Personalized medicine can be an approach to address the unsatisfactory treatment outcomes in inflammatory conditions such as cancer, arthritis, and cardiovascular diseases. A common feature of chronic diseases is the infiltration of pro-inflammatory macrophages at the disease loci. Infiltrating macrophages have been previously utilized for disease diagnosis. These features suggest that macrophages can be broadly applicable targets for simultaneous therapy and diagnosis. Cyclooxygenase-2 (COX-2), an enzyme involved in the biosynthesis of a lipid inflammatory mediator, prostaglandin E2 (PGE2), is over expressed in macrophages infiltrating the pathological site. Inhibition of PGE2 leads to reduced inflammation, pain and macrophage infiltration. To utilize macrophages for the purpose of simultaneous therapy and diagnosis, we proposed to integrate therapeutic and imaging capabilities on a single nanomedicine platform, referred as theranostics. A stable 19F MRI visible nanoemulsion platform was developed, incorporating celecoxib for COX-2 inhibition and near-infrared fluorescent dye(s) for fluorescence imaging. We hypothesized that inhibition of COX-2 in macrophages using a theranostic nanoemulsion will reduce the inflammation (and pain), and that this response can be visualized by monitoring changes in macrophage infiltration. In vitro characterization demonstrated that the theranostic displays excellent stability with no toxicity, and significant uptake in macrophages. Furthermore, it delivers celecoxib to macrophages and reduces PGE2 production from these cells. In vivo studies in a murine paw inflammation model showed nanoemulsion presence at the inflamed site, specifically in COX-2 expressing macrophages compared to neutrophils. Supporting our hypothesis, celecoxib delivered through a nanoemulsion demonstrated time-dependent reduction in fluorescence from the inflamed paw, indicative of reduced macrophage infiltration. In a neuropathic pain model, celecoxib delivered to macrophages led to reduced pain concomitant with reduced macrophage infiltration at the inflamed site compared to free drug control (cross reference: Kiran Vasudeva, Dissertation, 2015). In conclusion, inhibition of COX-2 in macrophages using theranostic nanoemulsions proves to be an effective and generalized strategy facilitating simultaneous therapy and diagnosis, which can be applied to many chronic diseases. The diagnostic information during therapy can be used to tailor the treatment and reduce patient variability leading to personalized medicine. / Mylan School of Pharmacy and the Graduate School of Pharmaceutical Sciences; / Pharmaceutics / PhD; / Dissertation;

Charles University in Prague Faculty of Pharmacy in Hradec Králové Department of Pharmacology & Toxicology Student: Jana Jeřábková Supervisor: Doc. PharmDr. Petr Pávek, Ph.D. Title of diploma thesis: Interaction of selected anthocyanidins with farnesoid X receptor Human farnesoid X receptor (FXR) is a member of nuclear receptor superfamily that act as ligand-activated transcription factors. FXR binds to specific regulatory DNA regions and induces expression of many target genes. These regulated genes are involved in bile acid metabolism and transport, maintaining blood lipids, liporoteins and glucose homeostasis and also contribute to maintain intestinal bacterial balance, hepatoprotection and liver regeneration. The interest of recent studies is to test the range of FXR ligands for treatment and prevention of many diseases such as cholestais, cholesterol gallstone disease, steato-hepatitis, dyslipidemia, atherosclerosis, type 2 diabetes mellitus, metabolic syndrome, liver cancer and other forms of cancer such as breast cancer. In this experimental diploma thesis we are focused on testing of potencial ligands of human farnesoid X receptor from the group of natural plant pigments anthocyanidins (cyanidin, delphinidin, malvidin, pelargonidin, peonidin and petunidin) using the human hepatoma cell line...

