Progress in brain research, volume 224

Woo Young Ahn Department of Psychiatry, Institute for Drug and Alcohol Studies, Virginia Commonwealth University, Richmond, VA, and Department of Psychology, The Ohio State University, C

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Mark Bear, Cambridge, USA.

Medicine & Translational NeuroscienceHamed Ekhtiari, Tehran, Iran

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Woo Young Ahn

Department of Psychiatry, Institute for Drug and Alcohol Studies, Virginia

Commonwealth University, Richmond, VA, and Department of Psychology,

The Ohio State University, Columbus, OH, USA

Nelly Alia-Klein

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York,

NY, USA

Albert Batalla

Department of Psychiatry and Psychology, Hospital Clı´nic, IDIBAPS, CIBERSAM,

University of Barcelona, Barcelona, Spain, and Department of Psychiatry,

Radboud University Medical Centre, Nijmegen, The Netherlands

Samantha Brooks

Department of Psychiatry and MRC Unit on Anxiety & Stress Disorders, University

of Cape Town, Cape Town, South Africa

Gregory G Brown

Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA

Jerome R Busemeyer

Department of Psychological and Brain Sciences, Indiana University,

Bloomington, IN, USA

Elizabeth Cabrera

National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health,

Bethesda, MD, USA

Salvatore Campanella

Laboratoire de Psychologie Medicale et d’Addictologie, ULB Neuroscience

Institute (UNI), CHU Brugmann-Universite Libre de Bruxelles (U.L.B.), Brussels,

Belgium

Nazzareno Cannella

Institute of Psychopharmacology, Central Institute of Mental Health, Medical

Faculty Mannheim/Heidelberg University, Mannheim, Germany

Daniele Caprioli

Behavioral Neuroscience Research Branch, Intramural Research Program,

NIDA, NIH, Baltimore, MD, USA

Sandra Carvalho

Department of Physical Medicine and Rehabilitation, Laboratory of

Neuromodulation, Spaulding Rehabilitation Hospital and Massachusetts General

Hospital, Harvard Medical School, Boston, MA, USA, and

Neuropsychophysiology Laboratory, CIPsi, School of Psychology (EPsi),

University of Minho, Braga, Portugal

v

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Ashkan Faghiri

Research Center for Molecular and Cellular Imaging, Tehran University ofMedical Sciences, and Department of Electrical Engineering, Sharif University ofTechnology, Tehran, Iran

Sarah W Feldstein Ewing

Department of Psychiatry, Oregon Health & Science University, Portland, OR,USA

Felipe Fregni

Neuromodulation, Spaulding Rehabilitation Hospital and Massachusetts GeneralHospital, Harvard Medical School, Boston, MA, USA

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Hugh Garavan

Departments of Psychiatry and Psychology, University of Vermont, Burlington,

VT, USA

Thomas E Gladwin

Addiction Development and Psychopathology (ADAPT) Lab, Department

of Psychology, University of Amsterdam, Amsterdam, and Research

Centre—Military Mental Health, Ministry of Defense, Utrecht, The Netherlands

David C Glahn

Department of Psychiatry, Yale University School of Medicine, New Haven, CT,

USA

Rita Z Goldstein

Department of Psychiatry, and Department of Psychiatry & Neuroscience, Icahn

School of Medicine at Mount Sinai, New York, NY, USA

Anna E Goudriaan

of Cape Town, Cape Town, South Africa, and Department of Psychiatry,

University of Amsterdam, Amsterdam, The Netherlands

Joshua L Gowin

Section on Human Psychopharmacology, Intramural Research Program,

National Institute on Alcohol Abuse and Addiction, National Institutes of Health,

Bethesda, MD, USA

Markus Heilig

Center for Social and Affective Neuroscience, Department of Clinical and

Experimental Medicine, Link€oping University, Link€oping, Sweden

Mary M Heitzeg

Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA

Marcus Herdener

Center for Addictive Disorders, Department of Psychiatry, Psychotherapy and

Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland

Derrek P Hibar

Department of Neurology, Imaging Genetics Center, Keck School of Medicine,

University of Southern California, Marina del Rey, CA, USA

Kent Hutchison

Department of Psychology and Neuroscience, University of Colorado Boulder,

Boulder, CO, USA

Joanna Jacobus

Neda Jahanshad

vii Contributors

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Lorenzo Leggio

Section on Clinical Psychoneuroendocrinology and Neuropsychopharmacology,Laboratory of Clinical and Translational Studies, National Institute on AlcoholAbuse and Alcoholism, and Intramural Research Program, National Institute onDrug Abuse, Bethesda, MD, USA

Jorge Leite

Neuromodulation, Spaulding Rehabilitation Hospital and Massachusetts GeneralHospital, Harvard Medical School, Boston, MA, USA, and

Neuropsychophysiology Laboratory, CIPsi, School of Psychology (EPsi),

University of Minho, Braga, Portugal

Chiang-Shan R Li

Department of Psychiatry, Yale University School of Medicine, New Haven, CT,USA

Edythe D London

Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York,

NY, and David Geffen School of Medicine, University of California at Los Angeles,Los Angeles, CA, USA

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Benjamin McKenna

Scott J Moeller

Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount

Sinai, NY, USA

Reza Momenan

Section on Brain Electrophysiology and Imaging, Institute on Alcohol Abuse and

Alcoholism, Bethesda, USA

Angelica M Morales

David Geffen School of Medicine, University of California at Los Angeles,

Los Angeles, CA, USA

Michael A Nader

Department of Physiology and Pharmacology, Wake Forest School of Medicine,

Winston-Salem, NC, USA

Mohammad-Ali Oghabian

Research Center for Molecular and Cellular Imaging, and Advanced Diagnostic

and Interventional Radiology Research Center, Tehran University of Medical

Sciences, Tehran, Iran

Vani Pariyadath

Neuroimaging Research Branch, Intramural Research Program, National

Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA

Muhammad A Parvaz

Departments of Psychiatry & Neuroscience, Icahn School of Medicine at Mount

Sinai, NY, USA

Martin P Paulus

VA San Diego Healthcare System and Department of Psychiatry, University of

California San Diego, La Jolla, CA, and Laureate Institute for Brain Research,

Tulsa, OK, USA

Experimental and Clinical Pharmacopsychology, Department of Psychiatry,

Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich,

and Neuroscience Centre Zurich, University of Zurich and Swiss Federal Institute

of Technology (ETH), Zurich, Switzerland

ix Contributors

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Tara Rezapour

Research Center for Molecular and Cellular Imaging, Tehran University ofMedical Sciences, and Translational Neuroscience Program, Institute forCognitive Science Studies, Tehran, Iran

Department of Psychiatry, School of Medicine, Yale University, New Haven, and

VA Connecticut Healthcare System, West Haven, CT, USA

Nadia Solowij

School of Psychology, University of Wollongong, Wollongong, NSW, AustraliaDan J Stein

Elliot A Stein

Intramural Research Program—Neuroimaging Research Branch, NationalInstitute on Drug Abuse, and Neuroimaging Research Branch, IntramuralResearch Program, National Institute on Drug Abuse, National Institutes ofHealth, Baltimore, MD, USA

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Jennifer L Stewart

Department of Psychology, Queens College, City University of New York, NY, USA

Julie C Stout

School of Psychological Sciences and Monash Institute of Cognitive and Clinical

Neuroscience, Monash University, Clayton, VIC, Australia

Susan Tapert

Laureate Institute for Brain Research, Tulsa, OK, USA

Rachel E Thayer

Department of Psychology & Neuroscience, University of Colorado Boulder,

Boulder, CO, USA

Paul M Thompson

Massimo Ubaldi

School of Pharmacy, Pharmacology Unit, University of Camerino, Camerino, Italy

Anne Uhlmann

Ruth van Holst

Department of Psychiatry, University of Amsterdam, Amsterdam, The

Netherlands

Jasmin Vassileva

Department of Psychiatry, Institute for Drug and Alcohol Studies, Virginia

Commonwealth University, Richmond, VA, USA

Dick Veltman

Department of Psychiatry, VU University Medical Center, Amsterdam, The

Netherlands

Marco Venniro

Behavioral Neuroscience Research Branch, Intramural Research Program,

NIDA, NIH, Baltimore, MD, USA, and Department of Public Health and

Community Medicine, Neuropsychopharmacology Laboratory, Section of

Pharmacology, University of Verona, Verona, Italy

Nora D Volkow

National Institute on Alcohol Abuse and Alcoholism, and National Institute on

Drug Abuse, National Institutes of Health, Bethesda, MD, USA

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Corinde E Wiers

Department of Psychiatry and Psychotherapy, Charite—Universita¨tsmedizin;Berlin School of Mind and Brain, Humboldt-Universita¨t zu, Berlin, Germany, andNational Institute on Alcohol Abuse and Alcoholism, National Institutes of Health,Bethesda, MD, USA

