National Institute on Drug Abuse

RESEARCH

MONOGRAPH SERIES

Medications

Development:

Drug Discovery,

Databases, and

Computer-Aided

Drug Design

U.S. Department of Health and Human Services • Public Health Service • National Institutes of Health

134
Medications Development:

Drug Discovery, Databases,

and Computer-Aided Drug

Design

Editors:

Rao S. Rapaka, Ph.D.

Richard L. Hawks, Ph.D.

NIDA Research Monograph 134

1993

US. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Public Health Service

National Institutes of Health

National Institute on Drug Abuse

5600 Fishers Lane

Rockville, MD 20657
ACKNOWLEDGMENT

This monograph is based on the papers and discussions from a technical

review on “Medications Development: Drug Discovery, Databases, and

Computer-Aided Drug Design” held on November 14-16, 1991, in Washington,

DC. The technical review was sponsored by the National Institute on Drug

Abuse (NIDA).

COPYRIGHT STATUS

NIDA has obtained permission from the copyright holders to reproduce certain

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 US. Department of Health and Human Services.

The U.S. Government does not endorse or favor any specific commercial

product or company. Trade, proprietary, or company names appearing in this

publication are used only because they are considered essential in the context

of the studies reported herein.

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.”

National Institute on Drug Abuse

NIH Publication No. 93-3638

Printed 1993

i i
Contents

Page

Strategies for Drug Discovery 1

Michael Williams

Mass Ligand-Binding Screening Strategies for Identification

of Leads for New Drug Discovery 37

Ronald M. Burch

Antibody- and Peptide Structure-Based Drug Design 46

Manfred E. Wolff

The Use of Cloned Human Receptors for Drug Design 56

Paul R. Hartig

Synthetic Peptide Combinatorial Libraries May Be Used

To Determine Peptide Ligands for Opioid Receptors 66

Richard A. Houghten and Colette T. Dooley

Preparation of Large Peptide Libraries With One Peptide per

Bead and Their Use for the Discovery of Peptides That Bind

to Acceptors 75

Victor J. Hruby, Kit S. Lam, Michal Lebl, Wieslaw Kazmierski,

Evan M. Hersh, and Sydney E. Salmon

Computer Design of Bioactive Compounds Based on

3-D Properties of Ligands 64

Y.C. Martin

iii
Toward the Design of New Inhibitors: A Computer

Modeling Approach 103

Carlos H. Faerman

Efforts To Improve the Utilization of Protein Sequence

and Structure Databases 114

Nagarajan Pattabiraman and Krishnan Namboodiri

Use of Computer Graphics in Drug Design 129

G. W.A. Milne and M.C. Nicklaus

Purification and Cloning of Opioid Receptors 146

Ling-Yuan Li, Ying-Fu Su, Zeng-Ming Zhang,

Chih-Shung Wong, and Kwen-Jen Chang

Computer-Assisted Design of Dopamine Transporter Ligands 165

Gilda H. Loew, Hugo 0. Villar, Patricia A. Maguire,

and M. Frances Davies

The Pharmacophore for Opioid Activity 178

Mark Froimo witz

The Molecular Basis of Opioid Potency and Selectivity:

Morphiceptins, Dermorphins, Deltorphins, and Enkephalins 195

Murray Goodman, Seonggu Ro, George Ösapay,

Toshimasa Yamazaki, and Alexander Polinsky

Molecular Determinants of Cannabinoid Activity: Toward

the Design of Cannabinoid Analgesics With Reduced

Psychoactive Liability 210

Patricia H. Reggio

Cocaine Receptor: A Structure-Activity Relationship Study 229

F. Ivy Carroll, Anita H. Lewin, Michael J. Kuhar,

and John W. Baja

The Use of Conformational Restriction and Molecular

Modeling Techniques in the Development of ReceptorSpecific Opioid Peptide Agonists and Antagonists 236

Peter W. Schiller, Thi M.-D. Nguyen, Grazyna Weltrowska,

Nga N. Chung, Carole Lemieux, Brian J. Marsden,

and Brian C. Wilkes

iv
Probes for the Cannabinoid Sites of Action 253

Alexandros Makriyannis

A Model for the Structural Basis of -Opiloid Receptor Selectivity 266

Henry I. Mosberg and Frank Porreca

List of NIDA Research Monographs 281

v

FOREWORD

We are at a point in the development and application of new technologies in

medications development that is expanding at an incredible rate. This meeting

brings together the various disciplines associated with new drug design, all of

which make use of cutting edge technologies.

Receptor characterization through computer modeling techniques and the

cloning and application of such clones in in vitro screening is greatly advancing

the power of discovery programs for new pharmacotherapies. New modeling

techniques, in particular the new means to use theoretical models as a basis

for rapidly searching large 3D SAR databases for the discovery of

pharmacophoric entities among previously synthesized compounds, is an area

of technological advancement that changes almost on a daily basis. These

changes are being spurred by rapidly decreasing costs and increasing

sophistication in the hardware and software available.

Another new technological area of rapid growth is the mass synthesis and

screening of peptides, where thousands of unique peptide structures can be

prepared, screened, and new active individual peptides identified in a very short

time. These techniques in combination with receptor clones, themselves

resulting from characterization based on ligands prepared with new modeling

techniques, are rapidly altering the way we search for new pharmacotherapies,

both in the drug abuse area and in the pharmaceutical industry in general.

Limited resources can now be focused in a discovery approach that is much

more effective and efficient than was ever conceived of a decade ago.

This monograph provides an overview of some of these new techniques and

approaches, particularly as they apply to pharmacotherapeutic development for

drug abuse, and will hopefully serve to stimulate new ideas and their

application and integration in the effort to solve the drug abuse problem.

Charles Grudzinskas, Ph.D.

Director, Medications Development

Division

National Institute on Drug Abuse

National Institutes of Health

vii

Strategies for Drug Discovery

Michael Williams

INTRODUCTION

From vaccines that have reduced many fatal diseases to the realm of history,

to psychoactive agents that have permitted many people to live normal lives,

to antihypertensive drugs that prolong and improve the quality of life, to

contraceptives that have permanently changed sexual mores, the products of

the ethical pharmaceutical industry have had a major and irrevocable impact

on the social fabric of society. Yet many disease states still remain untreated

or are currently treated by agents whose side-effect profile leaves significant

cause for concern. The aging of the population, partly a result of the availability

of more effective medications, has presented additional challenges in

therapeutic agent development, with an urgent need for drugs to treat

arthritis, cancer, neurodegeneration, and cognition impairment.

However, the process of drug discovery is complex and costly (Jack 1989;

DiMasi et al. 1991), and innovative science is especially so. But drug

discovery is more than the application of basic research knowledge and

technologies (Black 1986); it involves many facets of project management

and focus (Klimstra and Rafael 1992; Roussel et al. 1991; Jacques et al.

1992). To precisely define the drug discovery process and the parameters

crucial to success is a difficult task. For each successful drug there are many

accounts, both corporate and scientific, as to the process and the visionaries

and facilitators involved in bringing the compound to the marketplace.

Overviews of the drug research and development (R&D) processes (Sneader

1985, 1986; Williams and Malick 1987; Hesp and Resch 1987; Spilker 1988;

Hamner 1990; deStevens 1991; Gilbert 1992) have been complemented by

insightful case studies for several important drugs (Maxwell and Eckhardt

1990). Given the proprietary nature of the drug discovery process, the

failures, the blind alleys, and the persistence that are such an important

part of the drug discovery environment and that can account for up to 80

percent of the corporate research effort are rarely documented. Thus, the

database from which to derive lessons that may aid in the implementation of

new projects is limited. The commercial focus of the drug discovery process

equates the success of a project with a product and rarely with the successful

testing of a hypothesis (via the use of a new compound) that results in the

1
advancement of scientific knowledge. Given that discovery is a process of

“learning things not already known” (Roussel et al. 1991), success is equally

tenable when a project is terminated for technical or competitive reasons,

resulting in the avoidance of further “sunk costs” (Staw and Ross 1989).

In this chapter, some integral elements of the drug discovery process are

discussed, focusing on newer technologies and the scientific and business

environments that dictate the feasibility of bringing a needed therapeutic entity

to the marketplace.

THE DRUG DISCOVERY PROCESS

A uniform strategy for the drug discovery process has remained elusive despite

considerable analysis. Like the science on which the drug discovery process is

based, the approach to a problem involves many paths, with considerable trial

and error. Strategies for drug discovery are dependent on interrelated factors

that include:

Corporate, research, and marketing department cultures

Individual scientists working within a company

Synergies among various research disciplines

Morale and quality of research management

R&D leadership

Individual and corporate experience

Extent to which the management of a company is accustomed to risk

Serendipity

A particular company’s market franchise (the therapeutic areas in which

it markets drugs)

Inevitably, many drugs are described in terms of the individuals who were

associated with championing the research effort, often against considerable

scientific and corporate odds. Although making for interesting (and often

exciting) reading (Sneader 1985; Parnham and Bruinvels 1983; Teitelbaum

1989; Schwartz 1989; deStevens 1991; Djerassi 1992; Duncan, in press),

drug discovery is inevitably a team effort requiring constant iteration based

on experimental findings, planning, and synergies across many different

scientific, development, and marketing disciplines.

2
The drug discovery and development process can be divided into distinct

steps that interface with each other. The first step involves deciding on a

given therapeutic target, which entails an iterative process involving the

current state of the understanding of disease etiology, scientific knowledge

and available technology, unmet medical need, and commercial opportunity.

In diseases such as hypertension, for which there are many effective

medications already in the marketplace, new programs directed at this target

must focus on significant additional benefits. For agents being developed for

diseases such as Alzheimer’s disease (AD) or cancer, for which there are

currently no effective or safe treatments, the decision to target the disease

state is an easier one to justify even though the science involved represents

a significantly greater risk.

Once a decision has been made to target a particular therapeutic area (e.g.,

cardiovascular, anti-infective), diseases within the given area can be evaluated

and prioritized based on the existence of potential molecular targets, existing

technologies (both in-house and external) and scientific expertise, competitive

aspects of the marketplace, project maturity (and time to market), unmet need,

ease of clinical trials, and so on. After this prioritization has been done, the

process of drug discovery can begin.

From a historical perspective, drug discovery as a distinct scientific discipline

is very much a 20th century endeavor that has undergone continuous evolution

as the scientific skill bases that support the process have increased in

sophistication.

Through the early 1940s, much of the process of drug discovery was

dependent on plant sources and serendipity (Sneader 1985, 1990; Mann and

Plummer 1991). The technology base was synthetic chemistry, often based

on dyes, with qualitative testing of compounds as anti-infective agents, in whole

animals, with microbiology and limited biochemistry to support compound

evaluation. Among the drugs from this era were the fungal antibiotics.

A second phase of drug discovery emerged with advances in enzymology

and protein biochemistry. Many of the biological pathways and processes

were identified as enzymologists and pharmacologists defined new enzymes,

receptor ligands, and their functions. As a result, although serendipity was

still a major factor, drugs were now directed toward distinct molecular targets,

The evolution of this phase was rapid, with increased Federal support of the

biomedical sciences, growing sophistication in computational processing,

and the availability of the personal computer.

The third phase of drug discovery is one represented by the popular vision of

computer-driven discovery and development of compounds that not only treat

3
the symptoms of the disease but also lead to its cure. The potential for the

Intellectually rigorous targeting of therapeutic agents to molecular targets whose

genetics, structure, function, and pathophysiology are well understood is a noble

goal. Unfortunately, this somewhat naive view of several promising yet still

emerging technologies presumes more than biomedical science has learned

to date. Although there are superior medications to treat hypertension, asthma,

schizophrenia, and anxiety, for example, their etiology remains unknown,

leading to a major dependence on hypothesis testing.

Interestingly, the drug discovery wheel has come full circle as fermentation, plant,

marine, and invertebrate sources have again emerged as important sources of

novel therapeutic entities. Among the compounds identified in the past decade

from such sources are the cholecystokinin antagonist asperlicin (Chang et al.

1985) and the immunosuppressant FK 506 (Schreiber 1991).

Once a disease has been targeted and the appropriate test protocols have been

assembled, it is still uncertain whether a particular approach will result in a drug.

When little is known about the disease, to the extent that molecular targets are

unknown or sufficiently ill addressed by the available compounds, test procedures

usually depend on animal models. Such models are usually empirical, relying on

either the inducement of disease-like symptoms via the use of exogenous agents

or surgical procedures or on the expression of behaviors that have been defined

for compounds known to be clinically efficacious. In the former instance, asthma

can be induced by allergens in guinea pig and new compounds used to relieve

the symptoms. However, this process does not define the causative factors for

asthma in humans but does address the symptoms. Similarly, anxiolytic drugs

are currently tested in several variations of a passive avoidance situation. In

such a paradigm, an animal is taught to perform a behavior for a reward. It is

then exposed to aversive stimuli so that it does not perform the behavior. When

given a compound with antianxiety actions, disinhibition of the aversion behavior

occurs so that the animal will seek rewards in spite of the aversive stimuli.

Although the benzodiazepines and N-methyl-D-aspartate antagonists are active

in this paradigm, its relationship to human anxiety is empirical. Identification of

compounds in such paradigms is a useful indication of potential human activity,

When a molecular target can be selected, there is still uncertainty as to whether

stimulation or inhibition of that target will be effective in a given disease state.

Thus, to a major extent, the design, synthesis, and testing of new molecular

entities selective for a given target frequently provide the tools by which a

hypothesis can be evaluated. If the hypothesis proves to be in error, information

is added to the scientific literature but no drug is found. This is the working

approach to drug discovery that is frequently unappreciated and unreported.

If drug targets and the design of their ligands were as simple as the application

of new technologies, more than 20 percent of the effort in drug discovery would

reach fruition in the identification of clinical candidates. As it stands, 80 percent

of the effort is valuable, if ultimately nonproductive, hypothesis testing.

4
EMERGING TECHNOLOGIES IN DRUG DISCOVERY

As noted, a major target in the drug discovery process is to develop a high

degree of “rationality.” This would represent an intellectually rigorous approach

incorporating computer-assisted molecular design (CAMD), limited but

sophisticated chemical synthetic effort, and highly focused biological assays.

To many, rational drug design suggests that it is now possible, with the

knowledge of the three-dimensional (3-D) structure and sequence of various

drug targets, to design new compounds, by iteration, on a computer. Thus,

the drug design process may become significantly less of a risk and quantally

more resource efficient.

However, many of the enabling technologies that support the rational design

approach are still in the emergent stage; that is, they have theoretical promise

usually based on their use in the retrospective analysis of known compounds.

With the exception of the enzyme thymidylate synthase (Appelt et al. 1991),

molecular modeling techniques have yet to be generally used in a predictive

manner.

It is important to recognize that individual technologies, however sophisticated,

may become self-limiting if there is insufficient vision in their appropriate

application and integration into the mainstream drug discovery effort. A

goal-oriented discipline such as drug discovery is dependent on the coherent,

focused, and resource-efficient use of necessary technology rather than

technology for technology’s sake.

Using a building as an analogy, the architect represents the individual who

provides the global vision of the final product. In the process of building, he

or she makes use of various technologies provided by experts such as

carpenters, electricians, stonemasons, and plumbers in a highly integrated

and focused manner. To allow the electrician, the artesan of what was high

technology in the late 19th century, to drive the building process based solely

on what electrical wiring could do would not be considered a particularly wise

approach to completing the building project. To continue the analogy, the

architect of the drug discovery process is by necessity the pharmacologist,

who, because of training in a hierarchical systems approach, orchestrates

the biological testing technologies to the desired endpoint.

Receptor binding—the use of radiolabeled ligands to “tag” drug targets,

receptors, and enzymes-has revolutionized the ability to determine compound

structure-activity relationship (SAR) in a rapid, cost-effective manner (Williams

1991). This technology resulted from early work on characterizing the insulin

receptor by Roth and Cuatrecasas (Cuatrecasas 1974). In its present form (and

diversity), receptor binding was driven by the work of Snyder and coworkers such

5
that binding assays for nearly 100 receptors or enzymes have been developed.

The technology has also been used in the identification of new receptors and

recepto subtypes.

Advantages of the technology include a direct analysis of the interaction of a

ligand with a receptor (or substrate with an enzyme), the use of small amounts

of material, and rapid throughput, Thus, as little as 3 mg of compound

(sufficiently little to permit the analysis of intermediates from a synthetic

pathway) can be run in 30 to 40 assays in 2 to 3 weeks at an estimated cost

per assay of approximately $200. This allows for a binding profile to be

developed, which can then be used as a potential predictor of diverse activity

in a compound. The binding approach can be contrasted with more classical

functional assays where as much as 2 g of material was used and 3 to 4 weeks

elapsed before data on a compound were available. Although the cost in this

instance was typically in the range of $1,500 to $3,000, such assays also

provided information on whether a compound was an agonist or an antagonist

at the receptor target. Using binding as prescreen, researchers can reduce the

number of compounds put into more complex and time-consuming functional

assays.

Targeted screening (Burch 1991; Williams and Jarvis 1990) involves the use

of the receptor-binding technique to evaluate large numbers of compounds—

20,000 to 50,000 per year—in multiple assays to identify new pharmacophores.

Compound sources include herbal, marine, and bacterial fermentations as well

as chemical compound libraries, The latter include dissimilar pharmacophores

from chemical companies, compounds synthesized as part of a directed

chemical effort, and novel structures with no known biological activity.

Targeted screening is an iterative process dependent on a finite availability of

compounds and binding assays. Ideally, as newer targets are identified and

assay systems for them are developed, compounds should be reevaluated in

a continuous manner.

Molecular modeling, or CAMD, is an emerging technology that makes use

of knowledge of the steric and electronic aspects of the receptor/ligand,

enzyme/substrate interaction to identify pharmacophores or aid in their design

or both (Marshall and Naylor 1990; Blaney and Hansch 1990; Snyder 1992).

The target/ligand interaction can be studied from three vantage points: (1)

knowledge of the SAR within a series and among series of pharmacophores,

in effect approaching the receptor or enzyme from the drug perspective; (2)

knowledge of the structure of the receptor or enzyme, approaching the problem

from the receptor viewpoint (Hollenberg 1990); and (3) information regarding

the receptor/ligand, enzyme/substrate interaction derived by 2- or 3-D nuclear

magnetic resonance (NMR) (Fesik 1991), x-ray crystallographic, or other

structural protein analysis methods.

6
Each of these approaches has inherent limitations. The compound SAR

approach is limited in that the protein target (receptor or enzyme) is normally

configured on a computer database in a minimal energy configuration with

an approximation of water content. This approach has traditionally assumed

that the protein and ligand have limited degrees of flexibility, a constraint that

reflected the computational power available. With supercomputers such as

the Cray 2, the protein/ligand interaction can now be assayed in real time with

increased flexibility in the programing assumptions. It recently was noted that

the receptor/ligand interaction can involve more than a single step (Saunders

and Freedman 1989) and that receptors can induce changes in ligand

conformation (Wuthrich et al. 1991) features that had been known for enzymes

for a number of years. These facets of the receptor/ligand interaction present

additional dimensions to the CAMD process.

Knowledge of the structure and 3-D conformation of the protein target provides

an opportunity to identify the amino acid sequences and conformations that

are responsible for ligand recognition and efficacy. These can be derived by

knowledge of the primary sequence and, for a receptor, knowledge of which

transmembrane helices are involved in ligand recognition. The interaction of

various pharmacophores and compounds within a pharmacophore series can

be used to identify the critical amino acids, When molecular biology is used to

change these critical amino acids as point mutations, their importance in

defining the ligand recognition parameters can be assessed (Hollenberg 1990).

Additional information regarding the biophysical aspects of the protein/ligand

interaction in real time using NMR (Fesik 1991) can then be used to

hierarchically integrate information from the previous two approaches to gain

a more concise understanding of those properties of a molecule that impart

selectivity, activity, and efficacy at a given protein.

CAMD is frequently a visually attractive technology based on colorful and

complex computer images. From a theoretical perspective, it has the potential

to significantly enhance the drug design process, As a technology, it is limited

by several necessary assumptions already noted and by limitations in database

construction. Information from biological assays cannot yet be downloaded

into CAMD programs while knowledge on protein structure is currently being

built. In addition, the CAMD process is limited because few biological data

are available. Activity and efficacy represent two compound properties that

are amenable to existing technology. Bioavailability and metabolism have yet

to be addressed by this important technology. Consequently, to assume that

CAMD is presently at a stage where it can be used to design, iterate, and

select new chemical structures independently of biological testing and intuition

is to devalue the technology by overstating its present-day capabilities.

7
Molecular biology is the use of recombinant DNA (rDNA) technology to express

biologically important proteins and peptides in prokaryotic and eukaryotic cell

systems (Schoepke 1988). Although the phrase “molecular biology,” or

“biotechnology,” has become a broad descriptor for many facets of modern

biology, including molecular biology, biochemistry, and immunology, it also

includes the venture capital-driven revolution in biomedical research, which is

discussed further below. Included in the technique of molecular biology are

gene cloning, splicing, and expression and polymerase chain and ligase chain

technologies (Koshland 1991).

The drugs produced by molecular biology, proteins or peptides known as

biologics, were initially considered as replacements for the small molecules

produced by synthetic chemistry. Because biologics were “natural” drugs, they

were considered, in the absence of data, to be essentially free of the side-effect

profiles of more traditional drugs (Buell 1988). Although this has not proven to

be the case (Williams 1990; Williams et al., in press), many useful therapeutic

agents such as insulin, insulinotropin, human growth hormone, tissue

plasminogen activator, erythropoietin (EPO); various cytokines, including

interleukin 2 and colony stimulating factors; and other growth factors and

interferons, monoclonal antibodies, vaccines, and blood products (Factor

VIII and Factor IX) have proven the usefulness of biologics as therapeutics.

The majority of biologics are systemically active replacement therapies for

hormones and other blood-borne autacoids. Their effectiveness is then a

function of their being administered systemically and acting at sites proximal

to the blood supply. Although useful in certain circumstances, biologics in their

present forms cannot replace traditional drugs, which cover a broader spectrum

of activity and tissue specificity and have superior pharmacokinetic properties.

The role of molecular biology in the drug discovery process has been

increasingly enhanced as a tool that, when properly integrated, can offer

major benefits to the drug hunter. The cloning, transfection, and expression

of receptor and enzyme genes can be used to prepare mammalian cell lines

specific for a given drug target, These cell lines can then be used instead of

animals to evaluate the activity and efficacy of new compounds free of the

complexity of cells derived from animal sources, Deliberate alterations in

the genetic material can be used to develop point mutations in amino acid

sequences to assess the importance of various substitutions in the expressed

protein as well as to prepare chimeric receptors (Kobilka et al. 1988) that aid

in the understanding of receptor recognition and the transduction processes.

Complementary DNA technology can be used to identify new proteins and,

using tissues from patient populations, to determine the genetic bases of

various diseases.

Such information can then be used to transfect laboratory animals to make

transgenic animals that have the genetic code for human diseases. These

8
animals can be used to study the etiology of the disease and the effects of new

compounds on disease progress. Transgenic models of various cancers, AD, and

diabetes are being developed. In addition, the transgenic approach is being used

to produce biologics in cow’s milk.

In the AD area, models of cognitive impairment involve either chemical lesions of

the basal forebrain cholinergic system or the use of aged rats or primates. In both

instances, the experimental paradigms are artificial. Although it is known that the

cholinergic system undergoes degeneration in AD, there are major deficits in

other neurotransmitter systems as well as plaque and tangle formation associated

with amyloid deposition (Selkoe 1991). Although ablation of forebrain cholinergic

systems is an approach to an animal model of AD, it does not reflect the nuances

of the human disease state. Similarly, in aged animals, the cognitive impairment

is not necessarily reflective of AD. A primary focus in the past year by at least

four groups has been to transfect rodents with various amyloid precursor protein

constructs with the expectation that overexpression of amyloid protein will lead

to AD pathophysiology. To date, the results of these transgenic studies remain

controversial (Marx 1992).

Peptide combinatorial library technology is an emerging technology that involves

the production of many millions of different peptide sequences to enhance the

discovery of pharmacophores for peptide receptor targets. The technology exists

in varying forms as exemplified by the Affymax (Fodor et al. 1991), Selectide

(Lam et al. 1991), and Iterex/Houghten Pharmaceuticals (Houghten et al. 1991)

approaches. Given the attractiveness of peptide receptors, adhesion molecules,

and so forth as drug targets and the paucity of agonist pharmacophores identified

by conventional peptide chemistry approaches, the peptide library approach

represents an important new tool for drug discovery.

Antisense technology encompasses the use of synthetic oligonucleotides as

potential drugs. This technology has been a primary focus in venture capital

companies and has been somewhat modestly described as the “first revolution in

drug discovery since the discovery of the receptor” (Crooke 1991). Proponents

of the approach view traditional drugs as molecules acting on cellular proteins

(enzymes and receptors). These are the product of mRNA expression, By

selectively blocking events at the ribosomal or nuclear levels, antisense ligands

can potentially prevent the expression of aberrant proteins at a much earlier stage

“downstream” and theoretically may be more precise drugs than those blocking

the function of the expressed protein. The technology involves oligonucleotides

of 15 to 18 bases that act at three levels (Crooke 1992; Riordan and Martin 1991):

(1) as agents that bind to mRNA to selectively prevent protein synthesis, (2) as

triple helix (triplex) agents that block DNA promoter regions, and (3) as aptamers,

randomly coiled oligonucleotides that interact with conventional protein targets

(receptors and enzymes). Limitations to this technology are bioavailability and

selectivity. How does an antisense drug make its way, not only through the gut

9
and liver, but also inside the cell to reach the nucleus? And if this transition can

be attained, how is selectivity implied?

Intracellular receptors or hormone-responsive elements (HREs) represent

newer drug targets. These entities, nuclear receptors for the steroid receptor

superfamily (Evans 1988; O’Malley 1990), are present on DNA and act as

transcription factors that modulate promoter activity to stimulate or inhibit

gene expression. Several putative receptors belonging to the steroid receptor

family have no known ligands and have been designated as orphan receptors

(Denner et al. 1990) whose activity may be modulated by phosphorylation in

the absence of ligand. One such orphan receptor, the chick ovalbumin upstream

promoter transcription factor, is activated by dopamine (Power et al. 1991),

providing an unusual example of an intracellular HRE that is responsive to an

extracellular ligand. The HRE technology has been commercialized by Ligand

Pharmaceuticals and is being used by a number of companies on a contract

basis as a targeted screening approach for their in-house chemical libraries.

Technology Acquisition

The acquisition and incorporation of new technologies into a research

organization can be an expensive, continuous, and, frequently, incremental

process. Maintaining a competitive edge requires “technological literacy”

whereby a research organization has a complete and viable repertoire of

techniques necessary for the advancement of drug discovery projects within

the organization.

Keeping up with new technologies requires the addition of new staff with

expertise in the necessary disciplines or the retraining of existing personnel

(Williams et al., in press). The delay in the latter can be 1 or 2 years and may

represent a self-defeating catchup process. No sooner do existing staff

members become proficient in the new technology than the needs become

redefined. In addition, the transitional nature of the retraining process creates

a negative impact on the ongoing research effort, inasmuch as those individuals

being retrained are no longer able to perform their previous duties. Although the

addition of new staff requires incremental head count, the technology acquired

is typically available within 3 to 6 months of recruitment and does not involve

the transfer of staff from ongoing research efforts. Nonetheless, the issue of

redundant technology is not addressed, although retraining can occur when

a newer technology replaces an existing one.

Another approach to technology acquisition is contract research. A

pharmaceutical company can pay a university laboratory to evaluate a

compound or series of compounds in a test procedure ongoing in that

laboratory. Similarly, a commercial screening laboratory can also be used

to “hire” technology without the need for in-house retraining or increasing

head count.

10
As technologies have become increasingly sophisticated and proprietary via

the formation of biopharmaceutical startups, research collaborations have

become the means to acquire cutting-edge technology in a highly focused

and cost-effective manner. By investing in smaller, high-tech companies, the

larger pharmaceutical companies are able to acquire “turnkey” technology in

an area without the necessary commitment of increasing staff in-house. This

investment can provide a short- to medium-term solution to recruiting, which

may be limited by staff, space, and capital equipment availability, at the same

time allowing feasibility assessments of new technologies or research areas.

A company could provide seed money to four or five biopharmaceutical

companies involved in different technologies or approaches to a therapeutic

area and follow the relevant research activities over a 2- to 3-year period

before investing heavily in one particular approach. This strategy can provide

the means to enhance the amount of exploratory research a company can fund,

while avoiding the problem of investing in such research in a new technology

or area only to find that it is not relevant or practical and then having to find

alternative projects for the staff members who were hired.

ORGANIZATIONAL ASPECTS OF THE DRUG DISCOVERY PROCESS

The establishment of the project team necessary to integrate the various

complex technologies involved in the drug discovery process is highly

dependent on the structure of the discovery organization. Three main types

of organization exist within the pharmaceutical industry.

The functional or technical line organization (figure 1) is made up of distinct,

technologically based departments such as chemistry, biochemistry, and

pharmacology. Support groups for these major drug discovery disciplines

would include structural biology, CAMD, formulation, drug metabolism, and

so on. A project would function across these line organizations, with individuals

from each of the major disciplines involved in the ongoing project. Reporting

responsibilities would be through the technical directors, who typically would

work together to allocate and prioritize resources. The advantage of this type

of organization is that it permits technological specialization with an emphasis

on “cutting-edge” science. In addition, resource prioritization is highly flexible

in that head count can be moved to higher priority projects as necessary. In

addition, there is a central vision as to where the organization is directing its

efforts. The disadvantages are that the individual projects are superimposed

onto the technical line organization and that the directors of each of the

technologies (or the research director) decide on priorities. This situation can

often lead to friction between different projects and the technical department.

The aggregate resources requested are viewed by the technical director as in

excess of his or her resources, whereas the project members view resources

used in maintaining cutting-edge technology as negatively affecting their

project. Another disadvantage is a perceived diffuseness in the responsibility

11
FIGURE 1. Functional line organization

for project goals because most individuals will be working on two or more

projects.

The second type of organization is that of the dedicated project team

(figure 2). In this type of organization, the technical disciplines necessary for

drug discovery are contained within a single unit. Thus, a group of chemists

and biologists focus their efforts exclusively on the project. The reporting

relationship is to a project leader. The major advantages of this system relate

to the focus on a single project with shared therapeutic commitment and a

lack of conflicting pressures. The disadvantages are a lack of flexibility, which

makes it difficult to effectively reallocate resources without major changes in

global priorities; a diminished focus on enabling technology development; and

a narrow focus on the project, which has the potential to overlook scientific

advances outside the needs of the project.

The third type of organization is that of the project matrix (figure 3). This

is a mixture of the previous two types and involves a basic functional line

organization involving technical specialties with a project team cutting across

the organization. Individuals have line reporting relationships to a technical

director but are also responsible to a project leader. The advantages of this

system reflect the best of the previous two as well as an accentuation of the

negative. Thus, the matrix is flexible and technology is current, but reporting

priorities are complex. Unless there are sufficient resources, technical directors

perform a balancing act between the needs of the various projects while

providing time for their reports to maintain their technical skill base. The

matrix organization can on the one hand be very dynamic and creative

and on the other the source of considerable friction.

12
FIGURE 2. Dedicated project team organization

Variations on these three major themes abound. Some organizations have

line organizations based on therapeutic areas, and others superimpose a

therapeutic matrix on a line organization, Flexibility in some companies is

achieved by having chemistry “swat teams.” These function independently

of the mainstream and are used by the organization to push projects forward

rapidly by injecting a bolus of chemical resources. Such entities are extremely

valuable when the project is highly competitive and lead structures among

several companies are similar. Discovery organizations (that part of the R&D

organization conducting research) may undergo further modification via the

nature of their interface with the Development organization, the complementary

component of the R&D process involved in clinical evaluation of new

components.

The design of the line Discovery organization follows, for the most part,

organizational structure in academia. Deviations from this in terms of the

various forms of therapeutic and project matrices as well as the mode of

interfacing with Development frequently arise as the result of the evaluation

of an existing organization by a management consultant group. Spilker (1988)

has described such groups as “lack[ing] adequate knowledge about the drug

industry [and] drug development.” Nonetheless, management consultant

groups are often used by companies in an effort to increase research

13
FIGURE 3. Project matrix organization

productivity. This effort usually results in a reorganization of the research

department, either functionally or from a personnel perspective. For many

in the industry, this reorganization has become a seemingly continuous

process that in itself is an effective barrier to productivity. On the positive

side, constant reorganization does provide gainful employment for

management consultant groups.

Project management represents an important adjunct discipline to the project

team approach. Inevitably, irrespective of the type of project team or the

organizational backdrop, there is a need for the objective prioritization of

resources to meet project needs. A typical midsize pharmaceutical company

will have 12 to 25 research projects all with their own constituency and

resourcing needs that probably exceed those of the organization by at least

twofold. For the research director, the ability to prioritize the project needs

via a project management team that is independent of the individual projects

with a prime focus on compound flow can be a major benefit.

SCIENTIFIC SKILL BASES

Organizations dedicated to the discovery of new therapeutic entities are

multidisciplinary in nature, involving chemistry, biology, and computational

and physical sciences. Many subspecialties exist within the major disciplines,

reflecting the complexity of the drug process and the specialization of modernday science. Because an individual is unlikely to have more than passing

familiarity with these various disciplines, the drug discovery process has

become increasingly dependent on the team approach (Drucker 1988;

Roussel et al. 1991).

14
Included in the discipline of chemistry are organic, medicinal, physical, process,

theoretical, and computational chemists, each of whom represents a subspecialty

of chemical synthesis with different experiences and techniques. As an example,

the chemical goals involved in the novel synthesis of milligram quantities of a new

compound are very different from those involved in bulk drug manufacturing for

commercial sale. In the first case, the synthetic pathway is developed to reach

a new molecule as rapidly and easily as possible. There is little consideration for

economies of scale, cost of starting materials, number of steps, yield, timeframe,

or synthesis. As a result, synthetic costs per gram of compound at this stage

can be as much as $50,000. At the stage of bulk drug manufacturing, synthesis

is carried out in line with the U.S. Food and Drug Administration’s (FDA) Good

Manufacturing Practices conditions. The focus is on cost reduction, ease of

synthesis, yield, and quantity of material. Consequently, there is a very different

focus on how the compound is made. When kilogram quantities are made, the

cost per gram moves progressively downward from about $1,000 to $5.

Although the discipline of chemistry is primarily involved with compound

synthesis (deStevens 1991), there is a recent trend toward training “hybrid”

chemists with skills encompassing both compound synthesis and molecular

biology. Although one benefit from melding the two disciplines has been to

more effectively understand the interactions between compounds and their

protein targets, it has also augmented an increasing shortage in chemistry

graduates with interest and experience in organic synthesis.

Biological disciplines are considerably more diverse and encompass biochemistry

(including enzymology); pharmacology (including both in vitro and in vivo aspects

of cell, tissue, and whole animal function); molecular biology; cell biology;

pharmacokinetics; drug delivery; and drug formulations. It is probably a

truism that the biological sciences involved in the drug discovery process are

considerably more dynamic than the chemistry skill bases, because change

occurs much more rapidly in the biological disciplines and at an ever-increasing

pace.

Computational, physical, and organizational disciplines affecting the drug

discovery process should not be overlooked. These include CAMD, NMR,

and x-ray crystallography, which have already been discussed. Underlying

these disciplines are support services, such as research computing, that provide

a great deal of programing skill and assistance to benefit computers and the

other sophisticated instrumentation that drive these technologies forward.

Information scientists make a major contribution by providing the mechanisms

to maintain currency with the scientific literature as well as by designing and

supporting laboratory information management systems and databases. Tracking

compounds, their activities, and archival storage is an underestimated technology

that has a major impact as organizations mature and their information bases

15
expand. Patent activities, an increasingly complex facet of the biomedical

research arena, can also be included under this aspect (Grubb 1987; Vieillefosse

1990; Yevich 1991).

However, drug discovery should not be viewed as a technology-driven process,

a trend that unfortunately has emerged throughout the industry in the past

decade but, rather, as a technology-facilitated one. Technologies represent

the tools by which to move the discovery process forward. However, they are

fragmented parts of a whole and need a unifying discipline and vision to integrate

their contributions and drive the process toward goals that are more global.

The discipline that provides this focus is pharmacology. As a technology,

pharmacology has had a major role in establishing the pharmaceutical industry

(Swann 1988); as a discipline, it seeks the technologies necessary to discover

and characterize new drugs rather than abstract uses for the technologies

themselves. Although pharmacology is not as widely practiced a discipline as

it was 20 years ago and is not widely taught within the U.S. university system,

its rightful reemergence as the driving force for drug discovery is reflected in

the increasing numbers of pharmacologists from European universities being

hired by US. drug companies,

THE PROJECT TEAM APPROACH

Ideally, a project team should include the necessary skill bases, resources, and

decisionmaking authority to ensure that the project proceeds in an expeditious,

timely, and resource-effective manner (Jack 1983). The project team should

be viewed as a dynamic organization driven by goals rather than technology.

Thus, the project team process can be viewed in distinct stages as illustrated

in figure 4.

The initiatory stage involves the validation of the concept, which may be taken

from existing work from the literature and require internal repetition or may involve

an original idea that has to go through the same process of systematic evaluation

as any scientific project. In either event, other than the chemistry required for

small amounts of reference compounds, this stage of the project would involve

a nucleus of biological disciplines. The staff needs of such a project have been

estimated at between 8 and 12 scientists, Once the concept has been validated

and accepted by research management, additional resources would be added

and the project would then move to a planning stage. This would involve the

development of the appropriate screening procedures and their validation in

regard to compound flow; the sequence would be incorporated as the project

flowchart. At this point, dedicated chemical effort would be added to begin the

search for lead compounds. Project resources would increase to about 16

scientists. Once a lead series had been identified, the chemical effort would

be enhanced to effect a realized project—the fully resourced effort involving

chemistry and biology with appropriate milestones, compound target dates,

16
FIGURE 4. Schematic of the stages of a Discovery project

KEY: Biol.=biology; Chem.=chemistry; Form.=formulation; Process=process

chemistry

and corporate visibility. The size of the project at this point would be about

25 people, with additional support in terms of formulation, drug delivery,

and process chemistry to begin planning for the scaleup of synthesis of the

compound to the larger quantities required for toxicology and clinical testing.

With the emergence of a compound candidate for toxicological evaluation and

the identification of a backup compound, the project can be reduced in size,

releasing resources for new initiatory efforts. At this stage the project would

be termed mature and, although still involving formulation, drug delivery, and

process chemistry, would be reduced in size.

The timing for termination of effort on a given project has been the topic of

considerable debate. One school of thought would argue that a lead compound

and a backup compound distinguished by a different chemical structure with

related properties would be sufficient. Given that the time when a compound

is identified to its progress to marketing approval is in the range of 5 to 8 years,

others have argued that a gatekeeping effort should be maintained until the

compound reaches the marketplace. If unexpected problems occur, the

project then can be revived relatively easily to continue the search for a

better compound.

17
The number of individuals on a project obviously affects the rate of progress

toward lead compound status. Clearly, increasing resources, to a point,

can facilitate the objectives of the project. Inevitably, irrespective of the

organizational structure, there is considerable competition for both finite

and incremental resources.

Some projects in larger pharmaceutical companies have been rumored to have

100 to 150 people working on them to accelerate the pace toward compound

identification. Some companies use chemistry “swat teams” to move resources

from one project to another to ensure critical mass at crucial points.

The compound flowchart (figure 5) offers a convenient focal point for project

team efforts. In it the individual test paradigms are identified with appropriate

criteria established on an ongoing basis as compound properties are improved.

By this means, every member of the project is familiar with the immediate goals

of the project, when a compound has satisfied the criteria, and whether it should

continue forward in testing. Structuring the various test procedures used for

compound evaluation into a flowchart is a highly effective facilitatory

mechanism to enhance efficiency and support for project goals.

COMPOUND IDENTIFICATION AND OPTIMIZATION

A drug is a chemical entity that by interacting with a specific molecular target

effects a change in cell and tissue function to ameliorate the effects of trauma

or tissue pathophysiology. Such compounds are usually small molecules with

molecular weights in the range of 250 to 700 daltons and, when racemic, are

usually developed in their enantiomeric form unless additive or synergistic

benefit can be demonstrated for the individual components of the racemic

mixture. Although agonists are effective as drug entities, especially in the

area of rDNA-derived drugs (biologics), the majority of effective agents are

antagonists acting to reduce the actions of endogenous agonists that

contribute to the disease state.

Optimal characteristics of a drug are that it be (1) pure, preferably as a single

enantiomer if chiral; (2) efficacious on repeated administration; (3) safe in

terms of its beneficial actions; (4) affordable within the context of patient use

and cost of discovery and development; and (5), from a commercial viewpoint,

patentable, either by composition of matter or by use. Safety issues are highly

dependent on the disease or condition being targeted. For example, for an

appetite suppressant for use on a daily basis for reducing food intake, the

safety margins in terms of unwanted side effects would have to be extremely

high. For an agent to be used in the acute treatment of stroke, safety issues,

although present, are balanced against the brain damage resulting from the

reduction in blood flow to the brain. Irrespective of the nature of the disease

18
FIGURE 5. Compound flowchart

Each circle represents a predetermined decision point with appropriate criteria.

PI=phosphatidyl inositil; Na=sodium.

19
state, however, a compound with fewer safety aspects than existing medications

and no significant, additional benefits is unlikely to be a clinical entity.

The lead compound directed toward a disease target may arise from two sources,

by chemical modification of a natural receptor ligand or enzyme substrate (Black

1989) or by the screening of diverse compound sources against a molecular

target Williams and Jatvis 1990).

Once an active lead compound is found, it is refined by further synthetic effort

to develop an SAR for a number of properties of the molecule, including:

Activity: a measure of the interaction of a ligand with its molecular target,

expressed as a Ki (inhibitory constant) for a ligand-receptor interaction or

Km (Michaelis-Menten constant) for an enzyme.

Efficacy: the ability of a ligand once bound to effect a change in cell and

tissue function. This refers to whether a compound is an agonist or an

antagonist at a receptor or a substrate or an inhibitor for an enzyme.

For a receptor ligand, intrinsic efficacy is the relative ability of a ligand to

produce a functional effect, biochemical and/or physiological, compared

with a reference agonist. A full agonist has an intrinsic efficacy of unity,

whereas an antagonist, which lacks efficacy, has no agonist activity and

has an intrinsic efficacy of zero.