Personalized cancer medicine is a cancer care paradigm in which diagnostic and therapeutic strategies are customized for individual patients. Microsystems that are created by Micro-Electro-Mechanical Systems (MEMS) technology and integrate various diagnostic and therapeutic methods on a single chip hold great potential to enable personalized cancer medicine. Toward ultimate realization of such microsystems, this thesis focuses on developing critical functional building blocks that perform genetic variation identification (single-nucleotide polymorphism (SNP) genotyping) and specific, efficient and flexible cell manipulation on microfluidic surfaces. For the identification of genetic variations, we first present a bead-based approach to detect single-base mutations by performing single-base extension (SBE) of SNP specific primers on solid surfaces. Successful genotyping of the SNP on exon 1 of HBB gene demonstrates the potential of the device for simple, rapid, and accurate detection of SNPs. In addition, a multi-step solution-based approach, which integrates SBE with mass-tagged dideoxynucleotides and solid-phase purification of extension products, is also presented. Rapid, accurate and simultaneous detection of 4 loci on a synthetic template demonstrates the capability of multiplex genotyping with reduced consumption of samples and reagents. For cell manipulation, we first present a microfluidic device for cell purification with surface-immobilized aptamers, exploiting the strong temperature dependence of the affinity binding between aptamers and cells. Further, we demonstrate the feasibility of using aptamers to specifically separate target cells from a heterogeneous solution and employing environmental changes to retrieve purified cells. Moreover, spatially specific capture and selective temperature-mediated release of cells on design-specified areas is presented, which demonstrates the ability to establish cell arrays on pre-defined regions and to collect only specifically selected cell groups for downstream analysis. We also investigate tunable microfluidic trapping of cells by exploiting the large compliance of elastomers to create an array of cell-trapping microstructures, whose dimensions can be mechanically modulated by inducing uniform strain via the application of external force. Cell trapping under different strain modulations has been studied, and capture of a predetermined number of cells, from single cells to multiple cells, has been achieved. In addition, to address the lack of aptamers for targets of interest, which is a major hindrance to aptamer-based cell manipulation, we present a microfluidic device for synthetically isolating cell-targeting aptamers from a randomized single-strand DNA (ssDNA) library, integrating cell culturing with affinity selection and amplification of cell-binding ssDNA. Multi-round aptamer isolation on a single chip has also been realized by using pressure-driven flow. Finally, some perspectives on future work are presented, and strategies and notable issues are discussed for further development of MEMS/microfluidics-based devices for personalized cancer medicine.

This work takes us a step closer to realizing personalized medicine, complementing empirical and heuristic way in which clinicians typically work. This thesis presents mechanistic models of physiology. These models, given continuous signals from a patient, can be fine-tuned via parameter estimation methods so that the model's outputs match the patient's. We thus obtain a virtual patient mimicking the patient at hand. Therapeutic scenarios can then be applied and optimal diagnosis and therapy can thus be attained. As such, personalized medicine can then be achieved without resorting to costly genetics. In particular we have developed a novel comprehensive mathematical model of the cardiopulmonary system that includes cardiovascular circulation, respiratory mechanics, tissue and alveolar gas exchange, as well as short-term neural control. Validity of the model was proven by the excellent agreement with real patient data, under normo-physiological as well as hypercapnic and hypoxic conditions, taken from literature. As a concrete example, a submodel of the lung mechanics was fine-tuned using real patient data and personalized respiratory parameters (resistance, R_rs, and compliance, C_rs) were estimated continually. This allows us to compute the patient's effort (Work of Breathing), continuously and more importantly noninvasively. Finally, the use of Bayesian estimation techniques, which allow incorporation of population studies and prior information about model's parameters, was proposed in the contest of patient-specific physiological models. A Bayesian Maximum a Posteriori Probability (MAP) estimator was implemented and applied to a case-study of respiratory mechanics. Its superiority against the classical Least Squares method was proven in data-poor conditions using both simulated and real animal data. This thesis can serve as a platform for a plethora of applications for cardiopulmonary personalized medicine.

Mobile devices, such as smartphones and wearable devices, have become a popular platform to deliver recommendations and interact with users. To learn the decision rule of assigning recommendations, i.e. policy, neither one homogeneous policy for all users nor completely heterogeneous policy for each user is appropriate. Many attempts have been made to learn a policy for making recommendations using observational mobile health (mHealth) data. The majority of them focuses on a homogeneous policy, that is a one-fit-to-all policy for all users. It is a fair starting point for mHealth study, but it ignores the underlying user heterogeneity. Users with similar behavior pattern may have unobservable underlying heterogeneity. To solve this problem, we develop a personalized learning framework that models both population and personalized effect simultaneously. In the first part of this dissertation, we address the personalized policy learning problem using longitudinal mHealth application usage data. Personalized policy represents a paradigm shift from developing a single policy that may prescribe personalized decisions by tailoring. Specifically, we aim to develop the best policy, one per user, based on estimating random effects under generalized linear mixed model. With many random effects, we consider new estimation method and penalized objective to circumvent high-dimensional integrals for marginal likelihood approximation. We establish consistency and optimality of our method with endogenous application usage. We apply our method to develop personalized prompt schedules in 294 application users, with a goal to maximize the prompt response rate given past application usage and other contextual factors. We found the best push schedule given the same covariates varied among the users, thus calling for personalized policies. Using the estimated personalized policies would have achieved a mean prompt response rate of 23% in these users at 16 weeks or later: this is a remarkable improvement on the observed rate (11%), while the literature suggests 3%-15% user engagement at 3 months after download. The proposed method compares favorably to existing estimation methods including using the R function glmer in a simulation study. In the second part of this dissertation, we aim to solve a practical problem in the mHealth area. Low response rate has been a major issue that blocks researchers from collecting high quality mHealth data. Therefore, developing a prompting system is important to keep user engagement and increase response rate. We aim to learn personalized prompting time for users in order to gain a high response rate. An extension of the personalized learning algorithm is applied on the Intellicare data that incorporates penalties of the population effect parameters and personalized effect parameters into learning the personalized decision rule of sending prompts. The number of personalized policy parameters increases with sample size. Since there is a large number of users in the Intellicare data, it is challenging to estimate such high dimensional parameters. To solve the computational issue, we employ a bagging method that first bootstraps subsamples and then ensembles parameters learned from each subsample. The analysis of Intellicare data shows that sending prompts at a personalized hour helps achieve a higher response rate compared to a one-fit-to-all prompting hour.

