Invited reviewAddictions Neuroclinical Assessment: A reverse translational approach
Introduction
Psychoactive agents that are widely used tend to be widely abused. On a global basis, alcohol, nicotine, and cannabis are widely used, although quantity and qualitative aspects of use vary enormously across time and space, leading to disparities in impact on public health. On an overall basis, and for every population studied, the public health impact of addictive disorders is large. For example, alcohol is consumed worldwide, even in countries where it is illegal to sell or buy it. Correspondingly, alcohol is one of the largest preventable contributors to disease (Rehm et al., 2009), 4.6% of the global disease burden being attributable to alcohol, as measured in disability-adjusted life years. In the United States, roughly 14% of individuals are diagnosable with an alcohol use disorder (AUD) in a given year, and the lifetime prevalence rate of AUD is 29% (Grant et al., 2015). For several addictive disorders, including AUD, FDA-approved medications are available, and behavioral treatments are also helpful. Treatments for addictive disorders are partially effective, probably helping at least a third of patients who receive them, but treatment is usually not received: over 90% of individuals with AUD never receive specialized treatment (SAMHSAServices HaH, 2013). The treatment of addictions as end stage diseases in which neuroadaptive changes of addiction, and other damage to the body predominate, and may be difficult to undo, is inherently less satisfactory than prevention, and may be less efficacious than treatments targeted to specific types of vulnerability and stages of progression. However, developing new clinical approaches to addictions based on the neuroscience of addiction – a precision medicine of addictions – ultimately will require integration of relevant neuroscience-based measures into nosology.
Advances in our functional understanding of the pathophysiology of addictions are derived from studies in humans but also by investigations in animal models that capture different aspects of addictions. However, translating findings from animal models to humans, and reverse translating from humans to animal models, is hampered by the etiologic heterogeneity of addiction vulnerability in both humans and the diversity of animal models available (Belin et al., 2016). In humans, there is wide variation in progression and outcome, and limited ability to stage addiction using measures of progression emergent from studies in animal models. Animal models of addiction afford a degree of control of exposure and genotype, and access to tissue that is impossible to attain in human studies. Neuroadaptive processes (Koob and Volkow, 2016), and to a lesser extent, genes, gene transcripts, and proteins (Zhou et al., 2013), have primarily been identified in rodents, with translation to human. Translational studies at the genetic and neuroscience level reveal that mechanisms of addiction in mice, rats, and humans are orthologous, i.e., functionally similar and of a shared evolutionary origin. The roles of both genetic and environmental factors in interindividual variation in vulnerability and progression can bet traced in both humans and animal models of addiction. In rodents, genetically very similar, or identical (inbred and inbred F1) individuals can be divergent in behaviors such as novelty seeking and novelty-induced hyperlocomotion that predict addiction-like behavior, and, by extension, divergent in outcome (Belin et al., 2016), and divergent in addiction behaviors that develop subsequent to exposure. Preclinical studies are therefore an avenue to identify domains that are critical for predicting liability and staging response in human patients, provided relevant domains can be measured. Herein we link critical findings from animal models of addiction in humans to three neuroscience domains comprising the Addictions Neuroclinical Assessment (ANA) (Kwako et al., 2016), Fig. 1. While ANA focuses on addictions broadly, we have elected to focus on alcohol as an exemplar substance of abuse, with additional literature from other substances integrated throughout the manuscript as relevant.
Clinical heterogeneity is the major barrier to the treatment of addictive disorders and development of better treatments. In both the Diagnostic and Statistical Manual of Mental Disorders (DSM) and International Classification of Diseases (ICD), addictions are categorical diagnoses based on symptom counts of up to eleven intercorrelated symptoms, with a minimum of two in whatever of (11 × 10)/2 = 55 combinations to meet a threshold diagnosis of DSM addictive disorder. DSM-5 (APA, 2013) estimates level of severity, also based on symptom counts. Because a diagnosis of addictive disorder under ICD and DSM criteria requires that the patient meet a limited number of criteria from a larger list of partially inter-correlated criteria, there is considerable within-diagnosis heterogeneity. Any patient can reach the endpoints represented by these behaviorally focused criteria via different destinations, and beginning from distinctly different, and even polar opposite, starting points of vulnerability. For example, both anxiety (internalizing behavior: enhanced affective response, prior sensitization to stress/trauma) and risk-taking (externalizing behavior: impulsivity, enhanced responses to novelty, novelty seeking) can predispose to addiction, liability thus arising from genetic risk factors, and exposures. The diagnostic criteria for AUD and other addictive disorders focus on overt behavioral symptoms and consequences of use rather than underlying neurobiological differences that lead to vulnerability and can define progression.