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Preface: Neuroscience for Addiction

Medicine: From Prevention to

Rehabilitation

It is estimated that a total of 246 million people, i.e., over 5% of the world’s adult

population, have used an illicit drug during the last year Meanwhile, more than 10%

of these drug users are suffering from drug use disorders and the number of

drug-related deaths is estimated to be over 187,000 annually (UN Office of Drugs and

Crime, 2015) Adding disorders related to the nonpharmacologic or behavioral

ad-dictions such as pathological gambling, Internet and gaming adad-dictions, overeating

and obesity, and compulsive sexual behaviors to the drug addictions comprises a

group of brain disorders that contribute as one of the major challenges for humankind

in the current millennium

Addiction medicine has been regarded as a stand-alone specialty among other

medical professions in several countries; however, there are still serious concerns

regarding the availability and effectiveness of interventions in a wide range from

pre-vention to rehabilitation in addiction medicine Accumulating pathophysiological

evidences for “Addiction as a Brain Disorder” during last 20 years is extending

ex-pectations from neuroscience to contribute more seriously in the routine clinical

practices during prevention, assessment, treatment, and rehabilitation of addictive

disorders Neuroscience has made tremendous progress toward understanding basic

neural processes; however, there is still a lot of progress needed to be made in

uti-lizing neuroscience approaches in clinical medicine in general and addiction

medi-cine in particular

The basic idea of a book to provide the current status of the field of neuroscience

of addiction with particular emphasis on potential applications in a clinical setting

was jumped out during meetings in the 2nd Basic and Clinical Neuroscience

Con-gress in October 2013 in Tehran with Professor Vincent Walsh, theProgress in Brain

Research, PBR, Editor in Chief We, Martin and Hamed, started to work together for

a proposal to the PBR advisory board to compile a volume of reviews in June 2014 in

the Laureate Institute for Brain Research, Tulsa, OK After receiving the green lights

from the PBR office, the invitations went out to the senior scholars in the field from

October 2014 We received overwhelming positive feedbacks from over 120

contrib-utors from 90 institutes in 14 countries that ended up with 36 chapters in two volumes

in October 2015 During this 1 year of intensive efforts, all the chapters were peer

reviewed and revised accordingly to meet high-quality standards of the PBR and our

vision for the whole concept of the volumes The first volume, PBR Vol 223, is

mainly focused on the basic neurocognitive constructs contributing to

pathophysio-logical basis of pharmacopathophysio-logical and behavioral addictions, and the second volume,

xxv

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PBR Vol 224, depicts the contribution of neuroscience methods and interventions inthe future of clinical practices in addiction medicine.

The goal of these two volumes is to provide readers with insights into currentgaps and possible directions of research that would address impactful questions.The fundamental question that is addressed in these volumes is “how can neurosci-ence be used to make a real difference in addiction medicine”? To that end, we askedthe contributors to:

(1) review the recent literature with a time horizon of approximately 5–10 years,(2) identify current gaps in our knowledge that contribute to the limited impact ofthe area of research to clinical practice, and

(3) provide a perspective where the field is heading and how impactful questions can

be addressed to change the practice of addiction medicine

We envision that both neuroscientists and clinical investigators will be the primaryaudience of these two volumes Moreover, the common interest of these individualswill be the application of neuroscience approaches in studies to assess or treat indi-viduals with addictive disorders We think that these PBR volumes will provide theaudiences with most recent evidences from different disciplines in brain studies onthe wide range of addictive disorders in an integrative way toward “Neuroscience forAddiction Medicine: From Prevention to Rehabilitation.” The hope is that the infor-mation provided in the series of chapters in these two volumes will trigger new re-searches that will help to connect basic neuroscience to clinical addiction medicine

The EditorsHamed Ekhtiari, MD,Iranian National Center for Addiction Studies

Martin Paulus, MD,Laureate Institute for Brain Research

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Animal models for addiction

medicine: From vulnerable

1 Corresponding author: Tel.: +336-713-7172; Fax: +336-713-7180,

e-mail address: mnader@wakehealth.edu

Abstract

This chapter highlights the use of several animal models of abuse liability The overall goal is

to describe the most frequently used methods, unconditioned behaviors and conditioned

be-haviors, and how investigators can use these techniques to compare drugs and to understand

the mechanisms of action mediating abuse liability Thus, for each type of animal model

de-scribed, research will be highlighted on three general features related to the use of the model:

(1) determine abuse potential, (2) treatment efficacy, and (3) brain-related changes associated

with drug administration

Keywords

Animal models, Unconditioned behavior, Conditioned behavior, Drug discrimination, Drug

self-administration, Conditioned place preference, PET imaging, Microdialysis

In his brief history of behavioral pharmacology, Roy Pickens (1977) listed two

events in the 1940–1955 period that he considered the most significant advancements

for the field: the discovery of the antipsychotic effects of chlorpromazine and the

hallucinogenic effects of LSD The former was significant primarily because it

ad-vanced the predictive nature of animal models, while the latter was significant for

increasing attention on the relationship between biochemistry and behavior and

be-cause it led to the study of preclinical models of drug self-administration The focus

of this chapter will be on animal models of addiction and the foundation for these

Progress in Brain Research, Volume 224, ISSN 0079-6123, http://dx.doi.org/10.1016/bs.pbr.2015.07.012

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studies can be traced back to the preclinical work on chlorpromazine and LSD As aminimum, animal models must be predictive of some outcome in people This pre-dictive nature could be related to models of abuse liability (i.e., is this novel drugreinforcing?) or to potential treatment outcomes (i.e., does drug X decrease drug ad-diction?); methods for both types of models will be described In this chapter, twomain strengths of animal models will be emphasized: (1) the ability to start withdrug–naı¨ve subjects and determine phenotypic/trait characteristics that are associ-ated with addiction and (2) the ability to study the neurochemical, physiological,and pharmacodynamic consequences of chronic drug exposure Utilizing both ofthese qualities of animal models is necessary to develop novel treatment strategiesfor drug addiction.

