Strategies

for Research

on the Interactions

of Drugs of Abuse

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES • Public Health Service • Alcohol, Drug Abuse, and Mental Health Administration
Strategies for Research

on the Interactions of

Drugs of Abuse

Editors:

Monique C. Braude, Ph.D.

Division of Preclinical Research

National Institute on Drug Abuse

Harold M. Ginzburg, M.D., J.D., M.P.H.

Division of Clinical Research

National Institute on Drug Abuse

NIDA Research Monograph 68

1986

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Martin W. Adler, Ph.D.

Temple University School of Medicine

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Sydney Archer, Ph.D.

Rensselaer Polytechnic Institute

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Richard E. Belleville, Ph.D.

NB Associates, Health Sciences

Rockville, Maryland

Karst J. Besteman

Alcohol and Drug Problems Association

of North America

Washington, D.C.

Gilbert J. Botvin, Ph.D.

Cornell University Medical College

New York, New York

Joseph V. Brady, Ph.D.

The Johns Hopkins University School of

Medicine

Baltimore, Maryland

Theodore J. Cicero, Ph.D.

Washington University School of

Medicine

St. Louis, Missouri

Sidney Cohen, M.D.

Los Angeles, California

Mary L. Jacobson

National Federation of Parents for

Drug-Free Youth

Omaha, Nebraska

Reese T. Jones, M.D.

Langley Porter Neuropsychiatric Institute

San Francisco, California

Denise Kandel, Ph.D.

College of Physicians and Surgeons of

Columbia University

New York, New York

Herbert Kleber, M.D.

Yale University School of Medicine

New Haven, Connecticut

Richard Russo

New Jersey State Department of Health

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NIDA Research Monograph Series

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ASSOCIATE DIRECTOR FOR SCIENCE, NIDA

EDITOR-IN-CHIEF

Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
Strategies for Research

on the Interactions of

Drugs of Abuse
ACKNOWLEDGMENT

This monograph is based upon papers and discussion from a technical

review on strategies for research on the interactions of drugs of

abuse which took place on October 29 and 30, 1984, at Rockville,

Maryland. The review meeting was sponsored by the Office of Science

and the Divisions of Clinical Research, Preclinical Research, and

Epidemiology and Statistical Analysis of the National Institute on

Drug Abuse.

COPYRIGHT STATUS

Certain figures and tables and portions of the text in the chapter

by Dr. Clifford L. Mitchell on pages 41 to 64 are copyrighted by the

New York Academy of Sciences. All figures and tables and portions

of the text in the chapter by Drs. Jack H. Mendelson, Nancy K.

Mello, and Barbara W. Lex on pages 117 to 141 are copyrighted by

Williams and Wilkins. The National Institute on Drug Abuse has also

obtained permission from the copyright holders to reproduce certain

other previously published material as noted in the text. Further

reproduction of this copyrighted material is permitted only as part

of a reprinting of the entire publication or chapter. For any other

use, the copyright holder's permission is required. All other

material in this volume except quoted passages from copyrighted

sources is in the public domain and may be used or reproduced

without permission from the Institute or the authors. Citation of

the source is appreciated.

Opinions expressed in this volume are those of the authors and do

not necessarily reflect the opinions or official policy of the

National Institute on Drug Abuse or any other part of the U.S.

Department of Health and Human Services.

DHHS publication number (ADM)86-1453

Printed 1986

NIDA Research Monographs are indexed in the Index Medicus. They are

selectively included in the coverage of American Statistics Index,

BioSciences Information Service, Chemical Abstracts, Current

Contents, Psychological Abstracts, and Psychopharmacology Abstracts.

iv
Contents

Introduction and Overview

Monique C. Braude and Harold M. Ginzburg . . . . . . . . 1

Critical Factors in Studying Drug Interactions

Martin W. Adler . . . . . . . . . . . . . . . . 6

Issues in the Assessment of Multiple Drug Use Among Drug

Treatment Clients

Robert L. Hubbard; Robert M. Bray; S. Gall Craddock;

Elizabeth R. Cavanaugh; William E. Schlenger; and

J. Valley Rachal . . . . . . . . . . . . . . . . 15

Statistical Analysis of Drug Interactions

Clifford L. Mitchell. . . . . . . . . . . . . . . 41

Problems of Pharmacodynamlc Measurement Related to

Psychoactive Drug Interactions in Humans

Richard C. Frecker . . . . . . . . . . . . . . . 65

Pharmacokinetic Mechanisms of Ethanol-Psychotropic Drug

Interactions

Domenic A. Ciraulo and Jamie Barnhill . . . . . . . . . 73

Complex Interactions Between Drugs and Dietary Factors

Elliot S. Vesell . . . . . . . . . . . . . . . . 89

Interactions of Cannabis With Other Drugs in Man

Leo E. Hollister . . . . . . . . . . . . . . . .11O

Alcohol and Marijuana: Concordance of Use by Men and Women

Jack H. Mendelson; Nancy K. Mello; and Barbara W. Lex. . . .117

Cocaine and Other Drug Interactions: Strategy Considerations

Reese T. Jones. . . . . . . . . . . . . . . . .142

v
Cigarette Smoking: Interactions With Alcohol, Opiates,

and Marijuana

Nancy K. Mello and Jack H. Mendelson . . . . . . . . . . . . . . . . . . . . .154

Drug Combinations in Pleasure and Pain

Conan Kornetsky . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .181

Drug Interactions With Methadone in Humans

Mary Jeanne Kreek. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

List of NIDA Research Monographs . . . . . . . . . . . . . . . . . . . . . . . . . . .226

vi
Introduction and Overview

Monique C. Braude, Ph.D., and

Harold M. Ginzburg, M.D., J.D., M.P.H.

Ten years have passed since the Proceedings of the first Conference

on Interactions of Drugs of Abuse, held at the New York Academy of

Sciences, was published (Vesell and Braude 1976). Some of the

participants in the 1976 conference, such as Drs. Hollister. Kreek,

Mitchell, and Vesell, who have continued and expanded their interest

in the interaction area, also participated in the Technical Review

on "Strategies for Research on the Interactions of Drugs of Abuse"

which was held in Rockvllle, Maryland, October 29-30, 1984. This

Research Monograph is a summary of the papers and discussions of the

invited experts who participated in this 2-day meeting.

In the past two decades a large part of the counterculture has been

involved in self-experimentation with drugs, and the concurrent

self-administration of multiple substances of abuse, by a variety of

routes, has become normative behavior. Recently, for instance, the

use on the street of a combination of tripelennamlne and pentazocine

(Ts and Blues) elicited concern which was promptly remedied by the

pharmaceutical industry by the addition of naloxone to pentazoclne.

Since the early 1970s the National Institute on Drug Abuse (NIDA)

has supported a program encompassing interactions of drugs of abuse

(such as marijuana, stimulants, and depressants) or of new

therapeutic modalities. such as naltrexone, with other compounds

used either on the street or therapeutically. The need for this

type of research has increased with the use of polypharmacy by drug

abusers and also their simultaneous use of alcohol, which has proved

in many cases to be a potentiating agent for abused substances.

While the literature dealing with drug interactions from the

pharmaceutical standpoint is extensive, it often consists of

testimonial reports of adverse interactions and lacks in-depth

studies of the mechanisms underlying these effects. A more recent

approach has been to investigate interactions not simply from a

forensic standpoint but rather to obtain epidemiological and

clinical data to study pharmacological and behavioral interactions.

1
In as much as the first conference was about equally divided between

preclinical and clinical studies, the emphasis at this second

meeting was on clinical experimental and epidemiological data and

strategies to be used for future studies in this research area.

In the opening chapter of this monograph, Dr. Adler lists the

critical factors that would have to be adhered to in order to obtain

reproducible and meaningful information about interactions among

drugs of abuse. These include the necessity of performing full dose

response curves as most of these drugs, such as the barbiturates and

the cannabinoids, may have biphaslc effects at different dose

levels. This was discussed at length by the group, and it was the

general feeling that clinical studies cannot adhere to the rigorous

criteria set by Dr. Adler for preclinical studies. For instance, a

full dose response range may not be practical and would be too

expensive to use in clinical studies if one would want to look only

at the therapeutic (or abuse) range for treatment compounds.

However, for illicit drugs, often used in the street in the toxic

dose range, interaction studies cannot be done clinically and animal

models have to be used.

The group also noticed that basic science studies have not provided

any complex models for understanding drug interactions and that no

model to replicate the "street environment" of multiple drug use has

been developed. Too often, most of the preclinical studies have

used a single-drug or at most a two-drug dosing regimen while,

actually, street abusers as well as therapeutic users often use

multiple drugs simultaneously or sequentially. On the other hand,

clinicians have had difficulty in developing research protocols with

a definite set of hypotheses which would not be too costly to be

approved with high priority by a review committee.

Initial epidemiologic studies, which are descriptive, can provide

evidence of what types of drug interactions will precipitate

individuals into seeking treatment as well as demonstrate the

generallzabllity of laboratory findings. In the second chapter, Dr.

Hubbard and colleagues describe the multiple use patterns of a

treatment population of drug abusers, observed in the Treatment

Outcome Prospective Study (TOPS). This data, coupled with the Drug

Abuse Warning Network (DAWN) data, provides the basis for

determining new toxic drug interactions that will require careful

study both to develop therapeutic interventions that will minimlze

morbidity and to better understand the pharmacological reasons for

individuals wanting to use a given set of psychoactive substances.

Dr. Hubbard also emphasizes that, in order to understand the complex

nature of multiple drug use, a multidisciplinary approach is needed

with greater collaboration between epidemiologists, clinicians, and

pharmacologists.

Dr. Mitchell describes step by step the statistical approaches which

can be used to measure interaction quantitatively. Most often used

are the potency ratio and the isobolographlc methods. Both

approaches have the same conceptual basis. but the lsobolographic

method is more tedious and more difficult to use in clinical studies.

2
Dr. Frecker brought an interesting new perspective to the group,

that of an ophthalmologist with a doctorate in pharmacology who is

interested in developing engineering systems for biomedical studies

in humans. He identified some critical elements of the "man-machine

interface" as they relate to drug-effect measurements and described

what he considers to be the attributes of an "ideal" pharmacodynamic

measurement technique. Although his paper is mainly on pharmacodynamics, i.e., the study of drugs in organisms, he also reminds us

that pharmacokinetic parameters which provide indicators on, for

instance, the time course of drug effects should be taken into

consideration.

The pharmacokinetic mechanisms of alcohol-psychotropic drug

interactions were described by Ciraulo and Barnhill and relevant

examples cited. Alcohol alters drug metabolism through its effects

on hepatic biotransformation, and the concept of hepatic extraction

is important in predicting the effects of metabolic interactions.

(Excellent reviews on the pharmacokinetics of drug interactions by

G. Levy and J.R. Gillette were also included in the 1976 publication

of the New York Academy of Sciences and can be referred to for

additional information on that topic.)

The importance of dietary factors which can alter drug response by

changing several pharmacokinetic factors was reviewed by Dr.

Vesell. Large individual variations, from three to elevenfold, can

be observed in different subjects even when the same dose of a drug

is given by the same route and under the same environmental

conditions. Furthermore, food affects the absorption of drugs by

enhancing gastric blood flow and retarding gastric emptying. In

this monograph, Dr. Vesell discusses various approaches to

assessment of dietary contributions to the interindivldual

variations in drug disposition observed in many clinical studies.

The next two papers, by Dr. Hollister and Drs. Mendelson, Mello, and

Lex, illustrate the problem of making positive conclusions about

possible interactions of two drugs of abuse such as alcohol and

marijuana. In reviewing the interactions of cannabis with other

drugs in man, Dr. Hollister reports that only THC. the psychoactive

component of marijuana, shows a significant pharmacodynamic

interaction in man and animals with alcohol and that the interactive

effect tends to be additive. However, the data presented by Drs.

Mendelson and Mello regarding the concordant use of marijuana by men

and women shows that when alcohol and marijuana are concurrently

available, marijuana appears to affect alcohol use more dramatically

than alcohol influences marijuana use, and that the consumption of

alcohol is reduced during marijuana use even in heavy alcohol

drinkers. These data indicate that the simultaneous availability of

two recreational psychoactive drugs does not necessarily increase

drug use. As THC is known to affect cell membranes, Dr. Hollister

also feels that it would be of interest to study interactions

between THC and drugs, such as

Dr. Reese Jones in his critical

reminded the group that both 

lithium. that alter

 review of cocaine

nicotine and caffeine

3

membrane lipids.

Interactions

consumption need
to be taken into account when investigating the effects of cocaine

with other drugs. He believes that the most realistic conditions

for studying cocaine and other drug interactions are after repeated

use of both drugs of interest. Rapidly acquired tolerance and the

likelihood of cross tolerance to other drugs complicates any design

involving repeated administration of a drug in drug interaction

studies. In a repeated dose paradigm lasting more than a few days,

new signs and symptoms appear which are different from those

observed after acute or early drug effects. The mechanisms of these

long-term interactions are complex and difficult to delineate.

The chapter by Drs. Mello and Mendelson further demonstrates the

complexity of interaction studles. In studying the effect of

cigarette smoking on alcohol, opiates (heroin and buprenorphine),

and marijuana use, they confirm that a number of drugs from diverse

pharmacological classes influence tobacco smoking. Alcohol and

opiates increase cigarette smoking, whereas marijuana has no

apparent effect on tobacco use. Opiate antagonists like naltrexone

do not appear to alter cigarette smoking significantly. They

suggest that polydrug use may have less to do with the pharmacological properties of the drugs or their anticipated effects than

with their capacity to produce some change in subjective states.

Change may be the goal of the polydrug user, as it appears that any

drug or drug combination that has definitive stimulus properties and

behavioral effects for the user may have abuse potential.

Dr. Kornetsky reports the results of a series of experiments on the

effects of various drug combinations on the threshold for

brain-stimulation reward and brain-stimulation escape (pain). These

studies show that combinations of drugs often lead to synergism or

potentiation of effects which are manifested not only in the

euphoriant action of drugs but also in their analgesic action, as

with the enhancement of morphine analgesia by d-amphetamine.

Dr. Kreek in her extensive review of the interactions of methadone

with various drugs in humans shows that combined treatment of a

methadone-maintained patient with a second drug, such as rifampin or

phenytoin, can produce severe withdrawal symptoms due to

pharmacoklnetic interactions. In developing a strategy to identify,

define, and elucidate the mechanisms of interactions between drugs of

abuse or those used in treatment, Dr. Kreek concludes that the most

urgently needed studies are those which can be carried out in humans

to document pilot data obtained in in vitro or preclinical studies.

In the final analysis, however, the question is: What do we want to

learn from interaction studies? Are we interested in understanding

the cellular effects of drug interactions to develop better

treatment interventions or are we interested in the pathophyslology

of multiple concurrent drug use, independent of developing

therapeutic interventions? On the other hand, are we interested in

being able adequately to describe the phenomena as markers for the

effectiveness of interventions (primary, secondary, and tertiary)?

4
In summary, the review participants were very enthusiastic about

continuing research in the drug interactions area. They felt,

however, that this field needs to be encouraged by NIDA and that the

supportive group of clinical and preclinical research scientists

already involved in this type of research needs to be nurtured and

its ranks enlarged.

REFERENCE

Vesell, E.S., and Braude, M.C., eds. Interactions of Drugs of Abuse.

Annals of the New York Academy of Sciences, Vol. 281, 1976

AUTHORS

Monique C. Braude, Ph.D.

Senior Research Pharmacologist

Division of Preclinical Research

National Institute on Drug Abuse

Rockville, Maryland 20857

Harold M. Ginzburg, M.D., J.D., M.P.H.

Chief, Clinical Medicine Branch

Division of Clinical Research

National Institute on Drug Abuse

Rockville, Maryland 20857

5
Critical Factors in Studying Drug

Interactions

Martin W. Adler, Ph.D.

INTRODUCTION

This technical review met to develop etrategies that can be used

to study interactions involving drugs of abuse. The need for

information on this subject is obvious, both from a therapeutic

and a toxicological viewpoint. If we look at the list of pharmacological agents that we consider under the heading of drugs

of abuse, we see a variety of drugs and drug classes, including

the opioids, cannabinoids, alcohol, cocaine, amphetamine, PCP,

hallucinogens, barbiturates, anxiolytics, and nicotine. Many of

these agents have important therapeutic uses, while others are

of interest primarily because of their nomedical use. While

there is a paucity of information about the interactions of

these substances with all sorts of other drugs, perhaps the

greatest deficiency in knowledge lies in interactions among the

various drugs of abuse. When there drugs are used therapeutically, interactions can lead to either a desired increase in

efficacy or to adverse effects. When used nonmedically, they

can interact to alter the euphoric or dysphoric effects. If one

thinks in terms of basic pharmacological principles, it is often

possible to predict what will occur if two or more drugs are

administered concurrently or, at least, to understand unexpected

consequences of drug combinations. With that in mind, I will

review some of the factors that should be considered.

GENERAL CONSIDERATIONS

In general, one drug may be synergistic with (additive or potentiated) or antagonistic to another drug by acting at the same

receptors, by acting at different receptors or systems, by

interacting chemically or physically, or by altering pharmacokinetics. A word of caution should be given at this point,

however. Just because an interaction occurs, one rhould not

necessarily conclude that the interaction is meaningful either

in terms of clinical efficacy or toxicological consequencer;

that determination is dependent on a variety of considerations,

such as the degree of interaction, the therapeutic ratio, the

shape of the dose-response curve, and the general status of the

subject.

6
A natural way to proceed in this discussion might be to dissect

and explore in detail the pharmacokinetic and pharmacodynamic

factors which are vital elements in evaluating and predicting

drug interactions. For example, a consideration of drug absorption, distribution, metabolism, and excretion is important.

Good discussions of these pharmacokinetic factors may not only

be found in any authoritative text in pharmacology, but also in

three excellent papers presented at the New York Academy of

Sciences symposium entitled “Interactions of Drugs of Abuse”

(Reidenberg 1976; Levy 1976; Gillette 1976). Although this

chapter will address these and other basic considerations, they

will be used in a somewhat different way in order to focus

attention on those aspects that may be moat important in terms

of preclinical and clinical studies with the drugs of abuse.

CRITICAL FACTORS

Table 1 summarises some of the factors which should be taken

into account when conducting drug interaction studies.

TABLE 1

Critical Factors in Drug Interactions

1.

2.

3.

4 .

5.

6.

7.

8.

9.

10.

11.

12.

Ability of a Drug to Reach its Site of Action

Time Course of Drug Effects

Full Dose-Response Curves

Circadian and Seasonal Variations

Environment

Sex and Age

Choice of Endpoint

Route of Drug Administration

Choice of Vehicle and Proper Controls

Tolerance and Cross-tolerance

Effects on Neurotransmitter Systems

Appropriate Statistical Analysis

Reaching the Site of Action

The ability of the drugs to reach their sites of action is a

vital concern in interpreting the interaction or lack of

interaction Of drugs. Figure 1 diagrams the pharmacokinetics of

a drug in relation to its rite of action. Anything that might

affect the absorption of a drug, its binding to plasma proteins,

its metabolism, or its penetration into the central nervous

system could affect the final result and assessment.

Time Course

Due consideration must be given to the time course of the effects

of the drugs in terms of onset of action, time of peak effect, and

duration of action. The investigator must make aure that the

pharmacological effects of the drugs are being evaluated at a

time when both drugs are exerting their maximal effects, Looking

at interactions when one drug is just beginning to exert its

effects and the other drug’s effects are on the wane can result

in meaningless conclusions.

7
FIGURE 1

Schematic Representation of the Interrelationship of the Absorption, Distribution, Binding, Biotransformation, and Excretion of

a Drug and its Concentration at its Locus of Action. Possible

distribution and binding of metabolites are not depicted.

From Mayer, S.E.; Melmon, K.L.; and Gilman, A.G. Introduction:

The dynamics of drug absorption, distribution and elimination.

In: Gilman et al. 1980. Copyright 1980, Macmillan Publishing Co.

Dose-Response Relationship

The necessity of utilizing full dose-response curves when evaluating drug interactions is too often ignored. Conclusions are

sometimes reached on the basis of experiments using only one or

two doses of the drugs involved. Not only can such experiments

yield false negatives in terms of drug interactions, but they

also fail to provide the quantitative data needed for a full

evaluation of a positive effect. AS an example, one can take

the question of whether there is an interaction between the

opioid drugs and the barbiturates, using analgesia (or antinociception) as the endpoint. Various reports in the literature

concluded that there was synergism (Barlow and Duncan 1933;

Keats and Beecher 1950; Lesher and Spratto 1978; Smith et al.

1943), l antagonism (Clutton-Brock 1961; Dundee 1960; Neal 1965;

Shapero and Wilson 1964), or no interaction (Hart and Weaver

1948) between the drugs in terms of analgesia. The studies differed from each other in terms of the particular drugs used, the

doses, the species, the dose range tented, and the number of

times the drugs were administered. We decided to investigate

the problem by using the rat,

(tail-pressure), a full dose range of morphine, a full subone test of antinociception

anesthetic dose range of pentobarbital, and by giving the drugs

at the time of peak effect. One study was done in animals that

were drug naive and another in rats tolerant to morphine.

Although we raw an isolated instance of decreased analgesia in

one dose pair, the results were clear-- subanesthetic doses of Na

pentobarbital had neither antinociceptive nor hyperalgesic properties, and the barbiturate had no effect on the antinociceptive

8
action of morphine in either morphine-tolerant or

nontolerant rats (Geller et al. 1979). Bad we chosen only one

dose combination, we might have concluded otherwise.

Circadian and Seasonal Effects

The influence of circadian and seasonal variations on the

effects of one or both drugs must be assessed. Some drugs may

have markedly different quantitative actions depending on the

time of day the drug is administered. For instance, there is a

diurnal rhythm in responsiveness of mice to the hyperalgesic

activity of naloxone (Frederickson et al. 1977) and in

lorazepam-induced neurologic deficits (Henauer et al. 1984). A

study by Morris (1980) in mice and rats is intriguing. He found

that several drugs (e.g., cocaine, PCP, and morphine), when combined with alcohol, markedly increased lethality depending not

only on time of day, but also on the phase of the moon. The

circadian variations in drug effect are thus important to consider in evaluating drug interactions. Likewise, seasonal

variations in response to drugs have long been known in animals

and are likely to occur in human subjects as well. As an

extreme example, I can cite recent studies demonstrating that

dependence to morphine could not be produced in hibernating animals (Beckman et al. 19811.

Environment

The environment of the subject can play an important role in the

response to drugs. Thus, housing has been found to be one of

the determinants of the lethal dose of amphetamine; low humidity

may lead to infections and the consequent general debilitation

may modulate drug effects; high humidity can add to stressinduced effects of high temperatures; lighting can alter circadian rhythms; and ambient temperature can affect a variety of

measures such as the changes in body temperature produced by

morphine in rodents. Alterations in any of these environmental

conditions could thus markedly modify the results seen with

interactions of drugs of abuse.

Sex and Age

Sex and age of the subject are important considerations in

looking at interactions. One must be aware that a drug can have

markedly different effects depending on the sex of the subject.

For example, the toxicity of drugs such as cocaine seems to be

determined, in part, by the sex of the subject (Thompson et al.

19841. Based on studies which reveal a differential sensitivity

of leutinizing hormone to naloxone and morphine in males and

females at different points in development, it has been recently

postulated (Cicero et al., in press) that opioids may be responsible for the regulation of endocrine profiles. As for age, we

are becoming increasingly aware that the response of older individuals to drugs, especially those affecting the CNS, is

markedly different from that seen in younger adults because of a

combination of pharmacokinetic and pharmacodynamic factors. We

9
have long known that subjects with immature nervous systems

respond differently to centrally acting drugs than do mature

subjects. Yet, these factors are too often ignored, especially

in studies with mice and rats.

Endpoint

Choice of the endpoint to be rtudied is extremely important.

Drugs usually have multiple effects mediated through a variety

of different receptors and systems. Drug interactions may thus

be seen for some effects but not others.

Route of Administration

Route of drug adminstration is important in determining if

interactions occur. Not only does the route of administration

affect the amount of drug reaching its site of action and thus

influence the quantitative effect, but the route may alter the

qualitative effect. For example, studies in rats in our laboratory have demonstrated that meperidine increases brain

excitability vhen administered subcutaneously (s,c.) but

decreases excitability when given intracerebroventricularly

(i.c.v.) (Tortella et al. 1984). Table 2 compares effects on

flurothyl seizure thresholds and the action of naloxone on these

responses for several opioids given by the two routes.

Similarly, body temperature studies reveal that high doses of

morphine s.c. produce hypothermia, but these doses administered i.c.v.

produce only hyperthermia (Adler et al. 1985b). Whether the differences are due to the temporal sequence in which the drugs

reach the receptors or to some other mechanism in not yet known.

In any case, interactions may occur between drugs given by one

route but not by another.

TABLE 2

Influence of Route of Administration on Opioid-Induced

Changes in Flurothyl Seizure Threshold in Rats

ROUTE OF CHANGE IN EFFECT WITH

DRUG AMINISTRATION SEIZURE THRESHOLD NALOXONE

Morphine

Meperidine

Pentaxocine

Normeperidine

SC

ICV

S C

ICV

S C

ICV

SC

ICV

Blocked

Blocked

Potentiated

Blocked

Potentiated

Unchanged

Unchanged

Potentiated

10
Vehicle and Controls

Choice of vehicle and proper controls for the possibility of

interactions between the vehicles and the drugs is often

overlooked or given scant attention. Many chemicals used as

vehicles have effects by themselves and the possibility exists

that, when given together, those compounds can synergize with or

antagonize the actions of the drug or drugs being tested. As anexample, one can refer to studies involving delta-9-THC. This

agent is usually administered either in alcohol, emulphor, Tween

80, pluronic, or some combination of the above. All of these

agents have effects on the CNS. What started out as a straightforward experiment in our laboratory to determine the effects of

THC on the pupil in the rat became a major effort when we discovered that accompanying the change in pupil size with THC was an

increase in pupillary oscillations. The vehicles themselves

also caused some oscillation, although less than that seen with

the vehicle-THC combination (Adler et al. 1985a). In attempting

to determine whether there is an interaction between opioids and

THC on the pupil, we face a problem because opioids also produce

very profound pupillary fluctuations in the rat. In fact, the

degree of fluctuation is one means we use in postulating whether

an effect is exerted at a mu, kappa, or sigma receptor (Robin et

al. 1985).

Tolerance and Cross-Tolerance

Tolerance to a drug as well as cross-tolerance to other drugs

may develop. The tolerance may be due to pharmscokinetic or to

pharmacodynamic factors and may occur as the result of the

administration of a single drug or the previous or concurrent

administration of other drugs. There may be tolerance to some

of the effects of a drug and not to other effects, and there may

be reverse tolerance (increased responsiveness to an action of a

drug). A good example is seen with amphetamine vhere tolerance

occurs to effects such as the hyperthermic and the anorexic

actions (Gilman et al. 1980), while an increased sensitivity to

some stereotypic behaviors (Segal et al. 1980) can develop.

Repeated administration of cocaine leads to an increase in sensitivity to its lethal effects. Such factors are of obvious

significance in evaluating the drug interactions that may occur.

Effects on Neurotransmitters

Effects on a neurotransmitter system by one drug may affect the

actions of another drug. Thus, a drug affecting the adrenergic

system by altering levels of norepinephrine or by acting at

postsynaptic receptors can markedly modify the actions of drugs

of abuse, such as cocaine or amphetamine.

Statistical Analysis

Finally, correct statistical analysis of the data is not an easy

problem. This is especially true if one is trying to determine

1 1
if an interaction between drugs is an additive effect, an effect

that is something less than additive, or an effect that is

greater than additive, a true potentiation. There are several

ways that this problem can be approached and Dr. Mitchell

addresses this topic elsewhere in this volume.

CONCLUDING REMARKS

If future studies adhere to the criteria set forth above, I

believe that we will obtain reproducible and meaningful information about interactions among drugs of abuse. To conduct such

studies properly is admittedly tedious, time-consuming, and

expensive. Of course, not all studies can control all of the

factors I have discussed. This is especially true of clinical

studies with all their inherent difficulties. In such

situations, however, it would be useful if the investigators at

least took these factors into consideration when drawing conclusions. Pointing out potential pitfalls in interpretation vould

be most helpful to other researchers in the field and would aid

in resolving differences between studies and in determining the

significance of the drug interactions.

REFERENCES

Adler, M.W.; Kirby, A.; Zvil, A.; and Geller, E.B. The effects

of delta-9-tetrahydrocannabinol on pupil size in rats. In:

Harvey, D.J., ed. Marihuana ‘84. Proceedings of the

Oxford Symposium on Cannabis. Oxford: IRL Press, 1985a.

pp. 379-383. 

Adler, M.W.; Rowan, C.H.; and Geller, E.B. Intracerebroventricular vs. subcutaneous drug administration: Apples and

oranges? Neuropeptides 5:73:76, 1985b.

Barlow, O.W., and Duncan, J.T. The influence of morphine on the

pre-medication values of ethyl(l-methyl-butyl)barbiturate

(pentobarbital) and isoamylethylbarbituric acid(amytal).

J Pharmacol Exp Ther 49:60-66, 1933.

Beckman, A.L.; Llados-Eckman. C.; and Stanton, T.L. Physical

dependence on morphine fails to develop during the hibernating state. Science 212:1527-1529, 1981.

Cicero, T.J.; Schmoeker, P.F.; Meyer, E.R.; Miller, B.T.; Bell,

R. D.; Cytron, S.M.; and Brown, C.C. Ontogeny of the opioidmediated control of reproductive endocrinology in the male

and female rat. J Pharmacol Exp Ther, in press.

Clutton-Brock, J. Pain and the barbiturates. Anaesthesia

16(1):80-88, 1961.

Dundee, J. W. Alterations in response to somatic pain associated

with anaesthesia. II. The effect of thiopentone and pentobarbitone. Br J Anaesth 32:407-414, 1960.

Frederickson, R.C.A.; Burgis, V.; and Edwards, J.D.

Ryperalgesia induced by naloxone follows diurnal rhythm in

responsivity to painful stimuli. Science 198:756-750, 1977.

Geller, E.B.; Durlofsky, L.; Cowan, A.; Harakal, C.; and Adler,

M.W. The effect of pentobarbital on the antinociceptive

action of morphine in morphine-tolerant and non-tolerant

rats. Life Sci 25:139-146, 1979.

12
Gillette, J.R. Overview of factors affecting drug interactions.

In: Vesell, E.S., and Braude, M.C., eds. Interactions of

Drugs of Abuse. Annals of the New York Academy of Sciences

Vol. 281. New York: New York Academy of Sciences. 1976.

pp. 35-150.

Gilman, A.G.; Goodman, L.S. ; and Gilman, A., eds. Goodman and

Gilman’s The Pharmacological Basis of Therapeutics. 6th ed. New

York: MacMillan, 1980. 1843 DP.

Hart, E.R., and Weaver, O.M., Jr. The analgesic and hypnotic

actions of barbiturates. Anesthesiology 9:276-280, 1948.

Henauer, S.; Lombrozo, L.; and Hollister, L.E. Circadian

variations of lorazepam-induced neurologic deficits. Life Sci

5:2193-2197, 1984.

Keats, A. S., and Beecher. H.K. Pain relief with hypnotic doses

of barbiturates and a hypothesis. J Pharmacol Exp Ther

100:1-13, 1950.

Lesher, G.A., and Spratto, G.R. Potentiation of hexobarbital

and amphetamine effects in male and female rats physically

dependent on morphine. Psychopharmacology 57:175-183, 1978.

Levy, G. Pharmacokinetic approaches to the study of drug

interactions. In: Vesell. E.S.. and Braude. M.C., eds.

Interactions of Drugs of Abuse. Annals of the New York

Academy of Sciences. Vol. 281. New York: New York Academy

of Sciences, 1976. pp. 24-39.

Morris, R.W. Street drug interactions with ethyl alcohol circadian and lunar lethality rhythms. Life Sci 27:2577-2581,

1980.

Neal, M.J. The hyperalgesic action of barbiturates in mice. Br

J Pharmacol 24:170-177, 1965.

Reidenberg, W.M. Some extraneuronal interactions of druga of

abuse: An overview. In: Vesell. E.S.. and Braude. M.C.,

eds. Interactions of Drugs of Abuse. Annals of the New York

Academy of Sciences. Vol. 281. New York: New York Academy

of Sciences, 1976. pp. 1-10.

Robin, H.; Kirby, A.; Messner, S.; Geller, E.B.; and Adler, M.W.

Differentiating opioids by their pupillary effects in the rat.

Life Sci 36:1669-1677, 1985.

Segal, D.S.; Weinberger, S.B.; Cahill, J.; and McCunney, S.J.

Multiple daily amphetamine administration: Behavioral and

neuro-chemical alterations. Science 207:904-907, 1980.

Shapero, M., and Wilson, C. The inhibition of analgesia in mice

by thiopentone. J Pharm Pharmacol 16:759, 1964.

Smith, D.L.; D’Amour, M.C.; and D’Amour, F.E. The analgesic

properties of certain drugs and drug combinations. J

Pharmacol Exp Ther 77:184-193, 1943.

Thompson, M.L. ; Shuster, L. ; Casey, E.; and Kanel, G.C. Sex and

strain differences in response to cocaine. Biochem Pharmacol

33(8):1299-1307, 1984.

Tortella, F.C.; Cowan, A.; and Adler, M.W. Studies on the excitatory and inhibitory influence of intracerebroventricularly

injected opioids on seizure thresholds in rats.

Neuropharmacology 23:749-754, 1984.

13
ACKNOWLEDGMENT

Supported in part by grant DA-00376 from the National Institute on

Drug Abuse.

AUTHOR

Martin W. Adler, Ph.D.

Department of Pharmacology

Temple University School of Medicine

3420 N. Broad Street

Philadelphia, Pennsylvania 19140

14
Issues in the Assessment of Multiple Drug Use Among Drug Treatment Clients

Robert L. Hubbard, Ph.D.; Robert M. Bray, Ph.D.; S. Gail

Craddock, M.S.; Elizabeth R. Cavanaugh, B.A.; William E.

Schlenger, Ph.D.; and J. Valley Rachal, M.S.

Use of such drugs as marijuana, cocaine, and heroin continues to

be a significant problem, crosscutting health, legal, and behavioral domains. The use of multiple drugs of abuse, especially

marijuana and cocaine in combination with alcohol, appears to be

increasing in some segments of the population. Among persons

entering treatment, patterns of use are becoming more complex.

More effective approaches to prevention, intervention, and treatment may be developed and implemented with a better understanding

of multiple drug abuse patterns. Increased understanding of use

patterns of "street pharmacologists" could suggest hypotheses for

pharmacological studies in the laboratory. In addition, epidemiological studies can demonstrate the generalizability of laboratory findings.

Drug abuse treatment clients are the extreme in the continuum of

the drug-using population. Information on their muliple use

patterns may be useful and important. The Treatment Outcome

Prospective Study (TOPS) obtained detailed drug use histories of

11,750 clients entering 41 detoxification, methadone, residential, and outpatient drug-free treatment programs in 10 cities

from 1979-81 (Hubbard et al. 1984). A sample of 5,000 of these

clients was followed up 1 to 3 years after treatment.

The purpose of this chapter is, first, to describe the multiple

use patterns of treatment population; second, to outline factors

that need to be considered in epidemiological studies of multiple

drug use; and, finally, to suggest some strategies for a comprehensive and integrated approach to the study of multiple drug

use.

A COMPREHENSIVE ASSESSMENT OF DRUG USE

The complexity of current drug use patterns requires a thorough

assessment of use of alcohol and a variety of types of licit and

illicit drugs. The information needed for assessment includes

specific data on medical and nonmedical use of different types of

15
drugs as well as specific drugs. The history of use for the

major types of drugs should include the initiation of use, current use, and the termination of use. Frequency, quantity,

route, and concurrent use of other drugs provide data critical

for assessment of multiple use pattern.

Assessment of Drugs Used in TOPS

Clients were asked to provide information about their backgrounds, alcohol and drug use treatment, mental health, criminal

behavior, and employment in personal interviews conducted when

they applied for admission to a TOPS program. The data used to

develop drug use measures in TOPS are from the comprehensive

assessments of nonmedical drug use obtained in the interviews.

Clients were told, "In this section we would like to ask about

your nonmedical use of drugs--without a doctor's prescription or

not according to directions." Clients were then asked, "Have you

ever used [drug type]?" for each of the types of drugs listed

below.

1.

2.

3.

4.

5.

6

7.

8.

9.

10.

11.

12.

13.

Marijuana, hashish, THC

Inhalants-such as glue, gasoline, paint thinner

Hallucinogens or psychedelics-such as LSD, DMT,

mescaline, PCP

Cocaine

Heroin

Street or illegal methadone

Other narcotics or opiates-such as opium,

morphine, codeine, Demerol, Dilaudid, Talwin

Minor tranquilizers-such as Librium, Valium,

Miltown, Equanil

Major tranquilizers-such as Thorazine, Stelazine,

Lithium, Mellaril

Barbiturates-such as Nembutal, Seconal, Tuinal

Sedatives and hypnotics - such as Doriden,

Noludar, Quaalude, Sopor

Amphetamines, speed or diet pills - such as

Benzedrine, Dexedrine, Preludin

Any other drugs (Specify)

16
The drug type and examples of each type were read to the client.

Color pictures of tranquilizers, barbiturates, sedatives and

amphetamines were shown to the client.

of hallucinogens,

If a client reported use

minor tranquilizers or amphetamines, he or she

was asked about use of a specific drug within the general typePCP, Librium/Valium and Preludin, respectively.

Frequency of use of alcohol and frequency of use of drugs for

nonmedical purposes during the 3 months and 12 months before

treatment were obtained for all drug types. Clients were asked

to report their frequency of use on the following 9-point scale:

0

1

2

3

4

5

6

7

8

None

Less than 1 time a month

1 to 3 times a month

1 time a week

2 to 3 times a week

4 to 6 times a week

Daily/1 time a day

2 to 3 times each day

4 or more times each day

The 12-month period preceding treatment was chosen as the base

period for the TOPS analyses because it should allow a more

complete assessment of drug use than the 3 months immediately

preceding treatment. Although a client's life history of drug

use could also be considered, a period of a year prior to treatment should be a more appropriate baseline for examining change

during and after treatment. Detailed questions were asked on the

initiation of use. The nature of the drug problem and the types

of problems associated with use were also addressed.

At months 1, 3, and quarterly thereafter for up to 2 years while

the clients remained in treatment, additional indepth assessments

of drug use and other behaviors were conducted. These assessments are being continued in the posttreatment period by followup

interviews up to 3 years after termination.

Types of Drugs Used by Treatment Clients in TOPS

The need for a comprehensive assessment of drug use is shown in

table 1. In the year before treatment, clients in all modalities

used a variety of drugs weekly or more often. Weekly use of

alcohol and marijuana was quite common. Given the extent of

multiple drug use, weekly rather than daily use provides a more

appropriate measure for multiple use. The great variety of drugs

used suggests that use patterns may be episodic and considerable

substitution may occur. Consequently, a focus only on consistent

daily use may seriously understate the extent of multiple use and

the potential for drug interactions.

17
TABLE 1

Weekly or More Frequent Drug Use in Year

Before Intake: 1979 and 1980 Cohorts

Drug(s)

Outpatient Outpatient

Methadone Drug-Free Residential

(n=2660) (n=2014) (n=1789)

Alcohol

Marijuana

Inhalants

Hallucinogens

(PCP)

Cocaine

Heroin

Methadone

Other narcotics

Minor tranquilizers

(Chlordiazepoxide/Oiazepam)

Major tranquilizers

Barbiturates

Sedatives

Amphetamines

(Phenmetrazine)

46.8%

57.3

0.4

1.3

0.5

27.4

63.9

19.8

26.8

25.3

23.9

1.3

6.4

5.6

10.2

5.0

62.0%

67.5

1.4

5.6

2.6

15.6

10.5

2.0

15.6

18.1

16.6

1.9

7.9

11.2

22.0

3.6

63.8%

65.9

2.0

10.6

8.1

28.1

30.2

5.0

29.2

28.3

27.4

3.9

15.1

16.5

28.5

11.9

In table 2, we see that, even after treatment, many clients continued their use of multiple substances. Marijuana was used

weekly by almost half of the former clients. These data on the

extensive use of a variety of drugs provide the base from which a

description of multiple drug use must be made.

TABLE 2

Clients' Drug Use Weekly or More Often

During the First Year After Treatment:

Followup Samples of 1979 and 1980 Cohorts

Drug(s)

Outpatient Outpatient

Methadone Residential Drug-Free

(n=835) (n=731) (n=854)

Heroin

Cocaine

Marijuana

Other nonopiates*

21.4% 14.5% 7.5%

18.0 17.5 9.8

47.0 49.1 53.4

21.8 24.8 23.2

*Amphetamines. sedatives, barbiturates, minor tranquilizers

18
DESCRIPTION OF MULTIPLE USE PATTERNS

Many research efforts have been directed toward assessing multiple drug use, but no widely accepted system has emerged.

Numerous factors contribute to this situation: (1) many studies

focused on special populations or individual treatment programs

and have limited generalizability; (2) many studies have had

measurement shortcomings, mainly covering brief periods of use

that do not fully reflect the extent of multiple use; (3) the

number of possible drug combinations has resulted in varied and

complex patterns; (4) only a few drugs have been surveyed in some

research, e.g., Client Oriented Data Acquisition Process (CODAP)

data; and (5) selection of measures has varied, depending on the

purposes of research. In order to adequately assess and compare

the results of various studies, some common, comprehensive, and

meaningful measures are needed.

Issues in Describing Drug Use

Three key issues are central to the development of descriptions

of multiple drug use:

1. Emphasis on description of single drug use (such

as heroin) versus multiple drug use involvement;

2 Quantification (interval or ordinal scales) versus

classification (categories or patterns) of drug

use; and

3. Clinical versus analytic usefulness of the description.

In the following paragraphs, we discuss each issue and its impact

on the development of useful descriptions.

Single versus multiple drug use. Many previous studies have

focused on the use of a single drug, generally heroin or other

narcotics/opiates. Implicit in such studies is the belief that

heroin use is the most serious type of drug use and that knowing

the level of involvement with heroin or other narcotics/opiates

would be adequate for describing drug use involvement. The

parsimony of this approach must be weighed against evidence of

the prevalence of multiple drug use. Use of heroin and other

narcotics/opiates alone may no longer fully describe the intensity of drug use. Many addicts, for example, substitute other

drugs when heroin is not available and use many other drugs at

various levels of intensity throughout their drug-using careers.

Thus, even the most complete data on use of a single drug may not

adequately describe the nature and extent of multiple drug use.

19
Quantification versus classification. Approaches to assessing

drug use are, generally, either attempts to quantify the extent

of use or to develop classifications of use (i.e., patterns or

typologies). The resulting measures and classifications, for the

most part, have been developed independently by separate investigators working with different data sets. To date, there has been

little effort to compare and contrast these measures or to examine their relative utility for research in different populations of drug users (Clayton and Voss 1981). For quantifiable

measures, comparisons of the composite indices that are based on

a linear combination methodology would be particularly useful to

demonstrate the utility and/or interchangeability of these measures. A quantifiable measure of multiple drug use also has

advantages for empirical analysis using parametric statistics.

Of course, it should be evident that, despite the advantages of

the composite indices, they also have limitations. They provide

a univariate summary of drug involvement, for example, but do not

permit the specification of which drugs or drug combinations are

involved. Since drug combinations are of considerable interest

in understanding multiple drug use, it is useful to be able to

identify which drugs contributed to the index score.

Classification of drug use by patterns and typologies has

focused, respectively, on frequency of use of selected drugs and

on the specification of comprehensive typologies of drug use. As

with the quantifiable measures, few comparisons have been made

across samples to test for the stability and generalizability of

patterns and typologies. Furthermore, the utility of clustering

approaches has not been adequately demonstrated on large samples

and complex use patterns. Clustering algorithms which are designed to detect stable patterns and typologies within data sets

may not produce robust or stable solutions among clients with

complex multiple drug use patterns. Additional work is needed on

large samples that assess the full domain of drug use.

Clinical versus analysis use. The purpose for which a measure

will be used must be considered in its development. Two major

purposes of most drug abuse measures are: (1) to help make

judgments about treatment, and (2) to provide a useful analytic

tool. Ideally, a measure would serve both purposes. However,

the method of developing measures often emphasizes one purpose

over the other.

Approaches emphasizing clinical applications would more likely

focus on comprehensive use patterns or the use of particular

combinations of drugs. Such measures or patterns of use may not

be easily included in statistical analysis. Except for extreme

use patterns (such as heavy heroin and other drug use or solely

marijuana use), it may be difficult to scale or order these

categories for parametric analysis.

20
The analytic method often relies on statistical techniques to

structure the data. While such methods provide data in a form

that may be very useful for statistical analysis, the data may

not be readily interpretable by clinicians or program managers.

In addition, the analytic methods may be susceptible to small

statistical variations in the data that would not reflect clinically useful differences.

Measures of Single Drug Use

Measures that tap use of single drugs generally consist of the

percentages of persons who use particular drugs within a given

time frame (e.g., a week, a day) or examine use of the principal

problem drug. Such measures are useful when the focus is on use

of a particular drug (e.g., heroin) or when the primary drug

problem dictates the treatment strategy to be followed. Such

single use measures were predominant a decade and more ago when

intensive use of single drugs was the modal pattern among clients.

Primary drug of abuse was a major category of analysis in the

COOAP system. Tyler and Sheridan (1980) examined the relationship of various demographic characteristics and primary use of

such drugs as heroin, amphetamines, cocaine, or marijuana for

162,062 CODAP clients. They observed an interesting relationship

between age at first use and primary drug used: clients who

began use at age 15 or younger showed most frequent use of marijuana (45%) followed by heroin, amphetamines, and cocaine;

clients who began use at age 16 and older showed heroin as the

primary drug most often (69%) followed by marijuana, amphetamines, and cocaine. Blacks were found to abuse heroin more than

whites or Hispanics. Such data potentially have important implications for the targeting and focus of prevention and intervention strategies.

The usefulness of primary drug as an outcome measure seems apparent, though the exact nature of its usefulness is unknown. The

ability and willingness of clients to substitute other drugs in

whole or in part for the primary drug of abuse would greatly

diminish the usefulness of this measure. To better understand

the utility of primary drug as a measure, results from analyses

using only primary drug data and analyses using more Complete

drug use information should be compared.

Indices of Multiple Drug Use

Greater attention has been given recently to the development of

indices that capture multiple drug use. The approaches that have

been developed can be conveniently classified as procedures that

use scaling techniques or procedures that use some form of linear

combination of variables.

21
Scaling approaches. The attempts to use scaling for indexing

drug involvement have typically relied on Guttman scaling procedures (scalogram analysis) to form a unidimensional construct

(Guttman 1950). For drug use, such a scale assumes that different drug types are nearly always used in a hierarchical progression, such as alcohol to marijuana to heroin. Successful scaling indicates that users of any particular drug type are also

users of drug types preceding it in the ordering sequence. For

example, users of heroin in an ordering of alcohol, marijuana,

and heroin could be assumed to also use marijuana and alcohol.

Most of the studies that have successfully used Guttman scaling

procedures have focused on student and young adult populations.

Although they were unable to scale use patterns for their total

sample, Loiselle and Whitehead (1971) were successful in scaling

adolescent drug use patterns for the subset of subjects that had

smoked marijuana. The resulting four-item scale consisted of

stimulants, tranquilizers, hallucinogens, and opiates. Single

and coworkers (1974), analyzing survey data from 8,206 high

school students in New York State, found Guttman scaling useful

in describing drug use patterns in a series of analyses. The

best scale (based on a reproducibility coefficient of .98 and

scalability coefficient of .82) consisted of seven categories:

no drugs, any legal drug, cannabis, "pills," psychedelics,

cocaine, and heroin. Donovan and Jessor (1983) found problem

drinking to be a key stage after marijuana use in a Guttman

analysis of a national sample of high school students.

Other investigators have found Guttman scaling procedures useful

for identifying sequences and stages of drug use among high

school students (Gould et al. 1977; Kandel 1975; Kandel and Faust

1975; Kessler et al. 1976).

Linear combination approaches. The other tack followed in developing measures of multiple drug use has been to use some variant

of linear combinations. Two general approaches have been followed: (1) a multivariate procedure, such as factor analysis or

principal components analysis (e.g., Kessler et al. 1976); or

(2) a specially constructed index based on conceptually or empirically derived weights for drugs used by an individual. Most

efforts in this domain have followed the latter approach (Clayton

and Voss 1981: Douglass and Khavari 1978; Lu 1974; Phin 1978) and

are similar in that they have used a composite index of weighted

elements (drugs). These measures are distinct, however, in the

weighting scheme that is applied to the drugs to reflect the risk

or seriousness associated with the use of that drug.

Comparisons of multiple use indices. Five indices of multiple

drug use have been considered in TOPS: the Lu Index, the

Douglass/Khavari Index, the Number of Drugs Index, the Severity

Index, and the Illicit Drug Use Index. These indices share a

22
linear combination methodology in their construction. They all

attempt to derive a single number that represents the extent of

user involvement with the entire array of drugs being considered.

In general, the methodology multiplies the frequency of use

scores by the weights assigned to each drug and then combines

these products either by summing or averaging across the set of

drugs to obtain a final index value.

The principal difference among the indices is the procedure used

to obtain the weights associated with the drugs. The Number of

Drugs Index uses equal (unit) weights for each drug; the Lu Index

uses an empirical derivation based on frequency of use of each

drug in the population; the Douglass/Khavari Index uses a standardization procedure; the Severity Index uses conceptually based

weights; and the Illicit Drug Use Index uses weights derived from

national data sets.1

The similarity in construction of the various indices suggests

that there may be considerable overlap among the measures themselves. To address this question more directly, the intercorrelations of the indices were computed along with the correlations of selected drugs and are presented in table 3. As shown,

the Number of Drugs Index, the Lu Index, the Douglass/Khavari

Index, and the Severity Index are all highly related. For these

four indices the correlations range from .80 to .93, with an

average correlation of .89. Thus, it appears that the various

weighting schemes used to construct these indices have very

little effect on the final index. These four indices provide

reasonably comparable estimates of multiple drug use. Relationships are considerably lower, however, between these four indices

and the Illicit Drug Use Index which appears to be a better

indicator of heroin use than a total involvement index.

Table 3 also shows correlations between the five drug use

indices; the primary drug of abuse; the number of drug problems;

and the alcohol, marijuana, heroin, and barbiturate use during

the year prior to treatment. As already noted, heroin use is

strongly related to the Illicit Drug Use Index. For the other

indices, barbiturate use shows the highest relationship. Perhaps

barbiturates are a marker, in that clients who use barbiturates

tend also to use several other drugs, whereas those who do not

use barbiturates tend to use only a few drugs.

In selecting among indices, the three most important criteria are

construct validity, parsimony or simplicity, and conceptual

appeal. Examination of the five multiple use indices with respect to these criteria in TOPS led to the conclusion that the

Severity Index is the preferred multiple use index in the drug

treatment population.

23
TABLE 3

Correlations of Multiple Drug Use Indices

and Selected Other Drug Use Measures:

1979 Cohort

Selected Drug

Use Measure

Multiple Drug Use Index

Number of Douglass/ Illicit

Drugs Lu Khavari Severity Drug Use

Number of Drugs Index 1.00

Lu Index .80

Douglass/Khavari

Index .88

Severity Index .89

Illicit Drug Use

Index .48

Primary Drug Frequency .39

Number of Drug Problems .32

Alcohol Frequency .37

Marijuana Frequency .46

Heroin Frequency .34

Barbiturate Frequency .50

1.00

.91

.93

.42 .45 .59 1.00

.35 .39 .43 .55

.35 .36 .36 .19

.21 .17 .23 -.02

.52 .44 .38 . 10

.26 .24 .43 .90

.56 .62 .53 .08

1.00

.93 1.00

DRUG USE PATTERNS AND USER TYPES

Several approaches were used in TOPS analyses to examine drug use

patterns and user types based on use in the year prior to treatment: primary drug of abuse, Guttman scaling, pattern frequency

counting, development of general drug use patterns, and cluster

analysis.

For the primary drug index, a frequency analysis was first conducted to determine the pattern of drug use accompanying use of a

specified primary drug. Use of eight drug types (heroin/illegal

methadone, other narcotics, barbiturates/sedatives, cocaine,

amphetamines, minor tranquilizers, marijuana, alcohol) was

examined for clients who reported each of the primary drug groupings (heroin/illegal methadone, other narcotics, cocaine, barbiturates/sedatives/minor tranquilizers, amphetamines, alcohol/

marijuana). Discriminant analyses using these patterns misclassified 32% of the clients, thus showing this approach to be

insufficient.

24
Guttman scaling analysis, which provides information to determine

if clients use drugs in a particular cumulative sequence, was

also explored. If the drugs are able to be scaled, the analysis

indicates hierarchy of use, and specifies the set of drugs expected to be used by a client who appears at any point in the

scale. Weekly or greater use of eight drug classes reported by

TOPS clients was examined: heroin, other narcotics, barbiturates/ sedatives, cocaine, amphetamines, minor tranquilizers,

alcohol and marijuana. The results of the analysis showed that

clients use of these drug classes did not meet requirements for

Guttman scaling. The coefficient of scalability (which ranges

from 0 to 1) was only .22, far below the minimum acceptable level

of .60.

For pattern frequency counts, TOPS drug use data were first

recoded and compared to 28 patterns used in the Drug Abuse Reporting Program (DARP) (Simpson 1974, 1976). Eight of the major

drug classes were considered (heroin, other narcotics/ opiates,2

barbiturates, cocaine, amphetamines, hallucinogens, marijuana,

and other drugs) for four categories of use (no use, less than

weekly use, weekly use, daily use). Results showed that the DARP

patterns did not adequately describe the drug use of the TOPS

clients. The major difference was greater multiple drug use in

TOPS. Twenty-eight percent of DARP clients were heroin only

users as compared with 3% of TOPS clients. In contrast, 5% of

DARP clients fit the "poly" pattern (i.e., used heroin and other

narcotics [opiates] less than weekly and three or more nonnarcotics at any level) as compared with 19% of TOPS clients.

Similarly, 8% of OARP clients as compared with 14% of TOPS clients were classified in the pattern "opiates plus" (any use of

heroin or other opiates along with any use of one or two nonopiates).

A second approach was to examine a set of 25 drug use patterns

adapted from the DARP classification scheme. These 25 patterns

were based on the eight frequently used drug classes: heroin,

other narcotics, barbiturates (including sedatives/hypnotics),

cocaine, amphetamines, minor tranquilizers, marijuana, and alcohol. This drug set was examined using the 4-point use scale

noted above. Results showed that two-thirds of the clients from

both the 1979 and 1980 cohorts fell into three complex multiple

use patterns. However, these patterns were viewed as unsatisfactory because the classification rules (1) grouped clients together who had wide variability in amount and kind of drug use,

and (2) defined some patterns which did not appear discriminably

different from others.

Close examination of frequencies of the specific combinations of

use of the eight drug types revealed a complex picture. For

example, over 740 unique patterns were reported by the 3,389

clients of the 1979 cohort. This suggested that broad, rather

than specific, patterns may be required to describe drug use.

Thus, an alternate approach to using the eight drugs and the

25
4-point use scale focused on a more limited set of clinically

useful patterns. Patterns were defined by hierarchical rules

that emphasized weekly or greater use of key drugs or drug types.

The conceptually based rules produced seven patterns, which

appear in table 4 along with their respective percentages of

clients from the 1979 and 1980 cohorts. It should be noted that

the greatest number of clients fell in the heroin class and that

the majority of clients (58%, 1979; 55%, 1980) fell in the first

three classes which describe heavy narcotics users.

The defining characteristics of the patterns do not describe all

drugs used by the clients. Rather, they indicate the key drugs

and the use levels of these particular drugs. Additional illustration of the use of different types of drugs within the seven

patterns is provided in table 5.

It should be noted that a sizeable proportion of clients entering

treatment, including methadone treatment, were not active heroin

users in the year prior to their admission to the TOPS program.

We identified an eighth pattern of those who used heroin daily at

some time in their lives. Depending on the purposes of a study,

it might be advisable to separate these Former Daily Users from

individuals in the other pattern.

