Objectives: Addiction to opioid narcotics represents a major public health challenge. Animal models of one component of addiction, physical dependence, show this trait to be highly heritable. The analysis of opioid dependence using contemporary in-silico techniques offers an approach to discover novel treatments for dependence and addiction.
Methods: In these experiments, opioid withdrawal behavior in 18 inbred strains of mice was assessed. Mice were treated for 4 days with escalating doses of morphine before the administration of naloxone allowing the quantification of opioid dependence. After haplotypic analysis, experiments were designed to evaluate the top gene candidate as a modulator of physical dependence. Behavioral studies as well as measurements of gene expression on the mRNA and protein levels were completed. Finally, a human model of opioid dependence was used to quantify the effects of the 5-HT3 antagonist ondansetron on signs and symptoms of withdrawal.
Results: The Htr3a gene corresponding to the 5-HT3 receptor emerged as the leading candidate. Pharmacological studies using the selective 5-HT3 antagonist ondansetron supported the link in mice. Morphine strongly regulated the expression of the Htr3a gene in various central nervous system regions including the amygdala, dorsal raphe, and periaqueductal gray nuclei, which have been linked to opioid dependence in previous studies. Using an acute morphine administration model, the role of 5-HT3 in controlling the objective signs of withdrawal in humans was confirmed.
Conclusion: These studies show the power of in-silico genetic mapping, and reveal a novel target for treating an important component of opioid addiction.
aDepartment of Anesthesiology, Stanford University
bResearch Informatics Group, Roche Palo Alto
cVeterans Affairs Palo Alto Health Care System, Palo Alto, California, USA
Correspondence to Dr J. David Clark, Vapahcs, Anesthesiology, 112, 3801 Miranda Avenue, Palo Alto, CA 94304, USA
Tel: +1 650 493 5000, x67184; e-mail: firstname.lastname@example.org
Larry F. Chu and De-Yong Liang have contributed equally to this study.
Received 11 September 2008 Accepted 18 November 2008