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Atomoxetine is a treatment for attention-deficit hyperactivity disorder. It inhibits Norepinephrine Transporters (NET) in the brain. Renal impairment can reduce hepatic CYP2D6 activity and atomoxetine elimination which may increase its body exposure. Atomoxetine can be secreted in saliva. The objective of this work was to test the hypothesis that atomoxetine saliva levels (sATX) can be used to predict ATX brain Extracellular Fluid (bECF) levels and their pharmacological effects in healthy subjects and those with End-Stage Renal Disease (ESRD). The pharmacokinetics of atomoxetine after intravenous administration to rats with chemically induced acute and chronic renal impairments were investigated. A physiologically-based pharmacokinetic (PBPK) model was built and verified in rats using previously published measured atomoxetine levels in plasma and brain tissue. The rat PBPK model was then scaled to humans and verified using published measured atomoxetine levels in plasma, saliva, and bECF. The rat PBPK model predicted the observed reduced atomoxetine clearance due to renal impairment in rats. The PBPK model predicted atomoxetine exposure in human plasma, sATX and bECF. Additionally, it predicted that ATX bECF levels needed to inhibit NET are achieved at 80 mg dose. In ESRD patients, the developed PBPK model predicted that the previously reported 65% increase in plasma exposure in these patients can be associated with a 63% increase in bECF. The PBPK simulations showed that there is a significant correlation between sATX and bECF in human. Saliva levels can be used to predict atomoxetine pharmacological response. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Citation

Mo'tasem M Alsmadi, Laith N Al Eitan, Nasir M Idkaidek, Karem H Alzoubi. The Development of a PBPK Model for Atomoxetine Using Levels in Plasma, Saliva and Brain Extracellular Fluid in Patients with Normal and Deteriorated Kidney Function. CNS & neurological disorders drug targets. 2022;21(8):704-716

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PMID: 35043773

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