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Do behavioral pharmacology findings predict clinical trial outcomes? A proof-of-concept in medication development for alcohol use disorder.

ABSTRACT: Behavioral pharmacology paradigms have been used for early efficacy testing of novel compounds for alcohol use disorder (AUD). However, the degree to which early efficacy in the human laboratory predicts clinical efficacy remains unclear. To address this gap in the literature we employed a novel meta-analytic approach. We searched the literature for medications tested for AUD using both behavioral pharmacology (i.e., alcohol administration) and randomized clinical trials (RCTs). For behavioral pharmacology, we computed medication effects on alcohol-induced stimulation, sedation, and craving during the alcohol administration (k = 51 studies, 24 medications). For RCTs, we computed medication effects on any drinking and heavy drinking (k = 118 studies, 17 medications). We used medication as the unit of analysis and applied the Williamson-York bivariate weighted least squares estimation to preserve the errors in both the independent and dependent variables. Results, with correction for publication bias, revealed a significant and positive relationship between medication effects on alcohol-induced stimulation (β = 1.18 p < 0.05), sedation (β = 2.38, p < 0.05), and craving (β = 3.28, p < 0.001) in the laboratory, and drinking outcomes in RCTs, such that medications that reduced stimulation, sedation, and craving during the alcohol administration were associated with better clinical outcomes. A leave-one-out Monte Carlo analysis examined the predictive utility of these laboratory endpoints for each medication. The observed clinical effect size was within one standard deviation of the mean predicted effect size for all but three pharmacotherapies. This proof-of-concept study demonstrates that behavioral pharmacology endpoints of alcohol-induced stimulation, sedation, and craving track medication effects from the human laboratory to clinical trial outcomes. These results apply to alcohol administration phenotypes and may be especially useful to medications for which the mechanisms of action involve alterations in subjective responses to alcohol (e.g., antagonist medication). These methods and results can be applied to a host of clinical questions and can streamline the process of screening novel compounds for AUD. For instance, this approach can be used to quantify the predictive utility of cue-reactivity screening models and even preclinical models of medication development.

SUBMITTER: Ray LA

PROVIDER: S-EPMC8026961 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe care of traumatized children would benefit significantly from accurate predictive models for Posttraumatic Stress Disorder (PTSD), using information available around the time of trauma. Machine Learning (ML) computational methods have yielded strong results in recent applications across many diseases and data types, yet they have not been previously applied to childhood PTSD. Since these methods have not been applied to this complex and debilitating disorder, there is a great deal that remains to be learned about their application. The first step is to prove the concept: Can ML methods - as applied in other fields - produce predictive classification models for childhood PTSD? Additionally, we seek to determine if specific variables can be identified - from the aforementioned predictive classification models - with putative causal relations to PTSD.MethodsML predictive classification methods - with causal discovery feature selection - were applied to a data set of 163 children hospitalized with an injury and PTSD was determined three months after hospital discharge. At the time of hospitalization, 105 risk factor variables were collected spanning a range of biopsychosocial domains.ResultsSeven percent of subjects had a high level of PTSD symptoms. A predictive classification model was discovered with significant predictive accuracy. A predictive model constructed based on subsets of potentially causally relevant features achieves similar predictivity compared to the best predictive model constructed with all variables. Causal Discovery feature selection methods identified 58 variables of which 10 were identified as most stable.ConclusionsIn this first proof-of-concept application of ML methods to predict childhood Posttraumatic Stress we were able to determine both predictive classification models for childhood PTSD and identify several causal variables. This set of techniques has great potential for enhancing the methodological toolkit in the field and future studies should seek to replicate, refine, and extend the results produced in this study.

Project description:Injectable naltrexone for alcohol use disorders (AUDs) has been efficacious in several studies. It has not been (i) compared head-to-head with oral naltrexone or (ii) examined in the hospital setting as an intervention that might facilitate treatment attendance after hospital discharge.Fifty-four hospitalized veterans identified as having DSM-IV-TR alcohol dependence were randomized to receive (i) a 50 mg oral naltrexone plus a 30-day prescription or (ii) a 380 mg intramuscular naltrexone injection prior to discharge. Of 113 veteran inpatients deemed eligible based on screening criteria, 54 met final eligibility criteria and were enrolled and randomized. Baseline data included demographics, alcohol consumption, and comorbidity. Measures of treatment initiation and engagement and alcohol consumption were reassessed at 14- and 45-day follow-ups.Thirty-five participants (64.8%) completed the entire study protocol (received a study medication and completed 14- and 45-day follow-ups). Among those who received a study medication (n = 45), 77.8% completed all follow-up interviews. This pilot study was not designed to have sufficient statistical power for hypothesis testing, and thus, as expected, there were no significant differences between groups in medication adherence (self-report of >80% of daily doses taken in oral group; receipt of second injection in the injection group), treatment engagement (at least treatment 3 visits in the 30 days postdischarge, and 2 or more visits per month in each of the 3 months following discharge) or alcohol consumption at 14 or at 45 days (p > 0.05). The median number of drinks among the entire cohort in the 2 weeks prior to hospitalization (128 drinks) was significantly higher than at day 14 (0 drinks, p < 0.001) or day 45 (0 drinks, p < 0.001). Rates of medication adherence were 62% in the oral group and 61% in the injection group.Results indicate feasibility for larger, more definitive study. Both groups had significant reductions in alcohol consumption over time and high-treatment engagement rates. Both oral and injectable formulations are feasible to initiate prior to discharge for hospital inpatients identified as having an AUD.

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Do behavioral pharmacology findings predict clinical trial outcomes? A proof-of-concept in medication development for alcohol use disorder.

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