In animal models of addiction, the primary dependent variable is some behavioralendpoint—whether it is activity level, or time in a quadrant related to a conditionedstimulus (CS), or lever pressing or total drug intake These dependent variables will

be the focus of the studies described in this chapter In addition, the relationship tween behavior and brain will also be described Although there are many methodsused in the literature, this review will highlight the use ofin vivo microdialysis, mag-netic resonance imaging, and positron emission tomography (PET) in the study ofbrain–behavior relationships There are excellent reviews on this topic that willnot be repeated in this chapter (see Howell and Murnane, 2011; Murnane andHowell, 2011; Nader and Banks, 2014for recent reviews) Thus, for each type ofanimal model described, the goal of this chapter will be to highlight three generalfeatures for the use of the model: (1) determine abuse potential, (2) treatment effi-cacy, and (3) brain-related changes associated with drug administration

When assessing animal models for addiction medicine, there are two general gories of models: those that utilize unconditioned behaviors and those that requirethe study of conditioned responses While the majority of the chapter will be on con-ditioned responses, it is important to briefly describe some unconditioned models inorder to give researchers a more thorough representation of the breadth of experi-mental techniques available

Perhaps the simplest of behaviors to measure is overall activity in an enclosed vironment These measures can be used as trait markers for vulnerability or as aninitial screen for “stimulant-like” drug effects The best example of using locomo-tor activity as a trait marker for vulnerability to drug abuse was a study byPiazza

en-et al (1989)in which rats were first characterized as high responders (HRs) or lowresponders (LRs) in an open field When given access to cocaine under a fixed-ratio (FR) 1 schedule of reinforcement, the locomotor HRs were more likely to

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acquire cocaine self-administration compared to the LRs This behavioral

pheno-type has been well characterized in relation to corticosterone (Piazza and Le Moal,

1998; Piazza et al., 1991) and to dopamine (DA) D2-like receptor availability

(Dalley et al., 2007) Major strengths of this unconditioned behavior are (1) it

re-quires no training, (2) can utilize large numbers of animals, and (3) provides a

quantitative measure that can be used to compare animals on other endpoints

in-cluding neurochemical and behavioral (related to drug reinforcement, for

example)

It has been hypothesized that for stimulant drugs (e.g., cocaine, amphetamine,

nicotine) increases in locomotor behavior represent an initial screen for potential

abuse liability These simple procedures involving unconditioned behaviors can

be used to better understand the potential mechanisms of action related to drugs

of abuse, but they are not models of abuse potential For example, within the DA

D2 receptor family, drugs that act at different subtypes have been identified and these

subtypes (D2, D3, and D4) have implications for drugs of abuse.Li et al (2010)used

drug-elicited yawning and locomotor activity in mice to better understand the roles

of DA D3 versus D2 receptors, respectively, with the goal of identifyingin vivo

screens for each receptor subtype that could ultimately lead to medications for drug

abuse Interestingly,Collins et al (2008)showed that food restriction altered these

unconditioned behaviors suggesting an interaction between diet composition and

brain function, which could lead to increased or decreased vulnerability to drug

abuse With regard to the DA D4 receptor subtype, Katz et al (2003)examined

the effects of cocaine (1.0–10 mg/kg, i.p.) administered to wild-type (WT) and

DA D4 receptor knockout (KO) mice in order to better understand the role of this

receptor subtype in the behavioral effects of cocaine While the two groups did

not differ in baseline measures of activity, cocaine administration resulted in

signif-icantly larger increases in locomotor activity in the D4 KO mice compared to WT

animals Katz et al (2003) also found that D4 KO mice were more sensitive to

the discriminative stimulus effects of cocaine compared to WT littermates

Typically, in studies that utilize locomotor activity, other behavioral or

physiological measures are examined to more thoroughly characterize the behavioral

effects of drugs For example,Miller et al (2013)used an immunotherapeutic

ap-proach to attenuate the behavioral effects of methamphetamine and examined

multiple dependent variables These investigators reported that vaccination

against methamphetamine blocked the effects on locomotor activity, as well as wheel

running (another measure of activity) and changes in body temperature, suggesting

protection against physiological and behavioral disruptions induced by

methamphetamine

In a recent study,Vanhille et al (2015)characterized rats using two

uncondi-tioned behaviors, novelty-induced locomotor activity and open-arm access in an

elevated plus maze, and a conditioned behavior in which lever pressing and head

entries into the food magazine during presentation of the CS were used to assess

sign tracking and goal tracking, respectively Interestingly, all the behaviors were

characterized as normally distributed but not correlated with each other, indicating

5

2 Types of animal models

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independent constructs being assessed When used to phenotypically characterizevulnerability to cocaine abuse, neither elevated plus maze (high vs low anxiety)nor sign tracking versus goal tracking (i.e., CS vs food-maintained lever pressing)was related to a rat’s propensity to acquire cocaine self-administration However,HRs in the locomotor assay were more likely to choose saccharin over cocainethan LRs, who primarily chose cocaine over saccharin This interesting finding

is at odds with earlier work showing HR rats more vulnerable to cocaine ment when available under an FR 1 schedule of reinforcement (Piazza et al.,

reinforce-1989).Vanhille et al (2015) suggest that the difference is due to the importance

of environmental context in which drugs are self-administered; environmental text has been shown to influence the behavioral effects of drugs under many con-ditions (see Barrett and Katz, 1981 for review; e.g., Barrett and Stanley, 1980;McKearney and Barrett, 1975) There were important methodological differences

con-in the Vanhille et al (2015) study compared to earlier saccharin–cocaine choicestudies (see Ahmed, 2010) that may have biased initial choice toward cocaine(see comments from Ahmed, 2014) Certainly, there needs to be standardization

of protocols in order to better compare between studies, as has been pointedout earlier (Katz, 1990)

Other investigators have also used locomotor activity as a trait marker to identify

or “unmask” some other predisposition For example,Hamilton et al (2010, 2011)

studied two groups of adult rhesus monkeys—one group was prenatally exposed tococaine and the other group was control monkeys When they were approximately12–14 years old, each monkey was assessed in an open field for locomotor activity,along with other unconditioned behaviors including approaching a novel object

Hamilton et al (2011)reported that there were no differences in locomotor activity

or approaching a novel object between prenatally cocaine-exposed and control keys, even though other behaviors (e.g., drug-elicited yawning, resistance to extinc-tion, and cocaine self-administration) were different between groups (Brutcher andNader, 2012; Hamilton et al., 2010, 2011) This suggests that some characteristicsthat are hypothesized to influence vulnerability to drug abuse (e.g.,in utero cocaineexposure) may not be amenable to the predictive validity of behavioral assays hy-pothesized to measure “anxiety-like” behaviors, like locomotor activity in anopen-field apparatus

mon-There are some limitations to the use of locomotor activity to understand factorsrelated to abuse liability One major limitation is that while behavioral sensitization

to locomotor stimulation frequently occurs, this does not necessarily translate intosensitization to the reinforcing effects of cocaine, and vice versa (e.g., Ahmedand Koob, 1998; Lack et al., 2008) It is also the case that characterizing animals

as “high” and “low” responders does not necessarily translate into more or less nerable individuals, respectively (e.g.,Dalley et al., 2007) Thus, while the behavior

vul-is amenable to pharmacological manipulations, and the combination of other ditioned behaviors allows for rapid screening, some caution should be used whenthese are the primary behaviors under investigation

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2.2 CONDITIONED PLACE PREFERENCE

Conditioned place preference (CPP) studies are most frequently conducted in rodents

and are said to involve “reward.” CPP involves classical conditioning in which

stim-uli associated with one quadrant are paired with a drug dose, while stimstim-uli associated

with a distinctly different quadrant are paired with the drug vehicle (seeMucha et al.,

1982); the two compartments are separated by a neutral space CPP (i.e., reward) is

said to occur if the animal spends more time in the drug-paired side compared to the

vehicle-paired side (e.g.,Bali et al., 2015; see reviews byWise, 1989and by

Sanchis-Segura and Spanagel, 2006) In training a CPP, many investigators use an unbiased

procedure in which the initial phase consists of giving the animal access to both

partments of the apparatus If the animal spends significantly more time in one

com-partment over the other (e.g., some investigators use 80% vs 20%, others 67% vs

33% as criterion), then they are not used in the conditioning phase of the study In the

conditioning phases, drug is paired with one compartment and drug vehicle with the

other compartment; these compartments and drug/vehicle are typically

counterba-lanced across subjects Most drugs of abuse can produce CPP and recent literature

indicates that this methodology is frequently used to study drugs of abuse from

all classes, including stimulants (e.g., Aguilar et al., 2015), opiates (e.g., Wang

et al., 2015), alcohol (e.g.,Gubner et al., 2015), andD9

-THC, the active ingredient

in marijuana (e.g.,Manwell et al., 2014) Time spent on the drug-paired side is

typ-ically represented as an inverted U-shaped function of dose; very high doses can

in-duce a conditioned place aversion (e.g.,Kirkpatrick and Bryant, 2015)

