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Cellular and Synaptic Adaptations Mediating Opioid Dependence
Abstract
Although opioids are highly effective for the treatment of pain, they are also known to be intensely addictive. There has been a massive research investment in the development of opioid analgesics, resulting in a plethora of compounds with varying affinity and efficacy at all the known opioid receptor subtypes. Although compounds of extremely high potency have been produced, the problem of tolerance to and dependence on these agonists persists. This review centers on the adaptive changes in cellular and synaptic function induced by chronic morphine treatment. The initial steps of opioid action are mediated through the activation of G protein-linked receptors. As is true for all G protein-linked receptors, opioid receptors activate and regulate multiple second messenger pathways associated with effector coupling, receptor trafficking, and nuclear signaling. These events are critical for understanding the early events leading to nonassociative tolerance and dependence. Equally important are associative and network changes that affect neurons that do not have opioid receptors but that are indirectly altered by opioid-sensitive cells. Finally, opioids and other drugs of abuse have some common cellular and anatomical pathways. The characterization of common pathways affected by different drugs, particularly after repeated treatment, is important in the understanding of drug abuse.
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Self-citation
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Metadata
- Authors
- Williams, J. T., Christie, M. J., & Manzoni, O.
- Venue
- Physiological Reviews
- Publisher
- American Physiological Society
- Published
- 2001-01-01
- Volume
- 81
- Issue
- 1
- Pages
- 299-343
- ISSN
- 0031-9333 (ISSN-L)
- 1522-1210
- OpenAlex
- W2172734677
- Cited by
- 848
- References
- 488
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.