The last approach employed cluster analysis to identify some

general types of drug use clients. Several alternative clusterings were conducted and assessed according to their reproducibility and face validity. The best cluster solution based on

these criteria identified three major types of drug abuse clients. The first type, comprising 37% of the TOPS clients, was

viewed as traditional heroin users. The use pattern of this

group involved daily use of heroin coupled with periodic use of

cocaine, marijuana, and alcohol. The second type, comprising 23%

of the clients, was described as heavy multiple users, characterized by weekly or more frequent use of the eight drugs. Finally,

the third type, which made up 40% of the sample, was characterized by low average frequency of use of all drugs except alcohol

and marijuana Multivariate analysis of variance and discriminant analysis results confirmed that the clients in these three

clusters were reliably different from one another in terms of

their drug use.

These analyses, however, did not provide discrimination among the

user types that would be very useful for clinicians or researchers. Thus, for descriptive purposes, the seven drug-use patterns

described above seem to be the most useful approach.

26
TABLE 4

Distribution of Seven Patterns:

2979, 2980, and 1982 Cohorts

Drug Use

Patterns

Defining Characieristics

of Groupsa

Cohort Prevalence

1979 1980 1981

(n=3389) (n=3908) (n=3729)

Heroin/othernarcotics

Heroin

Other narcoticsb

Multiple nonnarcoticsc

Single nonnarcoticc

Alcohol/

Marijuana

Minimal

(residual)

Weekly or greater use of

heroin or illegal methadone

and other narcotics

Weekly or greater use of

heroin or illegal methadone

and no use of other narcotics as often as weekly

Weekly or greater use of

other narcotics but less

than weekly use of heroin/

methadone

Weekly or greater use of at

least two nonnarcotics in

addition to marijuana and

alcohol use

Weekly or greater use of

one nonnarcotic in addition

to marijuana and alcohol

use

Weekly or greater use of

alcohol and/or marijuana.

No other drug used as

often as weekly

All remaining clients

11.2% 10.3% 15.4%

37.1 33.0 34.0

9.7 12.1 11.6

6.0 6.7 7.4

10.4 13.5 11.9

19.5 17.7 14.2

6.1 6.7

100.0% 100.0%

5.5

100.0%

Note: Patterns were defined hierarchically in their tabled order. Thus,

for example, a client appears in a pattern 5 in the table only if he did

not qualify for any of the preceding patterns (1 to 4).

aDefining characteristics do not describe all drugs used by these clients.

Rather, they indicate the key drugs and use levels for classification.

b Other narcotics are defined as opioids other than heroin and methadone.

Included in this category are codeine, propoxyphene (Darvon), meperidine,

hydromorphone, morphine, opium, paregoric, oxycodone (Percodan), and

pentazocine.

cThe nonnarcotics included in constructing these patterns were barbiturates/

sedatives/hypnotics, cocaine, amphetamines, and minor tranquilizers.

27
TABLE 5

Use of Selected Drugs within Drug

Pattern: 1979, 1980, and 1981 Cohorts

Drug Type

Use During 12 Months Before Intake

No Use Monthly Weekly Daily

Heroin/Other-Narcotics (n=1379)

Heroin

Other narcotics

Barbiturates/sedatives

Cocaine

Amphetamines

Minor tranquilizers

Marijuana

Alcohol

2.1%

0.0

49.8

26.3

52.2

28.7

15.8

16.0

Heroin (n=3854)

Heroin

Other narcotics

Barbiturates/sedatives

Cocaine

Amphetamines

Minor tranquilizers

Marijuana

Alcohol

Other Narcotics (n=1259)

Other narcotics

Barbiturates/sedatives

Cocaine

Amphetamines

Minor tranquilizers

Marijuana

Alcohol

2.6% 29.0% 66.3%

0.0 40.0 60.0

27.0 14.3 8.9

40.1 20.3 13.3

20.9 12.2 14.7

26.0 23.9 21.4

21.1 25.2 37.9

27.9 29.7 26.4

3.8 2.7

80.5 19.5

81.8 11.7

29.2 29.3

84.3 9.4

59.9 22.5

22.1 20.9

23.1 22.8

66.4

0.0

46.5

42.5

45.9

32.3

17.1

14.7

33.6

0.0

25.6

39.8

20.6

26.5

19.5

26.8

Multiple Nonnarcotics (n=750)

Heroin 78.9

Other narcotics 54.6

Barbiturates/sedatives 18.1

Cocaine 18.1

Amphetamines 20.3

Minor tranquilizers 19.9

Marijuana 7.5

Alcohol 6.3

21.1

45.4

16.1

29.2

17.1

23.1

12.2

21.4

20.9

0.0

4.4

21.1

3.4

11.4

27.9

30.1

72.6

0.0

2.1

20.4

2.9

6.2

29.0

23.9

0.0 0.0

40.0 60.0

15.7 12.1

11.1 6.5

16.4 17.1

24.5 16.7

23.7 39.7

32.5 26.0

0.0

0.0

42.8

36.4

34.7

35.5

26.5

36.7

0.0

0.0

22.9

16.3

27.8

21.5

53.8

35.6

(Continued)

20
Table 5 (Continued)

Drug Type

Use During 12 Months Before Intake

No Use Monthly Weekly Daily

Single Nonnarcotic (n=1334)

Heroin 83.1

Other narcotics 73.8

Barbiturates/sedatives 52.9

Cocaine 33.9

Amphetamines 43.5

Minor tranquilizers 49.4

Marijuana 13.7

Alcohol 11.9

0.0

0.0

7.3

7.1

15.3

9.2

38.3

25.1

Alcohol/Marijuana (n=1901)

Heroin 85.0

Other narcotics 81.2

Barbiturates/sedatives 74.5

Cocaine 58.4

Amphetamines 68.3

Minor tranquilizers 72.6

Marijuana 10.8

Alcohol 8.2

Minimal (n=675)

Heroin

Other narcotics

Barbiturates/sedatives

Cocaine

Amphetamines

Minor tranquilizers

Marijuana

Alcohol

85.7 14.3

91.6 8.4

89.6 10.4

81.4 18.6

92.2 7.8

86.2 13.8

58.0 42.0

46.4 53.6

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

Former Heroin Users (n=2176)*

Heroin 60.4

Other narcotics 56.9

Barbiturates/sedatives 71.0

Cocaine 49.2

Amphetamines 70.0

Minor tranquilizers 54.5

Marijuana 23.2

Alcohol 22.1

16.9 0.0

26.2 0.0

27.6 12.2

41.0 18.0

20.4 20.9

30.4 11.0

17.9 30.1

24.4 38.7

15.0

18.8

25.5

41.6

31.7

27.4

13.7

21.1

0.0

0.0

0.0

0.0

0.0

0.0

39.4

46.4

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.0

39.6

15.9

17.7

34.6

15.9

23.5

22.9

27.9

0.0

9.2

6.3

10.9

6.8

12.7

26.6

27.4

0.0

0.0

0.0

0.0

0.0

0.0

36.1

24.3

0.0

18.0

4.9

5.3

7.2

9.3

27.3

22.6

Note: Rows sum to 100%.

*The Former Heroin Users pattern is a supplementary classification

which includes individuals from five of the seven patterns.

29
DYNAMIC NATURE OF MULTIPLE USE PATTERNS

The patterns of multiple drug use are dynamic. In TOPS, the

focus was on the pattern in a 1-year period. Over an individual's life, however, a number of patterns may be experienced.

Three sets of results demonstrate the dynamic nature of drug use

patterns.

A Decade of Changing Patterns

As previously noted, the drug use patterns of OARP clients entering treatment in 1969-73 were markedly different from those for

TOPS clients entering treatment in 1979-81. As shown in table 6,

the TOPS clients were much less likely than DARP clients to use

only opioids on a daily basis regardless of the modality they

entered. In methadone programs, TOPS clients were more likely to

be daily opioid users who also used nonopioids. In the other

modalities, nonopioid use was much more common among TOPS clients

than among DARP clients. More detailed analysis also demonstrated the more extensive use of multiple substances among TOPS

clients than among DARP clients. These results suggest the need

for monitoring trends in multiple use patterns to determine

changes in patterns, especially patterns that might adversely

affect treatment outcomes.

Substitution

In TOPS, clients were asked about any drugs they used as a substitute for heroin. As table 7 shows, substitution was common.

Over one-third of the clients who had a history of daily heroin

use reported using illegal methadone or other narcotics to substitute for heroin. About one in four used diazepam or chlordiazepoxide. These reports of substitution suggest that the life

history of use should be carefully considered in order to accurately describe multiple use patterns. A number of clients

entering treatment had not used heroin in the year prior to

admission. Thus, a limited assessment might misclassify them as

nonopioid users. The TOPS data suggest that former daily heroin

users should be identified and their use patterns described

separately from users who have not used heroin. Reasons for

abstinence from heroin also need to be ascertained for the former

daily users to better understand the nature of their current use

patterns.

30
TABLE 6

Classification of Pretreatment Drug Use:

DARP and TOPS Clients, 1979-1981

Drug Use

Classification

Outpatient Outpatient

Methadone Residential Drug Free

DARP TOPS DARP TOPS DARP TOPS

(n=11,023) (n=4505) (n=5785)

(n=3223) (n=1192) (n=1949)

Daily opioid 45 21 22 6 19 2

use only

Daily opioid use; 49 60 40 24 16 8

some nonopioids

Less-than-daily 5 15 20 29 17 26

opioid use

Nonopioid 1 4 17 41 48 64

use only

Total 100 100 99 100 100 100

Note: DARP data are for the 2 months before admission. TOPS data

are for the 3 months before admission. Data are for comparable

TOPS and DARP samples of clients using drugs in the 2 to 3

months before treatment.

TABLE 7

Nonmedical Use of Substitutes for Heroin by

Heroin and Cocaine Use in the Year Before Intake:

1979, 1980, and 1981 Cohorts

Substitute

Drug

Weekly Heroin Use Former Daily Users

Weekly No Weekly No

Cocaine Cocaine Cocaine Cocaine

(n=2069) (n=3069) (n=445) (n=2173)

Illegal methadone 44.9% 34.0% 33.8% 34.0%

Other narcotics 29.4 39.2 40.5 38.9

Diazepam or

chlordiazepoxide 19.5 23.8 25.0 23.0

Barbiturates 7.9 10.7 17.6 14.7

31
Change in Patterns Before and After Treatment

Table 8 shows considerable shifting in use patterns. The data

for residential clients are presented because there is a distribution of clients in each pattern. As can be seen from the data

for clients staying 1 week or less, about 65% of the opioid users

move to a less serious use pattern. However, 24% of nonopioid

users before treatment use opioids weekly in the year after

treatment. About one-third of the marijuana/ alcohol/minimal

user group increases use to weekly opioid or other nonopioid use.

The table also shows a clear tendency for clients with longer

treatment stays to move to less serious patterns of use. The

consideration of multiple use patterns after treatment provides a

more complete description of the effects of treatment on drug use

patterns.

TABLE 8

Drug Use Pattern in the Year After

Residential Treatment by Drug Use Pattern

in the Year Before Intake: Followup

Samples of 1979 and 1980 Cohorts

Weekly or Greater Pattern After Treatment

Duration of Non- Alcohol/

TOPS Treatment n Opioids opioids Marijuana Minimal

Weekly or Greater Heroin and/or Opioid Users (n=354)

1 week or less 26 35% 15% 23% 27%

2-13 weeks 153 39 19 25 17

14-52 weeks 153 39 19 35 22

More than 2 years 48 13 10 40 37

Weekly or Greater Nonopioid Users (n=202)

1 week or less 25 24% 20%

2-13 weeks 79 13 25

14-52 weeks 65 9 29

More than 1 year 33 6 3

Alcohol/Marijuana or Minimal Users (n=164)

28% 28%

47 15

34 28

42 49

1 week or less 17 12% 18% 53% 17%

2-13 weeks 80 23 19 38 20

14-52 weeks 38 13 11 37 39

More than 1 year 29 3 21 31 45

Note: Rows sum to 100%.

32
Conclusions and Recommendations

The results of these analyses of multiple drug use among

individuals entering publicly funded drug treatment suggest three

general conclusions:

1. Multiple use is complex, and it is increasing. In

addition to the TOPS data for drug treatment clients,

studies of youth in high schools (Johnston et al. 1984;

Rachal et al. 1980) show that marijuana and alcohol are

now commonly used together.

2. The pattern or configuration of use is the most empirically and clinically useful approach to multiple drug

use assessment.

3. The results strongly suggest that multiple use requires

consideration of factors in addition to the pharmacological interactions.

The complex, often pharmacologically incompatible patterns of use

suggest that many users often do not know or care what they use.

The complex and rapidly changing array of substances used may

confound any statistical or biochemical approach. A consideration of psychosocial factors, coupled with more detailed understanding of pharmacological effects, is needed to disentangle the

epidemiology of multiple use and to design more effective treatment approaches.

After considering all of the evidence concerning measures of drug

use, several recommendations seem warranted for further research

on this critical issue of multiple drug use. First, it is clear

that there are a number of options available for measuring drug

use, each of which has strengths and weaknesses. Second, it is

also clear that the choice of a measure can be greatly influenced

by the clinician's, researcher‘s, and policymaker's purposes, the

population sampled, and the questions under consideration.

Third, basic and complex measures of multiple use are available,

depending on the level of specificity needed in research.

Recommended Measures

The simplest measures of drug use are the single use measures,

which are appropriate when the study focus is on one specific

drug. For treatment clients, heroin use and primary drug appear

to be the best single use measures. Heroin use is the focus of a

major proportion of the treatment effort and is still the most

serious problem treated in terms of social cost and personal

suffering. A measure of primary drug use can be calculated for

all clients. Since the use of the primary drug is likely to be

the focus of treatment, it may be an appropriate indicator of

treatment effectiveness. However, single use measures are not

able to reflect the broad spectrum of drug use.

33
The multiple drug use indices are somewhat more complex and

attempt to reflect the full range of drug-taking behavior along a

single dimension. Such measures can provide a broad overview of

drug use. The Severity Index was judged to be the best unidimensional multiple use index for the treatment population. While

such an index is helpful in indicating a general level of drug

use, it may be insensitive to patterns of use and drug substitution.

The most complex measures are those that attempt to reflect the

multiple dimensions of drug use behavior. A Multidimensional

Index was constructed using principal components analysis of drug

use among TOPS treatment clients. It indicated three factors

underlying clients' drug use: a polydrug factor, a heroincocaine factor, and an alcohol-marijuana factor. The Multidimensional Index provides a more detailed picture of drug use than is

possible with a single index, but it has limited clinical utility.

Clearly, multiple drug use is more common among clients entering

treatment today than it was when the DARP studies were initiated

or, even later, when the CODAP system was developed. The CODAP

system and much of drug treatment research has focused on the

primary drug of abuse as a descriptor of use patterns. Although

useful, classification by primary drug does not capture the full

scope of drug use. Based on the original work of Simpson (1974,

1976), the seven drug patterns developed for TOPS provide a

hierarchical classification of use patterns that can be readily

understood and used by clinicians, policymakers, and researchers.

Treatment Implications

Compared to drug use patterns observed in treatment populations

in the late 1960s and early 197Os, the increased use of multiple

drugs by current clientele indicates a need for careful consideration of treatment approaches. The seven patterns provide a

framework for the design, conduct, and evaluation of current

treatment efforts. The patterns were chosen and defined with

treatment needs in mind and clearly separate heroin and narcotic

users from clients who abuse nonnarcotic drugs. Use of these

patterns can help answer such questions as:

1. Do clients with various patterns of use require different treatment approaches?

2. What types of users benefit most or least from particular modalities and environments?

3. Are different types of ancillary services required by

various types of users?

4. How do patterns of use change after a client enters

treatment?

34
Given the prevalence of multiple drug use, descriptions based on

a single or a limited number of drugs would provide insufficient

information to address the more complex issues now facing treatment programs. The patterns, in combination with information on

the severity of use and primary drug of abuse, could certainly

provide a more complete assessment of the nature and extent of

total drug use. Consequently, the increased information should

help treatment programs design and deliver services more effectively.

Research Directions

In the TOPS analyses, drug use measures developed in prior research have been examined and refined, and new measures have been

constructed, both for quantitative measures (e.g., Severity

Index) and for classification measures (e.g., the seven patterns). The result has been a set of recommended measures to be

used in the further analyses of the TOPS data and in other research. Major questions on the epidemiology of multiple drug

abuse and the association of multiple drug abuse with other

factors can now be examined more comprehensively with these

measures in the TOPS and other data sets. Epidemiological questions for general and treatment population include:

1. How do patterns of use develop?

2. Are patterns of use episodic, persisting, or random?

3. What are the demographic characteristics of different

types of users?

4. Has and will the prevalence of patterns change over

time?

The association of these measures with other variables should

provide important information on the causes and consequences of

multiple drug use.

Questions that are in need of careful examination include the

following:

1.

2.

3.

4.

What is the relationship between pattern classes and

drug-related problems?

Do such factors as depression, criminal behavior, and

employment vary systematically with drug use severity

or the pattern classes?

What individual characteristics or environmental conditions are associated with patterns of use?

How are community and treatment factors related to

changes in the patterns of use over time?

35
Further TOPS research has taken the next logical step of using

the drug use measures developed to address many of the questions

posed above for the drug treatment population. We would strongly

recommend the use of these measures in other appropriate studies

where multiple drug use is common. The use of these measures in

a variety of studies should significantly increase our understanding of the dynamics and nature of multiple drug abuse.

Coordination of Pharmacological, Epidemiological, and

Psychosocial Research

To advance the understanding of the complex nature of multiple

drug use, a multidisciplinary approach is needed. Increased

interaction of epidemiological, psychosocial, clinical, and

preclinical researchers should provide a stronger foundation for

research design and interpretation of study results. As a first

step, we suggest a focus on three areas for collaboration:

1. Identification of patterns of use and potential combinations of drugs in the field and in the laboratory;

2. Confirmation of research findings in field, clinical,

and preclinical studies; and

3. Assessment of effect of patterns of multiple use on

dependency.

More specifically, we would suggest a series of meetings with

epidemiologists, clinicians, and pharmacologists. The objective

would be the development of known and potential patterns of

multiple drug use. Specific patterns could be identified for

monitoring in epidemiological or treatment studies and/or pharmacological analysis in the laboratory. Known and potential correlates and consequences, particularly dependencies, of the

patterns could also be investigated across the multidisciplinary

settings. Such initial collaborative efforts should lead to

research that will expand our knowledge of both the basic mechanisms and multiple drug use and efficacy of therapies to ameliorate

adverse effects of multiple use patterns.

FOOTNOTES

1

The weights developed for the Lu Index and the T-scores of the

Douglass/Khavari Index reflect both the importance of each drug

and the frequency of use of each drug by clients. Thus, these

values were used in computing the final index for these measures

(i.e., these weights were not multiplied by frequencies prior to

combining them).

2The term "other narcotics or opiates" is used in TOPS. The term

"other opiates" that was used in DARP generally describes the

same class of drugs.

36
REFERENCES

Abelson, H.I.; Fishburne, P.M.; and Cisin, I. National Survey on

Drug Abuse: 1977. DHEW Pub. No. (ADM) 78-618. Rockville,

MD: National Institute on Drug Abuse, 1978.

Ball, J. The reliability and validity of interview data obtained

from 59 narcotic drug addicts. Am J Social 72(6): 650-654,

1967.

Benvenuto, J., and Bourne, P. The Federal Polydrug Abuse Project: Initial report. J Psychedelic Drugs 7(2): 115-120,

1975.

Beschner, G.; Adams, K.M.; Wesson, D.R.; Carlin, A.S.; and

Farley, E. (1978). Introduction. In: Wesson, D.R.;

Carlin, A.S.; Adams, K.M.; and Beschner, G., eds. Polydrug

Abuse. The Results of a National Collaborative Study.

New York: Academic Press, 1978 pp. 1-13.

Bonito, A.M.; Nurco, D.N.; and Shaffer, J.W. The veridicality

of addicts' self-reports in social research. Int J Addict

11(5): 719-724, 1976.

Braucht, G.N.; Kirby, M.W.; and Berry, G.J. Psychological

correlates of empirical types of multiple drug abusers.

J Consult Clin Psychol 46(6):1463-1475, 1978.

Bray, R. M.; Hubbard, R .L.; Rachal, J.V.; Cavanaugh, E.R.;

Craddock, S.G.; Collins J.J.; Schlenger, W.E.; and

Allison, M. Client Characteristics, Behaviors, and Intreatment Outcomes of Clients in TOPS: 1979 Admission

Cohort (RTI/1500/04-04F). Research Triangle Park, NC:

Research Triangle Institute, 1981.

Clayton, R.R., and Voss, H.L. Young Men and Drugs in Manhattan:

A Causal Analysis. National Institute on Drug Abuse.

Research Monograph 39. DHHS Pub. (ADM) 81-1167. Washington, DC: Supt. of Docs., U.S. Govt. Print. Off., 1981.

Cox, T., and Longwell, B. Reliability of interview data concerning current heroin use from heroin addicts on methadone.

Int J Addict 9(2): 161-165, 1974.

Craddock, S.G.; Hubbard, R.L.; Bray, R.M.; Cavanaugh, E.R.; and

Rachal, J.V. Client Characteristics, Behaviors and Intreatment Outcomes: 1980 TOPS Admission Cohort (RTI/1901/01-OlF).

Research Triangle Park. NC: Research Triangle Institute,

1982.

Domestic Council Orug Abuse Task Force. White Paper on Drug

Abuse: A Report to the President. Washington, D.C.:

Supt. of DOCS., U.S. Govt. Print. Off., 1975

Donovan, J.E., and Jessor, R. Problem drinking and the dimension

of involvement with drugs: A Guttman Scalograph Analysis of

Adolescent Drug Use. Am J Pub Health 73(5): 543-552, 1983.

Douglass, F.M., and Khavari, K.A. The drug use index: A measure

of the extent of polydrug usage. Int J Addict 13(6):

981-993, 1978.

Douglass, F.M.; Khavari, K.A.; and Farber, P.D. Three types of

extreme drug users identified by a replicated cluster

analysis. J Abnorm Psychol 89(2): 240-249, 1980.

37
Drug Abuse Warning Network Phase V Report. May 1976-April 1977.

Ambler, PA: IMS America, Ltd., 1978.

Duncan, D. The acquisition, maintenance and treatment of polydrug dependence: A public health model. J Psychedelic

Drugs 7(2): 209-213, 1975.

Gould, L.C.; Berberian, R.M.; Kasl, S.V.; Thompson, W.D.; and

Kleber, H. Sequential pattern of multiple-drug use among

high school students. Arch Gen Psychiatry 34:216-222,

1977.

Guttman, L.A. A basis for scaling qualitative data. Am Social

Rev 9:139-150, 1944.

Guttman L.A. The basis for scalogram analysis. In: Stouffer,

S.A. ; Guttman, L.A.; Suchman, E.A.; Lazarsfeld, P.; Star,

S.A. ; and Clausen, J.A., eds. Measurement and Prediction.

New York: Wiley, 1950.

Hubbard, R.L.; Rachal, J.V.; Craddock, S.G.; and Cavanaugh, E.R.

Treatment Outcome Prospective Study (TOPS): Client characteristics and behaviors before, during, and after treatment.

In: Tims, F.M., and Ludford, J.P., eds. Drug Abuse Treatment Evaluation: Strategies, Progress, and Prospects.

National Institute on Drug Abuse. Research Monograph 51.

DHHS Pub. No. (ADM) 84-1329. Washington, D.C.: Supt. of

Docs., U.S. Govt. Print. Off., 1984. pp. 42-68.

Johnston, L.D.; O'Malley P.M.; and Bachman, J.G. Highlights

from Drugs and American High School Students,

DHHS Pub. No. (ADM) 84-1317. Washington. D.C.: Supt. of

Docs., U.S. Govt. Print. Off., 1984.

Kandel, D. The measurement of "ever use" and "frequency-quantity"

in drua use surveys. In Elinson. J., and Nurco. D.N.. eds.

Operational Definitions in Sociol-behavioral Drug Use

Research. National Institute on Drug Abuse. Research Monograph 2. DHEW Pub. No. (ADM) 76-292. Washington, D.C.:

Supt. of Docs., U.S. Govt. Print. Off., 1975. pp. 27-35.

Kandel, D., and Faust, R. Sequence and stages in patterns of

adolescent drug use. Arch Gen Psychiatry 32: 923-932,

1975.

Kessler, R.C.; Paton, S.M.; and Kandel, D.B. Reconciling unidimensional and multidimensional models of patterns of drug

use. J Stud Alcohol 37(5): 632-647, 1976.

Loiselle, P., and Whitehead, P.C. Scaling drug use: An examination of the popular wisdom. Can J Behav Sci 3(4): 347-356,

1971.

Lu, K.H. The indexing and analysis of drug indulgence. Int J

Addict 9(6): 785-804, 1974.

Maddux, J.F., and Desmond, D.P. Reliability and validity of

information from chronic heroin users. J Psychiatr Res 12:

87-95, 1975.

Marini, J. L.; Bridges, C.I.; and Sheard, M.H. Multiple drug

abuse: Examination of drug abuse patterns in male prisoners.

Int J Addict 13(3): 493-502, 1978.

38
National Institute on Drug Abuse. Data from the Client Oriented

Data Acquisition Process (Annual Data for 1980 Statistical

Series E. Number 21). DHHS Publication No. (ADM) 81-1153.

Washington, D.C.: Supt. of DOCS., U.S. Govt. Print. Off.,

1981.

O'Donnell, J.A.; Voss, H.L.; Clayton, R.R.; Slatin, G.T.; and

Room, R.G.W. Young Men and Drugs A Nationwide Survey.

National Institute on Drug Abuse. Research Monograph 5.

DHEW Pub. No. (ADM) 76-311. Washington, D.C.: Supt. of

Docs., U.S. Govt. Print. Off., 1976.

Pandina, R.J.; White, H.R.; and Yorke, J. Estimation of substance use involvement: Theoretical consideration and

empirical findings. Int J Addict 16(1):1-24, 1981.

Phin, J. Nonpatient polvdrug users. In: Wesson, D.R.; Carlin,

A.S. ; Adams, K.M. ; and Beschner, G., eds. Polydrug Abuse:.

The Results of a National Collaborative Study. New York:

Academic Press, 1978. pp. 31-57.

Rachal, J.V.; Guess; L.L.; Hubbard, R.L.; Maisto, S.A.;

Cavanaugh, E.R.; Waddell, R.; and Benrud, C.H. Adolescent

Drinking Behavior (RTI/1322/01-O1F). Research Triangle

Park, NC: Research Triangle Institute, 1980.

Sells, S.B., ed. Effectiveness of Drug Abuse Treatment. Vols. 1

and 2. Cambridge, MA: Ballinger Publishing Company, 1974.

Sells, S.B., and Simpson, D.D., eds. Effectiveness of Drug Abuse

Treatment. Vol. 3. (Further studies of drug users treatment

typologies and assessment of outcome during treatment in the

DARP.) Cambridge, MA: Ballinger Publishing Company, 1976a.

Sells, S.B., and Simpson, D.D., eds. Effectiveness of Drug Abuse

Treatment. Vol. 4. (Evaluation of treatment outcomes for

the 1971-1972 admission cohort.) Cambridge, MA: Ballinger

Publishing Company, 1976b.

Sells, S.B., and Simpson, D.D., eds. Effectiveness of Drug Abuse

Treatment. Vol. 5. (Evaluation of treatment outcomes for

the 1972-1973 admission cohort.) Cambridge, MA: Ballinger

Publishing Company, 1976c.

Simpson, D.D. Patterns of multiple drug abuse. In: Sells, S.B.,

ed. Effectiveness of Drug Abuse Treatment. Vol. 1.

Cambridge, MA: Ballinger Publishing Company, 1974.

Simpson, D.D. Pretreatment drug use by patients entering drug

treatment programs during 1971-1973. J Drug Educ 6(1):

53-71, 1976.

Simpson, D.D.; Lloyd, M.R.; and Gent, M.J. Reliability and Validity of Data: National Followup Study of Admissions to Drug

Abuse Treatments in the DARP During 1969-1972 (IBR Report

76-18). Fort Worth: Texas Christian University,

Institute of Behavioral Research, 1976.

Single, E.; Kandel, D.; and Faust, R. Patterns of multiple drug

use in high school. J Health Soc Behav 15(4): 344-357,

1974.

Stephens, R.C. The truthfulness of addict respondents in

research projects. Int J Addict 7(3): 549-558, 1972.

39
Tuchfeld, B.S.; McLeroy, K.R.; and Waterhouse, G. Patterns of

Multiple Drug Use Among Persons with Alcohol-related Problems. Research Triangle Park, NC: Research Triangle

Institute, 1975.

Tyler, J., and Sheridan, J.R. Patterns of primary drug abuse.

Int J Addict 15(8): 1169-1178, 1980.

Wesson, D.R.; Carlin, A.S.; Adams, K.M.; and Beschner. G., eds.

Polydrug Abuse. The Results of a National Collaborative

Study. New York: Academic Press, 1978.

AUTHORS

Robert Hubbard, Ph.D.

Robert M. Bray, Ph.D.

Elizabeth R. Cavanaugh, B.A.

William E. Schlenger, Ph.D.

J. Valley Rachal, M.S.

Research Triangle Institute

Box 12194, Hill Building

Cornwallis Road

Research Triangle Park, North Carolina 27709

S. Gail Craddock, M.S.

University of North Carolina

601 South College

Wilmington, North Carolina 28403

40
Statistical Analysis of Drug

Interactions

Clifford L. Mitchell, Ph.D.

INTRODUCTION

The study of drug interactions poses the problem of how best to

characterize the effects observed when two or more agents are

administered together. As with other types of experiments, the

experimental designs and statistical techniques used depend on

the questions asked. Clearly, any reasonable evaluation of a

drug interaction presupposes that a precise question is being

asked.

This article deals only with the question of whether to characterize an interaction as one of addition, antagonism, synergism

or potentiation. In this discussion the following definitions

are used for these terms: (1) ADDITION - the situation in

which, within the limits of the dose-response curve, the effect

produced by the agents is the algebraic sum of their independent

actions; (2) ANTAGONISM - the situation in which the effect is

less than the algebraic sun of their independent action; (3)

SYNERGISM - the situation in which for drugs exhibiting the same

observable response, the effect produced is greater than the

algebraic sum of their independent actions, (4) POTENTIATION -

the enhancement of action of one drug by a second drug that

exerts no detectable response on the variable measured when it

is administered alone. In practice, the terms synergism and

potentiation are often used interchangeably, The terms underadditive and overadditive have been used in place of antagonism,

synergism and potentiation (Pöch and Holzmann 1980). Also,

other definitions of addition are sometimes used (cf. Pöch

1981). The procedures discussed require the definition of

addition given above.

Much of the material in this article has been presented previously (Mitchell 1976). That article also contains a

discussion of experimental design and its statistical basis

which is not presented here.

NOTE : Portions of the text of this article and figures and tables

as noted were originally publlshed in Mitchell, C.L. The design and

analysis of experiments for the assessment of drug interactions.

Ann NY Acad Sci 281:118-135. Copyright 1976, The New York Academy

of Sciences. Reproduced by permission.

41
IMPORTANCE OF CHARACTERIZING DRUG INTERACTIONS

Characterizing drug interactions as to addition, antagonism,

syngergism and potentiation is merely a descriptive exercise; it

does not yield information concerning the mechanism for the

interaction. Since one is presumably more interested in the

mechanism, why spend time and effort in characterizing the

interaction? Loewe (1957) answered this question succinctly when

he stated "the ‘why?’ of a phenomenon cannot be studied without

adequate knowledge of the ‘what?’, the phenomenon itself."

Defining a drug interaction as addition, antagonism, syngergism,

or potentiation is a problem of determining the quantitative

relationship between the doses of the combinations and the

effect observed. Since the nature of the interaction may change

depending on the doses and time intervals studied (cf. Gessner

1974) it behooves one to have dose-effect and time-effect data

for the combinations in order to have a rationale for the doses

and times to be utilized in mechanistic studies. This is no

different than what is expected in single drug studies. Unfortunately, much of the drug interaction literature suffers from

a lack of quantitative evidence for the type of interaction

(cf. Mediphor 1984, for listing of unsubstantiated cases of drug

interactions) . Obviously the extent to which dose-effect and

time-effect studies are done should vary according to the question(s) asked and the (perceived) importance of the interaction.

It is also important to obtain both dose-effect and time-effect

information about the substances individually on the responses

to be measured before designing the drug interaction experiment.

This is necessary for two reasons. First, this information provides a rationale for the doses used in the interaction studies

and the time after dosing at which the effect is to be measured.

Second, it aids in determining the statistical procedures to be

used in the interaction studies. These procedures depend on

whether only one or both of the substances affect the response

when given alone and whether the measurements are quantitative

or quantal in nature.

PROCEDURES WHICH MAY BE USED WHEN BOTH SUBSTANCES GIVEN ALONE

AFFECT ME MEASURED RESPONSE

Quantitative Data

The first step is to determine the potency of one substance

relative to the other in affecting the response. The purpose of

this is to provide a rational basis for the doses used in subsequent studies. The most simple method assumes that the two

compounds yield dose-effect responses which are linear and

parallel. The most commonly used parallel line bioassays are 4

point (two dose levels of each substance) and 6 point (three

dose levels of each substance) assays (Finney 1952). The ad42
vantage of the latter is that it provides a test for nonlinearity. Both, however, encompass a test for deviation from

parallelism. This is important because if the dose-response

curves are not parallel, then the relative potency will change,

depending on the doses used. If this is the case, then the

methods described in the paper are of limited value.

If the onset and duration of action of the compounds are different (a likely case), then obviously different relative potencies will be obtained depending upon when the measurements are

taken. The two most common procedures utilize either the time

of peak effect or the area under the time-effect curve. For

drug interaction studies, the former is generally preferred since

it avoids some of the complications that can arise if the compounds have grossly different time-effect curves. Better yet

might be to determine relative potencies at more than one time

interval. Most important, though, is to have a reason for the

time(s) examined, based on previous experiments or published

information.

Once the relative potency has been determined, subsequent studies involve combining fractional doses of the substances and

comparing the responses observed against those obtained using

standard doses of each substance individually. The hypothesis

to be tested is that, as far as the response being measured is

concerned, the two compounds behave as though they were different forms of the same substance. Thus, doses of the combination and single substance(s) are picked such that equivalent

responses should be obtained if the effect of the two together

is additive. For example, given two compounds (A and B) in

which A is four times as potent as B, a mixture of one part A

and four parts B should yield a response equivalent to two parts

A or eight parts B when each is administered alone if the effect

of the two together is additive. If the effect of the mixture

is significantly greater than this, synergism is indicated; if

it is significantly less, one infers antagonism. The statistical procedures used depend upon the design of the experiment.

The Student's t-test can be used if one is only comparing two

means. If more than two means are being compared, a one or two

way analysis of variance(depending on the design) followed by

designated comparisons of means (i.e., multiple comparison procedures) is more proper. If one has reason to believe that certain requirements of parametric tests are not met (e.g.,

equality of population variances and/or normal distribution of

data), non-parametric equivalents may be used (e.g., MannWhitney U test instead of Student's t, Kruskal-Wallis one-way

analysis of variance instead of analysis of variance, the

Cochran Q Test, or Friedman two-way analysis of variance).

An example of this technique is presented in Figures 1 and 2.

The experiment involved determining the effect of morphine and

chlorpromazine alone and in combination on the escape response

elicited by tooth-pulp stimulation in cats (Mitchell 1966).

43
Dose-Response Curves for Chlorpromazine Hydrochloride and

Morphine Sulfate as Determined in the 4 point bioassay.

C = chlorpromazine, M = morphine, and R = relative potency.

(Modified from Mitchell 1966).

Figure 1 shows the potency of morphine sulfate relative to

chlorpromazine hydrochloride when the measurements were taken at

the time of peak effect for both compounds. Since the two drugs

were virtually equipotent in this test, the combination of one

part morphine and one part chlorpromazine was compared against

two parts morphine and two parts chlorpromazine, each separately, as shown in figure 2. An analysis of variance was performed on these data, following which the means were compared

using one of the multiple comparison procedures, Duncan’s new

multiple range test (Steel and Torrie 1960). No significant

difference (p>0.05) was obtained between the effect of morphine,

4 mg/kg, chlorpromazine, 4 mg/kg, and the mixture of 2 mg/kg of

each. Moreover, morphine, 2 mg/kg, chlorpromazine, 2 mg/kg, and

the mixture of 1 mg/kg of each were not significantly different

(p>0.05). Thus, it is inferred that the effects of morphine and

chlorpromazine are additive in this model under the conditions

of the experiment.

44
Effect of Morphine Sulfate, Chlorpromazine Hydrochloride,

and Their Canbination on the Latency for an Escape Response

to Tooth-Pulp Stimulation in the Cat.

Bars represent mean change (± standard error) in the average

latency for 10 trials after drug and the average latency for 10

control determinations. The observations were made 45 minutes

following the administration of morphine and 60 minutes

following the administration of chlorpromazine. In all instances, the number of animals was 8. The doses were 2 and 4 mg/kg

of morphine (M) and chlorpromazine (C) and 1 and 2 mg/kg of each

for the combination (WC) (modified from Mitchell 1966).

Quantal Data

These data are first analyzed by probit or logit analysis, with

the relative potency determined by the ratio of one ED50 versus

the other (Finney 1952). This second step is sometimes neglected in practice, but it should not be since it encompasses a test

for deviation from parallelism. The methods outlined below

illustrate probit analysis and, like that for quantitative data,

assume parallel dose-effect curves. Again different ED50s may

be obtained, depending on when the effect is measured. Thus,

the comments made above are also relevant here. Once the ED5Os

(and, hopefully, the potency ratio) have been determined, subsequent studies are conducted in a manner similar to that

45
described for quantitative data except that the statistical procedures are different owing to the nature of the data.

Specifically, one might use either the chi-square or Fisher’s

exact probability tests, depending on the sample size and the

expected frequencies.

Tables 1 and 2 show an example of this technique. In this

experiment the effects of ethanol alone and in combination with

chlorpromazine or chlordiazepoxide on the ability of mice to

remain on an inclined screen were examined. Table 1 shows the

ED5Os and the potency ratios. Table 2 shows that when one-half

of the ED50s for ethanol and chlorprormazine were combined. additive effects were obtained. Potentiation was observed when onehalf of the ED50s for ethanol and chlordiazepoxide were combined.

This was determined by comparing the frequency of mice falling

from the inclined screen for the ethanol drug combination versus

the ED50s of ethanol alone. For the combination, each drug was

administered such that their time of peak effect coincided. It

should be emphasized that the findings of addition with the combination of ethanol and chlorpromazine does not mean that under

other conditions potentiation might occur. Indeed, in the same

article Gebhart et al. (1969) did demonstrate potentiation using

another endpoint (see below). Also, the study would have been

more complete if other fractional doses had been combined and

compared with their predicted equivalent doses of the substances

alone.

PROCEDURES WHICH MAY BE USED WHEN ONLY ONE OF THE SUBSTANCES

GIVEN ALONE AFFECTS THE MEASURED RESPONSE

The condition in which one of the substances is inactive when

given alone results, on the one hand, in a simpler situation and

on the other in a more complex situation than when both are

active. It is simpler because the potency of one substance

relative to the other need not (indeed, cannot) be determined.

It is more complex because the investigator must now search for

another rationale for the doses and times of measurement to be

used in the experiment. Once these parameters have been chosen,

the procedure is simply to compare the effects of the combinations with those for the active agent alone. The hypothesis

to be tested is that, as far as the response being measured is

concerned, the “inactive” substance exerts no influence on the

other when the two are given in combination. The statistical

procedures used depend upon the experimental design and whether

the data are quantitative or quantal in nature. In general, any

of those mentioned above may be used. It must be remembered,

however, that it is mandatory to demonstrate that the one

substance is inactive in the dose used unless it is known a

priori that it is.

46
TABLE 1

Effect of Drugs on the Ability of Mice to Remain on an Inclined

Screen*

Drug

50% Effective Dose †

(mg/kg) Potency Ratio+

Ethanol 1820 --

(1436-2043)§

Chlorpromazine 1 1

(9-14) (121-205)

Chlordiazepoxide 65 28

(57-78) (21-34)

* After Gebhart et al. 1969. Mice were placed on a 60° inclined

screen at the following times after drug administration: ethanol(5O% v/v), immediately; chlordiazepoxide, 30 min. and

chlorpromazine, 60 min. All drug injections were given intraperitoneally.

† The dose causing 50% of the mice to fall from the inclined

screen as determined by the probit method. For each agent, at

least 3 doses were used.

+ The potency ratios were calculated relative to ethanol.

§ 95% Confidence interval.

TABLE 2

Effect of Drugs in Combination with Ethanol on the Ability

of Mice to Remain on an Inclined Screen*

Nunber of Mice

Drug-Ethanol Combination Tested Respondingf p

+

50% effective dose of ethanol 20 13

1/2 50% effective doses, 20 10 >0.30

ethanol + chlorpromazine

1/2 50% effective doses, 20 19 <O.05

ethanol + chlordiazepoxide

* After Gebhart et al. 1969. Chlorpromazine was given 60 min.

before ethanol: chlordiazepoxide 30 min before ethanol. Mice

were placed on the 60o inclined screen immediately after

ethanol administration.

† The number of mice falling from the 60o inclined screen.

+ Probability of X2 (ethanol-drug combinations versus ethanol

alone): p<0.05 is considered significant.

47
TABLE 3

Sleeping Time of Mice Treated with Saline or Iproniazid

1 Hour Before Injection of Hexobarbital*

Drug

Sleeping Time

N (in min)+

Saline + hexobarbital 18 24 + 0.65

Iproniazid + hexobarbital 15 65 ± 4.78†

* The iproniazid dose was 100 mg/kg the hexobarbital dose, 80

mg/kg; all injections were administered i.p.

+ Defined as length of time between losing and regaining

righting reflex. Figures represent mean ± standard error.

† Significant difference at p <0.05, t' test.

From Mitchell 1976. Copyright (1976), New York Academy of

Sciences.

An example using quantitative data is illustrated in Table 3,

above, which shows the effect on the sleeping time of mice of

pretreatment with iproniazid or saline 1 hour prior to the

injection of hexobarbital. Iproniazed is a monoamine oxidase

inhibitor which, by itself does not produce sleep. Since the

data are quantitative in nature and only two groups are

involved, the first impression might be to analyze the data

using the Student's t-test. One of the assumptions of the

Student's t-test is that the samples are drawn from populations

having equal variances. There is a simple test for this assumption which, unfortunately, is too often ignored by biologists.

For the data shown in Table 3 the assumption of equality of

variance is rejected at p<0.0005. In such a case either the t'-

test or a non-parametric method (in this instance, the

Mann-Whitney U test) might be used. Another option is to use an

appropriate transformation (logarithmic, square root or other)

which "normalizes" the data and meets the criteria for parametric tests. The purpose of this illustration was twofold;

first, to show how a simple statistical method can be used to

study drug interactions; and second, to caution you about inequality of variance. Inequality of variance occurs more often

than recognized in biological data. Situations in which one

compound inhibits the metabolism of the other frequently present

such a case.

The results of an experiment using quantal data and the Fisher’s

exact probability test are shown in Table 4. An ED50 for ethanol producing a loss of righting reflex (LRR50) in mice was

determined and found to be 4.3 g/kg (95% Confidence interval of

3.9 to 5.2 g/kg; Gebhart et al, 1969). LRR50s for chlorpromazine and chlordiazepoxide could not be determined that did not

48
also produce Varying degrees of lethality. The ED50s for the

three agents which caused mice to fall from an inclined screen

could be determined and were called PD50s (see above). Onehalf of the LRR50 of ethanol was then given in combination

with one-half the PD50 for ethanol, chlorpromazine, or chlordiazepoxide. Singly, these doses produced no LLR. Statistical

comparisons were made using Fisher’s exact probability test

because the expected frequency in the cell number of mice

losing the righting reflex” was less than five. Otherwise a

chi-square test could have been used. Potentiation of the

effect of ethanol by both chlorpromazine and chlordiazepoxide

was concluded. It should be emphasized that doses of both

chlorpromazine and chlordiazepoxide much larger than those used

here are required to produce loss of righting reflex in mice.

TABLE 4

Ability of Drugs in Combination with Ethanol to Produce

a Loss of Righting Reflex (LRR)*

Number of Mice

Treatment

Tested Respondng† p+

* After Gebhart et al. 1969. Chlorpromazine was given 60 min.

before ethanol; chlordiazepoxide 30 min. before-ethanol. The

†

mice were tested immediately after ethanol administration.

The number of mice losing righting reflex.

Fisher’s exact probability (l/2 LRR50 ethanol + PD50 of ethanol versus l/2 LRR50 of ethanol + l/2 PD50 of drug): p<0.005

is considered significant.

§ One-half the dose causing 50% of the mice to fall from a 60°

inclined screen.

Probit analysis may also be used for studying drug interactions

where one of the compounds is inactive. One approach using this

method is shown in Table 5. These data from Chau et al. 1973,

illustrate the enhancement, by vasopression, of pentazocine

lethality.

49
TABLE 5

Lethal Effect of Pentazocine and Vasopressin in

Unanesthetized Rats

Compounds i.m LD50 of

Pentazocine*

mg/kg

Pentazocine 153

(131-177)

Pentazocine and vasopressin 139

(5 U/kg) (115-168.2)

Pentazocine and vasopressin 39

(10 U/kg) (21.6-68.4)

Vasopressin alone at 100 U/kg no lethality

* Ten animals per dose level.

From Chau et al. 1973. Copyright (1973) American Society for

Pharmacology and Experimental Therapeutics.

Another example is shown in figure 3. These data, from a report

by Tulunay and Takemori (1974) illustrate the enhanced efficacy

of naloxone induced by pretreatment with morphine. The tail

flick analgesia assay in mice was used. The animal responses

were made quantal by establishing an end-point at the mean peak

effect which represented an increase in the reaction of an individual animal of greater than three standard deviations of the

control mean reaction time for all animals used in the group.

In control animals, the ED50 ± 95% confidence intervals of

morphine sulfate, subcutaneously, 30 minutes after administration, were 4.1 (3.2-5.3) mg/kg without naloxone and 35.3

(28.2-41.0) mg/kg with concomitant administration of naloxone,

0.16 mg/kg. In animals pretreated 3 hours earlier with 30 mg/kg

of morphine sulfate the ED50s were 4.8 (3.7-6.2) mg/kg and 82.8

(65.1-108.4) mg/kg, respectively. Thus, pretreatment with a

single dose of morphine did not change the ED50 of morphine

alone but significantly altered the ED50 for the morphine plus

naloxone combination, demonstrating an increase in the antagonistic potency of naloxone.

50
The Enhanced

Morphine

FIGURE 3

Efficacy of Naloxone Induced by Pretreatment with

See text for details. From Tulunay and Takemori (1974).

Copyright (1974) American Society for Pharmacology and

Experimental Therapeutics.

Had either Chau et al. (1973) or Tulunay and Takemori (1974)

desired, they could have quantified the degree of enhancement by

obtaining the ratio of the ED50s for the two naloxone groups.

Figure 4 illustrates this type of approach just mentioned.

Although the data concern the efficacy of morphine in adrenalectomized versus nonadrenalectomized animals, the analogy to the

study of drug interactions is apparent. Analgesia was determined 30 minutes after the S.C. administration of morphine in

rats using the hot plate method. The 50% analgesic dose (AD50)

was defined as that dose permitting 50% of the animals to remain

on the heated plate for 30 seconds. As can be seen in figure 4,

under the condition of the experiment, adrenalectomized rats are

more sensitive to the analgesic effect of morphine than sham

operated animals. This is demonstrated by the fact that in sham

operated rats 1.7 (1.2-2.3) mg/kg of morphine is equivalent to 1

mg/kg in adrenalectomized animals.

51
FIGURE 4

Dose-Response Curves Generated While Determining Equi-analgesic

Morphine Doses on the Hot Plate.

AD50 morphine analgesic &se in 50% of the animals. See text

for further details. From Gebhart and Mitchell 1972.

Copyright (1972) European Journal of Pharmacology.

52
FIGURE 5

Dose-Response Curves as Determined in the 6 Point Bioassay. R;

Relative Efficacy.

See text for further details. From Gebhart and Mitchell 1972.

Copyright (1972) European Journal of Pharmacology.

53
The bioassay technique may also be used for quantitative data as

shown in figure 5, from the same article by Gebhart and Mitchell

(1972) as cited above. The experimental procedure was the same

as that already described except that no cutoff time was used.

Before proceeding with the analysis it was verified that adrenalectomy alone did not alter the control reaction times. Thus

the data analyzed were the actual increases in reaction times

from the control determinations. Results similar to those seen

using quantal data were obtained. The utility of this technique

for drug interaction studies where one of the compounds is inactive in the test procedure is, I trust, obvious. One advantage

of this method lies in the fact that it is possible to quantify

the potency of one agent relative to another in enhancing or

antagonizing the effects of the compound of central interest

(i.e., the one which is active in the model when given alone).

ISOBOLOGRAHIC METHOD

Loewe and Muischnek (1926) introduced the isobolographic method

several years before the statistical methods in biological assay

were formulated (Bliss 1939; Finney 1952). Until recently, the

method has never been widely accepted for a variety of reasons.

Among these are the complex and exotic terminology associated

with it; the tedious nature of the method; and the fact that, as

introduced by Loewe, it was never truly quantitative. Recent

drug interaction literature, however, indicates an increase in

its use (cf. Fircio et al. 1978; Yeung and Rudy 1980; Masuda et

al. 19811 Foltin et al. 1983; Marshall et al. 1983; Kissin et al.

1984) . The material which follows will point out the similarities and differences between this approach and those presented

above. The reader interested in examining the method in more

detail should consult the articles by De Jongh (1960) and

Gessner (1974) for more complete discussions of the method and

for additional references to Loewe's work.

An isobole (from the Greek, isos [equal] and bolos [effect]) is

a line connecting equi-effective doses. To determine an isobole, one first must choose an effect to be measured. This can

be any measurable effect, as for example, a decrease in blood

pressure of 20 mm mercury, sleeping time of 25 minutes, or the

incidence of oeath in 50% of the animals. Most uses of the

method have focused on quantal responses (e.g. determining

ED50s or LD50s). The amount of different mixtures of the two

substances necessary to produce the specified effect is then

determined. A graph is prepared using doses of one component as

the abscissa and doses of the other as the ordinate. Points

representing the various combinations tested and found to produce the specified effect are placed on the graph. The line

drawn to connect these points is called an isobole: its points

represent combinations which cause identical effects. A series

of such lines is called an isobologram (Loewe 1953). There are

several types of isoboles, of which only two will be presented

in this article.

54
The first type (Type I) is represented by those situations in

which the desired effect can be produced by both substances

alone. Figure 6 shows a theoretical isobologram illustrating

addition, potentiation, and antagonism. If the effect of

substances A and B together is additive, the isobole is a

straight line connecting the two points obtained for each agent

alone (Figure 6a). In such a case the specified effect is

reached with three-quarters the effective dose of A in combination with one-quarter the effective dose of B, or one-half

of each or one-quarter of A and three-quarters of B, and so on.

In other words, so far as the effect measured is concerned, one

compound is acting as an analytical concentration (or dilution)

of the other one. (N.B., This does not imply the same mechanism

of action for both substances, however.) If the substances

enhance each other’s effect, one needs less of both substances

in combination to produce the specified effect; hence, this

isobole is a curved line situated below the line for addition

(Figure 6b). If the substances have antagonistic effects, one

needs more of both in combination to produce the specified

effect; thus this isobole is a curved line situated above the

line for addition. One might also obtain isoboles which indicate mixed effects. One example might be potentiation for a

certain range of combinations and addition over the remainder

(see below ).

A second type (Type II) of isobole occurs when only one of two

substances given alone produces the desired effect. A theoretical isobologram illustrating potentiation, antagonism, and no

effect for one substance on the other is shown in Figure 7.

When substance B has no influence on A, the isobole will run

parallel to the abscissa (Figure 7a). If substance B potentiates A, then less of substance A is needed, and this isobole

will lie below the neutral isobole (Figure 7b). Antagonism

(Figure 7c) is indicated by an upward shift in the line.

Gessner (1974) has modified Loewe’s method in

order to apply a quasi-statistical treatment to the data. He

determines the ED50 and its 95% confidence interval for each

substance alone (if both are active), constructs the expected

isobole assuming additivity, and places a confidence interval on

it by connecting the lower and upper confidence limits for the

two compounds with dotted lines (see Figure 9). ED50s and their

confidence intervals for different doses of the combination are

then determined using either (1) different doses of both compounds combined in various mixed ratios, or (2) different doses

of one compound in the presence of a fixed dose of the other.

Additivity or non-additivity is determined by comparing the

observed versus expected ED50s and the 95% confidence intervals.

55
FIGURE 6

Type I Isoboles Illustrating Addition (a). Potentiation (b).

Antagonism (c).

The numbers for the doses are in arbitrary units. See text for

further details. From Mitchell 1976. Copyright (1976) New York

Academy of Sciences.

56
FIGURE 7

Type II Isoboles Illustrating No Effect of Substance B (a),

Potantiation (b), and Antagonism (c).

The numbers for the doses are in arbitrary units. See text for

further details.

57
Actual examples of Gessner’s approach with Type I isoboles

illustrating addition only and both potentiation and addition

are shown in Figures 8 and 9, respectively. These data are from

Gessner and Cabana (1970). Figure 8 shows the isobole for the

interaction of the hypnotic effect of trichloroethanol and ethanol in mice. Each of the points represents the ED50 (± 95% confidence interval) for trichloroethanol in producing loss of

righting reflex in the presence of a fixed dose of ethanol. It

is apparent from this graph that the experimental points do not

depart from simple additivity. The isobole for the interaction

of the hypnotic effects of chloral hydrate and ethanol in mice

is shown in Figure 9. The points represent the ED50 (± 95% confidence interval) for either chloral hydrate (vertical confidence interval lines) or ethanol (horizontal confidence

interval lines) in the presence of a fixed dose of the other

agent. Potentiation was concluded for all mixtures having a

chloral hydrate/ethanol ratio greater than 1:7. Addition was

concluded for ratios less than this value. The conclusions were

based upon a comparison of the observed versus expected ED50s

and their 95% confidence intervals.

The primary disadvantage of the isobolometric approach as

modified by Gessner is its tediousness. This arises due to the

requirement of determining the exact amounts of a series of mixtures needed to produce a specified effect. It is much easier

to measure the effects of given quantities of a mixture. This

latter approach is the one utilized in the methods described

earlier. It has an advantage, I suppose, in that it forces one

to examine numerous different concentrations of the mixtures.

This is not excluded in the other methods, however. Indeed, the

prudent investigator would do this anyway.

Conceptually, both approaches are similar. When the experimental substances, each alone, are active in the test preparation,

the working hypothesis is that the two compounds behave as

though they were different forms of the same substance, one of

which is possibly (depending on the potency ratio) diluted with

an inert substance. This is tested in one case by comparing the

responses of different portions of the mixtures to the. responses

obtained with certain doses of the compounds alone. In the

other (isobolometric) case, it is tested by determining the dose

of the mixture necessary to produce a response equivalent to

that for certain doses of the compounds alone. In either case,

the doses of both the mixtures and the compounds separately

which should give equal effects if the null hypothesis is true,

are specified by the potency of one compound relative to the

other in the test procedure. One infers additivity if the null

hypothesis is accepted. One infers either potentiation or antagonism if the null hypothesis is rejected, depending upon the

direction of the deviation from additivity. When one substance

is inactive when given alone in the test situation, the null

hypothesis for both approaches is that it has no effect when

given with the other.

58
FIGURE 8

Isobole for the Interaction of the Hypnotic Effect of

Trichloroethanol and Ethanol in Mice.

The ED50 points are plotted with their 95% confidence limits.

The expected location of the ED50 points, given the occurrence

of a simple additive synergism, s given by the solid diagonal.

See text for further details. From Gessner and Cabana (1970).

Copyright (1970) American Society for Pharmacology and

Experimental Therapeutics.

59
FIGURE 9

Isobole for the Interaction of the Hypnotic Effects of Chloral

Hydrate and Ethanol in Mice.

ED50 points are plotted with their 95% confidence limits. The

expected location of the ED50 points, given the occurrence of a

simple additive synergism,is given together with the 95% confidence limits by the solid and dotted straight line diagonals,

respectively. In the area of significant pdtentiation, the ED50

isobol has been generated by connecting adjacent points. See

text for further details. From Gessner and Cabana (1970).

Copyright (1970) American Society for Pharmacology and

Experimental Therapeutics.