In addition to examining abuse liability, CPP can be used to better understand the

neurochemical and neuropharmacological mechanisms of action for drugs of abuse

Two examples will be provided here, one involving systemic drug administration

and the other central administration.Northcutt et al (2015)trained rats using an

un-biased CPP protocol with 10 mg/kg cocaine and saline in the different compartments

over 4 training days For one group, during conditioning they received 10 mg/kg

co-caine plus (+)-naloxone When place preference was determined on Day 5, 10 mg/kg

cocaine induced a CPP, but the group that was coadministered (+)-naloxone did not

show a preference Throughin silico computer modeling and in vitro assays, the

in-vestigators hypothesized that cocaine and (+)-naloxone were binding to the same

proinflammatory central immune signaling cascade; the CPP data suggested a

func-tional consequence to thesein vitro findings

Using a slightly different version of CPP,Galaj et al (2014)first trained the CPP

with cocaine (10 mg/kg) and then examined the effects of a DA D1 receptor

antag-onist, SCH23390, administered via microinjection directly into the ventral tegmental

area The investigators found that SCH23390 (0, 2.0, 4.0, and 8.0mg/0.5 ml) dose

de-pendently reduced cocaine CPP The difference between the results of this study and

the previous one is related to neurochemical mediation involving acquisition

(Northcutt et al., 2015) and expression (Galaj et al., 2014) In the latter case, the

model addresses issues related to treatment efficacy, since conditioning had already

7

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taken place, while in the former study, neuropharmacological considerations related

to vulnerability were addressed

Most recently, CPP has been used to investigate environmental and social ables that influence vulnerability to drug abuse One hypothesis is that when com-bined with social enrichment, lower drug doses induce a CPP (e.g.,Thiel et al., 2008,

vari-2009; see review by Trezza et al., 2010) For example,Watanabe (2011) studiedthree groups of mice in a methamphetamine CPP study: (1) individually housed an-imals in standard CPP training with a low methamphetamine dose of 2.0 mg/kg; (2)paired animals in which both mice received the exact treatment (i.e., saline on oneside and 2.0 mg/kg methamphetamine on the other); and (3) control pairs in whichCPP training was reversed such that when one animal received methamphetaminethe other received saline The pair group, in which both animals received the iden-tical treatment, resulted in greater CPP than the individually housed and controlpairs, indicating an enhancement of methamphetamine reward when the cage matealso received methamphetamine It is important to note that merely the presence of acage mate did not enhance CPP, but rather only when both animals received drugtogether was there evidence of methamphetamine reward Interestingly, when timespent on the nondrug side was examined, the control pairs showed a profound placeaversion That is, when one animal received methamphetamine and the partner re-ceived saline, there was a place aversion on the saline side, perhaps indicating a neg-ative consequence on social behavior related to drug use

One final example to close out this section involves using CPP in combinationwithin vivo brain imaging to better understand the neurochemical consequences as-sociated with drug use.Schiffer et al (2009)first trained rats in CPP using 5.0 mg/kg(i.v.) cocaine and saline This dose of cocaine was chosen because this group hadpreviously shown, usingin vivo microdialysis, that the cocaine-paired side wouldelicit increases in extracellular DA in the ventral striatum (Gerasimov et al.,

2001) After the CPP was established, each rat underwent two PET scans using[11C]raclopride The investigators hypothesized that if the cocaine-paired side eli-cited DA release, the [11C]raclopride binding potential would be significantly re-duced compared to the PET signal when rats were placed on the saline-pairedside In fact,Schiffer et al (2009)found an approximate 20% lower [11C]raclopridebinding potential in the dorsal and ventral striatum on the cocaine-paired side relative

to the saline-associated side and a direct relationship between changes in bindingpotential and cocaine preference These findings highlight the amenability of CPP

toin vivo imaging studies

There are some limitations to the use of CPP as a model to understand factorsrelated to abuse liability As mentioned above, CPP does not measure “drug seeking”

or “drug taking,” two hallmarks of addiction A second limitation is the ability tostudy multiple pharmacological manipulations—once the conditioning has beenestablished, any tests without the drug of abuse can decrease the effectiveness ofthe CS, thereby making repeated, longitudinal studies more challenging In general,these models are good initial screens that can lead to follow-up studies involvingdrug discrimination (DD) and drug self-administration procedures

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2.3 DRUG DISCRIMINATION

By definition, a discriminative stimulus “sets the occasion” for responding by

pro-viding information related to the contingencies mediating stimulus–response

rela-tionships In models of DD, the discriminative stimulus is the presence or absence

of the training drug Training a discriminative stimulus in animal models typically

involves two operant responses in which responding on one manipulandum (e.g.,

le-ver, key, nose poke, finger poke) is reinforced following administration of the

train-ing drug while respondtrain-ing on the other manipulandum is reinforced followtrain-ing

administration of the drug vehicle For example, when the subject is administered

a dose of 0.2 mg/kg cocaine (the training drug and dose), responding on the left lever

results in food reinforcement; responding on the right lever would have no scheduled

consequence (or may reset the FR value on the correct lever) When the subject is

administered saline, responding on the right lever would be reinforced, but left-lever

responding would not It has been hypothesized that the “interoceptive”

discrimina-tive stimulus effects of a drug in an animal, model the subjecdiscrimina-tive effects in humans

A particular strength of DD procedures is that the behavioral effects of drugs are

thought to be mediated centrally (i.e., receptor changes in the brain; see Carter

and Griffiths, 2009andStolerman et al., 2011for reviews) In addition to

understand-ing the mechanisms of action mediatunderstand-ing the discriminative stimulus effects of a drug,

substitution studies are also used as an index of the abuse liability of compounds and

impact the scheduling of drugs by the US Food and Drug Administration (FDA; see

Nader et al., 2015for examples)

In DD studies, the two primary dependent variables are % responding on the

drug-associated lever and overall response rates Most investigators operationally

define substitution as occurring when at least 80% of the total responses occurred

on the drug-appropriate lever Including response rate data is important for several

reasons If a test drug substitutes for a drug of abuse, but only at doses that result in

significant rate-decreasing effects, that may suggest less abuse liability because

doses that disrupt ongoing behavior are required to produce subjective-like effects

similar to the drug of abuse Conversely, if a novel drug is studied and that drug does

not substitute for the training drug, it may not be clear that high enough doses were

tested unless response rates were altered Related to both substitution and response

rate effects of test drugs is the dose of the drug used to train the discriminative

stim-ulus As pointed out byStolerman et al (2011), “… training dose may show an

im-pact on qualitative aspects of a discrimination, as defined by changes in the drugs to

which generalization occurs, and sensitivity to antagonists” (p 415) One example

will be given in order to demonstrate the types of questions that can be addressed by

manipulating the training dose

2.3.1 Influence of training dose

Grant et al (2000)trained male (n¼8) and female (n¼10) cynomolgus monkeys to

discriminate either 1.0 g/kg ethanol from water or 2.0 g/kg ethanol from water (all

solutions were administered intragastrically) in a two-lever, food-reinforced operant

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procedure In addition to determining an ethanol dose–response curve, pentobarbital,midazolam, muscimol, and morphine dose–response curves were determined Notsurprisingly, the training dose influenced the ED50 values for ethanol substitution,with ethanol being more potent in the 1.0 g/kg training groups compared to the2.0 g/kg groups Pentobarbital and midazolam, two GABAA agonists, substitutedfor 1.0 g/kg and 2.0 g/kg ethanol, but only the potency of pentobarbital was influenced

by training dose.Grant et al (2000)did not observe sex differences with regard to anymanipulation in the study These findings were extended to N-methyl-D-aspartate(NMDA) glutamate receptors by examining PCP, ketamine, and dizocilpine(Vivian et al., 2002) At the low training dose condition (1.0 g/kg ethanol), all threeNMDA receptor compounds substituted for ethanol in both males and females Incontrast, at the 2.0 g/kg ethanol training dose, PCP, ketamine, and dizocilpine didnot substitute for ethanol in the males One possible mechanism for these sex differ-ences was the greater sensitivity to the rate-decreasing effects of NMDA receptorantagonists in males compared to females; these sex differences were only apparentwhen the higher ethanol training dose was studied Taken together, using differentethanol training doses, Grant and colleagues concluded that the NMDA receptorsystem is less prominent than the GABAAreceptors in mediating the discriminativestimulus effects of ethanol in nonhuman primates, especially with higher ethanoltraining doses Such mechanistic understanding of ethanol’s effects would not havebeen obtained if only one training dose had been studied

