• Innovation of Vancomycin Treatment in Neonates Via A Bayesian Dose Optimization Toolkit For Adaptive Individualized Therapeutic Management

      Pastoor, Devin DeForest; Gobburu, Jogarao (2018)
      Personalized medicine continues to gain momentum as a topic for discussion, yet directly linking patient-level decision support to more advanced analytical techniques, such as nonlinear mixed effects modeling, is not being practiced in most hospitals. Current practice for Vancomycin therapy uses dosing nomograms to determine the dosing regimen for patients. For simplicity, these nomograms stratify patients into bins based on some combination of weight, serum creatinine, and/or age to adjust starting regimens. Yet, studies across the US and Europe have shown as few as 37% of neonates achieve recommended target concentrations using such nomograms. The purpose of this research was to develop a bayesian decision support toolkit to provide adaptive, individualized dose recommendations for neonates. First, a bayesian nonlinear mixed effect model was developed and qualified for predictive forecasting in individual patients. Second, this model was used to develop a novel algorithm for dose individualization. Finally, a web application was developed to allow clinicians to provide decision support for clinicians involved in vancomycin dosing decisions. The proposed strategy can decrease the number of patients improperly dosed up to 90%, drastically reducing the chance for treatment failure, toxicity-related adverse events, and resistance development.
    • Learn & Apply Paradigm to Inform Drug Development & Optimize Clinical Therapeutics in Oncology

      Mehrotra, Shailly; Gobburu, Jogarao (2017)
      Application of learn-apply paradigm in drug development and clinical therapeutics increases efficiency and supports decision making. The current research highlights the role of pharmacometrics to inform trial design and propose individualized management of chemotherapy induced peripheral neuropathy (CIPN) in oncology. The first project focuses on learning from early clinical trial of veliparib to inform future investigations. Population pharmacokinetics and exposure-response analyses were conducted to evaluate the contribution of intrinsic and extrinsic factors on veliparib PK, and assess the adequacy of veliparib dosing for the future trial. A 28% increase in AUC with mild renal impairment increases mucositis by only 7%, thus supporting the inclusion of patients with mild renal impairment in future trials without the need of dose adjustment. Exposure-response for efficacy (objective response rate and overall survival) and safety (mucositis) along with in vitro IC50 information supported 80 mg BID dose for veliparib. Multivariate exposure-response analysis provided supportive evidence to further evaluate veliparib in patients with myeloproliferative neoplasms and with 14 day treatment duration. The second project proposes a novel strategy based on precision therapeutics for the management of CIPN in clinical setting. An indirect response model with linear drug effect was able to describe the longitudinal-CIPN data reasonably well for paclitaxel, nab-paclitaxel and ixabepilone. The model was utilized to identify an early time point of 3 months that predicted later time course of CIPN (concordance probability ~ 75%). Utilizing the dose-CIPN model, a novel strategy to use patients own early CIPN data to predict their future CIPN time course was proposed. 'CIPN management dosing card' and 'CIPN precision therapeutics tool' were developed to prospectively manage CIPN in patients who may be at risk of developing CIPN later in the therapy. For paclitaxel, nab-paclitaxel and ixabepilone, the proposed CIPN management dosing card resulted in 61%, 48% and 35% fewer patients with CIPN after 6 cycles as compared to administering cycle 3 doses for 4th, 5th and 6th chemotherapy cycle. With CIPN precision therapeutics tool, oncologists can visualize the predicted CIPN time course and tailor the dosing to manage CIPN in an individual patient based on overall benefit/risk.
    • Learn and Apply Paradigm to Optimize Pharmacotherapy in Neonatal Abstinence Syndrome Using Pharmacometrics

      Liu, Tao; Gobburu, Jogarao; 0000-0002-9943-2131 (2017)
      Every one hour a baby with neonatal abstinence syndrome (NAS) is born in the United States. NAS is a clinical syndrome of opiate withdrawal in infants exposed to drugs either prenatally in the form of maternal use (non-iatrogenic), or postnatally in the form of medical therapy (iatrogenic). The syndrome is comprised of a combination of central nervous system, digestive system and autonomic system abnormalities that result from uninhibited excitatory neurotransmitter release from the neurons. Between 2000 and 2009, a 3-fold increase in the use of opiate drugs among pregnant women led to an increase in NAS and associated higher health care costs. Currently, morphine is the first line pharmacotherapy for NAS. Pharmaceutical companies have no incentive to invest in therapy optimization for NAS, and the current dosing strategies vary from hospital to hospital. This research is based on a virtual consortium between the Center for Translational Medicine at the University of Maryland Baltimore, Johns Hopkins Medical Institute and Thomas Jefferson University Hospital. The purpose of this research is to optimize the morphine dosing strategy in NAS patients who require pharmacotherapy by using a pharmacometrics approach with the "learn and apply" philosophy. First, a comprehensive morphine pharmacokinetic model that accounts for prognostic factors, such as body size and age, was developed in neonates. The results suggested a uridine diphosphate glucuronic acid dependent morphine clearance during the first week of life. Second, an exposure-response (ER) relationship between morphine plasma concentrations and modified Finnegan scores was built, and the model prediction was evaluated for the primary and secondary clinical outcomes, such time-on-treatment and total morphine dose. Lastly, different morphine dosing strategies were simulated based on the ER relationship and then optimized dosing strategies were proposed. The proposed dosing strategies will minimize suffering due to the withdrawal symptoms and ultimately lead to an earlier hospital discharge.