Development of a physiologically based pharmacokinetic model of actinomycin D in children with cancer

AIMS: Use of the anti‐tumour antibiotic actinomycin D is associated with development of hepatotoxicity, particularly in young children. A paucity of actinomycin D pharmacokinetic data make it challenging to develop a sound rationale for defining dosing regimens in younger patients. The study aim was to develop a physiologically based pharmacokinetic (PBPK) model using a combination of data from the literature and generated from experimental analyses. METHODS: Assays to determine actinomycin D Log P, blood:plasma partition ratio and ABCB1 kinetics were conducted. These data were combined with physiochemical properties sourced from the literature to generate a compound file for use within the modelling‐simulation software Simcyp (version 14 release 1). For simulation, information was taken from two datasets, one from 117 patients under the age of 21 and one from 20 patients aged 16–48. RESULTS: The final model incorporated clinical renal and biliary clearance data and an additional systemic clearance value. The mean AUC(0‐26h) of simulated subjects was within 1.25‐fold of the observed AUC(0‐26h) (84 ng h ml(−1) simulated vs. 93 ng h ml(−1) observed). For the younger age ranges, AUC predictions were within two‐fold of observed values, with simulated data from six of the eight age/dose ranges falling within 15% of observed data. Simulated values for actinomycin D AUC(0‐26h) and clearance in infants aged 0–12 months ranged from 104 to 115 ng h ml(−1) and 3.5–3.8 l h(−1), respectively. CONCLUSIONS: The model has potential utility for prediction of actinomycin D exposure in younger patients and may help guide future dosing. However, additional independent data from neonates and infants is needed for further validation. Physiological differences between paediatric cancer patients and healthy children also need to be further characterized and incorporated into PBPK models

Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy

<p>Abstract</p> <p>Background</p> <p>Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.</p> <p>Methods</p> <p>Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.</p> <p>Results</p> <p>The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.</p> <p>Conclusion</p> <p>The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.</p

Schematic of three-tier system architecture of hospital pharmacotherapy decision support system comprising a back end database tier, a business logic middle tier and data presentation/user interface front-end tier

<p><b>Copyright information:</b></p><p>Taken from "Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy"</p><p>http://www.biomedcentral.com/1472-6947/8/6</p><p>BMC Medical Informatics and Decision Making 2008;8():6-6.</p><p>Published online 28 Jan 2008</p><p>PMCID:PMC2254609.</p><p></p