2.3.2 Other methodological considerations

In addition to the importance of training dose and sex, there are other independentvariables that have recently been identified that can impact the substitution profile ofdrugs In many DD studies, the subjects are modestly food restricted in order to studyfood-maintained operant responding Depending on the drugs under study, this mayinfluence the outcome of substitution studies (e.g., Baladi and France, 2010) Forexample, the DA D2/D3 receptor agonist quinpirole can be trained as a discrimina-tive stimulus, and this typically involves food-restricted animals (e.g., Katz andAlling, 2000).Baladi et al (2010)trained free-feeding rats to discriminate quinpirolefrom saline under a schedule of stimulus–shock termination DA D2/D3 receptor ag-onists apomorphine and lisuride substituted for quinpirole and, as reported byBaladi

et al (2010), similar findings have been reported in food-restricted animals ever, using DA receptor antagonists, differences between free-feeding and food-restricted animals became apparent In free-feeding rats, a D2/D3 receptor antagonist(raclopride) and a D3 receptor-selective antagonist (PG01037), but not a D2receptor-selective antagonist (L-741,626), blocked the discriminative stimulus ef-fects of quinpirole, shifting the quinpirole dose–response curve to the right Thesefindings suggest that the discriminative stimulus effects of quinpirole in free-feedinganimals are primarily D3 receptor mediated, while in food-restricted animals, quin-pirole’s discriminative stimulus effects are thought to be mediated by D2 receptors(cf.Baladi et al., 2010)

How-10 CHAPTER 1 Animal models for addiction medicine

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2.3.3 DD in combination with brain imaging

As mentioned above, it is believed that the discriminative stimulus effects of drugs

are centrally mediated Studies have been conducted that combine DD techniques

within vivo microdialysis to study how drugs that share discriminative stimulus

ef-fects influence neurotransmitter concentrations (e.g.,Czoty et al., 2000; Kimmel

et al., 2012) In one study, Czoty et al (2004) trained monkeys to discriminate

0.32 mg/kg methamphetamine from saline under an FR 10 schedule of stimulus–

shock termination Monkeys were also implanted with guide cannulae above the

cau-date nucleus and microdialysis experiments were conducted in the same operant

chambers as the DD procedures The investigators found that methamphetamine,

as well as cocaine and methylphenidate, resulted in dose-dependent increases in

methamphetamine-appropriate responding when studied in the DD protocol

Doses that occasioned 100% methamphetamine responding produced similar

in-creases in extracellular DA concentrations Interestingly, the time course for

eleva-tions in DA and substitution in DD was not identical, indicating the involvement of

other neurotransmitter systems in mediating the discriminative stimulus effects of

methamphetamine

There are some considerations regarding the use of DD that investigators should

address In terms of scheduling of drugs, the FDA suggests that if a novel drug

sub-stitutes for a drug of abuse, it has abuse liability, but if it does not substitute it may

still have abuse liability Considering the examples provided in this section on how

dose, environmental context, and sex can influence these profiles, the use of DD in

scheduling of drugs appears less than straightforward Also of relevance for the

de-velopment of treatment agents is the time course of substitution The FDA does not

distinguish the importance of time course, so if a novel drug does not substitute for

cocaine (for example) until 2 h after administration, this information does not factor

into “abuse liability,” but it should If pharmacological agonists become a treatment

strategy, a profile in which the subjective effects occur at a later time after

admin-istration and last longer than the drug of abuse, should positively impact compliance

and reduce drug taking

There is probably no behavioral model that is more predictive of human disease than

animal drug self-administration models of abuse liability Readers interested in the

history of drug self-administration are referred to the original pioneering studies of

Spragg (1940),Weeks (1962),Thompson and Schuster (1964), andDeneau et al

(1969); see also Griffiths et al (1980) The behavioral process mediating drug

self-administration is reinforcement, which can be either positive reinforcement

or negative reinforcement Positive reinforcement is defined as response-contingent

presentation of a stimulus (e.g., drug) increases the probability of the response that

produced the stimulus Negative reinforcement is also an increase in responding, but

in this case it is based on the response contingency of removing a stimulus (e.g.,

with-drawal symptoms) In the initial work (Spragg, 1940; Thompson and Schuster, 1964;

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Weeks, 1962), animals were made physically dependent on morphine and the drugself-administration behavior was thought to be mediated by negative reinforcement.That is, responding leading to morphine presentation was believed to be maintained

by removing withdrawal symptoms However, for all drugs of abuse, there are itive reinforcing effects and, most likely, negative reinforcing effects (see Czoty

pos-et al., 2015 for more discussion of this distinction) Drugs are self-administered

by animals using the same routes of administration as humans including oral(e.g.,Baker et al., 2014; Carroll and Meisch, 1978; Grant and Samson, 1985), inha-lation (e.g., Carroll et al., 1990; Evans et al., 2003; Newman and Carroll, 2006;Pickens et al., 1973), and intravenous (some examples provided below) For the pur-poses of this chapter, basic information regarding schedules of reinforcement will beprovided, as well as some examples involving different drug classes using the intra-venous route Because much work has been done with intravenous stimulants, espe-cially cocaine, that will be the most frequently described drug in this section.2.4.1 Use of simple schedules of reinforcement

Depending on whether the investigator is simply examining a drug for abuse liability

or wanting to compare it to other drugs, different schedules of reinforcement areused For example, answering the question “does the drug have reinforcing effects?”most investigators use an FR schedule of reinforcement in which a particular number

of responses are required for each drug injection For example, an FR 30 schedule,the thirtieth response results in drug presentation If it is a within-subject design, be-havior is compared to when saline is self-administered and if it is significantlyhigher, the drug has abuse potential Less ideal is the use of an “inactive” lever inthe chamber—responding that is higher on the “active,” drug-contingent lever rela-tive to the inactive lever also represents reinforcement Some investigators may use afixed-interval (FI) schedule of reinforcement, in which a response after a specificperiod of time has elapsed results in drug presentation For example, under an FI3-min schedule, the first response after 3 min results in drug presentation; respondingduring the interval has no scheduled consequence If the drug under investigation hassubstantial response rate-decreasing effects, this may be a better schedule than FRschedules because only one response is required after the interval has timed out Irre-spective of the schedule of reinforcement, behavior (response rates or number of in-jections) is represented as an inverted U-shaped function of dose (e.g.,Pickens andThompson, 1968; Weeks, 1962) The shape of this curve is influenced by severalfactors (Zernig et al., 2004), including reinforcing effects (increasing the probability

of future responding) and rate-decreasing effects (decreasing likely responding) Forthis reason, it is not appropriate to compare drugs and rank them in terms of abusepotential using simple schedules of reinforcement Later in this section, measures ofreinforcing strength will be described; these models can be used to directly compareand rank drugs

The use of animals allows investigators to begin with drug–naı¨ve subjects andstudy vulnerability to drug abuse As described earlier with high and low locomotorresponders, phenotypic characteristics can be used to identify more or less vulnerable