Selectivity: a measure of the ability of a ligand to interact predominantly

with a single molecular target. The concept of selectivity often involves

considerable semantic issues. Thus, selectivity may be anywhere between

a 10-fold and a 1,000-fold delineation between the activity or efficacy at

the desired target compared with its ability to interact with other targets,

In vivo, selectivity—as defined by the ability of a compound to produce

a preclinical action thought to reflect the therapeutic action of an agent

compared with unwanted side effects—is the therapeutic index.

Bioavailability and pharmacokinetics: crucial, yet frequently elusive,

properties of a compound series. The susceptibility of a compound to

glucuronidation, sulfation, and so forth, and its ability to cross the gut

and blood-brain barrier are difficult parameters to define. In vivo

estimates are compounded by a multiplicity of factors contributing to the

pharmacodynamic actions of the compound. These include the enzymic

modification of the compound, especially when given orally; its lipophilicity,

plasma levels, and half-life; and in the case of central nervous system

(CNS)-active agents, the relationship between plasma and brain

concentrations and the retention of the compound in the brain.

20
Species differences in metabolism, rat vs. primate vs. human, become a major

confounding feature of bioavailability and pharmacokinetic studies. Compounds

may be 90-percent bioavailable in one species and less than 10 percent in

another. Predicting what the situation might be in humans becomes very difficult.

Extensive efforts in defining such properties using in vitro study approaches (e.g.,

human liver slices) have yet to prove predictive (Kaphegian and Traina 1990).

Compound Status

As seen in the flowchart (figure 5), the establishment of project norms for

identifying compounds of interest can greatly enhance communication within

a project and avoid misunderstandings as to the priority of a compound. With

an extensive synthetic effort, many hundreds of compounds may be made in a

year. Although each of these is a well-known entity to the chemist who made

it, the biologist, especially one working in a tunctional line organization where

he or she is involved in several projects, needs additional descriptors for the

compounds beyond their company numbers. A system used by companies

is a list of new compounds according to their biological activity and evaluation

status. One such system involves designating compounds by a numerical

system. A category 4 compound is one that has reached a predetermined

cutoff point for activity in the primary assays of the flowchart. When selectivity

and function have been established, the compound becomes a category 3

compound. With further evaluation in whole animal tests, bioavailability and

half-life determination, and preliminary toxicological evaluation, a compound

would achieve category 2 status and high visibility within the R&D organization.

At this time it would undergo full review as a potential drug candidate and further

workup in terms of formulation as a toxicological candidate. This would lead to

the synthesis of a sufficient amount of compound for toxicological as well as

stability testing. On entering toxicology, the compound would be designated

as category 1. On completion of the toxicology required for the Notice of

Claimed Investigational Exemption for a New Drug (IND) submission, the

compound shifts into a similar prioritization system within Development. The

advantage of such a scheme is that each compound, when of sufficient interest

to a project team to be elevated to a given status, achieves instant recognition

within the organization so that it can be appropriately prioritized.

Compound Value

The lifeblood of any drug company is reflected in the proprietary compounds

it has patented. Patents are of two major types: composition of matter patents,

which cover the molecules synthesized, and use patents, which cover specific

uses for these entities. Typically, within a drug company both composition of

matter and use are contained within a single patent series. In some instances,

however, where unexpected data regarding the biological activity of a compound

have been derived, a use patent can be obtained. This can extend the

21
proprietary life of a compound beyond that of the initial patent. This strategy is

useful when the patent applicant in both instances is the same but can become

complex when a second party has a use patent on a compound owned by

another party. For a further discussion of patents, the reader is referred to

articles by Grubb (1987), Vieillefosse (1990), Yevich (1991), and Hueni (1992).

Compound Safety

Once a compound has been found to meet criteria for potential use as a drug,

its safety must be extensively evaluated to ensure that the potential risk-tobenefit ratio is such that a drug will not produce significant damage under

specific conditions of use. Toxicity should be clearly dissociable from efficacy

(Cavagnaro and Lewis 1987).

After preclinical evaluation, a potential new drug is evaluated in acute (14 to

20 days), subchronic (28 to 90 days), and chronic (3 to 24 months) toxicity

paradigms. Acute studies are performed in rodents of both sexes using oral

and parenteral routes of administration. Such studies are required before

Phase I studies may be conducted in humans. Dose-ranging studies are

performed to identify a dose that is safe in normal subjects and that can be

used as a basis for efficacy testing in the target population in Phase II trials.

Subacute mutagenicity studies (Ames test) represent the first test in which

a new pharmacophore is assessed before recommendation for indepth

toxicological evaluation.

The duration of initial toxicity testing is very dependent on the type of drug

and the plans for Phase I trials. In the case of an antibiotic that will be used

for the acute treatment of opportunistic infections, the data required for human

trials are significantly less than those required for a contraceptive pill for which

usage is measured in years. Carcinogenicity, mutagenicity, teratogenicity,

and behavioral toxicity testing occurs in more lengthy trials as a compound is

moving through Phase I and Phase II trials in humans. For further information

on toxicological evaluation, refer to Cavagnaro and Lewis (1987).

DRUG DEVELOPMENT

Research/Discovery and Development may be separate units, organized

along therapeutic lines reporting to separate heads of R&D. Alternatively, the

R&D organization can exist in a self-contained therapeutic organization with

a single head often known as a Strategic Business Unit. In either event, the

relationship between R&D in terms of compound movement is a crucial one.

Without synergy, Research may bring forward compounds for which there is

no interest in Development. This can be of special concern in the present

business environment where there is a continuing need for new products.

For a Development organization charged with a well-balanced, temporally

22
sequential product pipeline, compounds can be taken from the internal

organization or in-licensed from other companies. Resources committed to

the latter usually affect Research resources and the ability to develop internal

candidates. It is therefore important that communication between the two

groups is optimal as well as flexible, with Discovery responding to Development

needs and Development actively responding to high-risk, innovative

approaches to drug therapy.

Given the nature of such innovation, the enthusiasm of the Development

organization for a totally novel compound is crucial to its introduction to the

clinic and the risk taking necessary to establish a novel therapeutic target.

This is exemplified in Glaxo’s portfolio of serotonin receptor antagonists

(Waldholz 1991), which were novel compounds in search of diseases for

which no known treatments existed. A popular example of perceived

shortsightedness in developing new medications is that of the antiulcer

medication, the histamine H2

, blocker cimetidine (Slack 1989; Duncan 1990).

When this compound was developed in the 1960s treatments for gastric

ulcer encompassed antacids and surgery. The leap of faith in developing a

totally new approach to ulcer therapy in light of existing effective medications

was considerable. Although the retrospective viewpoint suggests that the

mechanistic approach of blocking H2

, receptors was obvious, there was

considerable risk in bringing cimetidine into the clinic with the long lead time

to the marketplace. Inevitably, development and marketing are forecasting

the medical need for a new compound 8 to 10 years in the future. For an

area in which there are existing medications, there are known criteria against

which new compounds can be compared. In new areas, or in areas where a

totally new mechanistic approach is being targeted, the value of the innovation

can be assessed only in the clinic. Yet before a compound reaches this stage,

the momentum to the marketplace takes considerable faith and an element of

risk taking that is not always present within large corporations. As an example,

hypertension was effectively treated in the mid-1960s with diuretics and

 -adrenergic blockers. The vision of Ondetti, Cushman, Horowitz, and their

colleagues in the 1970s led to the discovery of the first angiotensin-converting

enzyme inhibitor, captopril (Cushman and Ondetti 1980). This compound

was considered by many to be not only superior to existing treatments for high

blood pressure but also the definitive drug for this indication. Statements were

made at the time that further medications for the treatment of hypertension

were unnecessary, this in the face of a good percentage of “nonresponding”

patients, especially in the African-American population. In the 1990s however,

renin inhibitors and angiotensin-II antagonists represent newer approaches

to the regulation of blood pressure that may have additional indications

related to congestive heart failure and atherosclerosis, two complications

of cardiovascular function that are in need of improved approaches,

23
The synergies between R&D have been the subject of numerous articles and

have assumed the status of a research project on their own. This should not

result in the trivialization of the interaction or the impact that this relationship

has on a technology-based organization (Roussel et al. 1991). Frequently,

a productive Research organization has less than optimal channels of

communication with the Development organization. In the pharmaceutical

industry, the nature of the science is such that there is a large need for buy-in

from the Development organization to ensure that a compound with high

potential receives appropriate prioritization.

A lack of synergy between R&D can result in compounds appearing as

toxicological candidates taking the Development organization by surprise.

Another facet of the R&D interface, which operates more in the management

of R&D, relates to the balance in needs and resources. A highly productive

Discovery group can present the Development function with more compounds

than the Development organization can handle. Management is then left with

establishing priorities, either by increasing Development resources, usually at

the expense of the Discovery effort, or putting compounds on the shelf and

thus raising the question of the need for urgency in the Discovery process.

In the past, not a few R&D operations have disbanded Discovery efforts as

compound candidates were produced, the logic being that the product

pipeline was full and that further compounds from Discovery could not be

accommodated. Because attrition is a major feature of the various stages of

the Discovery process, the loss of compound candidates caused by unexpected

findings in clinical trials with no Discovery organization to find replacement

candidates made such organizations extremely vulnerable within the global

marketplace.

CLINICAL TRIALS

Clinical evaluation of a new compound is divided into discrete stages

designated by FDA and comparable organizations in the European Economic

Community (EEC). Before a drug can be tested in humans, an IND package

containing information on the safety, activity, and chemical properties of a new

compound is filed with FDA. In EEC, this process is known as the Clinical Trial

Certificate (CTC).

FDA is obligated to respond to the IND within 30 days, at which time, if

there are no objections from FDA, a company may proceed with clinical trials.

Phase I studies focus on the evaluation of compound safety in normal human

volunteers using dose-ranging studies to determine safety and a therapeutic

window. Side effects as well as human pharmacokinetics are established at

this stage. A dose in excess of that at which a therapeutic response is

anticipated must also be established. Phase II studies involve open-label,

single- and multiple-dose studies in the patient population. Efficacy is

24
determined using placebo controls. The therapeutic dose range is established

as well as the route and frequency of compound administration. There is a major

trend in relating compound efficacy to plasma levels rather than to dosage (Peck

et al. 1992) because there are many individual patient variables that affect the

amount of drug that reaches the bloodstream. Phase III studies expand on

the initial patient population, involving multicenter trials to establish uncommon

(<2 percent) side effects and drug interactions. The data derived from Phase III

studies are used as the basis for the new drug application (NDA), the data

required for registration of the drug with a regulatory agency for approval for

sale. In EEC, the NDA is known as the Product License Application. Phase IV

studies are postmarketing studies to further elucidate common side effects, to

focus on patient populations not adequately covered in Phase III trials, and to

gather definitive data on additional indications for the compound.

The data derived from clinical trials and for which a company seeks regulatory

approval relate to the uses of the drug and its side effects and safety. The

instrument to define these parameters is the package insert, the documentation

accompanying a prescription drug. In seeking regulatory approval, a company

should have data that will support the claims made in the package insert. Thus,

the claims are the driving force in determining clinical trials and the interactions

of a company with FDA. Rather than an isolated, two-stage (IND, NDA) process,

frequent meetings with FDA to seek advice, opinions, and clarification can

significantly accelerate the movement of a compound from the laboratory to

the pharmacy.

Clinical Markers of Drug Action

The predictivity of animal and biochemical test procedures is unclear because,

as discussed above, many models are highly empirical. The ability to diagnose

disease states on the basis of blood or tissue pathology and to use such tests

to determine compound efficacy is a major goal within the pharmaceutical

industry. For 3-hydroxy-3-methylglutaryl CoA reductase inhibitors (lovastatin

[Mevacor]) that prevent cholesterol formation, clinical endpoints were based

on pharmacoepidemiological studies linking elevated cholesterol levels to

atherosclerosis and other disorders of the vascular system. In the clinic,

therefore, determination of efficacy is related to a decrease in plasma

cholesterol levels produced by lovastatin rather than direct effects on

atherosclerotic pathophysiology.

CNS DRUG DISCOVERY—AN OVERVIEW

The pressures and complexity of modern society have increased the use of

psychotropic drugs and the incidence of anxiety and depression, creating a need

for medications that are more efficacious and free of side effects than those

currently available. The need for effective treatments to treat addictive disorders

25
(e.g., substance abuse, obesity, alcohol and nicotine addiction) is a high priority

in neuroscience research because of their high cost and negative impact on

society.

The unmet needs in CNS disease treatment (Subcommittee on Brain and

Behavioral Sciences 1990) have led the U.S. Congress to declare the 1990s

as the Decade of the Brain, a major initiative (Judd 1990; National Advisory

Neurological and Communicative Disorders and Stroke Council [U.S.] 1989;

Williams 1992) to facilitate the discovery and development of new medications

for a variety of CNS disease states.

CNS drug discovery may be conveniently divided into three phases. The first,

the age of serendipity, occurred from the early 1950s through 1975 and involved

the clinical discovery of compounds originally targeted at other disease states

that had unexpected CNS actions. Iproniazid, chlorpromazine, imipramine, and

L-dopa were products of this era (Sneader 1985). A second phase began in the

early 1970s with the search for second-generation compounds with improved

efficacy and reduced side effect liability compared with those agents discovered

in the first phase. The use of receptor binding and targeted screening led to the

discovery of antagonists for the cholecystokinin (CCK-B) MK 329 (Chang et al.

1985) and substance P receptors CP 98,345 (Snider et al. 1991) and RP 67580

(Garret et al. 1991). Despite the increased emphasis on molecular pharmacology,

drugs discovered in this period were, to a large extent, incremental in nature or,

like buspirone (New 1990) dependent on serendipitous evaluation in the clinic.

The antipsychotic clozapine (Bonate 1991) and the antidepressant fluoxetine

(Fuller et al. 1991) were major therapeutic agents from this era.

Advances in molecular biology and molecular pharmacology have resulted in

the identification of new drug targets: receptor subtypes and enzyme isoforms.

These permit elucidation of receptor/enzyme function with the development of

ligand/substrate SAR and compound selectivity. Such advances, fueling the

third phase of CNS drug discovery, also have the potential to facilitate disease

diagnosis and the understanding of disease etiology.

The emerging computer-based structural technologies, such as CAMD and NMR,

have been limited in the CNS area because many potential drug targets have yet

to be crystallized and thus require approximations for structural analysis,

LIMITATIONS IN CNS DRUG DISCOVERY

Present-day understanding of brain function is limited and has become overly

reductionist with minimal focus on the four principal levels of research: molecular,

cellular, systems, and behavior (Shepherd 1988; Bloom 1991). Objectively

integrating and interpreting data obtained at these various levels are crucial to

make allowances for the complexity of function that distinguishes the brain from

26
other organs within the body. Although considerable emphasis is placed on the

density of receptors within the brain (usually 10-fold to 100-fold greater than

found in other tissues), the possibility that these add to functional complexity

is rarely taken into account. Similarly, although glial cells are an important

component of nervous tissue, they are frequently ignored in defining hypotheses

related to drug action. It also has been noted that “neuroscience stands . . .

today [1991] where atomic physics was in 1919 . . . or . . . molecular biology . . .

in 1944” (Ridley 1991). A lack of knowledge regarding CNS disease etiology and

the descriptive nature of current psychiatric diagnosis (Baldessarini, in press) are

other contributing factors that affect progress in CNS drug discovery.

Another critical issue relates to the predictive animal models. These have

been highly empirical and have not always been useful in moving a compound

into the clinic. In the area of depression, for instance, there are a number of

biochemical and behavioral models (e.g., -receptor down-regulation, muricidal

rat, swim test) in which classical antidepressants have effects, These are not

especially robust test procedures, and few are on the critical path for compound

characterization. A limitation to such tests has been that research in the area

of animal behavior as related to human CNS disease states, both in terms of

determining efficacy and side effect potential, has undergone a significant

deemphasis in the past decade in favor of more molecular approaches (Williams

1992). Transgenic animals have been heralded as a more useful approach to

disease pathology, but their use, to date, has been limited in the CNS. Various

mouse strains (e.g., DBA) have been used in anticonvulsant testing, yet these

also are empirical, rather than based on definitive genetic defects related to

defined CNS function.

The limitations in behavior become self-defeating as research becomes

more focused on molecular approaches. To judge the potential of behavioral

paradigms based on current models, which are being improved in a very

limited fashion because of funding constraints, is impractical. New knowledge

regarding disease etiology cannot readily be integrated because of a paucity

of researchers and the complexity of test paradigms. It is crucial that a greater

focus on animal models of CNS diseases be part of the Decade of the Brain

initiative.

MANAGEMENT OF THE DRUG DISCOVERY PROCESS

The need for innovative research to find drugs that allow the treatment or more

effective treatment of human disease is a sine qua non for the pharmaceutical

industry. Yet understanding of the scientific approach and what factors motivate

scientists is not always a given within a research-based organization (Roussel

et al. 1991). The R&D organization is frequently viewed as an unpredictable,

uncontrollable entity, a black hole into which vast sums of money are poured

in hope that products will emerge. Communication among scientists, research

27
management, and corporate management is not always optimal (Williams and

Neil 1988; Spilker 1988; Cuatrecasas 1990; Roussel et al. 1991) so research

may often be perceived as a necessary evil rather than the lifeblood of an

organization. To the necessarily pragmatic businessperson, the abstractly

focused scientist may on one level be an icon representing the dynamics and

vision of the R&D-based commercial enterprise. On another level this individual

may represent a challenge to be molded and redirected into a more goaloriented, productive path. The challenges of managing and motivating

scientists are formidable (Roussel et al. 1991; Williams and Stork, in press)

and are in need of attention in today’s changing scientific marketplace.

The numbers of qualified scientists entering industry have been dramatically

reduced in the past decade, and this is already leading to major shortages

in the areas of chemistry and pharmacology. In a highly controversial public

debate, some participants have focused on the inferior quality of education in

the United States as a major causative factor of the shortages (Shakhashari

1991) whereas others have directed attention to more lucrative career

alternatives in investment banking and the legal profession. Whatever the

reasons, there has been an exponential increase in the hiring of foreign

nationals because of an absence of qualified U.S. scientists.

With such shortages and considerable in-house investments in the continued

training of research staff, the effective management, motivation, and retention

of scientists are as much a part of an effective strategy for drug discovery as

the acquisition of enabling technologies. Many research organizations are

“flat,” having few managerial levels (Drucker 1988). As a result, career

advancement has become limited, placing a major focus on job satisfaction.

For a research organization to provide this requires an understanding of

motivational factors within the scientific arena.

Classic scientific training, especially at the graduate level, places a major

premium on independent thought, on the dissemination of research results

through the global scientific community, and on peer respect and acceptance.

These qualities usually undergo modification within the organizational context

of a corporation that places a high premium on teamwork and loyalty. However,

such qualities do not disappear completely and must be factored into the

research environment. This is of special concern when project priorities

change and scientists have to refocus their efforts on different activities.

Ideally, scientists in industry should have the opportunity to work on interesting

projects, to make their own individual contributions to research projects, to be

part of the process of new project initiation, to be rewarded for their efforts in

drug discovery, to publish original research findings, to attend important

scientific meetings, and to have clear career goals. The assimilation of an

individual into a Research organization should be an active process, melding

28
with the culture of the Research organization in a synergistic manner from both

a technical and a personal viewpoint (Badawy 1985). Most large Research

organizations have both managerial and scientific hierarchies to enable scientists

to align their careers with science rather than having to compete for limited

managerial positions for which they may not be suited. Active recognition and

support from upper management for scientific excellence is a major aspect of

the culture in many pharmaceutical companies. Some scientists may align their

careers with a project or a therapeutic area so that when priorities change, they

are more inclined to seek other opportunities than to realign with corporate

needs. Such individuals are highly focused, dedicated, and often very

entrepreneurial in outlook and in their ability to motivate others. Fitzgerald

(1990) has described these “drug hunters” as “having a broad knowledge of

corporate science, . . . being non-compliant, disliking the status quo, risk taking,

having strong convictions which they forcefully express, and being ambitious

for drugs rather than themselves.” Several major drugs have been discovered

because of the efforts of such “swashbuckling” individuals, Although their

activities were not always appreciated at the time, they have since become

both corporate and industry folk heroes,

As the 21st century approaches, the urgency of scientific research has increased

considerably. Capable scientists are changing companies more frequently to

enhance their career prospects. This urgency has been fueled by venture capital

interest in pharmaceuticals (Klausner and Rodgers 1990; Hamilton 1992) and

by the global consolidation within the pharmaceutical industry (Mergers and

other alliances . . . 1991) and driven to a major extent by a short-term focus on

research (Poste 1990; Mergers and other alliances . . . 1991; Williams 1992)

that has led to a number of mergers and takeovers with staff realignments,

downsizing, and cultural dissidence. Fifteen years ago, a scientist in the

pharmaceutical industry could expect a well-paid, secure career with one

company and a comfortable retirement (Williams and Neil 1988). Today, such

security is relative, even though many scientists, as evidenced by events at

Genentech, Amgen, and so on, can become millionaires by (re)aligning their

careers with biopharmaceutical/biotechnology startups.

The continuing high premium placed on innovative, focused biomedical

science has thus provided an increasing number of career options for

experienced, productive drug hunters. The high element of risk in the

venture capital arena with the attendant high rewards presents a challenge

to the major pharmaceutical companies to be competitive in hiring and retaining

their scientific personnel. In the era of major expansion in industry research

from 1976 to 1989, career needs could be met as organizations expanded.

When organizations stabilize or even shrink, career opportunities become

fewer. Additional dynamic factors are imposed on the Research organization

as technologies and goals change.

29
A major challenge to research management for the future will be to provide

the leadership and the culture that effectively mesh the needs of the individual

scientist with those of the Research organization. In this context, Cuatrecasas

(1990) has noted that “few, if any, large organizations have ever come close

to adjusting their operational models to encourage creativity and invention.

Most R&D organizations tend to be bulky, awkward, regimented, controlling,

inflexible, ‘conformist,’ bureaucratic, formalized, overstructured, and intolerant.”

Against such a litany of negatives, one wonders how any technology reaches

the marketplace. The imperative in upward communication (Roussel et al.

1991) is exceedingly high.

CLOSING COMMENTS

The drug discovery process is a complex human endeavor involving many

different scientific disciplines and personalities. The potential benefits to

society in terms of quality of life represent a major goal of knowledge building

in the biomedical sciences. Although issues regarding the economic benefits

resulting from new medications are ongoing (Vagelos 1991; O’Reilly 1991),

society continues to seek relief from the pain and suffering of disease, aging,

and trauma. Whereas the scientific basis of present-day drug discovery has

changed considerably from the plant medicines of the 15th century, the

expectations remain the same.

The promise for biomedical research in the 21st century is to develop drugs

that not only will treat disease symptomatology but also will be either

prophylactic or curative. This hope places considerable expectations on the

biomedical community as well as the pharmaceutical industry. It also requires

that society prioritize its efforts in providing health care to the maximum

number of people. Concerns continue to emerge regarding the quality and

cost of health care to elderly persons (Callahan 1987, 1990), the need for

medications that will effectively treat drug abuse (Johnson and Vocci 1991),

and the realization that the highly effective products of the drug industry

represent only 5 to 7 percent of the health care dollar (Pharmaceutical

Manufacturers Association 1991).

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ACKNOWLEDGMENTS

The author acknowledges Drs. Steve Arneric, Jim Kerwin, and Jim Sullivan for

critical review of the manuscript and Drs. Alice Sapienza and Diana Stork for

many insightful discussions,

35
AUTHOR

Michael Williams, Ph.D., D.Sc.

Area Head, Neuroscience

Department 464

Abbott Laboratories

Building AP10/LL

Abbott Park, IL 60064-3500

36
Mass Ligand-Binding Screening

Strategies for Identification of Leads

for New Drug Discovery

Ronald M. Burch

SCREENING METHODS

Historically, screening for new drug leads has involved primarily studies in

animals and isolated tissues. Recently, cultured cells as well as isolated

enzyme preparations and receptor-binding preparations have been used for

assays. Screening in animals has the advantage of most relevant feedback.

If a reasonable animal model can be used to demonstrate the desired effect,

it provides a largely proven bioavailability and biological effect. This type of

screening is so successful that Beyer (1977) has noted that in the search for

novel diuretic agents in the 1950s while many laboratories searched for

carbonic anhydrase inhibitors and synthesized very potent agents, his group,

which relied on screening in animals, synthesized and developed a new class

of diuretic agents, the thiazides.

However, there are several drawbacks to the use of animals in screening. The

use of animals tends to be slow and requires large quantities of experimental

compounds. The use of animals is also very labor intensive, and together

these disadvantages make such screening an expensive proposition. In

addition, if the animal model is not chosen carefully, nonspecificity may result.

For example, if inflammatory reactions are initiated in animals, then, if the

animal becomes ill with the experimental agent, the inflammatory reaction

generally will not occur.

Screening in isolated tissues or cultured cells generally requires less compound

than in animals and often is less labor intensive. In addition, screening at this

level of complexity allows the determination of whether an agent is an agonist

or an antagonist. As with animals, inhibition of a response may reflect toxicity

rather than efficacy.

The use of receptor binding requires the least amount of compound. In many

laboratories, a single disbursement of 1 mg of compound may be sufficient to

perform screening in as many as a dozen assays in different receptors.

37
Receptor binding is also rapid. In the author’s small laboratory, as many

as 1,000 compounds often are screened per day in a given receptor assay.

In receptor-binding assays, specificity often may be readily determined

by assaying the compound in preparations of several different receptors.

However, receptor assay screening gives little or no clue of bioavailability

and ultimate efficacy in an animal. In addition, it is impossible to determine

from a single receptor assay whether a compound will be toxic to an animal.

The theory of ligand-binding assays is quite simple. A preparation of the

receptor is prepared using either tissue homogenates or intact cells and

incubates replicate tubes with a radiolabeled ligand. Certain tubes contain

saturating concentrations of unlabeled ligand to allow the determination

of nonspecific binding (figure 1). It is possible to determine potency of a

competitor by adding various concentrations of competitor to different tubes

and determining the amount of ligand bound. Then, one can either determine

percent inhibition of binding or the IC50. value for inhibition of binding by the

competitor. In mass ligand-binding screening, a single concentration of

competitor is often chosen for testing. Any compounds that inhibit binding

a certain specified percentage are further evaluated to determine IC50. This

approach dramatically speeds the process because very few compounds

will generally be shown to be active.

CHARACTERISTICS OF THE IDEAL RECEPTOR ASSAY

Optimization of a receptor-binding assay requires attention to two primary

components, the tissue and the ligand. The goal is to achieve the highest

signal-to-noise ratio possible, that is, to maximize specific binding and minimize

nonspecific binding. The ideal tissue will have a high density of binding sites,

Often, there are 10,000 to 50,000 binding sites per cell in a tissue. Tissues with

only a few hundred binding sites per cell tend to have very low signal-to-noise

ratios. In addition, the tissues should express a pure receptor subtype. For

example, to simply homogenize brain and then attempt to perform dopamine

binding, it is necessary to deal with distinguishing among binding to dopamine

D1, D2, D3, D4, D5, and D6 subtypes. With currently available ligands, this

would be impossible. Evaluating compounds against one of these dopamine

receptor subtypes would require the identification of a tissue that expresses

only one subtype or would require access to a cell line that stably expresses a

cloned subtype. Stably transfected cell lines expressing only a single receptor

subtype are becoming more common and will probably be the norm within the

next few years. Finally, an ideal tissue must be readily obtained in quantity.

A ligand must be highly specific for its receptor. Norepinephrine, for example,

would make a poor candidate for a binding ligand because it binds to virtually all

subtypes of -and - adrenergic receptors. When novel receptor subtypes are

screened, a specific ligand rarely will be available. Generally, the goal of the

38
FIGURE 1. Theory of ligand-binding assays

screening exercise will be to discover such ligands. Thus, in practice the

option is to choose a tissue that expresses only a single subtype of receptor

to achieve this specificity. In addition, a ligand must be of as high specific

activity as possible to evaluate binding. 125l-labeled ligands are preferable

to tritiated ligands because, after completion of the binding experiment,

processing of samples is minimized. Tritiated ligands may require solubilization

in liquid scintillation cocktail, which greatly increases the labor involved in a

binding assay because of filling and capping vials. However, because 125I has

a relatively short half-life, in practice tritiated ligands tend to be more stable

and easy to use.

In choosing a ligand, an antagonist is often better than an agonist. In particular,

affinity shifts for agonists in G protein-coupled receptors in response to ions

make for increased nonspecificity when dealing with unknown competitors.

Of course, for the reasons mentioned above, an agonist may have to be

used because discovery of an antagonist is often the goal of the exercise.

39
Receptors for many biogenic amines and many peptide hormones are expressed

at 10,000 to 50,000 per cell. This translates to about 10 12 sites per gram of

tissue wet weight and allows the evaluation of 200 to 300 experimental tubes

per gram of tissue. In contrast, many cytokines are expressed at 200 to 3,000

sites per cell. This often necessitates the use of 106 cells per tube or more for

experimental assays. For those receptors that are expressed in the least

abundance, as few as 2 to 5 tubes may be assayed using a 10 cm2

 dish

for contact-dependent cells or perhaps only 10 to 20 tubes per T-75 flask for

contact-independent cells. Increased receptor densities may often be obtained

in transfected cell lines expressing cloned receptors in which the expression

is commonly as high as 50,000 to 100,000 receptors per cell. Alternatively,

transformed cells may overexpress certain receptors. For example, certain

leukemia cell lines express as many as 50,000 to 100,000 interleukin-1 (IL-1)

receptors per cell compared with 200 per cell in nontransformed lines.

Assays with high signal-to-noise ratios make screening a much simpler

process. For example, binding of bradykinin to B2, receptors in guinea pig ileum

exhibits about 95-percent specific binding. If in a given tube total binding is

3,000 disintegrations per minute (DPM), then nonspecific binding is about

150 DPM, leaving a window of 2,850 DPM over which to observe competition.

Fifty-percent competition at this site would result in observing 1,575 DPM. If

errors in addition of experimental compounds, ligand, and tissue, together with

counting error, total 10 percent, this would amount to about 300 DPM in an

assay, which would change apparent percent inhibition by only about 10 percent.

In contrast, the binding of ligands to the thromboxane A2 receptor in human

platelets shows about 50-percent specific binding. In such an assay, total

binding might be 2,000 DPM per tube and nonspecific binding 1,000 DPM.

In this example, the window over which to observe competition is reduced to

1,000 DPM, and 50-percent competition would amount to 1,500 DPM being

observed in a tube. With such low specific binding, a lo-percent error in

addition of all reagents would change the apparent percent inhibition of

binding by 20 percent. In many of these low-signal assays, all the experimental

compounds are often assayed at least twice to reduce the number of false

negatives and false positives.

DISCOVERY OF A NEW LEAD FOR A SINGLE RECEPTOR

Screening efforts may be set up on a variety of levels-from those in which

all parts of the assay are performed manually by the binding technicians, to

those in which teams of technicians subdivide the various tasks and in which

computerization of all steps in the process leads to very high throughput

and generation of extensive databases. In the manual approach, a single

technician harvests and prepares the tissues, sets up and terminates the

assays, calculates results, and places them into a database. Using this type

40
of system, it is often not possible to perform assays more than 2 days in a

given week. In such a system, a single technician should be able on a given

day to set up and assay twelve 48-tube racks or six 96-tube racks. This

amounts to 576 tubes. Each rack has duplicate total and nonspecific binding

tubes, and a standard compound is run each day. Therefore, about 520 tubes

are available for test compounds. In this scenario, a single technician disburses

compounds. Each disbursement technician can weigh and solubilize about

250 compounds per day. Thus, in a given week, somewhat more than 1,000

compounds may be solubilized and assayed by these two people. If such an

approach is used to screen a library of 10,000 compounds, then about 10

weeks, or 20 technician-weeks, would be required (figure 2).

Considerable time and cost benefits can accrue using a high-throughput

approach. In this scenario, two disbursement technicians are required for

each binding technician. Each binding technician performs an assay each day.

Therefore, about 2,500 compounds are assayed per week. The disbursement

technicians weigh bar-coded compounds, and the computer attached to the

balance notes how much diluent to add to each vial. Scintillation counters

also are accessed by the computer, which calculates percent inhibition by

the test compounds. Before commitment of data to a database, the binding

technician quickly calls up the assay for examination to determine that total

and nonspecific counts are within normal values and that the standard

compound inhibited with the expected potency. Using this kind of approach,

FIGURE 2. Time required to screen a library of 10,000 compounds through a

single receptor assay

41
the 10,000-compound library can be screened through one receptor assay in

about 4 weeks (figure 2), requiring only about 12 technician-weeks.

This high-throughput approach can be extended to other receptor assays with

minimal cost. If a single batch of compound is disbursed, it may be stored in a

cold room in dimethyl sulfoxide or other appropriate solution and used for up to

several dozen different assays. Low signal-to-noise assays require more time

because each compound is assayed multiple times.

DEFINING SUCCESS

Depending on the definition of an active compound or a “hit,” success may be

great or little. Certain assays tend to have many apparent hits (false positives),

whereas others have very low hit rates. Receptor-binding assays for excitatory

amino acids tend to have hit rates of 10 to 20 percent in libraries of “random”

compounds, when hit is defined as 50-percent inhibition at a concentration of

10 μm. In contrast, in the IL-1 receptor-binding assay in fibroblasts in the

author’s laboratory, the hit rate is less than 1 in 5,000 using the same criteria.

Given these examples, to avoid being swamped following up false positives,

inclusion criteria must be strict. Of course, if criteria are too restrictive, then

promising leads may be missed. In the author and colleagues’ work, an

example of the weakest compound in a bradykinin receptor-binding assay

that can be shown to be an antagonist in a functional assay is NPC 361,

which has a Ki

, of 400 nm. The prototype cholecystokinin antagonist, asperlicin,

had a Ki of 600 nm in a binding assay (Chang et al. 1985). For either of these

examples, even our relatively strict inclusion criteria of 50-percent inhibition of

binding at a concentration of 10 μm would have tagged these compounds as

active. However, Wong and colleagues’ (1988) angiotensin II antagonist lead

had a Ki

, of 40,000 nm in their assay. Such a compound would have been

excluded in any of the assays in the author’s laboratory as inactive.

If such weakly active compounds are included as hits, then 50-percent inhibition

at 100 μm would be required. Unless the assay is very “clean” (as described

above for IL-l), a method is required to make certain that most nonspecific

(false positive) compounds are weeded out quickly. This is done by running the

compounds in several different binding assays and excluding those that are hits

in multiple assays. In situations in which compounds are routinely screened at

only a single site, all apparent hits are collected and run in additional binding

assays. Thus, a “receptor profile” or a “cross-selectivity report” is obtained.

In the cross-selectivity mode, an attempt is made to determine Ki, values in

each assay. Concentrations to be evaluated are chosen by noting the apparent

Ki, in the assay of interest, then bracketing that value. In these studies, two

compounds are evaluated per 48-tube rack (11 duplicate concentrations of

each test compound plus duplicate total count and nonspecific count tubes).

42
Each binding technician can perform determinations for 24 compounds per day.

For the specialized laboratory that performs screening in only its target of interest,

Ki

, determinations in multiple assays may be contracted commercially with

several companies.

FUNCTIONAL SCREENING

Functional screening is the vital next step after a hit is identified in a binding

assay. The functional screen is necessary to determine whether a given

compound is an agonist or antagonist. Functional screens also serve to

provide an idea of selectivity or, at times, toxicity. When determining whether

a compound has agonist activity, one must always evaluate the compound

in an assay alone, even if the antagonist assay suggests that the compound

does reduce the activity of an agonist. It must be remembered that a partial

agonist will appear to be an antagonist when assessed in an antagonist assay.

The first choice in deciding on a functional screen is whether to use an isolated

tissue response (e.g., smooth-muscle contraction) or a second-messenger

assay (e.g., increase in intracellular inositol phosphates [IP3] or cyclic AMP

[CAMP]). The second-messenger assays often lend themselves to very high

throughput. For example, using a radioreceptor assay for IP3, the author’s

laboratory cultures fibroblasts in 24-well plates. Cells are then stimulated

with bradykinin to determine whether test compounds inhibit bradykinin-induced

IP3 accumulation. This assay also lends itself to determination of affinity by

determining Kb

, or pA2 values. Such assays may be used to assess selectivity

of test compounds. In the assay just cited, the cells also express receptors for

angiotensin II, vasopressin, a,-adrenegic agonists, and bombesin, all of which

increase the accumulation of IP3

. Similarly, the cells express -adrenergic and

adenosine receptors, whose activation increases the accumulation of CAMP.

These, too, can be used to assess selectivity and may be preferred, because

assessing the effect of a compound on a second transduction system adds an

additional chance to detect nonspecific effects.

The utility of a functional screen is increased if it measures a “positive activity”

rather than a “negative activity.” In a negative-activity assay, an attempt is

made to block the ability of an agonist to elicit a response in a resting cell or

tissue. This type of assay often is used and represents the common agonistinduced muscle contraction or agonist-induced IP3

 accumulation. Here, the

test compound is assessed for its ability to inhibit the response. A problem

that often arises when assessing unknown compounds is that, if the compound

nearly kills the cell, usually an agonist will not cause increased IP3

, accumulation,

appearing to confirm activity of the test compound as an antagonist. Thus, the

negative-activity assay may be prone to generating false negatives. In the

positive-activity assay, the agonist does something to decrease the function

of the cell or tissue, and an antagonist restores a normal state. For example,

43
tumor necrosis factor (TNF) induces cytotoxicity in many cell types. If a test

compound blocks this effect, then the cell, rather than dying in response to

exposure to TNF, continues to act normally. In this type of assay, a toxic

compound tends to augment the ability of TNF to kill the cell rather than make

the cell function more normally. Thus, nonspecifically toxic compounds will

tend not to appear as active receptor antagonists.

PROCESS TECHNOLOGY

Process technology refers to throughput methodologies. Many laboratories

spend extraordinary amounts of money and time on automation of assays.

In my laboratory I find automation largely wasteful. Given hand-held automatic

pipetting devices, multiple-sample filtration manifolds, and dry scintillant pads,

my laboratory always outperforms automated laboratories. Often, a library

of compounds may be screened in the time it takes to reprogram all the

automation devices. The only situation in which automation may be arguably

worth the bother and expense is when a library of many thousands of

compounds will be screened.

I find that the real rate-limiting step in mass ligand-binding screening is

tabulation of data. To this end, all aspects of the process have been

computerized. Compounds are bar coded with molecular weights and

structural formulas. Bar-code readers are used to track compounds

through the computerized weighing procedure, and disbursements are

easily solubilized to the correct stock concentrations using computercalculated printouts. Disbursed compounds retain bar coding, and assays

are set up in standard fashion in the racks so that the computer accesses

the scintillation or )y-counter data and calculates results. Before ordering

the computer to commit the data to the database, the binding technician

looks at the onscreen display of the assay and ensures that the standard

compound acted properly in the assay. This data-handling system has

been previously described in some detail by Burch and Kyle (1991).

Beyond simple lead identification, this online system can search the

database for hits (the criteria for which can be specified with a few key

strokes) and, because the structures are also present, perform one of

several structural search paradigms for identification of pharmacophores

for given activities.

CONCLUSION

Mass ligand-receptor binding has proven useful in the identification of structural

leads in several instances where no prototype candidates were available. The

keys to success with the approach are acquisition of diverse and large libraries

of compounds, diversity being at least theoretically more important than size,

44
and constant attention to selectivity, both in other binding assays and in

functional assays.

REFERENCES

Beyer, K.H., Jr. A career or two. Rev Pharmacol 17:1-10, 1977.

Burch, R.M., and Kyle, D.J. Mass receptor screening for new drugs. Pharm Res

8:141-147, 1991.

Chang, S.L.; Lotti, V.J.; Monaghan, R.L.; Birnbaum, J.; Stapley, E.O.; Goetz,

M.A.; Albers-Schonberg, G.; Patchett, A.A.; Liesch, J.M.; Hensens, O.D.; and

Springer, J.P. A potent nonpeptide cholecystokinin antagonist selective for

peripheral tissues isolated from Aspergillus alliaceus. Science 230:177-180,

1985.

Wong, P.C.; Chiu, A.T.; Price, W.A.; Thoolen, M.J.M.C.; Carini, O.J.; Johnson,

A.L.; Taber, R.I.; and Timmermans, P.B.M.W.M. Nonpeptide angiotensin II

receptor antagonists. I. Pharmacological characterization of 2-n-butyl-4-

chloro-1-(2-chlorobenzyl)imidazole-5-acetic acid, sodium salt (5-8307).

J Pharmacol Exp Ther 247:1-7, 1988.

AUTHOR

Ronald M. Burch, M.D., Ph.D.

Director

Inflammation Programs and Research Technology

Nova Pharmaceutical Corporation

6200 Freeport Centre

Baltimore, MD 21224

Current affiliation:

Director

Immunology/inflammation and General Pharmacology

Rhône-Poulenc Rorer Central Research

640 Allendale Road

King of Prussia, PA 19406

45
Antibody- and Peptide

Structure-Based Drug Design

Manfred E. Wolff

INTRODUCTION

For more than a century, the search for new drugs has been rooted in an organic

chemistry-driven technology based on the synthesis and biological evaluation

of large numbers of candidate compounds (Burger 1980). Highlighting the

inefficiency of this process is the fact that US. pharmaceutical companies were

estimated to have spent $7.3 billion on domestic research and development in

1991 (Pharmaceutical Manufacturers Association 1991) to achieve U.S. approval

of only 11 new chemicals (Scrip Review Issue 1992).

Hoping to improve the success rate in drug discovery, today most major

pharmaceutical companies and several startup companies have organized

efforts in rational drug design. Several distinct strategies are employed,

including approaches based on computer modeling (DesJarlais et al. 1988)

of data obtained from binding assays and enzyme crystallography as well as

methods depending on bioorganic chemistry (Walsh 1984). These applications

of rational drug design thus require at least the purification and sometimes the

crystallization of targets that are central to each disease process. Yet many

prospective drug targets are membrane-bound macromolecules that cannot

readily be crystallized or even identified. For example, in a recent inhibitor

design study involving the crystal structure of the RNase H domain of human

immunodeficiency virus-1 reverse transcriptase (Davies et al. 1991), only an

inactive RNase H fragment could be obtained as diffraction quality crystals.

