Grant-Funded Lab Projects
A double-masked randomized Phase II study to compare the effectiveness of suvorexant vs. placebo in participants with co-occurring AUD and PTSD
The purpose of this study is to see how well a study medication (suvorexant) works at treating people with alcohol use disorder (AUD) and posttraumatic stress disorder (PTSD) who have trouble sleeping. This medication has been approved by the Food and Drug Administration (FDA) for the treatment of insomnia (trouble sleeping). However, suvorexant is not approved for the treatment of AUD or PTSD; therefore, it is an investigational drug. A total of 76 men and women veteran and non-veterans aged 21-65 with co-occurring AUD + PTSD symptoms and sleep disturbance will be recruited at 2 clinical sites, UTHealth in Houston and UCLA in Los Angeles. Eligible participants will be randomly assigned to receive suvorexant or matched placebo, following a 7-day placebo run-in. Randomized participants will be asked to take study medication every night, complete daily online surveys, and undergo a real-world practice quit attempt for 2 weeks. There will be 3 in-person study visits over the 14-day study period that will include exposure to alcohol cues and questionnaires.
The primary objective of the study is to assess the initial efficacy of suvorexant (20mg) in improving sleep metrics. The secondary objectives of the study are to evaluate the effect of suvorexant (20mg) on: 1) cue-induced alcohol craving, 2) number of drinks per drinking day, 3) number of drinks per day over a 14-day quit attempt period and 4) reducing PTSD symptoms
A double-masked randomized Phase II study to compare the effectiveness of suvorexant vs. placebo in participants with co-occurring AUD and PTSD.
Translational underpinnings of motivation for alcohol in humans
The purpose of this study is to understand different reasons why people are motivated to self-administer alcohol, building upon the extensive work from our laboratory on the development of a translational task for drinking motivation in humans. This study combines an IV alcohol challenge and self-administration task to test the effects of the three dimensions of the Addiction Neuroclinical Assessment (ANA), namely incentive salience, negative emotionality, and executive dysfunction on motivation for alcohol use. Individuals (N=210) with mild to severe Alcohol Use Disorder (AUD) will complete the ANA phenotyping assessment and a progressive ratio alcohol self-administration task, in which they will be allowed to work (button press) to be infused more alcohol following a progressive ratio schedule.
The Specific Aims of this project are: 1) To characterize the incentive salience dimension of the ANA. 2) To characterize the negative emotionality dimension of the ANA. 3) To characterize the executive dysfunction dimension of the ANA. Additional exploratory aims are (a) to test the role of AUD severity across the three dimensions of ANA, (b) to use machine learning modeling to elucidate determinants of alcohol self-administration; and (c) to test blood-based biomarkers of HPA axis activation (ACTH, cortisol) and inflammation (IL-6, IL-10, TNF-α, CRP). The successful completion of this project will advance translational science of AUD to better inform assessment, treatment, and biomarker development.
Integrating findings across stages of medication development for AUD
Medication development for alcohol use disorder (AUD) is a time-consuming and costly process. Unfortunately, no new medications for AUD have been approved in the past two decades, despite significant investments. A typical path to developing a new medication for AUD includes testing in animals, followed by safety testing in humans, followed by randomized clinical trials. Recently, it has been proposed that testing in humans using experimental psychopharmacology paradigms can detect the initial efficacy of a compound under development. As such, the “signal” of medication benefit over placebo is initially identified in animal models, followed by human laboratory testing, and ultimately tested in randomized clinical trials (RCT). In essence, at each phase in testing, scientists are tasked with making “go/no-go” decisions about candidate pharmacotherapies. In this context, approval by the FDA constitutes the final “go” decision and requires compelling efficacy demonstration in RCTs, which is the gold standard. While a host of factors are involved in making “go/no-go” decisions, the paradigms used in animal and human testing to detect an efficacy signal are crucial to the success of medication development. Further, how to evaluate the preclinical and human evidence for a compound in order to decide, is of paramount importance. To date, the question of which models should be used in preclinical studies and human laboratory studies and how the evidence they provide should be evaluated remains highly subjective. Scientists can argue for models they are most familiar with and preliminary data can be presented with a range of plausible interpretation, all of which is inherently subjective. The proposed R21 application seeks to conduct novel meta-analytic models to test the relationship between AUD medication effect sizes obtained in animal models, human laboratory models, and randomized clinical trials (RCTs). These analyses will test the degree to which models used at each stage of medication development for AUD are predictive of clinical outcomes in RCTs, the gold standard for improving healthcare.