12 CHAPTER 1 Animal models for addiction medicine

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individuals Others have shown that a particular drug history is needed for certain

drugs to function as reinforcers For example, Nader and Mach (1996) and

Collins and Woods (2007)showed that monkeys and rats required a cocaine

self-administration history before DA D3 receptor agonists would function as reinforcers,

implying that a cocaine history alters DA D3 receptor function Investigators

fre-quently operationally define acquisition of some performance criterion (e.g., number

of sessions needed to earn 30 injections) or acquisition of reinforcement The latter

implies a within-subject design and compares self-administration of a drug to

vehicle-contingent responding In order to show reinforcement, responding

contin-gent on administration of a drug dose needs to be higher than responding leading to

drug vehicle administration

In a recent study,Gill et al (2012)tested the hypothesis that adolescent exposure

to methylphenidate would increase vulnerability to cocaine abuse For this

experi-ment, adolescent rhesus monkeys (30 months old) were treated with

extended-release methylphenidate or vehicle for 12 months At the end of that treatment

period, monkeys were trained to respond under an FR 30 schedule of food

presen-tation (methylphenidate treatment had terminated and there was a 3- to 5-month

washout) When responding was deemed stable, saline was substituted for the food

pellets until responding declined to less than 20% of baseline for three consecutive

sessions There was a return to food-reinforced baseline and then ascending doses of

cocaine were made available for at least the same number of sessions as was required

for saline extinction, beginning at a very low cocaine dose (0.001 mg/kg per

injec-tion) and making half log unit higher doses available until cocaine functioned as

re-inforcer There was a return to food-reinforced baseline before different cocaine

doses were tested This procedure allowed for a quantitative measure of cocaine

acquisition—defined as the dose that maintained higher responding than when saline

was available Survival curves were generated for both groups and compared

statis-tically.Gill et al (2012)did not find any differences in vulnerability (i.e., cocaine

acquisition) in the group treated with methylphenidate and controls A similar

pro-cedure has been used and shown to differentiate female monkeys based on their

so-cial rank (Nader et al., 2012b)

As mentioned above, most drugs that humans abuse, animals will self-administer

One drug class that has proven challenging is marijuana or THC, the nonselective

partial cannabinoid agonist One of the first efforts to maintain THC

self-administration in monkeys was reported byHarris et al (1974) In that study, rhesus

monkeys were given access to THC (0.025–0.3 mg/kg/injection over 10 s) under an

FR 1 schedule of reinforcement during daily 12 h sessions No dose maintained

responding higher than vehicle-contingent behavior Next, the investigators gave

monkeys noncontingent THC in an effort to make them physically dependent and

studied 0.025 mg/kg THC self-administration (perhaps as a negative reinforcer)

Again, the behavior was not maintained above response rates leading to vehicle

in-jections Others have also reported negative results (Li et al., 2012; Mansbach et al.,

1994) However,Tanda et al (2000)andJustinova et al (2003, 2008)reported THC

self-administration in squirrel monkeys responding under an FR 10 schedule of

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reinforcement There are several possibilities for the different outcomes includingthe species used (squirrel monkeys vs rhesus monkeys), the drug vehicle, the pumpduration, and the schedule of reinforcement Clearly, much additional work is re-quired (seePanagis et al., 2008) since recreational marijuana use continues to in-crease across the world.

In addition to acquisition (vulnerability), simple schedules of drug administration have also been modified so as to assess other phases of addiction in-cluding “loss of control,” by studying long-access conditions (e.g.,Ahmed and Koob,

self-1998), long-term consequences during maintenance of drug self-administration (e.g.,

Nader et al., 2006), and relapse/reinstatement (e.g.,Achat-Mendes et al., 2012; de Witand Stewart, 1981), including the study of “incubation” (see reviews by Lu et al.,2004; Weiss, 2010) A recent series of studies have examined the powerful role ofenvironment on drug self-administration, including alternative physical activities(e.g., Smith and Lynch, 2011) and social variables (e.g., Morgan et al., 2002;Nader et al., 2012b; Smith, 2012; Yap et al., 2015; see alsoSmith et al., 2014) Finally,

it should be mentioned that the use of simple schedules of reinforcement and drug administration has recently been used to better understand the role of specific brainregions related to drug addiction, usingin vivo imaging, such as PET, in vitro imagingusing receptor autoradiography, optogenetics, and DREADDS A full description ofthese protocols is beyond the scope of this chapter, but it is relevant that investigatorsstudying the neurochemistry associated with addiction utilize self-administrationmodels rather than noncontingent drug administration

self-2.4.2 Use of complex schedules of reinforcement

Several investigators have suggested that more complex schedules of reinforcementthat measure reinforcing strength (efficacy) are a better model of the human condi-tion than simple schedules of reinforcement (Ahmed, 2010; Ahmed et al., 2013;Badiani, 2013; Banks and Negus, 2012; Banks et al., 2015) The two most frequentlyused models of reinforcing strength are the progressive-ratio (PR) schedule and drugchoice procedures (either drug vs drug or food vs drug) For responding maintainedunder PR schedules, the number of responses required for a drug injection increasewith each injection; this may occur within the same session (e.g.,Czoty et al., 2010a;Kimmel et al., 2008) or across sessions (e.g.,Griffiths et al., 1978; see alsoRowlett

et al., 1996) For these studies, the primary dependent variable is the final ratio pleted, termed the break point (BP), when no injections have been received after aspecified period of time (termed the limited hold) or at the end of the session As withall schedules of drug self-administration involving reinforcement, the shape of thedose–response curve is an inverted U-shaped function; for PR studies, BPs for dif-ferent drugs can be compared statistically (seeStafford et al., 1998for review)

com-PR schedules are quite amenable to examining the effects of treatments on drugself-administration, including cocaine self-administration (e.g.,Czoty et al., 2006,2010b, 2013) As an example, the effects of d-amphetamine on cocaine BP will

be described Amphetamine has been shown to have efficacy as a cocaine cotherapy (Grabowski et al., 2001; Negus and Mello, 2003a,b) In one study,Czoty

pharma-et al (2011)had monkeys self-administering cocaine under a PR schedule; the dose

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of cocaine was on the ascending limb of the dose–response curve Monkeys received

a continuous infusion ofd-amphetamine at a rate of 0.4 ml/h and every 7 days they

were given access to cocaine If the amphetamine treatment decreased the cocaine

BP, they were retested 1 week later to examine for tolerance to these effects; if

tol-erance developed or if the initial amphetamine dose had no effect on cocaine BP, the

daily amphetamine dose was increased In this study,d-amphetamine decreased the

BP for cocaine and, importantly from a clinical perspective, tolerance did not

de-velop to these effects Also of relevance is that different amphetamine doses

pro-duced optimal effects in monkeys, so if all animals had been tested with the same

doses and mean data presented, the effects would not have been statistically

signif-icant Studies of this type highlight the importance of individual subject variability in

drug responses

For studies involving drug choice, the primary dependent variable is percentage

of trials the drug is chosen There are two general variations of the choice procedure:

drug versus drug choice and food versus drug choice In one sense, if an investigator

wanted to directly compare the reinforcing strength of a novel drug with a known

drug of abuse, the drug–drug choice procedure is ideal (e.g., Johanson and

Schuster, 1975) For these studies, animals are implanted with double-lumen

cathe-ters in which drug A is available through one lumen and drug B through the other For

example,Lile et al (2002)compared the reinforcing strength of a novel DA

trans-porter (DAT) blocker, PTT, with cocaine When first studied under a PR schedule,

the BP for PTT was significantly lower than that for cocaine (Lile et al., 2002)

How-ever, when monkeys were given the opportunity to choose between cocaine and PTT,

at the highest dose of each, PTT and cocaine were chosen on 50% of the completed

trials Interestingly though, cocaine intake was reduced by nearly 90% relative to

when choice was between cocaine and saline That is, the monkeys did not complete

many trials when both drugs were available (although half the trials resulted in

co-caine and the other half PTT), suggesting that perhaps a long-acting DAT blocker

would be an effective treatment for cocaine addiction in the context in which cocaine

is still being used (seeNader et al., 2015for additional discussion)

The second variation of drug choice involves comparing self-administration in

the context of alternative nondrug reinforcers However, the food–drug choice

pro-cedure is too labor intensive to use to directly compare novel drugs in terms of

mea-sures of reinforcing strength That is, how different drugs dose–response curves

appear in the context of a nondrug alternative are difficult studies to interpret For

example,Nader and Woolverton (1991)had different groups of monkeys, one

choos-ing between cocaine and food the other between procaine and food Under baseline

conditions, the shapes of the dose–response curves for both drugs appeared similar

