio il TNF-α. Nonostante il forte impatto clinico, l’utilizzo di farmaci biologici, come l’anti-TNF-α ha mostrato diversi svantaggi, tra cui un’alta percentuale di non-responsività al trattamento oppure la perdita di risposta nel corso del tempo. La grande variabilità che si riscontra nella risposta clinica riflette di fatto la variabilità che sussiste tra i diversi individui, ed è dovuta principalmente a differenze a livello genetico, nello stile di vita e nello stato infiammatorio. Di conseguenza, cresce sempre più la necessità di definire nello specifico lo stato infiammatorio e le complicanze caratteristiche di ciascun paziente e di sviluppare nuovi sistemi di screening in-vitro che possano predire la risposta al trattamento e quindi consentire un approccio terapeutico specifico per ciascun individuo. Nell’ottica di una medicina predittiva e della terapia personalizzata, in questa tesi sono stati sviluppati differenti modelli in-vitro, che prendono in considerazione diversi aspetti e compartimenti implicati nelle malattie infiammatorie croniche intestinali, quali il sistema nervoso enterico, la fibrillina-1, componente della matrice extracellulare, così come colture tridimensionali a breve e lungo termine derivate da campioni bioptici umani. Questi modelli sperimentali di infiammazione cronica intestinale potranno costituire uno strumento clinico utile per applicazioni di medicina personalizzata.

Given the emerging importance of personalized medicine (PM) in primary care, now should be the ideal time for engaging with health care professionals (HCPs), both physicians and nurses, about integrating PM into practice. The question then becomes: what is the most effective way to engage with HCPs about emerging technologies that are not in routine clinical use and which are unfamiliar to many? The overall aim of this pilot study was to develop and compare two professional engagement (PE) approaches for engaging with HCPs about PM to inform their development and design of a future formal evaluation. The first PE intervention was a structured in-person focus group and the second was an online version, also incorporating an educational component, but without group interaction. The pilot study showed that while participants evaluated both interventions positively, the in-person workshop consistently scored higher; however, recruitment challenges were a major obstacle for this approach.

Surgeons constantly need to customize or personalize solutions to suit the unique 3D (3 Dimensional) challenges each patient presents. Technologies such as 3D printing which enables delivery of customized or personalized medicine can play an important role in surgery. 3D tools have played an important role in Otology for many decades. However, despite a significant volume of literature in this area validating its benefit, 3D technologies are not part of routine clinical care. Why is this so? This PhD examines the challenges and feasibility of clinical translation of personalized medicine in Otology by methodically studying applications in the outer, middle and inner ear. It includes a narrative review of 3D technologies in Otology, a 3D visualization study of the human vestibule (an inner ear application), a feasibility study of 3D printing for ossiculoplasty surgery (a middle ear application) and an animal trial to study 3D bioprinting for chondrogenesis in microtia reconstruction (an outer ear application). It also assesses the non-technical challenges for clinical translation of personalized surgery in Otology namely; regulatory and ethical barriers. There are both specialty and technology specific barriers but due to the anatomical constraints in Otology, there are limitations in overcoming specialty specific barriers. Technology specific barriers are easier to overcome in the outer ear as it is larger in size and easier to be visualized. This region sees more advanced application of personalized medicine such as bioprinting. Though further work to apply bioprinting in the middle ear is required, 3D printing applications can assist in improving surgical outcomes. For potential application of precision medicine to the inner ear, future developments in other developing technologies such as nanoscience is necessary. In the interim, 3D anatomical studies on the inner ear can overcome smaller challenges to assist this translation.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 141-161).
The ability to accurately detect and quantify biological molecules in complex mixtures is crucial in basic research as well as in clinical settings. Advancements in genetic analysis, molecular diagnostics, and patient-tailored medicine require robust detection technologies that can obtain high-density information from a range of physiological samples in a rapid and cost-effective manner. Compared to conventional microarrays and methods based on polymerase chain reaction (PCR), suspension (particle-based) arrays offer several advantages in the multiplexed detection of biomolecules, including higher rates of sample processing, reduced consumption of sample and reagent, and rapid probe-set modification for customizable assays. This thesis expands the utility of a novel hydrogel-based microparticle array through (1) the creation of a microfluidic, flow-through fluorescence scanner for high-throughput particle analysis, (2) the development of a suite of techniques for the highly sensitive and specific detection of microRNA (miRNA) biomarkers, and (3) the investigation of new methods for directly measuring biomolecules at the single-cell level. Graphically-encoded hydrogel microparticles synthesized from non-fouling, bioinert poly(ethylene glycol) (PEG) and functionalized with biomolecule probes offer great promise in the development of high-performance, multiplexed bioassays. To extend this platform to applications in high-throughput analysis, particle design was optimized to ensure mechanical stability in high-velocity flow systems, and a single-color microfluidic scanner was constructed for the rapid fluorescence interrogation of each particle's spatially-segregated "code" and "probe" regions. The detection advantages of three-dimensional, probe-laden hydrogel scaffolds and the operational efficiencies of suspension array technology were then leveraged for the rapid multiplexed expression profiling of miRNA. The graphical encoding method and ligationbased labeling scheme implemented here allowed for scalable multiplexing with a simple workflow and an unprecedented combination of sensitivity, flexibility, and throughput. Through the rolling circle amplification of a labeling oligonucleotide, it was possible to further enhance the system's sensitivity and resolve single-molecule miRNA binding events on particle surfaces, enabling the first direct detection of low-abundance miRNA in human serum without the need for RNA extraction or target amplification. Finally, by arraying cells and gel particles in polydimethylsiloxane (PDMS) microwells, it was possible to dramatically improve the particles' target capture efficiency and thereby move closer to a regime in which miRNAs and other biological molecules may be directly detected without target amplification from single cells.
by Stephen Clifford Chapin.
Ph.D.