60
From the statistical point of view there is, however, a major

difference between Gessner’s modification of Loewe's isobolometric technique and the more conventional methods. With conventional techniques& both the expected response (assuming

additivity) and the real” response can be determined experimentally. This is done by comparing the response of a given dose

of the mixture with the responses obtained for the appropriate

doses of the substances alone, the latter constitute the

expected response. (Of course, the experiment should be

designed such that possible differences due to the time of day

in which the three responses are measured are minimized.) With

the isobolometric technique, the expected dose cannot be-determined exoerimentallv. It can onlv be inferred from the line

drawn connecting the ED50s for the two compounds. Moreover,

because of the time-consumina nature of the method. this line is

generally based on historicai data rather than data obtained on

the same dav as that for the experimental ooints. From a practical point-of view, this may not matter in most cases, since

the evidence for potentiation or antagonism may be striking

enough that this is of no consequence.

Whatever approach one uses, it is important to be aware of the

underlying assumptions. Two specific problems deserve comment.

First, great day to day variation can exist in the determination

of 50% effective doses (LD50, ED50, etc.). Thus, the precision

with which these parameters are known may not be great (the 95%

confidence limits give some indication of this, however).

Second, dose response curves for 2 agents can appear to deviate

considerably from parallelism before one has statistical

evidence to reject the null hypothesis of parallelism. Thus,

deviation from parallelism may, in fact, be true even though the

data are treated as supporting parallelism of the two dose

response curves. Either of these conditions can result in artifacts interpreted as “significant interactions” due to the misestimation of the parameters used in determining the

interactions. It is likewise important that all possible precautions be taken before, during, and after the experiment to

ensure that the reported results are meaningful. This responsibility rests squarely upon the shoulders of the investigator.

SUMMARY

The design of drug interaction experiments focusing on whether

to characterize the interaction as addition, antagonism,

synergism or potentiation is based, in part, on whether or not

both substances when given alone affect the response. If both

substances are active, one determines the potency of one

substance relative to the other in affecting the response. This

can be done for either quantitative or quantal data. Once the

relative potency has been determined, subsequent studies involve

combining fractional doses of the substances and comparing the

results against those obtained using standard doses of the

61
substances individually. Doses of the combination and the

single substances are picked such that equivalent responses

should be obtained if the effect of the two together is additive. The null hypothesis is that the two compounds behave as

though they were different forms of the same substance, one of

which is possibly (depending on the potency ratio) diluted with

an inert substance. Equivalence of response can be tested using

such parametric tests as Student’s t or analysis of variance (or

their nonparametric equivalents) for quantitative data. The

chi-square or Fisher’s exact probability test may be used for

quantal data. Additivity is inferred if the null hypothesis is

accepted. One infers either antagonism or synergism (depending

upon the direction of the diviation from additivity) if the null

hypothesis is rejected. If one substance is inactive when given

alone the null hypothesis is that it has no effect when given

with the other. This is tested using the same techniques as

mentioned above, except that there is no need, obviously, to

determine relative potency.

The isobolographic method for studying drug interactions was

compared with those mentioned above. Both approaches have the

same conceptual basis. The isobolographic method is more

tedious, however, since it entails determination of doses

required to cause a specific response, whereas the other methods

focus on the responses caused by specific doses.

It was cautioned that, whatever the approach, it is the investigator’s responsibility to know what assumptions are being made

and to take all possible precautions before, during, and after

the experiment to ensure that the reported results are

meaningful.

REFERENCES

Bliss, C.I. The toxicity of poisons applied jointly. Ann

Appl Biology 26:1585, 1939.

Chau, T.T., Dewey, U.L. and Harris, L.S. Mechanism of the

synergistic lethality between pentazocine and vasopressin

in the rat. J Pharmacol Exp Ther 186:288, 1973.

DeJongh, S.E. Isoboles. In: Dejonge, H., ed. Quantitative

methods in Pharmacology. Amsterdam: North Holland Publishing

co., 1960. p. 318.

Finney, D. J. Statistical Method in Biological Assay. New York:

Hafner Publishing Co., Inc., 1952.

Foltin, R.W., Woolverton, W.L. and Schuster, C.R. The effect of

d-amphetamine and haloperidol alone and in combination on milk

drinking in rats. Psychooharmacology 80:342, 1983.

Gebhart, G.F. and Mitchell, C.L. The effect of adrenalectomy on

morphine analgesia and tolerance development in rats. Eur J

Pharmacol 18:37, 1972.

62
Gebhart, G.F., Plaa, G.L. and Mitchell, C.L. The effects of

ethanol alone and in combination with phenobarbital, chlorpromazine, or chlordiazepoxide. Toxicol Appl Pharmacol 15:405,

1969.

Gessner, P.K. The isobolographic method applied to drug

interactions. In: Morselli, P.L., Cohen, S.N. and Garattini,

S., ed. Drug Interactions, New York: Raven Press, 1974.

pp. 349-362.

Gessner, P.K. and Cabana, B.E. A study

the hypnotic effects and of the toxic

hydrate and ethanol. J Pharmacol Exp

Kissin, I., Kerr, C.R. and Smith L.R.

interaction in rats. Anesthesiology

Loewe, S. Antagonisms and antagonists.

Loewe, S. The problem of synergism and

of the interactions of

effects of chloral

Ther 174:!247, 1970.

Morphine-Halothane

60:553, 1984.

 Pharm Rev 9:237, 1957.

antagonism of combined

drugs. Arzeim-Forch 3:285, 1953.

Loewe, S. and Muischnek. Uber kombinationswirkungen. I.

Mittelung: Hilfsmittel der Fragestellung. Naunyn

Schmiedebergs Arch Exp Pathol Pharmakol 114:313, 1926.

Marshall, A .G., Kissin, I., Reves, J.G., Bradley, Jr., E.L., and

Blackstone, E.H. Interaction between negative inotropic

effects of halothane and nifedipine in the isolated rat heart.

J Cardiovasc Pharmacol 5:592, 1983.

Masuda, Y., Utsui, Y., Shiraishi, Y., Karasawa, T., Yoshida, K.

and Shimizu, M. Evidence for a synergistic interaction

between phenytoin and phenobarbital in experimental animals.

J Pharmacol Exp Ther 217:805, 1981.

Mediphor Editorial Group. Mediphor (Drug Interaction Facts),

St. Louis: Facts and comparisons Division, J.B. Lippincott

Co., 1984, pp. 536.

Mitchell, C.L. Effect of morphine and chlorpromazine alone and

in combination on the reaction to noxious stimuli. Arch Int

Pharmacodyn 163:387, 1966.

Mitchell, C.L. The design and analysis of experiments for the

assessment of drug interactions. Ann NY Acad Sci 281:118,

1976.

Pircio, A.W., Byniski, J.P. and Roebel, L.E. Pharmacological

effects of a combination of butorphanol and acetaminophen.

Arch Int Pharmacodyn 235:116, 1978.

Poch, G. The confusion about additive combinations. Trends In

Pharmacol Sci 2:256, 1981.

Poch, G. and Holzmann, S. Quantitative estimation of overadditive and underadditive drug effects by means of theoretical

additive dose-response curves. J Pharmacol Methods 4:179,

1980.

Siegle, S. Nonparametric Statistics. McGraw-Hill Book Co.,

Inc., 1956.

Steel, R.G.D. and Torrie, J.H. Principles and Procedures of

Statistics. New York: McGraw-Hill Book Co., Inc. 1960.

Tulunay, F.C. and Takemori, A.E. The increased efficacy of

narcotic antagonists induced by various narcotic analgesics.

J Pharmacol Exp Ther 190:395, 1974.

63
Yeung, J.C. and Rudy, T.A. Multiplicative interaction between

narcotic agonisms expressed at spinal and supraspinal sites

of antinociceptive action as revealed by concurrent intrathecal and intracerebral injections of morphine. J Pharmacol

Exp Ther 215:633, 1980.

AUTHOR

Clifford L. Mitchell, Ph.D.

Laboratory of Behavioral and Neurological Toxicology

National Institute of Environmental Health Sciences

Research Triangle Park, North Carolina 27709

64
Problems of Pharmacodynamic

Measurement Related to Psychoactive Drug Interactions in Humans

Richard C. Frecker, M.D., Ph.D.

INTRODUCTION

In measuring the effects of psychoactive drugs, a large number of

factors need to be taken into account. These relate to properties

of the drug itself (Netter 1983; Oates and Shand 1972), to the

organism upon which it is acting (Crooks 1983; Munte et al. 1984),

and to the measurement strategy being employed. In situations

where more than one active agent is present, a number of special

problems exist with respect to experimental design and the

interpretation of data (Vesell 1984; Oates and Shand 1972). These

arise fran both pharmacokinetic and phannacodynamic sources

(Brodie and Mitchell 1972; Ellinwood et al. 1984; Netter and

Netter 1983). The organism itself is constantly changing in

response to both its internal and external environments, and it is

adapting to the presence of exogenous substances which alter

arousal and a variety of processes serving physiological and

psychological equilibrium (Netter 1983; Vesell 1984; Munte et al.

1984). In these circumstances, sensitive and selective drugeffect detection as well as inmunity fran measurement noise are

difficult to achieve.

This paper looks at some critical elements of the man-machine

interface as they relate to drug-effect measurement, and it

comments on problems which may arise because of the highly

adaptive nature of many biological control systems. Current

models of central neural processes still lack sufficient accuracy

to permit a genuine mechanistic interpretation of the measured

effects of psychoactive agents on human information processing,

affect, and cognition. An integrated, neurotransmitter-based

understanding of systems affected by mood-altering substances is

comnonly lacking.

Modern instrumentation techniques are becoming available which

have great temporal precision and high sensitivity to changes in

central nervous system (CNS) function--whether these perturbations

are produced by rhythmic biological activity, disease states, or

65
the introduct ion of exogenous, pharmacologically active chemicals

(Aufdenbrinke 1982; Bigler 1977; Fink 1978; Grunberger et al.

1982; Itil 1978; Linnoila et al. 1978; Young and Sheena 1975). A

persistent problem is the difficulty of separating the effects of

concanitantly active drugs which act at related receptor sites,

and through similar basic mechanisms. This is especially so when

the drugs are pharmacokinetically interactive, share a cannon time

course of action, and act on processes which have poorly

characterized fluctuations in endogenous activity.

The author will identify sane critical elements of the man-machine

interface as they relate to drug-effect measurement and will

suggest what he considers to be the attributes of an “ideal”

pharmacodynamic measurement technique. From this will arise a

consideration of characteristics which render dependent variables

suitable for use as pharmacodynamic probes. A brief concluding

comment will be made concerning the special problems which arise

in drug-effect measurement when drugs interact.

PHARMACOKIKETICS versus PHARMACODYNAMICS

This paper is mainly about pharmacodynamics, but it would be

neither wise nor accurate to imagine that these two aspects of

pharmacology are functionally distinct or independent. To avoid

the possibility of confusion, the following definitions will

govern the sense in which these terms are used in this paper:

Pharmacokinetics is the study of the movement of drugs into,

through, and from an organism or system, including analysis

of data related to the absorption, distribution, metabolism,

and excretion of drugs and their metabolites, and to the

time course of drug effects. That is, pharmacokinetics is

the study of the effects of organisms on drugs.

Pharmacodynamics is the study of the mechanism of drug

action, and of the responses of an organism or system to the

administration of drugs, including analysis of the

relationship between drug concentration and effect. That is,

pharmacodynamics is the study of drugs on organisms.

THE MAN-MACHINE INTERFACE AND DRUG-EFFECT MEASUREMENT

Man and other animal forms are canplex biological entities which

contain many control systems which are highly integrated with each

other, and which are autoregulatory within certain limits.

Pharmacodynamic studies seek to measure drug-induced perturbations

in this complex of adaptive control. However, control is

frequently nonlinear, and input/output relations are frequently

incanpletely understood, and, in quantitative terms, poorly

modeled. Control parameters change over time, for a variety of

reasons, and the integrated system, and its functional subsystems, are subjected to a multitude of internal and external

sources of noise.

66
Therefore, it is difficult to separate system responses which

result from “drug perturbation” from other forms of system

disturbance. Further, biological organisms (even unicellular ones

possessing irritability) do not “like” being probed. Engineered

physical systems, while they may “respond”, are indifferent to the

various sensing systems which are used to measure their

performance (even if, in so doing, they are either disrupted or

destroyed). [It is unlikely that one can measure any process

without taking “something” from it, even if only a few electrons

or photons.]

The various interfaces, and associated transducers, which are

established to permit the noninvasive acquisition of physiological

information can be unstable over time. In sane senses, the less

invasive the technique the more prone to degradation is the

information-containing signal , and the more information extraction

relies on inferential and probabilistic processes. An additional

cunplication is that in the clinical and clinical research

environments ergonanic considerations are often paramount. They

may dictate further reduction in the “direct coupling” which one

might logically seek between event, transducer, and data

acquisition system. It is not hard to imagine that in sane

circumstances an adequate level of “ecological validity” may be

difficult to secure.

SOME ATTRIBUTES OF THE MEASUREMENT TECHNIQUE

What then might be the attributes of an “ideal” pharmacodynamic

measurement technique? Perhaps the most obvious consideration is

that the detection technique ought not to pervert the response

unacceptably. An example of such “perversion” might occur in a

system for measuring finger tremor which employs a heavy

accelerometer attached to the finger. The alteration in mass of

the tremoring digit would be likely to lower artificially the

frequency or amplitude of the measured tremor.

While there may be a basis for argument about the importance or

relative importance of the following in particular circumstances,

it is necessary to consider the implications of whether or not

sensing the dependent variable is:

a. Without subject constraint;

b. Without attachment to the subject;

c. Without subject awareness;

d. Objective, autanatic, and rapid;

e. Accurate, and sufficiently precise; and

f. Immune to extraneous “environmental” influences.

The impact on experimental design and the validity of experimental

data will vary fran circumstance to circumstance, depending on the

care, attention, and relative weighting the above factors receive,

and on the robustness of the dependent variable. A lively debate

on the above would not be beyond the realm of possibility, but it

is beyond the scope of this paper.

67
CHARACTERISTICS OF THE DEPENDENT VARIABLE

Less debatable, perhaps, would be the assertion that the following

characteristics of the dependent variable are highly desirable:

a.

b.

c.

d.

e.

f.

Low complexity for ease of extraction of single response

elements;

High sensitivity and selectivity for the drug(s) being

studied;

Appropriate differential sensitivity to such influences

as heat, humidity, pressure, light, sound, diet,

exercise, sleep, arousal, and other factors which may

themselves be components of the evoking stimulus or of

the response;

Low between- and within-day random variability ("random"

noise) ;

Infradian, circadian, and ultradian rhythms (where

present) which have been adequately characterized

("periodic" noise); and

Derivation directly from the relevant behavior, and

presentation in an ecologically valid manner.

Where it is not possible to achieve "f" (above), the dependent

variable should probably be derivative of involuntary responses

requiring little, if any, subject participation; and in turn,

would ideally have been shown on an empirical basis to be

sensitive to the drug(s) being investigated in a dose-response

manner . Of all the characteristics listed above, "b" and "c"

appear to be the most difficult to achieve in reality.

DATA ACQUISITION AND ANALYTICAL GENERALITIES

Much useful pharmacodynamic data can be collected by the use of

such instruments as questionnaires, visual analog scales, and a

variety of observational techniques using the usual sensory

modalities of the observer (vision, hearing, touch, etc.). While,

in sane senses, the following comments could be generalized to

cover that sort of data collection, they are more specifically

focused on data acquired using electronic, optoelectronic,

mechanical, and other sorts of transducers. In general, the term

"output signal" may be taken to mean a tine-varying electrical

event bearing a known or calibrated relationship to changes in the

dependent variable. It is not intended in this context to examine

the physical characteristics of various transducers; whether they

are resistive or capacitive devices; or whether the primary

transduction yields current, voltage, or a change in resistance.

The “output signal" may be thought of simply as the electrical

analog of information which indicates the state of the dependent

variable. It is usually a voltage, varying within a range

acceptable to the monitoring device.

However generated, output signals should be readily amendable to

available signal reduction and analysis techniques. Where copious

68
information is made available by a particular measurement system,

it may need to be "reduced" in quantity by intermittent sampling

over time, by electronic preprocessing, or by other techniques--

to yield a set of "raw" data which can be accatmwdated by the

investigator. "Accommodated" may mean direct analog or digital

recording on magnetic tape, temporary residence in a computer's

main memory, long-term storage on one of a variety of mass media,

or even being directly recorded onto paper as a pen trace or a

series of nunbers.

Whenever possible, the raw data should be entered onto/into the

storage medium of choice as the experiment proceeds. This will

serve to minimize later translational errors, and immediately will

create a permanent record of the raw data. It may be desirable to

create a "back-up" copy of the original data set, or to copy it in

whole or in part to a medium which will facilitate later analysis

or re-analysis. While it is often desirable to perform parameter

extraction and to produce numerical or graphical sumnaries of

results within the time frame of the experiment, a number of

precautions need to be observed. Within the limits of the

available data storage and retrieval systems, the data should be

permanently stored in that state closest to "raw data" which is

practical. This facilitates subsequent re-analysis, and may, in

the future, permit the use of an analysis technique which was not

previously thought of or available. However, in instances where

parameter extraction is analytically trivial, proven, and robust,

it may be sufficient to store the parameters themselves, or even

their summary statistics.

The time frame in which graphical and statistical sumnaries are

made available, and the persons who will have access to these

data, will depend very much on local circumstances, system

capacity, the need for "real-tine" results, and both ethical and

scientific propriety.

CHANGES OBSERVED IN THE DEPENDENT VARIABLE

Generally, it should be possible to correlate observed changes in

the dependent variable with known indicators of a particular

drug's effects, at least for purposes of calibration. A problem

arises when there are no other known or accepted indicators. When

a new measure of drug effect has been developed which is either

more sensitive or selective, this may be ill-advised other than as

a means of pointing out differences. Further, the fact that a

given measure of drug effect is both well known and well accepted

does not per se make it valid.

If one is monitoring drug concentrations in the course of a

pharmacodynamic experiment, it should be possible to correlate

observed changes in drug effect over time with changes in the

relevant effective drug concentration. The key word is relevant.

The relevant concentration/time profile (corrected for

distributional time lags) is that of the compound thought to

elicit the particular drug effect (whether receptor-mediated or

69
not). It may be total plasma concentration of the parent drug

whch is in simple and rapid equilibrium with a known site of

action. On the other hand, it may be the "unbound" (free) drug

concentration distributed across a number of transporting

membranes (differentially distributed)-a minute portion of which

is actually in dynamic, time-varying equilibrium with a poorly

identified, but specific, receptor, which is, in turn, distributed

unequally among a variety of sites. A myriad of states of

intermediate complexity came to mind. Pharmacodynamic measures of

sufficient sensitivity and selectivity would be clearly preferable

in the more complex case; and what one would seek is a measure

which might be repeated sufficiently often to resolve, in time,

the drug's effect (however rapid).

TABLE 1

Examples of Pharmacodynamic Dependent Variables

A) COMPLEX VOLUNTARY

Autanobile Driving (real)

Various Task Simulators

Maze-following Equivalents

Cognitive Psychometrics

Affective Psychometrics

B) ENCEPHALOGRAPHIC

Spontaneous EEG

Evoked Potentials

Cortical

Brainstem

C) VISUAL SYSTEM

Flicker sensitivity

Pursuit movements

Vergence movements

Nystagmus (various)

Miniature movements

Microsaccades

Tremor

Drift

D) AUDITORY SYSTEM

Pitch discrimination

Threshold detection

Flutter fusion

Acoustic impedance

E) VOICE ANALYSIS

Vocal Tremor

Feature extraction

F) NEUROMUSCULAR

Tremor measurements

Standing steadiness

Reflex kinetics

G) REACTION TIMES

Various

H) CARDIOVASCULAR

Heart rate

Blood pressure

Photoplythysmography

I) RESPIRATORY

Respiratory rates

Carbon dioxide drive

Respiratory volumes

J) SKIN CHANGES

Color

Temperature

Impedance (GSR)

K) GASTROINTESTINAL

Bowel sounds

For further discussion of sane of the above variables, the

interested reader is referred to Aufdembrinke (1982), Bigler

(1977), Grunberger et al. (1982), Itil (1978), Linnoila et al.

(1978), and Young and Sheena (1975).

70
In the real world of the intact human being, one has a limited

number of biological fluids in which to measure drug levels

(blood, urine, saliva, cerebrospinal fluid, bile, etc.). It is,

generally, not possible to measure drug concentration over time in

the micro-envirotxnent of the receptor, and the ideal pharmacodynamic dependent variable awaits discovery. In the meantime,

available samples are taken, and corrections in timing are made to

accomodate known pharmacokinetic behavior. Newer nuclear

medicine techniques such as positron emission tomography and

nuclear magnetic resonance imaging will afford better

distributional information; and the search continues for more

accurate and more precise noninvasive measures of drug effect with

which to correlate this new information. Sane examples of

currently available pharmacodynamic dependent variables are listed

in table 1.

WHEN PSYCHOACTIVE DRUGS INTERACT

The situation which exists when more than one drug OK substance is

active concurrently is somewhat more complex. If no kinetic

interaction occurs, and the drugs have distinct and different

mechanisms, sites, or time courses of action .(the simplest case),

the above principles apply in a fairly straightforward manner. In

the slightly more complex case of drugs with similar mechanisms of

action, but distinctly different time courses, it is sometimes

possible to separate their effects on a pharmacodynamic basis if

good kinetic data are also available. Due consideration must be

given to the difference between simple additive effects and those

which are produced by synergistic drug interaction. The phenomena

of cross-tolerance and inhibition further complicate the

situation.

Given these complexities, one is, perhaps, well advised to select

for investigation drugs whose dynamic and kinetic interaction are

particularly illustrative from a mechanistic perspective, or drugs

whose interactions pose special hazards for the exposed

population. It seems likely that the time is not too far distant

when the technical advances of modern noninvasive imaging will

greatly increase our ability to produce accurate kinetic models of

drug behavior within the CNS. Presently, there are pharmacodynamic

probes being developed which have greater precision, speed, and

selectivity. Given this convergence, one may reasonably expect

that increasing knowledge of the relationship of psychoactive drug

kinetics and dynamics will lead to a more perceptive and

mechanistically based view of psychoactive drug interactions in

general.

REFERENCES

Aufdembrinke, B. The measurement of CFF: Some methodological

considerations. Pharmacopsychiatry 15(1):5-8, 1982.

Bigler, E.D. Neurophysiology, neuropharmacology and behavioral

relationships of visual system evoked after-discharges: A

review. Biobehav Rev 1:95-112, 1977.

71
Brodie, B.B., and Mitchell, J.R. The value of correlating

biological effects of drugs with plasma concentration. In:

Davis, D.S., and Frichard, B.N.C., eds. Biological Effects of

 Drugs in Relation to Their Plasma Concentrations. London

Macmillan, 1972. pp. 1-12

Crooks, J. Aging and drug disposition - pharmacodynamics. J Chron

Dis 36:85-90, 1983.

Ellinwood E.H.; Nikaido, A.; and Heatherly, D. Diazepam:

Prediction of pharmacodynamics from pharmacokinetics.

Psychopharmacology 83:297-298, 1984.

Fink, M. EEG and psychopharmacology. In: Cobb, W.A., and Van

Duijn, H., eds. Contemporary Clinical Neurophysiology

Amsterdam: Elsevier Scientific Publishing Canpany, 1978. pp. 41-

56.

Grunberger, J.; Saletu, B.; Berner, P.; and Stohr, H. CFF and

assessment of pharmacodynamics: Role and relationship to

pychometric, EEG and pharmacokinetic variables. Pharmacopsychiatry 15(1):29-35, 1982.

Itil, T.M. Effects of psychotropic drugs in qualitatively and

quantitatively analyzed human EEG. In: Clark, W.G., and Del

Giudice, J., eds. Principles of Psychopharmacology. New York:

Academic Press, 1978. pp 419-443.

Linnoila, M.; Erwin, C.W.; Cleveland, W.P.; Logue, P.E.; and

Gentry,W.D. Effects of alcohol on psychomotor performance of men

and women. J Stu Alcohol 39(5):745-758, 1978.

Munte, T.F.; Heinze, H.J.; Kunkel, H.; and Scholz, M. Personality

traits influence the effects of diazepam and caffeine on CNV

magnitude. Neuropsychobiology 12:60-67. 1984.

Netter, K.J., and Netter, P. Pharmacokinetic factors: Sane basic

principles and their influence on psychotropic drug response.

In: Janke, W., ed. Response Variability to Psychotropic Drugs.

Oxford: Pergamon Press Ltd., 1983. pp 3-29.

Netter, P. Somatic factors as prediciont of psychotropic drug

response. In: Janke, W., ed. Response Variability to

Psychotropic Drugs. Oxford: Pergamon Press Ltd., 1983. pp 67-95

Oates, J.A., and Shand, D.G. Are we measuring the right things?

The role of active metabolites. In: Davies, D.S., and Prichard,

B.N.C., eds. Biological Effects of Drug in Relation to Their

Plasma Concentrations. London: Macmillan, 1972. pp. 97-106.

Yours and Sheena, D. Survey of eye movement recording

methods. Behav Res Meth Instrumentation 7(5):397-429, 1975.

Vesell, E.S. Complex effects of diet on drug disposition. Clin

Pharmacol Ther 36(3):285-296, 1984.

AUTHOR

Richard C. Frecker, M.D., Ph.D.

Clinical Pharmacology Program

Addiction Research Foundation

and

Institute of Biomedical Engineering, &

Department of Pharmacology

University of Toronto

Toronto, Canada M5S 1A4

72
Pharmacokinetic Mechanisms of

Ethanol-Psychotropic Drug

Interactions

Domenic A. Ciraulo, M.D., and Jamie Barnhill, Ph.D.

Given the ubiquitous nature of alcoholic beverages in our culture,

ethanol-drug interactions have substantial social importance, With

29% of women and 13% of men in the United States taking prescribed

psychotropic drugs (Parry et al. 1973), and a large number of

others taking nonprescribed psychotropics. the interaction of

ethanol* with psychoactive agents is particularly important. Yet,

the different kinds of interactions, their frequency, and the

clinical and social impact are not easily studied.

Although the problems of experimental investigatjon are somewhat

simplified by a pharmacokinetic/pharmacodynamic division of ethanoldrug interactions, the patterns of alcohol and psychotropic drug

intake may confound the findings of even the most carefully

designed studies. In designing or evaluating clinical research

protocols, it is necessary to consider several factors which have

the potential to affect the results and applicability to real

life situations. Drug use or alcohol intake may be acute or

chronic, and may interact in the clinical situation in any of several

ways. An occasional social drinker with the acute onset of stress

may be prescribed a bentodiazepine and decide to take it with a

cocktail at a social gathering (acute ethanol/acute drug). An

alcoholic who has been hiding the severity of the problem from his

physician may be prescribed a benzodiazepine for "anxiety" or an

antidepressant for "depression" (chronic ethanol/acute drug).

Another example is the occasional drinker who is maintained on an

antidepressant and after having a cocktail finds the enhanced effects

impair his driving ability (acute ethanol/chronic drug). Another

common clinical situation is that of the polysubstance abuser with

pharmacodynamic and pharmacokinetic interactions'of alcohol and a

variety of abused drugs (chronic ethanol/chronic drug).

In addition to duration of drug or ethanol ingestion, other factors

such as the types of drugs taken, dosage combinations, routes of

administration, pharmacokinetic and pharmacodynamic characteristics

of the particular psychoactive agent and ethanol, and the presence

of coexisting organic or psychiatric illness also influence the

nature and importance of the interaction.

73
There are several excellent reviews on ethanol-drug interactions

(Sellers and Holloway 1978; Linnoila et al. 1979; Weller and

Preskorn 1984), and this chapter will not be a repetition of those

efforts. Instead, the pharmacokinetic mechanisms of psychotropic

drug-ethanol interactions will be discussed and relevant examples

cited. It is hoped that this conceptualization will provide a

framework for understanding and evaluating the pharmacokinetic

mechanisms and clinical significance of ethanol-psychotropicdrug

interactions. Although the following discussion will be limited

to the effect of alcohol on pharmacokinetic processes, it should

be kept in mind that psychotropic medications may in turn alter

the metabolism and pharmacologic action of ethanol (Sellers et al.

1972; Mezey 19761.

Pharmacokinetic interactions arise from drug-induced alterations

in absorption, distribution, biotransformation, or excretion of

another drug. Acute alcohol intake inhibits gastric emptying

(Nimmo 1976), resulting in a lag in onset and decreased rate of

absorption (MacLeod et al. 1977; Greenblatt et al. 1978). The

clinical significance of these findings may not be as great as the

acute metabolic effects. Inhibition of "first-pass" effects can

result in greater systemic availability.

All orally administered drugs must pass first through the liver

before reaching the systemic circulation, so that inhibition of

metabolism by acute alcohol would produce greater systemic

availability of the drug dose. Acute alcohol intake inhibits

demethylation and/or hydroxylation of benzodiazepines, increasing

peak plasma levels of most drugs of this class (MacLeod et al.

1977; Laisi et al. 19791. Similar effects are seen when ethanol

and amitriptyline are given concurrently (Preskorn and Hughes

1983). Acute alcohol intake increases splanchnic blood flow,

irritates the gastric mucosa, and increases motility in the

duodenum (Magnussen 1968). Chronic intake results in structural

defects in the gastrointestinal tract and changes in enzymatic

activity (Findlay et al. 1976). Despite this, there is little

evidence to suggest that altered oral absorption is an important

mechanism of alcohol-drug interactions, particularly since acute

alcohol has been shown to decrease the clearance of intravenously

administered drugs, such as diazepam (Sellers et al. 1980a) and

lorazepam (Hoyumpa et al. 1981).

Drug distribution is another potential process that can be altered

by ethanol. After absorption, a drug is distributed to tissues

with high blood flow, such as brain, liver, and kidney, and then

to tissues with lower blood flow but higher affinity, such as

muscle or fat. Distribution factors are important in terminating

clinical action of single doses of such drugs as benzodiazepines

or thiopental. For example, despite a shorter elimination halflife, the duration of action of intravenous lorazepam is greater

than that of diazepam because of the greater lipid solubility of

the latter drug (Arendt et al. 1982).

Ethanol may potentially affect drug distribution by altering

membrane permeability, cardiac output, and protein or tissue

74
binding. Modification of albumin binding sites or alterations in

other binding substances (e.g., alpha1-acid glycoprotein),

intermediary metabolism, or endogenous substances are potential

areas of binding interactions (Sellers and Holloway 1978).

Before discussing any of these problems in any further detail,

some basic principles of pharmacokinetics require review.

One of the basic and most important concepts of clinical

pharmacokinetics is clearance. It is that pharmacokinetic

parameter that relates concentration to the rate of drug

elimination. It is expressed as volume per unit time and

indicates the volume of blood completely removed of drug per unit

time. Total systemic clearance (Cl) is the sum of hepatic

clearance (ClH) and extrahepatic clearance (ClEH) (Wilkinson and

Shand 1975).

Cl = ClH + ClEH

(1)

When a drug is only eliminated through hepatic metabolism, which

is the case for a majority of the psychotropic drugs, then total

systemic clearance equals hepatic clearance.

Total systemic clearance can be calculated by one of the following

formulas:

(2)

(3)

V is the volume of distribution, and t1⁄2 is the half-life of

elimination determined from the elimination rate constant, Kel.

FD represents the fraction of the drug dose reaching the systemic

circulation and AUC is the area under the drug concentration-time

curve. Clearance may also represent loss of a drug across an

eliminating organ (i.e., liver) and the equation describing it can

be of value in understanding the effects of altered blood flow,

plasma protein binding, enzyme activity, and secretory activity

on drug clearance.

ClH = QH · EH

(4)

Equation 4 demonstrates that hepatic clearance is a function of

hepatic blood flow (QH) and the hepatic extraction ratio (EH).

The extraction ratio relates the rate of drug extraction to the

rate at which a drug is presented to the liver. It can be

expressed as:

Clint

EH = QH + Clint (5)

75
where Clint is the intrinsic clearance, defined as the maximum

ability of the eliminating organ (liver) to remove the drug from

the blood when there are no flow limitations. Substituting

equation 5 into equation 4 gives:

(6)

Since it is the unbound drug which is available for hepatic

elimination, the intrinsic clearance of unbound drug (equation 7)

can be substituted into equation 6 to give the hepatic clearance

of unbound drug:

(7)

where fB equals the unbound fraction in the blood.

(8)

For a drug with a high intrinsic clearance in the liver, the

extraction ratio is high and approaches unity, and hepatic

clearance approaches organ blood flow (equation 9).

(9)

These drugs are therefore flow-limited in their clearance. Some

psychotropics with high E (>0.7) include desipramine, morphine,

meperidine, propoxyphene, and propranolol (Williams and Benet

1982).

For drugs with intrinsic clearance much smaller than liver blood

flow, the extraction ratio is low (<O.3) and clearance is

considered capacity-limited (equation 10).

(10)

Low E drugs include amobarbital, phenobarbital, diazepam, chlordiazepoxide, and phenytoin (Williams and Benet 1982).

The effect that alterations in protein binding will have on

clearance is dependent on extraction ratio. hanges in protein

binding are most important in low E (<O.3) drugs since the

clearance of these drugs is dependent on unbound concentration

(equation 10). Changes in the protein binding of drugs with in

E>O.7 should have little to no effect on clearance since

clearance is dependent on blood flow (equation 9). For these

high intrinsic clearance drugs, the unbound drug is very

rapidly removed from the blood, but the instantaneous association/

dissociation of the drug from its binding sites provides a

constant source of unbound drug while passing through the liver.

76
Displacement from binding sites in the blood

increases in the volume of distribution (V).

by the following relationship (Wilkinson and

can result in

This can be seen

Shand 1975).

(11)

Where VB equals the volume of the blood, VT equals the tissue

volume, fB is the fraction unbound in the blood, and fT is the

fraction unbound in the tissue. If the unbound fraction in the

blood increased, then the volume of distribution would

increase. For a drug with an E>0.7, this increase in the

volume of distribution will cause an increase in the half-life

for elimination of the drug, since:

(12)

This lengthens the time that the drug stays in the body as well

as the amount of time required for achievement of steady state,

although time averaged steady-state levels of total drug (bound

plus unbound) (Css) should be unchanged (Gibaldi and Koup 1981).

Average steady-state levels of unbound drug in blood (Cuss) will

be elevated, as can be seen by equation 13.

(13)

On single dosing, there will be a longer elimination half-life

with lower peak levels of total drug.

On the other hand, an increase in the volume of distribution for

a drug with an E<0.3 could be offset by the increase in clearance

and have no significant effect on elimination half-life. The

increase in volume of distribution and clearance will cause a

decrease in average steady-state blood levels of total drug.

The average steady-state levels of unbound drug will be unchanged,

although within a dose interval there will be fluctuations,

including an initial, transient, higher than normal unbound

concentration.

Diazepam can be used to illustrate these points. Diazepam has a

low E and is highly bound, and changes in protein binding affect

its systemic clearance as well as steady-state concentration of

total drug (bound plus unbound), but steady-state concentration

of unbound drug is relatively unaffected (Sellers et al. 1979;

Sandor et al. 1983). For a highly bound, low E drug, alterations

in unbound fraction have two important clinical implications

(Sandor et al. 1983). First, total drug concentration at steady

state may not correlate well with clinical effect. Second, an

increase in unbound drug in plasma is associated with a decrease

in total drug concentration in plasma as unbound drug enters

tissue. There is a transient rise in unbound concentration but

a more rapid distribution of drug. The clinical consequences are

77
the possibility of increased therapeutic or toxic effects early

in the dosing interval and diminished therapeutic effect late in

the dosing interval. There is no change in the time averaged

unbound concentration over the dosing interval, although greater

fluctuations are seen.

An increase in the average steady-state concentrations of

unbound drug for a drug with E>0.7 could have a greater clinical

significance since larger concentrations of the unbound species

are available at sites of pharmacological action. Total drug

concentration in the blood could, once again, not correlate with

the observed clinical effect.

There are relatively few studies of protein binding changes with

acute and chronic ethanol administration. In vitro work has

shown that chronic ethanol administration can impair synthesis and

secretion of proteins and glycoproteins in the liver (Sorrell et

al. 1983). Acute ethanol administration in perfused rat liver

studies has demonstrated a depression of albumin production

(Rothschild et al. 1983). Malnutrition, often found in chronic

alcoholics, can also cause hypoalbuminemia (Rothschild et al.

1983). It is known that disease, stress, and trauma can increase

levels of alpha1-acid glycoprotein (AGP) (Edwards et al. 1982;

Piafsky 1980) which has a strong affinity for basic drugs, but

whether it increases with acute ethanol is not known, symptomatic

withdrawal from chronic ethanol administration may be stressful

enough to cause increases. In a recent study of 15 alcoholics

in withdrawal, the unbound fractions of diazepam and propranolol

were determined on each of the first 3 days of withdrawal and

again between 5 and 7 days postadmission (Sandor et al. 1983).

Unbound diazepam fraction was elevated and propranolol unbound

fraction was decreased relative to normal values during the first

day of withdrawal. Over the 7-day withdrawal period.the diazepam

unbound fraction decreased and the fraction of unbound propranolol

increased. The mechanism of diazepam binding changes is uncertain,

although altered liver metabolism and/or nutritional status may

be involved. Levels of free fatty acids, which increase during

chronic alcohol consumption (Shaw and Lieber 1976) and are known

to displace drugs from binding sites (Sellers et al. 1980b),

were only weakly associated with diazepam binding changes. There

was an elevation of AGP levels durinq the symptomatic phase of

withdrawal and this correlated well with a decreased unbound

fraction of the basic drug propranolol (pka = 9.45).

We have examined the protein binding of desipramine and imipramine,

two basic drugs that bind to albumin and alpha1-acid glycoprotein.

The decreased unbound fraction seen in alcoholics compared to

normal controls correlated with increased alpha1-acid glycoprotein

(table 1). We did not find hypoalbuminemia in our alcoholics,

although it has been observed in alcoholics with liver disease.

78
TABLE 1

Comparison of Desipramine Protein Binding

in Alcoholics and Normal Controls

Alcoholics (n=4) Normals (n=4)

Free Fraction, % 14.07 1.62 17.15 1.29

Total Protein, g/dl 6.87 0.33 6.98 0.34

Albumin, g/dl 4.63 0.57 4.66 0.14

-Acid Glycoprotein, mg/dl 107.60 14.66 84.80 2.82

The acute effects of low-dose ethanol are to increase hepatic

blood flow by increasing cardiac output, vessel dilation, and

splanchnic blood flow (Stein et al. 19631, while acute high-dose

ethanol can lower hepatic blood flow by decreasing cardiac output. The effects of chronic alcoholism on liver blood flow are

unknown. Although intrahepatic shunting of blood flow has been

found in cirrhosis (Groszman et al. 1972), the effects of chronic

alcoholism, short of alcoholic liver disease, has not been

documented. For drugs with E>0.7, increases in hepatic blood

flow result in increased hepatic clearance, while decreases in

hepatic blood flow decrease hepatic clearance. Acute low doses

of ethanol would be expected to increase hepatic clearance of

these drugs, while acute high doses might be expected to reduce

clearance. Blood flow changes should not have a significant

effect on the clearance of low E (<0.3) drugs.

Alcohol alters drug metabolism through its effects on hepatic

biotransformations. The purpose of drug metabolism by the liver

is to render lipid soluble (nonpolar) substances into water

soluble (polar) derivatives for renal excretion (Hoyumpa and

Schenker 1982). Acute alcohol intake usually decreases (by

inhibition or competition) drug metabolizing enzymes (i.e.,

decreases Cl int) (Rubin et al. 1970; Cinti et al. 1973).

Barbiturates, benzodiazepines, methadone, and meprobamate are so

affected (Hoyumpa and Schenker 1982; Williams and Benet 1982).

Chronic alcohol intake increases the activity of a variety of

microsomal enzymes, the content of cytochrome P-450, and the

activity of NADPH-cytochrome P-450 reductase (Ishii et al. 1973).

As a result, in the absence of severe liver damage, metabolism of

drugs (Clint) is usually enhanced. Without liver biopsies or

pharmacokinetic assessments, it is difficult to predict whether

enzyme induction or liver damage will predominate. Chronic.

79
ethanol consumption enhances the metabolism of chlordiazepoxide,

diazepam, phenytoin, imipramine, meprobamate, barbiturates, and

others (Sellman et al. 1975; Sandor et al. 1981; Ciraulo et al.

1982; Misra et al. 1971). Chronic ethanol consumption may cause

an increase in glucuronidation by inducing glucuronyltransferase

(Ideo et al. 1971). On the other hand, acute ethanol consumption

was shown to decrease the clearance of lorazepam (a drug eliminated by glucuronidation) by inhibitin UDP-glucuronic acid

synthesis (Hoyumpa and Schenker 1982 , In general, cirrhosis

tends to impair oxidative metabolism, while it spares or only

slightly impairs glucuronidation (Williams and Benet 1982).

The concept of hepatic extraction ratio is important in predicting

the effects of metabolism interactions. For a drug with a high

extraction ratio, such as propranolol, induction does not affect

clearance or half-life, although oral availability is decreased,

due to a larger portion of drug being removed in the first pass

through the liver before it reaches the systemic circulation.

For a drug with a low extraction ratio (e.g., diazepam), enzyme

induction results in enhanced clearance and decreased half-life.

The extraction ratio of imipramine is intermediate (about 0.65).

Enzyme induction, as occurs with chronic alcoholism, enhances

metabolism of imipramine. Figures 1 and 2 show the semilogarithmic

plots of plasma concentration versus time in a representative

alcoholic and a normal control given single intravenous and oral

doses of imipramine (Ciraulo et al., unpublished data). Oral

availability was lower for the alcoholic subject. The alcoholic

had enhanced clearance and a shorter elimination half-life

(table 2).

TABLE 2

Pharmacokinetic Parameters for Imipramine in a

Representative Alcoholic and Normal Control

Parameters

Clearance, L/hr/kg

Elimination Half-life, hr-1

AUC, ng · hr/ml

Vol. of Distribution, L/kg

Free Fraction, %

Imipramine

50 mg P.O. 12.5 mg IV

Alcoholic Normal Alcoholic Normal

2.28 1.26 0.94 0.65

7.79 15.64 12.00 16.81

261.00 446.00 156.00 206.00

25.60 28.44 15.40 15.69

16.40 16.11 17.41 17.06

80
FIGURE 1

Plot of imipramine plasma concentrations following an IV infusion of

a 12.5 mg dose. The solid line represents the computer generated

curve.

81
FIGURE 2

Plot of imipramine plasma concentrations following a 50 mg oral

dose. The solid line represents the computer generated curve.

82
Chronic dosing studies show that steady-state levels of imipramine

and its metabolites are lower in depressed alcoholics compared to

nonalcoholic depressed patients (Ciraulo et al. 1982) (table 3).

A standard 150 mg dose of imipramine given to alcoholics yields

a mean steady-state concentration well below what many consider

to be a minimum effective therapeutic level (Ciraulo and Jaffe

1981). Clearance of desipramine is enhanced in alcoholics when

given intravenously (Ciraulo et al., unpublished data). Although

there is also a trend for enhanced clearance of oral and intravenous

desipramine (Ciraulo et al., unpublished data), there is such

great variability that differences are not statistically significant. The reasons for this are unclear, although such factors

as enterohepatic recycling and genetic variability in hydroxylation capacity may be involved.

Contrary to our findings with antidepressants, we have preliminary

evidence that hydroxylation of alprazolam, a triazolobenzodiazepine, is inhibited in acute alcohol withdrawal. Figure 3

presents data from a multiple dose study in which panic disorder

patients, two of whom were alcoholics, received three different

dosing regimens of the drug. The alcoholic patients had impaired

clearance, higher steady-state plasma concentrations, and more

drug-related side effects as compared to nonalcoholics. The

TABLE 3

Plasma Levels (ng/ml) of Imipramine and Its

Metabolites 8 Hours After the Last Dosea

IMI % free DMI % free 2-OH-IMI 2-OH-DMI Total Tricyclic

Species

Alcoholics

Mean

SD

N

Controls

Mean

SD

N

IMI=imipramine; DMI=desipramine; 2-OH-IMI=2-hydroxyimipramine;

2-OH-DMI=2-hydroxydesipramine; SD=standard deviation. *p<O.O05,

**p<0.01, ***p<O.O5 (significance level using unpaired t test).

83
FIGURE 3

Mean steady-state alprazolam plasma concentration versus daily dose

of alprazolam for six subjects. The two uppermost lines represent

data from two chronic alcoholics (nondrinking). The four lower lines

represent data from nonalcoholic subjects.

84
number of subjects is too small for meaningful statistical

comparisons, but the clinical differences were dramatic. If

these findings are substantiated in a larger number of subjects,

it would suggest that not only are different oxidative processes

(e.g., demethylation, hydroxylation) altered differentially as a

result of chronic alcoholism, but also that drugs metabolized by

the same biotransformation reaction (i.e., hydroxylation) may be

affected differently by chronic ethanol consumption. Both

desipramine and alprazolam are hydroxylated, yet in chronic

alcoholics,clearance of the former drug is slightly enhanced or

unchanged while that of the latter is impaired.

For those drugs with an intermediate extraction ratio, there will

be a combination of effects caused by blood flow changes, intrinsic

clearance changes, and alterations in protein binding. Only

careful experimentation can show which change or combination of

changes will predominate.

SUMMARY

Acute and chronic ethanol consumption alters psychotropic drug

pharmacokinetics. An understanding of the processes of drug

absorption, distribution, biotransformation, and elimination

provide a rational basis for predicting and evaluating drug

interactions. Careful application of clinical pharmacokinetic

models describing these physiological processes are particularly

appropriate for the task of understanding drug and alcohol

interactions.

(1) Absorption. (a) Acute alcohol inhibits first-pass effect

increasing systemic bioavailability. (b) Ethanol inhibits

gastric emptying and may delay drug absorption (increase lag

time of absorption) and decrease the rate of absorption. (c) The

effects of chronic alcohol intake are unknown.

(2) Distribution. (a) Hypoalbuminemia may be present in alcoholics

with liver disease. (b) Fluctuations in free fractions of drugs

may occur in the alcohol withdrawal period. The clinical effects

of protein binding changes are dependent on degree of binding,

hepatic extraction ratio, and binding protein. (c) Acute low-dose

ethanoi increases hepatic blood flow while high doses decrease it.

The effects of chronic alcohol intake on liver blood flow are

unknown. Hepatic blood flow changes show the greatest effects on

drugs with high extraction ratios.

(3) Metabolism. (a) Acute alcohol ingestion usually inhibits

drug metabolism and chronic intake (in the absence of severe liver

disease) enhances metabolism. (b) Cirrhosis impairs oxidative

metabolism, but spares glucuronidation.

Although these generalizations may serve as useful guidelines for

predicting alcohol and psychotropic drug interactions, they should

be applied with caution as exceptions do exist.

85
FOOTNOTE

1The terms alcohol and ethanol are used interchangeably.

REFERENCES

Arendt, R.M.; Greenblatt, D.J.; Divoll, M.; Abernethy, D.R.;

Giles, H.D.; and Sellers, E.M. Predicting in vivo bentodiazepine distribution based on in vitro lipophilicity Clin

Pharmacol Ther 31:200-206, 1982

Cinti, D.L.; Grunding, R.; and Orrenius, S. The effect of

ethanol on drug oxidations in vitro and the significance of

ethanol-cytochrome P-450 interaction. Biochem J 134:367-372,

1973.

Ciraulo, D.A., and Jaffe, J.H. Tricyclic antidepressants in the

treatment of depression associated with alcoholism. J Clin

Psychopharmacol 1(3):146-150, 1981.

Ciraulo, D.A.; Alderson, L.M.; Chapron, D.J.; Jaffe, J.H.;

Subbarao, B.; and Kramer, P.A.

alcoholics.

Imipramine disposition in

J Clin Psychopharmacol 2(1):2-7, 1982.

Edwards, D.J.; Lalka, D.; Cerra, G.; and Slaughter, R.L. Alpha-1-

acid glycoprotein concentration and protein binding in trauma.

Clin Pharmacol Ther 31:62-67, 1982

Findlay, J.; Sellers, E.; and Forstner, G. Lack of effect of

alcohol on small intestinal binding of the vitamin B12-

intrinsic factor complex.

1976.

Can J Physiol Pharmacol 54:469-476,

Gibaldi, M., and Koup, J.R.

binding, apparent volume of distribution and clearance. Eur

Pharmacokinetic concepts - Drug

J Clin Pharmacol 20:299-305, 1981.

Greenblatt, D.J.; Shader, R.I.; Weinberger, D.R.; Allen, M.D.;

and MacLaughlin, D.S. Effect of a cocktail on diazepam

absorption. Psychopharmacology 80:217-220, 1978.

Groszman, R.; Lotelansti, B.; Cohn, J.N.; and Khatri, I.M.

Quantification of portasystemic shunting from the splenic

and mesenteric beds in alcoholic liver disease. Am J Med

53:715-722, 1972.

Hoyumpa, A.M., and Schenker, S. Major drug interaction: Effect

of liver disease, alcohol, and malnutrition. Annu Rev Med

33:113-149. 1982.

Hoyumpa, A.; Patwardhan, R.; Maples, M.; Desmond, P.V.; Johnson,

R.F.; Sinclair, A.P.; and Schenter, S. Effect of short-term

ethanol administration of lorazepam clearance. Hepatology

1:47-53, 1981.

Ideo, G.; de Franchis, R.; and del Ninno, E. Ethanol increases

liver uridine-diphosphate-glucuronyltransferase. Experientia

27:24-29, 1971.

Ishii, H.; Joly, J.G.; and Lieber, C.S. Effect of ethanol on the

amount and enzyme activities of hepatic rough and smooth microsomal membranes. Biochim Biophys Acta 291:411-418, 1973.

Laisi, U.; Himberg, J.J.; Seppala, T.; Linnoila, M.; and Mattila,

M.M. Pharmacokinetic and pharmacodynamic interactions of

diazepam with different alcoholic beverages. Eur J Clin

Pharmacol 16:263-269, 1979.

86
Linnoila, M.; Mattila, M.J.; and Kitchell, B.S. Drug interactions

with alcohol. Drugs 18:299-311, 1979.

MacLeod, S.M.; Giles, H.G.; Patralek, G.; Thiessen, J.J.; and

Sellers, E.M. Diazepam actions and plasma concentrations

following ethanol ingestion. Eur J Clin Pharmacol 11:345-349,

1977.

Magnussen, M.P. The effect of ethanol on the gastrointestinal

absorption of drugs in the rat. Acta Pharmacol Toxicol

54:469-476, 1968.

Mezey, E. Ethanol metabolism and ethanol drug interactions.

Biochem Pharmacol 25:345-354, 1976.

Misra, P.S.; LeFevre, A.; Ishii, H.; Rubin, E.; and Liever, C.S.

Increase of ethanol meprobamate and pentobarbital metabolism

after chronic ethanol administration in man and rats. Am J Med

51:345-351, 1971.

Nimmo, W.S. Drugs, diseases and altered gastric emptying. Chin

Pharmacokinet 1:189-203, 1976.

Parry, H.J.; Barter, M.B.; Mellinger, G.D.; Cisin, I.H.; and

Manheimer, D. I. National patterns of psychotherapeutic drug

use. Arch Gen Psychiatr 28:769-783, 1973.

Piafsky, K.M. Disease-induced changes in the plasma binding of

basic drugs. Clin Pharmacokinet 5:246-262, 1980.

Preskorn,S., and Hughes, C. Ethanol effects on brain concentrations of amitriptyline and the relationship to psychomotor

function. Psychopharmacology 80:217-220, 1983.

Rothschild, M.A.; Oratz, M.; and Schereiber, S.S. Effects of

nutrition and alcohol on albumin synthesis. Alcoholism:

Clin Exp Res 7(1):28-30, 1983.

Rubin, E.; Gang, H.; Misra, P.L.; and Lieber, C.S. Inhibition of

drug metabolism by acute ethanol intoxication. Am J Med

49:801-806, 1970.

Sandor, P.; Sellers, E.M.; Dunbrell, R.N.; and Khouw, V. Effect

of short- and long-term alcohol use on phenytoin kinetics in

chronic alcoholics. Clin Pharmacol Ther 30:390-397, 1981.

Sandor, P.; Naranjo, C.A.; Khouw, V.; and Sellers, E.M. Variations

in drug free fraction during alcohol withdrawal. Br J Clin

Pharmacol 15:481-486, 1983.

Sellers, E.M., and Holloway, M.R. Drug kinetics and alcohol

ingestion. Clin Pharmacokinet 3:440-452, 1978.

Sellers, E.M.; Lang, M.; Koch-Weser, J.; LeBlanc, A.E.; and

Kalant, H. Interaction of chloralhydrate and ethanol in

man. Clin Pharmacol Ther 13:37-59, 1972.

Sellers, E.M.; Abel, J.G. Romach, M.K.; Khouw, V.; and

Naranjo, C.A. Sources of variations in binding of psychotherapeutic drugs to plasma proteins. Proceedings of the

Foundation Fund in Psychiatry. Chicago, 1979.

Sellers, E.M.; Naranjo, C.A.; Giies, H.G.; Frecker, R.C.; and

Beeching, R.N. Intravenous diazepam and oral ethanol interaction. Clin Pharmacol Ther 28:638-643, 1980a.

Sellers, E.M.; Naranjo, C.A.; Abel, J.G.; Khouw, V.; Sandor, P.;

and Alexander, P. Changes in fatty acids modulate reciprocal

variations in diazepam and warfarin free fraction. Clin

Res 28:666A, 1980b.

87
Sellman, R.; Kanto, J.; and Raijola, E. Human and animal study

on elimination from plasma and metabolism of diazepam after

chronic alcohol intake. Acta Pharmacol Toxicol 36:33-42, 1975.

Shaw, S., and Lieber, C.S. Characteristic plasma amino acid

abnormalities in the alcoholic: Respective roles of alcoholism,

nutrition, and liver injury. Clin Res 24:291A, 1976.

Sorrell, M.F.; Nauss, J.M.; Donohue, T.M.; and Tuma, D.J. Effects

of chronic ethanol administration on hepatic glycoprotein

secretion in the rat. Gastroenterology 84:580-586, 1983.

Stein, S.W.; Lieber, C.S.; Leevy, C M.: Cherrick, G.R.; and

Abelmann, W.H. The effect of ethanol upon systemic and

hepatic blood flow in man. Am J Clin Nutr 13:68-74, 1963.

Weller, R.A., and Preskorn, S.H. Psychotropic drugs and alcohol:

Pharmacokinetic and pharmacodynamic interactions. Psychosomatics 25(4):301-309, 1984.

Wilkinson, G.R., and Shand, D.G. A physiological approach to

hepatic drug clearance. Clin Pharmacol Ther 18:377-390, 1975.

Williams, R.L., and Benet, L.Z. Hepatic function and pharmacokinetics. In: Zakim, D., and Boyer, T.D., eds. Hepatology.

Philadelphia: W.B. Sanders, 1982. pp. 230-246.

AUTHORS

Domenic A. Ciraulo, M.D.

Jamie Barnhill, Ph.D.

Department of Psychiatry

Veterans Administration Medical Center

and

Tufts University School of Medicine

Boston, Massachusetts 02111

88
Complex Interactions Between

Drugs and Dietary Factors

Elliot S. Vesell, M.D.

Multiple dynamic interactions occur among host factors that affect

drug response (figure 1); these host factors and their

interactions are responsible for large interindividual variations

in drug disposition (Vesell 1982, 1984b). The critical role in

drug disposition played by host factors depicted in figure 1, and

by dynamic interactions among them, is insufficiently appreciated.

Too often, patients are incorrectly treated as being equivalent to

one another and unchanging with respect to their dosage

requirements. Too few physicians recognize that a particular

patient’s dosage requirements can change with an alteration in

even one of the host factors shown in figure 1. Thus, in a given

patient, a dose of a particular drug that is therapeutic at one

time may become either toxic or ineffective at another time. As

more active drugs with lower therapeutic indices are marketed,

physicians must individualize drug dosage according to the special

and changing requirements of each patient.

Diet is a multifaceted, complex host factor that can alter drug

response by changing several pharmaookinetic parameters (figure

1). The human diet is highly heterogeneous in its preparation,

chemical composition, volume, and times of consumption (figure 2).

Accordingly, dietary effects on drug kinetics vary widely in

subjects of different age, sex, socioeconomic status, ethnic

background, and geographic region. Indeed, these effects can vary

in the same subject over time and seasons as changes occur in his

or her dietary habits. Now, as a result of numerous studies on

food-drug interactions, several fundamental principles have

emerged, as well as certain problems whose resolution requires

more investigation and fresh insight. This review identifies

several of these problems and offers approaches to their solution.