However, when the magnitude of the alternative was manipulated (i.e., increases in

the number of food pellets available as an alternative to drug), the procaine dose–

response curve became much flatter than the cocaine curve, suggesting that procaine

had weaker reinforcing strength than cocaine

When only one drug is studied (e.g., cocaine), investigators can utilize a food–

drug choice procedure to compare different groups of subjects in terms of sensitivity

to environmental context and alternative reinforcers For example, when monkeys

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are placed in social groups, they form a linear hierarchy from most dominant to mostsubordinate (seeNader et al., 2012a) and the formation of these hierarchies results inchanges in brain DA receptors and initial vulnerability to cocaine abuse (Morgan

et al., 2002) However, if monkeys are permitted to continue self-administering caine under FR schedules, the differences between dominant and subordinate mon-keys dissipate (Czoty et al., 2005) When the conditions are changed to a concurrent

co-FR schedule of food and cocaine presentation, subordinate monkeys are more sitive to cocaine reinforcement, choosing lower doses of cocaine relative to foodcompared to dominant monkeys (Czoty et al., 2005) Importantly, the ability of drugs

sen-to alter cocaine–food choice also varies depending on the social rank of the monkey(Czoty and Nader, 2013, 2015)

More frequently, food–drug choice studies are used to identify potential ment drugs As described byBanks and Negus (2012), if addiction is conceptualized

treat-as a choice (Heyman, 2009), then drug versus nondrug choice behavior may have thegreatest face validity to the human condition (see Haney and Spealman, 2008;Hutsell et al., 2015) The primary objective of these studies is to examine novel treat-ment drugs on percent drug choice and a positive outcome would be represented by ashift in preference from drug to the food alternative This reallocation of behaviorwould model the human condition in which the drug user chooses an alternative re-inforcer (e.g., job) over continued drug use Some recent examples are described by

Nader and Banks (2014)andBanks et al (2015)

Models of drug addiction remain an integral preclinical research screen (seeTable 1).There will always be a need to screen novel drugs for abuse liability and with theincreases in recreational drug use (due in part to the legalization of marijuana)

Table 1 Summary of Animal Models

Model Strengths Limitations

discrimination

CNS-mediated effects

Because training dose and drug history can affect outcomes, care must be taken in designing studies Self-

administration

All routes of administration

Frequently requires surgery Predictive of

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and the huge expense of dealing with drug addiction, the use of these animal models

to evaluate novel treatments will be relevant for many years to come There is a need

to standardize research techniques and there is an important need to expand the study

of THC abuse beyond the few laboratories that are currently examining this drug

class There is also a need to better understand individual differences in drug

re-sponses Studies designed to investigate variable drug responses, rather than trying

to control for them, will yield important new insights that will move the field of

ad-diction treatment toward a personalized medicine approach This chapter has

highlighted the advantages of animal models for the understanding of brain changes

accompanying drug addiction It has been noted that there are certain behavioral

phe-notypes, as well as CNS markers (e.g., DA D2 receptor availability) that make certain

individuals more vulnerable than others How these phenotypic characteristics

im-pact later treatment strategies remains to be determined For example, when two

in-dividuals with long-term drug history are studied (in the animal or human lab or in

the clinic), they may have very similar symptomatology However, the treatment

out-come may be different, and we have typically attributed these equivocal outout-comes as

a “lack of effect.” For example, recovery of DA receptor function varies among

in-dividuals and this may be related to some behavioral phenotype (Nader et al., 2006)

Animal models of addiction can shed light on how these initial phenotypes impact

later treatment efficacy—perhaps there is truly order in these “equivocal” outcomes,

but only after understanding the long-term consequences of drug use can clinicians

make individualized treatment decisions

ACKNOWLEDGMENTS

Preparation of this review was supported by NIDA grants DA010584, DA017763, DA012460,

and DA06634 I thank Drs Alice Young, Travis Thompson, James E Barrett, and William L

Woolverton for years of mentorship and friendship

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Animal models of drug

relapse and craving: From

Marco Venniro*,†,1, Daniele Caprioli*, Yavin Shaham*,1

*Behavioral Neuroscience Research Branch, Intramural Research Program, NIDA, NIH,

Baltimore, MD, USA

† Department of Public Health and Community Medicine, Neuropsychopharmacology Laboratory,

Section of Pharmacology, University of Verona, Verona, Italy

1 Corresponding authors: Tel.: +443 740 2723; Fax: +443 740 2827, e-mail address: venniro.marco@nih.gov; yavin.shaham@nih.gov

Abstract

High rates of relapse to drug use during abstinence is a defining feature of drug addiction In

abstinent drug users, drug relapse is often precipitated by acute exposure to the

self-administered drug, drug-associated cues, stress, as well as by short-term and protracted

with-drawal symptoms In this review, we discuss different animal models that have been used to

study behavioral and neuropharmacological mechanisms of these relapse-related phenomena

In the first part, we discuss relapse models in which abstinence is achieved through extinction

training, including the established reinstatement model, as well as the reacquisition and

resur-gence models In the second part, we discuss recent animal models in which drug relapse is

assessed after either forced abstinence (e.g., the incubation of drug craving model) or

volun-tary (self-imposed) abstinence achieved either by introducing adverse consequences to

ongo-ing drug self-administration (e.g., punishment) or by an alternative nondrug reward usongo-ing a

discrete choice (drug vs palatable food) procedure We conclude by briefly discussing the

po-tential implications of the recent developments of animal models of drug relapse after

volun-tary abstinence to the development of medications for relapse prevention

Progress in Brain Research, Volume 224, ISSN 0079-6123, http://dx.doi.org/10.1016/bs.pbr.2015.08.004

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Voluntary abstinence, Forced abstinence, Conflict, Context, Cue, Extinction, Drug administration, Choice, Punishment, Reinstatement, Relapse, Resurgence, Reacquisition,Review

The central problem in the treatment of drug addiction is high rates of relapse to druguse after periods of forced or voluntary (self-imposed) abstinence (Hunt et al., 1971;Leshner, 1997; O’Brien, 2005) In human drug addicts, drug relapse and craving dur-ing abstinence typically involve one or more of the following factors: acute exposure tothe self-administered drug (de Wit, 1996; Jaffe et al., 1989), drug-associated cues orcontexts (O’Brien et al., 1986, 1992), stress (Sinha, 2001; Sinha et al., 2011), or short-term and protracted withdrawal symptoms (Wikler, 1948, 1973)

Since the 1970s, this clinical scenario has been modeled in monkeys (Stretch

et al., 1971), rats (Davis and Smith, 1976; de Wit and Stewart, 1981), and mice(Highfield et al., 2002) by using a reinstatement model in which drug seeking in-duced by different experimental manipulations is assessed after extinction of thedrug-reinforced responding (Bossert et al., 2013; Shaham et al., 2003) However, hu-man abstinence is typically either forced (e.g., incarceration or inpatient treatment)

or voluntary due to either the negative consequences of chronic drug use or the ability of alternative nondrug rewards in the drug user’s environment (Epstein andPreston, 2003; Katz and Higgins, 2003; Marlatt, 1996) Therefore, during the last

avail-15 years, investigators have incorporated these facets of human abstinence into

“alternative” models of drug relapse in which abstinence is not achieved by tion training (Caprioli et al., 2015a; Cooper et al., 2007; Lu et al., 2004; Marchant

extinc-et al., 2013a; Panlilio extinc-et al., 2005)

InSection 2, we discuss relapse models in which abstinence is achieved throughexperimenter-imposed extinction training: the reinstatement model (Shaham et al.,

2003), the reacquisition model (Carnicella et al., 2008), and the resurgence model(Podlesnik et al., 2006) InSection 3, we discuss animal models in which drug relapse

is assessed after either forced or voluntary abstinence The latter is achieved either byintroducing adverse consequences (punishment) to ongoing drug self-administration

or by introducing an alternative nondrug reward using discrete choice (drug vs atable food) procedures These include the incubation of drug craving and relatedforced abstinence-relapse models (Fuchs et al., 2006; Lu et al., 2004), punishment-and conflict-based relapse models (Cooper et al., 2007; Panlilio et al., 2005), and therecent choice-based voluntary abstinence-relapse model (Caprioli et al., 2015a) Ourgoal in this review is to introduce the different relapse models and then briefly pro-vide a historical perspective on each model InTables 1 and 2, we provide a summary

pal-of these models andFig 1depicts the number of published papers using the differentmodels since 1970

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Table 1 Extinction-Based Relapse Models

464 de Wit and Stewart (1981), Ettenberg (1990), McFarland

and Kalivas (2001), Mueller and Stewart (2000), Self et al.

