PmWebSpec: An Application to Create and Manage CDISC-Compliant Pharmacometric Analysis Dataset Specifications.

A well-documented pharmacometric (PMx) analysis dataset specification ensures consistency in derivations of the variables, naming conventions, traceability to the source data, and reproducibility of the analysis dataset. Lack of standards in creating the dataset specification can lead to poor quality analysis datasets, negatively impacting the quality of the PMx analysis. Standardization of the dataset specification within an individual organization helps address some of these inconsistencies. The recent introduction of the Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model (ADaM) Population Pharmacokinetic (popPK) Implementation Guide (IG) further promotes industry-wide standards by providing guidelines for the basic data structure of popPK analysis datasets. However, manual implementation of the standards can be labor intensive and error-prone. Hence, there is still a need to automate the implementation of these standards. In this paper, we present PmWebSpec, an easily deployable web-based application to facilitate the creation and management of CDISC-compliant PMx analysis dataset specifications. We describe the application of this tool through examples and highlight its key features including pre-populated dataset specifications, built-in checks to enforce standards, and generation of an electronic Common Technical Document (eCTD)-compliant data definition file. The application increases efficiency, quality and semi-automates PMx analysis dataset, and specification creation and has been well accepted by pharmacometricians and programmers internally. The success of this application suggests its potential for broader usage across the PMx community.

Considerations for the prediction of survival time in pancreatic cancer based on registry data.

Semi-parametric and parametric survival models in patients with pancreatic adenocarcinoma (PC) using data from Surveillance, Epidemiology, and End Result (SEER) registry were developed to identify relevant covariates affecting survival, verify against external patient data and predict disease outcome. Data from 82,251 patients was extracted using site and histology codes for PC in the SEER database and refined based on specific cause of death. Predictors affecting survival were selected from SEER database; the analysis dataset included 2,437 patients. Survival models were developed using both semi-parametric and parametric approaches, evaluated using Cox-Snell and deviance residuals, and predictions were assessed using an external dataset from Saint Louis University (SLU). Prediction error curves (PECs) were used to evaluate prediction performance of these models compared to Kaplan-Meier response. Median overall survival time of patients from SEER data was 5 months. Our analysis shows that the PC data from SEER was best fitted by both semi-parametric and the parametric model with log-logistic distribution. Predictors that influence survival included disease stage, grade, histology, tumor size, radiation, chemotherapy, surgery, and lymph node status. Survival time predictions from the SLU dataset were comparable and PECs show that both semi-parametric and parametric models exhibit similar predictive performance. PC survival models constructed from registry data can provide a means to classify patients into risk-based subgroups, to predict disease outcome and aide in the design of future prospective randomized trials. These models can evolve to incorporate predictive biomarker and pharmacogenetic correlates once adequate causal data is established.

Evaluating performance of a decision support system to improve methotrexate pharmacotherapy in children and young adults with cancer.

The management of high-dose methotrexate (MTX) therapy in patients with cancer depends on the routine monitoring of drug exposures in conjunction with leucovorin (LV), urine pH, patient hydration, and other clinical indices of patient well-being. A key factor in patient oversight is the facilitation of MTX clearance to minimize drug-related toxicity. The aim of this investigation was to evaluate the performance of a clinical decision support system and Bayesian forecasting algorithm in the prediction of MTX concentrations and assessment of LV dosing requirements in pediatric and young adult patients with cancer based on the current practice at the Children's Hospital of Philadelphia. Fifty patients ranging in age from 8 months to 21 years (weight range, 7.6-163.3 kg) contributing 80 total dosing events (183 MTX serum concentrations) were studied. The forecasting model was able to consistently predict future MTX concentrations with the knowledge of one prior concentration and continued to improve with additional concentration data made available through daily therapeutic drug monitoring. Precision was good at 12.9% with low bias at 2.2%. Comparison between the decision support system recommendations for LV rescue relative to the actual LV administration was also made. Sixteen patients would have initiated rescue therapy earlier, seven patients would have received a larger dose (42 smaller), and LV would have been given less often for 37 patients. The forecasting algorithm in the MTX dashboard was reasonably accurate in predicting MTX concentrations and should improve further as the underlying model and prediction algorithm evolves. This decision support system can be useful in helping physicians decide if a patient is clearing MTX as expected or if more aggressive rescue therapy is warranted.

Population Pharmacokinetic Model of Sublingual Buprenorphine in Neonatal Abstinence Syndrome.

