Browsing by Author "Hannemann, Robert"

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    Accumulation of high magnitude acceleration events predicts cerebrovascular reactivity changes in female high school soccer athletes
    (Springer, 2018) Svaldi, Diana O.; Joshi, Chetas; McCuen, Emily C.; Music, Jacob P.; Hannemann, Robert; Leverenz, Larry J.; Nauman, Eric A.; Talavage, Thomas M.; Neurology, School of Medicine
    Mitigating the effects of repetitive exposure to head trauma has become a major concern for the general population, given the growing body of evidence that even asymptomatic exposure to head accelerations is linked with increased risk for negative life outcomes and that risk increases as exposure is prolonged over many years. Among women's sports, soccer currently exhibits the highest growth in participation and reports the largest number of mild traumatic brain injuries annually, making female soccer athletes a relevant population in assessing the effects of repetitive exposure to head trauma. Cerebrovascular biomarkers may be useful in assessing the effects of repetitive head trauma, as these are thought to contribute directly to neurocognitive symptoms associated with mild traumatic brain injury. Here we use fMRI paired with a hypercapnic breath hold task along with monitoring of head acceleration events, to assess the relationship between cerebrovascular brain changes and exposure to repetitive head trauma over a season of play in female high school soccer athletes. We identified longitudinal changes in cerebrovascular reactivity that were significantly associated with prolonged accumulation to high magnitude (> 75th percentile) head acceleration events. Findings argue for active monitoring of athletes during periods of exposure to head acceleration events, illustrate the importance of collecting baseline (i.e., pre-exposure) measurements, and suggest modeling as a means of guiding policy to mitigate the effects of repetitive head trauma.
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    Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization
    (Public Library of Science, 2015) Jayachandran, Devaraj; Laínez-Aguirre, José; Rundell, Ann; Vik, Terry; Hannemann, Robert; Reklaitis, Gintaras; Ramkrishna, Doraiswami; Department of Pediatrics, IU School of Medicine
    6-Mercaptopurine (6-MP) is one of the key drugs in the treatment of many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP is a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed in the TPMT enzyme produces a significant variation in drug response among the patient population. Despite 6-MP's widespread use and observed variation in treatment response, efforts at quantitative optimization of dose regimens for individual patients are limited. In addition, research efforts devoted on pharmacogenomics to predict clinical responses are proving far from ideal. In this work, we present a Bayesian population modeling approach to develop a pharmacological model for 6-MP metabolism in humans. In the face of scarcity of data in clinical settings, a global sensitivity analysis based model reduction approach is used to minimize the parameter space. For accurate estimation of sensitive parameters, robust optimal experimental design based on D-optimality criteria was exploited. With the patient-specific model, a model predictive control algorithm is used to optimize the dose scheduling with the objective of maintaining the 6-TGN concentration within its therapeutic window. More importantly, for the first time, we show how the incorporation of information from different levels of biological chain-of response (i.e. gene expression-enzyme phenotype-drug phenotype) plays a critical role in determining the uncertainty in predicting therapeutic target. The model and the control approach can be utilized in the clinical setting to individualize 6-MP dosing based on the patient's ability to metabolize the drug instead of the traditional standard-dose-for-all approach.