MAGNITUDE OF INTERINDIVIDUAL VARIATION IN DRUG RESPONSE

Normal subjects exhibit large interindividual variations in

pharmacokinetic values when the same drug in the same dose is

administered by the same route to different normal adult male or

8 9
female subjects under similar environmental conditions. Table 1

shows that the magnitude of these interindividual pharmacokinetic

variations commonly ranges from threefold to elevenfold, depending

both on the drug and the population studied. Figure 1 identifies

27 different host factors that can cause large variations among

subjects in rates of drug elimination. Since some of these host

factors--including genetic constitution, diet, exposure to

numerous drugs, and other environmental chemicals--are broad in

scope, many additional specific items could be listed under each

factor. These and other host factors shown in figure 1 primarily

influence hepatic drug-metabolizing enzyme activity (HDMEA),

although effects on drug absorption, distribution, and excretion

can also occur. Since large interindividual pharmacokinetic

variations persist even when the other factors shown in the outer

circle of figure 1 are held constant, these large interindividual

variations, existing in normal subjects maintained under similar

environmental conditions, arise mainly from genetically controlled

differences in HDMEA (Vesell and Penno 1983).

FIGURE 1

This circular design suggests the multiplicity of either wellestablished or suspected host factors that may influence drug

response in man. A line joins all such factors in the outer circle

to indicate their close interrelationship. Arrows from each

factor in the outer circle are wavy to indicate that effects of

each host factor on drug response may occur at multiple sites and

through different processes, including drug absorption, distribution, metabolism, excretion, receptor action, and combinations

thereof.

90
DIETARY FACTORS THAT AFFECT DRUG RESPONSE

This review focuses on complex, multifaceted effects of dietary

factors on drug response. Most studies designed to identify the

influence of dietary factors have had to explore each dietary

factor in isolation from additional dietary factors and from other

host factors shown in figure 1. However, in the real world, many

of these dietary factors can and do interact with each other. The

extent and nature of such interactions among dietary factors and

also between dietary factors and other host factors need to be

explored and defined. Figure 2 suggests the extensive potential

for dynamic interactions among dietary factors.

FIGURE 2

This circular design suggests the possibility of dynamic

interactions among the several well-established or suspected

dietary factors that may influence drug response in humans.

Arrows from each factor in the outer circle are wavy to indicate

that effects of each dietary factor on drug response may occur at

multiple sites and through different processes. The inner circle

suggests that effects of dietary factors may be modified by many

other environmental factors, as well as by genetic factors.

91
Table 1. lnterindividual variations in plasma half-lives of drugs

metabolised by hepatic detoxifying enzymes

Drug Plasma

half-life

(hours)

Factor of No. of individuals

variation investigated

Aminopynne 1.1-4.5 4 12

Amylobarbitone 1.4-6.4 5 14 pairs of twins

Antipyrine 5-35 7 33

Carbamazepine 18-55 3 6

Diazepam 9-53 6 22

Dicoumarol 7-74 11 14 pairs of twins

lndomethacin 4-12 3 15

Nonripryline 15-90 6 25

Phenylbutazone 1.2-7.3 days 6 14 pairs of twins

Phenytoin 10-42 4 86

Primidone 3.3-l6.2 5 46

Theophylline 4-l8 5 45

Tolbutamide 3-27 9 50

Wartarin 15-70 5 40

As documented abundantly for such drugs of abuse as alcohol,

marijuana, cocaine, and heroin (DuPont 1976), drugs of abuse are

rarely used singly; rather, they are taken in various

combinations. Such multiple drug use by an individual enhances

opportunities for pharmacokinetic and pharmacodynamic

interactions between these drugs and numerous host factors shown

in figure 1 (Braude and Vesell 1976). The role of dietary factors

in affecting potential interactions among drugs of abuse

themselves and other host factors has been neither widely

recognized nor Investigated. Hopefully, this review (Vesell

1984b) will serve to focus attention on such interactions.

EFFECT OF FOOD ON ABSORPTION OF ORAL DRUGS

Figure 2 suggests not only that many dietary factors can interact

with each other to lnfluence drug disposition, but that each can

also affect drug disposition at several discrete sites, including

sites of drug absorption, distribution, metabolism, and excretion.

This section deals mainly with effects of food on absorption of

drugs taken by mouth. For decades, eating has been recognized to

of fact drug response, and patients have long been aware that

taking some medications with meals reduces gastrointestinal side

effects.

92
The general outcome of taking medication with meals is a retarded

rate rather than an altered total amount of gastrointestinal drug

absorption. Retardation of the gastrointestinal absorption rate

of drugs occurs mainly because meals slow gastric emptying. Meals

can increase, decrease, or be without effect on the absolute

systemic availability of a drug (Welling 1977). For the

relatively few drugs whose gastrointestinal absorption increases

after meals--griseofulvin (Crounse 1961; Kabasakallan et al.

1970), lithium (Jeppson and Sjogren 1975), nitrofurantoin (Bates

et al. 1974; Reidenberg and Vesell 1975), diazepam (Korttila and

Kangas 1977; Korttila et al. 1976), and riboflavin (Jusko and Levy

1967; Levy and Jusko 1966)--explanation is offered “in terms of

delayed gastric emptying and gastrointestinal transit which allows

more complete dissolution or prolonged residence at a site in the

intestine from which absorption is optimum” (McLean et al. 1978).

Divergent effects of meals on drug disposition depend on the

pharmacologic profile of the drug, its formulation, and the

composition and size of the meal. Excellent reviews (Melander and

McLean 1983; Welling et al. 1975) of the effeots of meals on drug

absorption obviate detailed discussion here. Only the

pharmacologic principles that determine the results of these fooddrug interactions will be described. Application of these

principles requires recognition that food-drug interactions may be

markedly modified by numerous host factors, such as age and

general condition of the patient; cardiovascular, hepatic, and

renal function; intercurrent diseases; and whether the patient is

ambulatory or bedridden (Welling et al. 1975). The appreciable

influence of numerous host factors (rarely defined adequately in

individual studies on this subject) may explain frequent

inconsistencies in the literature.

Two pharmacologic principles determine how food affects drug

absorption from the gut. Food enhances gastric blood flow but

retards gastric emptying. Since the rate of diffusion of a

chemical across a capillary is a function of its concentration

gradient across the capillary membrane, meal-induced increases in

splanchnic blood flow should accelerate drug absorption from the

gut lumen to the capillary. Complexity in practical application

of this seemingly simple principle arises from the fact that the

effect on blood flow depends greatly on meal content, i.e.,

chemical composition of the food. Blood flow can be doubled by a

high-protein liquid meal and slightly reduced by a liquid glucose

meal (Brandt et al. 1955). Effects of meal-induced enhancement of

splanchnic blood flow on drugs with high hepatic extraction are of

special significance and are discussed separately below.

The second fundamental principle is that meals slow the rate of

gastric emptying, thereby delaying rate of drug absorption from

the gastrointestinal tract. The influence of this factor on rate

of drug absorption is more complicated than might be suspected.

Changes in gastric emptying depend not only on the type of drug,

but also on the type of meal, as different foods have different

effects on gastric emptying. Delayed emptying is caused by hot

93
meals (Davenport 1961), solutions of high viscosity (Levy and

Jusko 1965), fat, and, to lesser extents, by proteins and

carbohydrates (Bachrach 1959). Compared to liquid meals, solid

meals almost double emptying time of the stomach (Marcus and

Lengemann 1962).

Complexity is further increased because the nature of the drug

under consideration can modify this effect. Pharmacologic

properties of drugs that influence absorption from the gut and,

thus, cause variable effects on gastric emptying include

solubility and stability in the highly acidic gastric juices as

well as the llpophilic character of the dissolved drug molecules,

e.g., whether the drug is ionized (thus, relatively resistant to

passage) or not ionized (hence, readily traversing lipid membranes) at gastric pH. The Important role of pH in drug absorption

is illustrated by the effect of antacids in altering rate but not

extent of diazepam absorption (Greenblatt et al. 1978).

After passage from the stomach to the small intestine, food

stimulates intestinal motility, thereby accelerating dissolution

of solid particles. Food also simultaneously decreases passage of

drugs from diffusion across intestinal mucosa by accelerating

their motion through the intestine. Further complication arises

from the fact that some foods, especially fat, stimulate bile flow

and secretion. As surface-active agents, bile salts enhance

dissolution of poorly soluble drugs, thereby accelerating drug

absorption (Gibaldi and Feldman 1970). On the other hand, bile

salts can impede absorption by forming insoluble complexes with

other drugs (Bates and Gibaldi 1970).

The pharmacologic character of the drug determines the precise

proportion absorbed in the stomach and in the small intestine. In

general, relatively little drug absorption occurs in the stomach

as compared to that in the small intestine, because of greater

surface area in the latter. By retarding stomach emptying, food

delays drug absorption, which takes place mainly in the small

intestine. Particular pharmacologic properties, such as weak

basicity or transportation by active carrier mechanisms, render

some drugs more likely than others to be absorbed in the small

intestine. Thus, complex characteristics of the patient, drug,

food, and physiologic mechanisms of gastrointestinal absorption

render difficult predictions and interpretations of how meals

ultimately influence drug absorption in a given patient at a given

time.

This section concludes with an illustration of a problem

associated with literal interpretation of principles governing the

influence of enhanced gastric blood flow on drug absorption. Let

us consider model drugs with high intrinsic clearance. Several

investigators have reported that meals elevated plasma

concentrations of such drugs subject to extensive first-pass

effect, including propranolol (McLean et al. 1981; Melander et al.

1977), metoprolol (Melander et al. 1977), and lidocaine (Elvin et

al. 1981). After high protein meals, the increase in oral

94
bioavailability of drugs subject to large first-pass effect has

been attributed to the enhanced hepatic blood flow associated with

such meals. The area under the curve (AUC) after intravenous

(i.v.) dosing is decreased for such drugs, while it is increased

after oral dosing; the kinetic basis for this difference has been

reported (Elvin et al. 1981).

Enhanced blood flow presumably serves to allow such highextraction drugs to pass rapidly through the liver, thereby

escaping hepatic removal and causing higher drug concentrations in

systemic circulation. Lidocaine kinetics were evaluated

thoroughly before and after a meal; enhanced systemic

bioavailability of oral lidocaine after a meal was attributed

entirely to transient food-induced elevations in rate of perfusion

of the splanchnic vascular bed (Elvin et al. 1981). Whereas

alterations in rates of hepatic lidocalne metabolism or of protein

binding of lidocaine could hypothetically have induced similar

results, Investigation of these possibilities indicated that these

measures did not ohange after meals. Thus, alteration in hepatic

blood flow was induced by food alone (Elvin et al. 1981). Several

recent studies, however, offered a different view. A thorough

computerized simulation performed to predict the magnitude of

change that meals produce in apparent oral bioavallability of

propranolol revealed that the meal-induced increase in hepatic

blood flow was of too short a duration to explain the extent to

which the bioavailability of an oral dose of propranolol rose

(Svensson et al. 1983). The authors concluded that “the effect of

food on drug metabolism appears to be a complex and time-dependent

process requiring further investigation.” In another

investigation, they tested their conclusion by measuring clearance

of indocyanine green given i.v. before and after a high

carbohydrate meal in six normal subjects (Svensson et al. 1984).

Again, they concluded that the meal-induced Increase In hepatic

blood flow probably made only a very small aontribution to the

increase in propranolol bioavailability (Svensson et al. 1984).

Related observations on altered aminopyrine disposition after

meals have been reported by Shively et al. (1981). In normal men,

aminopyrine t 1/2 was 34% longer acting and apparent volume of

disposition was 33% larger 3 hours after meals than during

fasting. Plasma protein binding of aminopyrine was reduced by

meals, explaining in part the meal-induced increase in aminopyrine

apparent volume of disposition. Melander and McLean (1983)

thoroughly and critically reviewed the influence of food on the

presystemic clearance of drugs. Meals commonly accelerate

presystemic clearance of lipophilic bases (such as propranolol and

amitriptyline), but rarely alter that of lipophylic acids (such as

sallcyllc acid and penicillin), although esters of such acids

(acetylsallcylic acid and pivampicillin) are subject to this mealinduced effect (Melander and McLean 1983). See Helling (1984) for

the most recent review on effects of meals on gastrointestinal

absorption of drugs.

95
DIETARY FACTORS THAT AFFECT DRUG METABOLISM

Pioneering experiments demonstrated that several discrete dietary

factors could alter the rate of drug metabolism and produce clear

results that have frequently been reviewed (Anderson et al. 1982;

Conney et al. 1977, 1980; Kappas et al. 1977; Vesell 1980). They

need only be summarized here. Antipyrine, theophylline,

phenacetin, and acetaminophen served as model drugs in these

studies. Carefully selected normal subjects received an oral dose

of one of these drugs before, during, and after a single dietary

manipulation lasting 3 to 14 days. All other environmental

factors during the studies were controlled as rigidly as possible.

The present inquiry will focus on the question of how much these

and related dietary factors contribute to large interindividual

variations in rates of drug metabolism.

Results of these studies showed that: 1) On a dally diet of 2,500

calories, a high ratio of protein (44%) to carbohydrate (35%)

content enhanced antipyrine and theophylline metabolism, whereas a

high ratio of carbohydrate (70%) to protein (10%) content retarded

their biotransformation (Kappas et al. 1976). Substitution of fat

for carbohydrate did not significantly change these results

(Anderson et al. 1979), which recently were extended from an

entirely oral route of dietary intake to a completely i.v. one

(Vesell 1984a). 2) Ingestion of charcoal-broiled beef increased

the rate of antipyrine and theophylline metabolism (Kappas et al.

1978), but not that of acetaminophen (Anderson et al. 1983). The

rate of biotransformation of phenacetln was not changed, although

charcoal-broiled beef induced a large first-pass effect, thereby

appreciably reducing plasma phenacetin concentrations (Conney et

al. 1976). 3) Cruciferous vegetables in large amounts induced the

metabolism of antipyrine and theophylline (Pantuck et al. 1979)

and the glucuronidation of acetamlnophen (Pantuck et al. 1984a).

4), Theobromine acetate in high concentrations retarded its rate of

biotransformation (Drouillard et al. 1978). Theobromine is a

major constituent of chocolate, and the same dose of theobromine

taken in chocolate rather than as the salt did not alter its

metabolism, presumably because of its reduced bioavailability In

chocolate. 5) Starvation of obese subjects for 10 days did not

alter antipyrine or tolbutamlde disposition (Reidenberg and Vesell

1975), but antipyrine metabolism was impaired in Indian (Narang et

al. 1977) and Sudanese (Homeida et al. 1979) children with protein

malnutrition. Nutritional rehabilitation of these children

restored antipyrine kinetics to normal. A few of the factors shown

in the outer circle of figure 2 have not been studied in man; but

in laboratory animals, they have been shown to affect drug

metabolism and disposition.

TO WHAT EXTENT DO DIETARY FACTORS CONTRIBUTE TO INTERINDIVIDUAL

VARIATIONS IN DRUG METABOLISM?

The preceding studies, each performed on a different dietary

factor, were carefully controlled: only a single dietary variable

was altered independently of all others. Furthermore, subjects

were selected so that their hepatic cytochrome P-450-dependent

monooxygenases would be under stable, near-basal conditions

96
(neither markedly induced nor Inhibited through the potential

influence of numerous environmental perturbations) (Anderson et

al. 1982; Conney et al. 1977, 1980; Kappas et al. 1977; Vesell

1980, 1982). None of the subjects took any drug regularly, smoked

cigarettes, or drank alcohol chronically. A single dietary

manipulation could be Introduced under these well-defined, uniform

environmental conditions. Any alteration in rate of metabolism of

a model drug (such as antipyrine) or the theophylline aould serve

as a reliable, sensitive index of the effect of that specific

nutritional change on the subject’s hepatic drug-metabolizing

capacity. Use of each carefully selected subject as his or her own

control eliminated not only all genetia but also most extraneous

environmental sources of variation in drug metabolism. Therefore,

alteration observed in disposition of a model drug after

imposition of a single dietary manipulation could reasonably be

ascribed to that nutritional change alone. Furthermore, this

experimental design permitted results to be verified in several

ways. The effect could be reproduoed in the same or in other

subjects. Dose-response relationships could be generated between

the particular dietary factor investigated and subsequent change

in model drug metabolism.

How far can these well-established, verified results on dietary

faators in normal subjects under near-basal aonditlons be extended

to other more complicated dynamic ciraumstanaes, such as those

prevailing among patients whose environments may be rapidly

changing in complex ways? What portion of total interindividual

variation among such patients, and among normal subjects with

unrestricted lifestyles, can be attributed to dietary factors?

These difficult questions cannot be answered simply. Complex

interactions between numerous factors probably explain the

totality of interindividual variation that occurs in rates of drug

metabolism among all subjects. In some individuals, dietary

considerations may play a major role, as as can smoking, age, and

genetic factors; or, several factors may share equally or interact

with these and additional factors to account for variability.

This discussion emphasizes the complexity of interactions that may

occur when all subjects are included. Interactions can take place

not only among different types of environmental factors, but also

among dietary factors themselves (figure 2). Therefore, the

magnitude of alteration in drug metabolism lnduaed by manipulation

of only single dietary faotors in subjects under carefully

controlled environmental conditions needs to be compared to the

magnitude of total interindividual variation in rates of

metabolism of the same model drugs before imposition of any

dietary change. Since several dietary alterations can ahange a

subject’s rate of drug metabolism, It might be expected that these

dietary factors account for a large proportion of the extensive

variation among normal subjects. The latter, however, does not

necessarily follow from the former well-established observations,

and It remains to be determined whether such extrapolations are

valid.

97
With respect, to changes in antipyrine and theophylline metabolism

after imposition of diets high in cruciferous vegetables and

charcoal-broiled beef, relatively small alterations (10% to 20% of

basal) are caused by these dietary manipulations. Such changes

are an order of magnitude less than those observed among normal,

carefully selected subjects of the same age and sex, who use no

drugs, are nonsmokers, and are not excessive users of alcohol

(Penno et al. 1981; Penno and Vesell 1983). Alvan (1978) doauments

600% interindividual variation in antipyrine clearance in a

representative number of subjects. Hence, these particular

dietary factors might initially appear to play only a very small

role in accounting for the 600% interindividual variation in

antipyrine metabolism among normal subjects under basal

environmental conditions. Although each factor may play a small

role by itself, a much larger synergistic effect could emerge in

combination with other factors. Figure 2 was designed to suggest

this possibility and shows each dietary factor in dynamic

interaction with other dietary as well as environmental and

genetic factors.

While the magnitude of metabolic change contributed by large

shifts in the proportion of protein to carbohydrate content on an

isocaloric diet is larger than that of diets high In cruciferous

vegetables, It is still considerably less than the 600% interindividual variation that is common when antipyrine kinetics are

compared in at least 12 normal subjects (Alvan 1978; Penno et al.

1981; Penno and Vasell 1983). Here again, It might initially

appear that even a very marked manipulation in the ratio of protein

to carbohydrate content might be insufficient to contribute

substantially to the much larger interindividual variations among

normal subjects in rates of drug metabolism. Superficially, such

a conclusion might seem to be supported by the observation of no

change in antipyrine clearance after starvation of obese subjects.

Studies on starvation, however, are compatible with a major effect

of changing the proportion of the macromolecular constituents of

isocalorlc diets. This reconciliation of seemingly contradictory

results offers an opportunity to stress the complex effects of

several dietary factors and the possibility for interactions among

them. Why doesn’t starvation change antipyrine metabolism, when

switching the proportion of carbohydrate to protein aontent alters

antipyrine metabolism profoundly? Possibly, the body is more

sensitive to the former dietary manipulation than to the latter.

Through detection of the gross diet change from starvation for a

limited time, the body can compensate by providing the amino acids

required for protein synthesis from another source. In contrast,

the body may not be able to detect and, hence, compensate for a

much more subtle switch in the proportion of calories supplied as

either carbohydrate or protein. If this change is uncompensated,

depletion of protein could reduce rates of synthesis of hepatic

drug-metabolizing enzymes, which, in turn, could induce retention

of antipyrine and theophylline.

98
Although fasting greatly alters hepatlc metabolism of some drugs

in rodents (Dixon et al. 1960; Furner and Feller 1971; Kato and

Gillette 1965), no change in drug metabolism occurred in obese,

but otherwise healthy, human subjects after 7 to 10 days on a diet

in which total daily carbohydrate intake was under 15 g

(Reidenberg and Vesell 1975). This diet induced ketoaaldosls and

weight loss ranging from 4 to 15 kg. When uncorrected for body

weight, the apparent volume of distribution of both antipyrine and

tolbutamlde was lower after fasting than before, presumably

because, during fasting, the early loss of body weight is mainly

from body water rather than from fat stores or muscle mass

(Reidenberg and Vesell 1975). In each subject, the extent of

decrease in the apparent volume of distribution was proportional

to loss of body weight; and when correction was made for body

weight, fasting had no effect on apparent volume of distribution

of either antipyrine or tolbutamide. These results, extended to

other hepatic microsomal oxidations, Including those for

sulfisoxazole, isoniazid, and procaine (Reidenberg 1977),

disclosed that, when allowance was made for body weight, neither

half-life values nor rates of hepatia metabolism of these drugs

changed in otherwise normal obese subjects who fasted 7 to 10 days.

Several reviews that describe effects of obesity on drug

disposition suggest that volumes of distribution of certain drugs

are increased in obesity. The extent of this increase appears to

correlate with the lipid-solubllity of the drug (Abernathy and

Greenblatt 1982; Abernethy et al. 1984).

General conclusions on the failure of absolute fasting to alter

hepatlc drug metabolism in normal subjects or in subjects without

liver dysfunction were extended by a study of seven female

subjects, which confirmed classic anorexia nervosa (Bakke et al.

1978). In these subjects, prolonged refusal to eat induced

differing degrees of dehydration, hyponatremia, hypoahloremia,

hypokalemia, and anemia. When compared with age- and sex-matched

normal nurses who served as aontrols and when values were

corrected for body weight, the subjects with anorexia had normal

antipyrine kinetics.

A study on Asian Indian subjects (Krishnaswamy and Naidu 1977)

revealed that in 15 men suffering from nutritional edema--a severe

manifestation of protein deficiency and resultant hypoalbumlnemia

--the mean plasma antipyrine half-life of 12.8 hours did not

differ from that of age- and sex-matched nonsmoking controls (11.2

hours), but it was higher than that of age- and sex-matched smoking

controls (8.9 hours). In the same study, another group of 13

undernourished, underweight men without edema had a short mean

antipyrine half-life of 8.6 hours (same range as that of smoking

controls); this could be because some of these 13 subjects smoked,

some drank alcohol, and some were agricultural laborers exposed to

pesticides known to induce hepatic drug-metabolizing enzymes.

Thus, in this study, severe malnutrition did not alter antipyrine

disposition, supporting observations already described for

subjects with anorexia nervosa and for obese, but otherwise

normal, subjects after a fast of 7 to 10 days. Several problems,

however, including chronic exposure of some subjects to inducing

chemicals, render the results inconclusive.

99
In a significant contribution, phenylbutazone kinetics were

measured in four normal male Asian Indian controls (mean age, 30

years) and in five undernourished male Asian Indian subjects with

hypoalbuminemia (mean age, 36 years), none of whom smoked

cigarettes or consumed alcohol for prolonged periods (Adithan et

al. 1978). The malnourished group had shorter mean plasma

phenylbutazone half-lives, but larger mean phenylbutazone apparent

volume of distribution and metabolic clearance, than did the

controls. These deviations in phenylbutazone disposition in

undernutrition presumably arose as a result of reduced binding of

phenylbutazone to albumin, with a corresponding increase in drug

availability for metabolism and elimination.

A STATISTICAL APPROACH TO DETERMINE CAUSES OF LARGE INTERINDIVIDUAL VARIATIONS IN DRUG METABOLISM RATES

Isolation and investigation of each environmental factor

Independent of all others and under rigidly controlled conditions

offer advantages enumerated above. Nevertheless, as we have also

seen, this method does not permit easy extrapolation to a

different circumstance and a different question: How much of

total extensive interindividual variation in drug metabolism in a

large population under nonbasal conditions oan be accounted for by

each dietary factor alone?

Statistical approaches to this question have been attempted. In

gathering 128 factory workers in London, investigators

deliberately chose subjects with different environments (Fraser et

al. 1979). Antipyrine was then given onoe to each of these

subjects. A computerized program based on multiple regression

analysis was used to relate antipyrine clearance of each subject

to numerous anthropometric and biochemical indices of nutritional

status. Five variables correlated independently with antipyrine

clearance: race, contraceptive pill usage, smoking status,

ponderal index (weight/height2), and serum albumin concentration

(Fraser et al. 1979). Because coefficients for race and diet were

virtually identical, the investigators concluded that it was

impossible to analyze the role of one factor independently of the

other. Most of their Asian Indian subjects were vegetarians,

whereas almost all of their population of white Londoners ate

meat; furthermore, many were subject to such additional

environmental perturbations as cigarette smoking and oral

contraceptives.

Recognizing that reliable conclusions regarding the role of

dietary factors in large interindividual variations in antipyrine

kinetics were impossible in this mixed and, hence, hopelessly

confounded experimental situation, the investigators decided to

apply their statistical method to another group of less confounded

subjects. The second group was considered more amenable to

investigation by multiple regression analysis because it was

racially homogeneous. Subjects in the second study were 36

healthy adult Indo-Pakistani immigrants to Britain. Among these

subjects, antipyrine clearance was much slower in the 16

lactovegetarians (0.54 ± 0.06 ml min-1 kg-1) than in the 20 regular

100
meat eaters (0.91 ± 0.07 ml min-1 kg-1) (Mucklow et al. 1982).

Since absence of meat from the diet was associated with a

significantly smaller daily intake of dietary protein (which, in

the 16 lactovegetarians, was abnormally low by Western standards),

Mucklow and coworkers concluded that this difference in daily

protein intake was the principal cause of differences in

antipyrine clearance,

The investigators recognized, however, that their methods are open

to criticism. For example, Mucklow et al. (1982) state:

“Retrospective dietary assessments made at a single interview,

even when this is conducted by an experienced dietician, are open

to criticism since they rely heavily upon the memory of the subject

interviewed, the estimates of quantity made by the dietician, and

subsequent expression of those quantities in mass units. The

opportunity for error is clearly increased when the interview is

conducted through an interpreter.” The 16 lactovegetarians had

high standard deviations in mean antipyrine clearance. This

extensive interindividual variation in antipyrine kinetics within

a population uniform with respect to low protein intake clearly

must arise from other factors. The investigators also attempted

to identify some alternative sources for these interindividual

variations: “significant correlations occurred between antipyrine

clearance and age, sex, religion, smoking, hemoglobin,

kilocalories, carbohydrate, and fat. No significant correlation

was observed between antipyrine clearance and weight, coffee/tea

index, alcohol intake or plasma albumin.” But, they did not

attempt to relate the socioeconomic or educational level of

subjects to their dietary habits, particularly protein intake.

This and additional confounding factors might have influenced

results.

Even for this limited group of 36 Indo-Pakistanis, the methodology

left the major conclusion open to question because, as Mucklow and

coworkers (1962) state, “when non-vegetarians alone were

considered, there was no significant correlation between

antipyrine clearance and daily meat intake, raising the

possibility that one or more other dietary factors might be of

greater importance. Moreover, many of the variables correlated

significantly with each other as well as with clearance. Stepwise

multiple regression analysis was therefore performed and

identified smoking, age and fat intake as independent correlates

with clearance.” Other discrepancies merit attention. Again,

according to the investigators, since “daily protein intake was

significantly lower amongst vegetarians, while daily fat intake

was similar in the two subgroups, it was surprising that

regression analysis identified fat intake as the only independent

dietary correlate with antipyrine clearance.” This anomaly should

be considered in light of the carefully controlled dietary studies

by Anderson et al. (1979) that assigned a small role to fat in

antipyrine kinetics, and in connection with the inconsistency of

these results on albumin with those of a previous study by Fraser

et al. (1979). Collectively, these difficulties suggest that the

statistical approach used here and elsewhere (Fraser et al, 1976)

may yield irreproducible, erroneous results.

101
In an earlier study with multiple regression analysis to determine

the basis of interindividual variations in antipyrine clearance

among 49 Gambians (Fraser et al. 1976), the principal causative

factor identified was another nutritional factor, the number of

cola nuts chewed per day (r = 0.4). In a prospective study on an

entirely different population of carefully controlled,

environmentally stable normal men living in south-central

Pennsylvania, chewing cola nuts did not alter antipyrine clearance

(Vasell et al. 1979). Because of multiple genetic and environmental differences between the groups investigated, these

apparently conflicting results are not necessarily contradictory.

Nevertheless, their divergence emphasizes the crucial role played

by the criteria used to select methods to analyze data and subjects

to receive drugs. This group has extensively applied their

statistical method to determine causation of large interindividual

kinetic variations without describing strengths and weaknesses;

therefore, others have attempted to critically assess this

application of multiple regression analysis (Vasell 1984a; Vesell

and Penno 1983). While this statistical method has great

potential, it requires considerable modification beyond its

initial applications in this area (Fraser et al. 1976, 1979;

Mucklow et al. 1982) if that potential is to be realized (Vesell

1984a; Vesell and Penno 1983). Thus far, its applications in

kinetics (Fraser et al. 1976, 1979; Mucklow et al. 1982) have been

disappointing, as those who have used it neither formulated nor

addressed, much less demonstrated fulfillment of, several

fundamental assumptions inherent in its use (Vasell and Penno

1983). As noted above, contradictory findings emerged in

different applications (Fraser et al. 1976, 1979; Mucklow et al.

1982; Vesell and Penno 1983).

Four fundamental assumptions of the particular statistical model

used are dubious when applied to investigations of sources of

interindividual differences in antipyrine kinetics and are

described in detail elsewhere (Vesell 1982, 1984a; Vesell and

Penno 1983). Briefly, too few subjects are used to be

representative of the population from which they are derived; the

method has, thus far, failed to account for most of the

intersubject variability; the results are conflicting and they

differ from much earlier work on age, sex, and genetic

constitution (Vesell and Penno 1983). The potential of multiple

regression analysis to resolve sources of kinetic variations is

much greater than has been realized by the particular model used.

The technique itself is both sensitive and powerful. For multiple

regression analysis to be used appropriately, however, a model

must be developed that encompasses nonlinear as well as linear

relationships (Cohen and Cohen 1975). With antipyrine, only a

linear model has, thus far, been used. Error terms especially need

to be appropriately modeled, rather than treated in a simply

additive manner as in past applications of this method. In

contrast to the use of this statistical approach in the assessment

of variations in antipyrine kinetics, investigators who applied

this model in the assessment of factors that cause large interindividual variations in theophylline clearance clearly recognized

102
its limitations and the requirement that the conclusions be

considered only tentative until tested by a prospective controlled

experiment (Jusko et al. 1979). The investigators state that:

“The factors identified as important in theophylline body

clearances are associations found by retrospective statistical

analysis which need not imply a cause-and-effect relationship,

especially where a pathophysiological or drug interaction

rationale does not exist. Often these factors need further

confirmation by prospective examination of cohorts of subjects

with the disease or history in question.”

A THIRD APPROACH TO ASSESSMENT OF DIETARY CONTRIBUTIONS TO

INTERINDIVIDUAL VARIATIONS IN DRUG DISPOSITION.

The magnitude of interindividual variation shown in table 1 for

some drugs can depend on the drug selected for study, the number of

subjects studied, the particular population from which the

subjects are drawn, and the "condition" of the subjects, including

their present and past health, genetic constitution, age, sex,

diet, and exposure to environmental chemicals and drugs that

induce or inhibit hepatic mixed-function oxidases. Also, the

extent of interindividual variation can reflect complex

interactions among these and other sources (figures 1 and 2). Even

in the same subject, however, kinetic values can fluctuate

markedly from one study to another if any of these factors change.

As already suggested, results of previously published reports also

reflect the specific method used and the degree to which the

assumptions underlying each method are fulfilled. Accordingly,

this review stresses the characteristics of each method and the

assumptions upon which it is based.

It should be emphasized that when subjects are under basal

environmental conditions with respect to the many factors that can

affect hepatic drug oxidation and when, accordingly, their hepatic

drug-metabolizing enzymes are relatively uninduced and uninhibited, large interindividual variations still remain (table 1).

Although well established, this important fact is often ignored.

Thus, under near-basal conditions, large interindividual kinetic

variations cannot be attributed convincingly to any specific

environmental factor. Twin and family studies have indicated that

these large interindividual kinetic variations in subjects under

near-basal environmental conditions arise from genetic factors

(Penno et al. 1981; Penno and Vesell 1983; Vesell 1982, 1984a;

Vesell and Penno 1983). Nevertheless, under other conditions in

which environmental factors are permitted to exert a differential

(unequal) influence, this pattern of genetic transmission can be

concealed. The major portion of interindividual variation

observed can then be attributed solely to environmental sources,

because underlying genetic factors that are also operative become

unrecognizable by conventional methods of detection. Accordingly,

the answer to the difficult question of what role nutritional

103
factors play in maintaining large interindividual kinetic

variations in normal subjects living unrestricted lifestyles

depends on the specific characteristics of the subjects selected,

the extent of environmental perturbation, and the particular

combination of environmental factors that exert effects on these

subjects at any given time. Results obtained in such studies will

often reflect, in large measure, the methods used.

To elucidate these precise relationships, a slightly different

approach from those thus far undertaken is recommended. This

approach incorporates parts of the several methodologies described

above. Selection of normal subjects with unrestricted lifestyles

is adopted from studies that used multiple regression analysis.

The part adopted from carefully controlled studies on subjects

under uniform near-basal environmental conditions is manipulation

of single nutritional factors independent of all others with

repeated use of the model drug to obtain kinetic measurements

before, during, and after this single dietary change. Such an

experimental design should permit assessment of the influence of a

single dietary change (generally, withdrawal of a particular

dietary factor) on model drug kinetics. For example, in normal

subjects living an unrestricted lifestyle, substitution for

several weeks of a vegetarian diet for one in which meat plays a

principal part could be assessed with respect to antipyrine

kinetics. All other dietary and environmental conditions of the

subjects in such a study should remain as constant as possible. In

separate experiments, effects of cruciferous vegetables and

charcoal-brolled beef could be estimated by removing each from the

diet for several weeks and comparing the kinetics of a model drug

before, during, and after such withdrawal.

The influence of such variables as age, sex, smoking, alcohol,

oral contraceptives, total calories, relative proportion of

macromolecular constitutents of the diet, exercise, and

environmental exposure to certain prevalent chemicals would

eventually need to be recognized and, to some extent, tested

before the generality of any conclusions drawn in one group of

subjects could be extended to other groups. As already

emphasized, on repetition under perturbed environmental

conditions, normal subjects are more variable in thin kinetic

values than are subjects under near-basal environmental

conditions. Therefore, the suggested experimental design needs to

establish the extent of intraindividual variation in each subject;

that is, kinetic values should be measured in each subject several

times before dietary manipulation. In some subjects, the

magnitude of intraindividual variation may possibly reach or,

perhaps, exceed that obtained after imposition of a specific

dietary change. Such studies are laborious, time-consuming; and

technically difficult to undertake, but they may represent the

next step in the extension of conclusions on dietary factors from

normal subjects under near-basal environmental conditions to

subjects under environmentally perturbed conditions.

104
In this connection, a study revealed markedly altered therapeutic

responses secondary to a change in drug kinetics produced by

switching the proportion of macromolecular constituents in the

diet. In 14 children with asthma, theophylline kinetics were

accelerated by a diet high in protein content and retarded by a

diet high in carbohydrate content (Feldman et al. 1980). Of

particular interest with respect to this discussion was the

observation that, within each of the three dietary groups, the

extent of interindividual variation was in the same range (300%)

(Feldman et al. 1980). Therefore, under none of these three

different dietary conditions did the proportion of the

macromolecular constituents of the diet appear to account for the

300% interindividual variations in theophylline clearance. More

studies of this kind should be performed to assess the proportion

of total interindividual pharmacokinetic variation ascribable to

dietary factors.

REFERENCES

Abernethy, D.R., and Greenblatt, D.J. Pharmacokinetics of drugs

in obesity. Clin Pharmacokinet 7:108-124, 1982.

Abernethy, D.R.; Greenblatt, D.J.; Divoll, M.; Smith, R.B.; and

Shader, R.I. The influence of obesity on the pharmacokinetics

of oral alprazolam and triazolam. Clin Pharmacokinet; 9:177-183,

1984.

Adithan, C.; Gandhi, I.S.; and Chandrasekar, S. Pharmacokinetics

of phenylbutazone in undernutrition. Ind J Pharmacol 10:301-

308, 1978.

Alvan, G. Individual differences in the disposition of drugs

metabolized in the body. Clin Pharmacokinet 3:155-175, 1978.

Anderson, K.E.; Conney, A.H. and Kappas, A. Nutrition and

oxidatlve drug metabolism in man: Relative influence of dietary

lipids, carbohydrate, and protein. Clin Pharmacol Ther 26:493-

501, 1979.

Anderson, K.E.; Conney, A.H.; and Kappas, A. Nutritional

influences on chemical biotransformation in humans. Nutr Rev

40:161-171, 1982.

Anderson, K.E.; Schneider, J.; Pantuck, E.J.; Pantuck, C.B.;

Mudge, G.H.; Welch, R.M.; Conney, A.H.; and Kappas, A.

Acetaminophen metabolism in subjects fed charcoal-broiled beef.

Clin Pharmacol Ther 34:369-374, 1983.

Bachrach, W.H. Physiology and pathologic physiology of the

stomach. Ciba Found Symp 11:3-28, 1959.

Bakke, O.M. Aandarud, S.; Syversen, O.; Bassoe H.H.; and Myking

0. Antipyrine metabolism in anorexia nervosa. Br J Clin

Pharmacol 5:341-342, 1978.

Bates, T.R., and Gibaldi, M. Gastrointestinal absorption of

drugs. In: Swarbrick, J., ed. Current Concepts i n the

Pharmaceutical Sciences: Biopharmaceutics. Philadelphia; Lea

and Febiger, 1970.

Bates, T.R.; Sequeira, J.A.; and Tembo, A.V. Effect of food on

nitrofurantoin absorption. Clin Pharmacol Ther 16:63-68, 1974.

Brandt, J.L. ; Castleman, L.; Ruskin, H.D.; Greenwald, J.; Kelly,

J.J.; and Jones, A. The effect of oral protein and glucose

feeding on splanchnic blood flow and oxygen utilization in

normal and cirrhotic subjects. J Clin Invest 34:1017-1025,

1955.

105
Braude, M.C., and Vesell, E.S. Preface. Ann NY Acad Sci 281:viiix, 1976.

Cohen, J., and Cohen, P. Applied Multiple Regression/Correlation

Analysis for Behavioral Sciences. Hillsdale, N.J.: Erlbaum

Assoc. , 1975.

Conney, A.H.; Pantuck, E.J.; Hsiao, K.-C.; Garland, W.A.;

Anderson, K.E.; Alvares, A.P.; and Kappas, A. Enhanced

phenacetin metabolism in human subjects fed charcoal-broiled

beef. Clin Pharmacol Ther 20:633-642, 1976.

Conney, A.H.; Pantuck, E.J.; Kuntzman, R.; Kappas, A.; Anderson,

K.E.; and Alvares, A.P. Nutrition and chemical

biotransformations in man. Clin Pharmacol Ther 22:707-720,

1977.

Conney, A.H.; Buening, M.K.; Pantuck, E.J.; Pantuck, C.B.;

Fortner, J.G.; Anderson, K.E.; and Kappas, A. In: Evered, D.,

and Lawrenson, G., eds. Environmental chemicals, enzyme

function and human disease. Proceedings of the Ciba Foundation

Symposium. Amsterdam: Excerpta Medica, 1980. pp. 147-167.

Crounse. R.G. Human pharmacology of griseofulvin: The effect of

fat intake on gastrointestinal absorption. J Invest Dermatol

37:529-533, 1961.

Davenport, D. W. Physiology of the Digestive Tract. Chicago:

Chicago Year Book Publishers, 1961.

Dixon, R.L.; Shultice, R.W.; and Fouts, J.R. Factors affecting

drug metabolism by liver microsomes. IV. Starvation. Proc Soc

Exp Biol Med 103:333-335, 1960.

Drouillard, D.D.; Vesell, E.S.; and Dvorchik, B.H. Studies on

theobromine disposition in normal subjects. Clin Pharmacol Ther

23:296-302, 1978.

DuPont, R.L. Polydrug abuse and the maturing national drug abuse

data base. Ann NY Acad Sci 281:311-321, 1976.

Elvin, A.T.; Cole, D.; Pieper, J.A.; Rolbin, S.H.; and Lalkam D.

Effect of food on lidooalne kinetics: Mechanism of food-related

alteration in high intrinsic clearance drug elimination.

Pharmacol Ther 30:455-460, 1981.

Clin

Feldman, G.H.; Hutohinson, V.E.; Pippenger, C.E.; Blumenfeld,

T.A.; Feldman, B.R.; and Davis, W.J. Effect of dietary protein

and carbohydrate on theophylline metabolism in children.

Pediatrics 66:956-962, 1980.

Fraser, H.S.; Bulpitt, C.J.; Kahn, C.; Mould, G.; Mucklow, J.C.;

and Dollery, C.T. Factors affecting antipyrine metabolism in

West African villagers. Clin Pharmacol Ther 20:369-376, 1976.

Fraser, H.S.; Mucklow, J.C.; Bulpitt, C.J.; Kahn, C.; Mould, G.;

and Dollery, C.T. Environmental factors affecting antipyrine

metabolism in London factory and office workers. Br J Clin

Pharmacol 7:237-243, 1979.

Furner, R.L., and Feller, D.D. The influence of starvation upon

hepatic drug metabolism in rats, mice and guinea pigs. Proc Soc

Exp Biol Med 137:816-819, 1971.

Gibaldi, M., and Feldman, S. Mechanisms of surfactant effects on

drug absorption. J Pharm Sci 59:579-589, 1970.

Greenblatt, D.J.; Allen, M.D.; MacLaughlin, D.S.; Harmatz, J.S.;

and Shader, R.I. Diazepam absorption: Effect of antacids and

food. Clin Pharmacol Ther 24:600-609, 1978.

106
Hoensch, H.P.; Steinhardt, H.J.; Weiss, G.; Haug, D.; Maier, A.;

and Malchow, H. Effects of semisynthetic diets on xenobiotic

metabolizing enzyme activity and morphology of small Intestinal

mucosa in humans. Gastroenterology 86:1519-1530, 1984.

Homeida, M.; Karrar, Z.A.; and Roberts, C.J.C. Drug metabolism in

malnourished children: A study with antipyrine. Arch Dis Child

54:299-302, 1979.

Jeppson, J., and Sjogren, J. The influence of food on side effects

and absorption of lithium. Acta Psychiatr Stand 51:285-288,

1975.

Jusko. W.J., and Levy. G. Absorption. metabolism, and excretion

of riboflavin-5'-phosphate in man. J Pharm Sci 56:58-62, 1967.

Jusko. W.J.: Gardner, M.J.; Hanglone. A.; Schentag, J.J.; Koup,

J.R.; and Vance, J.W.; Factors affecting theophylline

clearances: Age, tobacco, marijuana, cirrhosis, congestive

heart failure, obesity, oral contraceptives, benzodiazepines,

barbiturates, and ethanol. J Pharm Sci 68: 1358-1366, 1979.

Kabasakalian, P.; Katz, M.; Rosenkrantrz, B.; and Townley, E.

Parameters affecting absorption of griseofulvin in a human

subject using urinary metabolite excretion data. J Pharm Sci

59:595-600, 1970.

Kappas, A.; Anderson, K.E.; Conney, A.H.; and Alvares, A.P.

Influence of dietary protein and carbohydrate on antipyrine and

theophylline metabolism in man. Clin Pharmacol Ther 20: 643-653,

1976.

Kappas, A.; Alvares, A.P.; Anderson, K.E.; Garland, W.A.; Pantuck,

E.J.; and Conney, A.H. The regulation of human drug metabolism

by nutritional factors. In: Ulrich, V.; Roots, I.; Hildebrand,

A.; Estabrook, R.W.; and Conney, A.H., eds. Microsomes and Drug

Oxidation. New York: Pergamon Press, 1977. pp. 703-708.

Kappas, A.; Alvares, A.P.; Anderson, K.E.; Pantuck, E.J.; Pantuck,

C.B.; Chang, R.; and Conney, A.H. Effect of charcoal-broiled

beef on antipyrine and theophylline metabolism. Clin Pharmacol

Ther 23:445-450, 1978.

Kato, R., and Gillette, J.R. Effect of starvation on NADPHdependent enzymes in liver microsomes of male and female rats. J

Pharmacol Exp Ther 150:279-284, 1965.

Korttila, K., and Kangas, L. Unchanged protein binding and the

increase of serum diazepam levels after food intake. Acta

Pharmacol Toxicol 40:241-246, 1977.

Korttila, K.; Mattila, M.J.; and Linnoila, M. Prolonged recovery

after diazepam sedation: The influence of food, charcoal

ingestion and injection rate on the effects of intravenous

diazepam. Br J Anaesth 48:333-340, 1976.

Krishnaswamv. K.. and Naidu, A.N. Microsomal enzymes in

malnutrition as determined by plasma half life of antipyrine.

Br Med J 1:538-540, 1977.

Levy, G., and Jusko, W. Effect of viscosity on drug absorption. J

Pharm Sci 54:219-225, 1965.

Levy, G., and Jusko, W.J. Factors affecting the absorption of

riboflavin in man. J Pharm Sci 55:285-289, 1966.

Marcus, C.S., and Lengemann, F.W. Absorption of 45Ca and 85Sr

from solid and liquid food at various levels of the alimentary

tract of the rat. J Nutr 77:155-160, 1962.

107
McLean, A.J.; McNamara, P.J.; duSouich, P.; Gibaldi, M.; and

Lalka, D. Food, splanchnic blood flow, and bioavailability of

drugs subject to first-pass metabolism. Clin Pharmacol Ther

24:5-10, 1978.

McLean, A.J.; Isbister, C.; Bobik, A.; and Dudley, F.J. Reduction

of first-pass hepatic clearance of propranolol by food. Clin

Pharmacol Ther 30:31-34, 1981.

Melander, A., and McLean, A. Influence of food intake on

presystemic clearance of drugs. Clin Pharmacokinet 8:286-296,

1983.

Helander, A.; Danielson, K.; Schersten, B.; and Wahlln, E.

Enhancement of the bioavailability of propranolol and metoprolol

by food. Clin Pharmacol Ther 22:108-112, 1977.

Mucklow, J.C. Caraher, M.T. Henderson, D.B.; Chapman, P.H.;

Roberts, D.F.; arid Rawlins, M.D. The relationship between

individual dietary constituents and antipyrine metabolism In

Indo-Pakistani Immigrants to Britain. Br J Clin Pharmacol

13:481-486, 1982.

Narang, R.K.; Hehta, S.; Mathur, V.S.; and Phil, D. Pharmacokinetic study of antipyrlne in malnourished children. Am J Clin

Nutr 30:1979-1982, 1977.

Pantuck, E.J.; Pantuck, C.B.; Garland, W.A.; Min, B.H.;

Wattenberg, L.W.; Anderson, K.E.; Kappas, A.; and Conney, A.H.

Stimulatory effect of brussels sprouts and cabbage on human drug

metabolism. Clin Pharmacol Ther 25:88-95, 1979.

Pantuck, E.J.; Guck, C.B.; Anderson, K.E.; Uattenberg, L.W.;

Conney, A.H.; and Kappas, A. Effect of brussels sprouts and

cabbage on drug conjugation. Clin Pharmacol Ther 35:161-169,

1984a.

Pantuck, E.J.; Pantuck, C.B.; Weissman, C.; Askanazi, J.; and

Conney, A.H. Effects of parenteral nutrition regimens on

oxidative drug metabolism. Anesthesiology 60:534-536, 1984b.

Penno, M.B., and Vesell, E.S. Monogenic control of variations in

antipyrine metabolite formation: New polymorphism of hepatic

drug oxidation. J Clin Invest 71:1698-1709, 1983.

Penno, M.B.; Dvorchik, B.H.; and Vesell, E.S. Genetic variation

in rates of antipyrine metabolite formation: A study in

uninduced twins. Proc Natl Acad Sci USA 78:5193-5196, 1981.

Reldenberg, M.M. Obesity and fasting Effects on drug metabolism

and drug action in man. Clin Pharmacol Ther 22:729-734, 1977.

Reldenberg, M.H., and Vesell, E.S. Unaltered metabolism of

antipyrine and tolbutamide in fasting man. Clin Pharmacol Ther

17:650-656, 1975.

Rosenberg, H.A., and Bates, T.R. The influence of food on

nitrofurantoin bioavailability. Clin Pharmacol Ther 20:227-232,

1976.

Shively, C.A.; Simons, R.J.; Passanantl, G.T.; Dvorchik, B.H.; and

Vesell, E.S. Dietary patterns and diurnal variations In

aminopyrine disposition. Clin Pharmacol Ther 29:65-73, 1981.

Svensson, C.K.; Edwards, D.J.; Mauriello, P.M.; Barde, S.H.;

Foster, A.C.; Lanc, R.A.; Middleton, E.; and Lalka, D. Effect of

food on hepatic blood flow: Implications in the “food effect”

phenorcenon. Clin Pharmacol Ther 34:316-323, 1983.

Svensson, C.K.; Mauriello, P.M.; Barde, S.H.; Middleton, E.; and

Lalka, D. Effect of carbohydrates on estimated hepatic blood

flow. Clin Pharmacol Ther 35:660-665, 1984.

108
Vesell, E.S. The influence of host factors on drug response. III.

Diet. Ration Drug Ther 14(5):1-6, 1980.

Vesell, E.S. On the significance of host factors that affect drug

disposition. Clin Pharmacol Ther 31:1-7, 1982.

Vesell, E.S. Selection of subjects for investigation of host

factors affecting drug response: A method to identify new

pharmacogenetic conditions. Clin Pharmacol Ther 35:1-11, 1984a.

Vesell, E.S. Complex effects of diet on drug disposition. Clin

Pharmacol Ther 36:285-296, 1984b.

Vesell, E .S., and Braude, M.C. Interactions of Drugs of Abuse.

New York: New York Academy of Sciences, 1976 pp. 89.

Vesell, E.S., and Penno, M.B. Assessment of methods to identify

sources of interindividual pharmacokinetic variations. Clin

Pharmacokinet 8:378-409, 1983.

Vesell, E.S.; Shively, C.A.; and Passananti, G.T. Failure of cola

nut chewing to alter antipyrine disposition in normal male

subjects from a small town in south central Pennsylvania. Clin

Pharmacol Ther 26:287-293, 1979.

Helling, P.G. Influence of food and diet on gastrointestinal drug

absorption: A review. J Pharmacokinet Biopharm 5:291-334,

1977.

Welling, P.G. Interactions affecting drug absorption. Clin

Pharmacokinet 9:404-434, 1984.

Welling, P.G.; Lyons, L.L.; Craig, W.A.; and Trochta, G.A.

Influence of diet and fluid on bioavailability of theophylline.

Clin Pharmacol Ther 17:475-480, 1975.

AUTHOR

Elliot S. Vesell, M.D.

Department of Pharmacology

The Pennsylvania State University

College of Medicine

P.O. Box 850

Hershey, Pennsylvania 17033

109
Interactions of Cannabis With

Other Drugs in Man

Leo E. Hollister, M.D.

Interactions of cannabis constituents with other drugs are of

interest because cannabis is often used in the context of other

drugs. Of the many constituents present in cannabis, or in

marijuana smote, only a few can be studied in any systematic way.

These are the cannabinoids, such as the major active component,

delta-9-tetrahydrocannabinol (THC), as well as those without

psychoactivity, such as cannabinol (CBN) and cannabidiol (CBD).

Many possible interactions of cannabinoids might be studied.

Pharmacodynanic interactions would mainly involve THC, as the

other cannabinoids have little direct evidence of pharmacological

activity in man. Pharmacokinetic interactions might involve any

of the cannabinoids, as well as possibly still unstudied or

unidentified materials in marijuana. Interactions might be

tested between THC and drugs that might be used as probes for

investigating the mechanism of action of THC. Finally, some

interactions may depend on characteristics of the users, such as

their idiosyncratic metabolism of the drug, or the psychological

effects of the setting in which the drug is taken as well as the

expectations of individual subjects.

PHARMACODYNAMIC INTERACTIONS

Ethanol

This drug is commonly taken concomitantly with the use of

marijuana, with the idea that it will augment the effects of the

latter drug. Such an assumption is well based, as both drugs

have similar actions. At low doses they tend to produce euphoria

and stimulation; at higher doses, sedation. Comparison between

ethanol and THC confirmed the similarities, the major distinction

being the more distorted time sense and hallucinogenic action of

THC (Hollister and Gillespie 1970). One might guess that their

effects would be additive. Adding ethanol to both high and low

doses of THC increased impairment on several measures, as well as

adding to subjective experiences (Manno et al. 1971).

110
Sedatives

A situation similar to that of ethanol should prevail with

sedatives, but studies in man are few. Numerous studies in

animals support an additive effect. In man, THC given after

pentobarbital pretreatment induced hallucinations and anxiety in

five of seven volunteers, to the extent that four did not

complete the course of five doses of THC because of the severe

psychologic effects (Johnstone et al. 1975). The same

investigators found that in man THC potentiated the sedative and

respiratory depressant effects of the opioid oxymorphone.

Cardiovascular effects of THC seemed to be unaltered by the

presence of either drug. A second study in man confirmed an

additive effect when THC was combined with 150 mg secobarbital/70

Kg. No such interaction was found with CBD (Lemberger et al.

1976).

Stimulants

Stimulants combined with THC have relatively little effect in

man, seeming to act in a mildly additive fashion. A dose of 15

mg of dextroamphetamine in man followed by smoking an equivalent

of 15 mg THC from marijuana cigarettes revealed separate effects

from both drugs: marijuana caused tachycardia and conjunctival

injection; dextroamphetamine increased blood pressure and

respiratory rate. The impairment of performance on cognitive

tests produced by marijuana may have been overcome by dextroamphetamine, which improved performance when given alone (Zalcman

et al. 1973). Similar additive effects were seen in subjects

given 10 mg/70 Kg doses of dextroamphetamine and 50 ug/Kg doses

of THC by smoking. In this case, dextroamphetamine did not

counter the adverse effects of marijuana on motor performance

(Evans et al. 1974). Instances of combined use of amphetamines

and marijuana are infrequent in social practice. One would guess

that when they occurred, doses of amphetamine would be

considerably higher than those given experimentally. No studies

have examined an interaction with cocaine in man.

Other Cannabinoids

Two other major cannabinoids, CBN and CBD, are inevitably taken

with THC when marijuana is smoked. Their proportions may vary

considerably from one marijuana batch to another. Experiments

have been contradictory both in animals and man. Doses of from

15 to 60 mg of CBD orally were said to block the effects of 30 mg

oral doses of THC (Karniol et al. 1974). A similar finding was

reported from another study which showed attenuation of the

psychic “high” (Lemberger et al. 1976). CBD had no effect on the

action of barbiturates. On the other hand, smoked marijuana was

said to produce greater subjective , congitive, and physiological

effects than smoked THC in equivalent doses. Thus, the miniscule

amounts of CBN and CBD in marijuana seemed to enhance rather than

block effects (Galanter et al. 1973). A slight effect was seen

from 40 mg oral doses of CBD combined with 20 mg oral doses of

THC. Onset of THC effects was delayed, slightly prolonged, and

111
slightly more intense. Quite possibly some of these differences

might be due to the known proclivity of CBD to impair metabolism

of other drugs, but the overall effect was of no clinical

consequence. Similar doses of CBN produced no appreciable

alterations of THC effects (Hollister and Gillespie 1975a). In

rhesus monkeys CBD decreased operant behavior produced by THC

(Brady and Balster 1980).

Other Drugs

Virtually no studies of interactions with other drugs have been

done-possibly because few others, as well as the sedatives and

stimulants mentioned above, are used directly in combination with

marijuana. A single case report of marked sinus tachycardia when

marijuana was used by a patient taking nortriptyline would be

expected as an additive effect (Hillard and Vieweg 1983).

Alcohol is commonly used in such combination and its use for

augmenting marijuana effects seems to be based on good evidence.

No antidote for THC effects is known. Tamarind, reported to be

such, was not found to be active in our unpublished studies.