Given the significant advances in our understanding of the neurocircuitry of addiction, e.g., (Koob and Volkow, 2016), and the neurobehavioral differences that are involved in to vulnerability, and the capacity to measure the activity and output of the brain for relevant domains, the time may be ripe for leveraging knowledge towards a more nuanced approach to diagnosis and treatment. Such an effort would align with similar initiatives in mental health, e.g., the Research Domain Criteria (RDoC) program at the National Institute of Mental Health, and in medicine more generally, e.g., the NIH Precision Medicine Initiative Cohort Program (http://www.nih.gov/about-nih/who-we-are/nih-director/statements/preparing-launch-precision-medicine-initiative-cohort-program). One major distinction between ANA and these initiatives is that ANA is focused within a particular disease category, i.e., addiction, rather than across various diseases.
Historically, most preclinical studies of addiction, including alcohol, have focused on drug reinforcement (Belin et al., 2016). However, studies in rats and mice have revealed powerful predictors of interindividual variation in liability. Several of these measures correspond to heritable, disease associated intermediate phenotypes (endophenotypes) (Gottesman and Gould, 2003) in people (Kendler and Neale, 2010). These include trait measures in the anxiety (internalizing) and impulsive-like (externalizing) domains that are the basis of a substantial portion of the quantitative inheritance of addictive disorders (Kendler et al., 2012). While an ability to recognize broad differences in externalizing and internalizing behavior has advanced genetic analyses, and has improved translational alcoholism research, better measures of these domains are needed.
People vary in their preference for particular addictive agents both because of a shared vulnerability component and because of “unshared” – agent-specific- components of inheritance (Goldman and Bergen, 1998). For example, alcohol and nicotine use disorders are substantially cross-inherited, as was learned by measuring the sharing of risk for a different disorder by twin siblings of index patients, in large, epidemiologically representative twin samples such as the WWII Veterans, Vietnam Veterans, and Virginia Twin Studies. Opioid Use Disorder was not observed to be as strongly cross-inherited with other addictive disorders (Goldman and Bergen, 1998).
Substance-specific genes influencing addiction include ones whose actions are specific to the substance, or relatively so, and for example ALDH Glu487Lys (relatively specific to AUD, via alcohol-induced flushing) and CYP2A6 variants (relatively specific to nicotine, via nicotine metabolism). Shared liability genes influencing addiction include ones whose actions are shared across different substances such as DAT and HTR2B, whose variants can increase impulsivity, as well as genes such as NPY and FKBP5, whose variants alter emotionality and stress response (Bevilacqua and Goldman, 2011), potentially increasing or decreasing vulnerability to different addictions, and as also influenced by stress exposures.
Unique variations in exposures to addictive agents themselves (availability) can dictate variation in expression of liability is expressed by abuse of one addictive agent or another, or whether liability is penetrant at all. For example, a drug such as Khat is used nearly universally in certain countries, e.g., those in East Africa and Arabia, but is virtually unavailable in others, e.g., European countries without sizable East African immigrants, and rarely used (Griffiths et al., 2010). For any addictive agent, agent-specific measures are required to assess level and pattern of consumption, and to index response. For example, carbohydrate deficient transferrin (CDT) and plasma sialic acid index of apolipoprotein A for alcohol (Javors and Johnson, 2003) and cotinine and 3’-hydroxycotinine for nicotine (Strasser et al., 2011) and hair (Wilkins et al., 1995) and blood (Lowe et al., 2007) levels of cannabis can be used to augment history reports, such as the Timeline Follow-Back (Sobell and Sobell, 1992).