OBJECTIVE: Neonatal abstinence syndrome (NAS)--a clinical entity of infants from in utero exposure to psychoactive xenobiotic and buprenorphine--has been successfully used to treat NAS. However, nothing is known about the pharmacokinetics (PK) of buprenorphine in neonates with NAS. To our knowledge, this is the first study to investigate the population pharmacokinetic of sublingual buprenorphine in neonates with NAS. DESIGN: A retrospective population PK analysis of: (1) neonates with NAS treated with sublingual buprenorphine in randomized, double blinded clinical study and (2) data from healthy adults from a previously published pharmacokinetic study. SETTING: Neonatal intensive care unit and general clinical research unit. PATIENTS: Twenty-four neonates with NAS and five healthy adults. INTERVENTIONS: All participants received sublingual buprenorphine per study protocol. MEASUREMENTS AND MAIN RESULTS: A total of 303 PK data from 29 neonates and adults were used for model development. A population pharmacokinetic analysis was conducted using a first order conditional estimation with interaction in the NONMEM software program. A two-compartment linear PK model with first-order absorption process best described the pharmacokinetics of sublingual buprenorphine in neonates. The apparent clearance (CL) of buprenorphine was linearly related to body weight and matured with increasing age via two distinct saturated pathways. A typical neonate with NAS (body weight, 2.9 kg; postnatal age; 5.4 days) had a CL of 3.5 L/kg/hour and elimination half-life of 11 hours. Phenobarbital did not affect the clearance of buprenorphine compared to neonates of similar age and weight. CONCLUSIONS: This is the first study to investigate the population PK of sublingual buprenorphine in neonatal NAS. To our knowledge, this is also the first report to describe the age-dependent changes of buprenorphine PK in this patient population. No buprenorphine dose adjustment is needed for neonates with NAS treated with buprenorphine and concurrent phenobarbital.

Risk assessment of drug interaction potential and concomitant dosing pattern on targeted toxicities in pediatric cancer patients.

This investigation evaluated the impact of potential drug interactions on the incidence of reported toxicities seen with common dosing patterns in children with cancer, with the intent of being able to screen and reduce the incidence of adverse drug reactions (ADRs) in the future. Toxicity reported in pediatric cancer patients treated at the Children's Hospital of Philadelphia from 2004 to 2010 were abstracted from a cancer tumor registry and merged with drug order profiles from the medical record system. Analysis datasets were created in SAS and permutation algorithms were used to identify pairwise drug combinations associated with specific toxicity occurrence. Relative risk of toxicity based on dosing pattern was assessed via comparison to control patients. A total of 326 of 1,713 patients (19%) had reportable toxicities. Neutrophil count decreases and alanine aminotransferase increases represented the highest occurring, corresponding to 28.8% and 31.9% prevalence among patients reporting toxicity, respectively. Of coadministered drug pairs, acetaminophen-diphenhydramine occurred most frequently; however, methotrexate-vincristine was the highest occurring pair linked to a single toxicity (hepatotoxicity). Toxicity was highly associated with the diagnoses of leukemia (52.1%) or neuroblastoma (28.5%). Comparison of the dosing interval (≤30 versus >30 min) suggested that risk of toxicity can be associated with the timing of coadministration, with ≤30 min increasing the risk of hepatotoxicity with fentanyl-midazolam and methotrexate-midazolam combinations. Knowledge of drug interactions in children with cancer may help reduce the incidence of ADRs by providing pharmacotherapy options that may reduce the likelihood of toxicity.

Modeling and simulation approaches to evaluate pharmacokinetic sampling contamination from central venous catheters in pediatric pharmacokinetic studies of actinomycin-D: a report from the children's oncology group.

BACKGROUND: The binding of drugs to catheters can be a source variation in dosing chemotherapeutics. Drug contamination from the dosing central venous line (CVL) can impact the reporting of pharmacokinetic (PK) results and analysis. Peripheral venipuncture avoids binding complications from the CVL but dissuades patients from enrolling. Our group has developed a catheter clearing procedure to minimize the extent of contamination so that dosing and sampling from the CVL can ensue, promoting patient willingness to participate in phase I pediatric oncology trials. OBJECTIVES: To develop a population pharmacokinetic model of actinomycin-D (AMD) in children with cancer incorporating expressions for drug contamination from PK samples obtained via indwelling CVLs and to evaluate the efficiency of a catheter clearing procedure in removing contamination as well as the impact of contamination on PK results. METHODS: A dataset of 199 AMD plasma concentration measurements from 36 patients (age 1.6-20.3 years) was analyzed using nonlinear mixed-effects modeling. Quantitative modeling approaches, including baseline contamination model, covariate model, and catheter clearance model, were evaluated to describe catheter contamination. Monte Carlo simulations mimicking a prospective study in children with cancer were performed to assess the performance of the final model and impact of catheter contamination on PK reporting. RESULTS: The PK of AMD was best described by a linear 3-compartment model with first-order elimination. A baseline contamination model including a contamination factor proportional to the model-predicted concentration for samples obtained from central catheters was chosen as the most parsimonious and accurate among competing models. The final model parameters were allometrically scaled to a 70 kg person. The estimated mean parameter values were 11 L/h, 5.79, 24.2, 490 L, 17.7, and 42.8 L/h for total clearance, central volume of distribution, peripheral volume 1, peripheral volume 2, inter-compartmental clearance 1, and inter-compartmental clearance 2, respectively. The proportional contamination factor was 19.3 % immediately post-drug administration and decreased at a first-order rate of 0.0932 h(-1). Simulations precisely re-estimated kinetic parameters with catheter contamination adjustment. Large uncertainty and poor estimation were observed when contamination was ignored. CONCLUSIONS: Drug contamination from sampling catheter can impact AMD PK results and should be accounted for in the analysis. We provide a framework for evaluating catheter contamination and guidance on adjustment in the PK model.