Browsing by Subject "precision medicine"

Now showing 1 - 3 of 3
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    Drug development and novel combination strategies with phytochemicals for precision medicine in cancer
    (2017-08) Shin, Seung Ho
    Precision medicine refers to matching the most accurate and effective treatment to each individual, and has the potential to manage diseases. In cancer, however, developing drug candidates and finding effective combination strategies are in great demand. Here, we present a framework covering drug development against a specific oncoprotein, effective combinations of drug and natural compounds, and a physiologically-achievable chemoprevention strategy. First, HI-B1 is synthesized and identified as a direct β-catenin inhibitor. A colon cancer patient-derived xenograft (PDX) model with a high level of β-catenin is sensitive to HI-B1. Second, a combination of aspirin with a ginger extract shows synergistic effect. Combining a ginger extract with aspirin treatment can significantly reduce the effective dose of aspirin while retaining its therapeutic effects in PDX mouse models. Third, multiple phytochemicals at low doses accumulatively inhibit one key protein to exert their chemopreventive and therapeutic effects. Natural ERK2 inhibitors are discovered using chemoinformatics, crystallography, cell biology and biochemistry. Each outcome could be used in a precision oncology workflow to help prevent and treat cancer efficiently.
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    Statistical Inference for Optimal Treatment Regime and Related Problems
    (2020-06) Wu, Yunan
    Precision medicine is an innovative practice for disease treatment that takes into account individual variability in genes, environment, and lifestyle for each patient. Its main aim is to estimate and make inference about the optimal treatment regime. Though many successful estimation strategies have been developed, studies on statistical inference have not attracted much attention until recently. In this thesis, we attempt to study several statistical inference problems about the optimal treatment regime and some related problems in precision medicine. My thesis is composed of three parts. In the first part, we follow a nonparametric setup to estimate the optimal treatment regime, and propose a resampling approach for inference. The estimator based on a smoothed value function significantly saves the computational cost, provides adorable theoretical properties, and ensures the validity of resampling procedures. In the second part, we adopt a semiparametric model-assisted approach, and investigate inference about the effect of a group of variables on the optimal decision rule in the high-dimensional setting. Its theoretical properties are rigorously justified, and the proposed algorithm ensures its computational efficiency. The last part introduces a new approach for estimating a high-dimensional error-in-variable regression model. It enjoys the same computational convenience of standard Dantzig estimator in the non-contamination case and requires no additional tuning parameter. Theoretically, we derive its estimation error bound. The computational efficiency of all the proposed estimation and inference procedures are demonstrated by numerical studies.
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    The Use of Artificial Intelligence for Precision Medicine in the Metabolic Syndrome
    (2019-02) Kim, Era
    Type 2 Diabetes Mellitus (T2DM) is a chronic, progressive metabolic disorder, associated with an increased risk of developing micro- and macrovascular complications. Because of its interactive and heterogeneous nature, the management of T2DM is very complex. For the successful management of T2DM, the use of individualized and evidence-based clinical guidelines is necessary. Randomized controlled trials (RCTs) are considered the gold standard for clinical research. However, the results from RCTs can be inconclusive, leaving many aspects of T2DM management unaddressed. Therefore, there exists a huge gap between the optimal individualized and the current patient care. To fill some of the gap, there are opportunities of artificial intelligence (AI) in medicine, because big data and advanced machine learning (ML) techniques offer a new way to generate evidence that enhances clinical practice guidelines with more personalized recommendations. My overarching goal is to build clinically useful and transferable machine learning models on big data that can influence individual T2DM patient care towards the implementation of precision medicine. Under this goal, I had three specific aims, which I successfully achieved. • Specific aim 1: To develop a semi-supervised divisive hierarchical clustering algorithm for a subpopulation-based T2DM risk score. • Specific aim 2: To develop a Multi-Task Learning (MTL)-based methodology to reveal outcome-specific effects by separating the overall deterioration of metabolic health from progression to individual complications. • Specific aim 3: To demonstrate that even a complex ML model built on nationally representative data can be transferred to two local health systems without significant loss of predictive performance. In the management of T2DM, which is complex, the availability of reliable clinical evidence is critical for clinicians to make the right decision and produce high-quality care in healthcare delivery. Against the backdrop of RCTs, AI in medicine can reduce the gap between optimal individualized and current T2DM patient care. And building clinically useful and transferable ML models will especially facilitate the implementation of precision medicine in T2DM.