Antibody and peptide structure drug design (Wolff and McPherson 1990) is

aimed at avoiding these pitfalls. In the antibody approach, the remarkable

power of the mammalian immune system is first harnessed to access the threedimensional (3-D) structural information contained in pharmacological receptors,

enzymes, and viruses without the need for special purification or isolation efforts.

Then, the pharmacophoric information embedded in the resulting monoclonal

antibody is read out by one of several methods. From the data obtained, peptide

agents are produced whose structure is used as the basis for the computerassisted design of orally active nonpeptide peptidomimetic drugs with receptor

subtype specificity for many therapeutic applications. In those cases where the

46
structure of a lead peptide, such as a natural mediator, is known, the process

can begin without the creation of the antibody, which serves only as an

informational intermediate. These steps are considered briefly in the next

sections.

ANTIBODY TECHNOLOGY

Antibody-directed drug design begins with the selection of a drug target,

which can be a pharmacological receptor, an enzyme, or a virus. Through

monoclonal technology, idiotypic antibodies (Ab-1) to key drug action sites in

such receptors, enzymes, and viruses can be selected and produced in any

desired amount without the need to isolate the target (figure 1). The production

and selection of antibodies to drug targets represents a screening of a

repertoire of several billion different antibody structures generated by the

immune system and a fine tuning of these structures through somatic cell

mutation (Tonegawa 1983).

Antibody-directed drug design requires the antibodies to the drug target to have

functional (druglike) activity. Clearly, an antibody that binds to a nonfunctional

area of the target, or one that binds in some nonfunctional manner, will be

useless as a prototype for drug design. A list of functional antibodies that

have been obtained to targets such as peptide hormone receptors, autocoid

receptors, and enzymes is given in table 1 and is an impressive array of the

potential of this technology.

ldiotypic Antibody

Ab-1

FIGURE 1. Raising the antibody to the drug target

47
The idiotypic antibody is a mirror image of the target site, both in a spatial and

an electronic sense. For example, Conti-Tronconi and colleagues (1990) have

mapped the binding site of the nicotinic acetylcholine receptor a-subunit with

several monoclonal antibodies raised against native Torpedo receptor. The

binding of these antibodies to the receptor is inhibited by all cholinergic ligands,

and it was found that the antibodies bound to specific amino acid clusters

positioned discontinuously within the receptor sequence 181-200.

By using the idiotypic antibody as an antigen, an anti-idiotypic antibody (Ab-2)

(Linthicum and Farid 1988) can be produced as a positive image of the target

site (figure 2). Again, selection criteria exist for anti-idiotypic antibodies that

are faithful internal images of the original antigen (Ertl and Bona 1988).

COMPOUND LIBRARY SCREENING

ldiotypic antibodies have high affinity both for the antigens that were used to

raise them as well as their corresponding anti-idiotypic antibodies. Because

the interaction of these pairs can be observed conveniently using conventional

or sandwich enzyme-linked immunosorbent assay (ELISA) methodology, it is

possible readily to screen large numbers of compounds (compound libraries)

for their ability to inhibit this interaction. In this way, potentially useful compounds

that bind to either receptors or their anti-idiotypic images can be identified.

X-RAY CRYSTALLOGRAPHY IN ANTIBODY-DIRECTED DRUG DESIGN

Important drug design information is obtained from antibodies, their fragments,

and complexes and from peptides by crystallization using conventional protein

crystallization techniques (McPherson 1982) (figure 3). After x-ray diffraction

data are collected, the crystal structure is solved using the conventional multiple

isomorphous replacement technique requiring heavy atom derivatization of the

crystals. An alternative approach is molecular replacement, which depends on

some degree of knowledge of the structure of the antibody, the antigen, or both.

As an example, the 3-D structure (Sheriff et al. 1987) of an antilysozyme Fablysozyme complex is illustrated in figure 4. A closeup view (figure 5) shows the

spatial and electronic complementarity of the interacting surfaces of lysozome

residues 41-46 and light chain residues 55-59 that contribute to the hydrophobic,

electrostatic, and hydrogen bonding accounting for the nanomolar affinity of the

two ligands.

It must be emphasized that these crystal structures may or may not represent

a bioactive conformation. There is abundant evidence that only certain

conformations of peptides are bioactive (Sarantakis et al. 1973), and the

conformation that exists in the crystal may not be one of these. Moreover,

48
TABLE 1. Biologically functional antibodies

Receptor Antibody Type Action Reference

-adrenergic

Muscarinic acetylcholine

receptor

Nicotinic acetylcholine

receptor

Morphine

Morphine

Morphine

Insulin

Fibrinogen

Rat growth hormone

LFA-1

IL-l

IL-4

Angiotensin II

Renin

Human neutrophil elastase

Ab-1 to receptor

Ab-1 to receptor

Ab-1 to receptor

Ab-2 to morphine

Ab-2 to -endorphin

Ab-1 to HEAPI peptide

Ab-1 to receptor

Ab-1 to gpll-llla

Ab-2 to hormone

Ab-1 to receptor

Ab-1 to receptor

Ab-1 to receptor

Ab-2 to hormone

Ab-1 to enzyme

Ab-1 to enzyme

Stimulates cAMP

Guinea pig myornetrium

contractions

Blocks agonist-induced ion flux

Guinea pig ileum contraction

Blocks enkepthalin effect on cAMP

Suppresses cAMP

Stimulates 2-deoxyglucose uptake

Inhibits thrombus formation

Increases body weight

Blocks C5A-induced leukocyte

adhesion

Inhibits thymocyte proliferation

Inhibits B-cell proliferation

Binds to receptor in situ

Antihypertensive in monkeys

Enzyme antagonist

Chapot et al. 1987

Leiber et al. 1984

Donnelly et al. 1984

Ng and Isom 1985

Gramsch et al. 1988

Carr et al. 1989

Soos et al. 1989

Coller et al. 1988

Gardner et al. 1998

Argenbright et al. 1991

C. Escobar, personal

communication. January 1992

Maliszewsky et al. 1990

Berlove and Piekut 1990

Lykegaard 1980; Wood et al. 1989

E. Skaletsky, personal

communication, February 1998
Idiotypic Antibody

Ab-1

Anti-ldiotypic

Antibody Ab-2

FIGURE 2. Creating specific internal image anti-idiotypic monoclonal antibodies

FIGURE 3. Crystals of therapeutic lymphoma antibody

NOTE: See Harris et al. 1992 for the solution to this crystal structure.

SOURCE: ImmunoPharmaceutics, Inc.

50
FIGURE 4. Computer-generated Corey, Pauling, Koltun (CPK) model of

lysozyme-HyHEL-5 complex from coordinates in the Protein

Data Bank

SOURCE: V.N. Balaji and U.C. Singh, personal communication, December 1989

FIGURE 5. Stick model closeup view of the contact residues (4Å) of figure 4

SOURCE: V.N. Balaji and U.C. Singh, personal communication, December 1989

51
antibody hypervariable loops have been shown to undergo conformational

changes as a result of ligand binding (Rini et al. 1992).

The specific interactions between antibody and antigen demonstrate dramatically

the potential of the approach for directed drug design. Such analyses provide an

unequalled structural basis for the creation of synthetic molecules, peptides, or

peptidomimetics that share with the idiotypic antibody the ability to interact with

the antigen. Related information can be deduced for anti-idiotypic antibodies

that are replicas of receptors or other inaccessible macromolecules.

Through the use of molecular graphics algorithms, the interacting residues in

the antibody may be excised from the rest of the structure as a “virtual peptide”

(figure 6). The virtual peptide is a computer construct of the interacting residues

in their constrained conformation, although this conformation would not be

maintained if the peptide were synthesized free of the rest of the antibody

molecule. The virtual peptide is used as the pattern for the synthesis of

constrained peptidomimetic drugs as outlined in a later section.

FIGURE 6. Computer-generated CPK model of the virtual peptide of the

enzyme ligand in figure 4

SOURCE: V.N. Balaji and U.C. Singh, personal communication, December 1969

52
EXTRACTION OF INFORMATION

In addition to x-ray crystallography, three other techniques are used to read

out structural information from the embedded pharmacophore in the antibody

or from the anti-idiotypic antibody.

First, if specific leader sequences (Orlandi et al. 1989) are used in the

polymerase chain reaction, the amino acid sequence of the hypervariable

loops of the antibodies can be determined. With the knowledge of these

sequences and of the x-ray crystal structure of representative antibodies, a

combined knowledge-based and ab initio algorithm can be applied to model

the structure of the hypervariable loops (Martin et al. 1989). In this way,

structural information similar to that which can be obtained from x-ray

crystallography is at hand without the need to resort to the crystallographic

methodology.

Second, peptides that are complementary to the surface of anti-idiotypic receptor

surrogates can be selected from libraries of filamentous phage clones, each

displaying one peptide sequence on the virion surface (Scott and Smith 1990).

A properly selected peptide that is complementary to a receptor surrogate results

in a receptor antagonist.

Last, evidence for the location of key amino acid sequences in the hypervariable

loops of anti-idiotypic antibodies can be adduced from their homology with

sequences in the initial antigen (Williams et al. 1988).

Derivatives of these peptide sequences, chemically constrained to preserve their

spatial characteristics, have been evaluated as drugs in their own right. For

example, a synthetic mimetic derived from an omega loop structure that mimics

a prominent, exposed series of eight residues in interleukin-1 (IL-1) was

evaluated for its ability to bind to IL-1 receptors (Sarabee et al. 1991).

PEPTIDOMIMETIC SYNTHESIS

The use of peptides as design templates for peptidomimetic drugs is the key

step in the implementation of antibody- and peptide structure-based drug design,

In the preceding process, the end product is the design of a peptide drug that

may be an enzyme inhibitor, a receptor antagonist (or sometimes an agonist),

or an antiviral. Alternatively, the end product may be an antibody containing

the embedded pharmacophore for any of these activities. Because both

peptides and antibodies typically have short biological half-lives and poor oral

bioavailability, it remains to synthesize a peptidomimetic (Kemp 1990) based

on the peptide structure to afford a bioavailable, orally active drug.

53
The design of peptidomimetics (Morgan and Gainor 1990) requires two different

applications of computer-assisted drug design techniques. First, an estimate

of the bioactive conformation of the peptide must be obtained from x-ray or

nuclear magnetic resonance results and from modified data obtained from

primary sequence information through the application of computational chemistry

algorithms (Gibson and Scheraga 1987) such as those of V.N. Balaji and U.C.

Singh (personal communication, December 1989). This information is used to

interpret the structure-function studies on the molecule. For example, it has

been shown that the fragment Phe7

-Trp8-Lys9-Thr10 is essential for growth

hormone release inhibitory activity by somatostatin (Vale et al. 1978; Verber

et al. 1979) and that a ß II-type turn and a ß-sheet are also required for activity

(Van Binst and Tourwe 1992). Based on the predicted or observed 3-D spatial

and electronic characteristics of the peptide, a database and an algorithm of

peptidomimetic surrogate elements (V.W. Balaji and U.C. Singh, personal

communication, December 1990) are used to design candidate drugs for

synthesis. A measure of conformational flexibility is required in these compounds

to allow for the conformational changes that occur on binding to the target.

Docking routines may be used to optimize complementarity in those cases

where the 3-D structure of the receptor surrogate has been obtained.

A recent example of this approach involved the synthesis of a synthetic p-loop

structure that mimics the second complementarity-determining region of a

monoclonal antibody, which recognizes the cell surface receptor for reovirus type

3 (Reo3R) (Saragovi et al. 1991). The synthetic organic mimetic, termed the

87.1 mimetic, is a water-soluble, 10-membered macrocycle containing amide

bonds. Evidence was obtained that indicates the 87.1 mimetic binds efficiently

to Reo3R.

Antibody- and peptide structure-based drug design offers wholly new insights

and approaches in the design and optimization of new lead compounds for

therapeutic application, and risk is minimized by the multiplicity of available

strategies. As these methods are developed further, even greater savings

in time and effort will be at hand in the search for new drugs,

REFERENCES

Argenbright, L.W.; Letts, L.G.; and Rothlein, R. Monoclonal antibodies to the

leukocyte membrane CD18 glycoprotein complex and to intercellular adhesion

molecule-1 inhibit leukocyte-endothelial adhesion in rabbits. J Leukocyte Biol

49:253-257, 1991.

Berlove, D.J., and Piekut, D.T. Co-localization of putative vasopressin receptors

and vasopressinergic neurons in rat hypothalamus. Histochemistry 94:653-657,

1990.

Burger, A. History and economics of medicinal chemistry. in: Wolff, M.E., ed.

Burger’s Medicinal Chemistry, 4th ed., Pt. 1. New York: Wiley, 1980. pp. 1-54.

54
Carr, D.J.; Blalock, J.E.; and Bost, K.L. Monoclonal antibody against a peptide

specified by [Met]-enkephalin complementary RNA recognizes the delta-class

opioid receptor. lmmunol Lett 20:181-186, 1989.

Chapot, M.P.; Cervantes, P.; Kaveri, S.; Durieu-Trautmann, O.; DelavierKlutchko, C.; Emorine, L.; Couraud, P.O.; and Strosberg, A.D. Biochemical

and immunochemical analysis of avian beta 1 and mammalian beta 2-

adrenergic receptors. J Recept Res 7:1-15, 1987.

Coller, B.S.; Scudder, L.E.; Berger, H.J.; and luliucci, J.D. Inhibition of human

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AUTHOR

Manfred E. Wolff, Ph.D.

Senior Vice President for Research and Development

ImmunoPharmaceutics, Inc.

11011 Via Frontera

San Diego, CA 92127

57
The Use of Cloned Human

Receptors for Drug Design

Paul R. Hartig

INTRODUCTION

Cloned human receptors are increasingly used by pharmacologists and medicinal

chemists for the development of novel, site-specific drug therapies. This

approach can be expected to produce many new drugs with improved efficacy

and fewer side effects. The availability of sets of transfected human receptors

makes it possible to target drugs to single human proteins from the inception of

a drug design project. A critical requirement of this technology is that human

genes must express a human pharmacology when expressed in the host cell

line chosen for transfection. Studies on the human serotonin 5-HT2

, and 5-HT1D/

5-HT1B receptors demonstrate that the gene sequence appears to determine

the expressed receptor’s pharmacological properties, with only a minor role

played by the cellular environment in which the receptor is expressed. The

cloning and characterization of the 5-HT1B receptor also demonstrate another

emerging principle of molecular pharmacology: the equivalent G protein-coupled

receptor gene in different species can encode proteins with strikingly different

pharmacological properties. Another important issue is the relationship between

agonist and antagonist binding sites. Studies comparing agonist—1-(2,5-

dimethoxy-4-methylphenyl)-2-aminopropane (DOM)—and antagonist binding

sites of the human 5-HT2 receptor demonstrate the strong differences exhibited

by these two binding states of the same receptor protein, Finally, the fact

that receptors of the G protein-coupled or 7TM (7 transmembrane) receptor

superfamily exhibit many properties in common allows receptor homologies to

be used to predict certain drug-binding properties. Examples from the

serotonergic and adrenergic receptor families are presented.

A MOLECULAR PHARMACOLOGY

Cloned human receptors, conveniently expressed in transfected mammalian

cell lines, now make it possible to approach drug design from a truly molecular

perspective. In the past several years, the amino acid sequences of many

receptor genes have been determined. Most of these cloned receptors are

members of a closely related superfamily of genes known as the G proteincoupled receptors (or 7TM receptors), because of their characteristic single

58
subunit structure with 7TM-spanning segments. Similar cloning successes are now

being reported for multisubunit ligand-gated ion channels, including the GABAA

receptor, the 5-HT3 receptor, and the N-methyl-D-aspartate receptor. Molecular

pharmacologists are beginning to integrate this new information into their views of

physiological and pharmacological processes and are introducing cloned human

receptors into the drug development process, often from the

very start of a drug design effort.

Expression of cloned human receptors in mammalian cell lines allows

pharmacologists to develop assay systems that individually express each

receptor subtype important to a drug design project. Host cell lines can

be chosen that are devoid of any related receptor sites, producing clean,

unambiguous assay systems. These subtype-specific human receptor assays

will allow medicinal chemists to design drugs with high affinity for the desired

site of action and low affinity for those receptor subtypes that may induce side

effects. In many cases, this design strategy can be expected to produce more

potent medications with fewer side effects because of an improved molecular

targeting of the drug. Even in cases where a blended drug possessing a

spectrum of receptor activities may be the desired endpoint, pure human

subtype assays provide a drug design team with an important advantage:

unambiguous assays of the affinity of the drug candidate at each human

receptor site of interest.

The second advantage of the cloned receptor approach is that low abundance

sites, which have been difficult to study in tissue preparations (e.g., autoreceptors),

can now be isolated and expressed at high density for use in ligand screening

and basic science investigations. The third, and perhaps’most important,

advantage is the fact that many new receptor subtypes have been and continue

to be discovered by receptor cloning efforts, which are providing a rapid increase

in the number of potential drug target sites and can be expected to lead to

several new medications in the future.

SPECIES DIFFERENCES IN RECEPTORS

The pharmacological binding properties of receptors are often similar for the

same receptor subtype in different species, although many exceptions do occur.

When species differences do exist, they dictate that great care be used in the

choice of receptor assays for the purpose of human drug design. The best

situation is found when human receptors can be used to screen for drug activity

and selectivity. Cloned human receptors are making human receptor screening

possible. These clones are commonly expressed in mammalian or sometimes

in bacterial expression systems (Chapot et al. 1990) for use in drug-binding

assays. This leads to the important question of what influence the cell-line

will have on the pharmacological properties of the human receptors that they

express, For the purpose of drug screening, host cell lines must be chosen

59
so that cloned human receptors will properly reproduce the binding properties of

native human tissues.

The influence of the cell-line host on the pharmacological binding properties of

cloned human receptor subtypes has been studied in some detail. The binding

properties of the serotonin 5-HT2

, receptor have been known to differ in different

species, especially for certain ergot compounds. For example, mesulergine

exhibits approximately thirtyfold higher affinity for rat cortical membranes than

for comparable human tissue (Pazos et al. 1984). Transfection of a cDNA clone

encoding the human 5-HT2

, receptor subtype (Hartig et al. 1990) into mouse

fibroblast cells leads to expression of a serotonin receptor whose binding

properties match that of human rather than rat cortical membranes (table 1).

The largest drug-binding differences are seen for mesulergine, which binds to

both the transfected human receptor and to human cortical membranes with an

apparent affinity of approximately 150 nM, thirtyfold weaker than its affinity for

the rat cortex 5-HT2, receptor. This species difference also is seen for ritanserin,

which belongs to an entirely different chemical class (table 1). In both cases,

the transfected human receptor exhibits binding affinities in close agreement

with human cortical tissue, even though the human gene has been expressed

in a rodent, nonneuronal cell line. Together, these observations suggest that

TABLE 1. Ligand-binding properties of a human 5HT2 receptor gene

expressed in mouse fibroblast cells. A cDNA clone encoding the

human 5-HT2

 receptor (Hartig et al. 1990) was expressed in

mouse fibroblast L-M(tk-) cells. Membrane preparations were

labeled with [

3H]5-HT, and apparent dissociation constants were

determined as described in Branchek and colleagues (1990).

Comparative values from human cortex and rat cortex assays

(Hoyer et al. 1986) are also provided.

Apparent Dissociation Constant (K, in nM) for Displacement

of [

3H]Ketanserin Binding

Drug Human Clone Human Cortex Rat Cortex

Ritanserin 1.1 ± 0.16 1.3 7.2

Cyproheptidine 2.9 ± 0.10 6.3 1.8

Mesulergine 129±7.9 151 4.7

5-HT 598 ± 252 174 79

Quipazine 2,111 ± 295 3,802 1,549

5-CT 7,790 ± 50 8,130 21,878

SOURCE: Part of the data in this table was previously described in Hartig and colleagues

(1992).

60
it is the amino acid sequence of the receptor (nature), rather than the cellular

environment in which the receptor gene is expressed and processed (nurture),

that determines the species-specific pharmacological properties of the receptor.

Further studies are needed to determine whether this will prove to be a general

property of neurotransmitter receptors. For most cases examined so far, it

appears that transfection of a human 7TM receptor gene into mammalian cell

lines has produced ligand-binding properties in good agreement with previous

binding assays in human brain tissue preparations. Furthermore, the use of

different cell lines as transfection hosts was not found to substantially affect the

pharmacological binding properties of a series of human G protein-coupled

receptors that the author and colleagues at Synaptic Pharmaceuticals have been

investigating. This information provides a welcome degree of freedom in the

choice of host cells for transfection, which can then be chosen based on ease

of transfection, complement of native G proteins, or other desirable criteria.

A similar result was observed in the case of the serotonin 5-HT1D receptor,

which illustrates an extreme case of the variations that can occur in the

pharmacological properties of homologous genes in different species. The

5-HT1D receptor is not present in rat cortical membranes: however, the rat brain

contains a homologous receptor that exhibits such different pharmacological

properties that it has been named a separate serotonin receptor subtype

(5-HT1B). Recently, a gene encoding the rat 5-HT1B receptor was isolated

and shown to be highly homologous to the human 5-HT1D receptor gene

(Adham et al. 1992). This demonstrates that a previous suggestion that the

5-HT1B and 5-HT1D receptors are essentially the same receptor subtype

(Hoyer and Middlemiss 1989) was correct. This suggestion was based on the

fact that both receptor subtypes show similar distribution in the basal ganglia,

similar coupling to adenylate cyclase inhibition, and similar roles as terminal

autoreceptors on raphe neurons (Waeber et al. 1990). When a human 5-HT1D

receptor clone was expressed in the same mouse fibroblast cell line used to

express the 5-HT1B receptor, this human receptor exhibited the binding

properties appropriate for the human origin of its gene, rather than the mouse

origin of its cell host (Branchek et al. 1991). It appears that no counterexample

has been described where the host cell line dictates the pharmacological binding

properties of a transfected gene. Protein processing and glycosylation patterns

will differ from cell to cell, which may influence receptor kinetics, desensitization

phenomena, receptor turnover, and the membrane compartment distribution

of the receptor. Nevertheless, it appears that the pharmacological binding

properties of 7TM receptors are determined primarily by their gene sequences

with a much smaller role (if any) played by their cellular environments.

61
THE RELATIONSHIP BETWEEN AGONIST AND ANTAGONIST

BINDING SITES

G protein-coupled receptors cycle through a complex series of G protein- and

ligand-binding affinities. These binding states induce different affinity states for

agonist ligands. The availability of transfected cell lines expressing single

receptor subtypes now makes it possible to examine these binding states in

much greater detail. Two recent studies (Branchek et al. 1990; Teitler et al.

1990) have resolved a longstanding controversy in the serotonin receptor field,

namely, whether the serotonergic binding site for the agonist 4-bromo-2,5-

dimethoxyphenylisopropylamine (DOB) and other related compounds is the high

affinity agonist binding state of the 5-HT2

 receptor, which binds antagonist

ligands such as ketanserin, or is a separate, closely related receptor subtype.

Both interpretations have been advanced (Lyon et al. 1987; Pierce and Peroutka

1989) but the existing data now appear to strongly support the two-site rather

than the two-receptor interpretation, as two studies utilizing transfected human

(Branchek et al. 1990) and rat (Teitler et al. 1990) 5-HT2

 receptor clones have

shown. Both studies reached the same conclusion: Transfection of a single

cDNA clone into host mammalian cells produced two distinct binding sites

(distinct [

3H]DOB and [3H]ketanserin binding sites). Addition of guanine

nucleotides to these systems reduced the number of agonist high-affinity

binding sites with no change, or a slight increase, in the number of antagonist

binding sites. Thus, it appears that [3 H]DOB and [

3H]ketanserin binding sites

are distinct ligand-affinity states that exist at different times on the same 5-HT2

receptor protein. A summary diagram of these affinity states, based on a

review article by Freissmuth and colleagues (1989) is provided in figure 1.

The scientific community’s increasing molecular understanding of this ligand

affinity cycle needs to be better integrated into investigations of receptor function

and into drug design programs. Because a complex interaction cycle involving

two separate proteins and several forms of guanine nucleotides is involved in

agonist binding, the model systems used as templates for drug design must be

carefully chosen and carefully adjusted. They must properly mimic the natural

processes occurring in those regions of native human brain that are chosen

as a target for drug design. Because the types of G proteins, the relative

receptor excess (spare receptors), and the amounts of intracellular guanosine

diphosphate (GDP) and guanosine triphosphate (GTP) may vary widely in

different brain regions, this complexity should be dealt with from the start of a

drug design effort. Fortunately, the great freedom of choice of cell hosts and

transfection densities that is possible when using cloned human receptors

allows just the type of experimental freedom needed to address these issues.

It also must be kept in mind that two distinct, but partially overlapping, sets of

conformational states are involved in antagonist and agonist binding, and it is

important to be sure that the proper mix of the proper states is present in

biological screening models.

62
FIGURE 1. Model for the functional activity cycle of G protein-coupled

receptors (derived from Freissmuth and colleagues [1989]).

Binding of an agonist ligand (L) to the receptor (R) produces a

receptor-ligand complex (R•L), which binds a G protein complex.

Agonists induce dissociation of guanosine diphosphate (GDP) from

this complex, resulting in a “G empty” state, which is the

high-affinity agonist-binding state. The G protein can then bind

guanosine triphosphate (GTP), which induces dissociation of

the -subunit with GTP bound. This OTP complex can activate

effector mechanisms (such as adenylate cyclase or phospholipase

C) until such time as the intrinsic GTPase activity of the -subunit

hydrolyses GTP to GDP, starting the cycle again.

AMINO ACID AND PHARMACOLOGICAL HOMOLOGIES BETWEEN

RECEPTORS

Receptor researchers have traditionally organized their research groups around

a specific neurotransmitter or hormone that activates a set of closely related

receptors. For example, serotonin clubs and histamine meetings tend to

segregate away from neurokinin researchers, even though all three of these

biological mediators activate a group of 7TM receptors with many properties in

common. Recent advances in molecular understanding of receptor structures

suggest that this approach should change. Receptors of the 7TM superfamily

are much more closely related in amino acid sequence and function than had

been recognized previously. In addition, Hartig and colleagues (1992) have

found that 7TM receptors of the same family (e.g., serotonin receptors) are often

less closely related to each other than they are to other 7TM receptors. For

example, the serotonin 5-HT1A and 5-HT1D receptors are both more homologous

to several adrenergic receptors than they are to either the serotonin 5-HT2 or

5-HT1C receptors (Hartig et al. 1992). Interestingly, this relationship is also

63
reflected in the pharmacological binding properties of these clones. Many

compounds that are classically recognized as adrenergic compounds exhibit

moderate-to-high affinity for the 5-HT1A receptor, and a similar relationship

holds for the 5-HT1D receptor. For example, yohimbine, which is a classic

antagonist, exhibits a 25-nM affinity for the 5-HT1D receptor (Branchek

et al. 1991). This value is only sixfold weaker than its affinity at the site

(Kobilka et al. 1987).

These molecular homologies have implications that extend beyond nucleotide

sequence comparisons into the field of pharmacology. As is true for all

proteins, the form of a neurotransmitter receptor determines its function. As a

consequence, the pharmacological binding properties of 7TM receptors often

cross family lines in ways that are reflected in the amino acid sequences of

these receptors. In general, neurotransmitter receptors that display significant

amino acid sequence homologies often display some overlap in the chemical

structures that they will bind. Thus, the existence of greater than 50-percent

transmembrane amino acid sequence homology should alert researchers to

the possibility that receptors from different families may be more closely related

in their binding properties than had been previously appreciated. Increased

attention to these similarities in sequence and pharmacology could greatly

aid the medicinal chemist in efforts to find high-affinity compounds for each

receptor subtype and will help establish a much more directed approach to

drug design.

REFERENCES

Adham, N.; Romanienko, P.; Hartig, P.; Weinshank, R.; and Branchek, T. The

rat 5-HT1B receptor is the species homologue of the human 5-HT1DB receptor.

Mol Pharmacol 41 :1-7, 1992.

Branchek, T.; Adham, N.; Macchi, M.; Kao, H.-T.; and Hartig, P.R. [

3H]-DOB

(4-bromo-2,5-dimethoxyphenylisopropylamine) and [

3H]-ketanserin label two

affinity states of the cloned human 5-HT2

 receptor. Mol Pharmacol 38:681-688,

1990.

Branchek, T.; Zgombick, J.; Macchi, M.; Hartig, P.; and Weinshank, R. Cloning

and expression of a human 5-HT1D receptor. In: Saxena, P., and Fozard, J.R.,

eds. Serotonin: Molecular Biology, Receptors, and Functional Effects. Basel,

Switzerland: Birkhauser, 1991. pp. 21-32.

Chapot, M.; Eshdat, Y.; Marullo, S.; Guillet, J.; Charbit, A.; Strosberg, A.; and

Delavier-Klutchko, C. Localization and characterization of three different betaadrenergic receptors expressed in Escherichia coli. Eur J Biochem 187: 137-

144,1990.

Freissmuth, M.; Casey, P.; and Gilman, A. G proteins control diverse pathways

of transmembrane signaling. FASEB J 3:2125-2131, 1989.

Hartig, P.; Adham, N.; Zgombick, J.; Weinshank, R.; and Branchek, T. Molecular

biology of the 5-HT1

 receptor subfamily. Drug Dev Res 26:215-224, 1992.

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Hartig, P.; Kao, H.-T.; Macchi, M.; Adham, N.; Zgombick, J.; Weinshank, R.; and

Branchek, T. The molecular biology of serotonin receptors: An overview.

Neuropsychopharmacology 3:335-347, 1990.

Hoyer, D., and Middlemiss, D.N. The pharmacology of the terminal 5HT

autoreceptors in mammalian brain: Evidence for species differences. Trends

Pharmacol Sci 10: 130-132, 1989.

Hoyer, D.; Pazos, A.; Probst, A.; and Palacios, J.M. Serotonin receptors in the

human brain, II. Characterization and autoradiographic localization of 5-HT1c

and 5-HT2 recognition sites. Brain Res 376:97-107, 1986.

Kobilka, B.; Matsui, H.; Kobilka, T.; Yang-Feng, T.; Francke, U.; Caron, M.;

Lefkowitz, R.; and Regan, J. Cloning, sequencing, and expression of the gene

coding for the human platelet a,-adrenergic receptor. Science 238:650-656,

1987.

Lyon, R.A.; Davis, K.H.; and Titeler, M. [

3H]DOB (4-bromo-2,5-

dimethoxyphenylisopropylamine) labels guanyl nucleotide-sensitive state

of cortical 5-HT2

 receptors. Mol Pharmacol 31:194-199, 1987.

Pazos, A.; Hoyer, D.; and Palacios, J. Mesulergine, a selective serotonin-2 ligand

in the rat cortex, does not label these receptors in porcine and human cortex:

Evidence for species differences in brain serotonin-2 receptors. Eur J

Pharmacol 106:531-538, 1984.

Pierce, P., and Peroutka, S.J. Evidence for distinct 5-hydroxytryptamine2

receptor binding site subtypes in cortical membrane preparations.

J Neurochem 52:656-658, 1989.

Teitler, M.; Leonhardt, S.; Weisberg, E.; and Hoffman, B. 4-[125I]lodo-(2,5-

dimethoxy)phenylisopropylamine and [

3H]ketanserin labeling of 5-HT2

receptors in mammalian cells transfected with a rat 5-HT2 cDNA: Evidence

for multiple states and not multiple 5-HT2

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38:594-598, 1990.

Waeber, C.; Schoeffter, P.; Hoyer, D.; and Palacios, J.M. The serotonin 5-HT1D

receptor: A progress review. Neurochem Res 15:567-582, 1990.

AUTHOR

Paul R. Hartig, Ph.D.

Vice President for Research

Synaptic Pharmaceuticals (formerly Neurogenetic Corp.)

215 College Road

Paramus, NJ 07652

65
Synthetic Peptide Combinatorial

Libraries May Be Used To Determine

Peptide Ligands for Opioid Receptors

Richard A. Houghten and Colette T. Dooley

INTRODUCTION

Isolation of naturally occurring peptide sequences from biological sources

often requires years of laborious extraction, purification, and identification.

This was true for the naturally occurring opioid peptides methionine and

leucine enkephalin (Hughes et al. 1975). Optimization of these peptide

sequences over the past 17 years has involved the synthesis and screening

of thousands of analogs of the original biologically active sequences (Schiller

1990). Although thousands of analogs have been prepared and tested, they

represent only a small fraction of the possible pentapeptides (205 for the

L-amino acids alone). Existing methods for the synthesis and screening of

many peptides are severely limited in their ability to generate and screen

such numbers (i.e., millions) (Merrifield 1963; Geysen et al. 1984; Houghten

1985) and/or in their ability to generate unmodified free peptides in quantities

capable of interacting in solution at concentrations appropriate for relevant in

vitro assays (Fodor et al. 1991; Geysen et al. 1986; Scott and Smith 1990;

Devlin et al. 1990; Cwirla et al. 1990). Therefore, Houghten and colleagues

(1991, 1992) and Pinilla and colleagues (1992) have developed synthetic

peptide combinatorial libraries (SPCLs) composed of mixtures of free peptides

that in total exceed 50 million hexapeptides and in quantities that can be used

directly in virtually all existing assay systems. Thus, in the example presented

here, a library composed of 52,128,400 hexapeptides along with an iterative

selection process was used to confirm the utility of the SPCL approach for

the identification of active sequences in existing radio-receptor assays.

METHODS

Preparation of Synthetic Peptide Combinatorial Libraries

An SPCL consisting of six-residue peptide sequences having free N-terminals

and amidated C-terminals was synthesized in which the first two amino acids in

each peptide were individually and specifically defined, whereas the last four

66
amino acids consisted of equimolar mixtures of 19 of the 20 natural L-amino

acids. (Cysteine was omitted from this library.) This synthesis generated 400

different peptide mixtures, each represented by the formula O1O2XXXX-NH2

[O1O2=AA, AC, AD, AE, etc., through YV, YW, YY at positions O1 and O2;

each X represents an equimolar mixture of the 19 amino acids] and containing

130,321 combinations (1 9

4

), This library contains 400x130,321=52,128,400

hexapeptides.

The SPCL was assembled using the solid-phase approach (Merrifield 1963)

on methylbenzhydrylamine (MBHA) polystyrene resin in combination with

simultaneous multiple peptide synthesis (Houghten 1985) using t-Boc protected

amino acids. The XXXX-resin was prepared using a process of division,

coupling, and recombination (DCR) of individual resins, which ensures the

equimolarity of each peptide within the XXXX-resin (Houghten et al. 1991).

For this procedure, 19 equally weighed portions of resin (and, therefore, equal

numbers of milliequivalents) were placed into porous polypropylene packets

and then coupled to each of the protected N-a-t-Boc amino acids. Completion

of coupling was determined by Kaiser’s ninhydrin test (Kaiser et al. 1970). The

resins were combined and thoroughly mixed. The resulting mixture (X-resin)

was then divided into 19 equal portions, the N-a-t-Boc protecting groups were

removed, and trifluoroacetic acid salts were neutralized. Each individual portion

was then coupled to completion using one of the 19 different activated amino

acids to generate 361 dipeptide resin combinations (XX-resin). The DCR

process was repeated twice more to yield a XXXX-resin, representing an

equimolar mixture of 130,321 tetramers. This XXXX-resin was then divided

into 400 equal portions, and the two defined positions, O1

, and O2 were coupled

using the solid-phase multiple synthesis method (Houghten 1985). Amino

acid analysis confirmed the expected equimolarity (±10 percent). Following

deprotection and cleavage from the resins, each of the 400 peptide mixtures

was extracted with water to yield a final peptide concentration of 1 to 3 mg/mL,

which ensures that there is a sufficient concentration of each individual peptide

within a mixture for use in standard in vitro assays (i.e., receptor/ligand

interactions, antigen/antibody interactions, and enzyme/substrate interactions).

At 1.0 mg/mL, each of the 130,321 peptides within each peptide mixture is

present at a concentration of approximately 10.3 nM. Mixture concentrations

as high as 5.0 mg/mL can be utilized readily.

Radioreceptor Assay

Particulate membranes were prepared using a modification of the method

described by Pasternak and colleagues (1975). Rat brains frozen in liquid

nitrogen were obtained from Rockland, Inc. (Gilbertsville, PA). The brains were

defrosted, the cerebella were removed, and the remaining tissue was weighed.

Each brain was individually homogenized in 40 mL Tris-HCI buffer (50 mM, pH

7.4, 4 °C) and centrifuged (16,000 rpm) for 10 minutes. The pellets were

67
resuspended in fresh Tris-HCI buffer and incubated at 37 °C for 40 minutes.

Following incubation, the suspensions were centrifuged as before, the resulting

pellets were resuspended in 100 volumes of Tris-HCI buffer, and the suspensions

were combined. Membrane suspensions were prepared and used in the same

day. Protein content of the crude homogenates ranged from 0.15 to 0.2 mg/mL

as determined using the method described by Bradford (1976).

Binding assays were carried out in polypropylene tubes. Each tube contained

0.5 mL of membrane suspension, 8 nM [

3H]-DAGO (specific activity 36 Ci/mmol

160,000 cpm), 0.08 mg/mL peptide mixture, and Tris-HCI buffer in a total volume

of 0.65 mL. Assay tubes were incubated for 60 minutes at 25 °C. The reaction

was terminated by filtration through GF-B filters. The filters were subsequently

washed with 6 mL Tris-HCI buffer, 4 °C. Bound radioactivity was counted on an

LKB Beta-plate Liquid Scintillation Counter and expressed in counts per minute

(cpm). To determine interassay and intra-assay variation, standard curves in

which [

3H]-DAGO was incubated with a range of concentrations of unlabeled

DAGO (0.13 to 3,900 nM) were included on each plate of each assay (using a

96-well format). Competitive inhibition assays were performed as above using

serial dilutions of the peptide mixtures. IC50 (the concentration necessary to

inhibit 50 percent of [

3H]-DAGO binding) values were then calculated using the

software GRAPHPAD (ISI, San Diego) and were found to be consistent in

three determinations.

RESULTS

Screening and Selection of Peptide Ligands

There are at least three known subclasses of opioid receptors: μ, ,and 

(Schiller 1990). Enkephaiins, the natural ligands, bind to each of these binding

sites with differing affinities. For this study the authors used an analog of metenkephalin that is known to bind specifically to the μ-binding site, [

3H]-DAGO.

Each of the 400 different peptide mixtures of the SPCL (O1O2,XXXX-NH2) was

assayed to determine its ability to inhibit binding of [

3H]-DAGO to the μ-receptor.

IC50s were determined for each of the most effective peptide mixtures. YGXXXXNH,, with an lC50=3,452 nM, was found to be the most effective inhibiting peptide

mixture (figure 1, table 1).

An iterative process was then carried out in which the subsequent X positions

of YGXXXX-NH2

 were defined with each of the 20 natural L-amino acids. The

peptide mixture sequences at each step of the screening and selection process,

along with their respective IC50,s, are illustrated in table 1. Twenty new peptide

mixtures were synthesized in which the third position of the peptide mixture

YGXXXX-NH2 was defined (YGOXXX-NH2, i.e., YGAXXX-NH2, YGCXXX-NH2,

68
FIGURE 1. Initial screening of the SPCL (O1

,O2XXXX-NH2) for ability to inhibit binding of [

3H]-DAGO to crude rat brain

homogenates. Individual bar graphs are segregated by the first amino acid O1 with individual bars in each

graph representing the 20 amino acids making up the second position (O2). The Y-axis represents the

reciprocal of percent rota/ bound. Peptide mixtures (0.08 mg/mL) were incubated with [

3H]-DAGO (8 nM),

0.5 mL rat braii homogenate (0.15 to 0.2 mg/mL protein), and Tris-HCI buffer pH 7.4 at 25 °C for 60 minutes.
TABLE 1. Binding inhibition of [

3H]-DAGO by peptide mixtures in the iterative

process

IC5 0

(nM)

IC 5 0

Peptide Mixture Peptide (nM)

(a)

YGXXXX-NH2

YPXXXX-NH2

YCXXXX-NH2

YLXXXX-NH2

YWXXXX-NH2

YHXXXX-NH2

(b)

YGGXXX-NH,

YGXXXX-NH,

YGFXXX-NH,

YGNXXX-NH,

YGTXXX-NH,

YGSXXX-NH,

(c)

YGGFXX-NH,

YGGWXX-NH,

YGGXXX-NH,

YGGYXX-NH,

YGGRXX-NH,

YGGCXX-NH,

(d)

YGGFMX-NH,

YGGFFX-NH,

YGGFIX-NH,

YGGFLX-NH,

YGGFYX-NH,

YGGFXX-NH,

3,452

6,005

15,365

16,055

16,400

16,770

3,254

3,452

4,037

7,265

8,689

8,718

153

292

3,254

5,190

11,860

13,415

28

57

67

74

119

153

(e)

YGGFMA-NH,

YGGFMN-NH,

YGGFMX-NH,

YGGFMM-NH,

YGGFMS-NH,

YGGFMG-NH,

YGGFML-NH,

YGGFMH-NH,

YGGFMY-NH,

YGGFMQ-NH,

YGGFMT-NH,

YGGFMK-NH,

YGGFMR-NH,

YGGFMP-NH,

YGGFMI-NH,

YGGFMV-NH,

YGGFMD-NH,

YGGFME-NH,

YGGFMF-NH,

28

28

28

30

34

35

35

37

39

40

41

41

46

57

80

107

167

204

223

NOTE: IC50 values for the most effective inhibitory peptide mixtures at each

iterative step are illustrated for (a) peptide mixtures from the initial

screening of the SPCL, (b) the third position (YGOXXX-NH2), (c) the

fourth position (YGGOXX-NH2), (d) the fifth position (YGGFOX-NH2)

and (e) the sixth position (YGGFMO-NH2

). The IC50 value of the peptide

mixture of the previous iterative step is boxed for comparison.

70
and YGDXXX-NH2

). Of these, YGGXXX-NH2 (IC50=3,254 nM) was found to be

the most effective inhibiting peptide mixture. The next best case, YGFXXXNH2

, was approximately 50 percent less effective; all other substitutions in

the third position were found to have activities much less than YGGXXX-NH2

(table 1).