La médecine basée sur les preuves requiert des essais cliniques randomisés, qui permettent d'estimer un effet moyen du traitement. La personnalisation de l'estimation de l'effet du traitement nécessite l'estimation du risque spontané de la maladie (biomarqueur pronostique), la recherche de facteurs modifiant l'effet du traitement (biomarqueur théranostique). Des critères de substitution sont également proposés, dont la mesure devrait permettre d'évaluer l'effet du traitement sur l'événement clinique. La prise en charge des patients présentant un diabète de type 2 repose sur les médicaments hypoglycémiants. Plusieurs d'entre eux ont été associés à différents effets indésirables graves. Des études évaluant leur bénéfice cardiovasculaire sont nécessaires. La prise en charge de ces patients inclue également la prise en charge de l'hypertension artérielle. Celle-ci est basée sur des médicaments antihypertenseurs, dont l'intensité est ajustée au niveau de pression artérielle recherché. Cette stratégie basée sur la cible soulève plusieurs questions. Enfin, plusieurs biomarqueurs prédictifs de différents effets des médicaments hypoglycémiants ont été étudiés chez des patients présentant un diabète de type 2, avec des résultats contrastés. Une difficulté majeure dans la validation de biomarqueur théranostique est la puissance statistique nécessaire pour détecter une interaction dans un essai clinique randomisé. L'objectif de cette thèse était d'estimer les effets moyens des traitements hypoglycémiants sur les complications cardiovasculaires ; d'évaluer un potentiel critère de substitution ; et d'étudier les caractéristiques des études cliniques évaluant des biomarqueurs théranostiques. La première partie présente une méta-analyse en réseaux comparant les effets des hypoglycémiants contemporains chez des patients avec un diabète de type 2, sur la mortalité totale, cardiovasculaire et les évènements cardiovasculaires majeurs. Nous avons confirmé la supériorité des gliflozines et des agonistes du récepteur au GLP1 par rapport au traitement contrôle et aux inhibiteurs de la DPP4. Nous avons montré le besoin de comparaisons directes entre les différentes classes, notamment pour préciser la place de la metformine dans la stratégie thérapeutique. La deuxième partie présente une méta-régression évaluant l'association entre la diminution de la pression artérielle par des médicaments antihypertenseurs et les évènements cardiovasculaires majeurs. Nous avons confirmé la relation entre la baisse de la pression artérielle et le risque d'accident vasculaire cérébrale. Il n'y avait pas d'association avec la mortalité totale, la mortalité cardiovasculaire, les infarctus du myocarde. La troisième partie présente une comparaison statistique du plan expérimental en cross-over par rapport au plan en bras parallèle, concernant leur capacité à évaluer un marqueur théranostique. Nous avons montré que l'intérêt du cross-over, pour réduire le nombre de sujet nécessaire, dépend de la corrélation intra-sujet de la mesure du critère de jugement choisi, de façon similaire à l'estimation de l'effet propre du traitement. Ce travail met en lumière le besoin de comparaisons des médicaments hypoglycémiants sur les complications cardiovasculaires, et la difficulté d'évaluer une balance bénéfice—risque d'un traitement. Des approches de méta-analyses sur données individuelles permettraient de mieux estimer l'impact du contrôle glycémique sur les complications cardiovasculaires. L'accès aux technologies de séquençage du génome à haut débit permettrait d'identifier des facteurs pronostiques et théranostiques. Finalement, nous proposons une extension du modèle d'effet, qui permet d'appréhender la balance bénéfice—risque d'un traitement en fonction de différents biomarqueurs. L'évaluation d'un effet traitement moyen ou stratifié doit s'inscrire dans une vision globale de la balance bénéfice—risque du médicament concerné
Evidence based medicine requires randomized clinical trials for estimating a mean treatment effect. The personalization of this treatment effect needs prognostic biomarker for assessing the spontaneous risk of the disease and the absolute benefit of the treatment; and the search for potential theranostic biomarker, associated with a different relative treatment effect. Surrogate endpoints are also proposed, as their measure would reflect the treatment effect on the clinical outcome of interest. Taking care of patients with type 2 diabetes is based on hypoglycemic drugs. Several of them have been retrospectively associated with serious adverse events. They need to be assessed with cardiovascular outcome trials. Taking care of those patients also include handling other cardiovascular risk factor, as high blood pressure. Antihypertensive treatment is based on a “target to treat” strategy, which raise several questions. Finally, many theranostic biomarkers of the hypoglycemic drugs effect have been studied, with conflicting results. Statistical power is a high challenge in randomized trial looking for such interaction. We aimed to provide a mean treatment effect estimation of hypoglycemic drugs on cardiovascular outcomes and to explore potential tools for personalizing the treatment effect estimation. The first part of this thesis reports a network meta-analysis assessing the contemporary hypoglycemic drugs in type 2 diabetes patients on overall mortality, cardiovascular mortality and major adverse cardiovascular events. We confirmed the superiority of SGLT2 inhibitors and of GLP1 receptor agonists compared to control and to DPP4 inhibitors. We also showed the need for direct comparison, especially for clarifying the position of metformin in the pharmacological strategy. The second part of this thesis reports a meta-regression analysis, assessing the association between the decrease in blood pressure through antihypertensive drugs and the risk of cardiovascular events. We confirmed the association between the blood pressure control and the risk of stroke, but did not find any association regarding overall mortality, cardiovascular mortality and myocardial infarction. The third part reports a statistical comparison of the parallel group design and the cross-over design, regarding their capacity to assess a potential theranostic biomarker. We showed that the advantage of the cross-over for reducing the sample size lead on the intra-subject correlation, as already known for estimating the treatment effect. Finally, we highlighted the need for comparisons of hypoglycemic drugs for preventing macrovascular events. We emphasized pitfalls in estimating benefit—risk balance. Individual patient data meta-analyses would help better assessing the effect of glucose control on macrovascular events. High-throughput genome sequencing technologies would help to identify both prognostic and theranostic biomarkers. Lastly, we proposed an extended version of the effect model, which allow to grasp the benefit—risk balance of a treatment, according to different biomarkers. To conclude, assessing a mean and a stratified treatment effect should be conducted taking into account the global benefit—risk balance estimation

Personalised medicine aims to achieve the best treatment outcome with the highest safety margin tailored for each individual patient. It is essential to develop biomarkers of disease characteristics, prognosis and response to treatment for the implementation of personalised medicine. Widely used in postmenopausal women with estrogen receptor positive breast cancer, third-generation aromatase inhibitors (AIs) have nevertheless marked interindividual variability in efficacy and safety. Personalised treatment with AIs is required to achieve the best therapeutic response with highest safety margin. In the first specific aim, the current implementation of different approaches for personalised medicine of AIs in patients with breast cancer was reviewed, and future prospects were proposed. In the second specific aim, I reviewed details of the current research progress of personalised medicine with aromatase inhibitors specifically from a pharmacogenetic perspective. Schizophrenia is a genetically complex disorder. Even though it has been extensively studied, our understanding of the molecular etiology of schizophrenia remains incomplete, and current pharmaceutical treatments are not a complete solution. In the third specific aim, I conducted a genome-wide association study (GWAS) of schizophrenia in an Australian population. Post-GWAS analyses were subsequently conducted by leveraging GWAS summary statistics. The strongest finding (P=2.01×10-6, odds ratio (OR) =1.82, 95% confidence interval (CI) =1.42-2.33) in GWAS was with rs10252923 at 7q21.13, downstream of FZD1 (Frizzled Class Receptor 1). While this did not stand alone after statistical correction, the involvement of FZD1 was supported by gene-based analysis, which exceeded the threshold for genome-wide significance (P=2.78×10-6). The identification of FZD1, an independent association signal at the gene level, provided new insights regarding susceptibility for schizophrenia, as well as potential targets for developing new treatments. Very early- and early-onset schizophrenia (VEOS and EOS) are more severe forms of the disorder and have worse prognosis than adult-onset counterparts. There is evidence supporting schizophrenia onset at an early age to be associated with a greater genetic predisposition. In the fourth specific aim, a genome-wide association study for VEOS and EOS was carried out. Further functional follow-up analyses were performed using GWAS summary statistics. rs3825884 in NTRK3 (Neurotrophic Receptor Tyrosine Kinase 3), located at 15q25.3 showed the strongest genome-wide significant association (P=3.09×10-8, OR=2.63, 95% CI=1.87-3.71). Furthermore, immune alterations were implicated in VEOS and EOS through pathway enrichment analysis (T-cell and B-cell receptors signaling pathways, false discovery rate of 1.00×10−4 and 0.015, respectively). Our findings of genetic markers and pathways may aid in elucidating molecular mechanisms underlying VEOS and EOS, and offer insights into developing novel, tailored pharmacologic interventions. The combination of dabrafenib and trametinib is a standard of care for the management of BRAF mutant metastatic melanoma. Clinical trials of dabrafenib-trametinib combination therapy exclude patients with end-stage kidney disease (ESKD) and, as such, no data are available regarding the safety, efficacy and pharmacokinetics of these drugs in such patients. In the fifth specific aim, pharmacokinetic profiling of trametinib, dabrafenib and its metabolites were similar pre- and post- dialysis and comparable to those in patients with normal renal function. In the era of personalised medicine, this study provided evidence for the feasibility of administering dabrafenib and trametinib to patients with ESKD undergoing haemodialysis.

