Gabapentin Enacarbil Extended-Release Versus Placebo: A Likely Responder Reanalysis of a Randomized Clinical Trial

Emerging drugs in phase II and III clinical development for the treatment of alcohol use disorder

INTRODUCTION

Alcohol Use Disorder (AUD) poses an ongoing significant global health burden. AUD is highly prevalent and affects not only the individuals with AUD, but also their communities and society at large. Even though pharmacotherapy is an integral part of AUD treatment, the few available substances show limited efficacy and limited clinical impact. Thus, there is a need for new innovative pharmacotherapeutic approaches.

AREAS COVERED

This paper provides a comprehensive review of drugs approved for the treatment of AUD as well as those currently in phase II and III development. Data from recent clinical trials has been reviewed and supplemented by additional literature based on a systematic search of the PubMed database and clinical trials registries. Compounds discussed include disulfiram, naltrexone, nalmefene, acamprosat, baclofen, sodium oxybate, doxazosin, varenicline, zonisamide, gabapentin, apremilast, ibudilast, ivermectin, tolcapone, mifepristone, suvorexant, ketamine, psilocybin, semaglutide, oxytocin and cannabidiol.

EXPERT OPINION

Even though the majority of the discussed compounds lack sufficient evidence to support their efficacy, multiple promising new treatment options are currently under investigation. Future research has to consider specific phenotypes and subgroups of AUD as well as a possible enhancement of the effects of psychotherapy through combination with pharmacotherapy. Practitioners should be encouraged to use available compounds to support existing therapeutic regimens.

Sleep as an Important Target or Modifier in Alcohol Use Disorder Clinical Treatment: Example From a Recent Gabapentin Randomized Clinical Trial

OBJECTIVES

Alcohol consumption affects sleep both in healthy populations and in patients with alcohol use disorder (AUD). However, sleep has typically not been considered within AUD pharmacotherapy trials. We used data from a completed gabapentin clinical treatment trial to explore the medication's effect on patient-rated insomnia measured by a standard insomnia rating (Insomnia Severity Index [ISI]) and whether this influenced gabapentin's effects on alcohol consumption.

METHODS

This study included 90 individuals with current Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition AUD criteria reporting current or past alcohol withdrawal. Participants were assigned to placebo or gabapentin (up to 1200 mg/day) for a 16-week randomized controlled trial with percent heavy drinking days (PHDD) and percent abstinent days (PDA) as outcomes. Utilizing mixed-effects models, this study assessed medication effects on ISI over the trial. We then examined the interaction of baseline ISI and medication on drinking. Finally, given our previous finding of alcohol withdrawal influencing gabapentin efficacy, we added change in ISI as a potential "moderator" of the interaction of medication effects and alcohol withdrawal on drinking.

RESULTS

Sleep (ISI) improved more in those treated with gabapentin (60.6% reduction) compared with placebo (37.8% reduction; P = 0.013). Higher baseline ISI predicted drinking in gabapentin-treated individuals (lower PHDD [ P = 0.026] and higher (PDA [ P = 0.047]). ISI was an independent predictor of PHDD decrease and PDA increase ( P < 0.001; P = 0.002), but this did not significantly moderate gabapentin's effectiveness.

CONCLUSIONS

Although gabapentin positively impacts both alcohol use and sleep, its effect on drinking is not fully dependent on sleep improvement, implying a direct biological mechanism on alcohol use.

Beyond mediators: A critical review and methodological path forward for studying mechanisms in alcohol use treatment research

Understanding how treatments for alcohol use disorder (AUD) facilitate behavior change has long been recognized as an important area of research for advancing clinical care. However, despite decades of research, the specific mechanisms of change for most AUD treatments remain largely unknown because most prior work in the field has focused only on statistical mediation. Statistical mediation is a necessary but not sufficient condition to establish evidence for a mechanism of change. Mediators are intermediate variables that account statistically for the relationship between independent and dependent variables, whereas mechanisms provide more detailed explanations of how an intervention leads to a desired outcome. Thus, mediators and mechanisms are not equivalent. To advance mechanisms of behavior change research, in this critical review we provide an overview of methodological shortfalls of existing AUD treatment mechanism research and introduce an etiologically informed precision medicine approach that facilitates the testing of mechanisms of behavior change rather than treatment mediators. We propose a framework for studying mechanisms in alcohol treatment research that promises to facilitate our understanding of behavior change and precision medicine (i.e., for whom a given mechanism of behavior change operates and under what conditions). The framework presented in this review has several overarching goals, one of which is to provide a methodological roadmap for testing AUD recovery mechanisms. We provide two examples of our framework, one pharmacological and one behavioral, to facilitate future efforts to implement this methodological approach to mechanism research. The framework proposed in this critical review facilitates the alignment of AUD treatment mechanism research with current theories of etiologic mechanisms, precision medicine efforts, and cross-disciplinary approaches to testing mechanisms. Although no framework can address all the challenges related to mechanisms research, our goal is to help facilitate a shift toward more rigorous and falsifiable behavior change research.