The iterative process was repeated for the remaining three positions, On

defining YGGOXX-NH2, the 2 most active cases, YGGFXX-NH2 (IC50,=l53 nM)

and YGGWXX-NH2 (lC50=292 nM), were substantially more active than the

other 18 cases, and they were 34- and 18-fold better than the third most active

case YGGYXX-NH2 (IC50=5,190 nM) and 21- and 11-fold better than YGGXXXNH2

. This illustrates the specificity of the fourth position in the enkephalin

sequence. When the fifth position of YGGFXX-NH2

 was defined (YGGFOXNH,), it was found that YGGFXX-NH2

 was the most effective inhibiting mixture

(IC50=28 nM) and YGGFLX-NH, was the fourth most effective inhibitor (IC50=74 

nM). The first five residues of the peptide sequence mixture YGGFMX-NH,

(IC50=28 nM) exactly match the sequence of naturally occurring enkephalins

found in earlier studies to recognize this receptor (figure 2). The second and

third best cases at this position were YGGFFX-NH2 (IC50=57nM) and YGGFIXNH2

 (IC50=67 nM). When the final position (YGGFMO-NH2) was defined, little

improvement was found, and YGGFMA-NH2, the most effective peptide

YGXXXX-NH2 (IC50 = 3452 nM)

YGGXXX-NH2 (IC50 = 3254 nM)

YGGFXX-NH2 (IC50 = 153 nM)

YGGFMX-NH2 (IC50 = 28 nM) YGGFLX-NH2 (IC50 = 74 nM)

YGGFMA-NH2 (IC50 = 28 nM) YGGFLG-NH2

 (IC50 = 52 nM)

YGGFM-NH2 1C50 = 34 nM YGGFL-NH2

 IC50 = 41 nM

FIGURE 2. A summary of the most active peptide mixtures found at each

step of the iterative process; IC50 values obtained for the

pentameric enkephalin sequences are boxed for comparison.

71
(IC50=28 nM), was only tenfold greater than the least effective peptide

YGGFMF-NH2 (lC50=223 nM), which illustrates the redundancy of this position.

Using an SPCL composed of more than 52 million hexapeptides, combined

with an iterative selection and enhancement process, the authors were able

to determine a series of specific peptide sequences that inhibited binding

of [

3H]-DAGO to its receptor. The equimolar representation of all possible

individual peptide sequences in the SPCL enables precise identification of

peptide ligands. It should be noted that no information about the sequence

of the binding ligand is required to carry out determinations of this kind.

We used a library that was not acetylated at the N-terminal because the

enkephalins are known to need a free N-terminal tyrosine. Although this

example yielded the expected sequences of leucine and methionine

enkephalin as the first five residues in the hexapeptide sequence, the

same process carried out with an N-acetylated hexapeptide library yielded

sequences with no obvious relationship to the known enkephalins. This

work will be reported elsewhere.

CONCLUSION

We have employed SPCLs in a variety of other assay systems, including the

use of competitive enzyme-linked immunosorbent assay to study the binding

of synthetic peptide antigens to monoclonal antibodies in which the correct

antigenic determinant was identified (Houghten et al. 1991), in microdilution

assays for the development of novel antimicrobial peptides (S. aureus, P.

aeruginosa, C. albicans) (Houghten et al. 1992), and for potential antiviral

peptides in plaque inhibition assays (HIV-1 and HSV) (manuscripts in

preparation). SPCLs can be readily applied to the identification of peptide

ligands in the majority of existing assay systems.

Because large numbers of highly active, defined peptide sequences can be

readily generated through the use of SPCLs for any receptor system of interest,

we believe that information derived from SPCLs will complement existing

methods currently used for the detailed study of peptide/receptor interactions,

such as x-ray crystallography, nuclear magnetic resonance, and computer

modeling. We have found that SPCLs are of broad, general utility and greatly

facilitate basic research and drug discovery involving peptides.

REFERENCES

Bradford, M.M. A rapid and sensitive method for the quantitation of microgram

quantities of protein utilizing the principle of protein-dye binding. Anal

Biochem 72:248-254, 1976.

72
Cwirla, S.E.; Peters, E.A.; Barrett, R.W.; and Dower, W.J. Peptides on phage: A

vast library of peptides for identifying ligands. Proc Natl Acad Sci U S A

87:6378-6382, 1990.

Devlin, J.J.; Panganiban, L.C.; and Devlin, P.E. Random peptide libraries: A

source of specific protein binding molecules. Science 249:404-406, 1990.

Fodor, S.P.A.; Read, J.L.; Pirrung, M.C.; Stryer, L.; Lu, A.L.; and Solas, D.

Light-directed, spatially addressable parallel chemical synthesis. Science

251:767-773, 1991.

Geysen, H.M.; Meloen, R.H.; and Barteling, S.J. Use of peptide synthesis to

probe viral antigens for epitopes to a resolution of a single amino acid. Proc

Natl Acad Sci U S A 81:3998-4002, 1984.

Geysen, H.M.; Rodda, S.J.; and Mason, T.J. A priori delineation of a peptide

which mimics a discontinuous antigenic determinant. Mol lmmunol 23:709-

715, 1986.

Houghten, R.A. General method for the rapid solid-phase synthesis of large

numbers of peptides: Specificity of antigen-antibody interaction at the level of

individual amino acids. Proc Natl Acad Sci U S A 82:5131-5135, 1985.

Houghten, R.A.; Blondelle, S.E.; and Cuetvo, J.H. Development of new

antimicrobial agents using a synthetic peptide combinatorial library involving

more than 34 million hexamers. In: Epton, R., ed, Innovation and

Perspectives in Solid Phase Synthesis. Canterbury, England: Solid Phase

Conference Coordination, Ltd., 1992. pp, 237-239.

Houghten, R.A.; Pinilla, C.; Blondelle, S.E.; Appel, J.R.; Dooley, C.T.; and

Cuervo, J.H. Generation and use of synthetic peptide combinatorial libraries

for basic research and drug discovery. Nature 354:84-86, 1991.

Hughes, J.; Smith, T.W.; Kosterlitz, H.W.; Fothergill, L.A.; Morgan, B.A.: and

Morris, H.R. Identification of two related pentapeptides from the brain with

potent opiate agonist activity. Nature 258:577-579, 1975.

Kaiser, E.T.; Colescott, R.L.; Bossinger, C.D.; and Cook, P.I. Color test for

detection of free terminal amino acid groups in the solid phase synthesis of

peptides. Anal Biochem 34:595-598, 1970.

Merrifield, R.B. Peptide synthesis: The synthesis of a tetrapeptide. J Am Chem

Soc 85:2149-2154, 1963.

Pasternak, G.W.; Alexander-Wilson, H.; and Snyder, S.H. Differential effects of

protein-modifying reagents on receptor binding of opiate agonists and

antagonists. Mol Pharmacol 11:340-351, 1975.

Pinilla, C.; Appel, J.R.; and Houghten, R.A. Synthetic peptide combinatorial

libraries: The screening of tens of millions of peptides for basic research and

drug discovery. In: Lerner, R.A., ed. Vaccines 92. Cold Spring Harbor, NY:

Cold Spring Harbor Laboratory Press, 1992. pp. 25-28.

Schiller, P.W. Development of receptor-specific opioid peptide analogues. In:

Ellis, G.P., and West, G.B., eds. Progress in Medicinal Chemistry. Vol. 27.

Sweden: Elsevier Science Publishers B.V., 1990. pp. 301-340.

Scott, J.K., and Smith, G.P. Searching for peptide ligands with an epitope

library. Science 249:386-390, 1990.

73
AUTHORS

Richard A. Houghten, Ph.D.

Director

Multiple Peptide Systems

Colette T. Dooley

Research Assistant

Torrey Pines Institute for Molecular Studies

3550 General Atomics Court

San Diego, CA 92121

74
Preparation of Large Peptide Libraries

With One Peptide per Bead and Their

Use for the Discovery of Peptides That

Bind to Acceptors

Victor J. Hruby, Kit S. Lam, Michal Lebl, Wiesiaw Kazmierski,

Evan M. Hersh, and Sydney E. Salmon

INTRODUCTION

Most biological processes are controlled and modulated by intermolecular

interactions. These interactions can occur with a change in the covalent

structure of one or more of the participants (e.g., an enzyme substrate

interaction) or with no change in covalent structure (e.g., an antibody

antigen interaction). In the latter case, in general, the three-dimensional

structures of both the ligand and the acceptor molecule(s) change as a

consequence of the interaction, which in turn can lead to a change in the

physical and chemical properties of the acceptor molecule(s) in a way that

modulates cellular structure and function. Thus, these interactions are

critical requirements for the conformational, dynamic, and stereoelectronic

changes that are central to the “chemistry of life” in biological systems.

In this regard, peptides and proteins and their posttranslationally modified

derivatives constitute the most widely used “molecules of life” and can

serve many functional roles (e.g., as antibodies, enzymes, growth factors,

immunomodulators, hormones, neurotransmitters, and receptors) and the

many other functions that are critical to cellular function and to intercellular

communication in complex living systems. It is no accident that nature

uses proteins as the major functional molecules in living systems, which

undoubtedly is related to the enormous physical and chemical diversity

that is possible for these compounds. For example, if one utilizes only the

20 standard amino acid residues found in proteins and examines the number

of distinct chemical species available, millions of molecules already are

possible at the pentapeptide stage (3.2x106

); that number becomes 1.28x109

at the heptapeptide stage. Thus, for even a small protein of 100 amino acid

residues, there are an astronomical number of possibilities. It is not clear

how many of these possibilities nature has explored, but it is clear that only

a small fraction of those possible are used.

75
There is a great opportunity in the possibility for utilizing enormous chemical

diversity to obtain a better understanding of how nature works and for the

discovery of novel lead molecules that can serve as starting points for the

development of agonist or antagonist drugs with specific biological effects.

It also is a challenge to develop a simple method to prepare such large and

diverse chemical libraries in a cost-effective manner that will allow examination

of the binding properties of a particular species in the library and at the same

time have each chemical species present in sufficient quantities for structure

determination.

The authors recently proposed and developed a methodology for preparing

large, statistically diverse peptide libraries (104 to 108 individual species)

in a short time and for examining their ligand-binding activities with acceptor

macromolecules such as antibodies and enzymes (Lam et al. 1991, 1992).

This chapter briefly reviews the basic approach in this development and

discusses some of the possible applications for the future.

RESULTS AND DISCUSSION

The creation of a large peptide library creates several problems. The

most important is that of scale. The basic question posed was how much

of each peptide is needed so that, on the one hand, sufficient concentrations

are available to screen for binding and, on the other hand, sufficient amounts

are available for structure determination. After examining several approaches,

the authors devised a method that maximizes chances for success with the

minimum amount of peptide. This method exploits the idea of one peptide

for one bead. The synthetic methodology used is the Merrifield method

(Merrifield 1963) in which the peptide of interest is synthesized on a solid

support. This method has revolutionized peptide chemistry (and synthetic

chemistry in general) in that it greatly simplifies the multistep synthetic

processes of preparing a peptide. The growing peptide chain is assembled

on a resin bead, and as a consequence, the solvents and reagents required

for the synthesis can be removed by the simple expedient of washing. This

methodology provides two other previously unexploited advantages. First,

depending on the substitution level and size of the bead (in standard

Merrifield syntheses, the substitution level is generally 0.2 to 1.0 mmol/g

resin, and the bead size is generally from 50 to 200 microns), a high local

concentration of peptide can be present on each bead. Calculations suggest

that the local concentration of peptide on a single bead is in the millimolar

to micromolar range and that the amount of peptide present would be from

50 to 300 pmol, more than sufficient to determine the sequence, because

modern sequencers can sequence in the 2 to 5 pmol range. Second, a

bead provides a unique surface (like a cell) for interaction of the ligand

present on the surface of the bead with a macromolecule in the surrounding

76
medium. Such a surface should be excellent for large-scale screening in a

minimum volume but with a large surface area.

The next question posed was, given the above considerations, how to

prepare a large, chemically diverse peptide mixture in which each peptide

in the mixture would be localized to a single resin bead and each bead

would possess a single peptide and in which each peptide in the library

would be present in approximately the same concentration. The answer to

the latter part of the question is to have each bead in the mixture approximately

the same size and substituted to the same extent. To ensure that each bead

has a unique peptide and only that peptide, it is necessary that each bead be

exposed to only a single coupling reaction at a time and that the reaction be

driven to completion. The approach is similar to that presented by Furka and

colleagues (1988, 1991), who, however, did not recognize the potential of

having a unique peptide sequence on every bead. Finally, for diversity, one

must turn to statistics. For example, if the 20 standard amino acids are used

for the construction of a library, by dividing the resin into 20 equal parts, adding

each amino acid to only one of these parts, then thoroughly mixing all the parts,

by dividing into 20 equal parts again, adding each amino acid to only one

of these parts, and so forth, one can rapidly develop a library of millions

of peptides on millions of beads. Because a gram of standard-size solidphase resin contains about 1 million 100-micron beads, very large libraries

are feasible using Merrifield solid-phase methodology.

It is important to recognize two important synthetic issues in the construction

of such a large, diverse peptide library. First, different amino acid residues

have very different chemical reactivities. For example, activate -Fmocalanine is much more reactive than -Fmoc-isoleucine. Second, and even

more important, when a large peptide library is constructed, the peptide

products become very diverse chemically and hence differ greatly in their

reactivity (nucleophilicity). Therefore, every reaction must be driven to

completion. As a practical matter, we found that using a threefold to fourfold

excess of the amino acid, a 1- to 2-hour reaction time in a minimum volume,

and optimized Merrifield synthetic procedures generally leads to completion

of the reaction even for difficult sequences such as Val to Val-peptide coupling.

To check on the fidelity of the methodology for producing single peptide

moieties on a bead, we examined a large number of single resin beads

chosen both randomly and from our binding assay for their structure and for

the synthetic fidelity of the synthetic peptide on the bead using a Pulsed Liquid

Automatic Peptide Sequencer to determine the sequence and quantitate the

amount of peptide on a single bead and then used modern preview analysis to

determine the fidelity of the peptide on a single bead. These studies showed

that each bead contains between 50 and 300 pmols of peptide as predicted,

and preview analysis demonstrated that the peptides on each bead were

homogeneous, with the percentage of a single peptide species being between

77
97 to 99.9 percent in a pentapeptide library of more than 2 million peptides

(Lam et al., in press). The results of such an analysis for several pentapeptide

beads that interact with streptavidin (Lam et al. 1991, 1992) are given in table 1.

These results demonstrate that the synthetic methods used, optimized to

maximize completion of reactions on solid phase resins, can provide large

peptide libraries in which each peptide produced is present in enough yield

and each bead present has on its surface and throughout the bead a unique

peptide sequence. The realization that a single solid-phase bead could contain

enough peptide in sufficient purity for use in a screening process was a critical

element in the development of this methodology.

Next, these libraries were utilized to screen for binding to acceptor

macromolecules. For this purpose, the authors prepared the libraries to

leave the peptide attached to the bead, but with all the protecting groups

(amino terminal and side-chain moieties) removed. Thus, it was necessary

to develop a synthetic strategy so that all side-chain protecting groups could

be removed following construction of the peptide library while the peptide

remained on the resin and, in addition, to ensure that the peptide on the

surface of the bead was accessible for binding to the macromolecular

acceptor by construction of a spacer of suitable length that is “biocompatible.”

One approach is to use polyamide resins that have been modified with a

spacer arm that consisted of a -alanine, an -amino caproic acid moiety,

and an ethylenediamine moiety attached to the polyamide polymer.

In this strategy, Fmoc protection is used for -amino groups, and tertTABLE 1. Examination of the amount of peptide and preview analysis for

individual pentapeptide beads that interact with streptavidin

Sequence Determined

Peptide Recovered Percent Preview

(pmol) for Cycle 5

HPQGP 60 0.35

HPQAG 53 1.5

FHPQG 72 0.23

QHPQG 60 2.3

GHPQG 250 0.44

REHPQ 112 0.56

IQHPQ 192 1.8

GNHPQ 222 0

WMHPQ 257 2.7

TPHPQ 158 0

WNHPM 59 2.5

MHPMA 140 0.31

78
butyloxycarbonyl, tert-butyl esters, and tert-butyl ether type side-chain

protections are used ( -Boc protection in conjunction with Fmoc-based

side-chain protecting groups also can be used). The -Fmoc groups were

removed with piperidine, and following construction of the peptide library,

the side-chain protecting groups were removed by 90 percent trifluoroacetic

acid in N,N-dimethylformamide (v/v) containing 1 percent anisole and 0.9

percent ethanedithiol. The peptide-resin beads were then neutralized with

10 percent diisopropylethylamine in N,N-dimethylformamide and thoroughly

washed with several solvents (including water) before use in screening.

Utilizing this methodology, we obtained a number of peptide libraries

suitable for rapid screening of the type described below.

To screen a library composed of millions of different chemical species

requires careful consideration. Among the most common problems for

consideration are the signal-to-noise (background signal) ratio, nonspecific

chemical reactions, unexpected chemical reactions, and the usual problems

of sensitivity and specificity. To overcome these problems, the authors

developed a rapid screening method that utilizes an enzyme-linked

immunoassay method in which the acceptor molecule (e.g., a monoclonal

antibody) is coupled to the enzyme alkaline phosphatase, which can then

catalyze a reaction in which those beads that interact with the monoclonal

antibody become colored as a result of the enzyme-catalyzed reaction.

These beads can be easily distinguished from those that do not react

because they are colorless. The reacting beads (those that are colored)

can be removed easily from the incubation medium by a micromanipulator

under a low-power dissecting microscope as previously discussed

(Lam et al. 1991, 1992).

The colored beads are then washed, the bound monoclonal antibodyenzyme complex removed by treatment with denaturing reagents that

can solubilize proteins (e.g., 6M guanidine hydrochloride), and the single

bead subjected to sequence analysis. In this way, unique new structures

can be discovered that bind with high affinity (micromolar to nanomolar)

to acceptor molecules. Ordinarily, we separately synthesize the binding

peptides discovered by these procedures and examine their binding to the

acceptor molecules by standard radioreceptor binding assays. Generally,

there is a good correspondence between the binding seen on the bead

and that obtained in the solution binding experiments (Lam et al. 1991,

1992, in press).

SUMMARY AND OVERVIEW

The authors developed a simple, highly reproducible, and synthetically clean

method for preparing large, statistically diverse peptide libraries that can be

used to discover ligands that will bind to acceptor molecules. Several other

79
methods were developed using both biological and chemical methods to

provide polypeptide diversity for a variety of applications (Scott and Smith

1990; Geysen et al. 1984; Fodor et al. 1991; Cwirla et al. 1990; Houghten

et al. 1991; Oldenburg et al. 1992; Devlin et al. 1990; Scott et al. 1992;

Cull et al. 1992; Brenner and Lerner 1992). These methods, and others

to be discovered and developed, offer a number of opportunities to examine

in new ways the fundamental issues of molecular recognition and the

diversity of ways in which molecular recognition problems can be solved.

With the availability of large, diverse peptide libraries, it is likely that, for

the same acceptor site on a macromolecule, several different peptide

structures will bind specifically to the same site with an affinity within a

few orders of magnitude of each other.

From these and other fundamental studies, it will be possible to address

issues that could not be addressed before the availability of these libraries.

For example, there are the basic questions of whether a small peptide of

5 to 15 residues can mimic a protein in its activities, whether a small linear

peptide can serve as a mimotope for a discontinuous epitope on an antibody,

or how big an area an inhibitor needs to recognize to be able to block the

binding of the native ligand. The last question is an especially critical question

for protein-protein interactions, where fairly large surface areas appear to be

involved in molecular recognition. Can a small peptide serve as a competitive

inhibitor for one of the sites of interaction to block the entire interaction?

The answer is not clear.

Another major issue is the difference between a peptide agonist and a peptide

antagonist. It was suggested many years ago that peptide agonists and

antagonists, even competitive ones, bind differently to the acceptor molecule

(Meraldi et al. 1977). In this case, a peptide antagonist had greatly reduced

flexibility relative to the agonist, and the specific induced rigidity was critical

for antagonist bioactivity (Meraldi et al. 1977). Perhaps this effect of rigidity

explains why small, relatively rigid peptidomimetics generally are antagonists

of ligands for macromolecular acceptors.

Another issue is how many different ways a particular molecular recognition

problem can be solved. For example, it already has been noted that a

particular recognition favors a particular primary sequence, called a consensus

sequence, and that this sequence is conserved throughout evolution. The

question is whether this sequence is truly “essential” or whether some other

sequence can serve the same purpose. This question now can be explored

by constructing a large library of peptides (millions or more) that do not

possess this consensus to determine whether one or more new sequences

can accomplish the same binding interactions and whether a new consensus

sequence” will be developed. Another extension of these methods is molecular

80
recognition processes involving nonpeptides, such as sugars, polysaccharides,

nucleotides, and lipids. Again, some very critical issues can be addressed.

Many other possibilities exist for exploiting molecular diversity, and

acceleration can be expected in the development of assay methods,

chemical methods, and physical methods to examine complex diverse

mixtures. In this regard, it is likely that the development of new synthetic

methods to prepare large, diverse chemical structures will challenge the

current methods of structural elucidation.

REFERENCES

Brenner, S., and Lerner, R.A. Encoded combinatorial chemistry. Proc Natl Acad

Sci U S A 89:5381-5383, 1992.

Cull, M.G.; Miller, J.F.; and Schatz, P.J. Screening for receptor ligands using

large libraries of peptides linked to the C terminus of the lac repressor.

Proc Natl Acad Sci USA 89:1865-1869, 1992.

Cwirla, SE.; Peter, E.A.; Barrett, R.W.; and Dower, W.J. Peptides on phage:

A vast library of peptides for identifying ligands. Proc Natl Acad Sci U S A

87:6378-6382, 1990.

Devlin, J.J.; Pangamban, L.C.; and Devlin, P.E. Random peptide libraries:

A source of specific protein binding molecules. Science 249:404-406, 1990.

Fodor, S.P.A.; Reed, J.L.; Pirrung, M.C.; Stryer, L.; Lee, A.T.; and Solas,

D. Light-directed, spacially addressable parallel chemical synthesis.

Science 251:767-773, 1991.

Furka, A.; Sebestyén, F.; Asgedom, M.; and Dibó, G. Cornucopia of peptides

by synthesis. Abstracts, 14th International Congress of Biochemistry,

Vol. 5. Prague, 1988. p. 47.

Furka, A.; Sebestyén, F.; Asgedom, M.; and Dibó, G. General method for

rapid synthesis of multicomponent peptide mixtures. Int J Pept Protein Res

37:487-493, 1991.

Geysen, H.M.; Meloen, R.H.; and Barteling, S.J. Use of peptide synthesis to

probe viral antigens for epitopes to a resolution of a single amino acid.

Proc Natl Acad Sci U S A 81:3998-4002, 1984.

Houghten, R.A.; Pinilla, C.; Blondelle, SE.; Appel, J.R.; Dooley, C.T.; and

Cuervo, J.H. Generation and use of synthetic combinatorial libraries for

basic research and drug discovery. Nature 354:84-86, 1991.

Lam, K.S.; Hruby, V.J.; Lebl, M.; Kazmierski, W.M.; Hersh, E.M.; and Salmon,

S.E. The chemical synthesis of large random peptide libraries and their use

for the discovery of ligands for macromolecular acceptors. Bioorg med Chem

Lett, in press.

81
Lam, K.S.; Salmon, S.; Hersh, E.M.; Hruby, V.J.; Al-Obeidi, F.; Kazmierski,

W.M.; and Knapp, R.J. The selectide process: Rapid generation of large

synthetic peptide libraries linked to identification and structure determination

of acceptor-binding ligands. In: Smith, J.E., and Rivier, J.E., eds. Peptides:

Chemistry and Biology. Leiden, The Netherlands: ESCOM Publishers,

1992. pp. 492-495.

Lam, K.S.; Salmon, S.E.; Hersh, E.M.; Hruby,V.J.; Kazmierski, W.M.; and

Knapp, R.J. A new type of synthetic peptide library for identifying ligandbinding activity. Nature 354:82-84, 1991.

Meraldi, J.P.; Hruby, V.J.; and Brewster, A.I.R. Relative conformational rigidity

in oxytocin and [1 -penicillamine]oxytocin: A proposal for the relationship

of conformational flexibility to peptide hormone agonism and antagonism.

Proc Natl Acad Sci U S A 74:1373-1377, 1977.

Merrifield, R.B. Solid phase peptide synthesis. I. Synthesis of a tetrapeptide.

J Am Chem Soc 85:2149-2154,1963.

Oldenburg, K.R.; Loganathan, D.; Goldstein, I.J.; Schultz, P.G.; and Gallop,

M.A. Peptide ligands for a sugar-binding protein isolated from a random

peptide library. Proc Natl Acad Sci U S A 89:5393-5397, 1992.

Scott, J.K.; Loganathan, D.; Easley, R.B.; Gong, X.; and Goldstein, I.J.

A family of concanavalin A-binding peptides from a hexapeptide epitope

library. Proc Natl Acad Sci U S A 89:5398-5402, 1992.

Scott, J.K., and Smith, G.P. Searching for peptide ligands with an epitope

library. Science 249:386-390, 1990.

ACKNOWLEDGMENTS

Fahad Al-Obeidi and Ralph Hirschmann made helpful suggestions and

offered constructive criticisms; Farid Abdul Latif provided assistance in

sequencing; Ronald Ferguson assisted in the synthesis of peptide libraries

and ligands; and Richard Knapp helped in the determination of binding

affinities,

This work was supported in part by the Arizona Disease Control Commission

and the Selectide Corporation. Kit S. Lam is a recipient of a Leukemia Special

Fellow Award (1989-1992) and a Leukemia Scholar Award (1992-1997).

AUTHORS

Victor J. Hruby, Ph.D.

Regents Professor

Department of Chemistry

Kit S. Lam, M.D., Ph.D.

Assistant Professor

Arizona Cancer Center

82
Wieslaw Kazmierski, Ph.D.

Research Scientist

Department of Chemistry

Evan M. Hersh, M.D.

Professor

Arizona Cancer Center

Sydney E. Salmon, M.D.

Regents Professor

Arizona Cancer Center

Department of Chemistry and Arizona Cancer Center

University of Arizona

Tucson, AZ 85721

Michal Lebl, Ph.D.

Director of Chemistry

Selectide Corporation

10900 Stallard Place

Suite 122

Tucson, AZ 85737

83
Computer Design of Bioactive

Compounds Based on 3-D Properties

of Ligands

Y.C. Martin

INTRODUCTION

The computer design of bioactive molecules is now a reality (for a review,

see Martin 1992). The design is based on the observed or postulated threedimensional (3-D) properties of the ligand as bound to the target macromolecule.

Single crystal x-ray crystallography of the ligand macromolecule complex can

supply the necessary information. However, an x-ray structure may not be

available, at least at the beginning stages of a medicinal chemistry investigation.

As an alternative, ligand structure-activity relationships are a sensitive probe of

the shape and chemical properties of the binding site on a macromolecule of

unknown 3-D structure. This structure-activity relationship can be used to map

the binding site from the inside out instead of from the outside in as one would

do with protein crystallography (Martin 1991).

The resulting pharmacophore maps are 3-D summaries of the structure-activity

relationships on which they are based. Beyond this, they provide the necessary

information for computer techniques that design new compounds to meet

the 3-D criteria (Martin 1992) and for those that forecast the potency of

compounds suggested by people or computer (Cramer et al. 1988). Thus,

there are three aspects of the structure-activity-based molecular design strategy:

pharmacophore mapping, 3-D searching and molecular design, and 3-D

quantitative structure-activity relationships (Martin 1991). Each of these pieces

forms a part of the strategy to convert two-dimensional (2-D) structure-activity

information into a novel bioactive molecule by consideration of the 3-D

properties of the molecules.

PHARMACOPHORE MAPPING

Description

The goal of pharmacophore mapping is to establish the bioactive conformations

of the ligands and how to superimpose these conformations. For pharmacophore

84
mapping, one needs structure-activity relationships of structurally diverse

and conformationally informative molecules. From this information, one first

proposes a pharmacophore, that is, the chemical nature of the groups required

for bioactivity and the geometric relationships between them. The structureactivity relationships of the ligands establish the required groups, answering the

question, Activity is destroyed by removal of what group? Conformationally

constrained compounds that are also active may help establish the bioactive

conformation of all the compounds. The molecules are superimposed, in their

proposed bioactive conformations, over the atoms of the pharmacophore or

their projected binding points on the macromolecule. The union of the volumes

occupied by the active compounds as superimposed suggests the regions in

space that can be occupied by any newly designed active ligand. In addition,

new regions in space occupied by compounds that meet the pharmacophore

requirements but are inactive define “forbidden regions” that, if occupied,

destroy activity.

Application to D1 Agonists

Martin and colleagues’ work (1991) started with the observation that the phenyl

group of SKF 38393 (I in figure 1) increases the affinity for the D1 dopaminergic

receptor by 100X compared with II. The goal was to find potent D1

 agonists that

have little D2

 activity. Distance geometry generation of conformations (Crippen

and Havel 1988) and energy minimization revealed that the axial and equatorial

conformations shown in figure 2 are approximately equal in energy and are the

lowest found. To establish which is the bioactive conformation, we synthesized

several compounds that placed an added phenyl in an axial or equatorial

position. Only those with the phenyl in the equatorial position show the phenyl

boost. Thus, the bioactive conformation of I is equatorial.

FIGURE 1. The structure of SKF 38393 (I) and its analog

85
FIGURE 2. The two low-energy conformations of I. The equatorial

conformation is shown in fine lines and the axial conformation

in heavy lines.

The first potent mimic of I was designed by using molecular graphics of the

pharmacophore model. In screening, III (figure 3) had been identified as a potent

but nonselective dopaminergic agonist. Examination of the 3-D structure of this

molecule (figure 4) showed that a phenyl should be placed as in IV (figure 3).

IV is a potent and selective D1 agonist (table 1).

UTILITY OF 3-D DATABASES IN MOLECULAR MODELING

To organize the pharmacophore modeling of many compounds by the smallmolecule group at Abbott Laboratories, Martin and colleagues (1988) developed

III. R = H

IV. R =C6H5

FIGURE 3. The lead compound (Ill) and the designed compound (IV)

86
FIGURE 4. The 3-D structures of III (fine lines) and IV (dashed fine lines) over

the bioactive conformation of I (heavy lines)

a 3-D database system based on the Daylight Chemical Information System.

The coordinates and charges as well as literature and project biological

properties are stored. In addition, the compounds can be searched in the

database by substructure. An advantage to using a chemical information

database is that all sets of coordinates of a compound are together regardless

of their original source.

Abbott Laboratories’ small-molecule molecular graphics program reads and

writes directly to these 3-D databases (Martin et al. 1992). This permits the

graphics program to be used to display the bioactive conformation without

the user’s knowing its dataset name, to display all stored conformations of a

molecule, or to display all members of a group used to derive a pharmacophore

or 3DQSAR model. Because the bioactivity associated with each bioactive

conformation is identified, the database also can be asked whether the same

conformation is responsible for different bioactivities.

3-D SUBSTRUCTURE SEARCHING BACKGROUND

The discovery of IV created a problem. How could I design compounds that

mimic the arrangement of phenyl groups in the bioactlve conformation of I or II

while maintaining the proper geometric relationships between the pharmacophore

N and O atoms? As I pursued this goal at the molecular graphics screen, my

enthusiasm turned to frustration. No imagined molecule matched well enough,

and energy minimization often changed conformations that looked promising on

the screen. By the time I had designed three compounds, I lacked more ideas.

87
TABLE 1. Receptor affinity of dopaminergic agonists studied

-Log Affinity

Log

Compound Source D1

 D2

Selectivity

I

II

III

IV

V

VI

VII

VIII

Literature

Literature

Screening of Abbott

Laboratories compounds

Pharmacophore mapping plus

molecular graphics of III

3-D searching of Abbott library

of compounds based on

pharmacophore mapping

Pharmacophore mapping plus

molecular graphics of V

Traditional medicinal chemistry

and molecular graphics based on VI

Traditional medicinal chemistry

and molecular graphics based on

I, IV, VI, and VII

7.19 5.16

5.16 5.60

5.89 5.53

7.24 <4.0

4.88 <4.0

6.82 5.19

8.52 6.11

7.50 5.89

2.13

-0.44

0.46

>3.24

>0.88

1.63

2.41

1.61

SOURCE: J.W. Kebablan, R. MacKenzie, and R. Schoenleber, unpublished observations

On reflection, it became apparent that many of the structures I tried were not

built from scratch. Rather, I made them from molecules that I had studied

earlier and for which a shape, as remembered, resembled the design target.

In other words, I had searched my mental database of 3-D structures for those

with the desired features. Because coordinates were already being stored in a

chemical information database, a computer program could be written to do this

searching properly. We did so in the program ALADDIN (Van Drie et al. 1989).

Gund and colleagues (1974) and Gund (1977) had suggested 3-D searching

much earlier but had created no 3-D database. Esaki (1982) followed this work

with another prototype system. Simultaneously with the author and colleagues’

work, Jakes and Willett (1986), Jakes and coworkers (1987), Brint and Willett

(1987), DesJarlais and colleagues (1988), Sheridan and Venkataraghavan

88
(1987), Lewis and Dean (1989a, 1989b), Bartlett and coworkers (1989), and

Sheridan and colleagues (1989) were addressing the same problem from

different viewpoints. The field has grown so much that there are now six 3-D

database-searching systems available to researchers other than that of the

original developer (Martin 1992). Dozens of laboratories around the world

are doing research on the topic. The impetus for this work is the increasing

availability both of computer hardware for 3-D molecular modeling and of 3-D

protein structures.

Description

The aim of 3-D substructure searching is to find or design molecules that meet

3-D criteria by examining the 3-D structures of hundreds or thousands of

molecules (Martin et al. 1990; Martin 1992). In contrast to pharmacophore

mapping, a 3-D search needs no existing structure-activity relationships.

However, the more information used, the more likely the correct search

question will be posed.

There are three main types of searches. Geometric search questions deal with

the intramolecular relationships between geometric objects (points, lines, and

planes) calculated from a structure. They are independent of the enantiomer of

the compound and its orientation in space. Similarity searching asks how similar

a database molecule is in 3-D properties to the reference molecule. In contrast,

steric searches ask whether a candidate structure can fit into a particular volume.

The result of a steric search is different for enantiomers of a molecule. The

allowed orientations of the molecule in the target space are determined by

some steric searching programs or specified by the user in others.

Uses of 3-D Searching

3-D searching has three distinct applications in medicinal chemistry. First,

3-D searches may recognize known compounds that meet 3-D criteria (new

bioactivities in old molecules). Second, 3-D searches may test a proposed

pharmacophore. Only if the search identifies all active molecules is the

pharmacophore correct. Third, 3-D searching may help design new compounds

that mimic one or another of the low-energy conformations of an active molecule.

As described above with I (Martin et al. 1991), the synthesis and testing of such

a set of compounds may identify which is the bioactive conformation. Note

that 3-D searching may help design new compounds that mimic the bioactive

conformation of a molecule. These may show improved features such as

improved bioavailability, selectivity, ease of synthesis, or patentability.

89
 Sources of 3-D Structures To Search

Of course, one needs 3-D structures to search. In our original work, the only

coordinates available were in databases of modeled compounds, Somewhat

later the CONnectivity to COoRDinates (CONCORD) program, which generates

a 3-D structure of a molecule in a few seconds, became available.’ We used

CONCORD to build 3-D databases of our corporate and commercially available

compounds (M. Bures, unpublished data). Sometimes it would serve no use

for coordinates to be in a database; thus, files of 3-D structures also can be

searched with ALADDIN. Recently, we added the option to generate the

structure with CONCORD as part of the 3-D searching process (Y.C. Martin

and E.B. Danaher, unpublished data).

Design of Novel D1 Agonists With 3-D Searching of Existing Compounds

and Molecular Modeling To Suggest Analogs

ALADDIN was used to search Abbott’s small-molecule modeling group’s

database of compounds modeled for various projects to find existing compounds

that might have dopaminergic activity (Martin et al. 1991). This search identified

V (figure 5) as well as several other compounds that were later shown to be

active. Molecular graphics of V (figure 6) showed where to add the phenyl

group to impart D1

 selectivity, compound VI. Table 1 summarizes the D1 and

D2 binding affinity of the compounds.

V. R=H

VI. R =C6H5

VII. R =C6H5

VIII. R = adamantyl

FIGURE 5. Structures of the database compound V and molecules designed

from it

90
FIGURE 6. The superposition of the bioactive conformations of I (solid heavy

lines), IV (fine lines), VI (fine lines), VII (fine lines), and VIII (fine

lines)

Traditional medicinal chemistry of this new lead led to a more potent analog,

VII (DeNinno et al. 1991). This compound is the most potent and selective D1

dopaminergic agonist known. Figure 6 shows this compound compared with

the other D1

 selective compounds. Notice that the pharmacophore N and meta

OH groups are in roughly the same geometric relationship in these active

dopaminergic compounds. The pendant phenyl group occupies slightly

different regions in space in the different compounds. This suggested that

the phenyl might be replaced with other bulky groups. Accordingly, the

adamantyl analog VIII was synthesized. It is also a potent agonist. Thus, we

successfully applied the strategy of finding weakly active compounds with a

3-D search and using molecular graphics and traditional medicinal chemistry

to suggest more potent and selective analogs (Martin et al. 1991).

Strategy To Design Novel Compounds With 3-D Searching for Templates

and Computer Transformation of Structures

3-D searching also can design novel compounds that match a pharmacophore.

Table 2 shows the strategy (Martin 1990). The core of this strategy is to identify

molecules that match the pharmacophore in geometric relationships but not

atom types, The appropriate atoms are then mutated into those required for the

pharmacophore. Thus, the search covers a database of molecules included for

their geometric properties only. Notice that we use CONCORD to generate the

3-D structures of the designed molecules. The process shown in table 2

represents the computer invention of new molecules.

91
TABLE 2. Steps in the de novo design of bioactive compounds using 3-D

searching

Step in the Desian Process Result

Map the pharmacophore

Search for geometric

templates

Change the templates

into the 2-D structures

of the proposed

compounds

Generate 3-D structures

of designed molecules

Computer investigation

of active compounds

to suggest a bioactive

conformation and

superposition rule

Geometric model of

pharmacophore

3-D search based Molecules that match

on geometry of the pharmacophore

pharmacophore geometry

MODSMI transformation

of 2-D structures of the

templates

2-D structures of

new molecules that

might match the

pharmacophore

CONCORD generation

of 3-D structures from

2-D

3-D structures of

new molecules that

might match the

pharmacophore

KEY: MODSMI = MODify SMIles

To implement the strategy, we invented a language (MODify SMIles [MODSMI])

that is used to tell the computer how to change the structure of the database

molecule into that expected to be active (Martin and Van Drie 1990). This

MODSMI language transforms the SMILES (Weininger and Weininger 1988)

description of the 2-D structures of the database molecules into the target

2-D structures. The four verbs of MODSMI are “nibble,” to remove an atom:

“replace,” to change one atom into another; “axe,” to break a bond or change

its bond order; and “join,” to make a bond between two atoms. Because the

atoms to be transformed into the pharmacophore atoms are identified in the

SMILES of the database molecules, the transformations can be applied only

to certain atoms (Martin and Van Drie 1990).

The MODSMI language was used for two purposes in the design of potential

dopamine agonists (Martin 1990; Martin and Van Drie 1990). First, the

pharmacophore atoms were added to the appropriate places in the structure.

Second, geometrically irrelevant substituents were removed from the structures,

and nonpharmacophore heteroatoms were changed into carbons, This

92
prevents the computer suggestion of a series of close analogs. Figure 7 shows

the transformation of three database molecules into potential dopaminergic

agonists. Both the removal of irrelevant substituents and substructures and

the addition of the pharmacophore atoms are shown.

Application of the Strategy to the Design of Novel Dopaminergic Agonists

To evaluate this procedure, I tested its ability to design dopaminergic agonists

(Martin 1990). Compounds IX through XVII (figure 8) are the essential

substructures of known dopaminergic agonists in which ail bonds between the

N and O are in a ring.

FIGURE 7. The transformation of three database molecules into the same

molecule suggested for synthesis

93
FIGURE 8. Essential structures of known dopaminergic agonists

I searched three databases: (1) the Fine Chemicals Directory of 80,000

commercially available compounds, (2) the Pomona College Medicinal

Chemistry Project database of 27,000 compounds with measured solvent-water

partition coefficients (the 3-D structures in these databases were generated with

CONCORD), and (3) the Abbott small-molecule modeling group’s database of

carefully modeled compounds, 9,000 3-D structures from 3,000 compounds,

94
Compounds XVIII through XXI in figure 9 are examples of the types of

compounds suggested. Figure 10 shows one of the designed compounds

superimposed over apomorphine. There is a strong resemblance between

the 3-D structures of the two compounds.

Does this strategy design the known molecules? Yes, it suggested eight of nine

known classes of fused-ring dopaminergics. Does it design new molecules?

Yes, it designed 75 novel series compounds in which all atoms between the

O and N of the pharmacophore are in a ring (a total of >300 molecules). It also

designed more than 100 other compounds with one rotatable bond in the path

for a total of 508 molecules.

FIGURE 9. Examples from the >500 potential dopaminergics designed by

ALADDIN

SOURCE: Martin 1991

95
FIGURE 10. The superposition of XVIII (heavy lines) and apomorphine (fine

lines)

Evaluation of the Strategy by the Design of Morphine Mimics

Similar strategies can be used to design mimics of peptides in their bioactive

conformations (Bartlett et al. 1989). If one does not know the bioactive

conformation, one can use these methods to design mimics of the various

low-energy conformations. Similarly, one can use 3-D searching to design

compounds that probe the direction of binding of heteroatoms to a

macromolecule.

I decided to test the strategy by using the bioactive conformation of enkephalin

for the search criteria and asked whether morphine is designed (Y.C. Martin,

unpublished data). To not bias the results with structures we had modeled or

tested at Abbott, we searched the Fine Chemicals Directory for 3-D templates

and transformed them as had been done in the dopaminergic example. The

search criteria included an unsubstituted aromatic atom to which the required

OH would be added. Thus, morphine would not meet the search criteria.

However, the search identified XXII (figure 11), from which the morphine mimic

XXIII was designed with MODSMI. Thus, the computer would design morphine

from enkephalin distances.

96
FIGURE 11. A database molecule, XXII, that led to the design of a morphine

mimic, XXIII

QUANTITATIVE FORECAST OF POTENCY OF SUGGESTED MOLECULES

USING COMFA 3-D QUANTITATIVE STRUCTURE-ACTIVITY ANALYSIS

It is not sufficient to design novel compounds. These compounds also must be

potent. Thus, the third aspect of the computer design of bioactive molecules

involves the understanding of the 3-D quantitative structure-activity

relationships, We have successfully used a special implementation of

comparative molecular field analysis (CoMFA) (Cramer et al. 1988) for this

purpose (Martin et al. 1992).

