As malaria, caused by Plasmodium spp., continues to afflict millions of people worldwide, there is a dire need for the discovery of novel, inexpensive antimalarial drugs. Although there are effective drugs on the market, the consistent development of drug resistant species has decreased their efficacy, further emphasizing that novel therapeutic measures are urgently needed. Natural products provide the most diverse reservoir for the discovery of unique chemical scaffolds with the potential to effectively combat malarial infections, but, due to their complex structures, they often pose extreme challenges to medicinal chemists during pharmacokinetic optimization. In our laboratory we have performed unbiased, cell-based assays of numerous synthetic compounds from chemical libraries enriched with nature-like elements. This screening has led to the discovery of many original chemical scaffolds with promising antimalarial properties. In an attempt to further characterize these scaffolds, the most promising compounds were assayed in order to determine their cytotoxic effects on mammalian cells. In addition, the development of a drug resistant parasite line of Plasmodium falciparum to the most promising compound was done in order to determine the relative probability for parasite resistance development.

Crisis management simulation is important in training the next generation of surgeons. In this review, we highlight our experiences with the cavernous carotid injury model. We then delve into other crisis simulation models available for the neurosurgical specialty. The discussion focuses upon how these trainings can continue to evolve. Much work is yet to be done in this exciting arena and we present several avenues for future discovery. Simulation continues to be an important training tool for the surgical learner.

Lung cancer (LC) is the leading cause of cancer death in the US, claiming over 160,000 lives annually. Although CT screening has been shown to be efficacious in reducing mortality, the limited access to screening programs among high-risk individuals and the high number of false positives contribute to low survival rates and increased healthcare costs. As a result, there is an urgent need for preventative therapeutics and novel interception biomarkers that would enhance current methods for detection of early-stage LC. This thesis addresses this challenge by examining the hypothesis that transcriptomic changes preceding the onset of LC can be identified by studying bronchial premalignant lesions (PMLs) and the normal-appearing airway epithelial cells altered in their presence (i.e., the PML-associated airway field of injury). PMLs are the presumed precursors of lung squamous cell carcinoma (SCC) whose presence indicates an increased risk of developing SCC and other subtypes of LC. Here, I leverage high-throughput mRNA and miRNA sequencing data from bronchial brushings and lesion biopsies to develop biomarkers of PML presence and progression, and to understand regulatory mechanisms driving early carcinogenesis. First, I utilized mRNA sequencing data from normal-appearing airway brushings to build a biomarker predictive of PML presence. After verifying the power of the 200-gene biomarker to detect the presence of PMLs, I evaluated its capacity to predict PML progression and detect presence of LC (Aim 1). Next, I identified likely regulatory mechanisms associated with PML severity and progression, by evaluating miRNA expression and gene coexpression modules containing their targets in bronchial lesion biopsies (Aim2). Lastly, I investigated the preservation of the PML-associated miRNAs and gene modules in the airway field of injury, highlighting an emergent link between the airway field and the PMLs (Aim 3). Overall, this thesis suggests a multi-faceted utility of PML-associated genomic signatures as markers for stratification of high-risk smokers in chemoprevention trials, markers for early detection of lung cancer, and novel chemopreventive targets, and yields valuable insights into early lung carcinogenesis by characterizing mRNA and miRNA expression alterations that contribute to premalignant disease progression towards LC. / 2020-01-25

Cellular Thermal Shift Assay (CETSA) is a method of identifying protein-drug interactions by monitoring changes in protein thermal stability. CETSA is traditionally performed by using Western Blotting to examine the thermal stability shifts of a single protein of interest. By combining CETSA with Mass Spectrometry the shifts in thermal stability can be examined for an entire proteome in a single experiment in a technique known as CETSA-MS or Thermal Proteome Profiling (TPP). This can be used to identify targets of a compound of interest in order to further understand the compounds mechanism of interest, potentially making CETSA a powerful tool for target discovery. Here we attempt to develop a protocol by which CETSA can be used as a drug target discovery tool. Our work has allowed us to create a protocol that can reliably identify soluble drug targets. Our results demonstrate the capacity of CETSA to screen multiple compounds as well as to perform more in depth dose response studies, and highlight how future improvements could be made to the protocol