Proteins are the embodiment of the message encoded in the genes and they act as the building blocks and effector part of the cell. From gene regulation to cell signalling, as well as cell recognition and movement, protein-protein interactions (PPIs) drive many important cellular events by forming intricate interaction networks. The number of all non-redundant human binary interactions, forming the so-called interactome, ranges from 130,000 to 650,000 interactions as estimated by different studies. In some diseases, like cancer, these PPIs are altered by the presence of mutations in individual proteins, which can change the interaction networks of the cell resulting in a pathological state. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. To understand how these mutations can alter the PPIs, we need to look at the three-dimensional structure of the protein complexes at the atomic level. However, there are available structures for less than 10% of the estimated human interactome. Computational approaches such as protein-protein docking can help to extend the structural coverage of known PPIs. In the protein-protein docking field, rigid-body docking is a widely used docking approach, since es fast, computationally cheap and is often capable of generating a pool of models within which a near-native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. In the present thesis, we have characterized the synergy between combination of protein-protein docking methods and several scoring functions. Our findings provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts Then we used docking calculations to predict interaction hotspots, i.e. residues that contribute the most to the binding energy, and interface patches by including neighbour residues to the predictions. We developed and validated a method, based in the Normalize Interface Propensity (NIP) score. The work of this thesis have extended the original NIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This methodology was also applied to predict the location of 14,551 nsSNPs in 4,254 proteins, for more than 12,000 interactions without 3D structure. We found that 34% of the disease-associated nsSNPs were located at a protein-protein interface. This opens future opportunities for the high-throughput characterization of pathological mutations at the atomic level resolution, and can help to design novel therapeutic strategies to re-stabilize the affected PPIs by disease-associated nsSNPs.

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 191-200).
Electronic transmission of protected health information has become pervasive in research, clinical, and public health investigations, posing substantial risk to patient privacy. From clinical genetic screenings to publication of data in research studies, these activities have the potential to disclose identity, medical conditions, and hereditary data. To enable an era of personalized medicine, many research studies are attempting to correlate individual clinical outcomes with genomic data, leading to thousands of new investigations. Critical to the success of many of these studies is research participation by individuals who are willing to share their genotypic and clinical data with investigators, necessitating methods and policies that preserve privacy with such disclosures. We explore quantitative models that allow research participants, patients and investigators to fully understand these complex privacy risks when disclosing medical data. This modeling will improve the informed consent and risk assessment process, for both demographic and medical data, each with distinct domain-specific scenarios. We first discuss the disclosure risk for genomic data, investigating both the risk of re-identification for SNPs and mutations, as well as the disclosure impact on family members. Next, the deidentification and anonymization of geospatial datasets containing information about patient home addresses will be examined, using mathematical skewing algorithms as well as a linear programming approach. Finally, we consider the re-identification potential of geospatial data, commonly shared in both textual form and in printed maps in journals and public health practice. We also explore methods to quantify the anonymity afforded when using these anonymization techniques.
by Christopher A. Cassa.
Ph.D.

Orientador: Elenice Deffune
Resumo: Introdução: A Síndrome do Olho Seco (SOS) é uma doença multifatorial das lágrimas e da superfície ocular que resulta em desconforto, distúrbios visuais e instabilidade do filme lacrimal e afeta principalmente adultos com mais de 50 anos e mulheres. É uma doença que pode ter grande impacto negativo na qualidade de vida dos pacientes. A abordagem terapêutica com soro autólogo tem sido preconizada desde 1986 com prós e contras na literatura. Esta pesquisa teve como objetivo realizar uma revisão sistemática e metanálise para analisar a efetiva contribuição do Soro Autólogo segundo os protocolos, ensaios terapêuticos publicados nos últimos 10 anos e o atendimento dos pacientes portadores de Síndrome do olho seco (SOS) no Hospital das Clínicas da Faculdade de Medicina de Botucatu .Objetivos: 1) realizar revisão sistemática e metanálise para avaliar a eficácia do uso do soro autólogo em comparação com lágrimas artificiais comerciais no tratamento para adultos com olho seco. 2) realizar um trabalho retrospectivo da contribuição do soro autólogo de uso ocular no tratamento da SOS nos pacientes do ambulatório do Hospital das Clínicas de 2014-2018 comparando com o período anterior (2008-2013). Materiais e Métodos: Para a revisão sistemática e metanálise foi pesquisado na base de dados do Pubmed não usando restrições de língua, apenas trabalhos dos últimos 10 anos. Para os desfechos primários e secundários foi utilizado um intervalo de confiança de 95%. O levantamento de dados dos pacien... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: Introduction: Dry Eye Syndrome is a multifactorial disease of tears and ocular surface that results into discomfort, visual disturbances and tear film instability and affects mainly female adults, over 50 years. It is a disease which may cause a great negative impact on the patients’ quality of life. The therapeutic approach employing autologous serum has been advocated in 1986, with pros and cons in the literature. This study aimed to perform a systematic review and a meta-analysis to investigate the effective contribution of Autologus Serum according to the protocols and clinical trials published in the last 10 years, and the SOS patients care at Hospital das Clínicas, during two periods: 2008-2013 and 2014- 2018.Objectives: 1) to perform a systematic review and meta-analysis to evaluate the efficacy of the autologous serum, in comparison to commercial artificial tears for the treatment of dry eye in adults. 2) to carry out a retrospective study of the ocular autologous serum contribution for SOS out patients’ treatment, at Hospital das Clínicas (2014 to 2018), comparing with the previous period (2008-2013).Materials and Methods: For the systematic review and meta-analysis, searches were realized in Pubmed databases, with no language restrictions, for papers published in the last 10 years, with full text availability, for researches dated until 10/12/2018. For the primary and secondary outcomes, a 95% confidence interval was used. For the outpatients’ data collection, each ... (Complete abstract click electronic access below)
Mestre

Since the completion of the sequencing of the human genome at the turn of the century, genomics has revolutionized the study of biology and medicine by providing high-throughput and quantitative methods for measuring molecular activities. Microarray and next generation sequencing emerged as important inflection points where the rate of data generation skyrocketed. The high dimensionality nature and the rapid growth in the volume of data precipitated a unique computational challenge in massive data analysis and interpretation. Noise and signal structure in the data varies significantly across types of data and technologies; thus, the context of the data generation process itself plays an important role in detecting key and oftentimes subtle signals. In this dissertation, we discuss four areas where contextualizing the data aids discoveries of disease-causing variants, complex relationships in the human microecology, interplay between gene and environment, and genetic regulation of gene expression. These studies, each in its own unique way, have helped made possible discoveries and expanded the horizon of our understanding of the human body, in health and disease.

