• Journal of neurotrauma · Jan 2019

    Combining Brain Microdialysis and Translational Pharmacokinetic Modeling to Predict Drug Concentrations in Pediatric Severe Traumatic Brain Injury: The Next Step Toward Evidence-Based Pharmacotherapy?

    • Naomi Ketharanathan, Yumi Yamamoto, Ursula K Rohlwink, Enno D Wildschut, Mathôt Ron A A RAA 4 Department of Pharmacology, Academic Medical Center, Amsterdam, The Netherlands., de Lange Elizabeth C M ECM 2 Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands., Saskia N de Wildt, Andrew C Argent, Dick Tibboel, and Anthony A Figaji.
    • 1 Intensive Care and Department of Pediatric Surgery, Erasmus MC, Sophia Children's Hospital, Rotterdam, The Netherlands.
    • J. Neurotrauma. 2019 Jan 1; 36 (1): 111-117.

    AbstractEvidence-based analgosedation in severe pediatric traumatic brain injury (pTBI) management is lacking, and improved pharmacological understanding is needed. This starts with increased knowledge of factors controlling the pharmacokinetics (PK) of unbound drug at the target site (brain) and related drug effect(s). This prospective, descriptive study tested a pediatric physiology-based pharmacokinetic software model by comparing actual plasma and brain extracellular fluid (brainECF) morphine concentrations with predicted concentration-time profiles in severe pTBI patients (Glasgow Coma Scale [GCS], ≤8). Plasma and brainECF samples were obtained after legal guardian written consent and were collected from 8 pTBI patients (75% male; median age, 96 months [34.0-155.5]; median weight, 24 kg [14.5-55.0]) with a need for intracranial pressure monitoring (GCS, ≤8) and receiving continuous morphine infusion (10-40 μg/kg/h). BrainECF samples were obtained by microdialysis. BrainECF samples were taken from "injured" and "uninjured" regions as determined by microdialysis catheter location on computed head tomography. A previously developed physiology-based software model to predict morphine concentrations in the brain was adapted to children using pediatric physiological properties. The model predicted plasma morphine concentrations well for individual patients (97% of data points within the 90% prediction interval). In addition, predicted brainECF concentration-time profiles fell within a 90% prediction interval of microdialysis brainECF drug concentrations when sampled from an uninjured area. Prediction was less accurate in injured areas. This approach of translational physiology-based PK modeling allows prediction of morphine concentration-time profiles in uninjured brain of individual patients and opens promising avenues towards evidence-based pharmacotherapies in pTBI.

      Pubmed     Full text   Copy Citation     Plaintext  

      Add institutional full text...

    Notes

     
    Knowledge, pearl, summary or comment to share?
    300 characters remaining
    help        
    You can also include formatting, links, images and footnotes in your notes
    • Simple formatting can be added to notes, such as *italics*, _underline_ or **bold**.
    • Superscript can be denoted by <sup>text</sup> and subscript <sub>text</sub>.
    • Numbered or bulleted lists can be created using either numbered lines 1. 2. 3., hyphens - or asterisks *.
    • Links can be included with: [my link to pubmed](http://pubmed.com)
    • Images can be included with: ![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
    • For footnotes use [^1](This is a footnote.) inline.
    • Or use an inline reference [^1] to refer to a longer footnote elseweher in the document [^1]: This is a long footnote..

    hide…