Topiramate for cocaine dependence during methadone maintenance treatment: A randomized controlled trial

BackgroundDual dependence on opiate and cocaine occurs in about 60% of patients admitted to methadone maintenance and negatively impacts prognosis (Kosten et al. 2003. Drug Alcohol Depend. 70, 315). Topiramate (TOP) is an antiepileptic drug that may have utility in the treatment of cocaine dependence because it enhances the GABAergic system, antagonizes the glutamatergic system, and has been identified by NIDA as one of only a few medications providing a "positive signal" warranting further clinical investigation. (Vocci and Ling, 2005. Pharmacol. Ther. 108, 94).MethodIn this double-blind controlled clinical trial, cocaine dependent methadone maintenance patients (N=171) were randomly assigned to one of four groups. Under a factorial design, participants received either TOP or placebo, and monetary voucher incentives that were either contingent (CM) or non-contingent (Non-CM) on drug abstinence. TOP participants were inducted onto TOP over 7 weeks, stabilized for 8 weeks at 300 mg daily then tapered over 3 weeks. Voucher incentives were supplied for 12 weeks, starting during the fourth week of TOP induction. Primary outcome measures were cocaine abstinence (Y/N) as measured by thrice weekly urinalysis and analyzed using Generalized Estimating Equations (GEE) and treatment retention. All analyses were intent to treat and included the 12-week evaluation phase of combined TOP/P treatment and voucher intervention period.ResultsThere was no significant difference in cocaine abstinence between the TOP vs. P conditions nor between the CM vs. Non-CM conditions. There was no significant TOP/CM interaction. Retention was not significantly different between the groups.ConclusionTopiramate is not efficacious for increasing cocaine abstinence in methadone patients

Identifying drug (cocaine) intake events from acute physiological response in the presence of free-living physical activity

A variety of health and behavioral states can potentially be inferred from physiological measurements that can now be collected in the natural free-living environment. The major challenge, however, is to develop computational models for automated detection of health events that can work reliably in the natural field environment. In this paper, we develop a physiologically-informed model to automatically detect drug (cocaine) use events in the free-living environment of participants from their electrocardiogram (ECG) measurements. The key to reliably detecting drug use events in the field is to incorporate the knowledge of autonomic nervous system (ANS) behavior in the model development so as to decompose the activation effect of cocaine from the natural recovery behavior of the parasympathetic nervous system (after an episode of physical activity). We collect 89 days of data from 9 active drug users in two residential lab environments and 922 days of data from 42 active drug users in the field environment, for a total of 11,283 hours. We develop a model that tracks the natural recovery by the parasympathetic nervous system and then estimates the dampening caused to the recovery by the activation of the sympathetic nervous system due to cocaine. We develop efficient methods to screen and clean the ECG time series data and extract candidate windows to assess for potential drug use. We then apply our model on the recovery segments from these windows. Our model achieves 100% true positive rate while keeping the false positive rate to 0.87/day over (9+ hours/day of) lab data and to 1.13/day over (11+ hours/day of) field data