Identifying treatment responders in medication trials for AUD using machine learning approaches
Alcohol use disorder (AUD), as defined in DSM-5, represents a highly prevalent, costly, and often untreated condition in the United States. Pharmacotherapy offers a promising avenue for treating AUD and for improving clinical outcomes for this debilitating disorder. While developing novel medications to treat AUD remains a high priority research area, there remain major opportunities to further elucidate clinical response in completed medication trials. To that end, a key question in randomized clinical trials (RCTs) is which patients respond to a given pharmacotherapy. Identifying treatment responders provides major opportunities to advance clinical care for AUD by personalizing medication practices on the bases of variables/predictors of good clinical response. For example, while the effect size for medications such as naltrexone is deemed small-to-moderate, a host of studies over the past decade have shown that its effect size may be considerably larger for certain subgroups of patients. Towards advancing precision medicine for AUD and leveraging data from a host of carefully conducted RCTs for AUD, this R03 application seeks to conduct secondary data analysis. Specifically, we propose to analyze data from four RCTs conducted by the NIAAA Clinical Investigations Group (NCIG). These state-of-the-art RCTs for AUD have tested the following pharmacotherapies: (a) quetiapine, (b) Levetiracetam XR (Keppra XR®), (c) Varenicline (Chantix®), and (d) HORIZANT® (Gabapentin Enacarbil) Extended-Release. In this R03 application, we propose to use a machine learning approach to identify treatment responders in the NCIG RCTs. Machine learning represents a highly promising and underutilized data analytic strategy in the field of AUD treatment response. Machine learning models prioritize the ability to predict future outcomes over creating perfectly fitting models for the data at hand. This results in models which are more generalizable to future observations, which fits well with our goal of identifying responders in RCTs. Leveraging data from these pivotal RCTs through secondary data analysis and using novel analytic methods, namely machine learning, provides a cost-effective approach to identifying AUD pharmacotherapy responders.
UCLA Psychology Cognitive Behavioral Therapy (CBT) Treatment for Alcohol Use Disorder (AUD) Clinic
Alcohol problems are highly prevalent among adults in the U.S. Specifically, twenty-five percent of adults in the U.S. report either currently having alcohol-related problems or drinking patterns that put them at risk for developing problems. Further, only 1 in 5 individuals with alcohol problems seek treatment and the available treatments are often not informed by science. In order to address the service needs of our community and to provide a training opportunity for advanced doctoral students in clinical psychology at UCLA, we propose to develop an outpatient alcoholism clinic providing evidence-based cognitive behavioral therapy for individuals with alcohol problems. In keeping with our service, training, and research missions, this clinical protocol will combine a research component with evidence-based practices. Specifically, participants will be asked to complete an alcohol cue-reactivity testing session at the end of their intake session and will discuss their reactions to alcohol cues with the therapist. Participants will be asked to repeat the alcohol cue-reactivity testing session after completing the 12-week CBT protocol. The CBT protocol will be derived from the Project MATCH Treatment Manual, which is an effective and well-disseminated evidence-based intervention for alcohol use disorder. Participants will complete a battery of assessments at the intake visit (30 minutes of assessments at intake) as well as weekly assessments immediately prior to their clinic visits (10-15 minutes of assessments each week), including a breathalyzer test, and random urine toxicology tests (one at intake and three random tests over the course of the 12-week treatment).
