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Development of an MRI-Compatible Nasal Drug Delivery Method for Probing Nicotine Addiction Dynamics. | LitMetric

AI Article Synopsis

  • Substance abuse is a complex issue involving cycles of craving, drug use, reward, and satisfaction, and this research aims to model nicotine addiction in a controlled way.* -
  • A new MRI-compatible nicotine delivery system was developed to study brain responses by simulating the experience of craving and drug consumption in a controlled setting.* -
  • The study showed that this method can identify specific brain targets for potential treatments and allows for individualized understanding of addiction's impact on reward control mechanisms.*

Article Abstract

Substance abuse is a fundamentally dynamic disease, characterized by repeated oscillation between craving, drug self-administration, reward, and satiety. To model nicotine addiction as a control system, a magnetic resonance imaging (MRI)-compatible nicotine delivery system is needed to elicit cyclical cravings. Using a concentric nebulizer, inserted into one nostril, we delivered each dose equivalent to a single cigarette puff by a syringe pump. A control mechanism permits dual modes: one delivers puffs on a fixed interval programmed by researchers; with the other, subjects press a button to self-administer each nicotine dose. We tested the viability of this delivery method for studying the brain's response to nicotine addiction in three steps. First, we established the pharmacokinetics of nicotine delivery, using a dosing scheme designed to gradually achieve saturation. Second, we lengthened the time between microdoses to elicit craving cycles, using both fixed-interval and subject-driven behavior. Finally, we demonstrate a potential application of our device by showing that a fixed-interval protocol can reliably identify neuromodulatory targets for pharmacotherapy or brain stimulation. Our MRI-compatible nasal delivery method enables the measurement of neural circuit responses to drug doses on a single-subject level, allowing the development of data-driven predictive models to quantify individual dysregulations of the reward control circuit causing addiction.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708378PMC
http://dx.doi.org/10.3390/pharmaceutics13122069DOI Listing

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