Description of CoMFA

For CoMFA one needs to have a biological potency measured for at least

15 molecules, In addition, one needs the superposition rule and bioactive

conformations derived from pharmacophore mapping. A CoMFA starts with

calculating fields around each molecule at intersections of a 2Å lattice. The

fields are calculated using traditional potential energy equations. Typically,

energies are calculated for at least 1,000 points. In the analysis, high positive

steric energies are truncated to a more modest value (4 to 25 kcal/mol, for

example). Lattice locations with low standard deviations are discarded from

the calculations. The relationship between the remaining fields and potency

is evaluated using partial least squares, a variant of principal components.

Because there are more energy values per compound than compounds, crossvalidation is an essential aspect of the method. In cross-validation, the potency

of each compound is forecast from a model from which it had been deleted.

97
The result of a CoMFA analysis is an equation that describes the contribution of

each lattice energy value to potency. Such equations are used for forecasting

the potency of additional compounds, For ease of understanding, one displays

contours of the coefficients such as those shown in figure 12.

Evaluation of CoMFA

The Abbott implementation of CoMFA (Martin et al. 1992) differs from that

of Cramer and colleagues (1988) in that the molecular fields are calculated

using the program GRID (Boobbeyer et al. 1989; Goodford 1985).

Martin and colleagues (1992) showed that CoMFA performs as expected. First,

CoMFA fits and forecasts physical properties such as pKa’s and linear free

energy sterlc constants. Also, It provides a good fit for biological data that

previously had been shown to be correlated with octanol-water IogP. For these

three situations, we used different properties of the probe atoms. For pKa’s, a

positively charged probe with no hydrogen bonding character was used: for

steric effects, a neutral probe with no hydrogen bonding character was used:

and for IogP, a neutral probe that is both a hydrogen-bond donor and an

acceptor was used.

We also showed that CoMFA steric parameters correctly describe the structure

of the binding site. For this we derived a CoMFA for a set of quinones that bind

to the bacterial photosynthetic reaction center. The locations of the CoMFA

coefficients agree well with the 3-D structure of the protein determined by single

crystal x-ray crystallography.

FIGURE 12. The positive CoMFA contours for the D1

 dopaminergic binding

model in fine lines and VIII in heavy lines

98
Last, we showed that CoMFA relationships correctly forecast the potency of

compounds not included in its derivation.

INTEGRATION OF 3-D SEARCHING AND 3-DQSAR

We added an option to ALADDIN so that it now reads a set of CoMFA

coefficients, a coordinate set that was part of the CoMFA, and the superposition

rule (Y.C. Martin and E.B. Danaher, unpublished data). ALADDIN orients the

designed molecule (and its enantiomer if requested) and marks atoms of the

molecule that hit negative CoMFA coefficients. These atoms can be removed

with the MODSMI routines.

As noted above, ALADDIN designed 508 compounds that match the D2

pharmacophore. Of these, 110 did not hit negative D1 steric contours. Thus,

only 22 percent of the compounds designed to match a pharmacophore were

forecast to be active. MODSMI was used on the remaining 398 compounds

to remove the atoms that hit a negative D1

 CoMFA steric contour. Of these

redesigned compounds, 132 did not hit negative contours, and 26 of these

were not in the first 110. Thus, ALADDIN designed 136 compounds that fit

into the D1 steric contours.

The characteristics of the series of compounds tested will determine the

information gained from a CoMFA analysis. Does the computer design a

better set of compounds for CoMFA analysis than do chemists? A series of

20 chemist-designed D2

 agonists were composed with a series of the same size

selected from those designed by the computer (Lin et al. 1990). Shape was

described by the steric energies as used in CoMFA. To choose the series of

computer-designed compounds, a cluster analysis based on the 25 highest

principal components of the steric fields was used. Compared with the chemistdesigned series, the computer-designed series shows a larger variation in steric

properties and has less correlation between steric properties. it explores all

space explored by the former series. The mean and range of the forecast D1

and D2 dopaminergic receptor-binding affinities for the two series are not

different. Thus, multivariate statistical methods based on energy fields provide

the tools to choose a good series of molecules for a CoMFA analysis. These

methods also reduce a large set of suggested molecules to one that is

reasonable to synthesize.

SUMMARY

3-D database searching has many uses for a medicinal chemist. It can aid in

the design of compounds to probe or to mimic the bioactive conformation of a

natural ligand or to fit a hypothetical or experimental structure of a binding site.

It also can identify existing molecules that meet these criteria—new uses for old

99
molecules. If one has a database of active compounds, 3-D searching can

validate or refute a pharmacophore hypothesis.

The CoMFA method of 3DQSAR can be used to forecast the potency of the

designed analogs. Also, the integration of CoMFA and 3-D searching concepts

provides a framework for the design of a good series for CoMFA. In addition,

CoMFA 3DQSAR coefficients provide a model of the binding site to facilitate

the design of compounds that fit the pharmacophore and do not hit stericaiiy

unfavorable regions.

NOTE

1. Pearlman, R.S.; Rusinko, A. III; Skell, J.M.; Balducci, R.; and McGarity,

C.M. CONCORD, distributed by Tripos Associates, inc., Suite 303, 1699 S.

Hanley Road, St. Louis, MO 63944.

REFERENCES

Bartlett, P.A.; Shea, G.T.; Telfer, S.J.; and Waterman, S. CAVEAT: A program

to facilitate the structure-derived design of biologically active molecules. in:

Roberts, SM., ed. Molecular Recognition: Chemical and Biological Problems.

Roy Soc Chem 78:182-196,1989.

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Searching for pharmacophores in large coordinate databases and its use

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ACKNOWLEDGMENTS

The work presented in this chapter has been a collaborative effort with the

author’s past and present colleagues:

M. Bures—Built CONCORD databases of corporate and commercial

compounds. Collaborated on enhancements to 3D-searching.

E. Danaher—Collaborated on building CONCORD databases. Programed the

CoMFA evaluation of ALADDIN hits.

J. DeLazzer—Programed much of the molecular graphics.

K. Kim—Collaborated in CoMFA development and evaluation.

T. Lin—Implemented PLS and collaborated in CoMFA development and

evaluation.

P. Paulik—Modeled compounds for the D1

 pharmacophore and CoMFA.

R. Schoenleber—Synthesized the D1 dopaminergic compounds to elucidate

the bioactive conformer of I and supervised the synthesis of IV, VI-VIII.

J. Van Drie—Programed the initial version of ALADDIN, designed and

programed MODSMI.

AUTHOR

Y.C. Martin, Ph.D.

Senior Project Leader

Computer-Assisted Molecular Design Project

Pharmaceutical Products Division

Abbott Laboratories

Department 47E, AP9A LL

Abbott Park, IL 60064

102
Toward the Design of New Inhibitors:

A Computer Modeling Approach

Carlos H. Faerman

INTRODUCTION

There has been increased interest in the use of databases for the discovery

of potential new lead compounds (Sheridan and Venkataraghavan 1987;

Sheridan et al. 1989; Martin 1990). An excellent example of this effort is

provided by ALADDIN (Van Drie et al. 1989), a computer program that

searches a database of three-dimensional structures for the design of new

compounds with interesting biological properties. The author believes it is

important to try to use more than one method in combination with a database

of crystal structures for the design of new inhibitors. If one candidate is found

with more than one method, this would enhance the possibility of its being

a good template. This process is a distinct feature of this study. Initially, I

focused my work on searches based mainly on shape properties, and thus

I chose the following independent methods:

1. Docking techniques

2. CAVEAT analysis

3. QUEST programs

The Cambridge Crystallographic Database (CCD) version 4.5 of July 1991,

which contains 90,296 entries, was used in this work. What follows gives a

succinct description of each of the methods used in combination with the CCD.

Docking Techniques

I have used DOCK versions 1.0 and 2.0 by DesJarlais and colleagues (1988).

These programs use spheres to describe the active site of an enzyme, the

structure of which is known from, for example, x-ray crystallography. The

“negative” image of this receptor site is then used to test out compounds from

the CCD. Using a score to rank these molecules docked onto the receptor

site, one usually gets a family of compounds that fit well onto the receptor site.

Although many compounds of interest are rigid, this program has been used for

flexible molecules (DesJarlais et al. 1986). An example of the use of DOCK for

103
the design of inhibitors is haloperidol, a compound that inhibits the human

immunodeficiency virus 1 protease (DesJarlais et al. 1990).

CAVEAT Analysis

The program CAVEAT (Bartlett et al. 1990) makes use of the CCD in a

different way. For every molecule in the database, it stores the intramolecular

bonds as vectors. This new database of vectors is stored independently from

the original one, which contains atomic coordinates, and is then searched

for vector matching. The approach is to start with the crystal structure of

an inhibitor in complex with an enzyme and then search for new templates

based on the spatial arrangements of the bonds of this known ligand. The

user is then allowed to choose up to three vectors belonging to this ligand.

The natural choice for these vectors are those along the bonds that make

important chemical interactions with the target enzyme. It is then possible

to search for molecules that could have a similar relative orientation of their

bonds. Figure 1, panel A, shows two vectors chosen along two bonds of a

peptide of known structure. Figure 1, panel B, shows the structure of a hit,

a compound that satisfies the search, superimposed on the starting peptide.

This method has been applied to tendamistat, a 74-amino acid inhibitor of

-amylase (Bartlett et al. 1990).

QUEST Programs

These programs supersede the querying codes that belong to older versions

of the CCD (Allen et al. 1979). In particular, a similarity search (Willett et al.

1986) and a powerful connectivity search are now an integral part of this

package. It is also worth noting that a pharmacophore model for an inhibitor

can be built easily using QUEST. The pharmacophore model, which is the

set of distances and angles of a substructure that is important for a certain

biological activity, can also provide new ideas for new templates (Sheridan

et al. 1989).

SYSTEMS STUDIED

This chapter’s emphasis is on two systems—papain and thrombin.

Papain

Cysteine proteases have been implicated in various pathological conditions,

such as inflammation and malignancy. Papain belongs to the family of

cysteine proteases, and it is isolated from the latex of tropical papaya fruit.

Cathepsin B also is a cysteine protease found in, for example, human liver.

Mechanistic data gathered for cathepsin B suggest that it shares a common

basic enzymatic mechanism with papain (Khouri et al. 1991). Papain, which

104
FIGURE 1. (A) Peptide used to define a pair of vectors, which are indicated

by dashed arrows. (B) A hit, that is, a compound with a similar

spatial arrangement of its bonds, is shown superimposed on the

original peptide.

has been extensively studied, can therefore serve as a template for the design

of inhibitors aimed at related cysteine proteases of pharmacological importance

such as cathepsins B, H, and L. From the known crystal structure of BzoPhe-Ala-CH2CI, an inhibitor, in complex with papain (Drenth et al. 1976),

105
three vectors were chosen along bonds that make, for example, hydrophobic

interactions with papain. The choice of vectors is important because the basic

assumption of this method is that the shape of a compound can be described

reliably by vectors along its bonds. This will, in turn, allow choosing other

templates that interact with papain and, after some chemical modifications

(e.g., introduction of a chloromethylketone group), show inhibitory properties.

Using the CAVEAT analysis, I found two main classes of compounds.

The first family of compounds obtained with CAVEAT, shown in figure 2, panel

A, had been suggested earlier by DesJarlais and colleagues (1988) in an

attempt to find good inhibitors of papain using docking techniques. This is an

interesting result because it shows that, by using different methods, families

of compounds with similar geometrical features can be identified. The second

class of compounds found using CAVEAT, shown in figure 2, panel B, had also

been obtained independently by me with a similarity search available as part

of QUEST.

This similarity search was based on a known weak inhibitor of papain, 2,4-

dihydroxy-7-methoxy-1,4-benzoxazin-3-one (Perez and Niemeyer 1989), also

known as Dimboa. Figure 3, panel A, shows the structure of Dimboa, and

figure 3, panel B, gives the input data corresponding to the similarity search

based on Dimboa’s connectivity.

Furthermore, within QUEST it is possible to search the CCD database using

a model for the pharmacophore (see figure 4, panel A). This application has

provided some ideas for new lead compounds, one of which is shown in figure

4, panel B. This is a very promising candidate, not only because it is similar to

the starting compound, but also because it has many hydrogen-bonding donor

(acceptor) atoms. We at the Biotechnology Research Institute are in the

process of synthesizing new compounds based on this template.

Thrombin

Thrombin is an important enzyme that initiates blood coagulation. Fibrinogen,

thrombin’s natural substrate, interacts strongly with two sites of thrombin,

namely, the active site and the exosite. Because thrombus formation inside

the vessels, started by the hydrolysis of fibrinogen, may lead to thromboembolic

diseases, many scientists have been searching for new thrombin inhibitors to

control blood coagulation. One of the most potent inhibitors of thrombin is

hirudin, a 65-residue peptide from the salivary glands of the leech Hirudo

medicinalis (Walsmann and Markwardt 1981). A potent thrombin inhibitor

based on hirudin’s sequence has been synthesized (DiMaio et al. 1990) and

characterized (Yue et al. 1992) at the Biotechnology Research Institute. The

high affinity and specificity of hirudin as a thrombin inhibitor are the result of its

bivalent binding mode. In other words, in addition to blocking the active site,

106
FIGURE 2. Families of compounds obtained using CAVEAT analysis based

on a known inhibitor of papain, Bzo-Phe-Ala-CH2-Cl (Drenth et

al. 1976). The six (or eight) letter refcodes are from CCD. (a)

BEBNAX10 (Newkome et al. 1984). (b) CEJHUU (Shevyrev et

al. 1983).

hirudin interacts with the exosite via its C-terminal undecapeptide fragment.

Because the C-terminal hirudin fragment is available to digestion by many

enzymes (Chang 1983) and its intactness is necessary for antithrombotic

activity, a search for new analogs of the C-terminal fragment of hirudin, which

are resistant to proteolysis, is necessary. I have concentrated on the thrombin

exosite because it has been shown that blocking this site disrupts the cleavage

of fibrinogen. Using docking techniques, I have been able to design a cyclic

form of a hirudin analog. It is known that cyclic peptides may enhance not

107
A DIMBOA

B

SIMILAR TANI

at01 N 3

at02 C 3

at03 C 3

at04 0 2

at05 C 3

at06 C 3

at07 C 2

at08 C 2

at09 C 3

at10 C 2

at11 O 1

at12O 1 E

at13 0 1

at14 0 2

at15 C 1

at16 C 1

end

bo 1 2

bo 1 6

bo 1 13

bo 2 3

bo2122

bo 3 4

bo 3 11

bo 4 5

bo562

bo 5 10

bo 6 7

bo782

bo89

bo 9 10

bo 9 14

bo 14 15

FIGURE 3. (A) Structure of Dimboa (Perez and Niemeyer 1989), a weak

inhibitor of papain. (B) input for a similarity test available within

QUEST, based on the connectivity of Dimboa (Perez and

Neimeyer 1989).

only the biological activity (Al-Obeidi et al. 1989) but also the biological stability

(Sham et al. 1988; Szewczuk et al., In press). Between residues Phe56 and

IIe59 (hirudin’s numbering is used), there is space to accommodate extra

groups. Through the use of docking programs, many aromatic compounds

were successfully placed in this region. This suggests that aromatic rings in

this position are well accepted by thrombin. This fact prompted me to design

new inhibitors with an aromatic motif between the two residues mentioned

above. The aromatic ring between these two residues was approximately in

108
FIGURE 4. (A) A pharmacophore model for papain based on the known

structure of Bzo-Phe-Ala-CH2

-C1. (B) The structure of CEVNOG

(Toniolo et al. 1983) was retrieved with this pharmacophore

model, and it is presently under investigation.

the same position as the best docked candidate extracted from the CCD. This

constraint should allow the peptide to adopt the biologically active conformation

even in solution, resulting in an increase of the activity because of the entropic

gain during the interaction with thrombin. This compound, shown in figure 5, is

being synthesized at the author’s institute, and it will be reported elsewhere.

109
FIGURE 5. A cyclic analog of the C-terminal fragment of hirudin currently

being synthesized. Hirudin’s numbering scheme has been kept

for the natural amino acid residues.

CONCLUSIONS

This work has shown that, using three different techniques in combination

with the CCD, I could suggest new compounds for specific target enzymes.

Furthermore, it is very encouraging that some of these compounds (or parts

of them) were obtained by two independent methods. It is worth mentioning

that for both papain and thrombin I retrieved well-known inhibitors using

different vector triplets with CAVEAT. Thus, this method has successfully

withstood an important test. This project was undertaken to assess the

potential of computer modeling, used in combination with a database of

small molecules, for the design of new lead compounds. Work is in progress

at the institute to carry out biological assays to test the compounds synthesized

based on the templates suggested by computer modeling. I emphasize the

interplay between design of new compounds using databases and biological

assays. The latter will always be the acid test for any suggestion obtained with

computer modeling. For compounds that are very flexible, I will have to include

not only the single conformation stored in the CCD but also other conformations

that are compatible with the active site. There have been some attempts in this

direction using the commercial computer modeling package CHEM-X (Murrall

and Davies 1990).

110
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Perez, F.J., and Niemeyer, H.M. Reaction of Dimboa, a resistance factor from

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ACKNOWLEDGMENTS

The author acknowledges Dr. John Rodgers (National Research Council,

Canada, Canadian Institute for Scientific and Technical Information) for his

assistance with QUEST’s input commands and storage of data, Dr. Zbiguiew

Szewczuk (Biotechnology Research Institute, Canada) for the synthesis of the

compounds studied here and his helpful comments, and Dr. John Eyermann

(DuPont, Wilmington, DE) for his help with the use of the pharmacophore

model within QUEST. Professor Irwin D. Kuntz and Professor Paul Bartlett

provided copies of DOCK and CAVEAT.

112
AUTHOR

Carlos H. Faerman, Ph.D.

Research Associate

Biotechnology Research Institute

National Research Council, Canada

6100 Royalmount Avenue

Montreal, Quebec H4P 2R2

CANADA

113
Efforts To Improve the Utilization of

Protein Sequence and Structure

Databases 

Nagarajan Pattabiraman and Krishnan Namboodiri

INTRODUCTION

For the past several decades knowledge of cellular function has been growing

rapidly. This rapid growth was caused by the introduction of many technological

advancements in biology, which not only opened up a new field, biotechnology,

but also brought an information explosion. To illustrate this growth of

bioinformation, consider the basic constituent of all living organisms, a cell.

Vast amounts of information have been gathered on various types of cells, their

chromosomes, genes, nucleic acid, and protein. Most of this information has

been accumulated as published literature, generated by different laboratories

around the world. As this information began to flow, it was compiled into various

databases that were circulated among only a few individuals. When the size of

these databases started to increase, the need for quick archiving and retrieval

became critical. This need was filled by the advent of fast computers and disk

drives with large storage capacity, which in turn paved the way for the birth of

a new field called bioinformatics. This field addresses the issues of access,

update, and manipulation of various types of computer-based biological

information.

The biological databases contain a wealth of information for understanding the

structure and function of cells. The number of databases illustrates the current

status of bioinformatics. The database Listing of Molecular Biology Databases

(LiMB) contains various databases available in the field of molecular biology.

There are more than 3,000 cultured cell lines available in the database

maintained by American Type Culture Collection (ATCC). DNA and protein

sequences are maintained, respectively, by Genbank and National Biomedical

Research Foundation (NBRF) (George et al. 1986). A protein three-dimensional

(3-D) structural data bank is managed by Brookhaven National Laboratory (BNL)

(Bernstein et al. 1977). When the sources of these databases are examined,

it is clear that each database is managed by a different scientific agency.

114
An era of biological research is beginning in which scientists must rely on more

than one of these databases. A major problem confronting modern research in

bioscience is the availability and ease of utilization of the biological databases.

There is a need for either a certain uniformity among these databases, special

tools to access the information across different databases, or both. Some earlier

attempts in this direction were undertaken in Europe; OWL and SWISS-PROT

are the outcomes of this attempt to integrate protein and nucleic acids sequence

databases. It should be noted that a few in-house molecular modeling programs

(Blundell et al. 1988; Claessens et al. 1989; Thornton and Gardner 1989; Bryant

1989; Unger et al. 1989; Jones and Thrirup 1986) are available to automatically

search and extract the sequence and structure information from the Protein Data

Bank (PDB). Often these programs are found to be closely integrated with large

onsite graphics or molecular modeling programs and are not available for public

use because of cost and hardware limitations.

Advancements in the fields of protein sequencing and genetic engineering

will permit the addition of a large number of protein sequences to the Protein

Identification Resource (PIR) sequence database. In addition, many 3-D

structures of natural and genetically engineered proteins also will be determined

to atomic resolution, Thus, a direct link between the primary sequence database

(PIR) and the 3-D structure database (NBRF/PDB) becomes increasingly

important in understanding the structure-function relationships of proteins.

Prior to the author and colleagues’ work (Namboodiri et al. 1988; Pattabiraman

et al. 1990) these two important databases containing information about the

structure and function of protein molecules did not have a direct link. Therefore,

the authors created a sequence-structure database called NRL_3D derived from

protein entries in PDB and searchable within the PIR environment.

As the number of databases pertaining to a certain scientific area increases,

every effort should be made to cross-check and retrieve information. The most

significant effort to integrate biological databases started with the establishment

of the National Center for Biotechnology Information (NCBI) (Boguski et al., in

press) under the National Library of Medicine at the National Institutes of Health.

In July 1990 NCBI announced a new database, Genlnfo Backbone Sequence

Database (New molecular sequence . . . 1990). This new and integrated

database includes the MEDLINE records that correspond to protein or nucleic

acids sequences. Genlnfo will maximize the use of standard nomenclature

and gene names from original sources. It is the first major database to use

International Standard Organization format (Abstract Syntax Notation, ASN.1)

for data representation, Genlnfo is implemented as a relational database offering

a variety of advantages over other database systems. For the past few years,

the authors have been interested in integrating databases on protein molecules.

This chapter describes and gives examples of our efforts to improve the

utilization of the protein sequence database (PIR) and the 3-D structure data

bank (PDB).

115
DESCRIPTION OF A SEQUENCE-STRUCTURE DATABASE, NRL_3D

PIR’s protein sequence database contains most of the published protein primary

sequences (George et al. 1986). PIR also provides a variety of software tools

for protein sequence manipulation, sequence similarity searches, and sequence

alignments. Figure 1 shows a histogram of the number of protein primary

sequences added every year from 1983 through 1991 (D. George, personal

communication, September 1992). It is clear from the histogram that the growth

of this primary sequence database is exponential.

Protein Sequences in PIR

FIGURE 1. Histogram showing the exponential growth of protein primary

sequences deposited over a period of 9 years with PIR

116
BNL’s PDB (Bernstein et al. 1977) contains coordinates of 3-D structures of

several proteins and other related biomolecules obtained using single crystal

x-ray diffraction data. Figure 2 shows a histogram of the number of entries

added to PDB every 6 months from January 1988 through September 1991

(E. Abola, personal communication, September 1992; Bernstein et al. 1977).

Again, the growth is clearly exponential. In addition to x-ray diffraction data,

PDB also contains coordinates obtained from nuclear magnetic resonance,

modeling, and neutron diffraction data. A typical PDB entry for a biomolecule

contains not only the 3-D atomic coordinates but also information such as

sequence, bibliography, quality of the reported data, and secondary structure.

However, because PDB uses a rigid, cryptic, and flat-file format for the

distribution of these data, the general utility of this vast treasure of information

has been restricted to a few scientists.

FIGURE 2. Histogram showing the exponential growth of number of entries

deposited over a period of 4 years with BNL

117
in each PDB entry, a variety of protein structure information is stored as records

using a flat-file format, Each record is identified by a record identifier. For

example, the primary sequence of the protein is stored under the “SEQRES”

record identifier. It may appear simple to construct a new sequence database

similar to PIR by extracting the PDB protein sequences from these “SEQRES”

records and then performing sequence similarity searches on them. However,

this process is not straightforward for the following reasons. In PIR entries, the

N-terminal residue is always the first residue and is identified by the number 1.

The following residues are numbered contiguously. In PDB entries, the residue

numbers (residue identification scheme) may not be identified by contiguous

numbers starting with 1 from the N-terminal residue. in some cases, the

residue numbers also contain certain letters. The reasons for following this

residue identification scheme in PDB are as follows. The residue identification

was assigned according to the previously studied homologous protein. In

cases where there is an insertion of amino acids, the sequence numbers of

the inserted residues are the same as those of the homologous protein. in

addition, an alphabetical designator (e.g., A, B, C) is attached to the sequence

numbers of these inserted residues. For example, in table 1b residues 15A

and 15B are inserted between residues 15 and 16. If there is a deletion of

amino acids, the residue numbers before and after the deletion will be the

same as that of the homologous proteins. in other words, the residue numbers

at the deletion site are not contiguous. In table 1b, there is a deletion between

residues 19 and 31, which are linked by a peptide bond. Finally, because of

the quality of x-ray data and the nature of the structure refinement, it may not

be possible to trace the backbone atoms of some of the residues. Hence, the

3-D coordinates for these residues are not reported in the PDB entry. Thus,

using the primary sequence database derived from the “SEQRES” records

alone, one cannot extract the 3-D coordinates for a given sequence. In short,

when PDB sequences are reformatted, one has to keep track of the identification

for each residue given in the “ATOM” record.

In the development of the NRL_3D database, Pattabiraman and colleagues

(1990) extracted the sequence information from the “ATOM” records, based

on the distance between two consecutive atoms. The sequences were

then formatted according to PIR sequence database. In addition, the sequence

identifications were extracted from the “ATOM” record, and a separate database

file was created. Table 1 shows NRL_3D database files for the alpha-lytic protein

(PDB code is 2alp [Fujinaga et al. 1985]). In the current setup the NRL_3D

database can be used interactively within PIR using the SCAN and MATCH

commands (George et al. 1986; Pattabiraman et al. 1990). The authors and

collaborators also developed a computer program (PRENRL_3D) in C language

(Kusunoki et al. 1991) that automatically creates NRL_3D database files from

PDB protein entries.’

118
TABLE 1. Typical examples of NRL_3D database files

a. NRL_3D.SEQ file

>P1;2ALP1

Alpha-lytic protease (E.C. number not assigned-(lysobacter enzymoqenes)

ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARIGGAVVGJAARVFPGNDRAWVSLT

SAQTLLPRVANGSSFVTVRGSTEAAVCRSGRTTGYQCGTITAKNVTANYAEGAVRGLTQGNACMGR

GDSGGSWITSAGQAQGVMSGGNVQSNGNNCGUPASQRSSLFERLQPILSQYGLSLVTG*

b. NRL_3D.NUM File

>p1;2ALp1

Alpha-lytic protease (E.C. number not assigned-(lysobacter enzymogenes)

15A 15B 16

39 40 41

48C 49 50

59B 60 61

67 88 88A

105 106 107

120B 120C 120D

125 129 130

141 142 143

166 167 168

180 161 162

196 197 196

212 213 214

220A 221 222

230 231 232

243 244 245

c. NRL_3D.REF file

NRL_3D-Entry. 2ALP1

17

42

51

62

89

108

120E

131

156

169

183

199

215

222A

233

*

16

43

52

64

90

109

120G

132

157

170

184

200

216

222B

234

19

44

53

65

91

110

12OH

133

158

171

165

201

217

222C

235

31 32

44A 45

54 55

66 67

94 95

111 112

120I 120J

134 135

159 160

173 174

189 190

201A 202

218 219

223 224

236 237

33 34 35 36 38

46 47 48 48A 48B

56 57 58 59 59A

81 62 83 84 85

100 101 102 103 104

113 114 119 120 120A

120K 121 122 123 124

136 137 138 139 140

161 162 163 164 165

175 176 177 178 179

191 192 193 194 195

207 208 209 210 211

219A 219B 219C 219D 220

225 226 227 228 229

238 239 240 241 242

Alpha-lytic protease (E.C. number not assigned-(lysobacter enzymogenes)

PDB-entry 2ALP 07-MAR-85 NBRF-entry TRYXB4

HEADER. Hydroiase (serine proteinase)

RESOLUTION: 1.7 Angstroms. R-value: 0.131

PDB-depositor(s)=Fujinaga, M.; Delbaere, L.T.J.; Brayer, G.D.; and James, M.N.G.
APPLICATIONS OF NRL_3D IN PROTEIN MODELING

The authors’ laboratory has been interested in the structure-function

relationships of phospholipase A2 (PLA2) molecules. PLA2 catalyzes

specifically the hydrolysis of the ester bond at the C2 position of 3-snphosphoglycerides. PLA2 is a biologically important molecule involved in

arthritis and wound healing. In addition, the activities of PLA2 found in snake

venoms may be responsible for pharmacological effects such as myotoxicity,

anticoagulant properties, and hemolytic effects (Waite 1987). PLA2 includes a

class of low molecular weight proteins composed of approximately 120 amino

acids. In the current version of PIR, 53 primary sequences of PLA2 from

different sources have been included (Waite 1987). Six crystal structures of

PLA2 are available in PDB. Figure 3 shows a C

a

 backbone tracing of bovine

pancreas PLA2. This was the first molecule of this class whose structure was

determined (Dijkstra et al. 1978). Secondary structures such as a-helix and

 -strand are held together by six disulfide bridges, which are shown as dashed

lines (figure 3). The structure of the PLA2 molecule is rigid because of these

disulfide linkages.

Because of the linkage of PIR and PDB via the NRL_3D database, it is now

possible to align the sequences of PLA2 of known 3-D structure with those of

the sequences of unknown structure. The alignment of the sequences of pig,

horse, and a snake venom PLA2 (C. Atrox) with that of bovine pancreas PLA2

(Waite 1987), whose 3-D structure is known, is shown in figure 4. A star in the

aligned sequences denotes that the amino acid is the same as that of the bovine

pancreas PLA2. Shown within boxes of solid lines are the stretches of amino

acids for which the aligned residue in the bovine PLA2 structure is an a-helix.

Similarly, the -helices are represented by boxes with dashed lines. This gives

an idea of possible secondary structures for the aligned sequences. Also, in the

bovine pancreatic PLA2, the residues that are not solvent accessible are circled.

This surface is computed by rolling a spherical “solvent” molecule around the

exterior of the protein molecule, maintaining contact with the van der Waals’

surface of atoms near the exterior. The locus of the points on the surface of

the solvent molecule as it touches the protein during this journey generates the

molecular surface (Connolly 1983). The molecular surface of the protein was

calculated using the program MS (Connolly 1983). This information will help

to identify those residues that are buried inside the protein. The secondary

structure and the solvent-accessible information described here will be useful

to molecular biologists in their mutation experiments.

Now, let us examine another application of NRL_3D in the design of de novo

metal-binding proteins. Arnold and coworkers (Suh et al. 1991) have studied the

characterization of His-X3

-His sites in -helices of synthetic metal-binding bovine

somatotropin. Craik (1990) has demonstrated that changing the residue 97 in

trypsin to a His and adding copper causes the activity of the protein to be shut

120
FIGURE 3. -backbone atoms tracing (solid lines) of bovine pancreatic

PLA2. The six disulfide bridges are shown as dashed lines.

down. In this process, a Cu+2 ion chelates to the residues His-57 and the

nearby mutated His-97. To bind the metal, His-57, which is involved in the

protein catalytic activity, must undergo a conformational change. Because of

this metal-induced conformational change of His-57, the activity of the protein

is lost. Once the CU+2 ion is removed, the catalytic activity can be restored.

This is referred to as the active site “metal switch.”

We were interested in deactivating the PLA2 enzyme using the metal switch

mechanism as it was demonstrated in trypsin. In PLA2, His-48 is important for

the activity of the protein. If one of the active site residues in PLA2 is replaced

121
FIGURE 4. Comparison of PLA2 sequences from pig (2), horse (3), and

C. Atrox (4) with that of bovine pancreas PLA2

with a His, the modified residue and His-48 may bind to a Zn+2 ion. In PLA2,

the residue His-48 is in the a-helix region. Hence, to create a metal binding

site, there should be another His four residues away from His-48. To test this

hypothesis, the NRL_3D database was used to extract sequences and 3-D

coordinates corresponding to HXXXH (where X is any amino acid). The output

of our search results are summarized in table 2. Listed here for the matched

sequences are the PDB code, the name of the protein, the sequence, and

the sequence number range. Eleven fragments (a through k) containing

the sequence HXXXH from the structures in PDB were extracted. The

122
TABLE 2. Sequence search results for HXXXH

PDB

Fragment Code Protein Name

Sequence

Sequence Number Range

a 1MB5 Myoglobin (carbonmonoxymyoglobin) (neutron study)—sperm whale

(physeter catodon)

3GAP1 Catabolite gene activator protein-cyclic AMP complex (cap)-

(Escherichia coli)

c

d

e

f

1FC11

3TLN1

2CAB1

FC fragment (IGG1 class)—human (Homo sapiens) pooled serum

Thermolysin (E.C. 3.4.24.4)-(Bacillus Thermoproteotyticus)

Carbonic anhydrase form B (carbonate dehydratase) (E.C. 4.2.1 1)

—human (Homo sapiens) erythrocytes

1PYP1 Inorganic pyrophosphetase (E.C. 3.6.1.1)—Baker’s yeast

(sacchromyces cerevisiae)

1HMQ1 Hemerythrin (met)--sipunculid worm (themiste dyscritum)

3DFR1 DihydrofoLate reductase (E.C. 1.5.1.3) complex with NADPH and

methotrexate—(lactobecillus casei). dichloromethotreateresistant strain

i 4RHV1 Rhinovirus 14 (HRV14)—human (Homo sapiens) virus

grown in HELA cells

j

k

2CPP1 Cytochrome p450cam(camphor monooxygenase)

(E.C. 1.14.15.1) With bound camphor—(pseudomonas putida)

2MHR1 Myohemerythrin—sipunculan worm (themiste

zostericola) retractor muscle

HATKH 93-97

b HCHIH 17-21

HEALH 429-433

HELTH 142-146

HGSEH 103-107

HGYIH 86-90

HKKAH

HLPWH

73-77

18-22

9

h

HRAKH 245-249

HVPEH 17-21

HKKMH 73-77
extracted coordinates for the fragments were displayed and analyzed using

MIDAS (Ferrin et al. 1988).

In figure 5, a ray-traced Corey-Pauling-Koltan surface of the 11 fragments is

shown (a through k). The darker spheres are the atoms of the side chains of

the His residues. It is clear from figure 5 that several conformations are

available for this sequence (HXXXH). The coordinates of atoms of the

fragments were superimposed on the corresponding atoms of residues

48 to 52 of PLA2.

Fragments d, g, and h have a backbone conformation similar to that of

residues 48 to 52. For fragment d, which was extracted from thermolysin

crystal structure (Holmes and Matthews 1982), the His residues at the N- and

FIGURE 5. Ray-traced CPK molecular surface of 11 HXXXH fragments

extracted from PDB (refer to table 2 for identification)

124
C-termini are found to interact with a zinc ion. With the use of this fragment, a

zinc-binding site in PLA2 was built by replacing Tyr-52 with a His. In this model,

the conformation of His-48 had to be significantly changed to chelate the Zn+2

ion. Figure 6 shows the atoms tracing of all the residues of this mutated

PLA2. All the atoms and bonds for residues His-48 and His-52 are shown in

the figure. Because of the change in the conformation of His-48, we propose

that the activity of PLA2 will be lost if a zinc ion happens to Interact wlth this

modified protein. Mutation and metal-binding studies are needed to verify this

model and to confirm the possibility of zinc-induced metal switching In PLA2.

FIGURE 6. C-carbon backbone atoms tracing of PLA2. Residues His-48,

His-52 (substituted for Tyr), and zinc ion are shown.

125
CONCLUSION

This chapter discusses some issues concerning various databases used in

biology. Most of these databases are growing exponentially. To improve

the utilization of these databases in the fields of protein engineering and

drug design, efficient database management systems as well as database

integration efforts are critical. We describe in detail various aspects regarding

the linkage of two important databases (PIR and PDB) for proteins through the

sequence-structure database NRL 30. A few useful applications in protein

modeling under PIR environment using NRL_3D are discussed.

The linkage of these two protein databases is only the first step toward the goal

of integrating all other biological databases. As mentioned earlier, the Genlnfo

backbone database (Boguski et al., in press) is designed to be an integrated

database for several biological databases. Recently, we have started a

collaboration with NCBI to develop a sequence-structure database under the

Genlnfo backbone database. This proposed 3-D module of Genlnfo will be

similar to NRL_3D and will contain not only proteins but also other biomolecular

structures in PDB. A program in C language has been written to parse all the

PDB entries and convert them into various object modules based on structural

and other pertinent information. This conversion will enable us to manipulate

and extract various structural information from PDB entries as well as to

produce files that are compatible with ASN.1 format. The details of this

work will be published elsewhere.

NOTE

1. The PRENRL_3D program and the NRL_3D database can be obtained

from NBRF in Washington, DC. PDB can be obtained from BNL in Upton,

NY.

REFERENCES

Bernstein, F.C.; Koetzle, T.F.; Williams, G.J.B.; Meyer, E.F., Jr.; Brice, M.D.;

Rodgers, J.R.; Kennard, O.; Shimanouchi, T.; and Tasumi, M. The Protein

Data Bank: A computer-based archival file for macromolecular structures.

J Mol Biol 112:535-542, 1977.

Blundell, T.; Carney, D.; Gardner, S.; Hayes, F.; Howlin, B.; Hubbard, T.;

Overington, J.; Singh, D.A.; Sibanda, B.L.; and Sutcliffe, M. Knowledge-based

protein modeling and design. Eur J Biochem 172:513-520, 1988.

Boguski, M.S.; Ostell, J.; and States, D.J. Molecular sequence databases and

their use. In: Rees, A.R.; Wetzel, R.; and Sternberg, M.E., eds. Protein

Engineering: A Practical Approach. Washington, DC: IRL Press, in press.

Bryant, S.H. PKB: A program system and database for analysis of protein

structure. Proteins Structure Function Genet 5:233-247, 1989.

126
Claessens, M.; Van Cutsem, E.; Lasters, I.; and Wodak, S. Modelling the

polypeptide backbone with “spare parts” from known protein structure.

Protein Eng 2:335-345, 1989.

Connolly, M.L. Solvent-accessible surfaces of protein and nucleic acids.

Science 211:709-713, 1983.

Craik, C.S. In the seminar notes on “Computer-Aided Genetic and Protein

Engineering” for the American Chemical Society Satellite Television Seminar,

American Chemical Society, Washington, DC, November 16, 1990. p. 27.

Dijkstra, B.W.; Drenth, J.; Kalr, K.H.; and Vandermaelen, P.J. Threedimensional structure and disulfide bond connections in bovine pancreatic

phospholipase A2. J Mol Biol 124:53-60, 1978.

Ferrin, T.E.; Huang, C.; Jarvis, L.E.; and Langridge, R. The MIDAS display

system. J Mol Graphics 6:13-27, 1988.

Fujinaga, M.; Delbaere, L.T.J.; Brayer, G.D.; and James, M.N.G. Refined

structure of Alpha-lytic protease at 1.7 angstroms resolution. Analysis of

hydrogen bonding and solvent structure. J Mol Biol 184:479-502, 1985.

George, D.G.; Barker, W.C.; and Hunt, L.T. Protein identification resources,

PIR. Nucleic Acids Res 14:11-15, 1986.

Holmes, M.A., and Matthews, B.W. Structure of thermolysin refines at 1.6Å

resolution. J Mol Biol 160:623-639, 1982.

Jones, T.A., and Thrirup, S. Using known substructures in protein model

building and crystallography. EMBO J 5:819-822, 1986.

Kusunoki, M.; George, D.G.; Namboodiri, K.; and Pattabiraman, N.

PRENRL_3D: A computer program for an automatic creation of NRL_3D,

protein sequence-structure database, from the PDB. Protein Seq Data Anal

4:333-336, 1991.

Namboodiri, K.; Pattabiraman, N.; Lowrey, N.; and Gaber, B.P. Automated

protein structure data bank similarity searches and their use in molecular

modeling with MIDAS. J Mol Graphics 6:211 -212, 1988.

New molecular sequence database announced. Natl Library Med News

45(7-8):1-2, 1990.

Pattabiraman, N.; Namboodiri, K.; Lowrey, A.; and Gaber, B.P. NRL_3D:

A sequence-structure database derived from the Protein Data Bank (PDB)

and searchable within the PIR environment. Protein Seq Data Anal 3:387-

405,1990.

Suh, S.S.; Haymore, B.L.; and Arnold, F.H. Characterization of His-X3-His sites

in -helices of synthetic metal-binding bovine somatotropin. Protein Eng

4:301-305, 1991.

Thornton, J.M., and Gardner, S.P. Protein motifs and data-base searching.

Trends Biochem Sci 14:300-304, 1989.

Unger, R.; Harel, D.; Wherland, S.; and Sussman, J.L. A 3D building blocks

approach to analyzing and predicting structure of proteins. Proteins Structure

Function Genet 5:355-373, 1989.

Waite, M. Handbook of Lipid Research: The Phospholipase. New York: Plenum,

1987.

127
ACKNOWLEDGMENTS

Bruce Gaber, Ph.D., and Al Lowrey, Ph.D., Naval Research Laboratory,

provided helpful discussions during the development of NRL_3D. David

George, Ph.D., NBRF, Georgetown University, and M. Kusunoki, Ph.D.,

Institute for Protein Research, Japan, incorporated NRL_3D into PIR. David

States, Ph.D., and John Wootton, Ph.D., provided useful discussions during

the development of the object database. The Office of Naval Research and

NCBI provided partial financial support.