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology; and, (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2005.
Includes bibliographical references.
The promise of personalized medicine lies in its potential to fundamentally change healthcare. In the past, pharmaceuticals were prescribed on a "one size fits all" basis-patients with certain disease phenotypes were given what were thought to be appropriate drugs. There is growing evidence however that the effectiveness of these drugs may differ by individual and by sub-group; presumably due to fundamental genetic differences in disease and metabolic pathways. Drugs like Herceptin, Gleevec and Iressa are part of an emerging trend in the biopharmaceutical arena of drugs that are accompanied by genetic diagnostic tests and prescribed only for patients with genotypes in which the agents are most effective.
by Chad D. Holland.
S.M.M.O.T.
S.M.

Introduction: Chronic myeloid leukemia (CML) is a white blood cells cancer, which is characterized by a BCR-ABL fusion gene. The disease it is caused by a reciprocal translocation between chromosome 9 and 22, t(9; 22)(q34; 11) commonly known as the Philadelphia chromosome (Ph), resulting in an abnormally BCR-ABL tyrosine kinase, which is responsible for the pathogenesis of CML. The high efficacy of the tyrosine kinase inhibitors (TKIs) in the treatment of CML has caused the need for sensitive methods to monitor the course of therapy. Quantification of BCR-ABL transcripts with qRT-PCR Real-Time has demonstrated to be the most accurate method available. Following the European LeukemiaNet recommendations, the lack of initial response can be detected only after 3-6 months from the diagnosis. The ability to understand how patients respond to the different TKIs available as first-line treatment at the moment of diagnosis would help clinicians to prescribe more patient-tailored therapy, decreasing the onset of future drug resistance and decreasing treatment cost. Materials and Methods: We have developed and validated two devices (RQ-BCR-ABL p210 One-Step and RQ-BCR- ABL p190 One-Step) for monitoring therapy of CML. The RQ-BCR-ABL p210 One-Step kit has undergone a further external validation in three reference laboratories, belonging to LabNet network.?For what concern the prevision of therapy’s response the University of Verona has developed LeukoPredict, an in-vitro device to screen the inhibitory potential of several BCR- ABL-targeting drugs and to obtain the percentage of inhibition compared to the same non- treated samples. We took part to this project in the framework of industrial planning, performing a Freedom to Operate analysis and a Cost-Effectiveness analysis. Results: Both kits based in qRT-PCR Real-Time One-Step have showed high reproducibility and high sensitivity in quantification of BCR-ABL transcripts, proving to be suitable for CE-IVD mark. Moreover, RQ-BCR-ABL p210 One-Step kit has been verified by the LabNet network as a suitable device for the monitoring of CML, improving the reproducibility regarding the current system used in routine.?The Freedom to Operate analysis of LeukoPredict has found some close prior patents documents, but none could hinder entry into the market. The Cost-Effectiveness analysis has demonstrated that LeukoPredict is either cost saving or very cost-effective, depending on the scenario analyzed, generating significant savings for health systems. Conclusions: This project is able to connect actual principal issues in CML. On one side we have developed and validated two devices that completely satisfy actual request of therapeutic monitoring in pharmaceutical market making a complete panel to track CML. The develop of devices as LeukoPredict helps to decrease the risk of disease’s progression to more aggressive phase, personalizing the therapy and obtaining the maximum effectiveness of therapeutical choices. This can help physicians in an evidence based decisional therapeutic process, avoiding potential conflict of interest and giving a rational explanation to other kind of treatment when the risk of failure is too high. Finally, it has been considered as a technology that can be affordable and that could contain the cost of healthcare.
Introduzione: La Leucemia Mieloide Cronica (LMC) è un disordine mieloproliferativo delle cellule staminali pluripotenti caratterizzato per la presenza del gene di fusione BCR-ABL. Questo disturbo è causato da una traslocazione reciproca di materiale genetico tra il cromosoma 9 e 22 t(9; 22)(q34; 11) comunemente riconosciuta come cromosoma Philadelphia (Ph), che porta alla formazione di una proteina tirosinchinasica con un’attività alterata, causante della patogenesi della LMC. L’elevata efficacia degli inibitori della tirosinchinasa (TKIs) nel trattamento della LMC ha originato la necessità di metodi molto sensibili per monitorare il corso terapeutico. La quantificazione dei trascritti BCR-ABL con qRT-PCR Real Time si è dimostrato il metodo disponibile più accurato al giorno di oggi. Secondo le raccomandazioni degli esperti appartenenti alla European LeukemiaNet, la mancanza di risposta iniziale può essere rilevata solo dopo 3-6 mesi dal momento della diagnosi. La capacità di comprendere come i pazienti rispondono ai diversi TKIs disponibili nel momento della diagnosi aiuterebbe ai medici a prescrivere la terapia di prima linea più conveniente in ogni caso, riducendo l’insorgere di una futura resistenza ai farmaci e riducendo così i costi del trattamento. Materiali e Metodi: Abbiamo sviluppato e validato due dispositivi (RQ-BCR-ABL p210 One-Step e RQ-BCR- ABL p190 One-Step) per il monitoraggio terapeutico della LMC. Il kit RQ-BCR-ABL p210 One-Step ha subito un’ulteriore validazione esterna in tre diversi centri di riferimento per la LMC appartenenti alla rete italiana LabNet. Per quanto riguarda alla previsione della risposta terapeutica, l’Università degli Studi di Verona ha sviluppato LeukoPredict, un dispositivo in-vitro per individuare il potenziale inibitorio di diversi farmaci che hanno BCR-ABL come bersaglio, ottenendo il percentile d’inibizione rispetto agli stessi campioni non trattati. Abbiamo partecipato a questo progetto nell’ambito della pianificazione industriale, eseguendo un’analisi Freedom to Operate e un’analisi costo-efficacia sul prodotto. Risultati: Entrambi I kit basati sulla tecnologia qRT-PCR Real-Time One-Step hanno mostrato una elevata riproducibilità e un’alta sensibilità nella quantificazione dei trascritti BCR-ABL, dimostrando di essere adatti alla marcatura CE-IVD. Inoltre, il kit RQ-BCR-ABL p210 One-Step è stato certificato dalla rete LabNet come un dispositivo adatto per il monitoraggio della LMC, migliorando notevolmente la riproducibilità dei sistemi attuali utilizzati nella routine. L’analisi Freedom to Operate di LeukoPredict ha rilevato alcuni brevetti relazionati con la tecnologia presente nel dispositivo, ma nessuno potrebbe ostacolare la sua uscita nel mercato in questo momento. L’analisi costo-efficacia ha dimostrato che LeukoPredict ha un costo ridotto ed è molto conveniente a livello economico secondo lo scenario analizzato, generando rilevanti risparmi per i sistemi sanitari. Conclusioni: Questo progetto è in grado di collegare le problematiche attuali della LMC. Da un lato abbiamo sviluppato e validato due dispositivi che soddisfano completamente le richieste del mercato farmaceutico per il monitoraggio terapeutico, proporzionando un panello completo per la gestione della LMC. Lo sviluppo di dispositivi come LeukoPredict aiuta a diminuire il rischio di progressione della malattia verso fasi più aggressive, personalizzando la terapia e ottenendo la massima efficacia delle diverse scelte terapeutiche. Ciò aiuterebbe ai medici nella decisione della miglior terapia basandosi sull’evidenza, evitando potenziali conflitti d’interesse e fornendo una spiegazione razionale ad altri tipi di trattamento quando il rischio di fallimento terapeutico è troppo alto. Infine, la sua tecnologia è stata definita come molto conveniente potendo contribuire a contenere il costo dell’assistenza sanitaria.