Despite considerable effort considerable cost in both time and money, as many as two out of three persons with type 1 diabetes are not in control of their disease. As a result, 40% of these individuals will go on to develop at least one serious complication including retinopathy, nephropathy, neuropathy and cardiomyopathy. It is further estimated that as much as $4 billion could be saved annually if all persons with type 1 diabetes in the US were properly controlled. Adequate treatment of type 1 diabetes is predicated on the estimation of three clinical insulin therapy parameters: the basal dose, the insulin sensitivity factor and the insulin-to-carbohydrate ratio. Currently, these therapy parameters are determined by iterative titration procedures based on expert opinion. Unfortunately, there is evidence suggesting that for the majority of individuals, these titration protocols do not provide good results. In this work we develop an alternative to traditional insulin titration protocols that allows clinical insulin therapy parameters to be estimated directly from a set of easily acquired measurements. First, a simple model of type 1 diabetes is used to derive a series of equations connecting the model's parameters to the clinically important insulin therapy parameters of insulin sensitivity factor, insulin-to-carbohydrate ratio and basal insulin dose. The simplifying assumptions used to derive these equations are tested and shown to be valid and the Fisher Information Matrix is used to demonstrate parameter identifiability. Parameter estimation is then performed on two cohorts of virtual subjects, as well as two segments of real continuous glucose monitoring data from a person with type 1 diabetes. Identification of the true insulin therapy parameters is successful under most conditions for both cohorts of virtual subjects. Parameter estimation for one of the two segments of real continuous glucose monitoring data is also successful. Finally, because continuous glucose monitors are instrumental to successful implementation of our insulin therapy framework, the physiological environment in which continuous glucose monitoring takes place is modeled and a fundamental limitation on measurement precision is shown to exist. An examination of physiological variability in the parameters indicates that many of the challenges observed in real world continuous glucose monitoring may have a relationship to changes in capillary bed perfusion. A rationale for anecdotally reported sensor faults is also proposed based on the physical mechanisms explored. / text

Central to the goals of public health are obtaining and interpreting timely and relevant information for the benefit of humanity. In this dissertation, we propose methods to monitor and assess the spread HIV in a more rapid manner, as well as to improve decisions regarding patient treatment options. In Chapter 1, we propose a method, extending the previously proposed dual-testing algorithm and augmented cross-sectional design, for estimating the HIV incidence rate in a particular community. Compared to existing methods, our proposed estimator allows for shorter follow-up time and does not require estimation of the mean window period, a crucial, but often unknown, parameter. The estimator performs well in a wide range of simulation settings. We discuss when this estimator would be expected to perform well and offer design considerations for the implementation of such a study. Chapters 2 and 3 are concerned with obtaining a more complete understanding of the impact of treatment in randomized clinical trials in which multiple patient outcomes are recorded. Chapter 2 provides an illustration of methods that may be used to address concerns of both risk-benefit analysis and personalized medicine simultaneously, with a goal of successfully identifying patients who will be ideal candidates for future treatment. Riskbenefit analysis is intended to address the multivariate nature of patient outcomes, while “personalized medicine” is concerned with patient heterogeneity, both of which complicate the determination of a treatment’s usefulness. A third complicating factor is the duration of treatment use. Chapter 3 features proposed methods for assessing the impact of treatment as a function of time, as well as methods for summarizing the impact of treatment across a range of follow-up times. Chapter 4 addresses the issue of meta-analysis, a commonly used tool for combining information for multiple independent studies, primarily for the purpose of answering a clinical question not suitably addressed by any one single study. This approach has proven highly useful and attractive in recent years, but often relies on parametric assumptions that cannot be verified. We propose a non-parametric approach to meta-analysis, valid in a wider range of scenarios, minimizing concerns over compromised validity.

