Prediction and Evaluation of Hepatic Bile Acid Transporter-Mediated Drug Interactions Using In Vitro Tools and Pharmacokinetic Modeling

The objective of this doctoral dissertation research was to develop novel strategies to predict and evaluate drug interactions with hepatic bile acid transporters. Sandwich-cultured hepatocytes (SCH) and mechanistic pharmacokinetic modeling were employed. Altered disposition of the model bile acid taurocholate (TCA) in human SCH due to inhibition of multiple transporters was predicted based on the potency of inhibitors [e.g., inhibition constant (Ki)] and kinetic parameters of TCA using pharmacokinetic modeling. The accuracy of predictions using total and unbound inhibitor concentrations was assessed. The effect of bosentan and telmisartan (model inhibitors) was predicted adequately by using intracellular unbound concentrations of the inhibitors. In subsequent studies, a simulation-based method was proposed to determine the relevant inhibitor concentration when predicting the effect of hepatic efflux transporter inhibition. For inhibitors with high plasma protein binding and/or high relative inhibition potency, using intracellular unbound rather than total inhibitor concentrations was optimal. The utility of this method was evaluated using experimental data from human SCH. To circumvent the limitations of individual transporter Ki data, a model-based approach was proposed to obtain overall Ki values against each efflux clearance pathway (i.e., biliary and basolateral efflux clearance) of TCA in rat SCH. The study design was optimized using modeling and simulation to estimate Ki values of troglitazone sulfate (the model inhibitor). Using this study design, Ki estimation in different hepatocyte lots, and limitations on the accuracy, were evaluated using simulated data. In addition to inhibition, transporter induction by Farnesoid X Receptor agonists was investigated in human SCH. Basolateral efflux and biliary clearance values for TCA, determined by pharmacokinetic modeling, were significantly increased by the Farnesoid X Receptor agonists, obeticholic acid and chenodeoxycholic acid. These studies provided direct functional evidence for transporter induction. Immunoblot analysis results suggested that organic solute transporter alpha/beta may be the primary transporter responsible for the increase in the basolateral efflux clearance of TCA. This research leveraged pharmacokinetic modeling and simulation to integrate and interpret in vitro bile acid transport data. The approaches developed and the results detailed in this dissertation will improve the accuracy of predictions and mechanistic understanding of drug-bile acid interactions.Doctor of Philosoph

Machine Learning Methods for Magnetic Resonance Imaging Analysis.

The study of the brain and its connection to human activities has been of interest to scientists for centuries. However, it is only in recent years that medical imaging methods have been developed to allow a visualization of the brain. Magnetic Resonance Imaging (MRI) is such a technique that provides a noninvasive way to view the structure of the brain. Functional MRI (fMRI) is a special type of MRI, measuring the neural activity in human brain. The aim of this dissertation is to apply machine learning methods to functional and anatomical MRI data to study the connection between brain regions and their functions. The dissertation is divided into two parts. The first part is devoted to the analysis of fMRI. A standard fMRI study produces massive amount of noisy data with strong spatio-temporal correlation. Existing methods include a model-based approach which assumes spatio-temporal independence and a data-driven method which fails to exploit the experimental design. In this work we propose a Gaussian process model to incorporate the temporal correlation through a model-based approach. We validate the method on simulated data and compare the results to other methods through real data analysis. The second part covers the analysis of anatomical MRI. Anatomical MRI provides a detailed map of brain structure, especially useful for detecting small anatomical changes as a result of disease process. The goal of anatomical MRI analysis is to train an automated classifier that can identify the patients from healthy controls. We propose a multiple kernel learning classifier which will build classifiers in small regions in the segregating step and then group them in the integrating step. We study the performance of the new method using simulated data and demonstrate the power of our classifier on disease-related data.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/96103/1/gcen_1.pd

Differential variational-hemivariational inequalities: existence, uniqueness, stability, and convergence

The goal of this paper is to study a comprehensive systemcalled differential variational–hemivariational inequality which is com-posed of a nonlinear evolution equation and a time-dependentvariational–hemivariational inequality in Banach spaces. Under the gen-eral functional framework, a generalized existence theorem for differ-ential variational–hemivariational inequality is established by employ-ing KKM principle, Minty’s technique, theory of multivalued analysis,the properties of Clarke’s subgradient. Furthermore, we explore a well-posedness result for the system, including the existence, uniqueness, andstability of the solution in mild sense. Finally, using penalty methods tothe inequality, we consider a penalized problem-associated differentialvariational–hemivariational inequality, and examine the convergence re-sult that the solution to the original problem can be approached, as aparameter converges to zero, by the solution of the penalized problem