Around 12-15% of patients with locally advanced rectal cancer (LARC) undergo a pathologically complete response (Tumour Regression Grade 4 - TRG4) to neoadjuvant chemoradiotherapy; the remainder exhibit a spectrum of tumour regression (TRG1-3). Understanding therapy-related genomic alterations may help us better predict response, progression-free and overall survival, and also identify both novel and repurposed treatment strategies based on the underlying biology of the disease. The Northern Ireland Biobank provided 48 formalin fixed paraffin embedded (FFPE) rectal cancer biopsies and matched resections following neoadjuvant therapy (discovery cohort). These were analysed using high-throughput gene expression microarray, DNA mutational profiling and microsatellite instability profiling. Differential gene expression analysis (analysis of variance) was performed contrasting tumour regression grades in both biopsies and resections to identify predictive and therapy related features. Real time PCR was utilised for microarray validation while immunohistochemistry (IHC) was employed to measure CD56+ cell populations in an independent (validation) cohort (n=150). A NK cell-like gene expression signature was observed following long course chemoradiotherapy in a tumour regression-dependent manner. CD56+ NK cel, populations were measured by IHC and found to be significantly higher in TRG3 patients. Furthermore, it was observed that patients positive for CD56 ceils after therapy had a better overall survival (HR=0.282, 95%C,=0.109-0.729, x2=7.854, p=.OO5). In silico drug selection using QUADrATiC analysis identified clinically relevant therapeutic FDA-approved compounds based upon the NK cell-like signature. We demonstrated that identifying an independently validated predictive signature from biopsies for LARC patients treated with LCPCRT was not possible. However, we identified a novel post-therapeutic NK-like transcription signature in patients responding to neoadjuvant chemoradiotherapy. Furthermore, CD56 positive patients had better overall survival. Therefore, harnessing an NK-like response after therapy may improve outcomes for locally advanced rectal cancer patients.

A wide variety of formulations have been explored in attempt to improve the apparent aqueous solubility of poorly-water soluble drugs such as oil-in-water nanoemulsions (NE), oil-in-water microemulsions (ME) and drug nanoparticles (NPs). There would be advantage gained if two poorly-water soluble drugs could be combined in a single formulation, particularly with respect to the ability to personalise a patient’s medicine. In this study therefore, two novel combination formulations, which we have termed a “nanosusponanoemulsion (NSNE)” and a “nanosuspomicroemulsion (NSME)”, consisting of either NE or ME containing a low dose drug and NP comprised of a high dose drug, respectively have been studied. The particular aim of this study is to prove the principal that, by the rational design of NSNE and NSME, it is possible to prepare combination formulations suitable for the delivery of two poorly-water soluble drugs for use in personalised medicine. Studies involved the preparation and physico-chemical characterization of NSNE and NSME prepared from the mixing of NP, prepared by wet bead milling and stabilised by the anionic surfactant, sodium dodecyl sulphate (SDS), and either NE stabilised by the nonionic surfactant, Brij 97, and containing the triglyceride oil, glyceryl trioctanoate (TON) or a ME stabilised by SDS and containing either ethyl butyrate (EB) or ethyl caprylate (EC). Testosterone propionate (TP) was used as the low-dose model drug and was solubilised in the NE and ME, whilst griseofulvin (GF) was the high-dose model drug used to prepare the NPs. A range of physico-chemical techniques were used to characterize the individual systems, namely the NP, NE and ME, as well the NSNE and the NSME and included UV spectroscopy, photon correlation spectroscopy (PCS) as well as small angle neutron scattering (SANS) which was used to individually monitor the in situ stability of the individual components of the NSNE and NSME. Significantly in the combined formulations, some of the GF from the NPs was solubilised in the NE and the ME. In addition, while the solubility of TP in the NE remained constant in the presence of the SDS-stabilised GF-NPs, the amount of TP in the ME decreased upon contact with the GF-NP, suggesting that the GF displaced some of the TP molecules in the ME. PCS studies showed that the particle size of GF-NP, when in the form of a NSNE, i.e. in contact with the NE initially increased in size but thereafter remained relatively stable whilst the particle size of GF-NP in the form of a NSME remained unchanged. On the other hand, the SANS studies indicated that the TP-containing NE with a low amount of TON were stable for at least 24 hours contact with the GF-NP when in the form of a NSNE. These results suggest that the NSNE is more suitable than the NSME for the administration of two poorly water-soluble drugs in a single formulation for use in personalised medicine.

In neuropsychiatry, the development of brain imaging and dedicated data analysis for personalized medicine promises to predict both the evolution of diseases and responses of treatments. The ability to estimate the time course of the disease is the first step to understand the response to potential treatments, which implies the development of methods able to capture subject-specific features in addition to the discrimination between pathological conditions. However, methods that effectively characterize the neuronal activity at the whole-brain level are still lacking, and many efforts are currently made in the fields of clinical research and neuroscience to fill this gap. The above is particularly problematic to interpret functional Magnetic Resonance Imaging (fMRI) data, which are indirectly coupled with neuronal activity because of hemodynamics, yielding much slower signals than neuronal activity. We propose a multiscale method that combines a computational whole-brain model with machine learning to solve this issue. In our approach, the model relates the neuronal activity and the fMRI signals in a mechanistic fashion, allowing for access to neuronal activity down to millisecond precision. Specifically, we use a novel methodology that allows the extraction of space-time motifs at different timescales through binned time windows. Then, we use machine learning to study which range of timescales in the modeled neuronal activity is most informative to separate the brain's dynamics during rest, distinguishing subjects, tasks, and neuropsychiatric conditions. Our multiscale computational approach is a further step to study the multiple timescales of brain dynamics and predict the dynamical interactions between brain regions. Overall, this method raises outlooks to detect biomarkers and predict responses of treatments.
En neuropsiquiatría, el desarrollo de imágenes cerebrales y el análisis de datos dedicados a la medicina personalizada prometen predecir tanto la evolución de las enfermedades como las respuestas a los tratamientos. La capacidad de estimar el curso temporal de la enfermedad es el primer paso para comprender la respuesta a posibles tratamientos, lo que implica el desarrollo de métodos capaces de capturar características específicas del sujeto, además de la discriminación entre condiciones patológicas. Sin embargo, todavía faltan métodos que caractericen eficazmente la actividad neuronal a nivel de todo el cerebro, y actualmente se están haciendo muchos esfuerzos en los campos de la investigación clínica y la neurociencia. Lo anterior es particularmente problemático para interpretar los datos funcionales de las imágenes de resonancia magnética (fMRI por sus siglas en inglés), que están acoplados indirectamente con la actividad neuronal debido a la hemodinámica, lo que produce señales mucho más lentas que la actividad neuronal. En este trabajo, proponemos un método multiescala que combina un modelo computacional de cerebro completo con aprendizaje automático para resolver este problema. En nuestro enfoque, el modelo relaciona la actividad neuronal y las señales de resonancia magnética funcional de manera mecanicista, lo que permite el acceso a la actividad neuronal con una precisión de milisegundos. Específicamente, utilizamos una nueva metodología que permite la extracción de patrones espacio-temporales en diferentes escalas temporales a través de ventanas de tiempo. Después, usamos aprendizaje automático para estudiar qué rango de escalas de tiempo en la actividad neuronal modelada es más informativo, para separar la dinámica del cerebro durante el descanso, distinguiendo sujetos, tareas y condiciones neuropsiquiátricas. Nuestro enfoque computacional multiescala es un paso más para estudiar las múltiples escalas de tiempo de la dinámica del cerebro y predecir las interacciones dinámicas entre las regiones del cerebro. En general, este método aumenta las perspectivas para detectar biomarcadores y predecir la respuesta de tratamientos.