Such has also been the case with phenitrone, which revealed no

antagonism when tested in a number of animal species (Spaulding

et al. 1972).

PHARMACOKINETIC INTERACTIONS

Alterations of Absorption and Distribution

When given in a lipid vehicle, THC is much better absorbed than

it is in a hydroalcoholic vehicle; possibly the lipid matrix also

accounts for social use of the drug in the form of a cookie.

Alimentary lipemia does not seem to bind IHC in blood; clinical

actions were unchanged in our studies.

Alterations of Metabolism

No changes in the kinetics of THC were observed when 20 mg oral

doses were given together with 40 mg oral doses of CBN and CBD

(Agurell et al. 1981). Even a 1500 mg oral dose of CBD failed to

alter the kinetics of an intravenous dose of THC; a minimal

effect on the formation and excretion of metabolites was noted

(Hunt et al. 1981). CBD decreased the metabolism of

hexobarbital; maximum concentrations and bioavailability were

increased; and plasma half-life and volume of distribution were

decreased (Benowitz et al. 1980). No change in the disposition of

cocaine was produced by THC in rats (Vadlamani et al. 1984).

EXPLORATIONS OF MECHANISM OF ACTION

Alphamethylparatyrosine (AMPT) blocks tyrosine hydroxylase and

inhibits formation of new catecholamines. Its interaction with

THC was not associated with any qualitative or quantitative

changes in THC action (Hollister 1974). There may have been a

slight additive effect due to the sedative effects produced by

AMPT. Propranolol blocked the tachycardia produced by THC but

112
had no effect on the cognitive chanses (Drew et al. 1972).

 THC as decreased tachycardia and conjunctival injection.

Physostigmine amplified the lethargy and somnolence produced by

In many respects, physostigmine acts both similarly-to THC

(lethargy and somnolence) and different from THC (bradycardia).

The interactions were undoubtedly additive (Freemon et al. 1975).

INTERACTICINS BETWEEN CANNABIS AND CHARACTERISTICS OF SUBJECTS

Metabolism

Variable responses to the same dose of THC are seen not only

between subjects,but in the same subject as well. (One possible

explanation for between-subject variability would be different

rates of hydroxylation of drugs. An active metabolite of THC is

11-hydroxy-THC; subjects with a rapid rate of hydroxylation might

be expected to have a more precipitous onset and greater

intensity of THC effect than slow hydroxylators. Both metabolism

of antipyrine and phenylbutatone were used as indices of

hydroxylation rate. It was impossible to correlate either the

speed of onset, total intensity, or duration of effects with

speed of hydroxylation of drugs (Hollister and Gillespie 1975b).

Thus, it seems unlikely that differing rates of drug metabolism

account for the widely variable responses between individuals

exposed to THC.

Personality

It has become axiomatic that the setting in which psychoactive

drugs are taken is a strong determinant in the effects they

produce. The axiom is seldom tested. A study of marijuana and

placebo cigarettes smoked under “favorable” and “unfavorable”

conditions found no appreciable contribution of the extreme

settings in which the drug was taken (Hollister et al. 1975).

Quite possibly, the psychological expectations or “set” of

subjects are the major determinant of interindividual

differences. This factor has not yet been adequately studied in

the case of THC (Hollister and Overall 1976).

SUMMARY

Only THC, of all cannabinoids, has a significant pharmacodynamic

interaction with ethanol. Effects in man are additive as

expected. THC. but not CBD. showed a similar interaction with

barbiturates. Interactions with stimulants were weakly additive,

but the former drugs do not reverse impairments from THC. Interactions between cannabinoids are controversial. Some evidence

consistently suggests that CBD may block actions of THC, while

other evidence could not show a clinically significant

interaction. CBD did not alter the kinetics of THC, but it

decreased metabolism of hexobarbital. Preliminary studies of

interactions between THC and drugs affecting activity of

neurotransmitters have not provided good tests of the mechanism

of action of the drug, showing, at best, subtle effects of

questionable clinical significance. The variable responses of

113
subjects to THC is neither explained by differences in metabolism

of drugs nor by differences in the setting in which the drug is

taken.

FURTHER STRATEGIES

The paucity of recent studies of interactions of cannabis with

other drugs probably means that investigators no longer construe

these as very important. Adverse consequences of combined use of

cannabis with other drugs have been rarely reported. Of all

drugs, ethanol is the only one consistently used contemporaneously. Althogh studies of this interaction are sparse, and

by no means elegant, the conclusion of an additive effect seems

reasonable. Whether or not this interaction needs more

documentation is questionable.

One must realize that the clinical effects of smoking cannabis

are rather brief. The whole course is generally run within 3

hours, as judged both by assessment of subjective responses and

by measurement of plasma concentrations of THC. Thus, even

though cannabis users may also use other social drugs, unless

they were to use them concurrently with cannabis, significant

interactions would not be expected. Thus far, no such patterns

of combined use have emerged, other than for ethanol.

While it seems likely that many smokers of cannabis also use

tobacco, surprisingly little study of these interactions has been

done. As the potential for both a pharmacokinetic as well as a

pharmacodynamic interaction exists, it might be of some interest

to test these hypotheses. The clinical effects of cannabis, as

well as the kinetics of THC, might be studied in subjects who are

abstainers from tobacco and who are also heavy smokers. The

possibility that such studies would reveal any new important

interaction is remote, as this experiment has probably been done

many times in nature.

The number of interactions systematically studied to develop some

insight into the mechanism of action of THC have been sparse.

Work thus far in man has touched upon interactions involving

catecholamine neurotransmitters and acetylcholine. Clearly, more

work might be of interest, looking at specific alterations of THC

effect by modifying the actions of dopamine, serotonin, and

gamma-amincbutyric acid. In view of the fact that a possible

mode of action of THC might be one of disordering (fluidizing)

cell membranes, it might be well to study interactions between

THC and drugs that alter membrane lipids, such as chronic

exposure to alcohol, pretreatment with lithium, or other

techniques by which membranes may be made more or less rigid.

Such studies might very well be done on animals rather than in

man, however.

Factors influencing the variability of response to cannabis use

still remain relatively unexplored. More studies of the effects

of different rates of metabolism between subjects, as well as the

114
effects of mind-set of subjects, would be of interest but are not

crucial.

Many of the concerns about the adverse effects on health

of cannabis use still remain to be explored. It does not appear,

at least now, that such adverse effects are likely to be

associated with unexpected interactions between the active

components of cannabis and other drugs.

REFERENCES

Agurell, S.; Carlsson, S.; Lindgren, J-E.; Dhlsson, A.;

Gillespie, H.; and Hollister, L.E. Interactions of delta-ltetrahydrocannabinol with cannabinol and cannabidiol

following oral administration in man. Assay of cannabinol and

cannabidiol by mass fragmentography. Experientia 37:1090-

1092, 1981.

Benowitz, N.L.; Nguyen, T.-L.; Jones, R.T.; Herning, R.Z.;

and Bachman, J. Metabolic and psychophysiologic studies of

cannabidiol-hexobarbital interaction. Clin Pharmacol Ther

23:115-120, 1980.

Brady, K.T., and Balster, R.L. The effects of delta-9-tetrahydrocannabinol alone and in combination with cannabidiol on

fixed-interval performance in rhesus monkeys. Psychopharmacology 72:21-26 1980

Drew, W.G.; Kiplinger, G.F., Miller, L.L.; and Marx M. Effects

of propranolol on marijuana-induced cognitive dysfunctioning.

Clin Pharmacol Ther 13:526-533, 1972.

Evans, M.A.; Martz.; Lemberger, L.; Rodda, B.E.; and Forney,

R.B. Clinical effects of marihuana dextroamphetamine

combination. The Pharmacologist 16:281, 1974.

Freemon, F.R.; Rosenblatt , J.E.; and El-Yousef, M.K. Interaction

of physostigmine and delta-9-tetrahydrocannabinol in man. Clin

Pharmacol Ther 17:121-126, 1975.

Galanter, M.; Weingartner, H.; Vaughan, T.; Roth, W.T.; and

Wyatt, R.J. Delta-9-transtetrahydrocannabinol and natural

marihuana. A control comparison. Arch Gen Pschiatry 28:278-

281, 1973.

Hillard, J.R., and Vieweg, W.V.R. Marked sinus tachycardia

resulting from the synergistic effects of marijuana and

nortriptyline. Am J Psychiatr 140:526-627, 1983.

Hollister, L.E. Interactions in man of delta-9-tetrahydrocannabinol. I. Alphamethylparatyrosine. Clin Pharmacol Ther

15:18-21, 1974.

Hollister, L.E., and Gillespie, H.K. Marihuana, ethanol and

dextroamphetamine; mood and mental function alterations. Arch

Gen Pyschiatry 23:199-203, 1970.

Hollister, L.E., and Gillespie, H.K. Interactions in man of

delta-9-tetrahydrocannabinol. II. Cannabinol and cannabidiol.

Clin Pharmacol Ther 18:80-83, 1975a.

Hollister, L.E., and Gillespie, H.K. Action of delta-9-tetrahydrocannabinol. Clin Pharmacol Ther 18:714-719, 1975b.

Hollister, L.E., and Overall, J.E. Dimensions of marihuana

experience. Drug Alcohol Depend 1:155-164, 1976.

Hollister, L.E.; Overall, J.E.; and Gerber, M.L. Marihuana and

setting. Arch Gen Psychiatry 32:798-891, 1975.

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Hunt, C.X.; Jones, J.T.; Herning, R.I.; and Bachman, J. Evidence

that cannabidiol does not significantly alter the pharmacokinetics of tetrahydrocannabinol in man. J Pharmacokin Biopharm

9:245-260, 1981.

Johnstone, R.E.; Lief, P.L.; Kulp, R.A.; and Smith, T.C.

Combination of delta-9-tetrahydrocannabinol with oxymorphone or

pentobarbital: Effects on ventilatory control and

cardiovascular dynamics. Anesthesiology 42:674-684, 1975.

Karniol, I.G.; Shirakawa, I.; Kasinski, N.; Pfeferman, A.; and

Carlini, E.A. Cannabidiol interferes with the effects of

delta-9-tetrahydrocannabinol in man. Eur J Pharmacol 28:172-

177, 1974.

Lemberger, L.; Dalton, B.; Martz, R.; Rodda, B.; and Forney, R.

Clinical studies of the interaction of psychopharmacologic

agents with marihuana. Ann NY Acad Sci 281:219-228, 1976.

Manno, J.E.; Kiplinger, G.F.; Scholz,N.; Forney, R.B.; and

Haine, S.E. The influence of alcohol and marihuana on motor

and mental performance. Clin Pharmacol Ther 12:201-211, 1971.

Spaulding, T.C.; Ford, R.D. Dewey, W.L.; McMillan, D.E.; and

Harris, L.S. Some pharmacological effects of phenitrone and

its interaction with delta-9-THC. Eur J Pharmacol 19:310-317,

1972.

Vadlamani, N.L.; Pontani, R.B.; and Misra, AL. Effect of

diamorphine, delta-9-tetrahydrocannabinol and ethanol on intravenous cocaine disposition. J Pharm Pharmacol 36:552-554, 1984.

Zalcman, S.; Liskow, B.; Cadoret, R.; and Goodwin, D. Marijuana

and amphetamine: The question of interaction. Am J

Psychiatry 130:707-708, 1973.

AUTHOR

Leo E. Hollister, M.D.

Senior Medical Investigator

Veterans Administration

3901 Miranda Avenue

Palo Alto, California 94304

116
Alcohol and Marijuana: Concordance of Use by Men and Women

Jack H. Mendelson, M.D.; Nancy K. Mello, Ph.D.; and

Barbara W. Lex, Ph.D., M.P.H.

INTRODUCTION

There is abundant evidence that many American men and women smoke

marijuana. To what extent does marijuana use enhance risk for alcohol abuse and does alcohol use increase probability for marijuana

abuse? There is evidence that use of substances which have abuse

liability potential (for example, opiates, alcohol, and tobacco)

increases probability of concurrent use and abuse of other drugs.

For example, cigarette smoking increases when men drink alcohol

or self-administer opiates (Mello and Mendelson, this volume).

While it is reasonable to postulate that marijuana cigarette smoking may also contribute toward concurrent abuse of other substances, there are no empirical data available from controlled clinical

investigations which support this notion. The studies described

in this report were designed to examine the degree of covariance

between marijuana and alcohol use by men and women. We will first

describe our studies of alcohol and marijuana use by male subjects

who participated in controlled residential research ward investigat ions. Next, we will review studies of marijuana and alcohol

use by female subject volunteers assessed in a prospective, conmmitybased research paradigm.

MARIJUANA AND ALCOHOL USE BY MALES

This report describes the first attempt to examine the effects of

concurrent availability of marijuana and alcohol on drug use patterns under clinical research ward conditions (Mello et al. 1978).

This study was designed to explore patterns of polydrug use involving marijuana and alcohol. The following behavioral questions

were examined in a situation where subjects could work at a simple

operant task to acquire marijuana, alcohol, or money.

1. Does the simultaneous availability of marijuana and alcohol

change the use of either or both drugs in comparison to conditions

where marijuana or alcohol is available alone?

2. When both alcohol and marijuana are available, is there a tendency to use these separately or together?

NOTE: All figures and tables in this article and portions of the text

were originally published in Mello, N.K.; Mendelson, J.H.; Kuehnle,

J.C.; and Sellers, M.L. Human polydrug use: Marihuana and alcohol.

J Phamcol Exp Ther 207:922-935. Copyright 1978, William and Wilkins.

Reproduced by permission.

117
3. Is the temporal pattern of drug acquisition behavior different

under conditions of single or simultaneous availability of alcohol

and marijuana?

Since most of our information about nonopiate polydrug use has been

derived from survey data andfromself-report information (Benvenuto

et al. 1975; Bourne 1975; Carlin and Post 1971; Fisher and Brickman

1973; Grupp 1972; Smart and Liban 1980; Wechsler et al. 1980;

Johnston 1980 and 1981; Kandel 1980 and 1984; Hochhauser 1977;

Einstein et al. 1975; Welte and Barnes 1982), direct observations

of multiple drug use patterns and its behavioral consequences are

useful for distinguishing the actual from the anecdotal.

Volunteer subjects were recruited through advertisements in local

periodicals and newspapers. All subjects selected were in good

health and showed no evidence of psychiatric or medical abnormalities as determined by appropriate laboratory and clinical examinations. These included a complete physical examination, mental

status examination, chest x-ray, electrocardiogram, timed vital

capacity, and the following laboratory assessments: lactic dehydro

genase, serum glutamic oxaloacetic transaminase, bilirubin total,

total protein, albumin, globulin, agglutination ratio, alkaline

phosphatase, uric acid, blood urea nitrogen, creatinine, glucose,

total lipids, white and red blood counts, hemoglobin, hematocrit,

and differential.

Sixteen adult male volunteers who reported concurrent use of marijuana and alcohol were selected. Each subject was fully informed

about the nature and duration of each phase of the study and was

told he could withdraw at any time. Subjects were closely matched

for age (mean = 25; range, 21-29), years of alcohol use (mean =

6.60 ± S.E. 0.77), and years of marijuana use (mean = 7.5 ± S.E.

0.61). Subjects were matched as closely as possible with regard

to socioeconomic background and general intelligence and had an

average of 13.2 years of formal education (range, 11-16). Subject characteristics and self-reports of the frequency of alcohol

and marijuana use are summarized in table 1.

Subjects were studied in groups of 4 and lived on a hospital research ward for 34 days. The research ward contained 2 semiprivate

bedrooms, a nursing station, examining and testing rooms, kitchen

and lavatories, an operant equipment room, and a spacious, comfortable dayrooom with television, high fidelity equipment and other

recreational materials. Physicians, nursing personnel, and ward

assistants were present 24 hours each day.

Sequence of Procedures

The sequence of drug availability conditions is shown in table 2.

Each subject was used as his own control during a drug-free baseline of 5 days; a 5-day exposure to either marijuana or alcohol

only; a 5-day exposure to the opposite drug only; a 10-day period

when both marijuana and alcohol were simultaneously’available;

followed by a 5-day drug-free control period. Each 5-day exposure

to alcohol only or to marijuana only was followed by 1 drug-free

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day to reduce the possibility that withdrawal from one drug would

influence initial acquisition of the second drug. The alcohol or

marijuana only periods provided a single drug use baseline against

which to compare drug use patterns during concurrent alcohol and

marijuana availability. Eight subjects were given marijuana first

and 8 subjects were given alcohol first during the single drug

availability periods. An alternating sequence of only alcohol or

only marijuana access was intended to control for the possible influence of the order of single drug conditions on concurrent drug

use during the lo-day access period.

A single acute drug administration day (marijuana plus alcohol)

preceded the first 5-day single drug period and followed the 10-

day period when both alcohol and marijuana were available. These

acute drug administration days were included to examine the effects

of acute alcohol and marijuana administration on a series of biological and behavioral variables. Within a period of 11⁄2 hours, subjects smoked 3 marijuana cigarettes (1-g marijuana cigarette; 1.8-

2.3% tetrahydrocannabinol) and consumed a 43% alcohol solution in

a dose of 2.5 ml/kg in fruit juice which was sufficient to produce

a blood alcohol level between 100 and 120 mg/100dl.

Operant Behavioral Procedures

In order to earn purchase points for drugs or money, subjects

pressed the response button on a portable operant manipulandum.

Each response transmitted a discrete radiofrequency signal to the

programming and recording equipment. The manipulandum could be

easily held in one hand. It measured 4.5 x 9.4 x 1.6 cm and

weighed about 198g. Each manipulandum was color-coded and labeled

with the subject’s number to permit easy identification by the ward

staff and to discourage subjects from exchanging manipulanda.

Subjects worked for drugs or money on a second order fixed ratio

300, fixed interval 1 second schedule of reinforcement, an FR 300

(FI 1 sec:S). Only the first response after 1 sec elapsed was

recorded by the programming circuitry as an effective response.

Responses emitted at a faster rate had no programmed consequence.

This schedule of reinforcement was chosen to permit comparisons

with our previous studies of operant acquisition of marijuana

(Mendelson et al. 1976a).

Each purchase point required 300 effective responses on an FI 1

schedule. Thirty minutes of sustained performance resulted in 1800

effective responses or the accumulation of 6 purchase points. The

cost of 1 marijuana cigarette or 50 cents was 6 purchase points

(i.e., 30 min of work). This equation was made on the basis of prevailing prices for marijuana in the Boston area. The cost for 1

ounce (30 ml) of alcohol was 3 purchase points (i.e., 15 min of

work). The price differential between marijuana and alcohol was

established after pilot studies showed that subjects did not buy

as much alcohol as they usually drank at the price of 6 purchase

points (Babor et al. 1978).

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Subjects could work at the operant task whenever they wished as

long as it did not interfere with other ward procedures or data

collection routines. Subjects could only work at the operant task

within the general confines of the clinical research ward.

Available Drugs

Marijuana

Cigarettes which contained approximately 1g of marijuana were obtained from the National Institute on Drug Abuse (NIDA) in lot standard dosage form. Maximal standardization and equivalent dosage

“draw” characteristics of these cigarettes were ensured by machine

rolling. Each 1-g cigarette contained approximately 1.8 to 2.3%

tetrahydocannabinol (THC) as assayed by NIDA. Details of the Soxhlet and modified Lerner extraction procedures and the gas chromatographic assay procedure are available from NIDA. Content analysis

confirmed that the THC concentration of these cigarettes remains

stable over many months. Actual content analysis of the marijuana

indicated the following constituents (percent ± S.D.): cannabidiol =

0.18 ± 0.04%; 8-THC = 0.002 ± 0.002%; 9-THC = 2.06 ± 0.08%; cannabinol = 0.08 ± 0.12%.

Alcohol

Subjects could choose their preferred type of alcohol. Gin, vodka,

scotch, bourbon, beer, or wine and mixers were available. Prices

were established on the basis of the absolute alcohol ccmtent of

each beverage. One ounce (30 ml) of distilled spirits, one l2-

ounce (360 ml) can of beer, and one glass of fortified sweet wine

(2.5 ounces or 75 ml) cost 3 purchase points.

Reinforcement Choice

During the predrug baseline period, and the 2 drug-free control

days, subjects could work only for money. Money earned for operant work and for other performance and cooperation tasks was paid

upon completion of the study. During the two 5-day periods when

only marijuana or only alcohol were available, subjects could work

for the available drug or money. During the 10-day period when

both marijuana and alcohol were available, subjects could work for

marijuana, alcohol, or money.

Whenever a subject activated his operant instrument, he chose

whether to work for a drug or for money. When the subject pressed

the appropriate button on his control panel (i.e., money, alcohol,

or marijuana), this infonned the programning apparatus that points

earned should count towards the selected reinforcer. Once a subject chose to work for 1 reinforcer, he could not change to a second reinforcer until an interval of 5 min elapsed. This changeover delay contingency was included to prevent rapid alternation

between reinforcement conditions.

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Record of Earnings

A record of purchase points earned for money, alcohol, and marijuana was continuously available to each subject on his operant

control panel, located on one wall of the dayroom. When a drug purchase was made, the number of points required for that purchase was

automatically deducted from the subject’s total points earned for

that drug. Subjects were not allowed to purchase for another subject or to earn points using another subject’s manipulandum.

Control of Drug Use

All alcohol consumption and/or marijuana smoking had to be done at

the time of drug purchase, under observation of a staff member.

Subjects were not allowed to share drugs. Unused portions of

smoked marijuana cigarettes were collected by staff so that

“roaches” were not accumulated and smoked without staff knowledge.

Bach roach was weighed to determine the amount of marijuana actually used by each subject. Since studies were carried out on an inpatient clinical research ward, staff were able to ensure that subjects did not use drugs other than marijuana and alcohol.

Measurement of Effective Drug Levels

Blood levels of marijuana and its metabolites were not measured

directly,and effective dosages were inferred from the number and

amount of cigarettes smoked.

Alcohol purchase data were supplemented by blood alcohol level

measurements. A breathalyzer device was used in order to avoid

multiple venipunctures. It has been repeatedly shown that about

95% of all breathalyzer readings are equivalent to simultaneous

blood alcohol determinations within 10% (Dubowski 1970). Subjects

were required to rinse out their mouths and refrain from smoking,

eating, or drinking for 10 min before each breathalyzer measurement . Measurements were taken according to standard procedures.

Blood alcohol levels were measured at 8:30 a.m., 10:00 a.m., 2:00

p.m., 3:15 p.m., 8:00 p.m., 10:30 p.m., and every hour after 11:00

p.m. if subjects continued to drink. In addition, blood alcohol

levels were measured immediately before and 30 min after completion of the first and fourth drink of the day.

Medical and Neurological Assessments

Vital signs were taken 3 time each day and routine physical examinations were conducted periodically throughout the study. Upon conclusion of each drug availability condition, neurological examinations were conducted daily to ascertain the occurrence of withdrawal

signs and symptoms. A complete description of procedures used to

evaluate marijuana withdrawal appears in Mendelson et al. (1974).

A description of the alcohol withdrawal syndrome appears in Mello

and Mendelson (1977).

The pattern, rate, and duration ofoperantresponses for drug and

money acquisition were automatically recorded by the programming

121
circuitry on electromechanical counters; interresponse time counters; cumulative recorders; and running time meters. Data on individual patterns of drug use are presented graphically. T tests

were used to evaluate the statistical significance of individual

changes in mean alcohol and marijuana intake during periods of

single and combined drug availability. Group data were evaluated

with an analysis of variance.

RESULTS

Individual Drug Use Patterns by Men

There was considerable variability between individual subjects in

the amount of marijuana and alcohol consumed during each drug

availability period. Consequently, individual drug use patterns

are described before consideration of group trends. All subjects

used alcohol or marijuana during each 5-day single drug availability

period. Fifteen of the 16 subjects studied used both alcohol and

marijuana during the 10-day period of concurrent drug availability.

The single exception (S3-MA5) used only alcohol when both marijuana

and alcohol were available.

Analysis of the weight of marijuana cigarette roaches per subject

per day indicated approximately 98% of each 1-g cigarette was

smoked. Subjects used roach clips and consistently smoked as much

as possible of each marijuana cigarette. Subjects appeared to

drink all alcohol purchased, and the effective dose of alcohol, inferred from peak blood alcohol levels, was consistent with the

alcohol purchase pattern.

Average daily alcohol and marijuana use by individual subjects

during each drug availability period is shown in table 1. Subjects are grouped according to the volume of alcohol actually consumed during the alcohol only period, and are designated as heavy

(N=5) moderate (N=4) and light drinkers (N=7). Eleven subjects

were heavy marijuana users according to our previously established

criteria of 4 or more marijuana cigarettes per day (Mendelson et al.

1976a). The remaining 5 subjects could be classified as casual

users who smoked 3 or fewer cigarettes per day during the marijuana only period. During the single drug availability periods,

heavy drinking did not necessarily predict heavy smoking or the

converse. Heavy marijuana smokers accounted for 3 of the 5 heavy

drinkers, 3 of the 4 moderate drinkers, and 5 of the 7 light drinkers

(table 1, columns 2 + 5). No subject showed withdrawal signs or

symptoms after cessation of alcohol or marijuana use.

There was a marked lack of correspondence between actual marijuana

and alcohol consumption during the single drug availability periods and retrospective self-report data shown in table 1. Only 1

of the 5 heavy drinkers reported daily alcohol use and the others

reported no more than 2 to 4 beers and 2 ounces of distilled spirits per week. In fact, these subjects drank between 12 and 19

drinks per day (table 1, column 2). Although 6 subjects reported

using fewer than 20 marijuana cigarettes per month, 4 of those subjects smoked between 4 and 7.8 cigarettes per day (table 1, column

5). It is impossible to determine whether subjects do not remember

122
TABLE 1

or deliberately misinform to ensure selection for the study. However, an empirical classification based on actual drug use behavior

appears to be essential for analysis of these data.

Effects of Concurrent Marijuana and Alcohol Availability

Fourteen of the 16 subjects drank less alcohol during the period

of combined alcohol and marijuana availability, in comparison to

the 5-day alcohol only period (table 1, column 3). A reduction in

alcohol consumption by this number of subjects was significant

according to a nonparametric sign test (p<.01). Moreover, 7 subjects drank significantly less alcohol when marijuana was available, as evaluated by t tests (table 1, column 4).

Twelve of the 16 subjects smoked more marijuana during the period

of concurrent alcohol and marijuana availability in comparison to

the 5-day marijuana only period (table 1, column 6). An increase

in marijuana use by this number of subjects was significant according to a nonparametric sign test (p<.05). However only 2 subjects

smoked significantly more marijuana cigarettes (table 1, column 7).

Drug use was often quite variable within individuals over time.

Daily drug use and earning patterns for selected individual subjects are presented in figures 1, 2, and 3. The most common pattern observed, i.e., to increase marijuana use and decrease alcohol

use during the period of concurrent alcohol and marijuana availability, is shown in figure 1. This subject was a heavy marijuana

user who smoked an average of 7 cigarettes per day during the baseline period of marijuana availability. He was also a heavy drinker

and consumed an average of 14 drinks per day during the baseline

123
FIGURE 1

FIGURES 1, 2, and 3. Marijuana ad alcohol use and purchase points

for money and drugs earned over 34 consecutive days. The succeesive

conditions of drug availability are show at the top of each figure.

The first row show the numbsr of marijuana cigarettes smoked

each day. The dotted line and cross-hatched area (± S.E.) denotes

the average number of marijuana cigarettes smoked during the entire

5 or 10 day period. The second row shows the number of alcohol

drinks consumed each day (0) and the 24-h ntean and range of blood

alcohol levels observed each day The average number of drinks

consumed during the 5 or 10 day period is shown as a dotted line

and cross-hatched area (mean ± S.E.). The third row shows the number of purchase points earned for money , marijuana and

alcohol (0). Consecutive days of the study are shown on the abscissa. The single day (D) inmediately following and preceding the preand postdrug free baselines were days on which an acute combineddose of alcohol and marijuana was given. The single day following the 5-day period of marijuana and of alcohol availability

was a day on which no drugs were available.

124
FIGURE 2 See legend under figure 1

125
FIGURE 3 See legend under figure 1

126
period of alcohol availability. Peak blood alcohol levels ranged

from 50 to 160 mg/100 ml during the hours of maximum drinking.

When both alcohol and marijuana were available concurrently, marijuana smoking increased slightly to an average of 10 cigarettes per

day Alcohol consumption decreased significantly to an average of

4 drinks per day (p<.001) and peak blood alcohol levels never exceeded 75 mg/100 ml.

A decrease in both marijuana and alcohol use during the periods of

marijuana and alcohol availability is shown in figure 2. This subject was also a heavy drinker who consumed an average of 19 drinks

per day during the baseline period of alcohol availability. Peak

blood levels ranged between 100 and 280 mg/100 ml. This subject

also showed a significant decrease in alcohol consumption during

the period of concurrent alcohol and marijuana availability, despite a relatively low frequency of marijuana smoking (1.6-0.7

cigarettes per day).

Figure 3 shows data for 1 of the 2 subjects who increased both

marijuana and alcohol use during the period of concurrent alcohol

and marijuana availability. During the single drug baseline, this

subject was a moderate drinker and a heavy marijuana smoker. During the 10 days of concurrent alcohol + marijuana availability,

alcohol use increased slightly and marijuana smoking increased

significantly (p<.001) . This subject also smoked significantly

more marijuana during the final 5 days of the 10-day concurrent

drug access period than during the first 5 days as determined by

t tests (p<.05).

Group Trends in Concurrent Drug Use Patterns

Analysis of variance for repeated measures across days was done

separately for alcohol and marijuana use during the 10-day period

of concurrent availability. There were no significant linear

trends in alcohol consumption through time. Rather, alcohol consumption decreased when marijuana became available and remained

depressed in comparison to the alcohol-only baseline, throughout

the period of concurrent drug availability. In contrast, there

was a significant linear increase in marijuana smoking during

this 10-day period (p<.001). A trend analysis for the entire

15 days of marijuana availability also indicated a significant

progressive increase in marijuana smoking through time (p<.001).

The sequence of single drug conditions, alcohol, or marijuana

first, did not appear to determine drug use patterns during the

concurrent drug availability condition. There were no significant

differences between the 2 drug sequence groups in either alcohol

or marijuana consumption during the first 5 days of the concurrent

drug availability period. The 8 subjects exposed to marijuana

first also did not differ from the 8 subjects exposed to alcohol

first in baseline marijuana smoking. However, the alcohol-first

group drank significantly less alcohol than the alcohol-second

group during the alcohol only baseline (p<.001) as evaluated by

paired t tests.

127
In order to examine the temporal concordance of alcohol and marijuana use, the number of occasions that each subject used each drug

during consecutive 4-h blocks was tabulated. Group drug use patterns during the 10-day period of concurrent alcohol and marijuana

availability are compared with drug use patterns during the single

drug availability periods in figure 4.

During the period of concurrent availability, alcohol and marijuana

were usually used between noon and midnight (figure 4, row 1).

Although alcohol and marijuana were usually used together, there

were no instances of adverse reactions or other evidence of toxic

drug interaction. The temporal distribution of marijuana smoking

was similar during the marijuana only and concurrent drug availability periods (figure 4, rows 1 and 2). There were no significant

differences in the nunber of marijuana cigarettes smoked during

each 4-h time period as a function of the availability of alcohol.

A comparison of the amount of alcohol consumed during each 4-h period

revealed a significant decrease in drinking between 4:00 and 8:00

p.m. and between 8:00 p.m. and midnight when marijuana was concurrently available (p<.01). The changes in alcohol consumption during marijuana availability were not significant at any other 4-h

time period as evaluated by paired t tests.

Operant Work Patterns

Although only responses emitted at a rate of 1/sec counted toward

purchase points under the Fl 1 sec component of the schedule, all

subjects consistently responded at a faster rate. Usually response

rates were 2 or more per sec, and about 600 responses were emitted

for every 300 responses required. gates of 3 or more responses per

sec were not unusual. Subjects understood the schedule requirements

but preferred to respond at a comfortable rate.

The temporal pattern of operant work for purchase points for alcohol (0), marijuana and money by the entire group of subjects during each drug condition is shown in figure 4. Subjects

usually began operant work in the morning between 8:00 a.m. and

12:00 noon. Subjects earned the most points for money between noon

and midnight in both the drug-free and drug access conditions.

Operant work for alcohol and marijuana points was more equally distributed between the hours of 8:00 a.m. and 12:00 midnight in both

the single and concurrent drug availability periods. Comparison

of figures 4 and 5 indicate that the periods of maximum operant

work usually corresponded to periods of maximum drug use.

Despite the difference in price for alcohol and marijuana (3 vs.

6 purchase points), work patterns for each drug were similar during the alcohol or marijuana only periods. When both alcohol and

marijuana were available, subjects began to work for marijuana

earlier than for alcohol and earned consistently more marijuana

points through time. Purchase points earned for alcohol and marijuana were consistent with actual drug use. Subjects appeared to

earn about as many drug purchase points as were needed for daily

consumption. Subjects did not accumulate extra drug purchase

128
FIGURE 4 Amount of alcohol and marijuana cigarettes used as a

function of time of day, across drug availabitity conditions.

Numbers of drinks or marijuana cigarettes used (mean ± S. E. ) are

shown on the ordinate and consecutive 4-h periods are shown on

the abscissa. The top rot) shows the nwnber of alcohol drinks consumed (0) and marijuana cigarettes smoked during the 10-day

period of alcohol and marijuana availability. The second row

shows consumption of each drug during the 5-day period of single

drug availability.

129
FIGURE 5 Purchase points earned as a function of time of day

across drug availability conditions. Purchase points earned

(mean ± S. E. ) are shown on the ordinate and consecutive 4-h

periods are shown on the abscissa. The top row shows points

earned for money marijuana and alcohol (0) during

the 10-day period of concurrent alcohol and marijuana availability. The second row shows the purchase points earned for

money, alcohol, or marijuana during the two 5-day periods when

only alcohol or marijuana was available. The third row shows

points earned for money during the 5-day predrug baseline and

during the 5-day postdrug baseline.

130
points or plan ahead for a period of work-free drug use as was

previously observed in alcohol addicts (Mello and Mendelson 1972).

It is apparent that subjects earned more points for money than

for either drug during both the single and combined drug availability periods (figure 3, rows 1 and 2). Subjects earned the

most points for money during the drug-free baseline periods when

marijuana and alcohol were not available as alternate reinforcers

(figure 3, row 3). Throughout the course of the study, subjects

earned an average of $257.68 (± S.E. $21.60).

The average number of total points earned per day across each

successive baseline and drug access condition is summarized in

table 2. The total number of points earned by the entire group

tended to decrease through time as determined by a trend analysis (p<.025). Subjects earned fewer points during the 3 drug

availability conditions than during the predrug baseline, and

fewer still during the final drug-free baseline period. Differences between the pre- and postdrug baseline periods were significant as evaluated by matched pair t tests (p<.01).

Total points earned during the period when only alcohol was available and when alcohol and marijuana were available concurrently

did not differ significantly from the predrug baseline. However,

significantly fewer points were earned during the marijuana only

period than during the predrug baseline (p<.05).

The amount of operant work and total purchase points earned remained quite consistent across the 3 drug availability conditions.

There were no significant differences in total points earned during the alcohol, marijuana or concurrent drug availability periods.

Examination of total points earned as a function of alcohol consumption revealed differences in total earnings. Heavy drinkers

earned significantly fewer total points than light drinkers

(p<.05). Heavy drinkers also earned significantly less money

than light drinkers (p<.01). Light drinkers earned an average

of $8.92 per day or $303.45 for the study. Moderate drinkers

earned an average of $8.18 per day or $278.13 for the study.

Heavy drinkers earned an average of $5.21 per day or $177.25 for

the study.

Eleven of the 16 subjects were heavy marijuana users and 3 of

these were also heavy drinkers. However, if the effects of marijuana use are examined, independent of alcohol use, heavy marijuana users earned significantly more total points than light

users (p<.05). Heavy marijuana users also earned more money

than light smokers, but this difference was not significant.

Heavy marijuana users earned an average of $8.13 per day or

$276.42 per study, whereas light marijuana users earned an average of $6.37 per day or $216.58 per study.

Translation of average points earned per day on an FR 300 (FI 1

sec) schedule reveals that subjects worked between 8.6 and

131
TABLE 2

132
and 10.1 h per day during the pre- and postdrug baseline periods.

When drugs were available, subjects worked an average of 9.7 h

per day during alcohol only, 9.1 h per day during marijuana only

and 9.3 h per day when both drugs were available.

DISCUSSION (Males)

Alcohol and Marijuana Use in Men

When alcohol and marijuana were concurrently available, marijuana

appeared to affect alcohol use far more dramatically than alcohol

influenced marijuana use. A significant nwnber of subjects

reduced alcohol use in comparison to a period when only alcohol

was available. The magnitude of the decrease in alcohol consumption was also significant in half of those subjects. Although marijuana smoking increased when alcohol was also available, this change cannot be attributed solely to the effects of

alcohol. Marijuana smoking began to increase during the marijuana only period and this trend continued throughout the period

of marijuana and alcohol availability. The progressive increase

in marijuana smoking during the 10 days of concurrent alcohol and

marijuana access was comparable to that previously observed during 21 days of access to only marijuana (Mendelson et al. l976a,b).

These data may reflect an increased tolerance for marijuana. The

development of behavioral and physiological tolerance to marijuana during chronic use has been reported by several investigators (Mendelson et al. 1974; Babor et al. 1975; Jones and Benowitz

1976).

A marijuana-related suppression of drinking was unexpected and

the factors which account for this finding are unclear. There

were no progressive changes in alcohol consumption comparable to

changes in marijuana smoking over time. Rather, alcohol consumption decreased abruptly when marijuana became available, and remained suppressed in comparison to baseline throughout the period

of concurrent drug availability (cf. figure 1). Reanalysis of

drinking patterns in 18 control subjects from previous studies of

casual and heavy drinkers (Babor et al. 1978) also failed to reveal any significant linear trends in alcohol consumption through

time (J.H. Mendelson, unpublished observations). Therefore, it

is unlikely that the observed depression of alcohol consumption

reflects a time-related trend which is independent of marijuana

availability.

It could also be postulated that subjects drank less alcohol during marijuana use because the level of intoxication produced was

either equivalent to a higher dose of alcohol or was aversive.

It is generally acknowledged that the combined effects of alcohol

and marijuana are “additive” in the sense that performance impairments induced by the combined administration of both drugs are

greater than those observed with either drug alone (Burford et al.

1975; Franks et al. 1975; Manno et al. 1971; Smiley et al. 1975;

Smith 1976). Subjective aspects of marijuana intoxication have

also been reported to be enhanced by the concurrent use of alcohol

133
(Hollister 1976; Manno et al. 1971). However, alcohol consumption

decreased even in subjects who smoked only 1 to 3 marijuana cigarettes per day (cf. table 3), so this explanation is not compelling. The critical dose-response relationships between alcohol

and marijuana which influence drug use patterns remain to be

determined.

These subjects did not appear to be unusually intoxicated and did

not report that the combined effects of alcohol and marijuana

were aversive. We have seen no evidence for toxic interactions

between marijuana and alcohol in either the chronic self-administration or the acute dose studies in which 3 marijuana cigarettes

were smoked in combination with a 2.5 ml/kg dose of alcohol over

a 11⁄2-h period. Our findings are at variance with a report by

Sulkowski and Vachcm (1977) in which far lower doses of alcohol

(1 g/kg) and marijuana (1 cigarette containing 18 mg of 9-THC)

administered within a 1-h interval resulted in prolonged vomiting,

headaches, and severe intoxication in 4 of 7 healthy young men

with a history of alcohol and marijuana use. A lower dose of

alcohol (0.5 g/kg) did not result in comparable adverse effects.

Blood alcohol levels were not reported (Sulkowski and Vachon

1977).

These data indicate that the simultaneous availability of 2 recreational psychoactive drugs does not necessarily increase drug use,

as has been reported for alcohol and tobacco (Griffiths et al.

1976) Rather, the availability of marijuana modulated the use of

alcohol, even in heavy drinkers. Although marijuana has rarely

been considered a plausible addition to the armamentarium of drugs

arrayed against alcoholism, Rosenberg (1977) has proposed to use

marijuana to reinforce compliance with a disulfiram maintenance

program for alcoholics.

Operant Work Patterns by Men

Concurrent use of alcohol and marijuana did not change the amount

or temporal pattern of operant work in comparison to periods when

only alcohol. or only marijuana was available. Subjects continued

to work at the operant task during the periods of maximum drug use

in all 3 conditions of drug availability. Operant response rates

were consistently higher than required by an FI 1 sec schedule of

reinforcement. These data are consistent with our previous observations of sustained operant performance for marijuana and for

money during a 21-day period of marijuana availability (Mendelson

et al. 1976 a,b).

The sustained performance for drugs and money during intoxication

differs from that previously observed with alcoholic subjects

working at a simple operant task for alcohol or cigarettes. Alcoholic subjects alternated 2- to 3-day periods of work with 2- to

3-day periods of drinking even though periods of abstinent working

resulted in alcohol withdrawal signs and symptoms (Mello and

Mendelson 1972). Differences in operant task requirements do not

appear to account for observed differences in drug acquisition

patterns between alcohol addicts and multiple drug users. However,

134
the finding that heavy alcohol users earned fewer total points and

less money than light drinkers is consistent with our previous observations of alcohol-related decreases in operant work by alcohol addicts (Mello and Mendelson 1932).

The tendency to earn fewer total points as the study progressed is

consistent with our previous observations of both casual and heavy

marijuana smokers. The total number of points earned during the

postdrug baseline period were always fewer than during the predrug

baseline period (Mendelson et al. 1976a). Since there was no evidence of withdrawal signs and symptoms in these 16 polydrug users,

or in the 47 marijuana smokers studied previously (Mendelson et al.

1974, 1976a), this decrement may reflect boredom with the operant

task.

MARIJUANA AND ALCOHOL USE BY FEMALES

We are aware of no studies with women similar to those previously

described for human males. While such studies are entirely feasible

for women, a number of preliminary investigations are essential prerequisites before utilizing a residential research ward design analogous to that which has been described for studies with human males.

The major reason for the conduct of preliminary studies with women

is due to a possible influence of menstrual cycle function on marijuana use. Clinical studies have suggested that women may use alcohol or other psychoactive substances in greater or lesser amounts

as a consequence of menstrual cycle related phenomena (Belfer et al.

1971) . It has been postulated that women who experience premenstrual or menstrual related tension and dysphoria may be at higher

risk for using or abusing substances when they are symptomatic

(Podolsky 1963). Therefore, we have carried out a study to determine if alcohol and marijuana use vary as a function of menstrual

cycle phase. This study is, to the best of our knowledge? the

first major prospective investigation designed to ascertain the

frequency and amount of daily alcohol and marijuana use by women

during 3 consecutive menstrual cycle phases.

Thirty adult female volunteers between the ages of 21 and 36 (mean

age 26.4) were recuited via newspaper advertisements and provided

informed consent for participation in the study. All subjects were

selected following complete physical and mental status examination

and laboratory studies. No subject was pregnant, had past or current history of drug or alcohol abuse, or a recent history of amenorrhea or menstrual function disturbance. Findings reported in

this study were obtained within the context of an extensive multidisciplinary investigation of behavioral and biological concomitants

of marijuana use by women. Subjects were typically single women

with some college education and their experience with drugs other

than marijuana and alcohol was infrequent. Upon admission to the

study, subjects did not differ significantly on any background variables . Table 3 summarizes general background characteristics of

the subjects.

135
TABLE 3
Procedures

All subjects completed a detailed diary questionnaire each day.

The diary was designed to monitor use of marijuana, alcohol, tobacco

cigarettes, nonprescription drugs, recreational drugs (other than

marijuana), occurrence of unusual events, frequency of sexual activity, morning basal body temperature, menstrual cycle status, menstrual distress symptoms, and mood states each day during 3 consecutive menstrual cycles. Subjects began diary reporting on the

first day of menstruation following acceptance into the study and

continued to record daily responses for 3 consecutive menstrual

cycles. Subjects used an oral thermaneter to record morning basal

body temperature to the nearest tenth of a degree Fahrenheit each

morning before arising from bed. The remainder of diary data were

recorded each evening before retiring. Completed diaries were

mailed to the Harvard-McLean Alcohol and Drug Abuse Research Center

every day. This study reports patterns of marijuana and alcohol

consumption; mood state data and biological findings will be reported elsewhere.

During each 24-h period, subjects reported the total number of marijuana cigarettes (or other marijuana compounds) used and the 6 time

periods during the day (“early morning,” “late morning," “early

afternoon,” “late afternoon,” “early evening,” and “late evening”)

during which use occurred. Subjects also reported consumption

“all day,” which indicated that some amount of consumption had occurred during each of the 6 successive time periods but did not

necessarily signify continuous consumption throughout the day.

Subjects similarly noted the total nwnber and specific types of

alcoholic beverages consumed (beer, wine, or spirits or mixed

drinks) and times of alcohol use. The number of tobacco cigarettes

smoked and the time of their use was recorded. Subjects further

noted the quantity of other recreational or nonprescription drugs

used. They also reported onset and cessation of menses and occurrence of sexual activity. Occurrence of unusual events was recorded in response to the question: “Did anything unusually good or

bad happen?” during the daily reporting period.

Conclusions and Comments

Compliance of subjects for reporting daily diary events was excellent in this study. Less than 1.5% of over 2,700 questionnaires

were not completed. There was a highly statistically significant

correlation (p<.0001) between concordant use of marijuana cigarettes and alcoholic beverages during the 3 consecutive menstrual

cycles. In addition, subjects tended to consistently conswne alcohol, marijuana, marijuana and alcohol, or abstain from using either

substance in a similar manner across all 3 menstrual cycles.

The data obtained in this first prospective study designed to evaluate concordance of marijuana and alcohol use by women is in agreement with findings which have been obtained in previous investigations of relationships between alcohol and marijuana use (Fisher

and Brickman 1973; Smart and Fejer 1973; Wechsler and Rohman 1981).

137
However, studies of marijuana and alcohol use by women in controlled

research ward environments analogous to those described in the

initial portion of this paper for men have yet to be undertaken.

These studies should yield more precise information about acquisition and use patterns of marijuana and alcohol when both substances

are relatively freely available for women.

REFERENCES

Babor, T.F.; Mendelson, J.H.; Greenberg, I.; and Kuehnle, J.C.

Marihuana consumption and tolerance to physiological and subjective effects. Arch Gen Psychiatry 32:1548-1552, 1975.

Babor, T.F.; Mendelson, J.H.; Greenberg, I.; and Kuehnle, J.C.

Experimental analysis of the “happy hour”: Effects of purchase

price on alcohol consumption. Psychopharmacologia 58:35-41,

1978.

Belfer, M.L.; Shader, R.I.; Carroll, M.; and Hermatz, J.S. Alcoholism in women. Arch Gen Psychiatry 25:540-544, 1971.

Benvenuto, J.A.; Lau, J.; and Cohen, R. Patterns of nonopiate/

polydrug abuse : Findings of a national collaborative research

project. In: Problems of Drug Dependence Proceedings, 37th

Annual Scientific Meeting, Committee on Problems of Drug Dependence, Washington, D.C.: National Academy of Sciences-National

Research Council, 1975. pp. 234-254.

Boume, P.G. Polydrug abuse--Status report on the federal effort.

In: Senay, E. ; Shorty, V.; and Alksen, H., eds. Developments on

the Field of Drug Abuse. Nat’l Drug Abuse Conference. Cambridge

Schenkman Publishmg Co. 1975. pp. 197-207.

Burford, R.; French, I.W.; and LeBlanc, A.E. The combined effects

of alcohol and common psychoactive drugs. I. Studies on human

pursuit: tracking capability. In: Israelstam, S., and Lambert,

F., eds. Alcohol, Drugs and Traffic Safety. Toronto: Addiction Research Foundation, 1975. pp. 423-431

Carlin, A.S., and Post, R.D. Patterns of drug use among marihuana

smokers. J Amer Med Assoc 218:867-868, 1971.

Dubowski, K.M. Measurement of ethyl alcohol in breath. In:

F.J. Sunderman, and F.J. Sunderman, Jr., eds. Laboratory

Diagnosis of Diseases caused by Toxic Agents. St. Louis:

W.H. Green, Inc., 1970. pp. 316-342.

Einstein, R.; Hughes, I.E.; Hindmarch, I. Patterns of use of

alcohol, cannabis and tobacco in a student population. Br J

Addiction 70:145-150, 1975.

Fisher, G. and Brickman, H.R. Multiple drug use of marihuana

users. Dis. Nerv. Syst. 34:40-43, 1973.

138
Franks, H.M.; Starmer, G.A.; Chesher, G.B.; Jackson, D.M.:

Hensley, B.R.; and Hensley, W.J. The interaction of alcohol and

delta-9-tetrahydrocannabinol in man: Effects of psychomotor

skills related to driving. In: Israelstam, S. and Lambert, F.,

eds. Alcohol, Drugs and Traffic Safety. Toronto: Addiction

Research Foundation, 1975. pp. 461-466.

Griffiths, R.R.; Bigelow, G.E.; and Liebson, I. Facilitation of

human tobacco self-administration by ethanol: A behavioral

analysis. J Exp Anal Behav. 25:279-292, 1976.

Grupp, S.E. Multiple drug use in a sample of experienced marijuana smokers. Int. J. Addiction 7:481-491, 1972.

Hochhauser, M. Alcohol and marijuana consumption among undergraduate polydrug users. Am J Drug Alcohol Abuse 4:65-76, 1977.

Hollister, L.E. Interactions of delta-9-tetrahydrocannabinol with

other drugs. Ann NY Acad Sci 218:212-218, 1976.

Johnston, L.D. The Daily Marijuana User. Washington, D.C.:

National Alcohol and Drug Coalition, 1980.

Johnston, L.D. Frequent Marihuana Use: Correlates, Possible

Effects, and Reasons for Using and Quitting. Bethesda: American Council on Marijuana Conference, 1981.

Jones, R.T.,and Benowitz, N.L. The 30 day trip: Clinical studies

of cannabis tolerance and dependence. In: Braude, M.C., and

Szara, S., ed. Pharmacology of Marihuana. New York: Raven

Press, 1976. pp. 627-642.

Kandel, D.B. Drug and drinking behavior among youth. Ann Rev

Social 6:235-283, 1980.

Kandel, D.B. Marijuana users in young adulthood. Arch Gen

Psychiatry 41:200-209, 1984.

Manno, J.E.; Kiplinger, G.F.; Scholz, N.; Forney, R.B.; and

Haine, S.E. The influence of alcohol and marihuana on motor

and mental performance. Clin Pharmacol Ther 12:202-211, 1971.

Mello, N.K., and Mendelson, J.H. Drinking patterns during workcontingent and noncontingent alcohol acquisition. Psychosom.

Med 34:139-164, 1972.

Mello, N.K., and Mendelson, J.H. Clinical aspects of alcohol

dependence. In: Martin, W.R., ed. Handbook of Experimental

Pharmacology, vol. 45/I Drug Addiction I. Berlin: SpringerVerlag, 1977. pp. 613-666.

Mello, N.K.; Mendelson, J.H.; Kuehnle, J.C.; and Sellers, M.L.

Human Polydrug Use: Marihuana and Alcohol. J Pharmacol Exp

Ther 207:922-935, 1978.

139
Mendelson, J.H.; Kuehnle, J.C.; Greenberg, I.; and Mello, N.K.

Operant acquisition of marihuana in man. J Phannacol Exp Ther

198:42-53, 1976a.

Mendelson, J.H.; Kuehnle, J.C.; Greenberg, I.; and Mello, N.K.

The effects of marihuana on human operant behavior: Individual

data. In: Braude, M.C., and Szara, S., eds. The Pharmacology of

Marihuana, New York: Raven Press, 1976b. pp. 643-653.

Mendelson, J.H.; Rossi, S.M. and Meyer, R.E., eds. The Use of

Marihuana: A Psychological and Physiological Inquiry. New York:

Plenum Publishing Corp. 1974.

Podolsky, E. The woman alcoholic and premenstrual tension. J Am

Med Wom Assoc 18:816-818, 1963.

Rosenberg, C.M. The use of marihuana in the treatment of alcoholism.

In: Cohen, S. and Stillman, R.C., eds. The Therapeutic Potential

of Marihuana. New York: Plenum Medical Book Co. 1977 pp. 173-

182.

Smart, R.G.,and Fejer, D. Marihuana use among adults in Toronto.

Br J Addiction 68:117-128, 1973.

Smart, R.G. and Liban, C.B. Cannabis use and alcohol problems

among adults and students. Drug Alc Dep 6:141-147, 1980.

Smiley, A.; LeBlanc, A.E.; French, I.W.; and Burford, R. The combined effects of alcohol and common psychoactive drugs. II.

Field studies with an instrumented automobile. In: Israelstam,

S.,and Lambert, F., eds. Alcohol, Drugs and Traffic Safety

Toronto: Addiction Research Foundation, 1975. pp. 435-438

Smith, C. Interactions of drugs of abuse with alcohol. Ann NY

Acad Sci 218:384-392, 1976.

Sulkowski, A. and Vachon, L. Side effects of simultaneous alcohol

andmarihuanause. Amer J Psychiatry 134:691-692, 1977.

Wechsler, H.; McFadden, M.; and Rohman, M. Drinking and drug use

among college students in New England. J Am College Health 28:

275-279, 1980.

Wechsler, H., and Rohman, M. Extensive users of alcohol among

college students. J Stud Alc 42:149-l55, 1981.

Welte, J.W., and Barnes, G.M. The relationship between alcohol use

and other drug use among New York state college students. Drug

Alc Dep 9:191-199, 1982.

140
ACKNOWLEDGMENT

The studies described in this report were supported by grants

DA-00064, DA-00101, and DA-02905 from the National Institute on

Drug Abuse.

AUTHORS

Jack H. Mendelson, M.D.

Nancy K. Mello, Ph.D.

Barbara W. Lex, Ph.D., M.P.H.

Alcohol and Drug Abuse Research Center

Harvard Medical School--McLean Hospital

115 Mill Street

Belmont, Massachusetts 02178

141
Cocaine and Other Drug Interactions: Strategy Considerations

Reese T. Jones, M.D.

THE STATE OF THINGS

Although cocaine is by most criteria an old, almost ancient drug

(Van Dyke and Byck 1976), there is relatively little data on

cocaine and other drug interactions collected from controlled

laboratory experiments in humans. Many clinical observations,

reports, and speculations about drug interactions based on uncontrolled clinical observations date back virtually to the first use

of cocaine. Clinically significant cocaine interactions in humans

with epinephrine and most other sympthomimetics (National Institute on Drug Abuse 1977). with opiates, and with various therapeutic drugs-lithium (Cronsow and Flemenbaum 1981), antidepressants (Rowbotham et al. 1984), local anesthetics, and virtually

every class of psychoactive drug-indicate that interactions are

likely tut unpredictable and complicated as to mechanisms. A vast

amount of animal data illustrates numerous neurochemical, behavioral, and functional effects from cocaine’s interaction with other

drugs (Catravas and Waters 1981; Colpaert et al. 1978; Guinn

et al. 1980; Nigro and Enero 1981; Smith et al. 1981). However,

most of the published observations about humans consist of small

samples in not well-controlled case reports, in surveys of operating room fatalities, in self-reports from drug users, etc.

(National Institute on Drug Abuse 1977).

WHY STUDY COCAINE AND OTHER DRUG INTERACTIONS?

Information about drug interactions can be important when planning

therapeutic approaches. Host of the data supporting or refuting

current drug therapies for treating cocaine dependence (e.g.,

lithium or tricyclic antidepressants) come from patient reports and

clinical observations under conditions where the dose of cocaine,

the frequency of dose, the variety of adulterants that may have

been added, and the variation in route of administration are completely uncontrolled and, in the usual clinical situation, uncontrollable.

142
Cocaine users, like most drug users, use a variety of other drugs

often concurrently with cocaine or before and after. Alcohol,

opiates, sedatives, anxiolytics, stimulants, cannabis, tobacco,

and coffee are commonly used along with the cocaine. In addition

to the drugs ingested along with the cocaine, cocaine users are

exposed to a variety of adulterants commonly added to illicit

cocaine Cunningham et al. 1984). Besides the usual lidocaine,

lactose, or mannitol, a variety of local anesthetics are likely to

be present.

I will discuss some reasons why cocaine and other drug interaction studies are so seldom done and speculate about considerations leading to useful strategies for controlled human laboratory studies or more controlled clinical studies with cocaine.