Who responds to a multi-component treatment for cannabis use disorder? Using multivariable and machine learning models to classify treatment responders and non-responders

BACKGROUND AND AIMS

Treatments for cannabis use disorder (CUD) have limited efficacy and little is known about who responds to existing treatments. Accurately predicting who will respond to treatment can improve clinical decision-making by allowing clinicians to offer the most appropriate level and type of care. This study aimed to determine whether multivariable/machine learning models can be used to classify CUD treatment responders versus non-responders.

METHODS

This secondary analysis used data from a National Drug Abuse Treatment Clinical Trials Network multi-site outpatient clinical trial in the United States. Adults with CUD (n = 302) received 12 weeks of contingency management, brief cessation counseling and were randomized to receive additionally either (1) N-Acetylcysteine or (2) placebo. Multivariable/machine learning models were used to classify treatment responders (i.e. two consecutive negative urine cannabinoid tests or a 50% reduction in days of use) versus non-responders using baseline demographic, medical, psychiatric and substance use information.

RESULTS

Prediction performance for various machine learning and regression prediction models yielded area under the curves (AUCs) >0.70 for four models (0.72-0.77), with support vector machine models having the highest overall accuracy (73%; 95% CI = 68-78%) and AUC (0.77; 95% CI = 0.72, 0.83). Fourteen variables were retained in at least three of four top models, including demographic (ethnicity, education), medical (diastolic/systolic blood pressure, overall health, neurological diagnosis), psychiatric (depressive symptoms, generalized anxiety disorder, antisocial personality disorder) and substance use (tobacco smoker, baseline cannabinoid level, amphetamine use, age of experimentation with other substances, cannabis withdrawal intensity) characteristics.

CONCLUSIONS

Multivariable/machine learning models can improve on chance prediction of treatment response to outpatient cannabis use disorder treatment, although further improvements in prediction performance are likely necessary for decisions about clinical care.

Management of alcohol use disorder in patients with chronic liver disease

Alcohol use disorder (AUD) rates have risen dramatically in the United States, resulting in increasing rates of alcohol-associated liver disease (ALD), but many patients struggle to access alcohol use treatment. AUD treatment improves outcomes, including mortality, and represents the most urgent means by which care can be improved for those with liver disease (including ALD and others) and AUD. AUD care for those with liver disease involves 3 steps: detecting alcohol use, diagnosing AUD, and directing patients to alcohol treatment. Detecting alcohol use can involve questioning during the clinical interview, the use of standardized alcohol use surveys, and alcohol biomarkers. Identifying and diagnosing AUD are interview-based processes that should ideally be performed by a trained addiction professional, but nonaddiction clinicians can use surveys to determine the severity of hazardous drinking. Referral to formal AUD treatment should be made, especially where more severe AUD is suspected or identified. Therapeutic modalities are numerous and include different forms of one-on-one psychotherapy, such as motivational enhancement therapy or cognitive behavior therapy, group therapy, community mutual aid societies (such as Alcoholics Anonymous), inpatient addiction treatment, and relapse prevention medications. Finally, integrated care approaches that build strong relationships between addiction professionals and hepatologists or medical providers caring for those with liver disease are crucial to improving care for this population.