Introduction: Pharmacokinetic variability in response to pharmacotherapy contribute to adverse drug reactions, drug-drug interaction and therapeutic failure seen in clinical practice. Poor therapeutic response to medication has been attributed to inter-individual and interethnic variability in cytochrome P450 (CYP450)-dependent metabolism and altered drug absorption via expressed transport channels such as P-glycoprotein (P-gp). An individualised approach in therapeutic management would be beneficial in a South-African population considering the country’s large genetic diversity. A single time point, non-invasive capillary sampling, combined with a low dose probe drug cocktail, to simultaneously quantify in vivo drug and metabolite concentrations, would enhance the feasibility and cost-effectiveness of routine phenotyping in clinical practice and guide personalised prescribing to individual patients. A recent development in dried blood spot sampling is the Mitra™ device, using Volumetric Absorptive Micro Sampling (VAMS™) technology to collect an accurate volume (10-30 µL) of whole blood onto a hydrophilic polymeric tip as an alternative to plasma sampling. Small volume blood sampling however presents bioanalytical challenges in terms of the reproducibility and sensitivity of the quantitative method and the agreement between quantitative measurement from a dried blood spot (DBS) and that from plasma sampling. The physicochemical diversity of the structurally related aromatic probe drugs, used together in a drug cocktail, further require optimised analytical procedures for simultaneous quantification. Phenotyping cocktails are compounded from commercially available dosage forms and introduce challenges with regards to dosage homogeneity, chemical interference or degradation and possible incompatibilities of drugs when used in combination. Aim and objectives: The purpose of this study was to compound the validated “Geneva phenotyping drug cocktail”, from available API sources and develop a validated, targeted, analytical LC-MS/MS method to quantify the seven probe drugs and six respective metabolites in dried blood spots when using the Mitra™ volumetric absorptive micro-sampling device for blood collection. The aim was to assess inter-method agreement of the measured probe drug and metabolite concentrations between the low sample volume, from a dried blood spot, and conventional plasma sampling. Methods: An Agilent binary series LC system coupled to a Sciex 4000 QTRAP triple quadrupole tandem mass spectrometer was used for method optimisation and validation. Targeted LC-MS/MS methods, in both negative and positive ESI mode, were validated according to ICH guidelines for matrix effects, recovery, linearity, limits of quantitation and detection, carry-over, inter and intraday precision and accuracy and analyte stability. The selectivity of the structurally related ionisable analytes was compared between a Kinetex C18 and Kinetex Biphenyl column and the influence of changes in the analytical conditions (involving mobile phase pH and solvent mixture composition as well as the solvent type) studied. An initial assessment of statistical in vitro agreement between plasma and DBS sampling were carried out. USP assays were performed to determine the weight and content uniformity of the compounded phenotyping cocktail containing six of the seven probe drugs. Content uniformity was evaluated with an Acquity UPLC system coupled to a Synapt G2 QTOF mass spectrometer. Results and discussion: A biphenyl stationary phase in combination with methanol as the organic eluent, provided improved resolution and analyte selectivity of the structurally related aromatic compounds. Results from the robustness experiment further confirmed the importance of controlling analytical conditions to ensure reproducibility and reliability of the quantitative method. Separation selectivity and higher throughput were prioritised over optimised ionisation efficiency, although the sensitivity of the analytical method for individual analytes were still within the expected in vivo concentration ranges to infer metabolic and transport phenotypes. This study successfully validated the use of DBS, collected with the volumetrically controlled absorptive microsampling device Mitra™, to measure expected probe drug and metabolite concentrations using the “Geneva phenotyping cocktail”. The validated method met all the required standards accepted in bioanalytical chemistry for specificity, sensitivity, linearity, accuracy, precision, carry-over and stability. From the initial in vitro assessment of agreement, it was concluded that blood cell distribution kinetics are regulated by the blood-to-plasma concentration ratio and time dependent equilibrium between different blood compartments, the physicochemical properties of the analytes, temperature during extraction, analyte concentration and stability. A conclusive confounding factor was the extent to which the extraction procedure liberated bound drug from either plasma proteins or erythrocytes. It was further concluded that the compounded low dose phenotyping cocktail capsules could be used successfully to assess inter-method agreement of drug-based metabolic ratios and drug transport between plasma and DBS collected with the Mitra™ device. Conclusion: To our knowledge, this is the first DBS validation study using the Mitra™ device for the purpose of simultaneous phenotyping of the in vivo P-gp transport and CYP450 metabolic activity of the CYP1A2, -2B6, -2C9, -2C19, -2D6 and -3A4 enzymes and activity.
Thesis (PhD)--University of Pretoria, 2019.
National Research Foundation
Pharmacology
PhD (Pharmacology)
Unrestricted