Agent-specific responses to drug challenges can index aspects of vulnerability that are relatively agent-specific, serving as bioassays in the absence of genotypes or biochemical measures that might be used as predictive or explanatory surrogates. For AUD, responses that have been found to be predictive, and that have been correspondingly been traced in both animal models humans, include initial sedative sensitivity to sedative/hypnotic drugs such as alcohol (Schuckit, 1984), stimulant and sedative responses to alcohol (Hendler et al., 2011), withdrawal severity, and motor activation. The latter predicts shared vulnerability to addiction to different agents. Motor activation occurs on the ascending limb of the blood alcohol concentration curve when drinking is initiated, but is also triggered by novelty, by drugs such as cocaine that release dopamine, and by drugs that act directly on dopamine receptors. Shared sedative sensitivity between alcohol and benzodiazepine drugs may also underpin a portion of the shared sensitivity to alcohol-induced sedation, and as supported by genetic mapping studies in rodents (Belknap et al., 2008), by preliminary genetic studies in people identifying GABAA6 as a potential shared liability locus for alcohol and benzodiazepine effect on eye movements (Iwata et al., 1999), and by cross-sedation and cross-tolerance effects of alcohol and other sedative hypnotic drugs. However, genes, and processes critical to sedative response to alcohol would not be likely to be shared with stimulant drugs such as amphetamine and cocaine, or with gambling. Tolerance to the sedative effects of alcohol that can be mediated by induction of metabolism by cytochrome P450 enzyme activity (pharmacokinetic tolerance) and by neural resilience (pharmacodynamics) can be agent-specific, or specific to a class of agents sharing pharmacologic or physiologic mode of action. Capture of agent-specific responses has an important place in defining vulnerability, and progression.
However, whereas agent-specific assessments need to be developed, the broader need is to assess common domains of vulnerability that lead to cross-inheritance (Goldman et al., 2005), and to common pathways in outcome. The rationale for ANA is therefore both to subclassify patients addicted to particular agents, reducing diagnostic heterogeneity, but also to measure dimensions that act across agent-based diagnostic boundaries, and that can improve convergence between preclinical and clinical models. Here, we review prior classification attempts for AUD, a representative addictive disorder for which efforts to develop a classification schemes has been most intensive, noting that these schemes are primarily focused on a single addictive agent. We describe the stages of the addiction cycle and how they relate to the neuroscience domains for ANA. Next we review evidence for disruption in these domains from animals and humans associated with addiction. We build on the description of ANA in (Kwako et al., 2016), but specifically focus on linkage and consilience between animal and human findings in AUD.
Section snippets
Clinically-based classification of AUD
Subclassification of alcohol addiction began with Jellinek (Bowman and Jellinek, 1941), and was continued by Cloninger (1987), who identified the Type I versus Type II dichotomy that has become well-known, i.e., Type I patients who are characterized by relatively later onset of addiction, a combination of genetic and environmental mediators, and differing levels of severity, and Type II patients who have early onset, and who tend to have stronger genetic liability to alcohol addiction, and more
Stages of the addiction cycle and the Addictions Neuroclinical Assessment
Addiction does not occur immediately on exposure to a psychoactive agent, but develops over time. The addiction cycle may be understood from various perspectives, including social psychological; psychiatric, dysadaptational, and neurobiologic. In its totality, addiction is an allostatic process, meaning a transition to a new, more dysfunctional state that is then defended. The dynamic, progressive nature of the addictive process is a fundamental aspect of addiction that has been missing in
Improving translation and reverse translation
As reviewed above, there is considerable evidence for disruptions in three neuroscience domains associated with AUD and other addictions from both preclinical and clinical studies. What is less clear is how well specific experimental tasks and findings translate from animals to humans, e.g., the failure of CRF antagonism to reduce alcohol craving in humans, after robustly attenuating alcohol consumption in rodents. Similarly, the ability to reverse-translate human addiction constructs for study
Final points
In summary, we have presented a framework for understanding addiction, the Addictions Neuroclinical Assessment, and reviewed the literature supporting the application of the three ANA neuroscience domains to better understanding AUD and other addictions. A common framework of assessment of addiction neurobiology, e.g., as shown in Fig. 1, can enable aspects of vulnerability and consequences of addictions that are shared across different addictive agents to be tracked with the same tools, and
Acknowledgements
We acknowledge the Division of Intramural Clinical and Biological Research, the Office of the Clinical Director, the Office of the Director, the Laboratory of Neurogenetics, and the Division of Medications Development, all at NIAAA.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
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