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Title: Prediction of paraquat exposure and toxicity in clinically ill poisoned patients: a model based approach
Authors: Klintean Wunnapuk
Fahim Mohammed
Indika Gawarammana
Xin Liu
Roger K. Verbeeck
Nicholas A. Buckley
Michael S. Roberts
Flora T. Musuamba
Keywords: Medicine
Pharmacology, Toxicology and Pharmaceutics
Issue Date: 1-Oct-2014
Abstract: © 2014 The British Pharmacological Society. AIMS: Paraquat poisoning is a medical problem in many parts of Asia and the Pacific. The mortality rate is extremely high as there is no effective treatment. We analyzed data collected during an ongoing cohort study on self-poisoning and from a randomized controlled trial assessing the efficacy of immunosuppressive therapy in hospitalized paraquat-intoxicated patients. The aim of this analysis was to characterize the toxicokinetics and toxicodynamics of paraquat in this population.METHODS: A non-linear mixed effects approach was used to perform a toxicokinetic/toxicodynamic population analysis in a cohort of 78 patients.RESULTS: The paraquat plasma concentrations were best fitted by a two compartment toxicokinetic structural model with first order absorption and first order elimination. Changes in renal function were used for the assessment of paraquat toxicodynamics. The estimates of toxicokinetic parameters for the apparent clearance, the apparent volume of distribution and elimination half-life were 1.17 l h(-1) , 2.4 l kg(-1) and 87 h, respectively. Renal function, namely creatinine clearance, was the most significant covariate to explain between patient variability in paraquat clearance.This model suggested that a reduction in paraquat clearance occurred within 24 to 48 h after poison ingestion, and afterwards the clearance was constant over time. The model estimated that a paraquat concentration of 429 μg l(-1) caused 50% of maximum renal toxicity. The immunosuppressive therapy tested during this study was associated with only 8% improvement of renal function.CONCLUSION: The developed models may be useful as prognostic tools to predict patient outcome based on patient characteristics on admission and to assess drug effectiveness during antidote drug development.
ISSN: 13652125
Appears in Collections:CMUL: Journal Articles

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