Mitochondrial diseases involving dysfunction of the respiratory chain are the most common inborn errors of metabolism. Mitochondria are found in all cell types besides red blood cells; consequently, patients can present with any symptom in any organ at any age. These diseases are genetically heterogeneous, and exhibit maternal, autosomal dominant, autosomal recessive and X-linked modes of inheritance. Historically, clinical genetic evaluation of mitochondrial disease has been limited to sequencing of the mitochondrial DNA (mtDNA) or several candidate genes. As human genome sequencing transformed from a research grade effort costing $250,000 to a clinical test orderable by doctors for under $10,000, it has become practical for researchers to sequence individual patients. This thesis describes our experiences in applying "MitoExome" sequencing of the mtDNA and exons of >1000 nuclear genes encoding mitochondrial proteins in ~200 patients with suspected mitochondrial disease. In 42 infants, we found that 55% harbored pathogenic mtDNA variants or compound heterozygous mutations in candidate genes. The pathogenicity of two nuclear genes not previously linked to disease, NDUFB3 and AGK, was supported by complementation studies and evidence from multiple patients, respectively. In an additional two unrelated children presenting with Leigh syndrome and combined OXPHOS deficiency, we identified compound heterozygous mutations in MTFMT. Patient fibroblasts exhibit severe defects in mitochondrial translation that can be rescued by exogenous expression of MTFMT. Furthermore, patient fibroblasts have dramatically reduced fMet-\(tRNA^{Met}\) levels and an abnormal formylation profile of mitochondrially translated \(COX_1\). These results demonstrate that MTFMT is critical for human mitochondrial translation. Lastly, to facilitate evaluation of copy number variants (CNVs), we developed a web-interface that integrates CNV calling with genetic and phenotypic information. Additional diagnoses are suggested and in a male with ataxia, neuropathy, azoospermia, and hearing loss we found a deletion compounded with a missense variant in D-bifunctional protein, \(HSD_{17}B_4\), a peroxisomal enzyme that catalyzes beta-oxidation of very long chain fatty acids. Retrospective review of metabolic testing from this patient revealed alterations of long- and very-long chain fatty acid metabolism consistent with a peroxisomal disorder. This work expands the molecular basis of mitochondrial disease and has implications for clinical genomics.

As new therapeutic targets and drugs are discovered for B-cell lymphoma and other cancers, companion diagnostics are also needed to determine target engagement, therapeutic efficacy, and patient segmentation for clinical trials. We first employed synthetic chemistry to build a platform for modifying small molecule drugs into imaging probes, using the poly(ADP-ribose) polymerase 1 (PARP1) inhibitor AZD2281 (Olaparib) as a model for technology development. Our results showed that small-molecule companion imaging drugs can be used for fluorescence imaging in cells, as well as for pharmacokinetic studies and positron emission tomography (PET) imaging in vivo, without significantly perturbing their target binding properties or cellular uptake. To apply this approach to B-cell lymphoma drugs currently in clinical trials, we modified an irreversible inhibitor of Bruton's Tyrosine Kinase (BTK), PCI-32765 (Ibrutinib), with the fluorophore Bodipy FL (BFL), and used it for imaging in cells and in a mouse window-chamber xenograft model. The excellent co-localization of our probe (Ibrutinib-BFL) with BTK demonstrated its utility for studying additional BTK inhibitors and as a companion imaging probe. In parallel, we hypothesized that central nervous system (CNS) lymphoma diagnosis from paucicellular cerebrospinal fluid (CSF) samples could be improved with molecular profiling of putative lymphoma cells trapped in a customized microfluidic chip. Following fabrication and characterization of a polydimethylsiloxane (PDMS) diagnostic device containing an array of affinity-free single-cell capture sites, we were able to efficiently recover >90% of lymphocytes, perform immunostaining on chip, and apply an image-processing algorithm to group cells based on their molecular marker expression, such as kappa/lambda light chain restriction. Additionally, in combination with Ibrutinib-BFL or other imaging drugs, we demonstrated the potential for on-chip drug imaging for use in conjunction with drug development. Finally, we applied bioorthogonal conjugation chemistries on cellulose paper for potential applications in lowering the cost of drug screening. We anticipate that these approaches will enable direct, molecular information for personalized treatment decisions in B-cell lymphomas, as well as provide a roadmap for the development of companion diagnostic probes and devices for additional indications.

Lung cancer is the leading cause of cancer-related deaths among men and women. Non-Small Cell Lung Cancer (NSCLC) constitutes the most common type of lung cancer and is frequently diagnosed at advanced stages. In the past decade, discovery of Epidermal Growth Factor Receptor (EGFR) mutations have heralded a new paradigm of personalized treatment for NSCLC. Clinical studies have shown that molecular targeted therapies increase survival and improve quality of life in patients. Despite these advances, the realization of personalized therapies for NSCLC faces a number of challenges including the integration of clinical and genetic data and a lack of clinical decision support tools to assist physicians with patient selection. This thesis demonstrates the development of a predictive computational model for personalized therapeutic interventions in advanced NSCLC. The findings suggest that the combination of clinical and genetic data significantly improves the model’s predictive performance for tumor response than clinical data alone.