AUTHORS

Nagarajan Pattabiraman, Ph.D.1

Senior Scientist

Laboratory for the Structure of Matter

Code 6030

Krishnan Namboodiri, Ph.D.2

Research Assistant Professor

Center for Biomolecular Science and Technology

Code 6090

Naval Research Laboratory

Washington, DC 20375-5320

1 Permanent address: Geo-Centers, Inc., Fort Washington, MD 20774

2 Permanent address: Department of Bilochemistry, Georgetown University Medical Center,

Washington, DC 20007

128
Use of Computer Graphics in Drug

Design

G. W.A. Milne and M.C. Nicklaus

INTRODUCTION

A common approach to chemotherapeutic control of disease involves

intervention in a biochemical pathway by exogenous chemicals or drugs

(DeVita 1982). Compounds that block metabolic steps in this way usually

do so by occupying a specific enzyme site, which is therefore denied to the

normal substrate. For this reason, they are called enzyme inhibitors (Santi

and Kenyon 1980). Many of the drugs that have been used in cancer treatment

inhibit DNA biosynthesis; some examples are shown in figure 1. In the normal

cellular biosynthesis of DNA by DNA polymerase, all four 2'-deoxynucleotide

triphosphates are required (Kornberg 1980). Prior to its use by DNA

polymerase, 2'-deoxycytidine triphosphate (dCTP), for example, is produced

by the two major routes shown in figure 1, The de novo pathway can be

blocked at different stages by different compounds, each of which thus has

potential for the control of the growth of tumor cells. The inhibitors shown

here-hydroxyurea, 3-deaza-UTP, PALA (phosphonoacetyl aspartate), and

pyrazofurin-all are in use in cancer chemotherapy, although they all represent

compromises because they fail to block the pathway to dCTP from

deoxycytidine and, as a result, are not totally effective.

A variation on this strategy is the use of “fraudulent” precursors (Yarchoan

et al. 1988). Here, the chemotherapeutic agent is similar enough to the

natural chemical to be assimilated into the normal biochemical elaboration

and to proceed through early steps of biosynthesis of a needed compound,

such as DNA. Because of its structural differences, however, it fails a

subsequent step. An example of this is shown in figure 2. In the course of

reproduction of the human immunodeficiency virus (HIV), viral RNA is used

as a template by reverse transcriptase to build a new viral DNA from cellular

nucleoside triphosphates. A dideoxynucleoside such as 2',3'-dideoxyadenosine

(ddA) (1) (figure 3) will be incorporated into the growing DNA, but because

it has no 3'-hydroxyl group, growth will terminate at this point. If sufficient

quantities of such dideoxynucleosides are in the HIV-infected cell, they can

cause a shutdown of reverse transcription and the cell will die (Mitsuya and

Broder 1986). Dideoxynucleosides have shown some success in treating

129
FIGURE 1. Role of chemotherapeutic agents in blocking steps in DNA

biosynthesis

acquired immunodeficiency syndrome (AIDS); the only anti-AIDS drugs

available in the United States, AZT (2) and 2',3'-dideoxyinosine (ddl)

(3) (the major metabolite of ddA) (figure 3), are in fact both members of this

family (Mitsuya and Broder 1986; Balzarini et al. 1986; Mitsuya et al. 1985).

Design of potential enzyme inhibitors and fraudulent substrates is an active

area of medicinal chemistry, and some of the authors’ and colleagues’ recent

work at the National Cancer Institute is discussed in this chapter.

ENZYME-SUBSTRATE INTERACTIONS

Much of our effort in molecular modeling has been devoted to a detailed

study of the interactions that take place between an enzyme and a substrate.

Enzymes typically possess sites where substrate molecules can reside while

being chemically transformed. These sites, which can be likened to a

workbench or a jig, are highly structurally specific and often will accommodate

130
FIGURE 2. Reverse transcription of viral RNA into a new DNA

only a very few specific chemical structures. Provided that it has the correct

geometry, a substrate approaching a site will be able to enter and will be

held in place by several hydrogen bonds between its atoms and atoms in

the enzyme. If the geometry is not optimal or the hydrogen bonds are not

all completed, the molecule will fail to lodge in the site and will not serve as

a substrate to the enzyme. Some examples of enzyme-substrate relationships

are shown schematically in figure 4. A putative enzyme cavity is shown with

three attachment atoms. The first substrate shown can enter the cavity but is

too small to bind effectively, and the second is too big to enter the cavity. The

third one is the correct size and shape to get into the cavity and stay there.

In practice, the details of an enzyme cavity, or active site, are rarely revealed

as clearly as depicted in figure 4. The best source of such data is x-ray

diffraction measurements made on the crystalline enzyme—or better, the

enzyme-substrate complex. However, such data are difficult to measure;

proteins cannot be crystallized easily, and the diffraction patterns they afford

are extremely complex. The Cambridge Crystallographic Database (Allen et al.

1983) contains nearly 100,000 crystal structures of organic molecules, but

the Protein Data Bank (Abola et al. 1985), which is maintained at Brookhaven

131
FIGURE 3. Protein kinase C (PKC) inhibitors

Enzyme Site

(Schematlc)

Subrtrate

Molecules

FIGURE 4. Schematic view of substrates and an enzyme site

National Laboratory, contains only a few hundred examples of protein

structures that have been solved by x-ray diffraction. A well-known example

of the latter type, the structure of dihydrofolate reductase containing a known

inhibitor, methotrexate (Oeffner et al. 1988), is shown in figure 5.

However, even data of this sort are not easy to take advantage of because

they show only the positions of the atoms of the substrate and the enzyme;

the important bonds that hold the substrate in place have to be identified

deductively by means of energy calculations. This identification can be made,

132
FIGURE 5. Crystal structure of dihydrofolate reductase

and the results are shown in figure 6 where the six hydrogen bonds that hold

the methotrexate in the active site of dihydrofolate reductase can be seen. This

reveals that, to bind in this site, a substrate must have, at certain specific points

in space, hydrogen bond acceptors such as carbonyl oxygen or amino nitrogen,

or hydrogen bond donors such as hydroxyl groups. This information can be

used in the design of a substrate that may compete with the natural material for

that enzyme site; some examples of this approach, as it has been employed in

our laboratory, are given in the next sections.

A more common situation is one in which there is knowledge, not only of natural

substrates for an enzyme, but also of one or more inhibitors, which have been

discovered empirically. With this information it is often possible, by comparison

of these molecules, to determine which parts of each of them are necessary for

133
FIGURE 6. Crystal structure of dihydrofolate reductase with a merhotrexate

molecule

successful binding to the active site. The enzyme uridine phosphorylase, which

cleaves uridine to uracil and ribose-1 -phosphate, as shown in figure 7, is

inhibited by many uracil derivatives with the general structure shown (Baker

and Kelley 1970). It is clear from this that the uracil ring is what the enzyme

recognizes; if this ring is part of a uridine molecule, the expected cleavage is

observed, but if it is part of a 1-alkyl uracil, the enzyme is inhibited. It is fairly

straightforward to take this sort of information and deduce precise details of

the active site, including its shape and size and its binding points, and this is

the procedure that will be followed in the examples described here.

Anti-HIV Agents: Reverse Transcriptase lnhibitors

As mentioned earlier, ddA (1) possesses anti-HIV activity (Mitsuya and Broder

1986); and there is considerable information available concerning its mode of

134
FIGURE 7. Inhibitors of uridine phosphorylase

action. In mammalian systems, ddA is rapidly converted to ddl (3); this is the

pharmacologically equivalent form that now is administered to AIDS patients.

Both ddA and ddl are phosphorylated to their monophosphates (Johnson and

Fridland 1989), then phosphorylated again to the triphosphates (Ahluwalia et al.

1987), which enter into and ultimately terminate the reverse transcription step

of DNA synthesis in the HIV-infected cell.

Acceptance of these molecules by either of the phosphorylating enzymes

is known to be contingent on their molecular geometry because other,

quite similar compounds are not phosphorylated in this way. However,

the microbial metabolite oxetanocin A (compound 4 in figure 8) (Shimada

et al. 1986; Nakamura et al. 1986) (4) is known to inhibit reverse transcriptase

(Hoshino et al. 1987; Seki et al. 1989), and molecular modeling reveals that

significant dimensions in this Compound are very close to those in ddA, as

shown in figure 9. The 3'-hydroxymethyl derivative (5) of ddA also has these

same dimensions, but in the 2' isomer (6) the sugar ring adopts a different

“pucker.” As a consequence, the critical dimensions shown in figure 9 are quite

different. In fact, like ddA and oxetanocin A, the 3'-hydroxymethyl isomer does

provide a triphosphate that interferes with reverse transcriptase (Tseng et al.

1991). The 2'-isomer, on the other hand, does not inhibit reverse transcriptase.

This experience suggests that molecular modeling may be a legitimate means

with which to predict the shape of a molecule or an active site and whether

there may be an interaction between the two, and the authors attempted to

exploit the interaction in a study of the PKC problem.

135
FIGURE 8. Oxetanocin A, ddA, and derivatives

Protein Kinase C Inhibitors

PKC is of great interest because it controls transmission of signals through the

cell membrane (Berridge 1987). An event outside the cell can, through a PKCmediated mechanism, lead to consequences within the cell, and it is thought

that many cellular events, in particular cell growth, are regulated in this way.

The normal endogenous substrates for PKC are S-l ,2-diacylglycerols (Boni

and Rando 1985; Young and Rando 1984) such as S-1-oleyl-2-acetylglycerol;

however, several natural materials, such as phorbol (Wender et al. 1986) (7)

(figure 10) and aplysiatoxin (Nakamura et al. 1989) (8), are known to bind to

PKC more irreversibly than the diacyl glycerols with various consequences,

such as tumor promotion. The normal substrate, S-1 ,2-diacylglycerol, has five

oxygen atoms that could interact with the active site of PKC, and it is thought

(Nakamura et al. 1989; Brockerhoff 1986) that three of these, the two carbonyl

oxygens and the oxygen of the primary hydroxyl group, are necessary for

activity. It was hoped that this information could be used to design a PKC

inhibitor that could block the binding of diacyl glycerols while avoiding the

undesirable effects of the tumor promoters, phorbol and aplysiatoxin.

136
c(Å)

O5 Compound -N6

a (Å) b (Å)

O5'O4' O5

-N9

ddA (1) 2.91 3.94 7.45

Oxetanocin A (4) 2.84 3.51 6.95

3'-ydroxymethyl ddA (5) 2.87 3.68 6.96

2'-Hydroxymethyl ddA (6) 2.77 3.82 7.72

FIGURE 9. Dimensions of ddA, oxetanocin A, and analogs

(7) (8)

FIGURE 10. Dideoxynucleoside used in chemotherapy of AIDS

Molecular modeling suggests that the conformation of S-1 ,2-diacylglycerol

is as is shown in figure 11. However, the molecule is extremely flexible, and it

is difficult to decide on the most stable configuration. The structures of phorbol

(Brandt et al. 1971) and aplysiatoxin (Moore et al. 1984), on the other hand,

have been determined by x-ray diffraction. They are relatively rigid, and

because they both can fit into the active site of PKC, they can be used to

deduce the characteristics of this active site. The active form of phorbol is

137
FIGURE 11. Minimum energy conformation of 1,2-diacylglycerol

its 12-acetyl-13-myristyl ester, and the model shown in figure 12 was built

using the x-ray diffraction data of phorbol as a starting point. In the case of

aplysiatoxin, a C6O side chain was added to C-11 of the x-ray-based model

of debromoaplysiatoxin, and the resulting model also is shown in figure 12.

Studies (e.g., Nakamura et al. 1989) with analogs of phorbol have shown that

the three atoms that are involved in the binding to active sites of PKC are the

primary hydroxyl at C-20, the C-S hydroxyl oxygen, and the C-3 carbonyl group

in ring A, as shown in (7). Likewise, in aplysiatoxin (8), the two ester carbonyl

groups and the hydroxyl group that lies between them have been implicated

in PKC binding (Brandt et al. 1971). In phorbol, the carbonyl oxygen and the

C-9 hydroxyl oxygen serve as hydrogen bond acceptors, whereas the third,

the primary hydroxyl group, is a hydrogen bond donor. In aplysiatoxin, the

two carbonyl oxygens behave as hydrogen bond acceptors and the hydroxyl

group is the donor. Finally, the location of the hydrocarbon side chain must

be important because, when it is absent, binding is noticeably diminished;

138
(7) (8)

FIGURE 12. Molecular models of phorbol (7) and aplysiatoxin (8)

furthermore, if only three points were involved in the binding, optical isomerism

in the substrate would be irrelevant. It is known that this is not so; only

one of the isomers of 1,2-diacylglycerol, for example, binds efficiently to

PKC. Accordingly, in studies of the fit of substrates to PKC, the location

of the hydrocarbon side chain of the substrate was taken into account,

along with the three-atom fit,

Several carbohydrates related to the diacyl glycerols were modeled. These

all contained the diacyl glycerol skeleton but were cyclic and thus relatively

rigid. They included ribonolactones and their ring-expanded analogs, all of

which are shown in figure 13. The molecular models of all these compounds

were examined to determine how well each would fit into an active site that

accommodates phorbol acetyl myristate or the aplysiatoxin derivative. On a

three-point basis, most of the lactones fit this active site fairly well, but when

the substrate’s hydrocarbon side chain was required to overlay that of the

phorbol or aplysiatoxin esters, it became clear that compounds (9) and (12)

offered the better fits. When tested for their ability to compete with phorbol

for the active site in PKC, compounds (9) and (12) did in fact bind to this site

better than their isomers. The model of the binding of the trans-ribonolactone

(12) to the aplysiatoxin derivative in shown in figure 14.

139
FIGURE 13. Possible PKC inhibitors

The results obtained in this way suggest that molecular similarities between

known substrates and new molecules can be used to determine whether

the new molecules have the capability of binding to the active site. Other

considerations such as solubility, stability, and transport are also important,

but a fit to the receptor site is a necessary, if insufficient, condition for inhibition.

Modeling pharmacophores in this way seems to be a valid approach to defining

effective inhibitory molecules.

Three-Dimensional Databases

The approaches outlined above require, as a first step, that a molecule

containing the appropriate pharmacophore be defined. It then can be

modeled and, if promising, synthesized and tested. The requirement that

a candidate molecule be defined is a very limiting requirement, and it would

be much more useful if one could work with the precise pharmacophore,

searching databases in some way to find all compounds that contain the

specified polyatomic arrangement. This possibility quickly became clear to

many in the field in the late 1980s and much work went into developing such

databases and search systems.

The conventional substructure search systems operate in two dimensions

and deal in terms of topology, That is, they are concerned with what atom is

connected to what other atom, not with how close two atoms are together in a

spatial, three-dimensional (3-D) sense. Replacing connectivity with interatomic

140
FIGURE 14. Fit of aplysiatoxin to the trans-ribonolactone (12)

separation in these search systems is conceptually easy; in practice, methods

to do this have already been worked out if not yet perfected.

If a structure contains n atoms, then, counting all possibilities, there are

n

2

 interatomic distances. When all duplicates and zero separations are

removed, there will remain1⁄2n(n+1) distances, and it is thus clear that the

3-D database will be considerably larger than its two-dimensional precursor.

The precise nature of the atom at either end of such an interatomic separation

can be appended in each case to the number representing this distance, and

the collection of all these interatomic distances over a whole group of chemical

structures becomes the new “3-D database.” With such a database, it is

possible to search for all compounds containing a pharmacophore like

141
FIGURE 15. Structures retrieved from a 3-D search

those discussed here. It will involve searches, for example, for all molecules

containing a ketone oxygen between 4 and 5 Å from an aliphatic chlorine

and a hydroxyl separated from the ketone oxygen by 3.5 to 4.2 Å.

Databases that support such searches are becoming fairly common (Jakes

et al. 1987; Sheridan et al. 1989; Moock et al. 1990), and the searches

provide interesting lists of compounds that in principle all contain the

pharmacophore that was defined in the search query. The lists can often be

narrowed further with other criteria such as availability, solubility, toxicity, and

so on, and it is often possible to identify and test a candidate compound very

quickly. A search1 for compounds containing a three-oxygen pharmacophore

similar to that in phorbol, in which each of the three interatomic distances was

expressed as a range, gave as hits a number of compounds; some of them

are shown in figure 15. Many of these compounds are natural products, and

biological activities are not known for many of them. Such retrieved structures

offer many interesting possibilities with which to pursue the search for PKC

inhibitors.

NOTE

1. This search was conducted at Pfizer Central Research in Sandwich, U.K.

142
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144
AUTHORS

G.W.A. Milne, Ph.D.

Research Chemist

M.C. Nicklaus, Ph.D.

Research Chemist

Building 37, Room 5C02

Laboratory of Medicinal Chemistry

National Cancer Institute

National Institutes of Health

Bethesda, MD 20892

145
Purification and Cloning of Opioid

Receptors

Ling-Yuan Li, Ying-Fu Su, Zeng-Ming Zhang, Chih-Shung Wong,

and Kwen-Jen Chang

INTRODUCTION

The opioid receptor was one of the first receptors discovered in the early

1970s (Pert and Snyder 1973; Simon et al. 1973; Terenius 1973). Nearly

two decades after its discovery, opioid receptors have yet to be isolated

and purified in sufficient amount for biochemical analysis. With the advent

of gene-cloning techniques in the past decade, more than 50 genes of

hormone and neurotransmitter receptor proteins have been cloned to date.

Again, opioid receptor genes remain one of the elusive genes yet to be

cloned. The slow progress in this area has been attributed to the lack of

neuronal tissue sources rich in opioid receptors and the high lability of the

protein. On the other hand, accumulated biochemical and pharmacological

evidence suggests the existence of multiple types and subtypes of opioid

receptors, including μ-, -, -, and -receptors and their subtypes (Chang

1984). Today, all clinically useful opiates are p-agonists. These drugs

not only produce analgesic effects but also cause many side effects such

as addiction, physical dependence, and respiratory depression. The

undesirable side effects may be mediated by different subtypes of opioid

receptors. To improve opiate analgesic medication, more selective and

specific ligands for various opioid receptor subtypes must first be discovered.

The specificity of current biochemical and pharmacological methods for drug

screening is limited. One solution to the problem is to clone all possible types

and subtypes of opioid receptors and use the recombinant receptors for drug

discovery studies.

Formerly, a receptor protein was first purified by affinity chromatography

to obtain information on the partial amino acid sequences of the receptor

molecule (Noda et al. 1983; Kubo et al. 1986; Dixon et al. 1986; Kobilka

et al. 1987; Schofield et al. 1987). The corresponding oligonucleotides

were then synthesized to clone the full-length cDNA of the receptor’s gene

by the hybridization technique. The cloned cDNA was then expressed in

appropriate cells. The identity of the expressed receptor was verified by

either functional assay or receptor-binding assay with known agonists and/or

146
antagonists. Now, however, nearly all genes within a superfamily of

receptors are cloned by molecular biology techniques without going through

the traditional protein purification processes. The techniques include the

low-stringency hybridization screening technique with known homologous

oligonucleotides (Frielle et al. 1987; Emorine et al. 1989; Cotecchia et al.

1988; Peralta et al. 1987; Bunzow et al. 1988; Dearry et al. 1990; Sokoloff

et al. 1990; Van Tol et al. 1991; Matsuda et al. 1990), the polymerase chain

reaction (PCR) technique with degenerate oligonucleotide primers (Mahan

et al. 1991; Zhou et al. 1990; Gantz et al. 1991; Buck and Axel 1991), the

COS cell transient expression cloning system (Sakurai et al. 1990; Sasaki

et al. 1991; Murphy et al. 1991; lshihara et al. 1991; Juppner et al. 1991),

the ligand affinity panning technique (Davis et al. 1991), and the Xenopus

oocyte expression cloning method (Masu et al. 1987; Julius et al. 1988;

McEachern et al. 1991; Arai et al. 1990; Masa et al. 1991).

Evidence indicates that opioid receptors belong to the superfamily of G

protein-coupled receptors (Blanchard and Chang 1988). The signaltransduction system of this family of receptors consists of three protein

components: the receptor, the G protein, and the effector (Gilman 1987;

Birnbaumer et al. 1990). Many members of this family of receptors have

been cloned. A comparison of the secondary and tertiary structures of those

cloned receptors revealed a common topological feature, in that the receptors

exhibit seven transmembrane domains. The corresponding oligonucleotides

coding the seven transmembrane domains are also highly homologous

among these receptors’ genes. Because opioid receptors belong to this

receptor family, it is reasonable to expect that opioid receptors also have

a similar seven-transmembrane protein structure and should possess in the

cDNA an oligonucleotide sequence homologous to other receptor genes.

The authors and researchers in many other laboratories have designed

synthetic oligonucleotides homologous to many other receptor genes of

this family to clone opioid receptors from cDNA libraries prepared from

opioid receptor-rich cell lines and tissues. Both the low-stringency

hybridization and the PCR techniques were used for cloning with these

designed oligonucleotides and degenerate primers. So far, no one has

succeeded in cloning opioid receptors, These two methods are extremely

powerful and have been successful in cloning many subtypes of adrenergic

(Lefkowitz et al. 1988), muscarinic (Peralta et al. 1987), and dopaminergic

receptors (Bunzow et al. 1988; Dearry et al. 1990; Sokoloff et al. 1990;

Van Tol et al. 1991; Sunahara et al. 1991), as well as in cloning adenosine

(Mahan et al. 1991), histamine (Gantz et al. 1991), and odorant receptors

(Buck and Axel 1991). The failure in cloning opioid receptors with these

methods may be explained by the results of the recently cloned receptor

for endothelin (Sakurai et al. 1990; Arai et al. 1990) and the metabotropic

glutamate receptor (Masa et al. 1991). These receptors do not contain

147
sequences homologous to other G protein receptors, despite the presence

of seven transmembrane domains and their coupling to G proteins, Opioid

receptors may thus belong to a subfamily with a unique amino acid structure.

The Xenopus oocyte expression cloning method is an extremely powerful

technique in cloning receptors that are coupled to phospholipase C to generate

two second messengers, inositol-1,3,5-triphosphate and diacylglycerol (Masu

et al. 1987; Julius et al. 1988; McEachern et al. 1991; Arai et al. 1990; Masa

et al. 1991; Morel et al. 1992; Kimura et al. 1992). The increase of inositol1,3,5-triphosphate triggers release of Ca++ from the intracellular pool. This

results in increased cytoplasmic Ca++ concentration leading to activation of

Cl’ channel, which can be monitored by electrophysiologic techniques.

However, there is no evidence to suggest that opioid receptors are

functionally linked to the activation of phospholipase C and Cl-

 channels,

Thus, this method is unlikely to work for opioid receptor cloning.

The COS cell transient expression cloning method is suitable for receptors

that can be detected by 125I-labeled ligands. This method works for receptors

whose high-affinity binding activity for the labeled ligand does not require the

presence of affinity modifier such as G proteins. Currently available 125I-labeled

opioid ligands are all agonists that bind with a high affinity only to opioid

receptors that are associated with G proteins. Thus, the expressed opioid

receptors in COS cells must interact with appropriate G proteins to attain a

high affinity for the detection by 125I-labeled opioid agonists. This greatly

reduces the chance of successful cloning. The lack of 125I-labeled antagonists

for binding assay also hampers the molecular cloning of opioid receptors by

this technique.

There are only two other alternatives for cloning opioid receptors:

(1) the traditional method of purifying opioid receptor protein with affinity

chromatography to obtain partial amino acid sequences and (2) the ligand

affinity panning method. The latter method is exemplified by the recent

cloning of a receptor protein by Goldstein and colleagues (Xie et al. 1992).

They reported expression cloning of cDNA encoding an opioid-binding

protein from a -rich human placenta cDNA library. By repeated panning

of transfected COS-7 cells expressing -receptor binding activity, a cDNA

encoding a 440-residue protein was cloned. Hydropathy plot of the protein

revealed a seven-helix transmembrane domain typical of G protein-coupled

receptors as expected for opioid receptors. The expressed receptor showed

stereospecific binding for opioids and peptide as well as nonpeptide ligands,

but showed no -selectivity. The affinity of the cloned receptor for agonist

was about two orders of magnitude lower than that of native membrane

receptors, and the affinity for antagonist was even lower. The lack of

 -receptor selectivity and the lower affinity of the expressed receptor might

be caused by, among other reasons, missing G proteins or other accessory

148
proteins in the COS-7 cells or by improper, or lack of, posttranslational

modification of the expressed receptor for correct conformation. Interestingly,

the cloned protein showed a high degree of homology with the human

neuromedin K receptor at the transmembrane domain. The significance of

this observation is not clear at this time.

Li and colleagues (1992) recently succeeded in purifying μ-opioid receptor to

homogeneity. This chapter describes the methods used and discusses the

results of the purification of opioid receptors from rat brain membranes.

SOLUBILIZATION OF OPIOID RECEPTOR FROM RAT BRAIN

MEMBRANES

Rat brains (Sprague-Dawley) were homogenized in a buffer containing

50 mM Tris HCI, 1 mM ethylene glycol-bis ( -aminoethyl ether) N,N,N',N'

tetra acetic acid (EGTA), 10 mM MgSO4, 1 mM benzamidine, 0.5 mM

phenylmethyl sulphonylfluoride, 10 μg/mL leupeptin, and 10 μg/mL pepstatin

in 0.34 M sucrose. The homogenate was centrifuged at 40,000 x g for 20

minutes and the pellet was resuspended in a hypotonic buffer containing

5 mM Tris HCI, 1 mM EGTA, and protease inhibitors and incubated at 4 °C

for 25 minutes. The hypotonic treatment was repeated twice, and the final

pellet was washed and resuspended in 50 mM Tris buffer containing ethylene

glycol and protease inhibitors as described above. Opioid receptor was

then solubilized from the above brain membrane preparations by incubating

membranes in a mixture of CHAPS (3[(3-cholamindopropyl)-dimethylammonio]-

1 -propane-sulfonate), digitonin, and 1 M NaCl in the 50mM Tris buffer. After

centrifugation at 100,000 x g for 1 hour, the soluble fraction and pellet were

assayed for opioid-binding activity, which was carried out at 24 °C for 1 hour

with [

3H]diprenorphine (35 to 45Ci/mmol) in Tris buffer containing 1 M NaCl

and 0.4 percent bovine serum albumin. Nonspecific binding was determined

by the presence of 1 μM naloxone in the assay. When membrane material

was used, the binding reaction was terminated by filtration through GF/C

glass filter paper. When solubilized receptor was used, the reaction was

terminated by precipitation with saturated ammonium sulfate solution in

the presence of 0.1 percent r-globulin as protein carrier. The precipitate

was filtered with GF/C glass filter paper.

Both CHAPS and water-soluble digitonin were reported to be successful in

solubilizing opioid receptor from membranes (Simonds et al. 1980; Gioannini

et al. 1985). The authors have investigated the efficiency of CHAPS and

water-soluble digitonin in solubilizing opioid receptors from rat brain membrane

preparations, It was found that either one alone could only partially solubilize

receptor from membranes (figure 1). Optimal conditions appear to require both

7.5 mM CHAPS and 1 percent digitonin. It is also important to include 1 M

NaCl in the buffer. Elimination of NaCl from the buffer caused irreversible

149
FIGURE 1. Solubilization of opioid receptor from rat brain membranes.

Membranes were incubated for 1 hour at room temperature

with CHAPS (A) and digitonin (B) at concentrations indicated.

After centrifugation, the resulting supernatant and pellet were

assayed for specific binding of [

3H]diprenorphine, which is

expressed as percentage yield of starting membrane material.

Data were presented as means±SEM (n=3).

150
aggregation of solubilized proteins and, thus, inactivation of opioid receptors.

(It was also found later that the presence of 1M NaCl is essential for the

binding of opioid antagonist to the purified receptors.) At least 30 minutes

of incubation time is required to achieve effective solubilization (figure 2).

Room temperature (24 °C) appears to be more effective than 4 °C, and

the optimal protein concentration is about 7 mg/mL (figure 3). The final

protocol for solubilization includes CHAPS (7.5 mM), water-soluble digitonin

(1 percent), NaCl (1 M) in the solubilizing buffer, 60 minutes incubation at

24 °C, and about 6 to 10 mg/mL membrane protein.

Various protease inhibitors were systematically investigated for their abilities

to protect opioid receptor from proteolytic degradation during solubilization

and purification. A combination of EGTA, bezamidine, phenylmethyl

sulphonylfluoride, leupeptin, and pepstatin at concentrations described above

provides the best protection. The requirement of ethylene glycol was also

investigated. It was found that 25 percent ethylene glycol improved

(7.5 mM CHAPS, 1 M NaCI, 1 percent digitonin)

FIGURE 2. The effect of incubation time on solubilization yield. Membranes

were incubated up to 60 minutes at room temperature in the

solubilization solution indicated. The membrane protein

concentration was 4.0 mg/mL.

151
FIGURE 3.

(7.5 mM CHAPS, 1 M NaCI, 1 percent digitonin)

The solubilization yield as function of temperature and protein

concentration. Membranes were incubated for 60 minutes in

the solubilization solution indicated. Data were presented as

means±SEM (n=3).

the stability of the solubilized receptors. Using this protocol, the yield of

solubilization is routinely about 80 to 90 percent, and the solubilized receptors

obtained can be stored at -70 °C for months without losing binding activity.

PURIFICATION TO HOMOGENEITY

Sequential opioid antagonist-affinity column and wheat-germ agglutinin (WGA)

column were used to purify solubilized receptors (Li et al. 1992). The opioid

antagonist used was N-cyclopropyl-methyl-7-amino-6,14-endoetheno-7,8-

dihydromorphine (10 cd), which was covalently coupled to Affi-gel 202 by the

addition of 1-ethyl-3(3-dimethyl-aminopropyl)-carbodiimide HCI in 0.1 M HEPES

buffer (pH 5.4). Four separate 2.5 mL size opioid antagonist-affinity columns

were routinely set up in parallel. About 60 mL solubilized receptors (from eight

rat brains) were applied to each column, which were preequilibrated with 0.1

percent digitonin in the buffer. After thorough washing, opioid receptors were

152
eluted with 100 μM naloxone and readsorbed onto a WGA-agarose column

(1 mL) set up in series. The passed-through buffer was recycled back to opioid

antagonist and WGA columns four times. After thorough washing, opioid

receptors were then eluted from WGA column with N-acetyl- -D-glucosamine

(50 mM). Aliquots were tested for binding activity with [

3H]diprenorphine in the

presence of 1 M NaCI. Active fractions were pooled and further purified by

repeating the procedure described above but with smaller columns (0.5 mL size).

The active fractions were then concentrated tenfold with microconcentrators, and

the concentrated material was subjected to sodium dodecylsulfate polyacrylamide

gel electrophoresis (figure 4). A protein of diffused band with a medium molecular

mass of 62 kD was observed to correspond to the peak binding activity of

[

3H]diprenorphine. Two proteins of molecular mass of 67 and 58 kD were

eluted in fractions 1 through 3 and were separated from the active fractions

(fractions 4 through 6) in the last step of purification.

The kD value of purified receptor for [

3H]diprenorphine is 0.29 nM, a value

very close to that observed in membrane preparation. The purified material

binds the highly selective p-agonist PL017 ([N-Me-Phe3, D-Pro4]morphiceptin)

with an affinity similar to that observed in membrane preparations determined

Jnder identical conditions (Li et al. 1992). This material also binds selective

u-antagonist (-)-naloxone with a Ki value of 4.3 nM, which is in the range of

known values for μ-receptor. It shows a very low affinity for the biologic

inactive isomer (+)-naloxone (Li et al. 1992). [

3H]diprenorphine binding is

inhibited only 30 percent by (+)-naloxone at 100 μM. -Receptor antagonist

naltrindole (Portoghese et al. 1987) and k-receptor antagonist nor-binaltorphimine

(Portoghese et al. 1988) also show binding to the purified material, but the low

affinities and the binding characteristics of these compounds strongly suggest

that the purified material is predominantly μ-opioid receptor. It is estimated that

85 percent of the receptor is of the μ-type and 15 percent the -type. The specific

binding activity of [

3H]diprenorphine was calculated to be 18.8 pmol/μg protein,

which is near the theoretical value of homogeneity. The final product is about

65,000-fold purified over the initial solubilized material (Li et al. 1992).

CHARACTERIZATION AND RECONSTITUTION OF PURIFIED OPIOID

RECEPTOR

As discussed above, opioid receptors belong to the G protein-coupled receptor

superfamily, and the binding of opioid agonists is known to be regulated by

guanine nucleotides such as guanosine triphosphate (GTP), guanosine

diphosphate (GDP), or their stable analogs, GDP S Gpp(NH)p, and GTPS.

The binding of μ-agonist PL017 to membranes and to solubilized opioid receptors

was greatly reduced in the presence of GDP and GTPS (figure 5, panel A). This

reduction of binding was even greater in the presence of Na+

 (Li et al. 1992).

However, the binding affinity of PL017 was reduced for the partial as well as the

purified opioid μ-receptors, and the regulatory effect of guanine nucleotides was

153
FIGURE 4. The elution profile of receptor protein from the final WGA-agarose

affinity chromatography and the sodium dodecyl-sulfate

polyacrylamide gel eiectrophoresis result of the purified opioid

receptor. Opioid receptor was purified by chromatography

through repeated 10cd and WGA columns as described in the

text. The specific binding activity of eluted fractions from the

final WGA column was assayed, and aliquots of each fraction

were subjected to electrophoresis (7.5 percent polyacrylamide

gel). The top panel shows the silver staining pattern of the

purified protein material. The positions of molecular weight

standards are indicated by arrows. The bottom panel shows

the corresponding binding activity of each fraction. Fractions

4 through 6 contain most of the binding activity and exhibit

as diffused bands with a medium molecular mass of 62 kD.

Fractions 1 through 3 contain two proteins of 58 kD and 67 kD

with little or no binding activity.

154
lost (figure 5, panel B). These data suggest that purified μ-opioid receptors are

dissociated from G proteins and thus assume a low-affinity state for agonists.

The high-affinity binding of PL017 and the regulatory effect of guanine

nucleotides can be partially restored by reconstituting the purified receptor

with the G proteins purified from bovine brains in phospholipid vesicles

(figure 5, panel C).

Another interesting observation is the requirement of Na+ ions for the binding

of opioid antagonist to the purified opioid receptors (Li et al. 1992). During the

process of purification, it was found that the higher the purity of the receptor,

the higher the concentrations of Na+ ions required for the optimal binding of

[

3H]diprenorphine. To reach half-maximal binding, the purified receptor requires

about 250 mM of Na+, a concentration 10 times greater than that required for

membrane receptor. The reason for the increased Na+

requirement is unknown.

One can use this Na+ dependency of opioid antagonist binding to design an

affinity chromatography for the purification of opioid receptors (Li et al. 1992).

This can be achieved by loading receptors onto an opioid antagonist column

in the presence of 1 M NaCl and eluting receptors with a buffer without NaCI.

The realization of the importance of sodium ions in the binding of opioid

antagonist has eventually led the authors and colleagues to design an

appropriate affinity chromatography and binding assay for the successful

purification of μ-opioid receptor to homogeneity (Li et al. 1992).

The observation of the effect of sodium on opioid antagonist binding is not

new. It was reported in the early 1970s at the time opioid receptors were

discovered. The increase in opioid antagonist binding by sodium ions was

originally proposed as a result of conversion of agonist conformation to

antagonist conformation (Pert and Snyder 1974; Simon and Groth 1975;

Simon et al. 1975; Pasternak et al. 1975). Nijssen and Childers (1987)

reported that the effect of sodium ion on antagonist binding was to increase

the number of opioid binding sites for antagonist. Wong and colleagues

(1992) have recently confirmed this observation and further identified that

the increased binding sites of labeled antagonist by sodium ions are lowaffinity sites for an opioid agonist. This study on the purified receptor clearly

suggests that opioid agonists and antagonists either do not bind or bind with

low affinity to G protein-dissociated free opioid receptors in the absence of

sodium ions. However, the free opioid receptors have a high affinity for

antagonists but not agonists in the presence of sodium ions. This explains

why sodium ions increase binding sites for labeled antagonist.

MOLECULAR MODEL OF OPIOID RECEPTOR

Based on the model proposed for a G protein-linked receptor superfamily by

Gilman (1987) and Birnbaumer and colleagues (1990) and data presented

155
PL017 (Log M)

PL017 (Log M)

PL017 (LogM)

156
FIGURE 5. Effect of GTP S on ligand competitive binding curves of crude

membranes and solubilized, partially purified, and purified G protein

reconstituted receptor preparations. Highly selective μ-agonist

PL017 was used in competition binding against [

3H]diprenorphine

with rat brain membranes (A), partially purified receptor from WGA

affinity column (B), and final purified opioid receptor reconstituted

with G protein (C). Binding assay was carried out in the absence

(O - O) and presence (• - •) of 0.1 mM GTP S. The

reconstitution experiment was carried out in phospholipid vesicles

by a modification of the method described by Sternweis (1986).

Briefly, Gi-proteins (purified from rat brain according to Sternweis

[1986]) are incubated with alcoholic solution of phosphatidylcholine

to form a vesicle suspension. The suspension was passed through

a Sephadex G50 column to obtain a vesicle fraction containing

G protein (30 pmol/mL estimated from 35S-GTP S binding assay).

The vesicles (0.8 mL) were mixed with purified receptor solution

(1.4 mL containing 2.5 pmol of opioid binding site) in the Tris buffer

(same buffer described for homogenization in the text) containing

0.1 percent digitonin and 300 mM NaCI. [

3H]diprenorphine and

PL017 were added, and the mixture was incubated overnight at

4 °C. The binding assay was terminated by filtration through GF/C

paper. The results are expressed as percentage of control-specific

binding (mean ±SEM) of at least three separate experiments.

here and other data mentioned above, the authors propose a model of opioid

receptor-G protein activation cycle as depicted in figure 6. Opioid receptors

(R) exist in three forms: high-affinity (G•RH) complex, low-affinity (GDP-G•RL

)

complex, and free (RL

) receptors. At resting physiological condition, the

receptors are presumably in the low-affinity GDP-G•RL

•Na+ complex. When

a binding assay is conducted in the presence of high concentrations of

sodium ions and GDP, a condition that thermodynamically favors the formation

of GDP-G•RL•Na+ complex, all p-receptors detected are in the low-affinity state

for agonists. Mg++, together with opioid agonist Op, promote receptors into

the high-affinity G•RH•Op complex. In the absence of GTP, the reaction will

not proceed. Thus, in membranes prepared in the absence of GTP and

assayed with Mg++ and opioid agonist, all receptors stay in the high-affinity

state. Wong and colleagues (1992) have recently described that pretreatment

of brain membranes with sodium ions and GDP promoted the formation of a

homogeneous high-affinity state of μ-opioid receptors for an opioid agonist

when assayed in the presence of Mg++ ions.

GTP binds to the a-subunit of the high-affinity G protein-receptor complex.

The GTP-bound G protein promptly undergoes subunit dissociation and the

formation of the low-affinity free receptor (RL

). The GTP-bound a-subunit of

157
FIGURE 6. Mechanism of opioid receptor-G protein activation cycle.

In membranes, opioid receptor exists in the G proteincomplexed low-affinity (G•RL

) or high-affinity (G•RH) forms,

or the low-affinity free receptor form (RL

). A nascent receptor

form (R°) is also proposed, which expresses itself in the

membranes only in the presence of high sodium concentrations.

Op is an opioid agonist that together with Mg+ + facilitates the

GDP/GTP exchange reaction. G, is the -subunit of G protein;

G is the -subunit of G protein; GTP-G is the active GTPbound G -subunit. The GDP-bound G -subunit (GDP-G ) is

an inactlve form that reassociates with -subunit. Guanine

nucleotides, sodium, and magnesium ions regulate receptor

affinity and receptor-G protein coupling. The rate-limiting step

of effector activation is proposed to be the formation of highaffinity G•RH•Op complex. See text for details.

158
G protein induces effector activation. The hydrolysis of GTP to GDP, catalyzed

by an intrinsic GTPase on the -subunit, terminates the activation process

and returns the receptor to resting state. However, if GTP is replaced by

the nonhydrolyzable analogs of GTP such as Gpp(NH)p, the GTPase reaction

will not proceed and the effector will be permanently activated because of the

occupancy of the a-subunit by Gpp(NH)p. The subunits G and G remain

dissociated and unable to reform the G protein-receptor complex. Thus, all

p-receptors will be in the low-affinity state of free RL.

Sodium ions favor the low-affinity conformation and also facilitate effector

activation by promoting the breakdown of the high-affinity receptor-G protein

complexes. This is supported by the observation that all p-receptors are in

the low-affinity state for agonists in the presence of Na+, Mg++, and Gpp(NH)p

(Wong et al. 1992). When membranes are prepared with a buffer without

sodium ions and guanine nucleotides, part of opioid receptors are converted

to nascent receptors (Ro), which do not bind opioid agonists or antagonists.

These nascent receptors can be recruited back to the low-affinity receptor

pool by sodium ions. These sodium-associated receptors can further interact

with GDP-bound G proteins to form the GDP-G•RL

•Na+ complexes (Wong et al.

1992).

Under normal physiological conditions, the rate-limiting step for this cycle of

activation process is the interaction of opioid agonist with GDP-Ga&•RL

 complex

in the presence of Mg++ to induce the dissociation of GDP and the formation

of the high-affinity G •RH•Op complex. This is based on the fact that Na+

,

Mg++, and GTP exist in high concentrations under physiological conditions

such that opioid agonist triggers the breakdown of the high-affinity G •RH•Op

complex immediately. Wong and coworkers (1992) have recently demonstrated

that this step of the formation of the high-affinity G •RH•Op state is attenuated

in spinal membrane μ-receptors prepared from oproid-tolerant animals. This

reduced conversion to the high-affinity state correlates the degree of opioid

tolerance.

CLONING OF OPIOID RECEPTOR cDNA

As discussed above, many attempts to clone opioid receptor gene with

modern molecular biology techniques have not been successful for various

reasons. The authors have recently succeeded in purifying μ-opioid receptor

to homogeneity with a reasonable yield. Currently, we can obtain about 5 to

10 pmol of purified receptor in a week. It is estimated that to obtain 100 to

200 pmol quantity of receptor for partial amino acid sequence analysis, it will

take about 6 months to 1 year to accumulate enough quantity of purified

receptor. We are actively attempting to obtain this accumulation.

159
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163
ACKNOWLEDGMENTS

Part of the work presented in this chapter was supported by National Institute

on Drug Abuse grant DA-04240.

AUTHORS

Ling-Yuan Li, Ph.D.

Research Associate

Department of Anesthesiology

Ying-Fu Su, Ph.D.

Research Assistant Professor

Departments of Anesthesiology and Pharmacology

Zeng-Ming Zhang, M.D.

Research Associate

Department of Anesthesiology

Duke University Medical Center

Durham, NC 27710

Chih-Shung Wong, M.D., Ph.D.

Department of Anesthesiology

Duke University Medical Center

Durham, NC 27710

Associate Professor

Department of Anesthesiology

Tri-Service General Hospital

Taipei, Taiwan

REPUBLIC OF CHINA

Kwen-Jen Chang, Ph.D.

