Dynamic modeling of initiation of nonmedical opioid use

s / Drug and Alcohol Dependence 140 (2014) e86–e168 e161 Methods: The effects of JPC-077 at VMAT2 and the dopamine transporter (DAT), andas an inhibitor ofMETH in slicepreparations, were evaluated. Also, translation to the whole animal was pursued by determining JPC-077-mediated inhibition of responding for METH in self-administration assays. Results: Results show that JPC-077 exhibited a 6-fold increase in affinity for the [3H]dihydrotetrabenazine binding site onVMAT2, and a 5-fold increase in affinity for the dopamine (DA) translocation site on VMAT2 in relation to lobelane, as well as a competitive inhibition of DA uptake at VMAT2. JPC-077 evoked [3H]DA release from synaptic vesicles with 130-fold greater potency than lobelane or METH. JPC-077 had 370-fold greater selectivity for VMAT2 over the plasmalemma DAT, indicating that JPC-077 likely has low abuse liability. Importantly, JPC-077 inhibited METH-evoked DA release from striatal slices, while concurrently increasing extracellular DOPAC. JPC-077 (56mg/kg) decreased the number of methamphetamine infusions self-administered, but did not alter responding for food when given across repeated pretreatments. Conclusions: Thus, in vitro effects of JPC-077 translated to in vivo efficacy, decreasing METH self-administration. As a result of these studies, JPC-077 has emerged as a lead compound in the development of a treatment of METH abuse. Financial support: DA13519, DA016176, TR000117. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.453 Dynamic modeling of initiation of nonmedical opioid use Alexandra Nielsen1, Teresa D. Schmidt1, Dennis McCarty2, W.W. Wakeland1 1 Systems Science, Portland State University, Portland, OR, United States 2 PHPM, Oregon Health and Science University, Portland, OR, United States Aims: Drawing from empirical data and a panel of experts, a system dynamics simulation was developed to reproduce historical trends in nonmedical use of pharmaceutical opioids. System dynamics is a simulation method in which complex relationships and feedback loops are specified and mathematically formalized. The resultant set of differential equations was calibrated to replicate data from 1995 to 2005, used to predict behavior from 2006 to 2011, and used to evaluate policy interventions. Methods: Data to support assumptions and model parameters were drawn from publicly available sources. Data on initiation and nonmedical use were obtained from the National Survey of Drug Use and Health for 1995–2011. Results: The model contains 5 state variables, 13 exogenous parameter variables, 21 calculated parameters and 10 rates of change. Three principal feedback loops (a peer initiation epidemic loop, a global availability loop, and a personal accessibility loop) contribute to the nonlinear growth patterns in nonmedical opioid initiation and use. Peer initiation is modeled as the infection of a susceptible population by peers. Global availability of opioids for nonmedical use depends on the number of current opioid users and how much free leftover medicine they obtain from prescription holders. When availability diminishes, reduced personal accessibility requires transitions to paying for opioids. A demandside intervention appeared to be more effective than constraining supply; compelling susceptible non-users to resist initiation was more effective in reducing nonmedical use than compelling prescription holders not to share their medicines, and reducing global availability through prescription take back events. Conclusions: We offer a formalized model of a common pathway to nonmedical opioid initiation and provide a tool for comparing the impact of multiple policy interventions. System dynamics modeling sheds insights on the global dynamics of nonmedical opioid use and can be used to inform policy interventions to ameliorate the associated public health problems. Financial support: NIDA grant 5R21DA031361. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.454 Genetic variants of the dopaminergic system associated with subjective responses to cocaine David A. Nielsen1,2, C.J. Spellicy1,2, S.C. Hamon3, M.H. Harding1,2, James J. Mahoney III 1,2, T.R. Kosten1,2, R. De La Garza Ii 1,2, Thomas F. Newton1,2 1 Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States 2 Michael E. DeBakey V.A. Medical Center, Houston, TX, United States 3 Laboratory of Statistical Genetics, The Rockefeller University, New York, NY, United States Aims: To identify genetic markers of the dopaminergic system that modulate subjective responses to cocaine in cocainedependent subjects. Methods: Non-treatment seeking cocaine-dependent subjects (N=47) received in randomized order a single infusion of saline or cocaine (40mg, IV) delivered over a 2min period. Subjective effects (visual analogue scales: VAS) were acquired before (−15min) and at 5, 10, 15, and 20min after infusion. VAS scales ranged from zero (no effect) to 100 (greatest effect ever). Subjective values following cocaine were normalized to baseline values collected 15min prior to each infusion, and thenwere subtracted fromsaline session values. Data was analyzed using repeated measures ANOVA. DNA from subjects was genotyped for the DRD2 rs6277 and rs2283265, ANKK1 rs1800497, and CSNK1E rs1534891 variants. Results were corrected for population structure. Results: Our cohort had a mean age of 44 years, was 68% black, 87% male, and primarily smoked >2g cocaine per day (94%). Self-report of “High” and “Any Drug Effect” were found to be in association with DRD2 (rs2283265: p=1×10−3, p=3×10−4, respectively), and ANKK1 (p=6×10−5, p=3×10−4) variants. Associations were found with reports of “Like Cocaine” with ANKK1 (p=4×10−4) and “Anxious” with CSNK1E (p=7×10−4). Conclusions: This studyprovides evidence that inter-individual differences in genetic makeup modify subjective responses to cocaine. Knowledge of the genes and variants that modulate response to cocainemay aid in the development of novel therapies for the treatment of cocaine addiction. Financial support: Supported in part by NIH/NIDA P50 DA018197 (TK), for DN throughMDAnderson’s Cancer Center SupportGrantNIH/NIDADA026120, and the ToomimFamily Fund. This material is the result of work supportedwith resources and the use of facilities at the Michael E. DeBakey VAMedical Center, Houston, TX. http://dx.doi.org/10.1016/j.drugalcdep.2014.02.455