The ease or difficulty in solving a problemstrongly depends on the way it is represented. For example, consider the task of multiplying the numbers 12 and 24. Now imagine multiplying XII and XXIV. Both tasks can be solved, but it is clearly more difficult to use the Roman numeral representations of twelve and twenty-four. Humans excel at finding appropriate representations for solving complex problems. This is not true for artificial systems, which have largely relied on humans to provide appropriate representations. The ability to autonomously construct useful representations and to efficiently exploit them is an important challenge for artificial intelligence. This dissertation builds on a recently introduced graph-based approach to learning representations for sequential decision-making problems modeled as Markov decision processes (MDPs). Representations, or basis functions, forMDPs are abstractions of the problem’s state space and are used to approximate value functions, which quantify the expected long-term utility obtained by following a policy. The graph-based approach generates basis functions capturing the structure of the environment. Handling large environments requires efficiently constructing and utilizing these functions. We address two issues with this approach: (1) scaling basis construction and value function approximation to large graphs/data sets, and (2) tailoring the approximation to a specific policy’s value function. We introduce two algorithms for computing basis functions from large graphs. Both algorithms work by decomposing the basis construction problem into smaller, more manageable subproblems. One method determines the subproblems by enforcing block structure, or groupings of states. The other method uses recursion to solve subproblems which are then used for approximating the original problem. Both algorithms result in a set of basis functions from which we employ basis selection algorithms. The selection algorithms represent the value function with as few basis functions as possible, thereby reducing the computational complexity of value function approximation and preventing overfitting. The use of basis selection algorithms not only addresses the scaling problem but also allows for tailoring the approximation to a specific policy. This results in a more accurate representation than obtained when using the same subset of basis functions irrespective of the policy being evaluated. To make effective use of the data, we develop a hybrid leastsquares algorithm for setting basis function coefficients. This algorithm is a parametric combination of two common least-squares methods used for MDPs. We provide a geometric and analytical interpretation of these methods and demonstrate the hybrid algorithm’s ability to discover improved policies. We also show how the algorithm can include graphbased regularization to help with sparse samples from stochastic environments. This work investigates all aspects of linear value function approximation: constructing a dictionary of basis functions, selecting a subset of basis functions from the dictionary, and setting the coefficients on the selected basis functions. We empirically evaluate each of these contributions in isolation and in one combined architecture.

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学位プログラム名: 京都大学大学院思修館 / 京都大学 / 新制・課程博士 / 博士(総合学術) / 甲第23345号 / 総総博第18号 / 新制||総総||3(附属図書館) / 京都大学大学院総合生存学館総合生存学専攻 / (主査)教授 山敷 庸亮, 教授 杉山 弘, 教授 積山 薫 / 学位規則第4条第1項該当 / Doctor of Philosophy / Kyoto University / DGAM

Gene therapy has emerged as a revolutionary approach for treating genetic disorders, holding great promise for improving patient outcomes. Among the various viral vectors used for delivery of therapeutic transgenes, Adeno-Associated Viruses (AAVs) have gained prominence due to their favorable characteristics including low immunogenicity, long-term gene expression, and the ability to target both dividing and non-dividing cells. However, AAV’s are associated with the high costs of production and challenges with production of a high-quality virus, limiting AAV’s utilization and widespread use. In this study, we aimed to develop a high-throughput screening assay targeting AAV production enhancers, thus addressing the manufacturing obstacles and advancing the affordability and accessibility of gene therapies. To help overcome the limitations and expenses associated with AAV manufacturing, an innovative high-throughput screening assay was developed with the intent to identify cell culture additives/conditions which maximize AAV production. We optimized various parameters, including the transgene, producer and reporter cell lines, harvest timings and methods, and transduction techniques. The optimized screening assay was employed to evaluate novel compounds across several timings of addition, for their ability to enhance AAV production. Notably, several compounds indicated transfection enhancing capabilities up to 3.4-fold and the developed assays final variability was below 14%. Additionally, compound combinations were assessed to uncover potential additive and synergistic effects that could further enhance AAV productivity. In conclusion, our study presents a significant advancement in targeting the manufacturing challenges associated with AAV. By utilizing an optimized high-throughput screening assay, researchers and manufacturers can identify compounds that enhance AAV production, paving the way for cost-effective and scalable manufacturing processes. Ultimately, this progress holds the potential to improve the affordability, accessibility, and impact of gene therapies for patients worldwide.