Adjunct Professor

Department of Anesthesiology

Duke University Medical Center

Durham, NC 27710

Principal Scientist and Section Head

Division of Cell Biology

Burroughs Wellcome Company

Research Triangle Park, NC 27709

164
Computer-Assisted Design of

Dopamine Transporter Ligands

Gilda H. Loew, Hugo O. Villar, Patricia A. Meguire, and

M. Frances Davies

INTRODUCTION

Cocaine is a major drug of abuse that has potent effects in the central and

peripheral nervous systems (Washton and Gold 1987; Spitz and Rosecan

1987). These effects are elicited by cocaine’s interaction with multiple sites,

including the sigma (Sharkey et al. 1988a), muscarinic (Sharkey et al. 1988b),

and serotonergic (Ritz and Kuhar 1989) receptors. In addition, cocaine inhibits

the reuptake of the monoamines from the synaptic cleft (Javitch et al. 1984).

Among the monoamines there is growing evidence implicating the ability of

cocaine to block the dopamine transporter and inhibit its reuptake as one of

the major causes of its addiction liability (Dackis and Gold 1985).

For example, a specific link between the inhibition of dopamine reuptake

and the potential for addiction has been provided by the observation that the

reinforcing properties of several substances in self-administration studies in

primates varied directly with their affinity for the site labeled by mazindol on

the dopamine transporter (Ritz et al. 1987; Kuhar et al. 1991). The relationship

between the reinforcing properties of cocaine and its interaction with the

dopamine transporter has led to an increased interest in the molecular

mechanisms of monoamine transport at the cellular and molecular levels.

For many years, researchers at the authors’ laboratory have been involved

in multidisciplinary studies of molecular mechanisms of central nervous system

actions of abused drugs, which have recently been expanded to include cocaine.

The insights gained from these studies provide a strong foundation for the

rational design of other compounds that interact with a particularly targeted

subset of sites that are recognized by the abused drugs. There are three

important reasons for the design of drugs that interact with all or part of the

same sites that interact with drugs of abuse: (1) to obtain novel compounds

that permit probing the site of action at which drugs of abuse act; (2) to design

drugs that can be used in the pharmacotherapeutic treatment of the conditions

induced by drug abuse, such as the abstinence syndrome; or (3) to search for

165
therapeutic agents deprived of abuse potential that may substitute for commonly

abused drugs in clinical practice. All three goals are relevant to the design of

compounds that act at the same sites that cocaine does and, in particular, to

those that bind to the dopamine transporter. Experimental efforts aimed at

characterizing detailed aspects of the uptake mechanisms would profit from

more extensive and varied probes. Moreover, because the uptake mechanism

and the nature of the transporter protein are not fully understood, it is reasonable

to search for a compound that affects cocaine binding to the transporter without

altering dopamine uptake. Finally, there is a need in clinical practice for

compounds with some of the proper-ties of cocaine. Cocaine was used for

several years in nasal and ocular surgery because of its anesthetic properties

and was abandoned only because of its powerful addicting properties.

Of equal importance as the hypothesis-based generation of novel compounds

is the pharmacological evaluation of these novel drugs as a means of testing

the soundness of current hypotheses (i.e., the candidate molecular properties

identified as modulators of recognition and activation at the sites where abused

drugs act). If the new drugs evaluated fail to have the predicted pharmacological

profile, then the molecular hypothesis on which the design was based should be

refined. In this iterative process, valuable insights are gained about the nature

of the site recognized by the drug and useful new drugs ultimately are designed.

The use of the tools of computational chemistry has permitted the development

of detailed hypotheses for the mechanisms of drug recognition and activation

that have subsequently been applied to the design of therapeutic agents. The

methods and approach used are different, depending on whether the threedimensional (3-D) structure of the macromolecular target of the ligands is

known or unknown. In the first instance, ligand-macromolecular interaction

can be modeled explicitly, but with several important factors still to be resolved.

Modifications in the ligands aimed at improving the complementarity between

the properties of the ligand and those of the binding site can then be

suggested. In contrast, commonly abused drugs, and the drugs used

in the pharmacotherapeutic treatment of their effects, belong to the second

category. They act at membrane-bound receptors and, although many of

these receptors have been cloned and sequenced, none has a known 3-D

structure. For example, the dopamine transporter with which cocaine interacts

has been recently cloned and sequenced (Kilty et al. 1991; Shimada et al.

1991), but the information is of limited use because it is not possible to

deduce a 3-D structure for the protein from its sequence alone.

If no structural information on the site of action is available, then the design of

additional probes must be made by comparing the properties of known ligands

to the properties of chemically related compounds that fail to bind to and/or

activate the receptor. The most complete use of this approach involves

calculation and examination of three types of properties—steric, electronic,

166
and environmental—as candidate molecular determinants of recognition

and activation because all three types of properties relate to the ability of

the candidate compound to participate in intermolecular interactions with the

macromolecule. However, insights can be obtained at intermediate steps,

short of the final goal.

In the work reported in this chapter, the authors describe the current status

of our studies to identify and characterize the molecular determinants of

recognition by cocaine congeners and other ligands of the dopamine transporter.

A structural study of cocaine and its diastereoisomers has been made to

characterize its bioactive form (Villar and Loew 1990). In addition, study has

begun on the mechanistic significance of the three substructural elements

common to all ligands that bind to the cocaine-binding site of the dopamine

transporter: (1) an amine nitrogen, (2) a strongly lipophilic region, and (3) an

aromatic moiety. Finally, the authors illustrate how using these substructural

domains as the basis for even a two-dimensional (2-D) search of a chemical

database for new families of ligands can facilitate the drug design process.

CHARACTERIZATION OF THE BIOACTIVE FORM OF COCAINE

The first step in the characterization of the bioactive form of any substance is a

thorough study of its conformational and dynamic properties, If the compound

has only one energetically accessible structure (i.e., is a rigid ligand), this unique

structure would be the form recognized by the receptor and further studies are

not required. However, for more flexible ligands, determining which of many

accessible conformers is recognized by the binding site is a more complex

process and cannot be done by consideration of only one ligand. Several

ligands must be included, some of which do and some of which do not bind

with high affinity to the binding site being studied. Conformational profiles

of each must be obtained and properties calculated and compared for all

accessible conformations. In this case, the form of the ligands that maximizes

the similarities in the molecular properties of the high-affinity ligands should be

regarded as the bioactive form. As an additional requirement, this form should

not be energetically accessible in compounds that display low affinity. The

consequence of such an approach is that the molecular properties common to

all ligands with high affinity that are not present in the low-affinity analogs are

selected as the determinants of recognition at the binding site.

To determine the bioactive form of cocaine, a disubstituted tropane bicycle,

the authors carried out a systematic study of its conformational characteristics,

using the AM1 semiempirical, quantum mechanical technique (Dewar et al.

1985). Systematic rotations of the side chains were performed in each of the

four possible conformations for the tropane nucleus. The minimum energy

structure of the free base was found to correspond to a chair conformation with

the N-methyl substituent in an equatorial position. This preferred conformation

167
is shown in figure 1. The geometrical parameters characterized—bond distances,

angles, and torsional angles—are similar to those found in the x-ray determination

of crystal structure (Gabe and Barnes 1963). The only geometric difference

between the x-ray structure and the lowest energy minima characterized with

AM1 is in the torsion angle around the ester function in position 2. This difference

could be attributed to the fact that our studies were made for the free base,

whereas the x-ray determination was for the chlorohydrate form in which the

amine nitrogen is protonated. Indeed, the structure of the lowest energy form

for N-protonated cocaine (figure 2, panel A) is identical to the x-ray determined

structure.

As discussed above, the relevance of the lowest energy form of a compound to

the biological processes it initiates depends on how rigid it is at physiologically

relevant temperatures. Cocaine has only one low-energy conformer. One

method of assessing its flexibility is to perform a molecular dynamics calculation

on it. Such computations for cocaine were performed at 310 K for more than

200 psec. The study revealed that the only portion of the molecule that freely

rotates is the phenyl ring of the substituent in the 3 position. Despite the

presence of other rotatable bonds, the rest of the molecule is surprisingly rigid.

Therefore, the lowest energy structure of cocaine is likely to be the bioactive

conformation.

FIGURE 1. Lowest energy structure characterized for cocaine in its neutral

form

168
FIGURE 2. Structure of (A) cocaine and (B) pseudococaine in their

protonated forms. Note that cocaine has an internal hydrogen

bond that stabilizes ifs protonated form. The hydrogen bond

is not possible for pseudococaine because of its different

stereoisomery.

In addition to conformation, the state of protonation of the amine nitrogen in

cocaine in its bioactive form was not completely resolved. To resolve this

question, cocaine was compared with pseudococaine. These two compounds

differ significantly in their affinity for the site labeled by [3H] mazindol on the

dopamine transporter, The inhibition constant in monkey striatum for /-cocaine

is 0.64 μM; for d-pseudococaine it is 116 μM (Ritz et al. 1987). However, the

169
only structural difference between the two compounds is in the chirality of

carbon 2, as shown in figure 3.

The authors have computed the heat of protonation of both analogs using the

AM1 semiempirical method. For cocaine, the heat of protonation in vacuo is

139.4 kcal/mol, whereas for pseudococaine it is 148.3 kcal/mol, a much lower

proton affinity. The affinity for the receptor then varies directly with the protonaccepting capabilities of the free bases. Therefore, because the rest of the

structure remains unaltered, the ligands should be acting as proton acceptors

(i.e., in their unprotonated forms). The difference in heat of protonation can

be easily understood at the qualitative level. Cocaine can form an internal

hydrogen bond, between the protonated amine nitrogen and the carbonyl

oxygen, as shown in figure 2, panel A, whereas pseudococaine cannot form

such an internal hydrogen bond (figure 2, panel B) and, therefore, is less able

to stabilize its protonated form.

There is additional evidence to support the conclusion that the unprotonated

form of cocaine is responsible for its biological activity. The pKa of cocaine is

5.5, significantly lower than the physiological pH of 7.4. Hence, at physiological

pH, the ratio of protonated to unprotonated cocaine is 1:100. Moreover, in pH

dependence studies (e.g., Calligaro and Eldefrawi 1987), cocaine has been

shown to lose affinity for the site at pHs lower than 6.0.

Hence, theoretical and experimental evidence strongly suggests that the

bioactive form of cocaine is its free base in a chair conformation with the

N-methyl substituent in an equatorial orientation.

FIGURE 3. Structure of cocaine and its diastereoisomer pseudococaine

170
PHARMACOPHORE FOR THE DOPAMINE TRANSPORTER

Simple inspection of the ligands that displace [

3H] mazindol in striatum membranes

reveals that all these compounds have three substructural elements in common

(Villar and Loew 1990). Examples of those commonalities can be seen in figure 4.

First, there is the existence of an amine nitrogen, similar to the one in cocaine.

Second, there is a lipophilic region, an aliphatic lipophilic environment in which

the amine nitrogen is embedded. Third, there is at least one aromatic ring.

These are the three elements of an elementary pharmacophore for the site.

As discussed, the first element required for recognition, the amine nitrogen,

interacts with a proton-donating residue in the binding cavity. The second

element appears to be required for interaction with a lipophilic pocket, because

it is a single aliphatic chain that embeds the amine nitrogen. The role of the

third element, the aromatic rings, is more complex, and there are at least three

possibilities: It could be interacting with a second lipophilic pocket; it could form

a charge transfer complex with the transporter; or it could form a stacked

complex.

To study the mechanistic function of the aromatic moiety, the authors have

evaluated molecular properties that relate to each of its three plausible roles using

the quantum mechanical semiempirical program AM1. To this end, we chose a

series of four closely related analogs of one of the ligands with the highest affinity

for the site, 2ß-carbomethoxy-3ß+(4-fluorophenyl)-tropane (CFT) (WIN 35,428),

shown in figure 5. The four compounds differ only in the nature of the substituent

in position 4 of the aromatic ring. Hence, the differences in in vitro affinities can

be directly related to changes in this ring. The three properties computed for the

four ligands were the logarithm of the octanol water partition coefficient (IogP),

the nature and energy of the frontier orbitals and the product of the average

molecular polarizability times the ionization potential. The IogP, computed using

a technique recently developed in our laboratory (Kantola et al. 1991), should

indicate whether changes in the hydrophobic nature of the substituent affect the

affinity of the ligand. The energies of the frontier orbitals relate to the ability of

the ligand to form a charge transfer complex with the receptor. The energy of

the lowest empty molecular orbital is a measure of the electron affinity, or

electron-accepting ability, of the compound, whereas the energy associated

with the highest occupied molecular orbital is a measure of its ionization potential,

or electron-donating ability. Finally, the product of the polarizability times the

ionization potential derives from the London dispersion term and is related to

the ability of the ligand to form a stacking complex. The calculated properties

as well as the affinity data (Boja et al. 1990), using [3H] CFT and rat striatum

membranes, are shown in table 1.

171
FIGURE 4. Structure of compounds reported to interact with the dopamine

transporter. The brackets indicate the aromatic region, the solid

arrow indicates a lipophilic center, and the empty arrow shows a

proton-accepting atom.

172
Compound X

CFT or WIN 35,428 F

RTI-31 CH3

RTI-32 Cl

WIN 35,065-2 H

FIGURE 5. Analogs of CFT used in the analysis of the mechanistic role of the

aromatic ring in recognition

Comparing the affinities with the four properties calculated, no relationship is

observed between the variation in electron-donating or electron-accepting

ability of the ligands and the variation in affinities. However, the other two

computed properties do appear to vary with the affinity. A decrease in the

computed IogP for the ligand, as a consequence of the different substitutions

on the aromatic ring, parallels a decrease in the affinity. A similar observation

can be made for the decrease in the ability of the ligand to form a stacking

complex. Thus, both lipophilicity and the ability of the aromatic ring to form a

 stacking complex are potential determinants of recognition. Further studies

are necessary, using a larger congeneric series that differs only in the nature

of the substituents on the aromatic ring, to continue to probe the relative

importance of these two properties. For such a study, additional in

vitro pharmacological data, determined under similar experimental

conditions and not currently available, will be necessary.

TABLE 1. Properties computed for the compounds shown in figure 5

Compound LogP IP(eV) EA(V) IP(Å3

.eV) IC50 (mM)*

RTI-31 2.50 9.31 0.15 206.7 1.2

RTI-32 2.37 9.10 0.48 204.8 1.7

CFT 2.09 9.29 0.16 197.9 15.7

WIN 35,065-2 2.07 9.38 0.51 196.0 23.0

KEYS: Octanol water partition coefficient (IogP); energy of the highest occupied

molecular orbital (IP=ionization potential); energy of the lowest

unoccupied molecular orbital (EA=electron affinity): polarizability times

the ionization potential ( lp); and IC50 from the literature data

* Data from Boja et al. 1990

173
DESIGN OF NOVEL FAMILIES OF DOPAMIN TRANSPORTER LIGANDS

As mentioned in the introduction, the identification of novel ligands for the

dopamine transporter is essential for continued characterization of the

pharmacological endpoints that can be elicited by modification of the normal

functioning of this protein, Chief among this identification process is the

possibility of finding compounds that may affect cocaine binding to the

transporter without altering the uptake process itself. Compounds with these

characteristics have not yet been identified but are worth searching for

because the benefits of such a substance would be invaluable for efficient

pharmacotherapy of the cocaine abstinence syndrome.

The requirements for recognition developed at different stages of investigation,

using the tools of computational chemistry, can be useful not only in developing

mechanisms of ligand-binding site recognition but also for searching chemical

databases for novel compounds that satisfy these criteria. Searches using a

chemical database can be done at two different levels. The first involves the

use of 2-D, topological connectivity criteria to retrieve all compounds with the

stipulated characteristics. The second, a more desirable and stringent level,

is a search using 3-D structural information stored in the database. Only those

structures that fulfill the 3-D arrangements of structural elements proposed

in the pharmacophore are retrieved. Because this search does not depend

on connectivity, the nature of the compounds that can be retrieved is more

diverse than those found using the 2-D searching.

Because of the preliminary nature of our pharmacophore, we have not yet

developed a full understanding of the 3-D relationship among its elements.

Therefore, it is not possible to carry out a search of a 3-D database. However,

2-D searches can provide some useful initial ideas for the design of novel probes.

The purpose of such searches is to identify novel skeletons that might display

some affinity for the dopamine transporter. These basic structures can then be

used to develop structure-activity relationships (SARs) that may lead to the

discovery of a new family of ligands.

As a basis for the two-dimensional search, we used the presence of a secondary

or tertiary amine, bound to aliphatic carbon atoms and separated by three atoms

from an aromatic ring. A search in the Cambridge Crystallographic Database

(Allen et al. 1963) permitted the characterization of more than 150 compounds,

some of them closely related to 1 -methyl-4-phenyl-1,2,3,6-tetrahydropyridine

(MPTP), the neurotoxin known to interact with the dopamine transporter. From

the set of compounds retrieved, we selected four compounds for evaluation of

their affinity for the dopamine transporter. Three of them are novel and one,

a 4-phenylpiperidine, with known affinity for the dopamine transporter, was

included as a control. The three new compounds belong to two chemical families:

tetrahydropyridines and piperazines. They were tested for their ability to displace

174
[

3H] CFT at 0 °C from rat striatum membranes. The results shown in table 2

indicate that all the compounds have affinity in the low μM range similar to

cocaine and therefore can be regarded as appropriate starting points for a

more extensive SAR study.

CONCLUSIONS

The authors have developed a preliminary model for recognition of ligands by

the dopamine transporter. Three elements are essential for recognition: a

proton acceptor; a lipophilic region in which the proton acceptor is embedded;

and, finally, an aromatic region that could function as another lipophilic region

or in stacking with aromatic residues at the binding site. More complete

characterization of the molecular determinants of recognition of cocaine

congeners and other families of ligands by the dopamine transporter requires

TABLE 2. Inhibition constants (IC50) for four compounds retrieved from

the search in the chemical database using the elementary

pharmacophore for the dopamine transporter

Compound IC5 0 (μM)

175
the generation of an augmented database of ligands that have been

pharmacologically evaluated under the same experimental conditions.

However, using the elementary pharmacophore developed so far, we have

illustrated the power of chemical databases in compound design, even in the

initial stages of pharmacophore development. The results of a 2-D search

permitted the identification of two new chemical families that could potentially

be ligands for the site: tetrahydropyridines and piperazines. These analogs

now can form the basis of an extensive SAR study, which should permit

the generation of experimental data and the corresponding computations

necessary to continue the development of a 3-D pharmacophore for the site.

REFERENCES

Allen, F.H.; Kennard, O.; and Taylor, R. Systematic analysis of structural data as

a research technique in organic chemistry. Accounts Chem Res 16:146-153,

1963.

Baja, J.W.; Carroll, F.I.; Rahman, M.A.; Philip, A.; Lewin, A.H.; and Kuhar, M.J.

New, potent cocaine analogs: Ligand binding and transport studies in rat

striatum. Eur J Pharmacol 184:329-332, 1990.

Calligaro, D.O., and Eldefrawi, M.E. Central and peripheral cocaine receptors.

J Pharmacol Exp Ther 243:61-68, 1987.

Dackis, CA., and Gold, MS. Bromocriptine as a treatment for cocaine abuse:

The dopamine depletion hypothesis. Int J Psychiatry Med 15:125-135, 1985.

Dewar, M.J.S.; Zoebisch, E.G.: Heafely, E.F.; and Stewart, J.J.P. AM1: A new

general purpose quantum mechanical molecular model. J Am Chem Soc

107:3902-3909, 1985.

Gabe, E.J., and Barnes, W.H. The crystal and molecular structure of l-cocaine

hydrochloride. Acta Crystallogr 16:796-802, 1963.

Javitch, J.A.; Blaustein, R.O.; and Snyder, S.H. 3H mazindol binding associated

with neuronal dopamine and norepinephrine uptake sites. Mol Pharmacol

26:35-44, 1984.

Kantola, A.; Villar, H.O.; and Loew, G.H. Atom based parametrization for a

conformationally dependent hydrophobic index. J Comput Chem 12:681-689,

1991.

Kilty, J.E.; Lorang, D.; and Amora, S.G. Cloning and expression of a cocainesensitive rat dopamine transporter. Science 254:578-579, 1991.

Kuhar, M.J.; Ritz, M.C.; and Boja, J.W. The dopamine hypothesis of the

reinforcing properties of cocaine. Trends Neurosci 14:299-302, 1991.

Ritz, M.C., and Kuhar, M.J. Relationship between self-administration of

amphetamine and monoamine receptors in brain: Comparison with cocaine.

Science 248:1010-1017, 1989.

Ritz, M.C.; Lamb, R.J.; Goldberg, S.R.; and Kuhar, M.J. Cocaine receptors on

dopamine transporters are related to self-administration of cocaine. Science

237:1219-1223, 1987.

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Sharkey, J.; Glen, K.A.; Wolfe, S.; and Kuhar, M.J. Cocaine binding at

receptors. Eur J Pharmacol 149:171-174, 1988a.

Sharkey, J.; Ritz, M.C.; Schenden, J.A.; Hanson, R.C.; and Kuhar, M.J.

Cocaine inhibits muscarinic cholinergic receptors in heart and brain.

J Pharmacol Exp Ther 246:1048-1052, 1988b.

Shimada, S.; Kitayama, S.; Lin, C.L.; Patel, A.; Nanthakumar, E.; Gregor, P.;

Kuhar, M.; and Uhi, G. Cloning and expression of a cocaine-sensitive

dopamine transporter complementary DNA. Science 254:576-578, 1991.

Spitz, H.I., and Rosecan, J.S. Cocaine Abuse: New Directions in Treatment

and Research. New York: Bruner/Mazel, 1987.

Villar, H.O., and Loew, G.H. A conformational study of cocaine and its

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Guilford Press, 1987.

ACKNOWLEDGMENT

Compounds acquired from Aldrich Chemical Company, Milwaukee, WI, were

used in this study.

AUTHORS

Gilda H. Loew, Ph.D.

President

Hugo O. Villar, Ph.D.

Director

Computational Pharmacology

Patricia A. Maguire, Ph.D.

Staff Scientist

Biochemical Pharmacology

M. Frances Davies, Ph.D.

Director

Experimental Pharmacology

Molecular Research Institute

845 Page Mill Road

Palo Alto, CA 94304

177
The Pharmacophore for Opioid Activity

Mark Froimowitz

INTRODUCTION

Although the phenyl ring in prototypical morphine is constrained to be

axial on the piperidine ring, it is clear that phenyl-equatorial compounds

are also capable of opioid activity. A good example of the efficacy of both

classes of opioids are - and -azabicyclanes in which the piperidine and

cyclohexane rings are interchanged to produce compounds, with the phenyl

ring constrained to be either axial or equatorial (figure 1, panels A and B)

(Froimowitz et al. 1984). Despite this major structural difference, the potency of

both classes of opioids is enhanced by the addition of a phenyl meta hydroxyl

(Froimowitz et al. 1984). To deal with these data, an opioid ligand model has

been proposed that is consistent with the general structure-activity relationships

(SARs) of opioids (Tecle and Hite 1976; Fries and Portoghese 1976; Froimowitz

et al. 1984). To account for the potency-enhancing effects of a phenyl meta

hydroxyl in both phenyl-axial and phenyl-equatorial opioids, the model suggests

that the phenyl ring of both classes binds to the same region of the receptor.

Superimposing the phenyl rings of opioids such as -and -azabicyclane

(figure 1, panel C), one observes that, although the ammonium nitrogens are

placed quite differently, the ammonium hydrogens are oriented toward the

same point and could, therefore, interact with the same negatively charged

region of the receptor.

The opioid ligand model is also consistent with general SARs concerning

the N-alkyl group. An N-allyl or related group consistently produces opioid

antagonists in phenyl-axial opioids (Archer and Harris 1965) but never in

phenyl-equatorial opioids such as the phenylmorphans, meperidine, or the

prodines (Ong et al. 1974; Casy et al. 1968). Similarly, an N-phenethyl group

greatly enhances and is optimal for the potency of phenyl-axial opioids, but this

is less true for phenyl-equatorial compounds (Portoghese 1978; Froimowitz and

Matthysse 1986). This can easily be understood with the ligand model because

the N-alkyl group is positioned differently in phenyl-axial and phenyl-equatorial

opioids (figure 1) and, therefore, would interact with different portions of the

receptor.

178
FIGURE 1. Azabicyclane opioids in which the phenyl ring is constrained to be

(A) axial -azabicyclanes) or (B) equatorial ( -azabicyclanes) on

the piperidine ring, A superimposition of the phenyl rings for the

two compounds is shown in panel C.

Given the opioid ligand model, a more important structural factor may be

the orientation of the phenyl ring. Although the phenyl ring is conformationally

constrained in morphine, this is not the case for most phenyl-equatorial opioids.

It has been noted that 4-phenylpiperidines with a symmetrically substituted

piperidine ring, such as ketobemidone, 3-demethylprodine, meperidine, and

azabicyclanes, contain two mirror-image conformers that are equally preferred

(figure 2) (Froimowitz 1982; Froimowitz and Kollman 1984; Froimowitz et al.

1984), and it is unclear which one is responsible for the opioid activity. Both

mirror images have been observed in the crystal structures of the azabicyclanes,

ketobemidone, and meperidine (Froimowitz et al. 1984; Froimowitz and Cody,

submitted for publication). In 4-phenylpiperidines with an asymmetrically

substituted piperidine ring such as the prodines and phenylmorphans, one

conformer becomes preferred. In prodines with an axial 3-methyl group (or

5-methyl group), the energy difference between the two conformers is a

substantial 3 to 4 kcal/mol, and the concentration of one conformer will

be considerably higher than that of the other due to the Boltzmann factor

(Froimowitz and Kollman 1984). Interestingly, in a series of resolved prodine

analogs, the same conformer is consistently observed in the crystal structure

179
A

B

C

FIGURE 2. The two mirror-image conformers that are preferred in (A)

ketobemidone, (B) meperidine, and (C) 3-demethylprodine. The

phenyl rings, which are shown edge on, eclipse either one or the

other edge of the piperidine ring.

180
of the more active enantiomer (Pottoghese 1978). However, the phenyl

orientation of the preferred conformer of the more active prodine enantiomers

is the opposite of that in morphine with the above ligand model (Portoghese

1978; Froimowitz and Kollman 1984).

A second series of 4-phenylpiperidine opioids with an asymmetrically

substituted piperidine ring are the phenylmorphans. The (+)-enantiomer

of phenylmorphan has three to five times the potency of morphine, and the

compound is morphinelike in that it completely substitutes for morphine in

dependent rhesus monkeys and rats (May and Takeda 1970; Ong et al.

1974; Awaya et al. 1984a). The (-)-enantiomer, with about the same potency as

morphine for antinociception, appears to be an atypical opioid in that it does not

substitute for morphine in dependent monkeys and rats but, conversely, will

precipitate withdrawal symptoms in nonwithdrawn animals (May and Takeda

1970; Ong et al. 1974; Awaya et al. 1984a). Using the above ligand model, the

phenyl orientation for the preferred conformer of (+)-phenylmorphan is a good

match to that of morphine (Cochran 1974; Froimowitz 1984). Conversely, the

preferred phenyl orientation of the atypical (-)-phenylmorphan is an essentially

perfect fit to that of the more active prodine enantiomers. Interestingly, 

prodine also appears to be an atypical opioid agonist in that a phenyl meta

hydroxyl, which generally enhances potency in opioids, converts the compound

into a pure antagonist devoid of agonist properties (Zimmerman et al. 1978).

In general, the addition of a meta hydroxyl to potent agonists in the prodine

series abolishes or greatly weakens their activities (Portoghese et al. 1981;

Casy and Ogungbamila 1985).

Thus, of the two mirror-image conformers in symmetrically substituted 4-

phenylpiperidines, there appears to be a dichotomy between compounds in

which the preferred conformer has a phenyl orientation that is morphinelike and

those in which it has the mirror-image orientation. The morphinelike orientation

appears to be associated with morphinelike activity in compounds such as

morphine and (+)-phenylmorphan. Opioids with atypical properties or SARs

such as (-)-phenylmorphan and the prodines appear to be associated with the

nonmorphinelike phenyl orientation.

PHENYLMORPHANS

One question that must be addressed is whether phenyl-axial and phenylequatorial opioids do bind to the same receptors. Antinociceptive activity

alone does not prove that a particular receptor is involved because opioids

that are selective for u-, -, and -receptors are all capable of producing

antinociception and analgesia (Heyman et al. 1988; Millan 1990). Therefore,

Froimowitz and colleagues (1992a) have examined the binding of the

phenylmorphan enantiomers and some of their analogs to opioid receptor

subtypes. It was of particular interest to determine whether the atypical

181
pharmacological profiles of some of the compounds, most notably the mixed

agonist-antagonist (-)-phenylmorphan, were due to a change in affinity for

receptor subtypes. However, the affinities of all the compounds, except one,

were found to be greatest for μ-receptors, somewhat lower for -receptors,

and low for receptors. The exception was the (+)-9 -methyl analog, which

suffered a large decrease in μ-receptor affinity, causing it to have a slightly

higher affinity for -receptors. It was found that the in vivo antinociception

potency difference in favor of (+)-phenylmorphan relative to (-)-phenylmorphan

can be explained entirely by its proportionately increased affinity for μ-receptors.

In addition to determining the affinities of the enantiomers of phenylmorphan

for opioid receptor subtypes, some in vivo pharmacological properties also

were examined (Froimowitz et al. 1992a). Although the affinities of both

enantiomers are clearly greatest for μ-receptors, it is possible that another

opioid receptor subtype is responsible for the antinociceptive activity. To test

this, mice were pretreated with the selective opioid antagonists naloxonazine

(μ1), -FNA (μ1 and μ2), nor-BNI ( ), or naltrindole prior to testing in the

tail-flick assay. Only the y-receptor antagonists were found to significantly

attenuate the antinociceptive activity of (+)- and (-)-phenylmorphan. The

antagonism by the μ1-selective naloxonazine suggests that both compounds

are μ, agonists, whereas intrathecal administration suggests that both are

also μ2 agonists (Pick et al. 1991). In contrast, nor-BNI and naltrindole did

not significantly affect the antinociceptive activity at doses that attenuated the

activity of the x-selective agonist U50488 and the -selective agonist DPDPE,

respectively. Thus, both the morphinelike (+)-phenylmorphan and the atypical

(-)-phenylmorphan appear to be producing their antinociceptive effects

through μ-receptors.

As indicated above, the affinity of the (+)-9 -methyl analog is slightly higher

for x-receptors than for μ-receptors. This is due to a large decrease in affinity

for p-receptors relative to the two possible parent phenylmorphans. In terms

of in vivo activity, the compound is devoid of agonist activity in three mouse

assays of antinociception but is an opioid antagonist with a nalorphinelike

potency (Awaya et al. 1984a). In contrast, the (-)-9 -methyl analog is a weak

agonist relative to the parent compounds with atypical properties because the

compound does not substitute for morphine in morphine-dependent animals

(Awaya et al. 1984a). Because the pharmacological profiles of both 9 -methyl

analogs are modified by the introduction of a single methyl group to the parent

compounds, it was of interest to determine how this molecular change causes

these dramatically different pharmacological profiles.

The first necessity was to determine the absolute configuration of the 9 -methyl

analogs. This was accomplished through an x-ray crystallographic study of the

chloride salt of the (+)-9 -methyl analog (Froimowitz et al. 1992b). The absolute

configuration of this compound was found to be (1R,5S,9R); it therefore

182
corresponds to (-)-(1R,5S)-phenylmorphan. This implies that the (-)-9 -methyl

analog corresponds to (+)-(1S,5R)-phenylmorphan. Thus, the addition of the

9 -methyl group to (+)-phenylmorphan converts the potent morphinelike agonist

into a weaker agonist that no longer substitutes for morphine. The same change

to the atypical agonist (-)-phenylmorphan converts it into a pure antagonist with

no apparent agonist activity and is also associated with a tenfold drop in the

μ-receptor affinity.

The phenylmorphans and their analogs provide a series of compounds with

widely differing profiles of pharmacological activity (May and Takeda 1970;

Ong et al. 1974; Awaya et al. 1984a, 1984b). The morphinelike opioids are

(+)-phenylmorphan and the (±)-9 -hydroxy analog, which substitute for morphine

in dependent animals. (-)-Phenylmorphan, although maintaining good in vivo

antinociceptive activity and receptor-binding affinity, does not substitute for

morphine and appears to have the antagonist property of precipitating withdrawal

in morphine-dependent animals. The (-)-9 -methyl analog has even weaker

agonist activity and also does not substitute for morphine. The (+9 -hydroxy

analog is a weak agonist at best. Finally, the (+)-9 -methyl analog is devoid of

agonist activity but does appear to be an antagonist with nalorphinelike potency.

As indicated above, the orientation of the phenyl ring relative to the piperidine

ring may be an important factor for the pharmacological profile of an opioid.

This has been examined in figure 3, which shows those regions of the

conformation space between 0° and 180° that are within 1 kcal/mol of the

global minimum for the various phenylmorphans. The relatively rigid phenylaxial morphine (84°) (Gylbert 1973) and phenyl-equatorial racemic benzofuro

[2,3-c]-pyridin-6-ols(92°) (Hutchinson et al. 1989) are included for reference

purposes (Froimowitz et al. 1992a). The two compounds with the most

morphinelike profiles—(+)-phenylmorphan and the (±)-9 -hydroxy analog—

appear to most closely correspond to the rigid analogs, whereas the other

compounds deviate. The only possible exception is the (-)-9 -methyl analog.

However, for this compound the conformational energy of the region past 100”

rises rapidly (Froimowitz et al. 1992a, 1992b). Interestingly, the receptor affinity

of this compound is essentially the same as for (-)-phenylmorphan, although the

former appears to be considerably weaker in in vivo assays of antinociception.

The compounds that have the most difficulty in attaining this region of

conformation space due to unfavorable energies are the (+9 -hydroxy

and (+)-9 -methyl analogs. Additional compounds will allow further

exploration of this aspect of molecular structure.

U50488

The opioid U50488 (figure 4) is the prototype for selective k-receptor agonists

(Lahti et al. 1982; Piercey et al. 1982; Szmuszkovicz and VonVoigtlander 1982;

VonVoigtlander et al. 1983). Unlike previously described opioids that interact

183
FIGURE 3. The regions of the phenyl conformation space that are within

1 kcal/mol of the global minima for the various phenylmorphans.

The global minima are indicated by the "x's." The equivalent

value for the constrained phenyl-axial morphine and the phenylequatorial benzofuro[2,3-c]-pyridin-6-ols are indicated by the “m”

and “b, ” respectively.

184
FIGURE 4. The energy-minimized trans (with respect to conformer of

U50488 that is most similar to the three x-ray crystal structures

with -receptors, U50488 has little affinity for μ-receptors, and there is little

cross tolerance between it and μ-agonists such as morphine. It is important

to emphasize that the selectivity of U50488 for -receptors is primarily due to

its loss of affinity for μ-receptors rather than enhanced affinity for -receptors.

The conformational properties of U50488 have been studied using the

molecular mechanics computer program MM2-87 for the isolated molecule

and high-resolution 1H nuclear magnetic resonance (NMR) spectroscopy

for aqueous solutions of the compound (Froimowitz et al. 1992c). In addition,

two crystal structures of U50488 with different counterions and the crystal

structure of a closely related analog have been examined as well (Clark et al.

1988; Doi et al. 1990; Costello et al. 1991).

185
There are five dihedral angles that are important for the three-dimensional

structure of U50488 (figure 4). These are 1(C8-C7-C8-C5), 2(N9-C8-C7-C6),

3(C11 -N9-C8-C7), 4(C12-C11 -N9-C8), and 5(C18-N17-C16-C15). Preliminary

MM2-87 calculations and the conformational preferences of simpler molecules

were used to determine that there are two energy minima for 1 (±90°), three

for 2 (two gauche and one trans), two for 3 (0° and 180°), two for 4 (110° and

-70°), and three for 5 (two gauche and one trans). Permuting the different

combinations, there are 72 distinct conformational possibilities. All these were

generated and energy minimized with the MM2-87 program. Because there is

little energy difference between conformers that differ only with respect to 1,

these can be thought of as 36 pairs of minima. One of the pair of global

minima-aside from a pair of artifactual conformers (Froimowitz et al. 1992c) —

is shown in figure 5. This conformer was found to be especially stabilized by

attractive van der Waals’ interactions due to its compact structure.

FIGURE 5. The energy-minimized gauche (with respect to 52) conformer that

appears to be the biologically active form for U50488. This

conformer is especially stabilized by attractive van der Waals’

interactions due to its compact structure.

186
The NMR studies of U50488 in aqueous solution included one- and twodimensional high-resolution 1H NMR techniques with the interpretation

of 1H-1H vicinal coupling constants, one- and two-dimensional nuclear

Overhauser effect (NOE) experiments, and two-dimensional correlated

spectroscopy experiments. Especially important were the NOE data because

they provided important information regarding the conformation of the compound

in solution. In particular, conformationally important NOES were observed

between the N-methyl protons and both ortho phenyl protons, the methylene

protons H7, and the cyclohexyl proton H16 (see below).

There was generally good agreement between the MM2-87 calculations,

the NMR studies, and the three crystal structures, although there were some

notable differences as well. For 1, the MM2-87 calculations suggested that

there was little energy difference (tenths of a kcal/mol) associated with flipping

the phenyl ring over. This is consistent with the NMR data, which showed NOEs

between the N-methyl protons and both ortho phenyl protons, which indicated

that both conformers are populated in solution, However, all three crystal

structures had only one orientation of the phenyl ring. With respect to 2, the

MM2-87 calculations found that 2~-60° was required to produce the compact

and preferred structures that are stabilized by the attractive van der Waals’

interactions. This is consistent with the NMR data because NOEs between

the N-methyl protons and the phenyl protons are possible only in a gauche

conformer. In contrast, all three crystal structures had a trans value for 2. For

3, the MM2-87 calculations suggested a slight consistent preference for the

conformer in which the carbonyl group is trans to the N-methyl group. This is

consistent with both the NMR data, which showed NOEs between the N-methyl

protons and the methylene protons H7, and the three crystal structures, which all

had a similar conformation. With respect to 4, the MM2-87 calculations

consistently preferred a value of 4-110”. This is consistent with both the NMR

data, which showed NOEs between the N-methyl protons and the cyclohexyl

proton H16, and the three crystal structures, which all had values of 4~110°.

For 5, the MM2-87 calculations suggest a small consistent preference for

5~-60°. All three crystal structures had values in this vicinity, whereas the

NMR data did not provide any information regarding this dihedral angle.

Thus, there is good agreement between the MM2-87 calculations and either one

or both experimental methods for each of the five important dihedral angles.

With respect to 3 and 4, all three methods agree closely. For 1 and 2,

there is good agreement between the MM2-87 calculations and the NMR data,

which differ in reasonable ways from the three crystal structures. With respect

to 5, there is good agreement with the three crystal structures, whereas the

NMR data do not provide any information.

After the completion of the conformational analysis of U50488, the author became

aware of some recent work on novel analogs of U50488 in which the amide bond

187
has been incorporated into a lactam ring resulting in two diastereomers with

additional conformational rigidity (Cheng et al. 1990). The diastereomer that

maintains good affinity and selectivity for x-receptors is shown in figure 6. The

major conformational effect of the lactam ring is to fix 3-180° in agreement with

the results outlined above. For the more potent and -selective diastereomer,

the lactam ring also limits 2 to negative values, whereas that for the less active

diastereomer is limited to positive values. Thus, for the two gauche conformers

of U50488 with respect to 2, the one proposed for the biologically active form

is the only one that is consistent with the more active and selective lactamconstrained diastereomer.

The conformational analysis of U50488 and the conformationally restricted lactam

analog suggest that the energetically preferred conformer will be similar to that

shown in figures 5 and 6. There are several rigid multicyclic -agonists available,

FIGURE 6. The lactam-constrained analog of U50488 that retains good

receptor affinity and selectivity. The diastereomer with the phenyl

ring and hydrogen atom interchanged is much less active and

selective.

188
although these are relatively nonselective. This includes ketazocine (figure 7),

with which U50488 shares a carbonyl group. One would like to determine the

common molecular features of -agonists and the aspect of the molecular

structure of U50488 that makes it considerably more selective than compounds

such as ketazocine.

Comparing the U50488 conformer in figure 5 with the crystal structure of

ketazocine (Verlinde and De Ranter 1983), the overall geometries appear

to be similar (Froimowitz et al. 1992c). The distance between the center

of the phenyl ring and the ammonium nitrogen is 4.5 Å for both the U50488

conformer and ketazocine. Superimposing the two, it is possible to

approximately align the phenyl ring, the carbonyl oxygen, and the ammonium

nitrogen. Initially, however, it appeared that the orientation of the ammonium

hydrogen in the two structures varied considerably. To resolve this, the

pyrrolidine ring in U50488 was rotated to better match the orientation of the

ammonium hydrogen of ketazocine, although this increased the conformational

energy by 2.4 kcal/mol. However, it now appears that this is not necessary

FIGURE 7. The x-ray crystal structure of the -agonist ketazocine

189
because the structures of phenyl-equatorial opioids such as the (+)-9 -methyl

analog of phenylmorphan (figure 8) are a better match to the U50488 conformer

without the rotation of the pyrrolidine ring. Thus, the proposed biologically

active form of U50488 appears to be a better match to phenyl-equatorial opioids

than to the phenyl-axial ketazocine.

Superimposing the U50488 conformer in figure 5 with ketazocine suggests

that the -selectivity of the former is due to the presence of the cyclohexane

ring, which occupies space that is not occupied in nonselective -agonists such

as ketazocine. Thus, this added bulk appears to sterically hinder attachment to

μ-receptors, whereas the -receptor appears to tolerate bulk in this part of the

molecule. Additional -selective analogs of U50488 have been synthesized

with considerable steric bulk, including naphthalene rings, in this portion of the

molecule, and this does not adversely affect the affinity for -receptors (Lahti et

al. 1985; Halfpenny et al. 1989, 1990, 1991; Freeman et al. 1991).