The emphasis will be on considerations for human studies, even

though there is a large and growing body of literature from animal

studies with cocaine attempting to use drug interaction data to

get at mechanisms for various effects (e.g., Knapp and Mandell

1972; Nigro and Enero 1981). This valuable literature will only

be mentioned in passing where it has some specific relevance to a

point being made about human psychopharmacologic studies with

cocaine.

Cocaine pharmacokinetics, particularly relationships between

routes of administration and relationships between kinetics and

various physiological and psychological effects, have been studied

in our laboratories for the past few years. In the course of

these studies, we have had an opportunity to measure, in a limited

way, drug interactions that may have clinical relevance, even

though they don’t provide much information on mechanisms of cocaine

effects. A better understanding of mechanisms, of course, is one

of the ideal outcomes of a drug interaction study. Our experience

will illustrate potential research strategy problems and suggest a

few useful considerations to partially overcome them.

WHAT NEEDS TO BE CONSIDERED IN THE PERFECT STUDY

In Adler’s chapter (this volume) on critical factors in studying

drug interactions, a number of basic pharmacokinetic and pharmacodynamic principles are reviewed, Research with cocaine nicely

illustrates some of the problems in fully meeting those principles

in human studies. Table 1 lists a few relevant methodological

issues.

For example, a crucial and primary consideration is the ability of

cocaine to reach its sites of action; in the brain so far as the

user is concerned, but elsewhere in the body as well. The intense

vasoconstriction produced by cocaine (Brodsky and Goldwyn 1977) at

any but the weakest concentrations probably limits the rate of its

uptake with any route of administration except, perhaps. when

smoked. Thus, the time and magnitude of peak effects, duration,

and, perhaps, metabolites are altered as dose/drug concentration

increases.

143
TABLE 1 

Some Methodologic Problems in Cocaine

and Other Drug Interaction Studies

1.

2.

3.

4.

5.

6.

7.

8.

9.

Intense vaso (venous and arterial) constriction limits

cocaine uptake.

Metabolism not well understood:

a) Both blood (pseudocholinesterase) and hepatic

metabolism;

b) Possible active metabolites--benzolecgonine and

norcocaine.

Brief duration of action and relatively rapid clearance.

Rapid tolerance to many effects after a few repeated doses.

Marked species differences in metabolism and effects;

genetic differences within species.

Why routes of administration in common use.

Extremely wide range of doses commonly used.

Extremely varied pattern of use, both frequency and other

concurrent drug use.

Adulterants very comnon in illicit cocaine.

After intravenous (iv.) administration in distal forearm or hand

veins, we find lower peak blood cocaine levels and decreased area

under the time-concentration curves, presumably because of venoconstriction in smaller veins, and a relatively longer sojourn in

the venous return from the arm of injection Slower uptake and

distribution leads to longer exposure to pseudocholinssterase

metabolic activity when the cocaine is injected in small diameter,

more peripheral veins This characteristic not only affects

determination of time course of drug action, but pharmacokinetic

measures as well. Surprisingly, we find no greater variability

with nasal administration than with i.v., and less variability in

peak levels and area under the time-concentration curve with oral

administration The variability does not appear to be due to the

assay (Jacob et al. 1984). Presumably, the vasoconstrictive properties of cocaine alter and generally delay absorption even more

so in small animals where peripheral veins are smaller or where,

after intraperitoneal administration, absorption may be erratic.

Blood or tissue levels of the drug are seldom measured in animal

studies, so the degree of variability is unknown

144
WHAT IS PROPER TIME FOR TESTING BEHAVIORAL OR PSYCHOLOGICAL

EFFECTS?

The time course of most cocaine effects is relatively short when

compared to a number of drugs (e.g., opiates, alcohol, some sedative hypnotics) with which one might want to study the interaction. The great discrepancy in duration of effects poses design

problems when selecting the most appropriate time of testing,

particularly with pyschological or cognitive tests that do not

lend themselves to frequently repeated administration

Under usual laboratory conditions, the well-described euphoria or

stimulated state associated with cocaine is frequently, in fact

most often, followed by a sleepy, lethargic, and slightly depressed

state lasting for some hours. The problems associated with studying the brief euphoria are different than the problems of studying

interactions at the later state, though it’s the euphoria most

people are interested in Usually, because of rapidly acquired

tolerance with repeated testing (Fischman and Schuster 1981;

Teeters et al. 1963), it is difficult to study both phases of drug

effects in the same experiment

COCAINE METABOLISM

The metabolism of cocaine is not well understood for example,

most published human studies often involve only a small number of

subjects (Ambre et al. 1983: Fish and Wilson 1969; Holmstedt

et al. 1979; Inaba et al. 1978; Kogan et al. 1977; Javaid et al

1983; Stewart et al. 1979). Because of two major routes of metabolism-one involving pseudocholinesterase in blood and other

tissue, the other by hepatic microsomal enzyme systems-one might

expect relatively great variability in cocaine pharmacokinetics.

Thus, data from small samples of subjects can be misleading. The

question of active metabolites in humans has not been well explored.

There are some suggestions from animal studies that benzoylecgonine

(which is present in extremely high levels in cocaine users) and,

more likely, norcocaine have biological activity (Hawks et al.

1975). Levels and activity of other metabolites and shifts in

metabolic pathways when route, frequency of administration, and

other concurrent drug use are varied is not well worked out in

animals or in humans. Particularly in metabolic differences, the

limited evidence is for great species differences in cocaine

kinetics with good evidence for hepatotoxic metabolites in some

strains of mouse, for example; yet, very little evidence exists

for either the presence of such metabolites or hepatotoxicity in

humans or, for that matter, in other genetic strains of mice

(Freeman and Harbison 1981; Kloss et al. 1984).

DOSE EFFECT FUNCTIONS

Because of the very wide dose range used under illicit conditions,

a proper clinically relevant and full dose-response curve is difficult to achieve with cocaine when research subject safety and

selection characteristics are kept in mind as they should be

(Barnett et al. 1981). Probably because of acquired tolerance,

145
the frequent cocaine user outside of the laboratory can achieve

astronomical blood levels of cocaine and its metabolites with only

minimal cardiovascular/respiratory or behavioral toxicity. Such

levels cannot be safely reproduced in the laboratory under single

dose conditions where subjects have been relatively drug-free just

prior to testing.

The most realistic conditions for studying cocaine and other drug

interactions are after repeated doses of both drugs of interest

The research design issues associated with acute single doses are

complex. The even more complex issues and potential research

design criticisms of multiple dose subchronic cocaine studies are

such that it appears difficult to convince peer reviewers that

such studies are worthy of funding. For example, a relatively

straightforward chronic cocaine administration project by our

group has been reviewed four times by seperate peer review groups,

revised each time, and still is judged to be too expensive and not

important enough to support (three approvals, one disapproval).

Thus, studies of full dose-response curves and of subchronic drug

administration are usually either done in animals, with the resulting questions about species differences, or they are done under

quite unnatural and nonrealistic ways in humans, usually with a

limited dose range.

ENVIRONMENT AND TESTING SCHEDULES

Although there is no reason to think that there should not be

circadian or, possibly, metaholic differences in cocaine effects,

most laboratories cannot take this easily into account Our

laboratory is typical in that it usually starts experiments at 8

o’clock in the morning. Many subjects comment that, in their

experience, that’s a rather unnatural time to be taking cocaine

and that 8 p.m. or 1 a.m. would be more appropriate.

Environment is another important variable if behavior and psychological/cognitive consequences are of major interest (Post et al.

1981). With volunteer subjects drawn from the San Francisco area

subject pool, it’s extremely unusual for someone to take cocaine

alone with no other users present As with many, if not all,

psychoactive drugs, one might expect quite a different sequence

of effects depending on dose and setting interactions, with interpersonal aspects of the setting being most important.

ROUTE OF ADMINISTRATION

Route of drug administration is a particularly knotty problem with

cocaine, or at least one that makes for complicated designs when

both the demands of controlled scientific studies and relevance to

the real world are considered. Cocaine is taken by a substantial

number of users via virtually every possible route--i,v., oral, and

nasal, and by smoking, chewing, and, occasionally, other applications, mainly to mucous membranes (Van Dyke and Byck 1976). Proper

quantitative dosimetry studies, particularly by the smoking route

and by the buccal or chewing route (if swallowing is to be prevented), are particularly methodologically demanding. Despite our

146
laboratory’s experience with quantitative tobacco smoking and marijuana smoking studies, we find that the usual style of smoking

cocaine, the rapidity of onset, and the intensity of effects defy

proper measures of dose actually ingested.

I have already commented on the surprisingly similar and great

variability in peak blood levels and AUC when oral is compared to

nasal and i.v. routes (Van Dyke et al. 1978; Wilkinson et al.

1980). Bioavailability is similar when cocaine is taken orally or

nasally (about 30% to 35%). Oral administration offers many advantages for drug interaction studies, yet it would raise questions

Of relevance in the minds of many. There is only a slight advantage to the nasal route in initial uptake of cocaine, at least as

judged by venous blood levels. Perhaps, brain uptake is more

rapid by the nasal route, but we have no evidence for this as

judged by time of onset of various physiologic and subjective

effects. Without radioactive-labeled cocaine and suitable instruments, brain uptake cannot be as easily measured in humans as in

animals (Misra et al. 1975).

The balance between scientific precision and relevance to the real

world of drug use is a tricky one to judge, particularly when

planning drug interaction studies. Rapidly acquired tolerance and

the likelihood of cross-tolerance to other drugs on certain (but

not all) systems complicates any design involving repeated administration of a drug or involving frequent and infrequent users in

a drug interaction study. Most laboratories investigating cocaine

in humans find markedly diminished cocaine-induced physiologic and

psychological effects after relatively few doses (Fischman and

Schuster 1981). In contrast, in a repeated dose paradigm lasting

more than a few days, the appearance of other and new signs and

symptoms should be expected; for example, irritability, perceptual

acuity increases, and, perhaps, paranoid and delusional thinking

(Lesko et al. 1982; Post and Cbntel 1981). These later signs and

symptoms represent cocaine effects which are just as important and

real (but usually not studied) as the more commonly considered

acute or early drug effects To properly study tolerance, crosstolerance, and drug interactions with these later developing

cocaine effects in humans, relatively expensive experiments done

in rarely available test laboratories equipped for chronic human

studies are necessary. The history of Federal support for chronic

administration studies suggests that the peer review process is

not particularly sympathetic to the need for such studies with

cocaine, even though most individuals would agree that data associated with chronic administration is at least as important as

acute, single dose administration studies.

MECHANISM OF ACTION

The ideal and, to some scientists, the major justification for

drug interaction studies is to try to clarify mechanims of one

or the other of the drugs involved. All indications from animal

studies are that neurotransmitter systems affected by cocaine are

multiple, interacting, possibly species dependent, dose dependent,

and frequency-of-dose dependent Simple explanations of mechanisms

147
will not soon be forthcoming. Thus, if those who control research

funds believe that understanding mechanisms is a major criterion

or justification of interaction studies in humans, one might

assume fewer such studies will be undertaken.

ROUTE OF COCAINE ADMINISTRATION: AN EXAMPLE OF ITS SIGNIFICANCE

Route of administration has important consequences. For example,

we recently reported (Rowbotham et al. 1984) that a single 100 mg

dose of trazodone diminished a number of the physiologic and

subjective effects of a subsequent oral 2 mg/kg dose of cocaine.

Cocaine-induced increases in blood pressure and pupil size and

decrease in skin temperature were diminished by the trazodone

pretreatment. Trazodone did not alter cocaine-induced changes in

plasma epinephrine or norepinephrine levels and did not markedly

alter the subjective effects of the cocaine.

We repeated the exact protocol with eight subjects but used an

i.v. dose of cocaine, 0.2 mg/kg, which produced similar blood

levels to the oral cocaine dose. By neither the oral nor the i.v.

route was there a significant change in cocaine metabolism. As is

evident in table 2, there were no significant interactions between

the trazodone and the i.v. cocaine. Possible explanations include:

TABLE 2

Trazodone/Cocaine Interactions

Oral vs. Intravenous cocaine

Effect Cocaine cocaine

(Maximum Change Ran 2mg/kg Oral 0.2 mg/kg I.V.

Precocaine Levels)

Heart Rate (BPM)

Placebo

Trazodone

Systolic BP (mmHg)

Placebo

Trazodone

Skin Temperature (‘C)

Placebo

Trazodone

Pupil Size (mm)

Placebo

Trazodone

Subjective High (0-100)

Placebo

Trazodone

+27.1

+18.6

+26.5

+12.2

-7.3

-3.7

+1.02 0 .0001 +1.3 0.31

+0.80 +1.3

P P

0.14 +21.0 0.08

+17.0

0.01 +18.3 0.55

+2l.0

0 .0001 -4.2 0.05

-2.4

38

37

NS 33 0.24

29

Conclusion : No Change after 0.2 mg/kg i.v.

148
a more rapid bolus of i.v. cocaine is less likely to be blocked in

the central nervous system (CNS) than is the more gradual rise in

CNS cocaine levels after oral cocaine, The slower absorption of

cocaine orally might allow a different CNS pattern of metabolism

than that reflected in the peripheral blood, A rapid change in

CNS drug levels or function after i.v. doses might trigger off

mechanisms which cannot be blocked or altered by the trazodone.

The point is that there are route differences. Possibly, nasal

administration would be different from either i.v. or oral.

In the trazodone-cocaine interaction study, our group, as often is

the case in human studies, picked doses of drugs which we thought

were in a reasonable range in terms of clinical use, and in some

intermediate range of dose effect function. It would have been

better to use two doses or three doses of both trazodone and

cocaine. The single trazodone dose compared to placebo took 16

test sessions with innumerable blood samples and measures. The

additional complexity from multiple doses was not practical given

the money available for the study. A more serious limitation when

generalizing to clinical situations is that only a single administration of trazodone was used, which would not be the case for

most regular drug users

It would nave been more clinically relevant to examine not only

the single administration, but the events 2 or 3 weeks after

chronic trazodone and/or cocaine administration Such a study in

outpatient volunteers would not be possible and probably would be

viewed by the current research program peer review system as prohibitively expensive with hospitalized volunteers. Hence, the

alternatives are either subchronic animal drug interaction studies

or none.

TABLE 3

Caffeine-Cocaine Interactions

(Cocaine 2 mg/kg p.o.)

Peak Effects

Post/Pre Cocaine

Heart Rate

(BPM)

Caffeine

600 mg p.o. Placebo

Before cocaine Before Cocaine P

42.2 28.2 0.01

Systolic BP 33.4 22.3 0.05

(mmHg)

Skin Temperature

(‘C)

-7.8 -6.3 0.10

Pupil Size 1.3 0.99 0.06

(mm)

Subjective High 48.6 36.2 0.01

(O-100)

149
WHAT DRUG USE TO CONTROL? WHAT ABOUT COFFEE AND TOBACCO

A practical consideration facing any clinical investigator doing

drug interaction studies is the question of what should be done

about commonly used hugs like tobacco (nicotine) and coffee

(caffeine). It is unusall for a frequent cocaine user not to use

tobacco and coffee. It is even more common for other populations

of dependent individuals, for example, heroin- or alcohol-dependent persons, to use those drugs frequently and at high dose

levels. when setting up protocols, the question always arises,

should such people be allowedtocontinue their tobacco use

through the period of the experiment? Should they be deprived of

tobacco for 12 hours prior to the experiment? Similar questions

arise about coffee.

In some of our earliest pilot studies with cocaine before we were

set up to measure blood levels and pharmacokinetic parameters, we

measured caffeine-cocaine interactions. Table 3 lists a number of

effects of a modest oral dose of cocaine, given an hour after a

substantial dose of caffeine or after a caffeine placebo. Ten

subjects were studied in a crossover design

The table shows peak effects after the cocaine as compared to precocaine/postcaffeine levels, with heart rate, blood pressure, skin

temperature, pupil size, and subjective effects all being enhanced

by the caffeine pretreatment.

FIGURE 1

Percent Heart Rate Increase After Oral Cocaine:

Tobacco Smokers vs. Nonsmokers

150
Whether coffee drinkers, deprived or not, are different in cocaine

responses or drug interaction studies, of course, depends partially

on the importance of the multiple metabolic pathways of cocaine.

Since the significant metabolism of cocaine occurs in both blood

and liver (Foldes 1978; Yamamoto et al. 1953), the likely predictions as contrasted with dealing with a drug that has primarily

hepatic metabolism are more uncertain Whether a particular

research study deprives coffee drinkers of their caffeine or not

is probably less important than reporting just how this issue was

handled so that future investigators can take account of it.

The problem of what to do with tobacco smokers is also relevant.

Because both cocaine and nicotine have similar effects on the

noradrenergic system, one might expect some degree of interaction,

probably cross-tolerance. In fact, when a small group of nonsmokers was contrasted to a group of smokers who regularly used at

least one pack a day of cigarettes, there was some evidence of

cocaine/nicotine cross-tolerance on heart rate (figure 1).

The plots of smoothed functions in figure 1 compare percent increase in heart rate over precocaine levels with plasma levels of

cocaine on both the ascending and descending limbs of plasma

cocaine levels If there were no difference in cocaine effects at

a given plasma level on the ascending and ascending blood level

functions, there would be a relatively straight and narrow line.

Instead, the large, whale-shaped or balloon-shaped pattern in the

figure suggests that a certain degree of acute cocaine tolerance

develops even over the few hours after the single oral dose of

cocaine represented in that figure. Also, the peak increase in

heart rate at any given blood level was substantially greater for

nonsmokers than for smokers, suggesting cross-tolerance. Clearance and t-1/2 did not vary in the two groups.

When subjects receive prolonged (2 to 3 hours) infusions of cocaine

and are allowed to smoke tobacco cigarettes, we have, on each

occasion, measured less cardiovascular change blood pressure and

heart rate) from the tobacco cigarette smoked later in the cocaine

infusion than prior to it or at the beginning, again indicating

that as tolerance develops to the cocaine, there is cross-tolerance

with the tobacoo. Certainly, the study of interactions between

tobacco and cocaine are worthy and important in their own right

But, even if other drug interactions with cocaine are the primary

interest, both nicotine and caffeine consumption need to be taken

into account.

REFRENCES

Ambre, J.; Fischman, H.; and Rue, T.I. Urinary excretion of

ecgonine methylester, a major metabolite of cocaine. Clin Pharmacol Ther 33:2l3, 1983.

Barnett, G.; Hawks, R; and Resnick, R. Cocaine pharmacokinetics

in humans. J Ethnopharmacol 3(2):353-366, 1981.

Brodsky, J.R, and Goldwyn, R.M. Hemodynamic effects of intranasal and intravenous cocaine. N Engl J Med 296(17):1008, 1977.

151
Catravas, J.D., and Waters, I.W. Acute cocaine intoxication in

the conscious dog - Studies on the mechanism of lethality. J

Pharmacol Exp Ther 217(2):350-356, 1981.

Colpaert, F.C.; Niemegeers, C.J.E.; and Janssen, P.AJ. Neuroleptic interference with cocaine cue - Internal stimulus control

of behavior and psychosis. Psychopharmacology 58 (3):247-255,

1978.

Cronsow, AJ., and Flemenbaum, A Antagonism of cocaine highs by

lithium. In: Schecter, AJ., ed. Drug Dependence and Alcoholism Vol. I. New York: Plenum Publishing Corporation, 1981.

pp. 1139-1141.

Cunnigham, E.E.; Venuto R.C.; and Zielezny, M.A. Adulterants in

heroin/Cocaine: Implications concerning heroin-associated nephropathy. Drug Alcohol Depend 7:19-22, 1984.

Fischman, M.W., and Schuster, CR Acute tolerance to cocaine in

humans. In: Harris, L.S., ed. Problems of Drug Dependence,

1980: Proceedings of the 42nd Annual Meeting, The Committee on

Problems of Drug Dependence, Inc. National Institute on Drug

Abuse Research Monograph 34. DHEW Pub. No. (ADM) 81-1058.

Washington, D.C.: Supt. of Docs., U.S. Govt. Print. Off., 1981.

pp. 241-242.

Fish, F., and Wilson, W.D.C. Excretion of cocaine and its metabo1ites in man. J Pharmacol 21:1355-1385, 1969.

Foldes, F.F. Enzymes in anesthesiology. In: Foldes, F.F., ed.

 Enzymes in Anesthesiology. New York: Springer-Verlag, 1978.

pp. 91-168.

Freeman, R.W., and Barbison, R.D. Hepatic periportal necrosis

induced by chronic administration of cocaine. Biochem Pharmacol

30(7):777-783, 1981.

Guinn, M.M. Bedford, J.A.; and Wilson, M.C. Antagonism of intravenous cocaine lethality in nonhuman primates. Clin Toxicol

16(4):499-508, 1980.

Hawks, R.L.; Kopin, I.J.; Colburn, R.W.; and Thoa, N.B. Norcocaine: A pharmacologically active metabolite of cocaine found

in brain Life Sci 15:2l89-2195, 1975.

Holmstedt, B; Lindgren, J.E.; Rivier, L.; and Plowman, T. Cocaine

in blood of coca chewers. J Ethnopharmacol 1:69-78, 1979.

Inaba, T.; Stewart, AJ.; and Kalow, W. Metabolism of cocaine in

man. Clin Pharmacol Ther 23:547-552, 1978.

Jacob, P., III; Elias-Baker, B.A; Jones, R.T.; and Benowitz, N.L.

Determination of cocaine in plasma by automated gas chromatography. J Chromatogr 306:173-181, 1984.

Javaid, J.I.; Musa, N.M.; Fischman, M.W.; Schuster, CR; and

Davis, J.W. Kinetics of cocaine in humans after intravenous and

intranasal administration Biopharm Drug Dispos 4:9-18, 1983.

Kloss, M.w.; Rosen, G.W.; and Rauckman, E.J. Cocaine-mediated

hepatotoxicity: A critical review. Biochem pharmacol 33:169-

173, 1984.

Knapp, S., and Mandell, AJ. Narcotic drugs: effect on the

serotonin biosynthetic systems of the brain, Science 177:1209-

1211,1972.

Kogan, M.J.; Verebey, K.G.; dePace, A.C.; Resnick, R.R.; and

Mule’, S.J. Quantitative determination of benzoylecgonine and

cocaine in human biofluids by gas liquid chromatography. Anal

Chem 49:1965-1969, 1977.

152
Lesko, L.M.; Fischman, M.W.; Javaid, J.I.; and Davis, J.H. Iatrogenous cocaine psychosis. N Engl J Med 307:1153, 1982.

Misra, A.L.; Nayak, P.K.; Bloch, R.; and Mule’, S.J. Estimation

and disposition of [3H] benzoylecgonine and pharmacological

activity of some cocaine metabolites J Pharm Pharmacol 27:784-

786, 1975.

National Institute on Drug Abuse. Cocaine: 1977. Petermen, R.C,

and Stillman, R.C, eds. National Institute on Drug Abuse

Research Monograph 13. DHEW Pub. No. (ADM) 77-741. Washington,

D.C. Supt. of Docs., U.S. Govt. Print. Off., 1977.

Nigro D., and Enero, M.A. Cocaine facilitates the effects of

alpha-agonists and antagonists on adrenergic neurotransmission.

Gen Pharmacol 12(4):255-259, 1981.

Post, RM., and Cbntel, RR Cocaine-induced behavioral sensitization: A model for recurrent manic illness. In: Perris, C.;

Struwe, G.; and Jansson, B., eds. Biological Psychiatry 1981

vol. 5. Amsterdam: Elsevier/North-Holland Biomedical Press,

1981. pp. 746-749.

Post, R.R.; Lockfeld, A.; Sguillace, K.M.; and Contel, N.R. Drugenvironment interaction: Context dependency of cocaine-induced

behavioral sensitization, Life Sci 28(7):755-760, 1981.

Rowbotham, M.C.; Jones, R.T.; Benowitz, N.L.; and Jacob, P., III.

Trazcdone-oral cocaine interactionas. Arch Gen Psychiatry

41:895-899, 1984.

Smith, A.C.; Freeman, R.W.; and Harbison, RD. Ethanol enhancement of cocaine-induced hepatotoxicity. Biochem Pharmacol

30(5):453-458, 1981.

Stewart, D.J.; Inaba, T.; Lucassen, M.; and Kalow, W. Cocaine

metabolism: Cocaine and norcocaine hydrolysis by liver and

serum esterases. Clin Pharmacol Ther 25:464-468, 1979.

Teeters, W.R.; Koppanyi, T.; and Cowan, F.P. Cocaine tachyphylaxis. Life Sci 7:509-518, 1963.

Van Dyke, C., and Byck, R. Cocaine 1884-1974. In: Ellinwood,

E.H., and Kilbey, M., eds. Cocaine and Other Stimulants. New

York: Plenum Press, 1976. pp. 1-30.

Van Dyke, C.; Jatlow, P.; Barash, P.G.; and Byck, R. Oral

cocaine: Plasma concentrations and central effects. Science

100:211-213 1978

Wilkinson, P.; Van Dyke, C.; Jatlow, P.; Barash, P.; and Byck, R.

Intranasal and oral cocaine kinetics. Clin Pharmacol Ther

27(3):386-394, 1980.

Yamamoto, I.; Mikauni, R.; and Kurogochi, Y. The enzymatic breakdown of cocaine by the rabbit liver. Jpn J Pharmacol 3:39-49,

1953

AUTHOR

Reese T. Jones, M.D.

Drug Dependence Research Center

Langley Porter Psychiatric Institute

University of California, San Rancisco

San Rancisco, California 94143

153
Cigarette Smoking: Interactions

With Alcohol, Opiates, and

Marijuana

Nancy K. Mello, Ph.D., and Jack H. Mendelson, M.D.

A number of drugs from diverse pharmacological classes have been

shown to influence tobacco smoking. Alcohol, opiates, and certain

stimulants increase cigarette smoking, whereas marijuana has no

apparent effect on tobacco use. This chapter will describe some

clinical studies of the interactions of alcohol, opiates, and marijuana with cigarette smoking. Some factors that may influence cigarette and drug interactions will be discussed.

ALCOHOL EFFECTS ON CIGARETTE SMOKING

An association between smoking and drinking was first observed in

studies of alcoholics. Anecdotal observations that alcoholics

tended to be heavy smokers were conflrmed by survey data and selfreport studies (Dreher and Fraser 1967; Maletzky and Klotter 1974;

Walton 1972). Our clinical studies of alcoholic men working for

alcohol on a simple operant task (FR 1000) further strengthened the

impression of concordant alcohol consumption and cigarette smoking

(Mello and Mendelson 1972). Figure 1 shows the covariation between

cigarette smoking and alcohol consumption by a typical subject

(Subject E.S.) during 62 days of concurrent work-contingent cigarette and alcohol acquisition. There was an alternation between

periods of drinking and working for alcohol and cigarettes at the

operant task, even though these self-imposed abstinence periods

were often associated with alcohol withdrawal signs. Cigarette

acquisition declined from baseline during the alcohol availability

phase but smoking was temporally correlated with episodes of drinking. Periods of abstinent working for alcohol and for cigarettes

also covaried (Mello and Hendelson 1972).

The powerful association between cigarette smoking and drinking in

alcoholic men was demonstrated under clinical laboratory conditions

by Griffiths and coworkers (Griffiths et al. 1976). Smoking behavior of alcoholic men was measured during 6-hour sessions when

alcohol or a nonalcohol vehicle control solution was available.

Subjects could consume a total of 12 drinks (90 ml each containing

154
FIGURE 1

Earning and spending pattern of an alcoholic man working for

cigarettes during a 10-day baseline period; for both cigarettes and

alcohol during a 62-day period of alcohol availability; and for

cigarettes during a 10-day withdrawal period. Tokens to purchase

alcohol and cigarettes were acquired by working at a simple operant

task on an FR 1000. Tokens earned for alcohol were not interchangeable with tokens earned for cigarettes. Patterns of earning

(closed circles, shaded area) and spending (open circles, dotted

line) for cigarettes and for alcohol are shown in the top two rows.

Subjects wre allowed to work for alcohol tokens during the last 24

hours of the 10-day baseline period end these tokens could be spent

after 8:00 a.m. on the first day of drinking. Tokens earned that

day are shown at the arrow as first day earnings. Average daily

blood alcohol levels are shown in the third row and the occurrence

of measurable withdrawal signs during drinking are indicated as

asterisks. The type and duration of withdrawal signs and symptoms

after the subject stopped drinking are shown at the right of figure

1. Reprinted with permission from Mello and Mendelson 1972.

Copyright 1972, Elsevier Science Publishing Company, Inc.

155
11.14 g of ethanol) at a rate of one drink every 30 minutes. Subjects could smoke their preferred brand but all cigarettes were

cut to an equal length. Cigarettes were color coded for each

subject and dlscarded butts were collected and weighed. Access to

alcohol consistently induced an increase in cigarette smoking in

comparison to control sessions when no alcohol was available.

Measures of the number of cigarette puffs and butt weight indicated

that the effect was not due to smoking less of each cigarette

(Griffiths et al. 1976).

These findings in alcoholic men have been confirmed (Henningfield

et al. 1984) and extended to social drinkers (Mello et al. 1980a).

Tobacco cigarettes were freely available upon request in a study

designed to examine the covariance between alcohol consumption and

marijuana smoking (Mello et al. 1978), described more fully elsewhere in this volume (Mendelson et al.). Figure 2 illustrates the

dramatic covariance between dally alcohol consumption and tobacco

smoking in a male social drinker. This subject (3-MA5) rejected

marijuana except on one occasion and used only alcohol during the

5-day period of alcohol availability and the 10-day period when

alcohol and marijuana were concurrently available. This subject

was a heavy smoker who averaged over a pack a day during the predrug baseline period and the 5 days of marijuana availability.

When alcohol became available, he consumed 22 drinks to achieve a

peak blood alcohol level of 160 mg/100 dl, and almost doubled his

cigarette consumption. On the second day of alcohol availability,

he drank very little and cigarette consumption returned to baseline

levels. When alcohol consumption increased on the third day,

tobacco smoking also increased. This pattern of parallel

fluctuations in smoking and drinking continued throughout the 15

days of alcohol availability.

Pretreatment with a single dose of alcohol did not consistently influence smoking in social drinkers. Henningf ield and coworkers

(1984) compared the effects of a single alcohol dose (0.125 to 1.8

g/kg) on cigarette smoking in social drinkers and alcoholic men.

Alcohol given 30 minutes before smoking sessions had inconsistent

effects on smoking by social drinkers, Two subjects increased cigarette smoking, two subjects decreased cigarette smoking and smoking was unchanged in a fifth subject. Differences in the usual

pattern of alcohol consumption may have contributed to these inconsistent results in social drinkers. Subjects who increased smoking

after alcohol reported consuming about one drink each day. The

subject whose smoking did not change reported drinking three to

four times per week whereas the subjects whose smoking decreased

after alcohol usually drank once a month. However, in the alcoholic group, alcohol pretreatment was associated with significant

increases in cigarette smoking (Henningfield et al. 1984).

In summary, an association between cigarette smoking and alcohol

consmption by alcoholics has been shown consistently In clinical

studies with a variety of techniques (Mello and Mendelson 1971;

Griffiths et al. 1976; Henningfield et al. 1983, 1984). When

social drinkers were given unrestricted access to alcohol over

156
FIGURE 2

Tobacco, alcohol, and marijuana use by a male subject over 30 days.

The successive drug free and drug availability conditions are shown

at the top of the figure. The first row shows the number of alcohol drinks (closed circle) and marijuana cigarettes (open square)

used each day. The dotted line denotes the average number of

alcohol drinks consumed or marijuana cigarettes smoked during the 5-

or 10-day period of drug availability. The number of tobacco

cigarettes smoked each day in each condition is shown in the second

row. Reprinted with permission from Mello et al. 1980a.

Copyright 1980, The C. V. Mosby Company.

157
several days, there also was a striking covariation between alcohol

consumption and cigarette smoking (Mello et al. 1978).

The mechanisms by which alcohol influences tobacco use are unknown.

In a series of behavioral studies, Griffiths and coworkers (1976)

presented data showing that increased cigarette smoking by alcoholic men was not adjunctively maintained by patterns of drinking

and socializing. Moreover, increased cigarette smoking could not

be accounted for by alterations in number of puffs per cigarette or

amount of cigarettes smoked. Since increased smoklng occurred both

during ethanol sessions and during the remainder of ethanol days

(but not vehicle control days), sequence effects or instructions

probably did not influence the phenomenon (Griffiths et al. 1976).

In social drinkers, daily alcohol consumption (Mello et al. 1980a)

or admfnfstratfon of a single dose of alcohol to individuals reporting a history of daily drinking (Henningfield et al. 1984)

appears to be an important determinant of the facilitatory effect of

alcohol on cigarette smoking. It is interesting that a history of

sedative abuse appeared necessary for acute pentobarbital administratfon to increase cigarette smoking in alcoholic men

(Henningfield et al. 1983).

Metabolic hypotheses include the possfbflity that alcohol may

change the rate of nicotine metabolism. Since chronic alcohol consumption stimulates the activity of mfcrosomal enzymes which regulate drug metabolism (Lieber and DeCarli 1968; Rubin et al. 1970),

and induction of hepatic mitochondrial activity is associated with

an increased rate of nicotine metabolism (Russell 1976), smoking

during drinking could be less reinforcing. However, an alcoholrelated change in nicotine metabolism could account for only one

dimension of the reinforcing properties of cigarettes since many

complex interacting factors, including expectancy, affect drug use.

OPIATE EFFECTS ON CIGARETTE SMOKING

The opiate agonists, heroin and methadone, also have been shown to

be associated with increased cigarette smoking by opiate addicts

during intoxication (Mello et al. 1980b; Chait and Griffiths 1984).

It appears that heroin addicts, like alcoholics, are often heavy

cigarette smokers (Gritz 1980). There has been recent speculation

that endogenous opioids may mediate the reinforcing effects of cigarette smoking (Karras and Kane 1980; Chernick 1983; Tobin et al.

1983; Pomerleau et al. 1983). Studies of the interactions between

cigarette smoking, opioid agonists, opioid antagonists, and an

opioid mixed agonist-antagonist, buprenorphine, will be reviewed in

this section.

Opioid Agonist Interactions with Cigarette Smoking

The effects of heroin self-administration on cigarette smoking were

studied in 12 adult male volunteers with a history of heroin addiction and cigarette smoking (Mello et al. 1980b). Each volunteer

gave informed consent for participation in studies of the effects

of new pharmacotherapies on heroin self-administration in a

158
clinical research ward setting . Naltrexone (50 mg/day p.o.), a

long-acting narcotic antagonist, was compared with naltrexone placebo under double-blind conditions. Nine of the subjects in the

nal trexone study were cigarette smokers. Three other subjects were

involved in studies of buprenorphine, a new partial-agonist antagonist (cf. Jaslnski et al. 1978; Lewis et al. 1983). The effects

of buprenorphfne and buprenorphine placebo on heroin self-administration also were compared under double-blind conditions. Naltrexone and buprenorphine each significantly suppressed heroin selfadministration by heroin addicts under research ward conditions

(Mello et al. 1981, 1982; Mello and Mendelson 1980; Meyer and Mirin

1979). The effects of heroin and methadone on cigarette smoking by

eight subjects assigned to the placebo naltrexone condition, one

subject assigned to active naltrexone, and three subjects assigned

in the placebo buprenorphine conditions are described below.

Buprenorphine- and naltrexone-maintained subjects were closely

matched in age (X = 27 years, range 22 to31) and social and educational background. Subjects were fully informed about the nature

and duration of each phase of the study, and vere free to withdraw

at any time. All subjects were in good health and showed no evidence of psychiatric or medical abnormalities as determined by appropriate clinical and laboratory examinations. Subjects lived on

a clinical research ward throughout the study. Each subject served

as his own control during a drug-free baseline and each successive

drug condition. Consequently, it was possible to compare the effect of heroin, methadone, and control conditions on cigarette

smoking by each subject.

Naltrexone subjects were studied over 34 consecutive days in groups

of four. The sequence of conditions was as follows: A 9-day drugfree baseline, a 10-day period of heroin availability during which

naltrexone or naltrexone placebo was given; a 5-day detoxification

phase during which methadone was given to subjects on naltrexone

placebo; 7 drug-free days, followed by 3 days of inpatient maintenance on nal trexone. Subjects given naltrexone during heroin

availability continued to receive naltrexone throughout the study.

The eight cigarette smokers assigned to naltrexone placebo conditions each self-administered heroin. The one cigarette smoker who

was given active naltrexone did not self-administer heroin.

Buprenorphine subjects were studied over 39 consecutive days. The

sequence of conditions for the buprenorhine subjects was as follows : A 5-day drug-free baseline; a 14-day period during which

buprenorphine (or buprenorphfne placebo) vas administered in ascending doses; 10 days of maintenance with buprenorphine (8 mg/

day) or placebo when heroin was also available; 5 days during which

methadone was given to subjects on buprenorphine placebo and buprenorphine in decreasing doses was continued for subjects given

active buprenorphine; 3 drug-free days were followed by 2 days on

nal trexone prior to discharge. Three cigarette smokers were

assigned to the buprenorphine placebo condition and each

self-administered heroin.

159
Tobacco and Heroin Acquisition. Tobacco cigarettes were available

during all phases of the study. Each subject purchased his preferred brand of cigarettes. However, the nursing staff retained

the cigarettes and distributed them upon request. The time of each

cigarette request was recorded. Cigarette puff volume and puff

duration were not measured In these ambulatory subjects (cf. Jarvik

et al. 1977).

Subjects worked for heroin or for money at a simple operant task on

a second order fixed ratio 300, fixed interval 1-second schedule of

reinforcement FR 300 (FI 1-sec:S). Only the first response after a

1-second interval had elapsed counted as an effective response and

300 responses were required to earn one purchase point. Approximately 90 minutes of sustained performance on an PI 1-sec schedule

earned 18 purchase points which could be used to buy one dose of

heroin or exchanged for $1.50 in cash upon completion of the study.

Subjects could work for money throughout the study but points

earned for money could not be exchanged for points for heroin.

When both heroin and money mere available, subjects chose to work

for one or the other each time they activated the operant

instrument. Details of the operant apparatus and procedures are

presented elsewhere (Hello et al. 1981).

Subjects self-administered a fixed dose of heroin intravenously

under the supervision of a physician. Subjects could omit any

heroin injection but could not receive doses larger or smaller than

specified in the protocol. Medical considerations preclude unlimited access to heroin. Naltrexone subjects could take a maximum

of 40 mg of heroin each day in four 10 mg doses (at 8:00 a.m., 1:00

p.m., 8:00 p.m., and 2:00 a.m.). Buprenorphine subjects could take

a maximum of 21 mg of heroin in three doses during the first 5 days

of buprenorphine (or placebo) maintenance (7 mg at 9:00 a.m., 5:00

p.m., and 1:00 a.m.); and a maximum of 41.5 mg of heroin (13.5 mg

per dose at the same times) during the second 5 days of buprenorphine maintenance.

Effect of Drug Conditions on Smoking. Ten of the twelve heroin

addicts were heavy smokers. One subject smoked 1 1/2 packs of

cigarettes or more per day and nine subjects smoked one pack or

more a day during the drug-free baseline period. The other two

subjects smoked less than a pack per day. The naltrexone placebo

group smoked significantly (p< .01) more cigarettes per day during

the 10 days of heroin self-administration than during the drug-free

baseline as evaluated by t tests. Average cigarette smoking during

the period of heroin availability was also significantly greater

than during methadone detoxification (p< .001). There were no

significant differences in cigarette smoking during the drug-free

baseline periods which preceded and followed heroin and methadone.

Cigarette smoking during methadone detoxification also did not

differ from the drug-free baseline periods. Consequently, the

significant increase in cigarette smoking appeared to be specific

to the heroin condition.

160
The one subject given naltrexone, rather than naltrexone placebo,

did not self-administer heroin since naltrexone effectively blocked

heroin’s subjective and physiological effects. Although this subject lived on the research ward with other subjects who were taking

heroin and smoking more, there were no significant differences in

his smoking behavior across conditions. This subject smoked an

average of 25 cigarettes per day over the entire study. This suggests that heroin, rather than any nonspecific social interaction

factors, accounted for the increase in cigarette smoking seen in

the nal trexone placebo group.

Illustrative data for six individual naltrexone placebo subjects

are shown in figures 3 and 4. Heavy smokers who smoked an average

of over one pack of cigarettes per day during baseline are shown in

figure 3. Moderate smokers who smoked a pack a day or less during

baseline are shown in figure 4.

Each of the heavy smokers smoked significantly more cigarettes

during heroin use than during the drug-free baseline (p< .01).

 Two subjects (4-HG-1, 1-HT-1) took all or almost all of the four

heroin doses available each day. Therefore, variations in cigarette smoking observed during heroin self-administration were not

related to the specific daily dose of heroin. Although subject

2-HA-1 tended to smoke least on the days that he took the fewest

heroin doses, the fluctuations in cigarette smoking did not appear

to vary consistently with the heroin dose. An increase in smoking

to three packs a day during the final naltrexone period immediately

before discharge was unusual and has not been observed in other

heroin addicts studied under these conditions. Since the subject

who was maintained on active naltrexone throughout the study did

not show comparable elevations in smoking, it is difficult to

attribute this finding to the effect of naltrexone per se.

Among the moderate smokers shown in figure 4, the subject (3-HT-1)

who smoked least during baseline (an average of 15 cigarettes per

day), increased smoking most dramatically when heroin became available, to an average of 32 cigarettes per day (p< .001). This subject took all the available doses of heroin. Subject 1-HG-1 smoked

an average of 20 cigarettes per day during baseline. During the

period of heroin availability, he took most of the heroin available

and increased smoking by an average of seven cigarettes per day (p

< .01). Subject 4-HA-2 smoked an average of four cigarettes more

per day during the period of heroin availabillty (p< .05) and also

took most of the heroin available.

The number of cigarettes smoked per day across the successive conditions of the study by the three buprenorphine placebo subjects

is shown In figure 5. Since there were only three cigarette

smokers in the buprenorphine placebo group, changes in cigarette

smoking as a function of drug conditions were analyzed for Individual subjects with t tests. The introduction of buprenorphine

placebo did not result in significant changes in cigarette smoking

by any subject in comparison with baseline. Heroin self-administration was associated with an abrupt increase in cigarette smoking by

161
FIGURE 3

Cigarette smoking across successive drug conditions. Cigarette

smoking over 34 consecutive days is shown for three individuals who

were heavy tobacco smokers. Each subject was given naltrexone

placebo durlng heroin availability. The successive drug conditions

(drug free baseline, heroin plus naltrexone placebo, methadone

detoxification, drug free baseline, and naltrexone availability)

are shown across the top of the figure. Consecutive days in each

condition are shown on the abscissa. The number of heroin doses

taken each day by each subject is shown at the top of each row.

The number of cigarettes smoked each day is shown on the ordinate.

Reprinted with permission from Mello et al. 1980b.

Copyright 1980, Springer-Verlag, Heidelberg.

162
FIGURE 4

Cigarette smoking across successive drug conditions. Cigarette

smoking over 34 consecutive days is shown for three individuals who

were moderate tobacco smokers. Each subject was given naltrexone

placebo during heroin availability. The successive drug conditions

(drug free baseline, heroin plus naltrexone placebo, methadone

detoxification, drug-free baseline, and naltrexone availability)

are shown across the top of the figure. Consecutive days in each

condition are shown on the abscissa. The number of heroin doses

taken each day by each subject is shown at the top of each row.

The number of cigarettes smoked each day is shown on the ordinate.

Reprinted with permission from Mello et al. 1980b.

Copyright 1980, Springer-Verlag, Heidelberg.

163
FIGURE 5

Cigarette smoking across successive drug conditions. Cigarette

smoking by three individual subjects is shown over 39 consecutive

days. Each subject was given buprenorphine placebo prior to and

during heroin availability. The succcssive drug conditions (drugfree baseline, buprenorphine placebo, heroin plus buprenorphine

placebo, and methadone detoxification) are shown at the top of the

figure. Consecutive days in each condition are shown on the

abscissa. The number of heroin injections taken each day by each

subject is shown at the top of each row. The number of cigarettes

smoked each day are shown on the ordinate. Missing data are indicated by an asterisk. The day on which each subject left the study

is indicated by a star. Reprinted with permission from Mello et

al. 1980b. Copyright 1980, Springer-Verlag, Heidelberg.

all subjects. Each subject smoked more cigarettes on the average

during heroin self-administration than during the drug-free baseline or during buprenorphine placebo. However, only one subject

164
(1-HB-1) smoked significantly more during heroin use than during

the immediately preceding buprenorphine placebo conditions (p <

.001). Two subjects (1-HB-1, 2-HB-1) smoked significantly more

cigarettes (p< .01) during the period of heroin self-administration than during the subsequent methadone detoxification period.

The other subject (4-HB-1) left the study before the methadone

detoxification period.

Temporal Patterns of Cigarette Smoking as a Function of Heroin Use.

In an effort to determine if the significant increases in cigarette

smoking by the naltrexone placebo subjects during heroin availability were generalized increases or were temporally associated

with each heroin injection, two types of analyses were done. The

number of cigarettes smoked before and after each heroin injection

was compared and the temporal distribution of cigarette smoking

during bastline, heroin, and methadone conditions was examined.

The number of cigarettes smoked during the hour preceding and the

hour following each heroin injection was tabulated and the differences in cigarette smoking before and after heroin were evaluated

for individual subjects. An analysis of variance showed that the

time of day of the heroin injection did not result in significant

differences in cigarette smoking. Therefore, it was possible to

pool all pre- and post-heroin injection cigarette smoking data for

each subject for matched t test analysis.

Five of the eight subjects smoked significantly more cigarettes

during the hour immediately following heroin injection than during

the hour preceding heroin self-administration (p< .01 to .001).

The other three subjects smoked fewer cigarettes immediately

following a heroin injection and this decrease in smoking was

significant for two subjects (p< .05).

The average number of cigarettes smoked by the naltrexone placebo

group during consecutive 6-hour periods was tabulated for each of

three conditions: drug-free baseline, heroin availability, and

methadone detoxification. The temporal distribution of group

cigarette smoking during each of these conditions is shown in

figure 6. There were no significant differences in number of cigarettes smoked as a function of the drug condition during the morning to early afternoon (8:00 a.m. to 2:00 p.m.) and during the afternoon to early evening (2:00 p.m. to 8:00 p.m.) periods. However, significantly more cigarettes (p< .01) were smoked in the

evening and at night (8:00 p.m. to 2:00 a.m. and 2:00 a.m. to 8:00

a.m.) during heroin use than during baseline. Subjects smoked

significantly more cigarettes in the morning (p< .05) and at night

(p< .01) during heroin self-administration than during methadone

detoxification. Since heroin was given once every 6 hours (at 8:00

a.m., 1:00 p.m., 8:00 p.m., and 2:00 a.m.), this shift in the

temporal distribution of smoking could reflect the fact that subjects were awake longer and therefore smoked more. However,

165
FIGURE 6

Cigarettes smoked as a function of time of day. The number of

cigarettes smoked ( + S.E.) are shown on the ordinate and

consecutive 6-hour periods are shown on the abscissa. The top row

shows the number of cigarettes smoked by the naltrexone placebo

group (N=8) during the drug-free baseline (open circle), the 10

days of heroin availability (closed square), and the methadone

detoxification period (open triangle). The second row shows the

number of cigarettes smoked by the buprenorphine placebo subjects

(N=3) during the drug-free baseline (open circle), heroin

availability (closed square), and methadone detoxification (open

triangle). Reprinted with permission from Mello et al. 1980b.

Copyright 1980, Springer-Verlag, Heidelberg.

166
comparison of hours slept during baseline, heroin self-administration, and methadone detoxification revealed no significant differences by t test analysis. Consequently, there were equivalent

waking hours available for smoking during each condition.

The temporal distribution of smoking by the buprenorphine placebo

group was also analyzed as a function of consecutive 6-hour periods

during the drug-free baseline, buprenorphine placebo, and heroin

availability conditions. These data are shown in the lower panel

of figure 6. Heroin injections occurred three times a day, at 9:00

a.m., 5:00 p.m., and 1:00 a.m. Although more cigarettes were

smoked during heroin use in the evening and at night than during

the drug-free baseline, these differences were not statistically

significant. There were also no significant differences in hours

of sleep during baseline and heroin self-administration. However,

these subjects did sleep significantly more during methadone detoxification than during heroin self-administration (p< .05).

Intercigarette Interval Analysis. In order to determine if there

were marked changes in the overall rate of cigarette smoking as a

function of heroin self-administration, the distribution of intervals between successive cigarette requests was examined for three

representative placebo naltrexone subjects. The number of cigarette requests occurring at intervals of less than 15 minutes, 16

to 30 minutes, 31 to 45 minutes, and so on were tabulated for the

drug-free baseline condition and for the period of heroin selfadministration for individual subjects. The percent of the total

number of cigarettes smoked during each condition at each intercigarette interval was calculated. The distributions of intersmoking intervals are shown in figure 7.

Subject 3-HT-1 increased cigarette smoking during heroin selfadministration by an average of 17 cigarettes per day or 113% (cf.

figure 3). The peak intercigarette Interval distribution fell

between 16 and 30 minutes during both baseline and heroin conditions. However, during baseline, he smoked 19% more of the total

number of cigarettes at intervals of 46 to 60 minutes, 61 to 75

minutes, and 76 to 90 minutes than during heroin self-administration. When heroin became available, this subject smoked more

cigarettes more frequently, i.e., 72% of all cigarettes were smoked

at intervals between zero and 45 minutes. During baseline, he only

smoked 50% of his total cigarettes at this rate and 6% of the

cigarettes were smoked at intervals of 6 or more hours.

Subject 1-HA-1 smoked an average of 10 more cigarettes per day

during heroin self-admlnistration than during baseline and averaged

45 cigarettes per day (cf. figure 3). The peak and form of the

distribution of intercigarette intervals during baseline and heroin

conditions were very similar. However, he smoked 12% more cigarettes at 0- to 30-minute intervals during heroin self-administration than during baseline.

167
FIGURE 7

Distribution of intercigarette intervals during baseline and heroin

use. The interval between successive cigarette requests is shown

on the abscissa. The percent of the total cigarettes smoked during

the 9-day baseline period and 10 days of heroin availability is

shown on the ordinate. Intersmoking interval data are presented

for three naltrexone placebo subjects who were moderate or heavy

smokers. Reprinted with permission from Mello et al. 1980b.

Copyright 1980, Springer-Verlag, Heidelberg.

168
Subject 4-HG-1 also smoked an average of 10 more cigarettes per day

during heroin self-administration than during baseline and averaged

39 cigarettes per day during that period (cf. figure 3). However,

there was no appreciable change in the distribution of intercigarette intervals, except that he smoked fewer cigarettes at

intervals of 3 to 6 hours during heroin self-administration than

during baseline.

These distributions of intercigarette intervals suggest that the

increased smoking during heroin self-administration reflects a more

rapid rate of smoking, rather than smoking more cigarettes at long

intervals of 3 hours or more. The consistency of the peak of the

distribution of intersmoking intervals within and across subjects

and across conditions indicates that these moderate to heavy

smokers usually smoke a cigarette every 15 or 30 minutes (Mello et

al. 1980b).

Methadone Effects on Cigarette Smoking. These observations that

intravenous heroin self-administration is associated with increased

cigarette smoking have recently been extended to oral administration of another opioid agonist, methadone (Chait and Griffiths

1984). Five male volunteers with a history of opiate drug abuse,

and maintained in a methadone maintenance treatment program, were

studied. Dally 120-minute sessions were held five times each week.

Ninety minutes before the smoking session, subjects were given the

pretreatment drug (methadone, dextromethorphan, or placebo) and

then allowed to smoke ad libitum for 60 minutes. A 30-minute

period of cigarette deprivation preceded each smoking session.

These subjects were maintained on 40 to 60 mg of oral methadone.

The methadone pretreatment dose was 0.5, 1.0, or 2.0 times each

individual subject’s maintenance methadone dose (Chait and

Griffiths 1984).

Figure 8 shows that methadone pretreatment resulted in dose dependent increases in cigarette smoking in four of the five subjects studied. Group data shows that the mean number of cigarettes

smoked during the two-hour session increased twofold after the high

dose methadone in comparison to placebo. Pretreatment with placebo

or dextromethorphan had no consistent appreciable effect on

cigarette smoking. A number of other smoking related measures also

increased with increasing methadone doses. Subjects changed their

smoking behavior to increase their smoke intake, i.e., both the

number and duration of puffs per session increased at higher methadone doses. Moreover, the total time spent puffing during the

session increased 2.8-fold after the high dose of methadone in

comparison to placebo. Correlated increases in expired air CO

levels indicated that subjects were actually inhaling more smoke

during the high methadone dose smoking sessions. Similarly, there

was a decrease in the latency to the first puff of the session as

well as the mean intercigarette interval with increasing methadone

doses (Chait and Griffiths 1984).

these findings that methadone pretreatment can produce substantial

increases in cigarette smoking are concordant with previous

169
FIGURE 8

The number of cigarettes smoked per session as a function of drug

pretreatment for individual subjects and for the group (bottom

right panel). Bars show ±1 S.E.; P, placebo; D, dextromethorphan;

M, methadone. Reprinted with permission from Chait and Griffiths,

1984. Copyright 1984, American Society for Pharmacology and

Experimental Therapeutics.

170
observations of the effects of methadone detoxification. As the

daily methadone maintenance dose was decreased, the number of

cigarettes per day also gradually decreased (Bigelow et al. 1981).

One possible implication of the association between opioid agonist

administration and increased cigarette smoking is that endogenous

opioids may contribute to the reinforcing effects of cigarette

smoking . Pomerleau and coworkers (1983) have reported that after

cigarette smoking, increased plasma nicotine levels were significantly correlated with increased beta-endorphin levels, with no

change in ACTH levels. These findings suggest the importance of

examining the effects of opioid antagonists on cigarette smoking.

Opiate Antagonist Effects on Cigarette Smoking

Unfortunately, studies of the effects of opioid antagonists on

cigarette smoking have yielded inconsistent and contradictory findings. Nal trexone, a long acting opioid antagonist, did not change

cigarette smoking in one heroin addict subject (Mello et al.

1980b). The naltrexone-maintained (50 mg/day p.o.) subject smoked

an average of 25 cigarettes per day during a 9-day drug free baseline and a 10-day period of naltrexone maintenance (Mello et al.)

The effects of administration of a short acting opioid antagonist,

naloxone (10 mg s.c.), on cigarette smoking was studied in three

men and four women under ordinary working conditions (Karras and

Kane 1980). Subjects were offered a cigarette every 30 minutes for

3 hours following naloxone administration and asked to report their

desire for a cigarette, their mood, and their satisfaction with the

cigarette (if smoked). Subjects were allowed to smoke a cigarette

only for 2 minutes and were requested to abstain from cigarettes

except during the smoking trials. The experimenters recorded the

number of puffs taken and weighed the butts of the cigarettes.

Subjects took 32% fewer puffs and smoked 30% less often after

naloxone administration than after placebo administration. The

amount of each cigarette smoked, inferred from the weight of the

remaining cigarette butt, was also reduced by 33%. Karras and Kane

(1980) interpreted these data as consistent with the hypothesis

that beta-endorphin is released during smoking and may be partially

responsible for smoking maintenance.

These findings were not replicated under laboratory conditions in

subjects given a range of naloxone doses (0.625, 0.25, 1.0, and 4.0

mg/kg i.m.) when a number of smoking related measures including

expired carbon monoxide levels were also examined (R.R. Grifflths,

Personal Communication, 1984). Naloxone or placebo was administered 60 minutes before the 120-minute smoking sessions in seven

normal subjects. Each subject was used as his own control in three

treatment conditions with at least 48 hours between smoking

sessions. Naloxone administration had no effect on any smoking

related measures including carbon monoxide levels in expired air

across the naloxone dose range studied.

171
At present, most of the evidence suggests that narcotic antagonists

do not significantly alter cigarette smoking under both acute and

chronic administration conditions. However, the effects of opiate

antagonists have been studied in a total of 15 smokers in three

different laboratories and findings have been inconsistent. Additional studies should provide a more definitive answer to this

question.

Opioid Mixed Agonist-Antagonist Effects on Cigarette Smoking

Since opioid agonists consistently increase cigarette smoking

(Mello et al. 1980b; Chait and Griffiths 1984) and opiate antagonists either have no effect or decrease smoking behavior, it is

interesting to consider the effects of an opioid that has both

agonist and antagonist properties. Buprenorphine is a potent

opioid agonist with respect to analgesia, physiological, and

subjective reactions in man and is also a long acting opiate

antagonist like naltrexone (Houde 1979; Jasinski et al. 1978; Levis

et al. 1983). Buprenorphine has been shown to produce opiate

agonist-like subjective effects comparable to morphine and

methadone. A comparison of the effects of single doses of buprenorphine (0.2 to 2.0 mg), morphine (15 to 40 mg), and methadone

(30 mg) showed similar euphoria and liking scores on several selfreport measures. An 8 mg/day dose of buprenorphine produced subjective effects and euphoria equivalent to that produced by 120

mg/day of morphine (30 mg q.i.d.) or 40 to 60 mg of methadone

(Jasinski et al. 1978).