Building galaxies by accretion and in-situ star formation

We examine galaxy formation in a cosmological AMR simulation, which includes two high resolution boxes, one centered on a 3 \times 10^14 M\odot cluster, and one centered on a void. We examine the evolution of 611 massive (M\ast > 10^10M\odot) galaxies. We find that the fraction of the final stellar mass which is accreted from other galaxies is between 15 and 40% and increases with stellar mass. The accreted fraction does not depend strongly on environment at a given stellar mass, but the galaxies in groups and cluster environments are older and underwent mergers earlier than galaxies in lower density environments. On average, the accreted stars are ~2.5 Gyrs older, and ~0.15 dex more metal poor than the stars formed in-situ. Accreted stellar material typically lies on the outskirts of galaxies; the average half-light radius of the accreted stars is 2.6 times larger than that of the in-situ stars. This leads to radial gradients in age and metallicity for massive galaxies, in qualitative agreement with observations. Massive galaxies grow by mergers at a rate of approximately 2.6% per Gyr. These mergers have a median (mass-weighted) mass ratio less than 0.26 \pm 0.21, with an absolute lower limit of 0.20, for galaxies with M\ast ~ 10^12 M\odot. This suggests that major mergers do not dominate in the accretion history of massive galaxies. All of these results agree qualitatively with results from SPH simulations by Oser et al. (2010, 2012).Comment: 18 pages, 12 figures, submitted to MNRA

On the Robustness of Safe Reinforcement Learning under Observational Perturbations

Safe reinforcement learning (RL) trains a policy to maximize the task reward while satisfying safety constraints. While prior works focus on the performance optimality, we find that the optimal solutions of many safe RL problems are not robust and safe against carefully designed observational perturbations. We formally analyze the unique properties of designing effective state adversarial attackers in the safe RL setting. We show that baseline adversarial attack techniques for standard RL tasks are not always effective for safe RL and proposed two new approaches - one maximizes the cost and the other maximizes the reward. One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward. We further propose a more effective adversarial training framework for safe RL and evaluate it via comprehensive experiments. This paper provides a pioneer work to investigate the safety and robustness of RL under observational attacks for future safe RL studies.Comment: 30 pages, 4 figures, 8 table

Constrained Decision Transformer for Offline Safe Reinforcement Learning

Safe reinforcement learning (RL) trains a constraint satisfaction policy by interacting with the environment. We aim to tackle a more challenging problem: learning a safe policy from an offline dataset. We study the offline safe RL problem from a novel multi-objective optimization perspective and propose the $\epsilon$-reducible concept to characterize problem difficulties. The inherent trade-offs between safety and task performance inspire us to propose the constrained decision transformer (CDT) approach, which can dynamically adjust the trade-offs during deployment. Extensive experiments show the advantages of the proposed method in learning an adaptive, safe, robust, and high-reward policy. CDT outperforms its variants and strong offline safe RL baselines by a large margin with the same hyperparameters across all tasks, while keeping the zero-shot adaptation capability to different constraint thresholds, making our approach more suitable for real-world RL under constraints.Comment: 15 pages, 7 figure

Paeoniflorin inhibits the growth of bladder carcinoma via deactivation of STAT3

Bladder cancer (BCa) is one of the most common urinary cancers. The present study aims to investigate whether Paeoniflorin (Pae) can exert inhibitory effects on BCa. The results showed that Pae inhibited proliferation of human BCa cell lines in a concentration- and time-dependent manner. Pae and cisplatin (Cis) synergistically inhibited the growth of tumours in RT4-bearing mice. Pae treatment neutralized the body loss induced by Cis. Moreover, Pae induced apoptosis in RT4 cells and increased the activities of caspase3, caspase8 and caspase9. Western blotting and immunohistochemical analysis revealed that the phosphorylated signal transducer and activator of transcription-3 (p-STAT3) level were decreased in Pae-treated RT4 cells and Pae-treated tumour-bearing mice. Furthermore, STAT3 transcriptional target B-cell lymphoma-2 was decreased in Pae-treated RT4 cells. Interestingly, Pae prevented translocation of STAT3 to the nucleus in RT4 cells. Collectively, Pae inhibits the growth of BCa, at least in part, via a STAT3 pathway