Enhancing Methotrexate Pharmacotherapy in Children with Cancer: A Decision Support System Integrating Real-time PK/PD Modeling and Simulation with Patient Medical Records
Jeffrey S Barrett1, Sundararajan Vijayakumar2, Kalpana Vijayakumar2, Sarapee Hirankarn1, Bhuvana Jayaraman1, Erin Dombrowsky1, Mahesh Narayan1, Julia Winkler1,3, Marc Gastonguay3
1Laboratory for Applied PK/PD, Clinical Pharmacology & Therapeutics Division, The Children’s Hospital of Philadelphia; Pediatrics Department, School of Medicine, University of Pennsylvania; 2Intek Partners, Bridgewater, NJ; 3Metrum Institute,Tariffville, CT
Objectives: Methotrexate (MTX) is an anti-folate chemotherapeutic agent used in the therapy of several childhood cancers, including acute lymphoblastic leukemia, non-Hodgkin lymphoma, and osteosarcoma. Our objectives were to design an interface to the hospital's electronic medical records system facilitating the management of MTX therapy, develop a decision support system (DSS) that provides early assessment of high dose MTX renal toxicity and recommendation for leucovorin (LV) rescue, verify the outcomes of the DSS against historical controls and current best practices, and design a testing strategy for implementation.
Methods: Patient data obtained from source electronic medical records (EMR) included MTX concentrations, laboratory values and medical record number. Joined data was generated in NONMEM and SAS dataset formats and ultimately loaded into the Oracle database using SQL loader. Several generations of MTX population-based models have been evaluated and the current model is based predominantly on EMR data. The NONMEM-based Bayesian forecasting model incorporates population priors to forecast future MTX exposure events. The MTX dashboard was developed based on a three-tier architecture comprising a back end database tier, a business logic middle tier and a data presentation/user interface. The database tier consists of EMR patient data merged with data from patient registration, lab data and adverse event management systems. Predictions are conducted in an external computational platform (modeling and simulation workbench) which can execute code in a variety of languages that run in batch mode (e.g., NONMEM, SAS and R). The user interface is web-based and utilizes a combination of HTML, JavaScript and XML. Validation contained three distinct components: (1) qualification of the PPK model and forecasting algorithm derived from the model, (2) assessment of the clinical performance of clinical decisions derived from the forecasting routine and interface and (3) system validation of the dashboard integration with the EMR system.
Results: The MTX PPK model is generalizable across a broad range of pediatric patients. Clinical validation of the forecasting tool confirms the value of MTX exposure prediction and LV guidance. Screen captures and validation results show (A) the most recent MTX dose event with monitored MTX plasma concentrations and safety markers, (B) MTX exposure against the protocol-specific LV dosing nomogram, (C) MTX exposure projected after the dosing guidance menu button is selected, (D) Effect of the run number and the number of observations on the precision error of the current model in forecasting MTX concentrations and (E) representative evaluation of LV guidance nomogram overlaid with TDM and predicted data.
Conclusions: This application provides real-time views of complementary data related to the clinical care of these patients that is essential for the management of MTX therapy (e.g., urine pH, hydration, serum creatinine). Future development will provide prediction of increased risk of MTX toxicity and drug interaction potential. Clinical evaluation of the production application is ongoing; international test sites are being sought to provide additional feedback on the system.
References:
[1] Barrett JS, Mondick JT, Narayan M, Vijayakumar K, Vijayakumar S. Integration of Modeling and Simulation into Hospital-based Decision Support Systems Guiding Pediatric Pharmacotherapy. BMC Medical Informatics and Decision Making 8:6, 2008.
[2] Barrett JS, Vijayakumar K, Krishnaswami S, Gupta M, Mondick J, Jayaraman B, Muralidharan A, Santhanam S, Vijayakumar S. iClinical: NONMEM Workbench. PAGE 15, Belgium, 2006, PAGE 15 (2006) Abstr 1016 [www.page-meeting.org/?abstract=1016]
[3] Skolnik JM, Vijayakumar S, Vijayakumar K, Narayan M, Patel D, Mondick J, Paccaly D, Adamson PC, and Barrett JS. The creation of a clinically useful prediction tool for methotrexate toxicity using real-time pharmacokinetic and pharmacodynamic modeling in children with cancer. J. Clin. Pharmacol 46: 1093 (Abstr. 135), 2006
[4] Dombrowsky E, Jayaraman B, Narayan M, Barrett JS. Evaluating Performance of a Decision Support System to Improve Methotrexate Pharmacotherapy in Children with Cancer. (submitted J. Ther. Drug Monitoring)