The agonist and antagonist components of buprenorphine appear to

have different durations of action (Lewis et al. 1983). Buprenorphine can antagonize high doses of morphine (60 to 120 mg) for

almost 30 hours (Jasinski et al. 1978), whereas an analgesic dose

of buprenorphine effectively relieves pain for about 6 hours (Houde

1979).

The effects of huprenorphine on heroin self-administration and

cigarette smoking were evaluated under clinical research ward conditions (Mello and Mendelson 1980; Mello et al. 1982). Nine heroin

addict subjects were cigarette smokers. Three were sssigned to

buprenorphine treatment and two to buprenorphine placebo treatment.

Four subjects participated in two separate studies and received

both buprenorphine and placebo in a counterbalanced order; i.e.,

two received buprenorphine first and two received placebo first.

Consequently, final data are reported for seven subjects assigned

to buprenorphine and six subjects assigned to placebo under double

blind conditions.

After a 5-day drug free baseline, subjects were given ascending

doses of buprenorphine or its placebo for 14 days at 8 a.m. each

day. An initial subcutaneous buprenorphine dose of 0.5 mg/day was

gradually increased by 0.5 mg per day for 12 days and by 1 mg per

day for 2 days to a final dose of 8 mg/day s.c. The volume of

placebo and buprenorphine injections was equivalent at each dose

level. Subjects were maintained on 8 mg/day of buprenorphine (or

172
placebo) for 10 days during which they could work for i.v. heroin

at a simple operant task. They were then gradually withdrawn from

buprenorphine over 5 days in a dose reduction schedule of 7, 6, 5,

3, and 1 mg/day. All subjects assigned to placebo buprenorphine

used heroin and were detoxified with methadone in progressively

decreasing doses (25 to 5 mg/day p.o.) over 5 days.

Heroin self-administration was significantly suppressed by bupre

norphine treatment, whereas placebo maintained subjects self-administered 93% to 100% of all the heroin available (Mello and

Mendelson 1980; Mello et al. 1982). Placebo maintained subjects

increased cigarette smoking significantly during heroin selfadministration (Mello et al. 1980b).

Buprenorphine treatment was associated with a significant increase

in tobacco smoking in comparison to the drug free baseline. During

the 14-day ascending dose phase, buprenorphine doses of 2.0 mg/day

s.c. and above were associated with significant increases in cigarette smoking (p< .01). During the 10-day period of buprenorphine

maintenance at 8 mg/day, the significant increase in smoking observed during buprenorphine induction was sustained. Subjects

smoked significantly more cigarettes during the period of high dose

buprenorphine maintenance than during the buprenorphine induction

phase (p < .01). As the daily buprenorphine dose was decreased

from 7 to 1 mg/day, cigarette smoking also decreased and returned

to baseline levels during the postbuprenorphine drug free period.

Subjects in the buprenorphine treatment group and the placebo

treatment group smoked an equivalent number of cigarettes during

the predrug and postdrug free baseline periods (Mello et al. 1985).

These data suggest that the agonist properties of buprenorphine

remained salient in terms of its effects on cigarette smoking over

29 days of chronic administration. These findings are interesting

in view of the fact that the antagonist component of buprenorphine

appears to last for almost 30 hours (Jasinski et al. 1978), whereas

the agonist analgesic effects persist for about 6 hours (Houde

1979). Tolerance to most of the opioid agonist-like side effects

developed within 21 days of chronic buprenorphine maintenance in

most subjects (Mello et al. 1982).

Buprenorphine appears to act primarily like an opioid agonist in

its effects on cigarette smoking (Mello et al. 1985). Buprenorphine also acts primarily like an agonist rather than an antagonist on male pituitary hormones (Mendelson et al. 1980).

chronic buprenorphine maintenance was associated with the suppression of luteinizing hormone (LH) and prolactin (PRL) levels, a

finding comparable to that previously observed in heroin users and

heroin addicts treated with methadone and with LAAM (Mendelson and

Mello 1975, 1982; Mendelson et al. 1974, 1975 a,b,c). In contrast,

opioid antagonists such as naltrexone have been shown to be associated with an increase in LH in normal adult males (Mendelson et

al. 1980; Ellingboe et al. 1982), and naloxone is frequently used

to stimulate LB for endocrine studies in the males of several

species (Moult et al. 1981; Mirin et al. 1976; Cicero et al. 1979,

1982).

173
In summary, the facilitatory effects of opiofd agonists on cigarette smoking appears to be a robust finding consistently observed

under different experimental conditions (Mello et al. 1980b; Chait

and Griffiths 1984). The decrease in cigarette smoking associated

with gradual decreases In daily methadone maintenance doses

(Bigelow et al. 1981) was also seen during both buprenorphine and

methadone detoxification (Hello et al. 1985). However , despite

these converging findings, it is difffcult to account for the

apparent effects of opioid agonists on smoking. It is not clear if

more cigarettes were smoked during opioid use because they were

more reinforcing, or because their effects were attenuated by

opioid intoxication and more cigarettes were required (cf. Hello et

al. 1980b). Chait and Griffiths (1984) suggested that methadone

might block or attenuate the aversive effects of cigarette smoking

such as throat irritation which could limit smoking under drug free

conditions.

The possible contribution of endogenous opioids to cigarette smoking is a provocative hypothesis, but still quite speculative. The

notion that an opiofd antagonist such as naloxone or naltrexone

might lower cigarette smoking by antagonizing smoking related increases in plasma beta-endorphin levels (cf. Karras and Kane 1980;

Pomerleau et al . 1983) has been challenged by studies showing that

opioid antagonists have no effects on cigarette smoking (Mello et

al . 1980b; R.H. Griffiths, Personal Communication, 1984). The fact

that the opioid mixed agonist-antagonist buprenorphine significantly increased cigarette smoking is also inconsistent with the hypothesis that opioid antagonists might antagonize cigarette smoking

through related changes in endogenous opioids. Since buprenorphine

effectively antagonized both the subjective and physiological effects of heroin and decreased heroin self-administration by heroin

addicts over 10 days (Mello and Mendelson 1980; Hello et al. 1982),

it is likely that buprenorphine antagonized endogenous opioid

levels as well . Alternatively, if increases in beta-endorphin

levels associated with increased nicotine levels following smoking

(Pomerleau et al. 1983) are important for the reinforcing properties of cigarette smoking, it could be argued that more cigarettes

were smoked during buprenorphine treatment because they were less

reinforcing.

MARIJUANA EFFECTS ON CIGARETTE SMOKlNG

In contrast to alcohol, opiates, and certain stimulants, marijuana

does not appear to alter tobacco smoking in any systematic way

(Hello et al. 1980a; Hello and Mendelson, in press). Examination of

cigarette smoking in a study of the covariation between alcohol and

marijuana use (see Hendelson et al., this volume) showed that there

were no dose related changes in cigarette smoking during marijuana

availability. The introduction of marijuana did not change average

cigarette smoking in comparison to the drug free baseline, except

in one occasional tobacco smoker who smoked more during both

alcohol and marijuana availability (Hello et al. 1980a).

174
Recently, we have examined cigarette smoking during marijuana

smoking in women (Mello and Mendelson, in press). Sixteen of 21

women studied were cigarette smokers. Cigarettes were freely

available upon request. Women could earn one 1-gram marijuana

cigarette or 50¢ in 30 minutes of performance at a simple operant

task on a second order FR 300 (FI 1 sec:S) schedule of reinforcement. A 7-day drug free baseline was followed by 21 days of

marijuana availability and a postmarijuana drug free period of

7 days. The amount and temporal pattern of tobacco cigarette

smoking observed during drug free conditions were not changed

during concurrent marijuana use by either heavy or moderate

marijuana smokers (Mello and Mendelson, in press).

Although it might be expected that smoking marijuana and tobacco

would be antithetical, in fact, these drugs usually were smoked in

close temporal contiguity by both men and women (Mello et al.

1980a, Mello and Mendelson,in press). These data have unfortunate

implications for one medical consequence of marijuana and tobacco

smoking, impaired pulmonary function. Adverse effects of marijuana

smoking on pulmonary function have been consistently demonstrated

in numerous studies with men (Tashkin et al. 1973, 1976, 1980;

Vachon et al. 1973; Bernstein et al. 1976). Studies of pulmonary

function in these female marijuana users have shown that single

breath carbon monoxide diffusion capacity was signifcantly lower

than in tobacco cigarette smokers and nonsmoker control subjects

(Tlllea et aL,in press). These data were interpreted to suggest that

marfjuana smoking may cause significant impairment of pulmonary

function associated with the gas exchange surface of the lungs.

TOBACCO SMOKING AND DRUG INTERACTIONS

Alcohol, opioid agonists, and mixed agonist-antagonists as well as

certain stimulants (Schuster et al. 1979; Henningfield and

Griffiths 1981) have been shown to be associated with increased

cigarette smoking. Since these drugs have a broad spectrum of

actions, it is difficult to construct a plausible hypothesis concerning specific pharmacological effects on cigarette smoking.

Nicotine appears to be the primary pharmacological reinforcer in

cigarette smoking (Jaffe and Jarvik 1978; Russell 1976; Gritz

1980), but it has been difficult to separate the effects of nicotine from other constituents of tobacco smoke. The pharmacokinetics of nicotine make it an almost ideal drug reinforcer.

Nicotine inhaled from cigarettes reaches the CNS within seconds and

appears to produce rapid behavioral effects which diminish over 30

to 40 minutes (Jaffe and Jarvik 1978). There is increasing

evidence that subjects adjust their smoking behavior to maintain

constant nicotine levels when low nicotine content or short cigarettes are provided (Benowitz and Jacob 1984; Herning et al. 1981;

Gritz 1980; Chait and Griffiths 1982; Robinson et al. 1983). But,

analysis of several aspects of the microtopography of smoking

behavior, such as puff duration (Nemeth-Coslett and Griffiths 1984)

and puff frequency (Griffiths et al. 1982), has not shown

significant effects of nicotine dose. Consequently, the role of

175
nicotine in maintaining cigarette smoking is continually under

evaluation (Jarvik et al. 1977; Kumar et al. 1977).

In experimental animal studies, intravenous nicotine can be a

positive or a negative reinforcer depending upon the schedule of

nicotine reinforcement and the behavioral history of the animal

(cf. Goldberg et al. 1983 for review). Similar results have been

obtained in human studies of the reinforcing properties of intravenous nicotine (Henningfield and Goldberg 1983). These studies

Illustrate the complexity of analyzing the reinforcing properties

of nicotine alone.

The interactions between cigarette smoking and other drugs from

various classes presents an even greater challenge. This problem

illustrates aome as yet unresolved questions concerning the nature

of the reinforcer in polydrug use. The simultaneous use of drugs

which appear to have contradictory pharmacological effects is

especially puzzling. We have suggested elsewhere that polydrug use

may have less to do with the pharmacological properties of the

drugs or the anticipated effects than with the capacity to produce

some change in subjective states (Mello 1977, 1978, 1983). Change,

rather than any particular direction of change, may be the goal of

the polydrug user. It appears that any drug or drug combination

that has definite stimulus properties and behavioral effects for

the user may have abuse potential. Since multiple drug use appears

to be an increasingly common pattern, the way in which drugs interact to modulate use patterns is an important area for further

study.

REFERENCES

Benowitz, L., and Jacob, P. Nicotine and carbon monoxide intake

from high and low yleld cigarettes. Clin Pharmacol Ther 36(2):

265-270, 1984.

Bernstein, J.G.; Kuehnle, J.C.; and Mendelaon, J.H. Medical implications of marijuana use. Am J Drug Alcohol Abuse

3:347-361, 1976.

Bigelow, G.E.; Stitzer, H.L.; Griffiths, R.R.; and Liebaon, I.A.

Human methadone detoxification: Opioid self-administration

behavior, cigarette smoking and withdrawal signs and symptoms as

a function of progressive dose reductions. Fed Proc 40:296,

1981.

Chait, L.D., and Griffiths, R.R. Smoking behavior and tobacco

smoke Intake : Response of smokers to shortened cigarettes.

Clin Pharmacol Ther 32(1):90-97, 1982.

Chait, L.D., and Griffiths, R.R. Effects of methadone on human

cigarette smoking and subjective ratings. J Pharmacol Exp Ther

229(3):636-640, 1984.

Chernick, V. The brain’s own morphine and cigarette smoking: The

junkie in disguise? Chest 83:2-3, 1983.

Cicero, T.J.; Schainker, B.A.; and Meyer, E.R. Endogenous opioids

participate in the regulatlon of the hypothalamic-pituitaryluteinizing; hormone axis and testosterone’s negative feedback

control of luteinizing hormone. Endocrinology 104:1286-1291,

1979.

176
Cicero, T.J.; Newman, K.S.; Gerrlty, M; Schmoeker, P.F.; and Bell,

R.D. Ethanol inhibits the naloxone-induced release of luteinizing hormone-releasing hormone from the hypothalamus of the

male rat. Life Sci 31:1557-1596, 1982.

Dreher, K.F., and Fraser, J.G. Smoking habits of alcoholic outpatients. Int J Addict 2:259-270, 1967.

Ellingboe, J.; Veldhuis, J.D.; Hendelson, J.H.; Kuehnle, J.C.; and

Mello, N.K. Effect of endogenous opioid blockade on the amplitude and frequency of pulsatile LH secretion in normal men. J

Clin Endocrinol Metab 54:854-857, 1982.

Goldberg, S.R.; Spealman, R.D.; Risner, H.E.; and Henningfield,

J.E. Control of behavior by intravenous nicotine injections in

laboratory animals. Pharmacol Biochem Behav 19:1011-1020, 1983.

Griffiths, R.R.; Bigelow, G.E.; and Liebson, I. Facilitation of

human tobacco self-administration by ethanol : A behavioral

analysis. J Exp Anal Behav 25(3):279-292, 1976.

Griffiths, R.R.; Hennigfield, J.E.; and Bigelow, G.E. Human

cigarette smoking: Manipulation of number of puffs per bout,

interbout interval and nicotine dose. J Pharmacol Exp Ther

110:256-265, 1982.

Gritz, E.R. Smoking behavior and tobacco use. In: Mello, N.K.,

ed. Advances in Substance Abuse, Behavioral and Biological

Research, Vol. 1. Greenwich: JAI Press, Inc., 1980. pp. 91-

158, 1980.

Henningfield, J.E., and Goldberg, S.R. Control of behavior by intravenous nicotine injections in human subjects. Pharmacol

Biochem Behav 19: 1021-1026, 1983.

Hennningfield., and Grifflths, R.R. Cigarette smoking and

subjective response: Effects of d-amphetamine. Clin Pharmacol

Ther 30(4):497-505, 1981.

Henningfield, J.E.; Chait, L.D.; and Grifflths, R.R. Cigarette

smoking and subjective response in alcoholics: Effects of pentobarbi tal . Clin Pharmacol Ther 33:806-812, 1983.

Henningfield, J.E.; Chait, L.D.; and Griffiths, R.R. Effects of

ethanol on cigarette smoking by volunteers without histories of

alcoholism. Psychopharmacology 81:1-5, 1984.

Herning, R.I.; Jones, R.T.; Bachman, J.; and Mines, A.H. Puff

volume Increases when low nicotine cigarettes are smoked. Br

Med J 283:187-193, 1981.

Houde, R.W. Analgesic effectiveness of the narcotic agonistantagonists. Br J Clin Pharmacol 7(Supp. 3):297-308, 1979.

Jaffe, J.H., and Jarvik, H.E. Tobacco use and tobacco use disorder. In: Lipton, M.A.; DiMascio, A.; and Killam, K.F., eds.

Psychopharmacology: A Generation of Progress. New York: Raven

Press, 1978. pp. 1665-1676.

Jarvik, M.E.; Cullen, J.W.; Gritz, E.R.; Vogt, T.M.; and West,

L.J., eds. Research on Smoking Behavior. National Institute on

Drug Abuse Research Monograph 17. DHEW Pub. No. (ADM) 79-581.

Washington, D.C.: Supt. of Docs., U.S. Govt. Print. Off., 1977.

381 pp.

Jasinski, D.R.; Pevnick, J.S.; and Griffith, J.D. Human pharmacology and abuse potential of the analgesic buprenorphine. Arch

Gen Psychiatry 35:601-616, 1978.

177
Karras, A., and Kane, J.M. Naloxone reduces cigarette smoking.

Life Sci 17:1541-1545, 1980.

Kumar, R.; Cooke, E.C.; Lader, M.H.; and Russell, M.A.H. Is nicotine important in tobacco smoking? Clin Pharmacol Ther 21(5):

520-529, 1977.

Lewis, J.; Rance, M.J.; and Sanger, D.J. The pharmacology and

abuse potential of buprenorphine, a new antagonist analgesic.

In : Mello, N.K., ed. Advances in Substance Abuse, Behavioral

and Biological Research, Vol. III Greenwich:

Inc., 1983.

JAI Press,

pp. 103-154.

Lieber, C.S., and DeCarli, L.M. Ethanol oxidation by hepatic

microsomes: Adaptive increase after ethanol feeding. Science

162:917-918, 1968.

Maletzky, B.M., and Klotter, J. Smoking and alcoholism. Am J

Psychiatry. 131(4):445-447, 1974.

Mello, N.K. Stimulus self-administration: Some implications for

the prediction of drug abuse liability. In: Thompson, T., and

Unna, K.R., eds. Predicting Dependence Liability of Stimulant

and Depressant Drugs. Baltimore : University Park Press, 1977.

pp. 243-260.

Mello, N.K. Control of drug self-administration: The role of

aversive consequences. In: Petersen, R.C., and Stillman, R.C.,

eds. Phencyclidine Abuse: An Appraisal. National Institute on

Drug Abuse Research Monograph 21, DHEW Pub. No. (ADM) 78-728.

Washington, D.C. : Supt. of Docs., U.S. Govt. Print. Off., 1978.

pp. 289-308.

Mello, N.K. A behavioral analysis of the reinforcing properties of

alcohol and other drugs in man. In: Kissin, B., and Begleiter,

H., eds. The Pathogenesis of Alcoholism, Biological Factors,

Vol. VII. ‘New York: PlenumPress. 1983. pp. 133-198.

Mello, N.K., and Mendelson, J.H. A quantitative analysis of drinking patterns in alcoholics. Arch Gen Psychiatry 25(6):527-539,

1971.

Mello, N.K., and Mendelson, J.H. Drinking patterns durlng workcontingent and non-contingent alcohol acquisition. Psychosom

Med 34(2):139-164, 1972.

Mello, N.K., and Mendelson, J.H. Buprenorphine suppresses heroin

use by heroin addicts. Science 27:657-659, 1980.

Mello, N.K., and Mendelson, J.H. Operant acquisition of marihuana

by women. J Pharmacol Exp Ther in press.

Mello, N.K.; Mendelson, J.H.; Kuehnle, J.C.; and Sellers, M. Human

polydrug use : Marihuana and alcohol. J Pharmacol Exp Ther 207

(3): 922-9.35, 1978.

Mello, N.K.; Mendelson, J.H.; Sellers, M.L; and Kuehnle, J.C. Effects of alcohol and marihuana on tobacco smoking. Clin

Pharmacol Ther 27(2):202-209, 1980a.

Mello, N.K.; Mendelson, J.H.; Sellers, M.L.; and Kuehnle, J.C. Effects of heroin self-administration on cigarette smoking.

Psychopharmacology 67:45-52, 1980b.

Mello, N.K.; Mendelson, J.H.; Kuehnle, J.C.; and Sellers, M.L.

Operant analysis of human heroin self-administration and the

effects of naltrexone. Pharmacol Exp Ther 216(1):345-54, 1981.

Mello, N.K.; Mendelson, J.H.; and Kuehnle, J.C. Buprenorphine

effects on human heroin self-administration: An operant analysis. J Pharmacol Exp Ther 223:30-39, 1982

178
Mello, N.K.; Lukas, S.E.; and Mendelson, J.H. Buprenorphine effects on cigarette smoking. Psychopharmacology 86:417-425,

1985.

Mendelson, J.H., and Hello, N.K. Plasma testosterone levels during

chronic heroin use and protracted abstinence: A study of Hong

Kong addicts. Clin Pharmacol Ther 17(5):529-533, 1975.

Mendelson, J.H., and Mello, N.K. Hormones and psycho-sexual development in young men following chronic heroin use. Neurobehav Toxicol Teratol 4(4):441-445, 1982.

Mendelson, J.H.; Kuehnle, J.C.; Ellingboe, J.; and Babor, T.F.

Plasma testosterone levels before, during and after chronic

smoking. N Engl J Med 291:1051-1055, 1974.

Mendelson, J.H.; Kuehnle, J.C.; Ellingboe, J.; and Babor, T.F.

Effects of marihuana on plasma testosterone. In: Tinklenberg.

J.R., ed. Marihuana and Health Hazards: Methodological Issues

in Current Research. New York: Academic Press, 1975a. pp. 83-

93.

Mendelson, J.H.; Mendelson, J.E.; and Patch, V.D. Plasma testosterone levels in heroin addiction and during methadone maintenance. J Pharmacol Exp Ther 192:211-217, 1975b.

Mendelson, J.H.; Meyer, R.E.; Ellingboe, J.; Mirin, S.M.; and

McDougle, M. Effects of heroln and methadone on plasma

cortisol and testosterone. J Pharmacol Exp Ther 195:296-302,

1975c.

Mendelson, J.H.; Ellingboe, J.; Kuehnle, J.C.; and Hello, N.K.

Heroin and naltrexone effects on pituitary-gonadal hormones in

man: Interaction of steroid feedback effects, tolerance and

supersensitivity. J Pharmacol Exp Ther 214(3):503-506, 1980.

Meyer, R.E., and Mirin, S.M. The Heroin Stimulus. New York:

Plenum Press, 1979. 254 pp.

Mirin, S.M.; Mendelson, J.H.; Elllngboe, J.; and Meyer, R.E. Acute

effects of heroin and naltrexone on testosterone and gonadotropin secretion: A pilot study. Psychoneuroendocrinology 1:359-

369, 1976.

Moult, P.J.A.; Grossman, A.; Evans, J.M.; Rees, L.H.; and Besser,

G.M. The effect of naloxone on pulsatile gonadotropin release

in normal subjects. Clin Endocrinol 14:321-324, 1981.

Nemeth-Coslett, R., and Griffiths, R.R. Determinants of puff duration in cigarette smokers. I. Pharmacol Biochem Behav 10:965-

971, 1984.

Pomerleau, O.F.; Fertig, J.B.; Seyler, L.E.; and Jaffe, J. Neuroendocrine reactivity to nicotine in smokers. Psychopharmacology

81:61-67, 1983.

Robinson, J.C.; Young, J.C.; Rickert, U.S.; Fey, G.; and Kozlowski,

L.T. A comparative study of the amount of smoke absorbed from

low yield (less hazardous) cigarettes. Br J Addict 78:79-87,

1983.

Rubin, E.; Gang, H.; Misra, P.; and Lieber, C.S. Inhibition of

drug metabolism by acute ethanol intoxication: A hepatic micro

somal mechanism. Am J Med 49:800-806, 1970.

Russell, M.A.H. Tobacco smoking and nicotine dependence. In:

Gibbons. R.J.: Israel, Y.; Kalant, H.; Popham, R.E.; Schmidt,

W.; and Smart; R.G., eds. Research Advances in Alcohol and Drug

Problems. New York: John Wiley & Sons, 1976. pp. 282-295.

179
Schuster, C.R.; Lucchesi, B.R.; and Emley, C.S. The effects of

d-amphetamine, reprobamate and lobeline on the cigarette smoking

behavior of normal human subjects. In: Krasnegor. N.. ed.

Cigarette Smoking as a Dependence Process. Washington, D.C.:

supt. of Docs., U.S. Govt. Print. Off., 1979. pp. 91-99.

Tashkin, D.P.; Shapiro, B.J.; and Rank, I.M. Acute pulmonary

physiologic effects of smoked marljuana and oral tetrahydrocannabinol in heal thy young men. N Engl J Med 289:336-341,

1973.

Tashkin, D.P.; Shapiro, B.J.; Less, Y.E.; and Harper, C.E.

Subacute effects of heavy marijuana smoking on pulmonary function In heal thy men. N Engl J Med 294:125-129, 1976.

Tashkin, D.P.; Calvarese, B.M.; Simmons, M.S.; and Shapiro, B.J.

Respiratory status of seventy-four habitual marijuana smokers.

Chest 78:699-706. 1980.

Tilles, D.; Goldenheim, P.; Johnson, D.C.; Mendelson, J.H.; Hello,

N.K. ; and Hales, C.A. Marihuana smoking causes a reduction in

single breath carbon monoxide diffusing capacity. Amer J Med

in press.

Tobin, H.J.; Jenouri, G.; and Sackner, M.A. Effect of naloxone on

change in breathing pattern with smoking. Chest 82: 530-537,

1983.

Vachon, L.; Fitzgerald, H.X.; Solliday, N.H.; Gould, I.A.; and

Gaensler, E.A. Single dose effect of marihuana smoke--bronchial

dynamics and respiratory center sensitivity in normal subjects.

N Engl J Med 288:985-989, 1973.

Walton, R.G. Smoking and alcoholism: A brief report. Am J

Psychiatry 128:1455-1459, 1972.

ACKNOWLEDGMENTS

Preparation of this review was supported in part by Grants

DA-00101, M-00064, M-02519, DA-02905, and DA-01676 from the

National Institute on Drug Abuse, ADAMHA.

AUTHORS

Nancy K. Hello, Ph.D.

Jack H. Mendelson, M.D.

Alcohol and Drug Abuse Research Center

Harvard Medical School-McLean Hospital

115 Mill Street

Belmont, Massachusetts 02178

180
Drug Combinations in Pleasure and

Pain

Conan Kornetsky, Ph.D.

The use of more than one drug simultaneously is not new. Drugs

are combined for both medical and nonmedical use, e.g., codeine and

aspirin for the relief of pain, heroin and cocaine for the

enhancement of euphoria. In this essay, I will describe a series

of experiments from my laboratory on the effects of various

combinations of drugs on the threshold for brain-stimulation reward

(pleasure) and brain-stimulation escape (pain).

We, as well as others (e.g., Kelly and Reid 1977; Wise 1980), have

suggested that many abuse substances increase the sensitivity of

animals to rewarding brain stimulation and, by inference, these

substances cause an activation of those areas of the brain that are

involved in brain-stimulation reward. Olds and Milner (1954) were

the first to report that animals would work in order to receive

electrical stirmlation to various areas of the brain. Although

animals will work for stimulation to many midbrain and cortical

sites, the most reliable site commonly used for eliciting selfstimulation behavior is the medial forebrain bundle/lateral hypothalamic area. The phenomenon has been called intracranial selfstimulation (ICSS) as well as brain-stimulation reward. For the

most part, but not exclusively, the former term has been used when

the animals are in an operant paradigm and the dependent variable

is rate of lever pressing for the rewarding stimulation. We have

used the term brain-stimulation reward to designate threshold

determinations.

We (Marcus and Kornetsky 1974) first demonstrated that morphine

will lower the threshold for brain-stimulation reward and raise the

threshold for brain-stimulation escape. The dose-effect curve for

the brain-stirmlation reward is U-shaped, while it is a monotonic

positive dose-effect curve for escape from aversive brain stimulation. For a review of this work see Kornetsky and wheeling (1982)

and Kornetsky (1985).

181
REWARDING BRAIN STIMULATION

METHOD

Bipolar stainless steel electrodes (0.013cm in diameter and

insulated except at the tips) are stereotaxically implanted

bilaterally in the lateral hypothalamic region of the medial

forebrain bundle of male rats (300 g, CDF, Charles River

Laboratories, Wilmington, MA). Surgical anesthesia is produced by

systemic administration of Equi-Thesin (0.3 ml/100 g of

body weight). After surgery, animals are given at least 1 week for

postoperative recovery before behavioral testing is instituted.

Animals are maintained on a 12-hour light/dark cycle, housed in

standard steel cages, and have ad libitum access to food and water.

During the initial phase of animal training, each electrode is

tested to determine the current intensity needed to produce

appetitive behavior with little or no motor artifact. The

electrode which produces appetitive behavior at the lowest current

intensity and has the least or no motor artifact is used in the

subsequent study. Animals are trained and tested on a rate

independent threshold procedure in a plastic chamber (20 by 20 by

35 cm) A wheel manipulandum is located within one wall of the

test chamber. Four equally spaced cams on one endplate of the

wheel manipulandum operate a microswitch which results in immediate

delivery of a stimulation when the wheel is rotated one-quarter of

a turn. A constant current stimulator (Sunrise System, Pembroke,

MA) is used to deliver the biphasic symmetrical pulses. Each

stimulus consists of a 500 milliseconds (msec) train with a pulse

width of 0.2 msec and a delay of 0.2 msec between the positive and

negative pulses at a frequency of 160 Hz.

Thresholds are determined by a procedure involving the use of

discrete trials systematically presented over a range of stimulus

intensities. A trial begins with the delivery of a non-contingent

stimulus. A response of one-quarter wheel turn within 7.5 seconds

(sec) of this stimulus results in the delivery of a contingent

stimulus, identical in all parameters to the non-contingent

stimulus, and terminates the trial. Failure to respond has no

scheduled consequences and the trial is terminated after 7.5 sec.

The interval between trials varies around an average of 15 sec, and

responses made during the intertrial interval (error responses)

result in a 15 sec delay before the start of the next trial.

Stimulus intensities are varied using a modification of the

classical psychophysical method of limits. Stimuli are presentd

in an alternating descending and ascending series with a step size

of 5 or 10μA (depending on the sensitivity of the individual

animal) with five trials presented at each intensity level before

the next lower or higher intensity is presented. Subjects complete

four series (i.e., descending, ascending, descending and

ascending) prior to injection, then four or eight series postinjection, with the entire pre-, post-session lasting 2.5 to 3 hours.

All experimental data are collected and stored by an on-line micro182
computer. Each series’ threshold value is defined as the midpoint

in microamperes between the level at which the animal makes three

or more correct responses out of the five stimulus presentations (a

plus score) and the level where less than three correct responses

(a minus score) were made. The pre- and post-injection thresholds

are defined by the respective series means.

Animals require approximately six 1-hour training sessions to

learn the task and approximately four additional sessions for the

establishment of a stable threshold level whereupon saline injections are begun. Animals are tested with saline injections for

five days before drug administration is initiated. Also, saline

days are interspersed with drug treatment day so that animals

receive a drug only twice weekly.

Threshold values are calculated for both the preinjection and the

postinjection sessions, with the difference between the two scores

taken as the dependent measure. These difference scores are

transformed to standard scores (Z-scores) based on the mean and

standard deviation of the difference scores for all saline days. A

Z-score of ±2.0 or greater (95% confidence limits) is preselected

as the level for significance.

TRIPELENNAMINE AND PENTAZOCINE

Because of the street use of this combination, called “T’s and

Blues,” we determined the effect of these drugs alone and in

combination on the threshold for rewarding brain stimulation. In

previous studies, we had already found that pentazocine would lower

the threshold by itself (Kometsky et al. 1979) and, to our

surprise, we found that tripelennamine would also lower the threshold by itself (Unterwald et al. 1984). This finding is in

agreement with those of Jasinski (personal communication), who

found that tripelennamine causes an increase in euphoria as

measured by the Addiction Research Center Inventory. Although both

tripelennamine and pentazocine significantly lower the threshold

for reward, the effect is minimal and considerably less than that

seen after morphine.

Figure 1 (top) shows the mean effect (in Z-soorss) of five animals

after various doses of tripelennamine. The bottom panel of the

figure shows the effects of 2.5 mg/kg of tripelennamine administered simultaneously with various doses of pentaxocine (Unterwald

and Kometsky 1984). As can be seen, the 2.5 mg dose of tripelennamine has no significant effect by itself, but when combined with

ineffective doses of pentazocine, the effect is clearly and markedly significant. It is important to note that the results suggest

a real synergistic effect, for were the effect only due to

increasing the amount of pentazocine available at receptor sites,

then the dose/response curve simply would have moved to the left.

By combining the two drugs, however, the mamximum effect, or

efficacy, is clearly enhanced.

183
FIGURE 1

Mean Z-score Effects of

Various Doses of Tripelennamine (top panel) and

Pentazocine (bottom panel).

TheBottom Panel also shows

the Effects of 2.5mg/kg

of Tripelennamine with

Various Doses of Pentazocine. Error Bars Indicate

the Standard Error of the

x Z-score. A Z-score of

±2 Indicates the 95%

Confidence Limits.

(Adapted from Unterwald

and Kometsky 1984)

Copyright 1984, ANKHO

International, Inc.

NALBUPHINE AND TRIPEPENNAMINE

In order to further examine the effects of tripelennamine with a

mixed agonist-antagonist, we studied the simultaneous administration

of tripelennamine with nalbuphine. Nalbuphine is currently available in injectable form for clinical use as an analgesic. Jasinski

and Mansky (1971) reported that it has significantly less abuse

potential than morphine, although it does increase the euphoria

score on the Addiction Research Center Inventory. Also, animals

will self-administer nalbuphine (Steinfels et al. 1982). Figure 2

Shows the results obtained with an ineffective dose of tripelennanine combined with various doses of nalbuphine in the rat. As

can be seen, although nalbuphine alone will significantly lower the

threshold for rewarding brain stimulation, the addition of

tripelennamine causes a highly significant increase in efficacy, and

moves the We-effect curve to the left.

Figure 3 presents sane idea of the relative effects of these

tripelennamine mixtures as compared to morphine, cocaine, or

amphetamine.As can be seen,the combinations put tripelennamine,

with pentazocine and nalbuphine, in the same class with the latter

highly favored and abused substances.

MORPHINE AND D-AMPHETAMINE ON REWARD

Two experiments with morphine and d-amphetamine were carried out by

Hubner et al (1983). In the first one, a minimally effective dose

of morphine was given simultaneously with various doses of damphetamine. In the second experiment, the procedure was reversed;

184
FIGURE 2

Mean Effect of Various Doses of Nalbuphine Alone and

in Combination with 2.5 mg/kg of Tripelennamine

FIGURE 3

Mean Maximal Lowering Effect on Rewarding Stimulation

for Several Drugs. As in Previous Figures, Data are

Presented in Z-scores with Error Bars. (Data from

Unterwald and Kometsky 1984)

185
an ineffective dose of d-amphetanine was given simultaneously with

various doses of morphine. The procedure used in these experiments

was similar to that used in the previously described studies. In

both cases, there was clearly a greater effect of these two drugs

when given together than when given separately. However, it is not

clear whether the effect was simply additive or synergistic. A

summary of the effects of the combination in the three animals

used in the second experiment is shown in figure 4. The figure

shows doses of morphine from 0.25 to 2.0 mg/kg alone and with a

nonsignificant threshold-lowering dose of d-amphetamine. The dose

of d-amphetamine used varied between animals. In each case, a nonsignificant dose was selected. The doses were 0.063, 0.125, and

0.5 mg/kg, respectively. Although the effect of morphine alone, as

represented in the figure, indicates no significant effect at the

2.0 mg/kg dose, doses up to 1.0 mg/kg rarely show a significant

lowering of the threshold in an individual animal.

FIGURE 4

Mean Effects of Morphine Sulfate Alone (N=3) and in

Combination with d-Amphetanine. (Data from Hubner

et al. 1983)

AVERSIVE BRAIN STIMULATION

METHOD

The experimental chamber, the manipulandum, and the characteristics

of the electrical stimulation are identical to those we use in

determining the threshold for brain-stimulation reward. However,

186
in these experiments, the electrodes are stereotaxically implanted

into a brain site that, when stimulated, is only aversive. The

brain site that we use is the mesecephalic reticular formation

(MRF). The rat quickly learns to escape from such stimulation by

rotating the manipulandum a quarter turn. A trial is initiated by

the onset of the electrical stimulation. A response within 7.5 sec

immediately terminates the shock and the trial. If no response

occurs within 7.5 sec, the stimulus is automatically terminated.

Stimulus intensities are varied according to a modification of the

classical psychophysical method of limits. Stimuli are presented

in an alternating ascending and descending series with a step size

of 1 or 2μA, depending on the sensitivity of the animal. An

ascending series is initiated at a previously determined subthreshold intensity. Three trials are given in succession at each

intensity. Two or more escape responses at a particular intensity

are scored as a plus, while less than two responses are scored as a

minus. An ascending series is conducted until plus scores are

achieved in two successive steps. A descending series is then

initiated at one step size lower and current intensity continuously decreased until two successive minus scores are achieved. The

threshold for a particular ascending or descending series is

defined as the midpoint between those intensities which delimited

the transition from plus to minus scores.

Four series, two ascending and two descending, comprised a session.

A session threshold is computed as the mean of the four series

thresholds. Immediately after the first session is completed,

animals are injected subcutaneously with the test drug or saline,

and a second session of four series is conducted.

Threshold differences between pre- and post-injection sessions on a

drug test day are expressed as a Z-score based on the standard

deviation of the mean threshold differences for all saline test

days. As in the case of brain-stimulation reward, a Z-score of ±2.0

or greater (95% confidence limits) is preselected as the level for

significance.

Using this method, we have found that morphine (Wheeling et al.

1981), cyclazocine and pentazocine (Sasson and Kometsky 1986) and

ethylketocyclazocine (Sasson and Kometsky 1984) all raise the

escape threshold. Naloxone lowers the threshold (Sasson and

Kometsky 1983), and amphetanine either lowers it or has no effect

(Sasson et al. 1983).

AMPHETAMINE AND MORPHINE

It has been previously demonstrated that amphetamine combined with

morphine results in a greater degree of analgesia in man than

morphine alone (Forrest et al. 1977). In order to test whether our

model would also show a potentiation of the analgesic effect of

morphine by amphetamnine, we tested various doses of amphetamine in

combination with a dose of morphine that was ineffective in raising

the escape threshold (Sasson et al. 1983).

Four animals were tested. As in previous experiments, we found

that morphine alone in doses from 1.0 to 16.0 mg/kg (i.p.) caused

187
a dose-related increase in the escape threshold d-Amphetamine at

doses from 0.06 to 4.0 mg/kg (i.p.) either had no effect or lowered

the escape threshold. Figure 5 show the interaction of a minimally effective or ineffective dose of morphine in combination with

various doses of d-amphetamine in two of the four animals tested.

Similar results were obtained in the other two animals As shown,

the d-amphetam

the morphine

ine clearly potentiated the antinociceptive effect of

FIGURE 5

Effects of Morphine Alone and in combination with dAmphetamine in Two Separate Animals, Left and and Right

Two charts, Respectively. As in Previous Work, Data is

Expressed as Z-score charges. (Data from Sasson et al.

1983)

DISCUSSION

These experiments show that combinations of drugs often lead to

effects that exceed the simple algebraic sum of the actions of the

individual drugs. This complex interaction seems to exceed what

might be seen if one drug simply increased the amount of the other

188
drug’s availability at the receptor. These synergistic or

potentiation effects are manifested not only in the euphoriant

action of drugs, but also in their analgesic action.

The combining of the antihistmine tripelennamine with the mixed

agonist-antagonist raises sane interesting questions regarding the

role of histamine in the effects of these opiate drugs. The effect

is not confined to the euphoriant action, for Tagashira et al. (1982)

reported that tripelennamine enhanced the analgesic response of

pentaxocine in the mouse using the hot/plate method. Also, Shannon

and Su (1982) found that tripelennamine enhanced the morphinelike

discriminative stimulus effects of pentaxocine in rats trained to

discriminate morphine from saline.

Shannon and Su (1982) argued that the interaction seen at the

behavioral level is not due to a molecular interaction at the

opiate receptor. This conclusion is based on their finding that

tripelennamine did not alter the response of the stimulated guinea

pig ileum to pentazocine, did not modify the Ke for naloxone in

antagonizing pentaxocine, and did not change the inhibition of

specific (3H)-naloxone binding by pentazocine. However, other

evidence suggests that a central histamine system may account for

the interaction Mazurkiewicz-Kwilecki and Henwood (1976) found

that, in the rat, chronic treatment with morphine causes a decrease

in histamnine in the CNS. Also, Eroglu (1979) reported that

naloxone can reverse or block these effects of chronic morphine

treatment on brain histamine. Fran the above mentioned studies,

the mechanism involved in the synergistic effect of tripelennamine

with the mixed agonist-antqonist is not clear. It is clear,

however, that the role of histamine in the response of opiates is

still not understood.

The mechanisms involved in the enhancement of a putative analgesic

response of morphine by d-amphetamine and the drugs’ combined

increaesd effect on brain-stimulation reward are certainly not

apparent. It is highly possible that different mechanisms are

involved since, in the case of rewarding brain stimulation, each

drug independently will increase the animal's sensitivity.

However, in the case of brain-stimulation escape, there is clearly

no similarity in the effects of these two compounds.

Despite a great deal of evidence that dopaminergic pathways are

involved in the reward system (Fibiger 1978), some recent data

suggest that there may not be a close correspondence between the

boundaries of the reward system and those of the dopamine terminal

fields (Prado-Alcala and Wise 1984; Prado-Alcala et al. 1984).

However, the finding that naloxone will attenuate the threshold

lowering effect of d-amphetamine on brain-stimulation reward

(Esposito et al. 1980) suggests an interaction between the

dopaminergic and an endogenous opiate system. This is supported by

the finding that opiate receptors have been localized on

dopaminergic presynaptic neurons in the mesolimbic system and the

striatum (Pollard et al. 1977a, 1977b). However, the role that

these endogencus opiates play in the behavior of the dopamine

neuron is not clear. Some investigators have proposed that opiates

189
facilitate (Chesselet et al. 1981) the release of dopamine, while

other suggest inhibition (Pollard et al.).

Clearly, these experiments suggest that the neuronal mechanisms of

drug interactions require understanding of a greater magnitude than

does the action of either drug alone. There is, however, sane

advantage to studying drug combinations. First,of course, the

abuse liability of new drug combinations may be predicted,based on

animal models. Equally important, the study of combinations may be

helpful in understading the underlying mechmisms involved in the

action of each of the drugs alone. For example, the use of the

combination of the antihistamine with opiates has helped to

elucidate the role of histamine in the action of the opiate drugs.

Thus, while gathering information to understand more fully the

synergistic effects of drug combinations, we gain information

valuable in its own right.

REFERENCES

Chesselet, M.F.,; Cheramy,A.; Reisine,T.D.; and Glowinski,J.

Morphine and -opiate agonists locally stimulate in vivo

dopamine release in cat caudate nucleus. Nature 291:320-322,

1981.

Eroglu, L. Effects of morphine on brain histamine levels in

stress-exposed rats. Psychopharmacology 63:13-15, 1979.

Esposito, R.U.; Perry, W.; and Kornetsky, C. Effects of damphetamine and naloxone on brain stimulation reward.

Psychopharmacology 69:187-191, 1988.

Fibiger, B.C. Drugs and reinforcenmt mechanisms: A critical

review of the catecholamine theory. Annu Rev Pharmacol Toxicol

18:37-56, 1978.

Forrest, W.B.; Brown, B.W.; Brown, C.R,; Defalque, R; Gold, M.;

Gordon, E.; James, K.E.; Katz, J.; Mahler, D.L.; Schroff, P.;

and Teutsch, G. Dextroamphetamine with morphine for the treatment of postoperative pain. N Engl J Med 296:712-715, 1977.

Hubner, C.; Bain, G.T.; and Kometsky, C. Morphine and damphetamine: Effects on brain-stimulation reward. Abstract,

Soc for Neuroscience 9(2):893, 1983.

Jasinski, D.R., and Mansky, P.A. Evaluation of nalbuphine for

abuse potential. Clin pharmacol Ther 13:78-90, 1971.

Kelly, K., and Reid, L.D. Addictive agents and intracranial

stimulation: Morphine and thrseholds for positive intracranial

reinforcenmt. Bull Psychonomic Soc 10:298-300, 1977.

Kometsky, C. Brain-stimulation reward: A model for the neuronal

bases for drug induced euphoria. In: Brown, R.M., Friedman,

D.P., and Nimit, Y., eds. Neuroscience Methods in Drug Abuse

Researce National Institute on Drug Abuse Research Monograph

62. DHHS Pub. No. (ADM) 85-1415. Washington, D.C.: Supt. of

Docs., U.S. Govt. Print. Off., 1985. pp. 30-50.

Kometsky, C.; Esposito, R.U.; McLean, S.; and Jacobson, J.O.

Intracranial self-stimulation thresholds- A model for the

hedonic effects of drugs of abuse. Arch Gen Psych 36:289-292,

1979.

Kometsky, C., and wheeling, H.S. Theoretical and methodological

issues in the use of animal models of drug abuse. In: Levy,

190
A., and Spiegelstein, N.Y., eds. Behavior Models and the

Analysis of Drug Action. Amesterdam: Elsevier, 1982, pp.21-38.

Marcus, R., and Kometsky, C. Negative and positive intracranial

reinforcement thresholds: Effects of morphine. Psychopharmacologia

38:1-13, 1974.

Mazurkiewicz-Kwilecki, I., and Henwood, R.W. Alterations in

brain endogenous histamine levels in rats after chronic morphine

treatment and morphine withdrawal. Agents Actions 6:402-408,

1976.

Olds, J., and Milner, P. Positive reinforcement produced by

electrical stimulation of septal area and other regions of rat

brain. J. Comp physiol Psych 47:419427, 1954.

Pollard, H.C.; Llorens, C.; and Schwartz, J.C. Enkephalin

receptors on dopaminergic neurons in rat striatum. Nature

268:745-747, 1977a.

Pollard, H.; Llorens, C.; Schwartz, J.C.; Gros, C.; and Dray, F.

Localization of opiate receptors and enkephalins in the rat

striatum in relationship with the nigrostriatal dopaminergic

system: Lesion studies. Brain Res 151:392-398, 1977b.

Prado-Alcala, R., and Wise, R.A. Brain stinulation reward and

dopamine terminal fields. I. Caudate-putamen, nucleus

accumbens and amygdala. Brain Res 297:265-273, 1984.

Prado-Alcala, R.; Streather, A.: and Wise, R.A. Brain stinulation

reward and dopamine terminal fields. II. Septal and cortical

projections. Brain Res 301:209-219, 1984.

Sasson, S., and Kornetsky, C. Naloxone lowers brain-stimulation

escape thresholds. Pharmacol Biochem Behav 18:231-233, 1983.

Sasson, S., and Kometsky, C. Evidence for a supraspinal analgesic

effect with the kappa receptor agonist ethylketocyclazocine.

Abstract Society for Neuroscience 10(l):677, 1984.

Sasson, S. and Kometsky, C. Evidence for a supraspinal analgesic

effect with cyclazocine and pentazocine. Life Sci 38:21-26,

1986.

Sasson, S.; Unterwald, E.M.; and Kometsky, C. Effects of

concomitant administration of morphine and d-amphetamine on

escape behavior maintained by aversive intracranial stimulation.

Abstract. Society for Neuroscience 9(2):794, 1983.

Shannon, H.E., and Su, T. Effects of the combination of

tripelennamine and pentazocine at the behavioral and molecular

levels. Pharmacol Biochem Behav 17:789-795, 1982.

Steinfels, G.F.; Young, G.A.; and Khazan, N. Self-administration

of nalbuphine, butorphanol and pentazocine by morphine postaddict rats. Pharmacol Biochem Behav 16:167-171, 1982.

Tagashira, E.; Kachur, J.F.; and Dewey, W.L. Enhancement of

antinociceptive action of pentazocine and morphine by

tripelennamine. The Pharmacologist 24:188, 1982.

Unterwald, E.M., and Kometsky, C.

pentazocine and tripelennamine on brain-stimulation reward.

Pharmacol Biochem Behav 21:961-964, 1984.

Unterwald, E.M.; Kuchanski, L.T.; Williams, J.E.G.; and

Kometsky, C. Tripelennamine: Enhancement of brainstinulation reward. Life Sci 34:149-153, 1984.

Wheeling, H.S.; Sasson, S.; and Kometsky, C. Tolerance to

the effect of morphine on escape from reticular formation

stinulation. Substance and Alcohol Actions/Misuse 2:107-

114, 1981.

191
Wise, R. Actions of drugs of abuse on brain reward systems.

Pharmacol Biochem Behav 13(1):213-224, 1980.

ACKNOWLEDGMENTS

This work was supported in part by grart DA/02326 from the

National Institute on Drug Abuse and Research Scientist Award

DA/00099 from the National Institute on Drug Abuse.

AUTHOR

Conan Kometsky, Ph.D.

Division of Psychiatry

Boston University School of Medicine

80 East Concord Street

Boston, Massachusetts 02118

192
Drug Interactions With Methadone

in Humans

Mary Jeanne Kreek, M.D.

In a rigorous consideration of the desirable

strategies for research on the interactions of drug

abuse, and also interactions with drugs used to treat

drug abuse, it becomes evident that despite the usefulness of animal models and in vitro models, both

for delineation of mechanisms of observed interactions and for screening for possible interactions,

ultimately, controlled studies must be carried out in

normal human volunteer subjects, or patient volunteer

subjects, because of the profound dispositional,

pharmacokinetic, and pharmacodynamic differences with

respect to drug fate and action in humans as contrasted to other species. Also, because of the enormous number of theoretically possible drug interactions

of various kinds which might occur, it is essential

first to explore what kinds of drugs are being used

and/or abused in combination by humans using both

wide-scale epidemiological techniques as well as

small-scale, careful clinical observational

techniques. These findings could then direct both

clinical research, animal model and in vitro model

research. By this approach, priorities with respect

to which potential drug interactions should be

studied first can be more logically established.

Research on potential drug interactions should

include not only studies of the possible interactions

between drugs or chemical agents which are documented

to be used (or abused) concomitantly, but also

interactions between similar new drugs or chemical

agents which might be, in the near future, so used in

combination. Studies of model compounds which might

elucidate mechanisms of drug interactions -- such as

compounds known to alter drug disposition by specific

mechanisms, compounds known to alter receptor site

actions by antagonism or which may effect augmentation of action, and, finally, drugs which may

193
have dynamic actions that either compliment or

counteract some of the diverse actions of the primary

drug of interest -- should also be encouraged,

both in animal models and, when appropriate and

possible from both a regulatory and ethical

standpoint, in humans. For legal, ethical, and

scientific reasons, studies of many model compounds,

of combinations of new drugs,and of combinations of

new and old drugs probably should be carried out

initially in animal model; and/or in vitro systems.

However, studies of possible interacts of those

drugs of use and/or abuse which are already known by

clinical observations to be used in combination by

active drug-abusing, or former drug-abusing humans,

in treatment, and studies of drugs which must be used

together in the treatment of former drug-abusing

humans, should be carried out in controlled human

studies at the earliest possible time.

The use of human subjects, both volunteer subjects

and patient subjects, for studies of interactions of

drugs of use and/or abuse presents many diverse types

of ethical, feasibility-related, and methodological

problems. Studies of drugs of abuse in normal

volunteer subjects are frequently not acceptable on

ethical grounds. Similarly, the administration of

large doses of drugs of abuse, such as the amounts

actually taken in street drug abuse situations, may

not be easily accepted by ethical review committees,

even when studies are to be carried out in subjects

who themselves are substance abusers using those very

drugs in large amounts. However, as in other

scientific-clinical-ethical considerations,

thoughtful development of risk/benefit factor

analysis will undoubtedly facilitate the approval

for performance of needed research.

Feasibility is a second extremely important issue

when clinical studies are to be carried out for any

purpose. Although problems of feasibility will not

be discussed in detail here, it should be made clear

that the most ideal, scientifically and methodologically rigorous type of drug interaction studies,

such as the most desirable types of studies using

animal models designed to determine the time course

of action and pharmacokinetics of each drug alone and

in combination, as well as the full dose response

curve of each drug alone and in combination, are not

feasible in humans. Also, diverse factors which may

influence drug interactions--such as circadian

variations of drug disposition, sex and age of

subject, environment, route of drug administration--

and the various diverse pharmacodynamic effects of

each drug alone and in combination--such as the

194
effects on neurotransmitter or neuroendocrine

systems--obviously cannot be fully considered in even

the most basic clinical research. From a purely

scientific standpoint, however, it is not absolutely

clear that it would be desirable to carry out such

rigorous testing in humans since a relatively narrow

range of doses of each drug is used and abused in

humans and careful clinical observational studies may

promptly identify these dose ranges for each drug

when used alone and in combination. Similarly, the

approximate time course of action of most drugs used

or abused can be established by careful clinical

observations with respect to time of maximal clinical

effect. Also, the time of peak drug levels in plasma

may be determined and, therefore, the possible time

of maximum action at receptor sites predicted.

On the other hand, many of the criteria demanded for

excellent animal model research in drug interaction

studies certainly can and should be demanded of human

research as well. Perhaps the most important issue

to be addressed in appropriate rigorous clinical research is patient selection and characterization,

coupled with appropriate subset analyses when a

heterogeneous population of subjects is to be

included in the study. Many factors, including

circadian factors, effects of food and other drugs,

and effects of environment, can be controlled by the

environment selected for the clinical research

studies. For instance, in-patient clinical research

in a formal Clinical Research Center setting is far

preferable for such work than outpatient studies

conducted in outpatient clinics, which in turn are

preferable to studies carried out in a nonresearch

clinical setting. In human studies, when feasibility

must be a major consideration (despite the views of

many pharmacologists and pharmacokineticists), it is

most appropriate to study drugs by the routes of

administration which are used both in clinical

medicine and in abuse settings, rather than to

demand, purely for study purposes, one specific route

of administration (e.g., intravenous route with the

misconception that this is essential for pharmacokinetic studies), or to demand studies following

drug administration by all possible routes of

administration which is clearly the most desirable

way to carry out the studies in an animal model, but

is not necessarily feasible or desirable to be done

in human subjects. Increasingly, it is being

appreciated that the route of administration used

may significantly alter drug disposition,

pharmacokinetics, and pharmacodynamics; and, again,

careful consideration of possible species differences

in each of these parameters is extraordinarily

195
important in interpretation of data. In human

studies, appropriate statistical analysis of data

should be demanded, and the end-point to be studied

should be chosen in advance of controlled studies.

The technological methods to be used in human research will be addressed to some extent in this

report by giving examples of those methadologies

which have been used in studies of methadone

interaction with other drugs, which have allowed

scrutiny of such indices as dispositional

pharmacokinetics of both racemic methadone and each

of the enantiomers of methadone following oral dosing

in the setting of chronic drug administration.

Methadone is an orally effective opioid agonist

which, because of its long-acting properties in

humans (but not in any animal model, where it is

always a short-acting drug with rapid clearance), can

be effectively used to treat narcotic addiction in

humans. Methadone may also be used effectively in

the management of chronic pain, either by providing

analgesia for which the dosages used must exceed the

degree of tolerance developed by the individual, or

by preventing signs and symptoms of narcotic

withdrawal (effects similar to one of the effects

desired in the treatment of narcotic addiction) for

which the doses used should be less than the degree

of tolerance developed by the individual. In the

case of chronic pain patients, additional use of

short-acting narcotics is required to provide the

desired analgesic effects per se.

Methadone has been used in chronic maintenance treatment of addiction of several hundreds of thousands of

patients in the United States as well as an increasing number abroad. To a limited extent, methadone has remained a minor drug of abuse on the

street, just as it was prior to the introduction of

methadone maintenance treatment in 1964. Because of

the wide-spread clinical use and dramatic efficacy of

methadone in the maintenance treatment of addiction,

it has been essential to determine any significant

interactions between other drugs of use, and of

abuse, with methadone, since it is the steady state

of plasma levels and effects of methadone in

chronically treated individuals which allows

normalization of physiological functions, including

neuroendocrine functions, which are significantly

perturbed by chronic abuse of short-acting narcotics,

such as heroin.

Two general types of interactions between any two

drugs or chemical agents may occur: first, disposi196
tional or pharmacokinetic interactions, which either

increase or decrease the actual amount of drug available at sites of action; and, secondly, interactions

which alter the pharmacodynamics of either or both

drugs by competition at sites of action, such as at

specific receptor sites, or by a variety of actions

of each drug which may augment or counteract the

primary desired actions of each. Appropriate studies

of the first kind of interactions should include as

"end-points" the determination of the disposition of

one drug alone, and then with the addition of a

second drug. Observations should be made of clinical

symptoms of increased or decreased action of the

first drug, which may occur as a result of dispositional interactions when the second drug is

added. The end-points for study with respect to the

second type of drug interactions could be of a wide

variety of types depending upon the primary and

secondary actions of each drug.