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 205-215).
When we try to apply statistical learning in real-world applications, we frequently encounter data which include missing and uncertain values. This thesis explores the problem of learning from missing and uncertain data with a focus on applications in personalized medicine. In the first chapter, we present a framework for classification when data is uncertain that is based upon robust optimization. We show that adding robustness in both the features and labels results in tractable optimization problems for three widely used classification methods: support vector machines, logistic regression, and decision trees. Through experiments on 75 benchmark data sets, we characterize the learning tasks for which adding robustness provides the most value. In the second chapter, we develop a family of methods for missing data imputation based upon predictive methods and formal optimization.
We present formulations for models based on K-nearest neighbors, support vector machines, and decision trees, and we develop an algorithm OptImpute to find high quality solutions which scales to large data sets. In experiments on 84 benchmark data sets, we show that OptImpute outperforms state-of-the-art methods in both imputation accuracy and performance on downstream tasks. In the third chapter, we develop MedImpute, an extension of OptImpute specialized for imputing missing values in multivariate panel data. This method is tailored for data sets that have multiple observations of the same individual at different points in time. In experiments on the Framingham Heart Study and Dana Farber Cancer Institute electronic health record data, we demonstrate that MedImpute improves the accuracy of models predicting 10-year risk of stroke and 60-day risk of mortality for late-stage cancer patients.
In the fourth chapter, we develop a method for tensor completion which leverages noisy side information available on the rows and/or columns of the tensor. We apply this method to the task of predicting anti-cancer drug response at particular dosages. We demonstrate significant gains in out-of-sample accuracy filling in missing values on two large-scale anticancer drug screening data sets with genomic side information.
by Colin Pawlowski.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Precision healthcare, also known as personalised medicine, is based on our understanding of the fundamental building blocks of biological systems, with the ultimate aim to clinically identify the best therapeutic strategy for each individual. Genomics and sequencing technologies have brought this to the foreground by enabling an individual’s entire genome to be mapped for less than a thousand dollar in just one day. Recently, metabolomics, the quantitative measurement of small molecules, has emerged as a field to understand an individual’s molecular profile in terms of both genetics and environmental factors. This is crucial because a genome could only indicate an individual’s susceptibility to a particular disease, whereas a metabolome provides an immediate measurement of body function, enabling a means of diagnosis. However, the current approach of measurements depends on large-scale and expensive equipment such as mass spectroscopy and NMR instrumentation, which does not offer a single analytical platform to detect the entire metabolome. This thesis describes the development of an integrated CMOS sensor array technology as a single platform to quantify different metabolites using specific enzymes. The key stages in the work were: to construct instrumentation systems to perform enzyme assays on the CMOS sensor array; to establish techniques to package the CMOS sensor array for an aqueous environment; to implement and develop a room temperature Ta2O5 sputtering process on CMOS sensor array for hydrogen ion detection; to collaborate with a chemist and investigate an inorganic layer on top of the CMOS ISFET sensor to show an improvement of sensitivity towards potassium ion; to test several different enzyme assays electrochemically and optically and show the functionalities of the sensors; to devise microfluidic channels for segregation of the sensor array into different compartments and perform enzyme immobilisation techniques on CMOS chips; and integrate the packaged chip with microfluidic channels and enzyme immobilisation using 2D inkjet printer into a complete system that has the potential to be used as a multi-enzyme platform for detection of different metabolites. Two CMOS sensor array chips (1) a 256×256-pixel ISFET array chip and (2) a 16×16-pixel Multi-Corder chip were fully understood. Therefore, a high-speed instrumentation system was constructed for the ISFET array chip with a maximum readout speed of 500 frames per second, with 2D and 3D imaging capability, as well as single pixel analysis. Follow by that, a miniaturised measurement platform was implemented for the Multi-Corder chip that has three different sensor arrays, which are ISFET, PD and SPAD. All the sensor arrays can be operated independently or together (ionic sensor and one of the optical sensors). Several post-processing steps were investigated to allow suitable fabrication process on small 4×4 mm2 CMOS chips. Post-processing of the CMOS chips was first established using room temperature sputtering process for Ta2O5 layer, achieving Ta:O ratio of 1:1.77 and a surface roughness of 0.42 nm. This Ta2O5 layer was then fabricated on top of CMOS ISFETs, which improves the ISFET pH sensitivity to 45 mV/pH, with an average drift of 6.5 ± 8.6 mV/hour from chip to chip and a working pH range of 2 to 12. Furthermore, a layer of POMs was drop casted on top of Ta2O5 ISFET to make ISFET sensitive to potassium ions. This was investigated in terms of potassium ions sensitivity, hydrogen ions sensitivity and sodium ions as interfering background ions. The POMs Ta2O5 ISFET was found to have a net potassium sensitivity of 75 mV/pK, with a linear range between pH 1.5 to 3. Moreover, the POMs ISFET has -5 mV/pH in pH sensitivity, showing that it is selectivity towards potassium ions and not hydrogen ions. However, sodium ions were found to produce a large interference towards the pK sensitivity of POMs ISFET and reduced the pK sensitivity of POMs ISFET. Hence, further work is still required to modify POMs layer for better selectivity and sensitivity. Besides that, microfluidic channels were fabricated on top of the CMOS chips that could provide segregation for multiple enzyme assays on a single chip. In addition, a PDMS and a manual dam and fill method were developed to encapsulate the CMOS chips for wet biochemistry measurements. The CMOS sensor array was found to have the ensemble averaging capability to reduce noise as a function of √N , where N is the number of sensors used for averaging. Several enzyme assays that include: hexokinase, lactate dehydrogenase, urease and lipase were tested on the ISFET sensor array. Moreover, using an optical sensor array, namely a PD on the Multi-Corder chip and using LED illumination, quantification of cholesterol levels in human blood serum was demonstrated. Enzyme kinetics calculations were performed for hexokinase and cholesterol oxidase assays and the results were comparable to that obtained from a bench top spectrophotometer. This shows the CMOS sensor array can be used as a low cost portable diagnostic device. Several enzyme immobilisation techniques were explored but were unsuccessful. Alginate enzyme gel immobilisation with a 2D inkjet printer was found to be the best candidate to bio-functionalise the CMOS sensor array. The packaged chip was integrated with microfluidic channels and alginate enzyme gel immobilisation into a complete system, in order to perform an enzyme assay with its control experiments simultaneously on a single chip. As a proof-of-concept, this complete system has the potential to be used as a multiple metabolite quantification platform.

Alterations of CDKL5 give rise to several forms of neurological disorders generally characterised by epileptic encephalopathy, severe developmental delay, hypotonia and RTT-like features. To date no cure exists and only secondary symptoms can be treated. About 15% of CDKL5 patients carry a nonsense mutation and might benefit of a readthrough strategy as “personalised” medicine approach. The read-through process occurs when a near-cognate aminoacyl-tRNA binds a premature stop codon (PTC), allowing its suppression and the subsequent protein elongation. This mispairing event can rarely occur, but can be facilitated using a wide range of drugs. In order to test PTC suppression, we have chosen some human pathogenic CDKL5 nonsense mutations located in the two main domains of the protein: the catalytic N-terminus (R59X, R134X) or the C-terminal tail (Q347X, E364X, R550X, S855X). We then evaluated the read-through process using aminoglycoside and non-aminoglycoside drugs in cells transfected with the mutagenized constructs. In this study, we have demonstrated that tested CDKL5 PTCs can be suppressed by gentamicin and geneticin (G418) in a dose-dependent manner and that PTC position can be critical for read-through. In particular, G418 was found to be more effective than gentamicin. Considering the known aminoglycosides toxicity, we evaluated the activity of PTC124 and GJ072 but no PTC suppression was detectable in our experimental conditions. Finally, in order to understand whether the full-length derivatives may maintain the proper function of WT CDKL5, we analysed some features of read-through products compared to the WT protein. In particular, while premature truncated proteins showed an altered subcellular localisation, read-through products demonstrated a nucleo-cytoplasmic distribution more similar to the WT one. Moreover, by evaluating the auto-phosphorylation of the TEY motif, the read-through derivatives demonstrated to recover some catalytic activity, although remaining highly hypomorphic. Nevertheless, preliminary studies on Cdkl5-null neurons transfected with R134X construct suggested that G418 treatment can ameliorate impaired neuronal morphology. Collectively, our results indicate that: (i) aminoglycosides are able to induce read-through of different CDKL5 PTCs; (ii) the read-through derivatives recover some features characterizing the WT protein; (iii) PTC position can be crucial for read-though and for rescue of a proper function and (iv) neuronal morphological defects might be rescued by small amount of a possible hypomorphic CDKL5, therefore supporting the potential validity of a read-through therapy.