In 2020, cancer impacted an estimated 1.8 million people and result in over 600,000 deaths in the United States. Some cancer treatments options are limited due to drug resistance, requiring additional drug development to improve patient survival rates. It is necessary to continuously develop new therapeutic approaches and identify novel targets, as cancer is ever-growing and adapting. Experimental research strategies have limitations when exploring how to target certain protein classes, including membrane-embedded or protein-protein bound, due to the complexity of their environments. These two domains of research are experimentally challenging to explore, and in silico research practices provide insight that would otherwise take years to study. Computer-aided drug design (CADD) routes can support the areas of drug discovery that are considered difficult to explore with experimental techniques. In this work, we provide research practices that are easily adaptable and translatable to other difficult protein targets and interfaces. First, we identified the morphological impact of a single-site mutation in the G-protein coupled receptor (GPCR), OR2T7, which had been identified as a novel prognostic marker for glioblastoma. Next, we explored the blockbuster target, Programmed Cell Death Protein 1 – (PD-1) and the agonistic vs antagonistic response that can be exploited for Non-Small Cell Lung Cancer (NSCLC) therapeutic development. Last, we explored the sphingolipid transport protein, Spns2, which has been demonstrated to be important in regulating the metastatic cancer enabling microenvironment. This work utilized molecular dynamics simulations (MDS) to explore the protein structure-function relationship for each protein of interest, allowing for the exploration of biophysical properties and protein dynamics. We identified that the D125V mutation in OR2T7 likely influences activation of the MAPK pathway by impacting G-protein binding via reducing the helical plasticity in the TM6 and TM7 regions. PD-1 was identified to have a domain near the PD-L1 binding interface that increases β-sheet stability and increases residue-residue distances with the membrane-proximal region within PD-1, thus leading to an active conformation. Lastly, Spns2 was identified to follow a rocker-switch transport model and provided preliminary insight into sphingolipid-Spns2 channel binding, interacting with residues Thr216, Arg227, and Met230, as well as highlighting the role of Arg119 in a salt-bridge network of interactions essential in substrate translocation. Collectively, this work illustrates the advantages of computational workflows in the drug discovery process and provides a framework that can be applied for additional GCPRs, transport proteins, or protein-protein interfaces to enhance and accelerate the CADD research. / Doctor of Philosophy / Cancer is an ever-evolving disease that requires continuous development of new treatment options. Experimental research strategies can be timely, expensive, or lack atomistic insight into drug development processes. Computer-aided drug design (CADD) routes provide research strategies to support areas of drug discovery that can be difficult to explore with experimental techniques. Membrane-bound proteins and protein-protein interfaces are two domains of research that are typically difficult to explore, and computational research practices provide insight that would otherwise take years to study. In this work, we provide research practices that are easily adaptable and translatable to other difficult protein targets and interfaces. First, we identified the impact of a single-site mutation in the G-protein coupled receptor (GPCR), OR2T7, which had been identified as a novel prognostic marker for glioblastoma. Next, we explored the blockbuster target, Programmed Cell Death Protein 1 – (PD-1) and active vs inactive states that can be exploited for Non-Small Cell Lung Cancer (NSCLC) therapeutic development. Last, we explored the sphingolipid transport protein, Spns2, which has been demonstrated to be important in metastatic cancer growth. This work utilized molecular dynamics simulations (MDS) to explore the protein structure-function relationship for each protein of interest, allowing for the exploration of biophysical properties and protein movement. We identified that the D125V mutation in OR2T7 likely influences activation of the MAPK pathway, which supports multiple cancer-regulation pathways, by impacting G-protein binding via reducing the structural flexibility. PD-1 was identified to have a domain near the PD-L1 binding interface that increases structural stability, thus leading to an upregulation of cancer survival pathways. Lastly, Spns2 analysis provided insight into movement involved in sphingolipid transport, provided preliminary insight into sphingolipid-Spns2 binding, as well as highlighting the role of Arg119 in a network of interactions essential in substrate translocation. Collectively, this work highlights the usefulness of computational workflows in the drug discovery process and provides a framework that can be utilized for additional GPCRs, transport proteins, or protein-protein interfaces to enhance and accelerate the CADD research.

Doctor of Philosophy / Startups can and do fail. For an entrepreneur, product developer, or researcher with a physical and capital-intensive product idea, this dissertation can serve as a resource to bridge the gaps between business, engineering, and design and reduce the risk of failure when trying to create a startup. The process described in this dissertation describes how to evaluate the key elements of an idea and conduct a series of interviews with potential customers to find evidence that supports pursing that idea further, challenge the startup team to change some aspect of the idea, or drop it altogether. Once the startup team has found a problem, as well as a solution to that problem, this dissertation describes an approach creating that solution. Then this dissertation describes an approach for critically evaluating the foundational elements of the problem and the solution. The goal for a critical evaluation is to identify additional foundational elements which relate to the product that may increase its value or decrease the risk of product failure.