Most studies of drug interactions with methadone in

humans have dealt with dispositional drug interactions. The most obvious, though not always fully

recognized, interaction of the second type, which

will be considered briefly, is the interaction of

methadone with a specific opioid antagonist, naloxone. Studies were carried out to determine whether

the addition of naloxone to a methadone formulation

had any significant effect on methadone disposition

(Kreek 1973B). In these studies, naloxone was administered orally along with methadone in a 1 to 10 dose

ratio. Naloxone has very limited oral bioavailability; thus any dispositional interactions would

have to occur at the intestinal wall sites of

binding, absorption, or metabolism prior to the sites

of hepatic uptake, biotransformation, and clearance

of naloxone as its major glucuronide metabolite.

Naloxone's poor oral bioavailability is due not to

failure to absorb the drug but rather to extensive

metabolism to the inactive naloxone glucuronide after

initial up-take by the liver, or the classical "first

pass" effect. The studies which we carried out to

determine whether any dispositional interactions

occurred showed that naloxone, added to the oral

formulation of methadone, did not alter either plasma

levels of methadone or urinary excretion of methadone

in its major pyrrolidine metabolite. However, when

oral naloxone was given with the oral methadone

formulation to narcotic-tolerant and -dependent

individuals, profound gastrointestinal signs and

symptoms of narcotic withdrawal were observed,

presumably due to antagonist effects of naloxone at

opiate receptor sites within the intestinal wall

(Kreek 1973B). These gastrointestinal signs and

197
symptoms were associated with only minimal systematic

signs and symtoms of narcotic withdrawal, reflecting

the very small percentage of naloxone administered

which reached the systemic circulation in a

chemically active unmetabolized form (Kreek 1973B;

Kreek et al. 1976B, 1983B). In this case, there was

no dispositional interaction between the opiate

agonist methadone and the opiate antagonist naloxone,

but rather a profound pharmacodynamic interaction --

i.e.,precipitation of gastrointestinal narcotic

abstinence syndrome--was observed, probably due to

interaction at the opiate receptor sites within the

intestinal wall.

Studies of factors which may significantly alter

methadone disposition in humans have been carried out

to address hypotheses which we have developed

concerning the action of methadone in the treatment

of addiction. Our hypotheses state that a narcotic

drug must be available to critical receptor sites (or

binding sites) for finite and definable periods of

time for tolerance and physical dependence to

develop: that drug-seeking behavior follows as a

natural consequence when the symptoms resulting from

narcotic drug withdrawal in a tolerant and physically

dependent individual (the abstinence syndrome) are

identified by that individual as being related to

drug withdrawal and are observed by that individual

to be relieved when a narcotic drug is readministered;

that constant availability of a narcotic drug (in

this case, methadone) to critical receptor sites is

essential for the steady-state maintenance of a

tolerant and dependent stage; and, finally, that

availability of drugs to critical receptor sites

depends in part on various factors which may affect

overall drug disposition, pharmacokinetics and

excretion. We have shown that three types of factors

may significantly alter methadone disposition in

humans: 1) altered drug disposition in a setting of

chronic disease, including liver and renal disease:

2) altered drug disposition in a setting of the

altered physiological state of pregnancy; and 3)

altered drug disposition due to interaction with

other drugs or hormones (Kreek, Gutjahr, Garfield et

al. 1976B).

When methadone is administered orally on a chronic

basis, a steady state is achieved with relatively

steady plasma levels over a 24-hour dosing interval.

A small increment of methadone plasma levels is found

after oral dosing, with a peak reached in 2 to 4

hours (Kreek 1973B; Kreek, Gutjahr et al. 1976B).

After oral dosing, the peak plasma levels of methadone

rarely exceed a doubling of the nadir or steady-state

198
plasma levels and then these slowly decline to a

steady-state plateau level over the remainder of the

24-hour dosing interval (see figure 1). Using a

conventional model of analysis, the specific and

sensitive technique of gas liquid chromatography, it

has been shown that the apparent plasma terminal

half-life of methadone in humans receiving methadone

orally on a chronic basis ranges from around 18 to 28

hours (Kreek 1973B; Kreek et al. 1979).

One of the major problems encountered when animal

models are used in attempts to study methadone

disposition or methadone interactions with other

agents is that the pharmacokinetics of methadone in

amimal models is entirely different from that in

humans. Using gas chromatographic techniques, we

have determined that the apparent plasma terminal

half-life of racemic methadone in the rat is 90

minutes after chronic oral administration of drug and

70 minutes after chronic parenteral subcutaneous

(s.c.) administration of drug. The clearance of

methadone in the mouse is similarly very rapid: 112

minutes after chronic parenteral S.C. administration.

Therefore, the most commonly used rodent models are

very poor models to use for studies of methadone

interactions unless either a constant infusion of

methadone is given or, more realistically, the

profound differences in pharmacokinetics are

Figure 1

Plasma Levels of Methadone Over a 24-Hour Dosing

Interval in an Individual Maintained on 100mg/day

199
appreciated and drug interactions are studied only

during the first 1 hour following methadone

administration (Burstein et al. 1980; Kreek 1979) (see

figure 2).

To be able to rigorously determine the dispositional

fate and pharmacokinetics of a single dose of orally

administered methadone against a background of

chronic methadone dosage, a new technology has been

developed and used in basic clinical research

studies. For this purpose, various stable isotopelabeled species of methadone have been specially

synthesized. These include: dl(SR)- pentadeuteromethadone, d(S)-trideuteromethadone, l(R)-pentadeuteromethadone, and dl(SR)-octadeuteromethadone

(Hachey et al. 1977; Kreek et al. 1979; Nakamura et al,

1982) (see figures 3 and 4).

Figure 2

Plasma Levels of Methadone in the Rat During Chronic

Treatment Using the Oral or Subcutaneous Routes of

Administration. Copyright 1979, Phar Biochem

Behav, ANKHO Int. Inc. Kreek 1979.

200
Figure 3

Structure of Pentadeuteromethadone (Methadoned5,i.e., 2

H5-Methadone). Copyright 1979, Life Sci,

Pergamon Press, Ltd. Kreek et al. 1979.

Figure 4

Four Different Species of Stable Isotope Labeled

Methadone. Copyright 1982, J of Pharm Sci,

American Pharmaceutical Association. Nakamura

et al. 1982.

203
In some studies, the entire daily dose of methadone

has been administered to chronic methadone-maintained

patients as the racemic dl(SR)-pentadeuteromethadone.

Using gas chromatography-chemical ionization mass

spectrometry with selected ion monitoring, the enrichment of the labeled over the unlabeled species of

methadone has been determined over a period of several days following administration of the single,

stable isotope-labeled oral dose. In other studies,

a single, stable isotope-labeled species of one enantiomer of methadone has been administered along with

equal amounts of the other enantiomer in unlabeled

form and the fate of the single-labeled enantiomer

was determined, again using gas chromatographychemical ionization mass spectrometry, with selected

ion monitoring to determine the amounts of the

single-labeled species against a background of

unlabeled methadone. Finally, and most recently, the

technique has been significantly modified so that

each of the two enantiomers of methadone may be given

simultaneously as separate, stable isotope-labeled

species. Thus, the full daily dose of methadone can

then be given as racemic methadone, but with each

enantiomer labeled separately so that the fate of

each enantiomer may be determined simultaneously in a

single study. For these studies, dl(SR)-octadeuteromethadone is used as an internal standard in the gas

chromatography-chemical ionization mass spectrometry

measurements, so that the absolute amounts, as well

as the ratios of labeled to unlabeled species, of

methadone may be determined in a single analytical

procedure (Hachey et al.1977; Kreek et al. 1979;

Nakamura et al. 1982) (see figures 5 and 6).

Radioisotope-labeled species of methadone could not

be used in such studies because of the very wide distribution and tissue binding of methadone in the

body; the resultant low plasma levels of methadone

which ensue; and the appropriate ethical limits on

amounts of radioisotope-labeled compound which can be

given to human subjects. The ethically acceptable

amounts would be insufficient to obtain a sufficiently high specific activity of labeled methadone in

plasma for pharmacokinetic studies. Using the stable

isotope technology, it has been determined that the

plasma apparent terminal half-life of methadone

ranges from 19 to 38 hours and that the plasma

apparent terminal half-life of the active l(R)-enantiomex is significantly greater than that of the inactive d(S)-enantiomer. Using this stable isotope

technique, it has been shown that mean half-times for

disappearance of the two enantiomers of methadone in

urine are 34 hours for the inactive d(S) and 57 hours

for the active l(R)-enantiomers, respectively.

202
If studies of drug interactions between drugs of

abuse, or drugs for treatment of drug abuse and other

agents of use or abuse are to be carried out in human

subjects, appropriate subject selection, characterization of the health status of each subject, and

appropriate sub-group analysis are essential. The

very high prevalence of liver disease in substance

abusers, in general, necessitates a consideration of

the effects of liver disease on the disposition of

each drug to be studied, as well as on the effects of

drugs ox agents used concomitantly. We have carried

out extensive studies of the effects of liver disease

of various types and degrees of severity on the

disposition of methadone in humans. This was

absolutely essential because of the very high

prevalence of liver disease in both heroin addicts

and former heroin addicts in methadone maintenance

treatment. Several prospective studies by our group,

as well as similar studies by others (Beverly et al.

1980; Hartman et al. 1983; Kreek et al. 1972; Kreek

1973A; Kreek 1978A; Kreek 1981,; Novick et al. 1985A;

Figure 5

Urine Disappearance Curves

of d - and 1 - Methadone

in 3 Patients (After

Separate Oral Administration of Pentadeuteromethadone: Time in Hours).

Copyright 1979, Life Sci,

Pergamon Press, Ltd.

Kreek et al. 1979.

203
Novick et al. 1985B), have shown that over 60% of all

heroin addicts have biochemical, with or without

clinical, evidence of chronic liver disease and that

approximately the same percentage of patients

continue to have biochemical with or without

clinical evidence of chronic liver disease during

chronic long-term methadone maintenance treatment.

It has been shown that chronic liver disease in

former heroin addicts and in methadone maintenance

treatment patients is due to: 1) chronic sequelae of

hepatitis B virus infection, delta agent infection,

and, undoubtedly, also sequelae of non-A/non-B

hepatitis virus infection: and 2) alcohol-induced

hepatotoxicity, with alcohol-induced liver disease

(Kreek et al. 1972). In many patients, liver disease

has been documented to be of a mixed type, due both

to viral sequelae of hepatitis virus infection and to

alcohol-induced injury (Beverly et al. 1980; Hartman

Figure 6

Plasma Disappearance Curves of the Separate d - and 1

- Enantiomers of Stable and Plasma Levels of dl -

Methadone (After Simultaneous Oral Administration of

Separate Stable-Labeled Species). Copyright 1982,

J of Pharm Sci, American Pharmaceutical Association.

Nakamura et al. 1982.

204
et al. 1983; Kreek 1978A; Kreek 1978B; Kreek 1984;

Novick et al. 1985A; Novick et al. 1985B).

In one prospective study, it was shown that 20% of

all heroin addicts entering methadone maintenance

treatment were also chronic abusers of alcohol and

that approximately the same percentage of these

patients continued to abuse alcohol on a chronic

basis during methadone maintenance treatment (Kreek

1978A). In other studies, it has been shown that

from 20 to 50% of heroin addicts entering treatment

for narcotic addiction and chronic methadone

maintained patients, including both youthful drug

abusers who have become chronic long-term heroin

addicts and thus have been admitted to methadone

maintenance treatment and adult heroin addicts who

have been entered into methadone maintenance

treatment, remain chronic abusers of alcohol (Beverly

et al. 1980; Hartman et al.1983; Novick et al.1985A).

Several studies have shown that the prevalence

of all hepatitis B markers in heroin addicts and

methadone maintenance patients is around 95% and that

about 12% of heroin addicts and patients in the early

years of methadone maintenance treatment are chronic

carriers of hepatitis B antigen. (Kreek 1973A; Kreek

1978A; Kreek et al. 1972; Novick et al.1981A).

Studies of methadone disposition have been carried

out in patients with moderately severe, but fully

compensated, chronic liver disease of a cirrhotic

type. Each of these patients had been treated with

chronic methadone maintenance for extended periods of

time. In these studies, it was shown that the

apparent plasma terminal half-life of methadone was

prolonged to 35.5 +/- 7.6 hours in patients with

compensated alcoholic and/or viral cirrhosis, as

compared to 18.8 +/- 3.0 hours in the contrast group

(Novick et al.1981B). It was also found that plasma

levels of methadone were not increased in cases of

severe liver disease, as had been anticipated from

studies of the effects of chronic liver disease on

the disposition of other drugs. In more recent

studies of patients with very severe liver disease,

performed to determine the disposition of methadone

in that setting, the plasma levels of methadone

appear to be even lower than those in otherwise

healthy patients receiving similar doses of

methadone (Novick et al.1985C). These findings are of

considerable interest because they are the opposite

of what might have been anticipated.

Methadone is metabolized primarily by the hepatic

P450-dependent microsomal drug-metabolizing enzymes.

Methdone is first N-demethylated to form an inactive

205
pyrrolidine metabolite--the major metabolite of

methadone --which in turn undergoes, in part, a second

N-demethylation to form a second inactive metabolite,

a pyrroline. There are also several minor metabolites of methadone, only two of which, accounting

for less that 2% of the administered dose, are

pharmacologically active compounds (see figure 7).

The liver plays two important roles in the disposition of methadone: first, it is the major site

of biotransformation of methadone: and, second, it

has been shown to be an important site for nonspecific storage and release of unchanged methadone.

The liver thus serves as a reservoir for methadone,

probably accounting in large part for the long-acting

pharmacokinetic properties of methadone (Kreek et al.

1978). Using an isolated perfused rabbit liver

preparation, it has been found that methadone is

avidly extracted by the liver, but after extraction,

it is extensively bound non-specifically to plasma

membranes where it is stored for subsequent release

in an unchanged form (Kreek et al. 1978). Only a

small proportion of methadone is metabolized during

its first pass through the liver (Kreek et al. 1978).

Therefore, although methadone is avidly extracted by

the liver, it does not undergo the classical "first

pass" effect, implying uptake followed at once by

Figure 7

Structures of Methadol, Methadone, Pyrrolidine,

Pyrroline, Pyrrolidone Metabolite of Methadone.

Copyright 1983, Biomedical Mass Spec, John Wiley

& Sons Ltd. Kreek et al. 1983.

206
biotransformation usually to inactive forms, and

excretion. Similarly, using a whole rat model, it

has been shown that a single dose of methadone may

persist in various tissues for up to 6 weeks and

that, at all time points studied, the liver contains

both the highest concentrations and the highest total

amounts of methadone (Harte et al. 1977; Siring et al.

1981A; Ziring et al. 1981B). Thus, the liver appears

to be the major reservoir for methadone storage in

tissue and subsequent release. In the setting of

very severe chronic liver disease (such as severe

cirrhosis), the profoundly decreased hepatocellular

mass with resultant decreased reservoir capacity

plays an even greater role in altering methadone

disposition than does the reduction in hepatic

P450-dependent microsomal drug-metabolizing enzyme

capacity, which, though probably very compromised in

a setting of severe chronic liver disease, may

remain adequate to carry out the biotransformation of

methadone, since this drug is metabolized very

gradually by the liver over a 24-to-48-hour period,

as contrasted to short-acting drugs which are very

rapidly metabolized by the hepatic microsomal P450

drug-metabolizing enzymes.

Other studies have been carried out to determine the

effects of chronic liver disease of varying types and

degrees of severity on the urine and fecal excretion

of methadone and its metabolites. For this work,

techniques have been developed using direct probe

chemical ionization mass spectrometry, in which the

stable isotope-labeled methadone octadeuteromethadone

is used as an internal standard for the quantitation

of unlabeled methadone and its metabolites, but without the administration of stable isotope-labeled

compounds for use as in vivo tracers in these

studies (Kreek et al. 1980A; Kreek et al. 1983A). In

these studies, it was shown that from 46 to 55% of an

oral daily dose of methadone is excreted in urine as

the sum of unchanged methadone and its metabolites;

the major pyrrolidine metabolite accounts for 24 to

29% of urine clearance of the daily methadone dose,

whereas unchanged methadone accounts for 10 to 26% of

the total amount (Kreek et al. 1980A). In otherwise

healthy patients, a mean of 48% of the daily dose of

methadone is excreted by the urinary route of

methadone plus all methadone metabolites; whereas, in

patients with chronic liver disease of varying types

and degrees of severity, there was a significant

decrease in the total amounts of methadone and

metabolites excreted in urine, with a mean of 31% of

the daily dose being excreted by that route (Kreek et

al. 1980A) (see figure 8). In additional studies

which also used direct probe chemical mass

207
spectrometry, it was shown that, in a setting of

chronic liver disease, increased amounts of both

unchanged methadone and its metabolites are excreted

in feces. This suggests that, in the setting of

decreased hepatic reservoir capacity, methadone is

either more extensively biotransformed or passed as

unchanged methadone into the bile after hepatic

uptake and, thus, eliminated in feces (Kreek et al.

1983A) (see figure 9). In all of these studies, no

patients were observed to have any symptoms of

relative narcotic overdose. This fact is of clinical

importance, since patients who have stable chronic

liver disease may thus be treated with methadone

without undue concern about accumulation of drug in

plasma and, therefore, excessive amounts of active

compounds at various receptor sites. Thus, any

possible adverse central nervous system effects would

not be expected to occur in the setting of severe

chronic liver disease. These findings also should be

considered when planning and conducting studies of

drug interactions in narcotic-dependent persons, or

other parenteral drug abusers, in whom chronic liver

disease of one or more types is known to be of high

prevalence.

Figure 8

Urinary Excretion of Methadone and Its Metabolites in

Humans as Studied by Direct Probe Chemical Ionization

Mass Spectrometry

208
Limited studies have also been carried out to determine the effects of compromised renal function on

methadone disposition. In these limited studies, it

has been shown that both in a setting of oliguria in

a patient maintained on hemodialysis, as well as

following renal transplantation, there was no

evidence of accumulation of methadone in plasma

(Kreek et al. 1980B). Plasma levels of methadone in

those settings were found to be within the range

expected in otherwise healthy subjects for the dose

of methadone administered. Also in these studies

it was found that there were marked increases in the

fecal excretion of methadone, which accounted for

essentially the total elimination of methadone in

patients with oliguria or anuria.

Controlled clinical studies have also shown that

methadone disposition becomes significantly altered

during late pregnancy (Kreek et al. 1974; Kreek 1979;

Pond et al. 1985). In early observations and study

Figure 9

Fecal Excretion of Methadone and Its Metabolites in

Humans, as Studied by Direct Probe Chemical

Ionization Mass Spectrometry

209
of one case, it was found that, for a given dose of

methadone, the plasma levels of methadone during the

third trimester of pregnancy were significantly lower

than would be expected for the dose administered and,

yet, the levels returned to anticipated levels for

dose shortly following delivery (Kreek et al. 1974).

Subsequently, prospective studies have been carried

out in several methadone-maintained patients who were

documented to be free of any alcohol or polydrug

abuse and who had minimal to no evidence of chronic

liver disease (Kreek 1979) Pond et al. 1985). In

these studies, it was shown that, during the last

trimester of pregnancy, the plasma levels of

methadone become progressively lowered with

concomitant lowering of the area under the plasma

concentration time curve coupled with increased

nonrenal clearance of methadone (Kreek 1979; Pond et

al. 1985).

Although albumin levels are normally lowered in late

pregnancy, globulin levels become increased. Methadone binds to both albumin and globular fractions.

Specific binding studies were performed which showed

that the percentage of free methadone did not change

significantly and, thus, could not account for the

observed lowering of plasma levels and enhanced

clearance of methadone during late pregnancy (Pond et

al. 1985). In these studies, it was also shown that,

in the postpartum period, plasma levels of methadone

resulting from administration of a constant dose

increased promptly back to the normal anticipated

range (Pond et al. 1985). Each patient served as her

own control in this study, thus minimizing any

interindividual variation. In these studies, as well

as in all of our other controlled studies, we have

shown that both in otherwise normal healthy subjects

and in patients with chronic liver disease and

altered physiological states, there is very little

intraindividual variation of plasma levels of

methadone when studies are carried out on different

days but in a clinical research setting, where time

of day, time of dosing, and circadian factors, along

with careful monitoring of patients for use and abuse

of other substances which might alter drug disposition, can all be controlled. However, all of our

studies have indicated that there is highly significant interindividual variation with respect to

plasma levels of methadone after any given dose.

Both the lowering of plasma levels of methadone in

drug interactions discussed below, and the lowering

of plasma levels of methadone during late pregnancy,

exceeded what one would expect to see, even given the

range of interindividual variations in levels and,

far beyond expectation, given the very limited range

210
of intraindividual variation. In these studies,

since each patient served as her own control, it was

possible to see both the progressive lowering of

plasma levels and the area under the plasma concentration time curve as pregnancy progressed and, then,

a return to normal range of these indices in the

postpartum period (Kreek 1979; Kreek et al. 1974; Pond

et al. 1985). It is assumed, though not proven, that

the hormonal milieu of pregnancy resulting in an

enhanced biotransformation of methadone is a major

factor accounting for the reduction in plasma levels

of methadone. This formulation is supported in part

by the finding of increased urinary excretion of the

major pyrrolidine metabolite relative to the excretion of unchanged methadone during pregnancy. It has

been shown by others that progestins, which are

present at very high levels during late pregnancy,

may enhance the hepatic microsomal P450-dependent

enzyme activities for some substrates. Also, the

biotransformation of methadone to inactive compounds

by the placenta and the fetal liver may have contributed in part to the apparent enhancement of methadone metabolism and lowering of plasma levels

observed during late pregnancy.

The first clinical observations and documentation of

interaction of any other drug with a narcotic drug

were the observations of the profound interactions

between the antituberculosis drug, rifampin, and the

long-acting narcotic methadone, being used on a

chronic basis in the maintenance treatment of

addiction (Kreek et al. 1976A). Rifampin has been

shown to have several effects on the liver, including

a hepatocellular toxic effect resulting in nonspecific hepatitis in some cases and an effect on

hepatic uptake and biliary clearance of a variety of

substances. Also of specific importance for this

discussion, it has been shown that rifampin is a

potent enhancer of hepatic microsomal P450-dependent

drug-metabolizing enzyme activities. Based on

knowledge of this action of rifampin, it was

postulated that this drug, and also phenobarbital,

which similarly is a potent enhancer of hepatic

microsomal P450-dependent drug-metabolizing enzyme

activities, might result in acceleration of

metabolism of a drug such as methadone, which

normally is dependent upon the hepatic P450

microsomal enzymes for its biotransformation. The

first clinical observations of such an interaction

were made in a special clinic for the simultaneous

management of heroin addiction, by use of chronic

methadone maintenance treatment, and tuberculosis, by

use of a variety of anti-tuberculosis agents. Severe

signs and symptoms of narcotic withdrawal ensued when

211
the then experimental new drug rifampin was added to

the therapeutic regimen of some of the methadonemaintained patients who, up to that point, had been

successfully stabilized on both methadone maintenance

and anti-tuberculosis treatment, but who needed

additional antituberculosis treatment because of far

advanced resistant disease. Thirty of the 86

patients in this special treatment program for

tuberculosis and narcotic addiction were treated with

rifampin and, of these, signs and symptoms of

narcotic withdrawal developed in 21 cases, or 70%.

Of the 21 patients who developed narcotic withdrawal

symptoms, 14 had mild symptoms of withdrawal whereas,

in 7, severe withdrawal symptoms ensued. Mild

withdrawal symptoms included abdominal cramps,

rhinorrhea, lacrimation, yawning, and irritability:

in the severe cases, additional symptoms included

nausea, vomiting, anorexia, joint pains, chills,

tremulousness, insomnia, and severe anxiety (Kreek et

al. 1976A). Most of the severely affected patients

developed withdrawal symptoms within the first week

of combined rifampin and methadone treatment.

Studies were carried out to determine whether there

were any dispositional or pharmacokinetic interactions between methadone and rifampin in six of

these patients with severe narcotic withdrawal symptoms during combined treatment (Kreek et al. 1976A).

Each patient was studied while receiving methadone

alone, and while receiving methadone plus rifampin.

A highly significant lowering of plasma levels of

methadone, along with a decreased area under the

plasma concentration time curve, was observed in each

of the six patients studied during the combined

rifampin and methadone treatment as compared to the

period of treatment with methadone alone (see figure

10). Also in each case, the symptoms of narcotic

withdrawal reappeared when rifampin treatment was

reinstituted for study purposes. The apparent plasma

terminal half-life of methadone was not significantly

altered during combined rifampin treatment, despite

the fact that the plasma levels and area under the

plasma concentration curves were significantly

lowered. Further studies showed increased urinary

excretion of the N-demethylated pyrrolidine

metabolite in urine and great increases in fecal

excretion of the N-demethylated inactive metabolites

during combined rifampin and methadone treatment

(Kreek et al. 1976A; Kreek et al. 1976B). The results

of these studies indicate that when rifampin must be

added to the antituberculosis therapeutic regimen of

a patient receiving methadone on a chronic basis for

treatment of addiction or for the management of

chronic pain, the patient should be carefully

212
observed; increases in doses of methadone and/or

increases in frequency of doses will probably be

needed to sustain the desired effects of methadone in

this observed and documented interaction between

rifampin and methadone. The effectiveness of careful

clinical observations, followed by controlled

clinical research studies which elucidate both the

phenomena and the probable mechanisms of the drug

interaction, was demonstrated in this observed and

documented interaction between rifampin and

methadone. This is a kind of approach which may be

most effective from all standpoints in identifying

and elucidating interactions between drugs of abuse

or between drugs, such as methadone, used to

pharmacologically treat drug abusers and other drugs

of abuse or therapeutic agents, such as rifampin.

Figure 10

Plasma Concentration-Time Curves for Methadone During

Treatment with Methadone Alone and During Concomitant

Treatment with Methadone and Rifampin in 6 Patients.

Copyright 1976, New England Journal of Medicine

294:1104-1106, 1976. Kreek et al. 1976.

213
A second clinically very significant drug interaction

with methadone has also been observed clinically and

then studied in a controlled setting (Tong et al.

1981). It was noted that when the anticonvulsant

drug phenytoin (Dilantin) was added to the

therapeutic regimen of patients receiving methadone

on a chronic basis, symptoms of narcotic withdrawal

would frequently appear within 1 to 4 days,

suggesting that phenytoin enhances methadone

metabolism. A study was carried out in volunteer

chronic methadone-maintained patients without

polydrug abuse, alcohol abuse, or significant liver

disease. The disposition and pharmacokinetics of

methadone were studied in these volunteer subjects

while being treated with methadone alone and, then,

while also receiving the anticonvulsant drug

phenytoin in therapeutic doses (1000 mg in two

divided doses on the first day, followed by a single

daily dose of 300 mg on the second through fifth

day). Moderate to severe symptoms of narcotic

withdrawal appeared in each of these subjects by the

third or fourth day of concomitant phenytoin and

methadone administration. The drug disposition

studies showed that plasma levels of methadone became

significantly reduced during concomitant phenytoin

treatment as compared to treatment with methadone

alone (Tong et al. 1981) (see figure 11). Quantitative analyses of methadone and its metabolites

in urine documented that, during concomitant

phenytoin treatment, there was a very significant

increase in the relative amount of the inactive

N-demethylated pyrrolidine metabolite of methadone,

thus showing enhanced urinary excretion of the

biotransformation products of methadone during phenytoin treatment (Tong et al. 1981) (see figure 12).

Thus, both in clinical observations and in controlled

studies carried out in a clinical research setting,

it was shown that phenytoin enhances methadone

clearance, resulting in lower plasma levels of

methadone at all time points and a reduction in the

the area under plasma concentration time curves.

These changes in methadone disposition are accompanied by highly significant clinical signs and

symptoms of moderate to severe narcotic withdrawal.

Thus, again, as recommended in the case of rifampin,

when phenytoin is added to the therapeutic regimen of

a patient receiving methadone treatment on a chronic

basis, whether for the maintenance treatment of

addiction or for the management of chronic pain, it

is essential to observe the patient carefully and

adjust the dose or dosing intervals of methadone as

needed (Kreek et al. 1976A; Kreek et al. 1976B; Tong et

al. 1981).

214
Figure 11

Effects of Phenytoin (Dilantin) on Plasma

Levels of Methadone

Figure 12

Effects of Phenytoin (Dilantin) on Urinary

Excretion of Methadone and its Major

N-Demethylated Pyrrolidine Metabolite

215
Other clinical observations have suggested that

possibly the pharmacological agent used in an attempt

to manage alcohol abuse, disulfiram (Antabuse), might

cause dispositional interactions with methadone. A

study was carried out in seven male volunteer subjects, all of whom were receiving chronic methadone

maintenance treatment and none of whom had any alcohol or polydrug abuse or significant liver disease

(Tong et al. 1980). Dispositional studies of methadone metabolism were carried out first while subjects

received methadone alone and, then, while they received methadone plus disulfiram in therapeutic

doses. There were no significant changes in plasma

levels of methadone during combined disulfiram treatment, and the areas under the plasma concentration

time curves were not altered in the setting of combined treatment (Tong et al. 1980). However, quantitative analyses of the excretion of methadone and its

metabolites in urine showed that there was a relative

increase in urinary excretion of the N-demethylated

pyrrolidine metabolite of methadone during

concomitant disulfiram treatment as compared to

treatment with methadone alone, suggesting that some

minor dispositional interaction between methadone and

disulfiram had occurred. There were no clinical

signs and symptoms of narcotic withdrawal in the

study subjects (Tong et al. 1980). However, with the

suggestion that some minor dispositional interaction

might have occurred, it will probably be necessary to

carry out similar studies to determine whether there

are any significant dispositional interactions

between disulfiram and methadone in patients who are

former alcohol abusers in chronic methadone treatment

and, thus, might be receiving this combination of drugs,

including both those subjects without and also with

alcohol-induced liver disease or mixed type of liver

disease of varying degrees of severity.

Studies have also been carried out in humans to determine whether the acute social use of alcohol would

have any effect on either methadone disposition or

action (Cushman et al. 1978). For these studies,

chronic methadone-maintained volunteers without a

history of either alcohol abuse or polydrug abuse and

without significant chronic liver disease were

selected. Plasma levels of methadone were determined

during treatment with methadone alone and during

administration of an acute dose of 90 ml of ethanol

in juice solution. There were no significant changes

in plasma levels of methadone during combined

treatment as compared to treatment with methadone

alone (Cushnan et al. 1978). Also, there were no

unexpected or enhanced effects of either ethanol or

methadone observed clinically in this setting

(Cushman et al. 1978).

216
Studies are currently in progress to determine if

there are any significant interactions between

methadone and ethanol in the setting of chronic

alcohol abuse by chronic methadone-maintained

patients (Kreek 1984). Based on studies carried out

for other drugs, primarily in animal models, with

respect to possible interactions with ethanol, it has

been suggested that two types of dispositional

interactions may occur during chronic ethanol use or

abuse. When ethanol is administered in high doses

(probably doses too high to be tolerated by any

animal or human who is not already tolerant and

dependent on ethanol), inhibition of the metabolism

of a second drug may occur at the hepatic microsomal

P450-dependent enzyme activity level, since ethanol,

to an extent of approximately 30% of an administered

dose undergoes biotransformation by that system and

since the microsomal system is more extensively

utilized in the metabolism of alcohol in the setting

of high blood levels resulting from consumption or

administration of large amounts of ethanol. Conversely, it has been well documented that chronic

ethanol administration causes enhancement of the

hepatic microsomal P450-dependent enzyme activities;

therefore, when alcohol itself has been cleared from

the body, drugs which are normally metabolized by the

hepatic microsomal P450-dependent enzymes are

metabolized more rapidly than normal because of this

enhancement by ethanol. Thus, a biphasic effect of

ethanol on the metabolism of another drug occurs.

Whether or not each of these types of dispositional

drug interactions occur when large amounts of ethanol

are ingested by chronic alcohol-abusing methadonemaintained patients has yet to be established and is

currently under study by our laboratory group.

In each of these examples, it has been amply demonstrated that studies of drug interactions between

methadone and a second drug may be carried out in a

controlled clinical research setting and that the results have immediate and enormous clinical relevance.

However, it is essential sometimes either to conduct

pilot studies using an animal model or to use an

animal model to elucidate mechanism of action of such

drug interactions. In our laboratory, we have used

both whole animal models, primarily involving administration of one or two drugs to rats, as well as

isolated perfused liver preparations. Using organ

models, we have shown, for instance, that acute perfusion with ethanol does not significantly affect the

hepatic uptake of methadone, nor does it affect the

immediate metabolism of methadone (Kreek et al. 1981).

217
We have also shown that hepatic perfusion with

ethanol does not affect the immediate hepatic uptake

of other narcotics, including morphine (Kreek et al.

1981). Thus, the observed inhibition of drug

metabolism by ethanol is probably due to a sustained

action of competitive inhibition by ethanol of the

second drug at the hepatic microsomal drug-metabolizing enzyme. In studies carried out in the whole

rat, we have shown that, when animals have been

treated with both ethanol and methadone, or with

methadone alone on a chronic basis, and determination

of plasma levels of methadone is carried out 24 hours

following the last dose of ethanol and 1 hour following dosing with methadone (a time point which conforms with what is now known about the pharmacokinetics of methadone in the rat), significant reductions in plasma levels of methadone are observed

in the animals which have been treated with both

ethanol and methadone as compared to animals treated

with methadone alone (see figure 13). Thus, chronic

ethanol treatment does result in acceleration of

methadone metabolism in the whole rat model. However, if other time points had been selected for

study, other interpretations might have ensued simply

Figure 13

Plasma Levels of Methadone in Rats Treated with

Methadone Alone as Compared with Methadone and

Ethanol on Chronic Basis

218
because of the pharmacokinetic properties of methadone in a rat, i.e., the very rapid metabolism of

methadone in this animal (Kreek 1981, Kreek 1978).

Similarly, studies have been carried out using the

whole rat model to determine possible interactions

between phenobarbital and methadone. Again, in these

studies, animals are treated chronically with methadone plus phenobarbital or with methadone alone

(Kreek 1978B). These studies have shown that plasma

levels of methadone at 1 hour after dose administration are significantly lower in animals treated with

methadone plus phenobarbital than in animals treated

with methadone alone (Kreek 1978B) (see figure 14).

It was also shown that the fecal excretion of methadone plus metabolites was significantly enhanced in

phenobarbital-treated animals as compared to animals

treated with methadone alone. Furthermore, it was

critical to fully appreciate the pharmacokinetics of

both methadone and phenobarbital in the rat model

before this interaction could be properly studied.

Figure 14

Plasma Levels of Methadone in Rats Treated with

Methadone Alone as Compared with Methadone and

Phenobarbital on a Chronic Basis. Copyright 1978,

Raven Press, N.Y. Kreek 1978B.

219
These studies were carried out because phenobarbital

is the most potent enhancer of hepatic microsomal

P450-dependent drug-metabolizing enzyme activity of

all agents categorized as enhancers or "inducers" of

hepatic P450-dependent drug-metabolizing enzyme

systems.

This is an example of the use of animal models to

study the effect of a model drug, with a known type

of mechanism of action, on the disposition of a

second drug. Prom these studies, it was predicted

that, should any patient be placed on phenobarbital

treatment, or abuse phenobarbital, while on chronic

methadone treatment, methadone plasma levels probably

would be significantly reduced and, probably, narcotic withdrawal symptoms would ensue. Thus, it was

predicted that a drug interaction between methadone

and phenobarbital would occur similar to that clinically observed and documented to occur between methadone and rifampin, and between methadone and phenytoin, with the same resultant precipitation of the

narcotic abstinance syndrome. The first clinical

report of such an interaction between phenobarbital

and methadone in a chronically maintalned patient was

published recently (Liu and Wang 1984). In this report,

careful clinical observations, coupled with multiple

determinations of methadone plasma levels, were made

in a single patient observed to develop symptoms of

narcotic withdrawal while in chronic methadone maintenance treatment, first unexplained, and later documented and verified to be due to chronic phenobarbital abuse. In this patient, it was shown that, during the period of phenobarbital abuse, plasma levels

of methadone and the area under the plasma concentration time curve of methadone were significantly reduced as compared to similar indices of methadone

disposition when that same patient was studied both

before and some time following cessation of phenobarbital abuse, while taking methadone alone (Liu

and Wang 1984).

In developing a strategy to identify, define, and

elucidate the mechanisms of any drug interactions between drugs of abuse, or drugs of use and abuse, or

agents used therapeutically in the treatment of drug

abuse with other agents of use or abuse--although it

may often be appropriate and suitable to use animal

models or in vitro models--clearly, the most urgently

needed studies are those which can be carried out in

human beings and, thus, directly document the presence or absence of any pharmacokinetic or pharmacodynamic interactions of clinical significance in

the human species. Such work is extremely difficult

to carry out and is frustrating for a multitude of

220
reasons; this type of clinical research frequently is

not perceived by critics, at least in advance of

performance of such studies, to be of merit.

Nevertheless, it is quite clear that once performed,

especially when positive findings are made, clinical

studies are the basis for not only more appropriate

therapeutic management, or prevention, of the effects

of concomitant use of two interacting agents, but

also serve as the basis for making predictions of

future drug interactions which may be of clinical

significance in humans. Thus, properly conducted

studies of drug interactions in humans using

rigorously and appropriately selected study subjects

should be encouraged and supported.

REFERENCES

Beverly, C.L.; Kreek, M.J.; Wells, A.O; and Curtis,

J.L. Effects of alcohol abuse on progression of

liver disease in methadone maintained patients.

In: Harris, L.S., ed. Problems of Drug

Dependence, 1979: Proceedings of the

41st Annual Scientific Meeting, The

Committee on Problems of Druq Dependence,

Inc. National Institute on Drug

Abuse Research Monograph 27: DHHS Pub. No. (ADM)

80-901. Washington, D.C.: Supt. of Docs., U.S.

Govt. Print. Off., 1980. pp. 399-401.

Burstein, Y.; Grady, R.W.; Kreek, M.J.; Rausen, A.R.;

and Peterson, C.M. Thrombocytosis in the offspring of female mice receiving dl-methadone.

Proc Soc Exp Biol Med 164:275-279, 1980.

Cushman, P.; Kreek, M.J.; and Gordis, E. Ethanol and

methadone in man: A possible drug interaction.

Drug Alcohol Depend 3:35-42, 1978.

Dole, V.P., and Kreek, M.J. Methadone plasma level:

Sustained by a reservoir of drug in tissue. Proc

Natl Acad Sci USA 70:10, 1973.

Hachey, D.L.; Kreek, M.J.; and Mattson, D.H. Quantitative analysis of methadone in biological

fluids using deuterium labeled methadone and GLCchemical ionization mass spectrometry. J Pharm

Sci 66:1579-1582, 1977.

Harte, E.H.; Gutjahr, C.L.; and Kreek, M.J. Longterm persistence of dl-methadone in tissues. Clin

Res 24:623A, 1977

Hartman, N.; Kreek, M.J.; Ross, A.; Khuri, E.;

221
Millman, R.B.; and Rodriguez, R. Alcohol use in

youthful methadone maintained former heroin

addicts: Liver impairment and treatment outcome.

Alcoholism Clin Exp Res 7:316-320, 1983.

Kreek, M.J. Medical safety and side effects of methadone in tolerant individuals. JAMA 223:665-668,

1973A.

Kreek, M.J. Plasma and urine levels of methadone.

NY Stat J Med 23:2773-2777, 1973B.

Kreek, M.J.

patients.

Kreek, M.J.

Medical complications in methadone

Ann NY Acad Sci 311:110-134, 1978A.

Effects of drugs and alcohol on opiate

disposition and actions. In: Adler, M.W.;

Manara, L.; and Samanin, R., eds. Factors

Affecting the Action of Narcotics. New

York: Raven Press, 1978B. pp. 717-739.

Kreek, M.J. Methadone disposition during the

perinatal periods in humans. Pharmacol Biochem

Behav ll(Supp.):7-13, 1979.

Kreek, M.J. Metabolic interactions between opiates

and alcohol. Ann NY Acad Sci 362:36-49, 1981.

Kreek, M.J. Opioid interactions with alcohol.

Advances in Alcohol and Substance Abuse. New

York: The Hawarth Press, 1984. pp. 35-46.

Kreek, M.J.; Dodes, L.; Kane, S.; Knobler, J.; and

Martin. R. Long-term methadone maintenance

therapy: Effects on liver function. Ann Intern

Med 77:598-602, 1972.

Kreek, M.J.; Schecter, A.; Gutjahr, C.L.; Bowen, D.;

Field, F.; and Merkatz, I. Analyses of methadone

and other drugs in maternal and neonatal body

fluids: Use in evaluation of symptoms in a neonate of mother maintained on methadone. Am J Drug

Alcohol Abuse 1:409-419, 1974.

Kreek, M.J.; Garfield, J.W.; Gutjahr, C.L.; and

Giusti, L.M. Rifampin-induced methadone withdrawal. N Engl J Med 294:1104-1106, 1976A.

Kreek, M.J.; Gutjahr, C.L.; Garfield, J.W.; Bowen,

D.V.; and Field, F.H. Drug interactions with

methadone. Ann NY Acad Sci 281:350-370, 1976B.

Kreek, M.J.; Oratz, M.; and Rothschild, M.A. Hepatic

extraction of long- and short-acting narcotics in

222
the isolated perfused rabbit liver.

Gastroenterology 75:88-94, 1978.

Kreek, M.J.; Hachey, D.L.; and Klein, P.D. Stereoselective disposition of methadone in man. Life

Sci 24:925-932, 1979.

Kreek, M.J.; Field, F.H.; and Bencsath, F.A. Effects

of liver disease on urinary excretion of methadone

and metabolites in maintenance patients; Quantitation by direct probe-chemical ionization

mass spectrometry. Biomed Mass Spectrom

7:385-395, 1980A.

Kreek, M.J.; Schecter, A.J.; Gutjahr, C.L.; and

Hecht, M. Methadone use in patients with chronic

renal disease. Drug Alcohol Depend 5:197-205,

1980B.

Kreek M.J.; Rothschild, M.A.; Oratz, M.; Mongelli,

J.; and Handley, A.C. Acute effects of ethanol

on hepatic uptake and distribution of narcotics in

the isolated-perfused rabbit liver. Hepatology

1:419-423, 1981.

Kreek, M.J.; Bencsath, F.A.; Fanizza, A.; and Field,

F.H. Effect of liver disease on fecal excretion

of methadone and its unconjugated metabolites in

maintenance patients: Quantitation by direct

probe chemical ionization mass spectrometry.

Biomed Mass Spectrom 10(10):544-549, 1983A.

Kreek, M.J.; Schaefer, R.A.; Hahn, E.F.;and Fishman,

J. Naloxone, a specific opioid antagonist,

reverses chronic idiopathic constipation. Lancet

Feb 5:261-262, 1983B.

Liu, S.J.; and Wang, R.I.H. Case report of

barbiturate induced enhancement of methadone

metabolism and withdrawal syndrome. Am J Psych

141(101:1287-1288, 1984.

Nakamura, K.; Hachey, D.L.; Kreek, M.J.; Irving,

C.S.; and Klein, P.D. Quantitation of methadone

enantiomers in man using stable labeled methadone

2

H3, 

2

H5, 

2

H8. J Pharm Sci 71:40-43, 1982.

Novick, D.; Gelb, A.; Stenger, R.; Yancovitz, S.;

Adelsbert, B.; Chateau, F.; and Kreek, M.J.

Hepatitis B serologic studies in narcotics with

chronic liver diseases. Am J Gastroenterol

75:111-115, 1981A.

Novick, D.M.; Kreek, M.J.; Fanizza, A.M.: Yancovitz,

223
S.R.; Gelb, A.M.: and Stenger, R.J. Methadone

disposition in patients with chronic liver

disease. Clin Pharmacol Ther 30:353-362, 1981B.

Novick, D.M.; Enlow, R.W.; Gelb, A.M.; Stenger, R.J.;

Fotino, M.; Winter, J.W.; Yancovitz, S.R.;

Schoenberg, M.D.: and Rreek, M.J. Hepatic

cirrhosis in young adults: Association with adolescent onset of alcohol and parenteral heroin

abuse. Gut 26:8-13, 1985A.

Novick, D.M.; Farci, P.; Karayiannis, P.; Gelb, A.M.:

Stenger, R.J.; Kreek, M.J.; and Thomas, H.C.

Hepatitis B virus antibody in HBsAg-positive and

HBsAg-negative substance abusers with chronic

liver disease. J Med Virol 15:351-356, 19858.

Ncvick, D.K.; Kreek, M.J.; Arns, P.A.; and Lau, L.L.

Effects of severe alcoholic liver disease in the

disposition of methadone in maintenance patients.

Alcoholism and Clin Exper Res 9:349-354, 1985C.

Pond, S.M.;Kreek, M.J.; Tong, T.G.; Benowitz, N.L.

Changes in methadone pharmacokinetics during pregnancy. J Pharmacol Exp Ther 234:1-6, 1985.

Tong, T.G.; Benowitz, N.L.; and Kreek, M.J.

Methadone-disulfiram interaction during methadone

maintenance. J Clin Pharmacol 29:67-68, 1980.

Tong, T.G.; Pond, S.M.; Kreek, M.J.; Jaffery, N.F.;

and Benowirz, N.L. Phenytoin-induced methadone

withdrawal. Ann Intern Med 94:349-351, 1981.

Ziring, B.; Brown, L.; and Kreek, M.J. Effects of

route of administration on methadone disposition

in the rat. In: Harris, L.S.,ed. Problems of

Drug Dependence, 1980: Proceeding of the 42nd

Annual Scientific Meeting, The Commiytee on

Problems of Drug Dependence, Inc. National

Institute on Drug Abuse Research Monograph 34.

DHHS Pub. No.(ADM)81-1058. Washington, D.C.: Supt.

of DOCS., U.S. Govt. Print. Off., 1981A.

pp.145-151.

Siring, B.S.; Kreek, M.J.; and Brown, L.T.

Methadone disposition following oral versus parenteral dose administration in rats during chronic

treatment. Drug Alcohol Depend 7:311-318, 1981B.

ACKNOWLEDGMENTS

Dr. Kreek is a recipient of the ADAMHA-NIDA Research

224
Scientist Award DA-00049. The author would also like

to acknowledge the support of the New York State

Division of Drug Abuse Services, #C-001381. This

research was supported in part by a General Clinical

Research Center Grant, #RR00102, from the National

Institutes of Health and in part from an Extended

Range Mass Spectrometric Research Resource, #5, P41

RR00862-12.

AUTHOR

Mary Jeanne Kreek, M.D.

The Rockefeller University

1230 York Avenue

New York, New York 10021

225
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ed.

GPO out of stock NTIS PB #80-215171 $22.95

32 GC/MS ASSAYS FUR ABUSED DRUGS IN BODY FLUIDS. Rodger L. Foltz,

Ph.D.; Allison F. Fentiman, Jr., Ph.D.; and Ruth B. Foltz.

GPO out of stock NTIS PB #81-133746 $22.95

33 BENZODIAZEPINES: A REVIEW OF RESEARCH RESULTS, 1980. Stephen I.

Szara, M.D., D.Sc., and Jacqueline P. Ludford, M.S., eds.

GPO Stock #017-024-01108-8 $5 NTIS PB #82-139106 $16.95

34 PROBLEMS OF DRUG DEPENDENCE, 1980: PROCEEDINGS OF THE 42ND

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. NCDAI out of stock

GPO Stock #017-024-01061-8 $8 NTIS PB #81-194847 $34.95

35 DEMOGRAPHIC TRENDS AND DRUG ABUSE, 1980-1995. Louise G.

Richards, Ph.D., ed.

GPO out of stock NTIS PB #82-103417 $16.95

36 NEW APPROACHES TO TREATMENT OF CHRONIC PAIN: A REVIEW OF

MULTIDISCIPLINARY PAIN CLINICS AND PAIN CENTERS. Lorenz K.Y.

Ng, M.D., ed.

GPO out of stock NTIS PB #81-240913 $22.95

37 BEHAVIORAL PHARMACOLOGY OF HUMAN DRUG DEPENDENCE. Travis

Thompson, Ph.D., and Chris E. Johanson, Ph.D., eds.

NCDAI out of stock

GPO Stock #017-024-01109-6 $7 NTIS PB #82-136961 $28.95

38 DRUG ABUSE AND THE AMERICAN ADOLESCENT. Dan J. Lettieri, Ph.D.,

and Jacqueline P. Ludford, M.S., eds. A RAUS Review Report.

GPO Stock #017-024-01107-O $4.50 NTIS PB #82-148198 $16.95

39 YOUNG MEN AND URUGS IN MANHATTAN: A CAUSAL ANALYSIS.

Richard R. Clayton, Ph.D., and Harwin L. Voss, Ph.D.

GPO Stock #017-024-01097-9 $5.50 NTIS PB #82-147372 $22.95

40 ADOLESCENT MARIJUANA ABUSERS AND THEIR FAMILIES. Herbert

Hendin, M.D., Ann Pollinger, Ph.D., Richard Ulman, Ph.D., and

Arthur Carr, Ph.D. NCDAI out of stock

GPO out of stock NTIS PB #82-133117 $16.95

41 PROBLEMS OF DRUG DEPENDENCE, 1981: PROCEEDINGS OF THE 43RD

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. NCDAI out of stock

Not available from GPO NTIS PB #82-190760 $40.95

229
42 THE ANALYSIS OF CANNABINOIOS IN BIOLOGICAL FLUIDS. Richard L.

Hawks, Ph.D., eo. Not available from NCDAI.

GPO Stock #017-024-01151-7 $5 NTIS PB #83-136044 $16.95

43 PROBLEMS OF DRUG DEPENDENCE, 1982: PROCEEDINGS OF THE 44TH

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. NCDAI out of stock

GPO out of stock NTIS PB #83-252-692/AS $40.95

44 MARIJUANA EFFECTS ON THE ENDOCRINE AND REPRODUCTIVE SYSTEMS.

Monique C. Braude, Ph.D., and Jacqueline P. Ludford, M.S., eds. A

RAUS Review Report.

GPO Stock 6017-024-01202-5 $4 NTIS PB #85-150563/AS $16.95

45 CONTEMPORARY RESEARCH Ih PAIN AND ANALGESIA, 1983. Roger M.

Brown, Ph.D.; Theodore M. Pinkert, M.D., J.D.; and Jacqueline P.

Ludford, M.S., eds. A RAUS Review Report.

GPO Stock #017-024-01191-6 $2.75 NTIS PB #84-184670/AS $11.95

46 BEHAVIORAL INTERVENTION TECHNIQUES IN DRUG ABUSE TREATMENT. John

Grabowski, Ph.D.; Maxine L. Stitzer, Ph.D., and Jack E.

Henningfield, Ph.D., eds.

GPO Stock #017-024-01192-4 $4.25 NTIS PB #84-184688/AS $16.95

47 PREVENTING ADOLESCENT DRUG ABUSE: INTERVENTION STRATEGIES.

Thomas J. Glynn, Ph.D.; Carl G. Leukefeld, D.S.W.; and

Jacqueline P. Ludford, M.S., eds. A RAUS Review Report.

GPO Stock 8017-024-01180-1 $5.50 NTIS PB #85-159663/AS $22.95

48 MEASUREMENT IN THE ANALYSIS AND TREATMENT OF SMOKING BEHAVIOR.

John Grabowski, Ph.D., and Catherine S. Bell, M.S., eds.

GPO Stock #017-024-01181-9 $4.50 NTIS PB 84-145-184 $16.95

49 PROBLEMS OF DRUG DEPENDENCE, 1983: PROCEEDINGS OF THE 45TH

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed. NCDAI out of stock

GPO Stock #017-024-01158-3 $12 NTIS PB 85-151553/AS $34.95

50 COCAINE: PHARMACOLOGY, EFFECTS, AND TREATMENT OF ABUSE. John

Grabowski, Ph.D., ed.

GPO Stock #017-020-01214-9 $4.50 NTIS PB 85-150381/AS $16.95

51 DRUG ABUSE TREATMENT EVALUATION: STRATEGIES, PROGRESS, AND

PROSPECTS. Frank M. Tims. Ph.D., ed.

GPO Stock #0l7-020-01218-1 $4.50 NTIS PB 85-150365/AS $16.95

52 TESTING DRUGS FOR PHYSICAL DEPENDENCE POTENTIAL AND ABUSE

LIABILITY. Joseph V. Brady, Ph.D., and Scott E. Lukas, Ph.D., eds.

GPO Stock #017-024-0204-1 $4.25 NTIS PB 85-150373/AS $16.95

53 PHARMACOLOGICAL ADJUNCTS IN SMOKING CESSATION. John Grabowski,

Ph.D., and Sharon M. Hall, Ph.D., eds.

GPO Stock #017-024-01266-1 $3.50 NTIS PB No. to be assigned

230
54 MECHANISMS OF TOLERANCE AND DEPENDENCE. Charles Wm. Sharp,

Ph.D., ed. NCDAI out of stock

GPO Stock #017-024-01213-1 $8.50

55 PROBLEMS OF DRUG DEPENDENCE, 1984. PROCEEDINGS OF THE 46TH

ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG

DEPENDENCE, INC. Louis S. Harris, Ph.D., ed.

GPO Stock 6017-024-01242-4 $9.50

56 ETIOLOGY OF DRUG ABUSE: IMPLICATIONS FOR PREVENTION. Coryl LaRue

Jones, Ph.D., and Robert J. Battjes, D.S.W., eds.

GPO Stock #017-024-01250-5 $6.50

57 SELF-REPORT METHODS OF ESTIMATING DRUG USE: MEETING CURRENT

CHALLENGES TO VALIDITY. Beatrice A. Rouse, Ph.D., Nicholas J.

Kozel, M.S., and Louise G. Richards, Ph.D., eds.

GPO Stock #017-014-01246-7 $4.25

58 PROGRESS IN THE DEVELOPMENT OF COST-EFFECTIVE TREATMENT FOR DRUG

ABUSERS. Rebecca S. Ashery, D.S.W., ed.

GPO Stock 6017-024-01247-5 $4.25

59 CURRENT RESEARCH ON THE CONSEQUENCES OF MATERNAL DRUG ABUSE.

Theodore M. Pinkert, M.D., J.D., ed.

GPO Stock #017-024-01249-1 $2.50

60 PRENATAL DRUG EXPOSURE: KINETICS AND DYNAMICS. C. Nora Chiang,

Ph.D., and Charles C. Lee, Ph.D., eds.

GPO Stock 1017-024-01257-7 $3.50

61 COCAINE USE IN AMERICA: EPIOEMIOLDGIC AND CLINICAL PERSPECTIVES.

Nicholas J. Kozel, M.S., and Edgar H. Adams, M.S., eds.

GPO Stock #O17-024-01258-1 $5

67 NEUROSCIENCE METHODS IN DRUG ABUSE RESEARCH. Roger M. Brown,

Ph.D., and David P. Friedman, Ph.D., eds.

GPO Stock #017-024-01260-2 $3.50

63 PREVENTION RESEARCH: DETERRING DRUG ABUSE AMONG CHILDREN AND

ADOLESCENTS. Catherine S. Bell, M.S., and Robert Battjes,

D.S.W., eds.

GPO Stock #017-024-01263-7 $5.50

64 PHENCYCLIDINE: AN UPDATE. Doris H. Clouet, Ph.D., ed.

GPO Stock #017-024-01281-5 $6.50

65 WOMEN AND DRUGS: A NEW ERA FOR RESEARCH. Barbara A. Ray, Ph.D.,

and Monique C. Braude, Ph.D., eds.

GPO Stock #017-024-01283-1 $3.25

66 GENETIC AND BIOLOGICAL MARKERS IN ORUG ABUSE AND ALCOHOLISM.

Monique C. Braude, Ph.D., and Helen M. Chao, Ph.D. eds. In press.

67 PROBLEMS OF DRUG DEPENDENCE, 1985. PROCEEDINGS OF THE 47TH ANNUAL

SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS OF DRUG DEPENDEHCE,

INC. Louis S. Harris, Ph.D., ed. In press.

231

U.S. GOVERNMENT PRINTING OFFICE : 1986 O - 158-852
DHHS Publication No. (ADM)86-1453

Printed 1986