Current Projects

Grant-Funded Lab Projects

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.

A Novel Human Laboratory Model for Screening Medications for Alcohol Use Disorder (Practice Quit Study)
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 are major opportunities to refine the process of screening novel compounds. To that end, a key question in clinical studies of novel compounds for AUD is how to efficiently determine whether a novel medication has sufficient evidence of initial treatment efficacy to warrant proceeding to clinical trials.The process of screening novel compounds for initial efficacy, known as the early phase 2 of medications development, often consists of human laboratory studies assessing constructs of putative clinical relevance, such as alcohol craving, subjective response to alcohol, and alcohol self-administration under laboratory conditions. Nevertheless, these controlled human laboratory models lack the ecological validity of clinical trials in which medication efficacy is established via clinically meaningful endpoints in individuals motivated to change their drinking behavior. The scientific premise of this study is that screening novel AUD medications can be more efficient and clinically meaningful if early efficacy (phase 2) studies combine the internal validity of laboratory testing with the external validity of clinical trials. To that end, we propose to develop and validate a novel early efficacy paradigm informed by the smoking cessation medication development literature, to screen AUD medications in humans. Specifically, the proposed early efficacy paradigm consists of a study in which individuals with current AUD reporting intrinsic motivation to change their drinking (i.e., wanting to quit or reduce their drinking within the next 6 months) will complete a week-long “practice quit attempt” while on study medication (either varenicline, naltrexone, or placebo). The primary outcomes of the practice quit attempt are (a) percentage of days abstinent and (b) drinks per drinking day. The proposed laboratory protocol has been carefully developed and validated for screening smoking cessation pharmacotherapies. The objective of this proposal is to develop, refine, and validate this novel approach to screen pharmacotherapies for AUD.

A Randomized Controlled Clinical Trial of the Neuroimmune Modulator Ibudilast for the Treatment of Alcohol Use Disorder (Ibudilast Alcohol Study)
Alcohol use disorder (AUD) is a chronic and relapsing condition for which current pharmacological treatments are only modestly effective. The development of efficacious medications for AUD remains a high research priority with recent emphasis on identifying novel molecular targets for AUD treatment and to efficiently screen new compounds aimed at those targets. To that end, modulation of neuroimmune function represents a promising novel target for AUD. Chronic alcohol consumption produces a sustained inflammatory state, such that individuals with AUD have increased neuroinflammation throughout the brain, and alcohol-induced neuroinflammation is thought to contribute to chronic alcohol seeking behavior and to the behavioral and neurotoxic effects of alcohol. Ibudilast (IBUD) has been advanced as a novel addiction pharmacotherapy that targets neurotrophin signaling and neuroimmune function. IBUD inhibits phosphodiesterases -4 (PDE4) and -10 (PDE10) and macrophage migration inhibitory factor (MMIF). Additionally, IBUD enhances neurotrophin expression, reduces pro-inflammatory cytokine release, and attentuates neuronal death. Our laboratory has recently completed a randomized, double-blind, placebo-controlled crossover laboratory study of IBUD in non-treatment seeking individuals with AUD, and concluded that IBUD is well tolerated and associated with mood improvements during stress- and alcohol-cue exposures in conjunction with a reduction in tonic levels of alcohol craving. This current study seeks to advance medication development for AUD by conducting a 12-week, double-blind, placebo-controlled randomized clinical trial of IBUD. A total of 132 treatment-seeking drinkers that meet criteria for moderate or severe AUD will be randomized to either IBUD or placebo.The primary aims of this study are to (1) to test whether IBUD will decrease percent heavy drinking days, and (2) test the efficacy of IBUD on secondary alcohol consumption endpoints, alcohol craving, and negative mood, compared to placebo, and over the course of the 12-week trial.

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).