Dataset Information

The effect of reporting methods for dosing times on the estimation of pharmacokinetic parameters of escitalopram.

ABSTRACT: The objective of this study was to compare population pharmacokinetic models of escitalopram developed from dosage times recorded by a medication event monitoring system (MEMS) versus the reported times from patients with diagnosed depression. Seventy-three patients were prescribed doses of 10, 15, or 20 mg escitalopram daily. Sparse blood samples were collected at weeks 4, 12, 24, and 36 with 185 blood samples obtained from the 73 patients. NONMEM was used to develop a population pharmacokinetic model based on dosing records obtained from MEMS prior to each blood sample time. A separate population pharmacokinetic analysis using NONMEM was performed for the same population using the patient-reported last dosing time and assuming a steady-state condition as the model input. Objective function values and goodness-of-fit plots were used as model selection criteria. The absolute mean difference in the last dosing time between MEMS and patient-reported times was 4.48 +/- 10.12 hours. A 1-compartment model with first-order absorption and elimination was sufficient for describing the data. Estimated oral clearance (CL/F) to escitalopram was statistically insensitive to reported dosing methods (MEMS vs patient reported: 25.5 [7.0%] vs 26.9 [6.6%] L/h). However, different dosing report methods resulted in significantly different estimates on the volume of distribution (V/F; MEMS vs patient reported: 1000 [17.3%] vs 767 [17.5%] L) and the absorption rate constant K(a) (MEMS vs patient reported: 0.74 [45.7%] vs 0.51 [35.4%] h(-1)) for escitalopram. Furthermore, the parameters estimated from the MEMS method were similar to literature reported values for V/F ( approximately 1100 L) and K(a) ( approximately 0.8-0.9 h(-1)) arising from traditional pharmacokinetic approaches.

SUBMITTER: Jin Y

PROVIDER: S-EPMC2688454 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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The effect of reporting methods for dosing times on the estimation of pharmacokinetic parameters of escitalopram.

Jin Yuyan Y Pollock Bruce G BG Frank Ellen E Florian Jeff J Kirshner Margaret M Fagiolini Andrea A Kupfer David J DJ Gastonguay Marc R MR Kepple Gail G Feng Yan Y Bies Robert R RR

Journal of clinical pharmacology 20090201 2

The objective of this study was to compare population pharmacokinetic models of escitalopram developed from dosage times recorded by a medication event monitoring system (MEMS) versus the reported times from patients with diagnosed depression. Seventy-three patients were prescribed doses of 10, 15, or 20 mg escitalopram daily. Sparse blood samples were collected at weeks 4, 12, 24, and 36 with 185 blood samples obtained from the 73 patients. NONMEM was used to develop a population pharmacokineti ...[more]

PMID: 19179296

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Project description:The purpose of this work was to develop a consolidated set of guiding principles for reporting of population pharmacokinetic (PK) analyses based on input from a survey of practitioners as well as discussions between industry, consulting and regulatory scientists. The survey found that identification of population covariate effects on drug exposure and support for dose selection (where population PK frequently serves as preparatory analysis to exposure-response modeling) are the main areas of influence for population PK analysis. The proposed guidelines consider two main purposes of population PK reports (1) to present key analysis findings and their impact on drug development decisions, and (2) as documentation of the analysis methods for the dual purpose of enabling review of the analysis and facilitating future use of the models. This work also identified two main audiences for the reports: (1) a technically competent group responsible for in-depth review of the data, methodology, and results, and (2) a scientifically literate, but not technically adept group, whose main interest is in the implications of the analysis for the broader drug development program. We recommend a generalized question-based approach with six questions that need to be addressed throughout the report. We recommend eight sections (Synopsis, Introduction, Data, Methods, Results, Discussion, Conclusions, Appendix) with suggestions for the target audience and level of detail for each section. A section providing general expectations regarding population PK reporting from a regulatory perspective is also included. We consider this an important step towards industrialization of the field of pharmacometrics such that non-technical audience also understands the role of pharmacometrics analyses in decision making. Population PK reports were chosen as representative reports to derive these recommendations; however, the guiding principles presented here are applicable for all pharmacometric reports including PKPD and simulation reports.

Project description:PurposeT1 -weighted dynamic contrast-enhanced Magnetic Resonance Imaging (DCE-MRI) is typically quantified by least squares (LS) fitting to a pharmacokinetic (PK) model to yield parameters of microvasculature and perfusion in normal and disease tissues. Such fitting is both time-consuming as well as subject to inaccuracy and instability in parameter estimates. Here, we propose a novel neural network approach to estimate the PK parameters by extracting long and short time-dependent features in DCE-MRI.MethodsA Long Short-Term Memory (LSTM) network, widely used for processing sequence data, was employed to map DCE-MRI time-series accompanied with an arterial input function to parameters of the extended Tofts model. Head and neck DCE-MRI from 103 patients were used for training and testing the LSTM model. Arterial input functions (AIFs) from 78 patients were used to generate synthetic DCE-MRI time-series for training, during which data augmentation was used to overcome the limited size of in vivo data. The model was tested on independent synthesized DCE data using AIFs from 25 patients. The LSTM performance was optimized for the numbers of layers and hidden state features. The performance of the LSTM was tested for different temporal resolution, total acquisition time, and contrast-to-noise ratio (CNR), and compared to the conventional LS fitting and a CNN-based method.ResultsCompared to LS fitting, the LSTM model had comparable accuracy in PK parameter estimations from fully temporal-sampled DCE-MRI data (~3 s per frame), but much better accuracy for the data with temporally subsampling (4s or greater per frame), total acquisition time truncation by 48%-16%, or low CNR (5 and 10). The LSTM reduced normalized root mean squared error by 40.4%, 46.9%, and 53.0% for sampling intervals of 4s, 5s, and 6s, respectively, compared to LS fitting. Compared to the CNN model, the LSTM model reduced the error in the parameter estimates up to 55.2%. Also, the LSTM improved the inference time by ~ 14 times on CPU compared to LS fitting.ConclusionOur study suggests that the LSTM model could achieve improved robustness and computation speed for PK parameter estimation compared to LS fitting and the CNN based network, particularly for suboptimal data.

Dataset Information

The effect of reporting methods for dosing times on the estimation of pharmacokinetic parameters of escitalopram.

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The effect of reporting methods for dosing times on the estimation of pharmacokinetic parameters of escitalopram.

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