Alcohol use disorder in community management of chronic liver diseases

Rising rates of alcohol use disorder (AUD) combined with increases in alcohol-related liver disease (ALD) and other liver disease have resulted in the need to develop alcohol management strategies at all levels of patient care. For those with pre-existing liver disease, whether ALD or others, attention to alcohol use treatment and abstinence becomes critical to avoiding worsening liver-related consequences. Modalities to help patients reduce or stop alcohol include screening/brief intervention/referral to treatment, various therapeutic modalities including cognitive behavioral therapy, motivational enhancement therapy and 12-step facilitation, and alcohol relapse prevention medications. Harm reduction approaches versus total abstinence may be considered, but for those with existing ALD, particularly advanced ALD (cirrhosis or acute alcoholic hepatitis), total abstinence from alcohol is the recommendation, given clear data that ongoing alcohol use worsens mortality and liver-related morbidity. For certain populations, alcohol cessation is even more critically important. For those with hepatitis C or NAFLD, alcohol use accelerates negative liver-related outcomes. In women, alcohol use accelerates liver damage and results in worsened liver-related mortality. Efforts to integrate AUD and liver disease care are urgently needed and can occur at several levels, with establishment of multidisciplinary ALD clinics for fully integrated co-management as an important goal.

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

Mechanisms of Behavior Change in Substance Use Disorder With and Without Formal Treatment

This article provides a narrative review of studies that examined mechanisms of behavior change in substance use disorder. Several mechanisms have some support, including self-efficacy, craving, protective behavioral strategies, and increasing substance-free rewards, whereas others have minimal support (e.g., motivation, identity). The review provides recommendations for expanding the research agenda for studying mechanisms of change, including designs to manipulate putative change mechanisms, measurement approaches that expand the temporal units of analysis during change efforts, more studies of change outside of treatment, and analytic approaches that move beyond mediation tests. The dominant causal inference approach that focuses on treatment and individuals as change agents could be expanded to include a molar behavioral approach that focuses on patterns of behavior in temporally extended environmental contexts. Molar behavioral approaches may advance understanding of how recovery from substance use disorder is influenced by broader contextual features, community-level variables, and social determinants of health. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Predicting environmental risk factors in relation to health outcomes among school children from Romania using random forest model - An analysis of data from the SINPHONIE project

BACKGROUND

Few studies have simultaneously assessed the health impact of school and home environmental factors on children, since handling multiple highly correlated environmental variables is challenging. In this study, we examined indoor home and school environments in relation to health outcomes using machine learning methods and logistic regression.

METHODS

We used the data collected by the SINPHONIE (Schools Indoor Pollution and Health: Observatory Network in Europe) project in Romania, a multicenter European research study that collected comprehensive information on school and home environments, health symptoms in children, smoking, and school policies. The health outcomes were categorized as: any health symptoms, asthma, allergy and flu-like symptoms. Both logistic regression and random forest (RF) methods were used to predict the four categories of health outcomes, and the methods prediction performance was compared.

RESULTS

The RF method we employed for analysis showed that common risk factors for the investigated categories of health outcomes, included: environmental tobacco smoke (ETS), dampness in the indoor school environment, male gender, air freshener use, residence located in proximity of traffic (<200 m), stressful schoolwork, and classroom noise (contributions ranged from 7.91% to 23.12%). Specificity, accuracy and area under the curve (AUC) values for most outcomes were higher when using RF compared to logistic regression, while sensitivity was similar in both methods.

CONCLUSION

This study suggests that ETS, dampness in the indoor school environment, use of air fresheners, living in proximity to traffic (<200 m) and noise are common environmental risk factors for the investigated health outcomes. RF pointed out better predictive values, sensitivity and accuracy compared to logistic regression.

Neuronal adaptations in the lateral habenula during drug withdrawal: Preclinical evidence for addiction therapy

The epithalamic lateral habenula (LHb) regulates monoaminergic systems and contributes to the expression of both appetitive and aversive behaviours. Over the past years, the LHb has emerged as a vulnerable brain structure in mental illnesses including addiction. Behavioural and functional evidence in humans and rodents provide substantial support for a role of LHb in the negative affective symptoms emerging during withdrawal from addictive substances. Multiple forms of cellular and synaptic adaptations that take hold during drug withdrawal within the LHb are causally linked with the emergence of negative affective symptoms. These results indicate that targeting drug withdrawal-driven adaptations in the LHb may represent a potential strategy to normalize drug-related behavioural adaptations. In the current review we describe the mechanisms leading to functional alterations in the LHb, as well as the existing interventions used to counteract addictive behaviours. Finally, closing this loop we discuss and propose new avenues to potentially target the LHb in humans in light of the mechanistic understanding stemming from pre-clinical studies. Altogether, we provide an overview on how to leverage cellular-level understanding to envision clinically-relevant approaches for the treatment of specific aspects in drug addiction.