(1996), Stewart (1984), and Stretch et al (1971)

58 Ciccocioppo et al (2001), McFarland and Ettenberg

(1997), Weiss et al (2000), and Katner et al (1999) Runway 2

Context

Self-administration

60 Bossert et al (2007), Crombag et al (2002), Fuchs et al.

(2005), and Hamlin et al (2007) Stress

The table depicts the number of published papers in which investigators used the different

extinction-based relapse models We also include in the table selected historical citations Note: Many

papers published results that fit more than one category (e.g., assessment of both drug-priming- and

cue-induced reinstatement) Such papers are counted in more than one category in Tables 1 and 2

The data in both tables are based on PubMed research.

27

1 Introduction

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Our review does not include theoretical discussions of the validity of animalmodels of relapse or a comprehensive summary of the main findings in studies usinganimal models of relapse We refer the interested reader to earlier and more recentreviews in which we covered these topics (Bossert et al., 2005, 2013; Epstein et al.,2006; Lu et al., 2004; Marchant et al., 2013b; Pickens et al., 2011; Shaham et al.,2003; Shalev et al., 2002) Additionally, our review does not cover the more recent

Table 2 Abstinence-Based Relapse Models

Number of Papers Key Historical Citations Forced abstinence

A single test

during abstinence

37 Fuchs et al (2006), Grimm et al (2001), Tran-Nguyen

et al (1998), Shalev et al (2001), and Neisewander

et al (1996) Incubation of drug

craving

67 Adverse consequences-imposed abstinence

Punishment-based model

9 Cooper et al (2007), Marchant et al (2013a), Panlilio

et al (2003), and Economidou et al (2009) Conflict model 5

Voluntary abstinence

Incubation of drug

craving

1 Caprioli et al (2015a)

The table depicts the number of published papers in which investigators used the different

abstinence-based relapse models We also include in the table selected historical citations.

Relapse-related empirical papers Relapse-related review papers

0 100 200 300 400 500

Number of relapse-related empirical papers and reviews per 5-year period since 1970

Note: Data for 2011-2015 do not include papers published after August 2015.

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adaptation of the reinstatement model and the incubation of craving model to study

relapse to palatable food seeking (Calu et al., 2014; Grimm et al., 2002, 2005; Nair

et al., 2009) We also do not cover the “alcohol-deprivation effect”—the increase in

alcohol intake after an abstinence period (Sinclair and Senter, 1968)—that is widely

used in the alcohol field to study alcohol relapse (Le and Shaham, 2002; Vengeliene

et al., 2014)

In the learning literature, reinstatement refers to the recovery of a learned response

(e.g., lever-pressing behavior) that occurs when a subject is exposed,

noncontin-gently, to the unconditioned stimulus (e.g., food) after extinction (Bouton and

Swartzentruber, 1991) In the drug addiction literature, reinstatement typically refers

to the resumption of drug seeking after extinction following exposure to drugs, drug

cues or contexts, or stressors (Shaham et al., 2003)

In the operant self-administration variation of the reinstatement model,

labora-tory animals are trained to self-administer a drug During the extinction phase, lever

pressing (or nose poking) is extinguished in the absence of the drug During the

instatement test, the ability of acute exposure to the drug or nondrug stimuli to

re-instate drug seeking is determined under extinction conditions Non-reinforced

responding on the previously active lever or nose poke device is the operational

mea-sure of drug seeking (Stewart and de Wit, 1987)

In the operant runway variation of the reinstatement model, the dependent

mea-sure is therun time from a start box to a goal box where a drug infusion is given

During the training phase, rats are given a drug injection when they reach the goal

box and over time, their run time decreases During the extinction phase, the rats

in-crease their run time when drug injections are not available in the goal box During

reinstatement testing, noncontingent exposure to drug priming or drug-associated

cues results in decreased run time to the goal box (reinstatement) (Ettenberg,

1990; McFarland and Ettenberg, 1997)

In the conditioned place preference (CPP) variation of the reinstatement model,

laboratory animals are trained to associate one distinct compartment (context) with

drug injections and a second compartment with injections of the drug vehicle

Sub-sequently, rats are subjected to extinction training during which they are exposed to

both contexts in the absence of the drug Reinstatement of the preference for the

drug-paired compartment is then determined after noncontingent exposure to drug

or nondrug stimuli (Mueller and Stewart, 2000; Sanchez and Sorg, 2001)

In the paragraphs below, we describe the different usages of the model to study

reinstatement induced by drug priming, discrete cues, discriminative cues,

contex-tual cues, stress, and drug withdrawal For each reinstatement-related stimulus,

we describe the experimental procedure and then briefly discuss selected historical

citations

29

2 Extinction-based relapse models

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2.1.1 Drug priming

2.1.1.1 Experimental procedure

In the drug-priming-induced reinstatement procedure, the effect of noncontingent jections of the self-administered drug, or other drugs on reinstatement of the operantresponse in the self-administration or the runway procedures, or place preference inthe CPP procedure, is determined after extinction of the drug-reinforced learned be-havior (de Wit, 1996; Shaham et al., 2003)

in-2.1.1.2 Brief history

During the early 1970s, Stretch and Gerber showed that noncontingent priming jections of the self-administered drug reinstate amphetamine or cocaine seeking af-ter extinction in monkeys (Gerber and Stretch, 1975; Stretch et al., 1971).Subsequently, Davis and Smith (1976) and de Wit and Stewart (1981, 1983)

in-showed that priming injections of drugs reinstate opiate (heroin, morphine) andstimulant (cocaine, amphetamine) seeking in rats In 1990, Ettenberg (1990)

showed that priming injections of amphetamine reinstate operant responding inthe runway model In 2000, Mueller and Stewart (2000) and Parker andMcDonald (2000)showed that priming injections of cocaine or morphine reinstatedrug CPP

In the 1980s, Stewart and colleagues showed that intracranial injections of phine or amphetamine into ventral tegmental area (VTA) or nucleus accumbens(NAc) reinstate heroin or cocaine seeking, respectively (Stewart, 1984) These re-sults provided the first demonstration for a role of the mesolimbic dopamine system

mor-in remor-instatement of drug seekmor-ing In 1996,Self et al (1996) showed that D1-likeand D2-like dopamine receptor agonists have opposite effects on reinstatement

of cocaine seeking: D1-like receptor agonists inhibit cocaine-priming-induced instatement, while D2-like receptor agonists potentiate reinstatement These resultsprovide the first evidence that mechanisms of reinstatement of drug seeking can bedissociable from those that control ongoing drug self-administration in which thebehavioral effects of D1-like receptor and D2-like receptor agonists (and antago-nists) are similar (Self and Stein, 1991) In 2001, McFarland and Kalivas (2001)

re-made the first attempt to identify the neuronal circuits that mediate priming-induced reinstatement by manipulating dopamine, glutamate, and g-aminobutyric acid transmission in multiple brain areas This study has been the inspira-tion for many other studies on the circuitry of drug-priming-induced reinstatement

cocaine-in the last 15 years (Bossert et al., 2013; Kalivas and McFarland, 2003; Schmidt

et al., 2005)

During the last two decades, the drug-priming-induced reinstatement procedurehas been used in many studies using different drugs of abuse (Bossert et al., 2013;Self and Nestler, 1998; Shaham et al., 2003), including nicotine (Chiamulera et al.,

1996) and alcohol (Le et al., 1998), to identify neuropharmacological mechanismsunderlying this phenomenon

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