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.

Relatório de Estágio do Mestrado Integrado em Ciências Farmacêuticas apresentado à Faculdade de Farmácia
“Right drug, right patient, right moment, right space, and right dose” is a key concept of the current personalized medicine.Due to the limitations of the standard diagnostic and therapeutic strategies, the term “personalized medicine” has recently emerged as a promising way to optimize treatment for each patient, considering the interindividual variability in the therapeutic response to achieve maximal efficacy along with a high safety profile.Theranostics, the integration of therapeutic and diagnostic capability in a single system, may significantly contribute to the ever-growing field of personalized medicine.Nanoparticles are one of the possible agents of theranostics that, due to their high loading capacity, are able to combine target therapeutic and diagnostic functions into a single system.Polymers, dendrimers, liposomes, polymeric micelles, silica nanoparticles, iron oxide nanoparticles, gold nanoparticles and quantum dots, have been previously investigated and are candidate nanoplatforms for building up nanoparticle-based theranostics.The clinical application of nanotheranostics will enable earlier detection, treatment of diseases and monitoring of therapeutic response, which is expected to play a significant role in the era of personalized medicine.In this monograph, the recent advances in molecular imaging technologies and theranostic nanoparticles will be described, exhibiting the advantages and drawbacks of each imaging modality and nanoparticles used in theranostics. Finally, the use of theranostic nanomedicines for early diagnosis and therapy of glioblastoma will be addressed.
“O fármaco certo, para o doente certo, no momento certo e na dose certa”, é um conceito-chave da medicina personalizada.Devido às limitações das estratégias convencionais de diagnóstico e tratamento surgiu recentemente o termo “medicina personalizada” com o objetivo de otimizar o tratamento, específico para cada doente, considerando a variabilidade interindividual na resposta à terapêutica para atingir a máxima eficácia com um elevado perfil de segurança.A teranóstica, que combina o diagnóstico e terapêutica num único sistema, pode contribuir significativamente para o desenvolvimento da medicina personalizada.Um dos possíveis agentes de teranóstico são as nanopartículas que, devido à sua elevada capacidade de carga, podem combinar funções de diagnóstico e terapêutica num único sistema.Nanopartículas poliméricas, dendrímeros, lipossomas, micelas poliméricas,nanopartículas de sílica, nanopartículas de óxido de ferro, nanopartículas de ouro e pontos quânticos têm sido investigados e são possíveis “nanoplataformas” para o desenvolvimento de nanopartículas teranósticas.A aplicação da nanoteranóstica na clínica permite a deteção precoce, o tratamento de doenças, bem como a monitorização da resposta ao tratamento, desempenhando assim um papel importante na medicina personalizada.Nesta monografia serão descritos os avanços recentes na tecnologia de imagem molecular e nanopartículas teranósticas, referindo as vantagens e desvantagens de cada modalidade de imagem, bem como das nanopartículas utilizadas na teranóstica. Por fim, será abordada a aplicação de nanopartículas teranósticas no diagnóstico precoce e terapia do glioblastoma.

Relatório de Estágio do Mestrado Integrado em Ciências Farmacêuticas apresentado à Faculdade de Farmácia
Cancer is a collective term which comprehends a multiplicity of diseases, that, in spite of being different amongst each other, occur in the same form, more specifically through the uncontrolled invasive growth of cells which are not recognized by the Natural-Killer T-Lymphocytes of the immune system. Nowadays, there are thousands of people worldwide who live with a diagnosis of cancer, which makes the development of new treatments, such as immuno-oncology, an unquestionable necessity.In order to accomplish the development of new therapeutic methods, it is essential to proceed to the correct identification of therapeutic targets, that is to say the identification of genetic mutations in the tumor, which represent an important source of neo-antigens. Therefore, there has been a significant progress on several methods which aid the prognostic and development of specific treatments for each patient, through techniques such as next-generation sequencing, proteomics, flux cytometry, mass spectrometry and bioinformatics.This monography aims to present the recent advances of investigation in the context of the utilization of personalized mRNA vaccine technology in different types of cancer, recognizing its tremendous therapeutic potential.The clinical trials developed demonstrated that the mRNA vaccines show a positive safety and tolerability profile, as well as some implications on the capacity to induce cellular and humoral immune responses against countless antigens. Currently, there are several clinical trials taking place, which are not centered exclusively on the monotherapy with mRNA vaccines, but also on the conjugation of combined therapy, especially checkpoint-inhibitors.It is important to emphasize that every development aims to transform the complexity of cancer in its major vulnerability, unveiling a new Era of Personalized Medicine, as an emergent necessity in multiple cancer patients to which other pharmacological options are not satisfactory.
O cancro corresponde a um termo coletivo que abrange uma multiplicidade de doenças, que apesar de diferentes entre si, surgem da mesma forma, mais especificamente, através de um crescimento celular descontrolado e invasivo, não reconhecido pelos linfócitos T natural killer do sistema imunitário. Atualmente, existem a nível mundial, milhões de pessoas que vivem com um diagnóstico de cancro, pelo que o desenvolvimento de novas terapêuticas, como a imuno-oncologia, constitui uma necessidade inquestionável. Para o desenvolvimento de novas terapêuticas é fundamental a identificação correta de alvos terapêuticos, ou seja, a identificação das mutações genéticas tumorais que constituem uma fonte importante de neo-antigénios. Estão, por isso, a emergir significativamente vários métodos que auxiliam o prognóstico e desenvolvimento de terapêuticas específicas para cada paciente, através da sequenciação de próxima geração, proteómica, citometria de fluxo, espetrometria de massa e bioinformática.A presente monografia procura apresentar os recentes avanços inovadores das investigações no âmbito da utilização da tecnologia de vacinas personalizadas de mRNA em diferentes tipos de cancro, reconhecendo o seu enorme interesse e potencial terapêutico. Os ensaios clínicos desenvolvidos demonstraram que as vacinas de mRNA apresentam um perfil de segurança e tolerabilidade favorável, e implicações na capacidade de induzir respostas imunitárias celulares e humorais contra inúmeros antigénios. Atualmente, estão a decorrer vários ensaios clínicos que não se centram apenas na monoterapia com vacinas de mRNA, mas também na conjugação de terapêuticas combinadas sobretudo com checkpoint-inhibitors.É de realçar que todos os desenvolvimentos pretendem transformar a complexidade do cancro na sua maior vulnerabilidade, inaugurando uma nova Era da Medicina Personalizada, como necessidade emergente em múltiplos doentes cancerígenos na qual outras opções farmacológicas não apresentam respostas satisfatórias.