The Biostatistics and Modeling core will provide centralized biostatistics and modeling services for all three projects and the pilot studies described in this proposal. The core has three main objectives: 1) to provide guidance on study design and statistical analysis, 2) to provide pharmacokinetic, pharmacodynamic and disease progress modeling support, and 3) to provide medical infomatics support for drug specific decision support systems constructed from 1 and 2. Combining our preliminary data with literature information and public domain databases has allowed us to streamline essential study design elements, justify sample size and power calculations, and develop a strategy for SNP selection. An integrated pharmacogenetics/pharmacokinetics, pharmacodynamics and disease progress modeling system has been proposed to integrate clinical and demographic variables, genetic variants, protein expression markers, drug exposure measurements, and EPC measurement in predicting clinical outcomes (i.e. efficacies and side effects) for all three projects. A web-interface will be constructed to provide model based analytics which incorporate real-time predictions regarding efficacy and side effect relationships. Ultimately, these analytics will be benchmarked against caregiver assessment to ensure that patient-specific pharmacotherapy guidance superior to current standard of care if provided. This Biostatistics and Modeling Core will organize regular meetings among biostatisticians, pharmacometricians, clinicians, and basic scientists for data analysis and predictive model construction. The services provided will include functional SNP selection, genotype/phenotype associations, and genomic and metabolic biomarker - based signature construction as predictors of clinical endpoints. The primary goal of this core is therefore to provide cutting edge analyses to accomplish the objectives of each of the 3 main projects, but the unique expertise within the core investigators: Drs Bies, Li and Barret will also be offered to other sites via an organized training curriculum and schedule of exchange visits with trainees and investigators.
This core will analyze and integrate data from the main projects and pilot studies from this proposal and will use the dense data sets so generated to test for associations between biomarker and outcome data, then generate models that can be employed in clinical settings to predict drug response in individual children.
|Stark, Julie; Renbarger, Jamie; Slaven, James et al. (2017) Glutathione-S-transferase P1 may predispose children to a decline in pulmonary function after stem cell transplant. Pediatr Pulmonol 52:916-921|
|Sierra Potchanant, Elizabeth A; Cerabona, Donna; Sater, Zahi Abdul et al. (2017) INPP5E Preserves Genomic Stability through Regulation of Mitosis. Mol Cell Biol 37:|
|Taylor, Julia F; Ott, Mary A (2016) Fertility Preservation after a Cancer Diagnosis: A Systematic Review of Adolescents', Parents', and Providers' Perspectives, Experiences, and Preferences. J Pediatr Adolesc Gynecol 29:585-598|
|Akil, Ayman; Zhang, Qing; Mumaw, Christen L et al. (2015) Biomarkers for Diagnosis and Prognosis of Sinusoidal Obstruction Syndrome after Hematopoietic Cell Transplantation. Biol Blood Marrow Transplant 21:1739-45|
|McGuire, Jennifer L; Barrett, Jeffrey S; Vezina, Heather E et al. (2014) Adjuvant therapies for HIV-associated neurocognitive disorders. Ann Clin Transl Neurol 1:938-52|
|Nalepa, Grzegorz; Barnholtz-Sloan, Jill; Enzor, Rikki et al. (2013) The tumor suppressor CDKN3 controls mitosis. J Cell Biol 201:997-1012|
|Hennessy, S; Flockhart, D A (2012) The need for translational research on drug-drug interactions. Clin Pharmacol Ther 91:771-3|