FIGURE 8. The (+)-9 -methyl analog of phenylmorphan, which is a pure

opioid antagonist

190
CONCLUSIONS

A ligand model has been developed that is consistent with the diverse

molecular structures that elicit opioid activity. Constrained phenyl-equatorial

opioids like the phenylmorphans have been shown to produce their

pharmacological effects through the same μ-receptors as more traditional

phenyl-axial compounds such as morphine, and the opioid ligand model is

consistent with these structures. Additional evidence has been presented

regarding the importance of the phenyl orientation for morphinelike and

atypical opioid activity. Currently, 4-phenylpiperidines are being synthesized,

which will further explore the role of the phenyl ring orientation on opioid

activity. Finally, a conformational analysis of the prototypical selective

-agonist U50488 and its superimposition with less selective -agonists

suggests that its cyclohexane ring is responsible for sterically interfering

with its attachment to μ-receptors. This should make it possible to design

additional -selective opioids through the introduction of steric bulk in the

appropriate regions of opioid molecules.

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ACKNOWLEDGMENTS

The author acknowledges the contributions of his collaborators Alexandros

Makriyannis, Ph.D. (NMR spectroscopy), Christine DiMeglio, Ph.D. (NMR

spectroscopy), Gavril Pasternak, M.D., Ph.D. (receptor binding), and Vivian

Cody, Ph.D. (x-ray crystallography). This work was supported by National

Institute on Drug Abuse grant DA-04762.

AUTHOR

Mark Froimowitz, Ph.D.

Assistant Professor of Psychiatry (Molecular Pharmacology)

Harvard Medical School

Associate Biochemist

Alcohol and Drug Abuse Research Center

McLean Hospital

115 Mill Street

Belmont, MA 02178-9106

194
The Molecular Basis of Opioid

Potency and Selectivity:

Morphiceptins, Dermorphins,

Deltorphins, and Enkephalins

Murray Goodman, Seonggu Ro, George Ösapay, Toshimasa

Yamazaki, and Alexander Polinsky

INTRODUCTION

Naturally occurring peptide opioids have been isolated from the central nervous

system (Pasternak et al. 1975; Numa 1984), frog skin (Montecucchi et al. 1981;

Mor et al. 1989), and the enzymatic digestions of proteins (Chang et al. 1981).

All natural peptide opioids contain an amino terminal tyrosine, a phenylalanine,

and at least one spacer residue between them. Systematic modifications of

these residues have shown that the free amine and phenolic groups of tyrosine

at the first position and the aryl ring of the third or fourth residue represent the

pharmacophoric array (Hruby and Gehrig 1989; Morley 1980). It is well known

that each opioid peptide can bind to more than one receptor (μ, , . . .) and

their subtypes. Such poor selectivities create difficulties in clinical applications

and physiological studies of these molecules. Therefore, it is essential to

design and synthesize potent opioid ligands that bind to only one receptor.

As with other peptide hormones, opioids assume specific three-dimensional

structures when they interact with receptors. The flexibility of natural opioids

allows them to adopt many different conformations that may account for their

ability to bind to different receptors. To reduce such flexibility and to allow

specific analogs to interact favorably with only one receptor, various

constrained opioids have been devised and their conformations studied.

The authors’ laboratories have employed an integrated approach that includes

design, synthesis, bioassays and conformational analysis using nuclear

magnetic resonance (NMR) spectroscopy and computer simulations. Many

of the analogs synthesized in our laboratories have shown high activity,

selectivity, and metabolic stability. In addition, the conformational studies

of these analogs have suggested that the arrangements of the tyrosine and

phenylalanine aromatic groups are important to determine the potency and

195
selectivity of the opioids. The authors recently proposed topochemical models to

explain the bioactivities of morphiceptins, dermorphins, deltorphins, and a new

class of cyclic opioids based on lanthionine. This work extends their prior studies

on the structure-bioactivity relationships of enkephalin and related molecules.

STRUCTURAL CHARACTERISTICS OF MORPHICEPTIN AND DERMORPHIN

The biologically important Tyr1 and Phe3 are joined by a single amino acid

in the highly μ-receptor selective morphiceptin (Tyr1-Pro2-Phe3-Pro4-NH2)

(Chang et al. 1981) and dermorphin (Tyr1-D-Ala2-Phe3-Gly4-Tyr5-Pro6-Ser7-NH2)

(Montecucchi et al. 1981). As a result, the conformation and configuration of

the second residue must play a significant role in orienting the pharmacophores.

Interestingly, these two classes of opioids exhibit different structural features as

residue. Morphiceptin requires an L-chirality for Pro2, whereas dermorphin

requires an amino acid with D-chirality. Dermorphin exhibits only the trans amide

linkage between residues 1 and 2, whereas morphiceptin exhibits cis and trans

isomers about the Tyr1-Pro2 amide bond in a ratio of 30:70 (Goodman and Mierke

1989). The cis/trans configuration about the Tyr1-Pro2 amide bond is particularly

significant for the relative orientation of the biologically important Tyr1 and Phe3

residues.

In studies of morphiceptin analogs containing 2-aminocyclopentanecarboxylic

acid (2-AC

5

c) as the second residue in place of proline Tyr-2-AC5c-Phe-X4-NH2

[X4=Pro and (L and D)-Val], the amide bond between residues 1 and 2 adopts a

completely trans structure (Mierke et al. 1990; Yamazaki et al. 1991a ). Among

the four stereoisomers, only the analogs containing (1S,2R)-2-AC5c in position 2

show opioid bioactivity. Although the 2-AC

5

c analogs can adopt only a trans

amide configuration about Tyr-2-Ac5

c, the bioactive analogs Tyr-(1S,2R)-2-Ac5cPhe-X4

-NH2 are topochemically equivalent to morphiceptin with the Tyr-Pro

amide bond in a cis configuration (figure 1, structure A). However, the inactive

analogs Tyr-(1R,2S)-2-Ac5

c-Phe-X4-NH2 share similar preferred conformations

with morphiceptin adopting a trans configuration at the Tyr-Pro amide bond

(figure 1, structure B). We therefore proposed that a cis configuration about the

Tyr1-Pro2 amide bond is required for bioactivity of the morphiceptin-related

analogs containing a proline as the second residue (Yamazaki et al. 1991 a ) .

This suggestion is inconsistent with those proposed from the previous theoretical

studies, in which the requirement of trans configuration of Tyr-Pro amide bond is

implied for the bioactivity of morphiceptin (Momany and Chuman 1986; Lowe et

al. 1986). Thus, systematic conformational studies concentrated on the second

residue were undertaken to establish the configurational requirements of the Tyr1

-

X

2

 amide bond (where X

2

 represents residue) of morphiceptin. Such studies can

also provide insight into the “bioactive conformation” of dermorphin as well as

morphiceptin because, as noted above, the array of pharmacophoric groups is

determined primarily by the conformation of their second residues. Therefore, we

196
FIGURE 1. Superposition of preferred conformations for (A) Tyr-cis-(1S,2R)-2-

Ac5c-Phe-Pro-NH2 (active) and Tyr-Pro-Phe-Pro-NH2 with cis TyrPro amide bond and (B) Tyr-cis-2-Ac5

c-Phe-Pro-NH2 (inactive) and

Tyr-Pro-Phe-Pro-NH2 with trans Tyr- Pro amide bond

have designed and synthesized a series of analogs: Tyr1-X2-Phe3-D-Pro4-NH2

(where X

2

=Ala, D-Ala, Pro, D-Pro, L-[NMe]Ala, and D-[NMe]Ala) (Yamazaki et

al. 1991 b). The in vitro bioactivities of these tetrapeptide analogs determined

from the guinea pig ileum (GPI) and mouse vas deferens (MVD) assays are

summarized in table 1.

Because these analogs differ only at the second residue, the differences in

bioactivities can be explained in terms of the topochemistry resulting from the

effects of changes at residue 2. To assess accessible ( , ) space for the

second residues of the above-described analogs, energy calculations were

carried out for six N-acetylamino acid-N'-methylamide derivatives (Ac-X2

-NHMe;

X

2

=Ala, D-Ala, [NMe]Ala, D-[NMe]Ala, Pro, or D-Pro). The derivatives AC-X2-

NHMe display the greatest accessible space because they do not have

neighboring residues that could restrict geometries for the X

2

 residues in a

peptide sequence (Zimmerman et al. 1977).

All the space accessible to the trans isomers of Ac-Pro-NHMe and Ac-

(NMe)Ala-NHMe are also allowed for Ac-Ala-NHMe. The Tyr-Ala-Phe-D-ProNH2

 is inactive, whereas Tyr-Pro-Phe-D-Pro-NH, and Tyr-(NMe)Ala-Phe-D-ProNH2

 are active (table 1). Thus, the Tyr-X2 amide bond of these two analogs

cannot be trans. We therefore examined the conformational ( , ) space

accessible for the cis isomers of Ac-(NMe)Ala-NHMe and Ac-Pro-NHMe and

197
TABLE 1. GPI and MVD assays of rerrapeprides related to morphiceptin and

dermorphin

Analog

GPI

IC50/nM

MVD MVD/GPI

IC50/nM IC50-ratio

Tyr-Ala-Phe-D-Pro-NH2 28,100±7,500*

26,900±7,200†

Tyr-(NMe)Ala-Phe-D-Pro-NH2 32.4±8.1*

30.6±6.1†

Tyr-Pro-Phe-D-Pro-NH2

Tyr-D-Ala-Phe-D-Pro-NH2‡

Tyr-D-(NMe)Ala-Phe-D-Pro-NH2

28.7±2.4†

74±13

73.4± 15.9*

85.4±5.9†

Tyr-D-Pro-Phe-D-Pro-NH2§ Inactive

Inactive*

>100,000†

1,390± 1,430*

202±28†

1,508±180†

1,490±180

2,046±391*

603±147†

Inactive

—

—

42.9*

6.60†

52.5†

20.1

27.9*

7.06†

—

* Measured by the NIDA Opiate Compound Testing Program

† Measured by Dr. P.W. Schiller in the Clinical Research Institute of Montreal

‡ Measured by Matthies et al. 1984

§ Measured by Chang et al. 1981

noted that the two compounds share significant accessible space. Thus, we

conclude that the L-chirality for the second residue and the cis configuration at

the Tyr-X2 amide bond are required for morphiceptin bioactivity (Yamazaki et al.

1991 b). In the 500 MHz 1H-NMR studies, both Pro2 and (NMe)Ala2 analogs

exhibit about 30 percent of cis amide structure for Tyr-X2 bond, whereas TyrAla-Phe-D-Pro-NH, is 100 percent rrans about the Tyr-Ala amide bond

(Yamazaki et al. 1991 b ).

The p-receptor selective Tyr-D-(NMe)Ala-Phe-D-Pro-NH2 also exhibits cis/trans

isomerism at the Tyr-D-(NMe)Ala bond as does the inactive Tyr-D-Pro-Phe-DPro-NH, at the Tyr-D-Pro bond. The Tyr-D-Ala bond of dermorphin can adopt

only a trans configuration. We therefore proposed that the bioactivity of D-

(NMe)Ala2 analog must be caused by the form of the tetrapeptide in which the

Tyr-D-(NMe)Ala bond is trans. The opioid Tyr-D-(NMe)Ala-Phe-D-Pro-NH2

can be classified as a member of the dermophin family of opioids that requires

both D-chirality and trans amide configuration at the second residue for activity

at the μ-receptor. The ( , ) conformation of the second residue required for

dermorphin bioactivity could be estimated from accessible space for Ac-D-Ala198
NHMe and Ac-D-(NMe)Ala-NHMe but prohibited for Ac-D-Pro-NHMe (Yamazaki

et al. 1991 b).

The 1H-NMR and molecular modeling studies employing the above results

provide the preferred topologies for Tyr-(NMe)Ala-Phe-D-Pro-NH2 and Tyr-D-

(NMe)Ala-Phe-D-Pro-NH2 and their torsional angles (figure 2 and table 2).1

The Tyr-(NMe)Ala amide bond (figure 2, structure A) assumes a cis

configuration, whereas the Tyr-D-(NMe)Ala amide bond (figure 2, structure B)

adopts a trans configuration. As expected, most torsional angles, except for

those that belong to the second residues, are extremely similar (table 2).

Interestingly, in spite of the configurational and conformational differences of the

second residue, the relative spatial arrangements of the pharmacophoric groups,

that is, the amine and phenolic groups of Tyr1 and the aromatic ring of Phe3, are

almost the same in both topologies. The chirality change of the (NMe)Ala2

residue may be compensated by a 180° rotation about the amide bond between

FIGURE 2. The topochemical array for p-receptor bioactivity of (A) Tyr-

(NMe)Ala-Phe-D-Pro-NH2and(B)Tyr-D-(NMe)Ala-Phe-D-ProNH2

. In structure A, the Tyr-(NMe)Ala amide bond assumes a cis

configuration, whereas the Tyr-D-(NMe)Ala amide bond assumes

a trans configuration in Stucture B.

SOURCE: Yamazaki et al. 1991b. Copyright 1991 by Academic Press

(Orlando, FL).

199
TABLE 2. Conformations of Tyr1

-X2-Phe3-D-Pro4-NH2[X=(NMe)Ala,

D-(NMe)Ala]

Tyr1 X

2 Phe3 D-Pro4

(NMe)Ala 130 -159 14 -103 54 159 -105 93 -171 -177 76 -89

D-(NMe)Ala 129 -168 180 129 91 180 -144 130 -168 175 81 -82

residues 1 and 2. Because these analogs show high μ-receptor selectivity,

these topologies are considered to be closely related to conformations

necessary for the μ-receptor recognition.

CONFORMATIONAL STUDIES OF CYCLIC DERMORPHIN-DELTORPHIN

ANALOGS

Dermorphin and deltorphins-deltorphin (Tyr-D-Met-Phe-His-Leu-Met-AspNH,), deltorphin I (Tyr-D-Ala-Phe-Asp-Val-Val-Gly-NH2

), and deltorphin II (TyrD-Ala-Phe-Glu-Val-Val-Gly-NH2

)—are isolated from the same source, and the

possess common generalized N-terminal tripeptide sequences Tyr1-D-X2-Phe3

(where D-X is either D-Ala or D-Met). Although dermorphin is p-receptor

selective, deltorphins bind only to the -receptor (Erspamer et al. 1989). To

elucidate the topochemical requirements for the bioactivities of these two opioid

classes, conformationally constrained cyclic dermorphin-deltorphin analogs

have been studied.

Most of the reported cyclic dermorphin-deltorphin analogs have been formed

by side chain-to-side chain coupling with two different types of cyclization

methods: amide bond formation and disulfide bridge formation (Schiller et al.

1987). A representative analog of the former is Tyr-c[D-Orn-Phe-Asp]-NH2.

The bioactivity of this compound is similar to those of corresponding linear

analogs, but its selectivity for the p-receptor is one of the highest among the

cyclic and linear dermorphin analogs. Among a series of disulfide-bridged

analogs, Tyr-c[D-Cys-Phe-Cys]-NH2

 shows μ-selectivity with high activity at

the μ-receptor and low activity at the -receptor. Introduction of a D-chirality

at position 4 and a free C-terminal carboxylic group into this analog resulted

in Tyr-c[D-Cys-Phe-D-Cys]-OH that is highly active at both μ- and -receptors

and thus is nonselective.2 Incorporation of penicillamine at the fourth position

of this nonselective analog drastically decreases μ-receptor activity without

significantly affecting -receptor activity. The analog Tyr-c[D-Cys-Phe-D-Pen]-

OH shows higher selectivity for the - over the μ-receptor (Mosberg et al. 1988).

This result strongly suggests that the spatial arrays of the three pharmacophoric

200
groups within the N-terminal tripeptide sequence define the bioactivity and

selectivity of dermorphin and deltorphin.

Calculations of a series of molecules based on the structure Tyr-c[D-Orn-PheAsp]-NH, indicated that a tilted stacking orientation of the two aromatic side

chains at residues 1 and 3 is a structural requirement for high μ-receptor affinity

(Wilkes and Schiller 1990). However, another theoretical study that employed

various cyclic dermorphin-deltorphln analogs, including analogs from the Wilkes

and Schiller study, suggested different topologies for μ-receptor recognition

(Shenderovich et al. 1991). It should be noted that the bioactive conformation

of Tyr-D-(NMe)Ala-Phe-D-Pro-NH2proposed in the previous section is not in

agreement with any of the above topologies.

To avoid such ambiguities and to establish a conformational model for

dermorphin bioactivity, we synthesized and evaluated analogs with a new

cyclic structure formed by the cyclization between a side chain and the

backbone.3

 Among them, the 12-membered cyclic analog Tyr-c[D-Orn-PheGly], which contains the amino terminal tetrapeptide sequence of dermorphin,

shows interesting features. Its bioactivity profile is similar to that of dermorphin

(table 3). Although it is highly p-receptor selective, the analog shows bioactivity

at the a-receptor unlike the Tyr-c[D-Orn-Phe-Asp]-NH2

 that is active only at

the μ-receptor. The analog Tyr-c[D-Orn-Phe-Gly] is more constrained than

Tyr-c[D-Orn-Phe-Asp]-NH2 because the cyclization achieved by a side chainbackbone coupling produces a smaller ring (13 vs. 12 membered).

A conformational analysis was carried out employing NMR spectroscopy and

computer simulations. Among the resulting preferred conformations of both

analogs are conformations (figure 3, structures A, B, and C) that are

topologically similar to that responsible for the μ-receptor activity of the Tyr-D-

(NMe)Ala-Phe-D-Pro-NH2

 (figure 2, structure B). These topologies contain a

TABLE 3. Biological activities of cyclic dermorphin-deltorphin analogs

GPI* MVD* MVD/GPI In Vlvot

Analog IC50[nM] IC50[nM] IC50-ratio ED50[μg]

Tyr-c[D-Orn-Phe-Gly] 8.8±0.8 145±15 18.9 0.1

Tyr-c[D-Orn-Phe-Asp]-NH2 36.2±3.7 3,880±840 107 —

Tyr-D-Ala-Phe-Gly-NH2‡ 45.2±3.2 510±31.8 11.3 —

Morphine 58.8 844 11 5.8

*The GPI and MVD activities are measured in Dr. P.W. Schiller’s laboratories.

† Values are measured in Dr. T. Yaksh’s laboratories.

‡ Measured by Salvadori et al. 1982.

201
Tyr-c[D-Orn-Phe-Asp]-NH2

Tyr-c(D-Orn-Phe-Gly]

Tyr-c[D-A2bu-Gly- Nal(l)-Leu]

FIGURE 3. Preferred conformations of (A and 8) and (C and D). Tyr-c[DOrn-Phe-Asp]-NH2

 is biologically active at the μ-receptor. Tyrc[D-Orn-Phe-Gly] is μ-receptor selective but also shows

bioactivity at the -receptor. Structures E and F represent

bioactive conformations estimated for 14-membered cyclic

enkephalin analogs containing -naphrhylalanine(1)as the fourth

residue at the μ- and -receptors, respectively.

202
common structure in which the aromatic side chains of the Tyr1

 and Phe3 project

away from each other. We therefore propose that dermorphin must adopt a

topochemical structure with this topology for μ-receptor recognition. It is

worthwhile to mention that μ-receptor active conformations of 14-membered

cyclic enkephalin analogs (figure 3, structure E) (figure was estimated from a

study described by Yamazaki and colleagues [1991c]) and morphiceptin (figure 2,

structure A) also adopt similar topologies for the same receptor. Topochemical

similarity of the bioactive conformations of enkephalin, morphiceptin, and

dermorphin indicates that these three classes of opioids are likely to interact

with the same μ-receptor subtype.

Among the preferred conformations of the Tyr-c[D-Orn-Phe-Gly] analog,

a structure that places the two aromatic side chains in close proximity was

observed (figure 3, structure D). This structure is topochemically similar to

the conformation responsible for -receptor recognition of 14-membered cyclic

enkephalin analogs (figure 3, structure F) (Yamazaki et al. 1991c). This type

of molecular array was not observed among the preferred conformations of

-receptor inactive Tyr-c[D-Orn-Phe-Asp]-NH2

. Therefore, the bioactivity of

the 12-membered cyclic analog at the μ- and b-receptors can be explained by

the presence of extended and folded forms among the preferred structures.

Extended forms are defined as structures in which the phenol group of the

tyrosine at position 1 and the aromatic group of phenylalanine at position 3

project away from each other. Folded forms are defined as structures in which

the same aromatic groups are in close proximity to each other.

DESIGN, SYNTHESIS, AND CONFORMATIONAL STUDIES OF LANTHIONINE

ENKEPHALIN

A new class of peptide opioid structures was designed and synthesized based on

the incorporation of a lanthionine residue in the sequence. Lanthionine structures

have been observed in naturally occurring “Iantibiotics” such as nisin, subtilin, and

epidermin (Shiba et al. 1986). In figure 4, the first lanthionine-bridged enkephalin

analog, H-Tyr-c[D-AlaLGlys

-Phe-Ala,]-NH2, is depicted where AlaL denotes each

of the lanthionine amino acid ends linked by a monosulfide bridge. The ring size

in this new analog is reduced to 13 atoms as compared with 14 in the Tyr-c[DCys-Gly-Phe-Cys]-NH2

 analog (DCLCE) resulting in a more conformationally

constrained molecule (Schiller et al. 1981).

For the preparation of lanthionine opioid analog, the method that most closely

simulates the biosynthesis of lanthionine peptides was selected: The sulfide

bridge was formed by the addition of the Cys5 SH group to the double bond of

dehydroalanine-2 in the solid phase. The peptide synthesis was carried out

on a methylbenzhydrylamine resin using Boc chemistry with the symmetrical

anhydride peptide coupling method. Serine was incorporated at position 2, then

converted to dehydroalanine using disuccinimidocarbonate. The S-protecting

203
FIGURE 4. The structure of Tyr-c[D-AlaL

-Gly-Phe-AlaL]-NH2

fluorenyl methyl group was selectively removed with piperidine. A slightly basic

milieu (5 percent piperidine/DMF) promoted the Michael addition of the SH

group to the double bond. “Low-high HF cleavage” was used to cleave the

peptide from the resin and to remove the benzyl and benzyloxycarbonyl

protecting groups. Purification of the resulting crude product was achieved

by preparative reverse phase high-performance liquid chromatography.

In vivo bioactivity of the compound was determined from the rat hotplate test after intrathecal injection (Yaksh et al. 1991). The analog

Tyr-c[D-AlaL-Gly-Phe-AlaL-NH2 shows 37 times higher bioactivity than

morphine (table 4).ln vitro assays with GPI and MVD preparations, this

analog exhibits 400 times greater bioactivity at the p-receptor and 20 times

higher at the -receptor than (Leu5]-enkephalin. These values are higher

than those of its disulfide-bridged counterpart, DCLCE (Schiller et al. 1981).

The lanthionine analog shows preference to neither the μ- nor the -receptors

with an IC50 ratio (MVD/GPI) of 0.88.

To determine the conformational features of this molecule that are responsible

for such behavior, we carried out a rigorous conformational analysis employing

both NMR spectroscopy and molecular modeling. The torsional angles

remain quite stable during molecular

dynamics (figure 5). Although no methyl substituents are present on

the -carbon of the D-AlaL

2

 to provide steric hindrance that would restrict

rotations about the C -C and C, -S bonds, this part of the ring is nonetheless

204
TABLE 4. Biological activities of the lanthionine-bridged enkephalin analog

Analog

GPI* MVD* MVD/GPI In Vivo†

IC50[nM] IC50[nM] IC50-ratio ED50[μg]

Tyr-c[D-AlaL

-Gly-Phe-AlaL]NH2 0.6 0.5 0.88 0.15

Tyr-c[D-Cys-Gly-Phe-Cys]-NH2 1.5 0.8 0.50 —

Leu5-enkephalin 246 11.4 0.05 >100‡

Morphine 58.6§ 644§ 11 5.6

* The GPI and MVD activities are measured in Dr. P.W. Schiller’s laboratorles.

† Values are measured in Dr. T. Yaksh’s laboratorles.

‡ It only shows 50 percent of maximum effect at 100 μg dosage.

§ Data taken from Hruby and Gehrlg 1989.

constrained. Dynamics reveals that some local flexibility exists about residues 4

and 5 (Phe and AlaL

5

), enabling pairs of torsional angles in this region to undergo

concerted transitions. This small degree of flexibility does not influence the

mutual orientations of the two aromatic rings, because the D-AlaL

2

-Gly region is

highly constrained. Although the flexibility of the main ring has been decreased,

the distance between the aromatic rings is still highly variable and therefore no

increase in selectivity has been achieved compared to parent analog DCLCE

FIGURE 5. Superposition of structures of Tyr-c[D-AlaL

-Gly-Phe-AlaL]-NH2

resulting after every 10 psec of dynamics simulation

205
(Schiller et al. 1981). The bioactivity profile of the new 13-membered lanthionine

analog is consistent with conformational models for opioid activities of the (14-

membered) cyclic enkephalins (Yamazaki et al. 1991 a ).

CONCLUSIONS

From the above studies, the authors obtained several analogs with novel

structures and of reduced ring size such as lanthionine enkephalin. Because

they are highly potent and exhibit interesting conformational features, they can

be key analogs for opioid drug discovery.

These studies also enabled us to classify peptide opioids into three families

by their structural characteristics as well as their origin. The morphiceptin

family requires an L-chiral residue between Tyr1 and Phe3 that adopts a cis

configuration at the Tyr-X2

-(spacer) amide bond. The dermorphin-deltorphin

family contains a D-chiral X

2

 residue that assumes a trans amide configuration.

The enkephalin family can have a D-chiral or an achiral residue at position 2

and an additional achiral residue at position 3. According to such classifications,

the authors are now synthesizing cyclic morphiceptin analogs with N-alkylated-Lamino acids at position 2.

For each family of opioid peptides, topochemical models have been developed

to explain bioactivity and receptor selectivity based on the accessible space

of conformationally constrained analogs. The analogs incorporate various

peptidomimetics and cyclic structures. All the topochemical structures proposed

as “bioactive conformations” of the three families for each μ- and -receptor

recognition are quite similar. Therefore, we propose that in general the folded

structure with the tyrosyl aromatic ring in close proximity to another aryl ring is

required for -receptor activity. For bioactivity at the μ-receptor, the structure in

which the two aromatic side chains project away from each other with a large

separation (>11Å) is required.

NOTES

1. The details of our conformational studies will be discussed in a subsequent

paper.

2. Unpublished data of Dr. M. Goodman’s laboratories.

3. Similar work also was carried out by Darlak and colleagues (1991).

206
REFERENCES

Chang, K.J.; Killian, A.; Hazum, E.; Cuatrecasas, P.; and Chang, J.-K.

Morphiceptin (NH,-Tyr-Pro-Phe-Pro-CONH,): A potent and specific agonist

for morphine (μ) receptors. Science 212:75-77, 1981.

Darlak, K.; Burks, T.F.; Wire, W.S.; and Spatola, A.F. Small ring enkephalins.

In: Giralt, E., and Andreu D., eds. Peptides 1990. Proceedings of the First

European Peptide Symposium. Lieden, The Netherlands: ESCOM, 1991.

pp. 401-403.

Erspamer, V.; Melchiorri, P.; Falconieri-Erspamer, G.; Negri, L.; Corsi, R.;

Severini, C.; Barra, D.; Simmaco, M.; and Kreil, G. Deltorphins: A family of

naturally occurring peptides with high affinity and selectivity for opioid

binding sites. Proc Natl Acad Sci U S A 86:5188-5192, 1989.

Goodman, M., and Mierke, D.F. Configurations of morphiceptins by 1H and 13C

NMR spectroscopy. J Am Chem Soc 111:3489-3496,1989.

Hruby, V.J., and Gehrig, C.A. Recent developments in the design of receptor

specific opioid peptides. Med Res Rev 9(3):343-401, 1989.

Lowe, G.; Keys, C.; Luke, B.; Polgar, W.; and Toll, L. Structure-activity studies

of morphiceptin analogs: Receptor binding and molecular determinants of

μ-affinity and selectivity. Mol Pharmacol 29:546-553, 1986.

Matthies, H.; Stark, H.; Hartrodt, B.; Ruethrich, H.-L.; Spieler, H.-T.; Barth, A.;

and Neubert, K. Derivatives of -casomorphins with high analgesic potency.

Peptides 5:463-470, 1984.

Mierke, D.F.; Nö ner, G.; Schiller, P.W.; and Goodman, M. Morphiceptin

analogs containing 2-aminocyclopentane carboxylic acid as a peptidomimetic

for proline. Int J Pept Protein Res 35:35-45, 1990.

Momany, F.A., and Chuman, H. Computationally directed biorational drug

design of peptides. Methods Enzymol 124:3-18, 1986.

Montecucchi, P.C.; de Castiglione, R.; Piani, S.; Gozzini, L.; and Erspamer, V.

Amino acid composition and sequence of dermorphin, a novel opiate-like

peptide from the skin of Phyllomedusa sauvagei. Int J Pept Protein Res

17:273-283, 1981.

Mor, A.; Delfour, A.; Sagan, S.; Amiche, M.; Pradelles, P.; Rossier, J.; and

Nicolas, P. Isolation of dermenkephalin from amphibian skin, a high-affinity

-selective opioid heptapeptide containing a D-amino acid residue. FEBS Lett

255(2):269-274, 1989.

Morley, J.S. Structure-activity relationships of enkephalin-like peptides. Annu

Rev Pharmacol Toxicol 20:81-110, 1980.

Mosberg, H.I.; Omnaas, J.R.; Medzihradsky, F.; and Smith, C.B. Cyclic,

disulfide- and dithioether-containing opioid tetrapeptides: Development of a

ligand with high delta opioid receptor selectivity and affinity. Life Sci 43:1013-

1020, 1988.

Numa, S. Opioid peptide precursors and their genes. In: Udenfriend, S., and

Meienhofer, J., eds. The Peptides. Vol. 6. New York: Academic Press, 1984.

pp. 1-23.

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Pasternak, G.W.; Goodman, R.; and Snyder, S.H. An endogenous morphinelike factor in mammalian brain. Life Sci 16:1765-1769, 1975.

Salvadori, S.; Sarto, G.; and Tomatis, R. Synthesis and pharmacological activity

of dermorphin and its N-terminal sequences. Int J Pept Protein Res 19:536-

542, 1982.

Schiller, P.W.; Eggimann, B.; Dimaio, J.; Lemieux, C.; and Nguyen, T.M.-D.

Cyclic enkephalin analogs containing a cystine bridge. Biochem Biophys Res

Commun 101:337-343, 1981.

Schiller, P.W.; Nguyen, T.M.-D.; Maziak, L.A.; Wilkes, B.C.; and Lemieux, C.J.

Structure-activity relationships of cyclic opioid peptide analogues containing a

phenylalanine residue in the 3-position. Med Chem 30:2094-2099, 1987.

Shenderovich, M.D.; Nikiforovich, G.V.; and Golbraikh, A.A. Conformational

features responsible for the binding of cyclic analogues of enkephalin to

opioid receptors. III. Probable binding conformations of p-agonists with

phenylalanine in position 3. Int J Pept Protein Res 37:241-251, 1991.

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dermorphin analogues. Biopolymers 29:89-95, 1990.

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intrathecal administration of drugs. Life Sci 48:623-633, 1991.

Yamazaki, T.; Pröbstl, A.; Schiller, P.W.; and Goodman, M. Biological and

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analogues containing 1-naphthylalanine at the fourth position: Chirality effect

of leucine at the fifth position on biological activity and receptor selectivity.

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using ECEPP. Macromolecules 10(1):1-9, 1977.

ACKNOWLEDGMENTS

This research was supported by National Institute on Drug Abuse (NIDA) grants

DA-05539 and DA-06254. Dr. P.W. Schiller of the Clinical Research Institute of

Montreal, the NIDA Opiate Compound Testing Program, and Dr. T.L. Yaksh of

the University of California, San Diego, School of Medicine, carried out

biological assays of our analogs.

208
AUTHORS

Murray Goodman, Ph.D.

Professor

Seonggu Ro, Ph.D.

Postgraduate Research Chemist

George Ösapay, Ph.D.

Postgraduate Research Chemist

Toshimasa Yamazaki, Ph.D.

Postgraduate Research Chemist

Alexander Polinsky, Ph.D.

Postgraduate Research Chemist

Department of Chemistry

University of California, San Diego

La Jolla, CA 92093-0343

209
Molecular Determinants of Cannabinoid

Activity: Toward the Design of

Cannabinoid Analgesics With Reduced

Psychoactive Liability

Patricia H. Reggio

INTRODUCTION

Cannabinoids are the group of C21 compounds typical of and present in the

Indian hemp, Cannabis sativa L. Both hashish and marijuana are derived

from cannabis and have been used for centuries for their medicinal effects

as well as for their psychotomimetic effects (Mechoulam and Gaoni 1967).

Current scientific interest in the cannabinoids followed Mechoulam and

Gaoni’s (1967) and Gaoni and Mechoulam’s (1971) report that (-)- transtetrahydrocannabinol ( -THC) (figure 1, panel A) is the major psychoactive

component of cannabis. Subsequent to this report, more than 300 cannabinoids

have been synthesized and tested (Razdan 1986). Some cannabinoids have

been shown to have possible therapeutic value as analgesics, bronchodilators,

anticonvulsants, antiemetics, and antiglaucoma agents (Mechoulam 1986).

For example, interest in the development of a cannabinoid analgesic has been

spurred by the demonstration that some classical and nonclassical cannabinoids

possess analgesic activity comparable to that of the strong opiates. However,

these cannabinoid analgesics do not act at opiate receptors and have neither

the physical dependence liability nor the respiratory depression properties of the

strong opiates, yet they possess oral activity with long duration (Razdan 1986).

11 -Hydroxylation has been shown to be a primary metabolic event for molecules

such as THC (Nilsson et al. 1970). Although studies have shown that -THC

can exert its psychotomimetic effects without metabolic activation (Martin et al.

1975, 1977; Carney et al. 1979), it has been proposed that 11-hydroxylation

in the tetrahydrocannabinols (THCs) is a prerequisite for the production of

analgesic activity (Wilson and May 1975). During the course of their work on

a metabolic activation requirement for cannabinoid analgesia, Wilson and

coworkers (1976) synthesized (-)-9-nor-9-alpha-hexahydrocannabinol [(-)-9-nor9 -OH-HHC]and(-)-9-nor-9-beta-hexahydrocannabinol (-)-9-nor-9 -OH-HHC].

The 9 compound (in which the 9 hydroxyl group is axial) (figure 1, panel B)

210
A B C

FIGURE 1. (A) One numbering system commonly employed for (-)- -THC.

(B) A drawing of the structure of (-)-9-nor-9 -OH-HHC.

(C) A drawing of the structure of (-)-9-nor-9 -OH-HHC.

was found to possess low psychoactivity and no analgesic activity, whereas its

9 epimer (in which the 9 hydroxyl group is equatorial) (figure 1, panel C) was

found to possess both significant analgesic and psychotomimetic activity.

Later, Johnson and Melvin (1986) worked with (-)-9-nor-9 -OH-HHC, making

modifications to this structure that improved analgesia. Unfortunately, these

modifications also produced a parallel increase in psychoactivity, rendering

the new cannabinoid compounds less desirable as therapeutic agents.

Results such as those for cannabinoid analgesics discussed above underscore

a major problem in the cannabinoid field: the inability to separate the

psychotomimetic effects of the cannabinoids from their therapeutic effects.

When the author began work on the cannabinoids, the initial research goal

was to use the methods of theoretical chemistry o shed some light on the

molecular basis for the actions of the cannabinoids. As work on the molecular

determinants for cannabinoid activity progressed, I became interested in this

problem of separation of activities. One of the major goals of this work is to

accomplish such a separation, that is, to design cannabinoid analgesics with

reduced psychoactive liability. This chapter reviews the progress made toward

this goal. For a more detailed discussion of the work presented here, the

reader is referred to Reggio and Mazurek (1987), Reggio and colleagues

(1989, 1990a, 1990b, 1991), and a recent review by Reggio (1992).

Historically, the well-known high lipophilicity of the cannabinoids led many

investigators to hypothesize that the cannabinoids exerted their effects by

perturbing biological membranes (Roth and Williams 1979). However, evidence

of stereospecificity of action in the cannabinoids led other investigators to

hypothesize the existence of a cannabiniod receptor (Binder and Franke

1982). This second group was recently rewarded by the discovery of the first

cannabinoid receptor. The potent radiolabeled nonclassical cannabinoid ligand

[

3H]CP-55,940 was used by Devane and coworkers (1988) to develop membrane

homogenate and tissue section binding assays for the characterization and

211
localization of a cannabinoid receptor in rat brain. The [

3H]CP-55,940 binding

site has been reported to exhibit characteristics expected for a neuromodulator

receptor associated with a G protein. Autoradiographic techniques using [

3H]CP55,940 have revealed a heterogeneous distribution of this receptor throughout

the brain, with greatest abundance of sites in the basal ganglia, hippocampus,

and cerebellum (Herkenham et al. 1990). The cloning and expression of a

complementary DNA that encodes a cannabinoid receptor both in rat (Matsuda

et al. 1990) and in humans (Gerard et al. 1991) also has been reported recently.

To date, no cannabinoid antagonist has been discovered.

The site or sites of the analgesic action of the cannabinoids in the central

nervous system (CNS) are not yet established. The concentration of low

levels of [

3H]CP-55,940-labeled receptors in the spinal cord gray matter in

the substantia gelatinosa of the dorsal horn suggests an action at the spinal

level. However, this finding does not rule out action at higher levels in the

brain (Howlett et al. 1990).

Agonist binding data for the [

3H]CP-55,940 binding site are rather limited

(Devane et al. 1988; Herkenham et al. 1990). Over the years, a wealth of

pharmacological data for the cannabinoids has been published. The majority

of these data has come from whole animal assays such as the rhesus monkey

behavioral assay (Razdan 1986), the rat drug discrimination assay (Weissman

1978; Browne and Weissman 1981) and a battery of tests in mice that have

been shown to be highly predictive of cannabinoid effects (Razdan 1986; Martin

et al. 1984). In the work presented here, results have been correlated with

those of the selected whole animal assays mentioned above.

Because one of the major goals of the work presented here is the design of

cannabinoid analgesics with reduced psychoactive liability, efforts have been

directed toward the elucidation of the molecular determinants for cannabinoid

psychopharmacological activity and for cannabinoid analgesic activity. To this

end, the methods of theoretical chemistry have been used. Several assumptions

are implicit in this work. (1) It is assumed that the interactions between drugs

and their biological targets are dependent on the same molecular parameters

that determine chemical interactions and reactions. A molecular reactivity

characteristic (MRC), such as the molecular electrostatic potential (MEP), is a

molecular property that determines specific chemical reactivity. In this work the

calculation of the MEP has been emphasized as an MRC. (2) Because studies

have shown that differences in the penetration and distribution of cannabinoids

in the CNS do not correlate with activity (Ohlsson et al. 1980; Binder et al. 1984),

it has been assumed that changes in activity caused by molecular modification

can be attributed to changes in the ability of the molecule to interact at its

receptor or site of action. (3) Because the compounds studied are clearly

structurally related, it has been assumed that they may be unambiguously

superimposed on one another and that all compounds interact in analogous

212
orientations with their receptor or site of action, However, the recent report

(Pacheco et al. 1991) that a new class of compounds, the aminoalkylindoles,

interact with a cannabinoid receptor may soon produce the first test of these

assumptions of superimposability and analogous orientations of all compounds.

TEMPLATE SELECTIONS AND HYPOTHESES

This work of determining the MRCs necessary for cannabinoid

psychopharmacological activity and for analgesic activity began by

identifying template molecules for each activity. These template molecules

are compounds with demonstrated activities whose MRCs can be used

as a basis for comparisons with those of other cannabinoids. For

psychopharmacological activity, -THC (figure 1, panel A) was chosen

as the template molecule because this molecule has been reported to

be the major psychoactive component of cannabis (Mechoulam and Gaoni

1967; Gaoni and Mechoulam 1971) and because studies indicate that it

can exert behavioral effects without metabolic activation (Martin et al.

1975, 1977; Carney et al. 1979). The first working hypothesis concerning

cannabinoid psychoactivity employed in the work reported here was that

there are two components of the structure of -THC that confer on the

molecule reactivity characteristics crucial to activity. These components

are (1) the orientation of the carbocyclic ring, ring A, and its C9 substituent

relative to the phenyl group hydroxyl and (2) the orientation of the lone

pairs of electrons of the phenyl group hydroxyl oxygen,

For cannabinoid analgesic activity, (-)-9-nor-9 -OH-HHC was chosen as the

template molecule (figure 1, panel C) because 11- or 9 -hydroxylation has

been reported to be a prerequisite for the production of cannabinoid analgesia

(Wilson and May 1975; Wilson et al. 1976) and because this molecule seems to

embody the minimum requirements for activity as evidenced by the fact that the

simple change of the 9-OH group from equatorial to axial abolishes analgesic

activity (Wilson et al. 1976). The author’s working hypothesis concerning

cannabinoid analgesia was that the analgesic activity of 9-nor-9 -OH-HHC

is caused by the presence and relative location of two regions of negative

potential in the top half of the molecule. These negative potential regions

are generated by the equatorial alcohol (i.e., the 9 -OH group) and by the

phenyl group hydroxyl at C-1 (Reggio et al. 1990a).

MRC COMPARISONS

Having identified a template molecule for each activity, the strategy was to

characterize each template and then to compare the MRCs calculated for

each template molecule with those of other active and inactive cannabinoids

to refine the hypothesis for each activity. The initial focus was on the MRCs

for psychopharmacological activity.

213
Carbocyclic Ring Orientation

Molecular mechanics calculations [MMP2(85)] (Molecular Design, Ltd.,

San Leandro, CA) revealed that -THC (figure 1, panel A) is a nonplanar

molecule. Because the carbocyclic ring (ring A) is in a half-chair conformation,

the top of this ring lifts up off the page and toward the viewer, protruding

into what is called the top or face of the molecule. The spatial relationship

of the carbocyclic ring with the rest of the molecule can be better visualized

by turning the molecule sideways so that the side chain comes forward and

the carbocyclic ring (ring A) goes to the back (i.e., viewing the molecule

parallel to the direction of the vector from C2 to C10b) (figure 2, panel A).

From this perspective the “top” ( ) face will be to the left of the molecule,

and the “bottom” ) face will be to the right. In figure 2, panel A, it is clear

that the top portion of ring A clears the bottom ( ) face of the molecule and

protrudes instead into the top ( ) face. The shape of the template molecule,

-THC, was assumed to be characteristic of active molecules, with the idea

that only molecules that possess no portion of their carbocyclic ring

protruding into the bottom ( ) face will be active.

Because the first part of the working hypothesis for cannabinoid

psychopharmacological activity concerned the orientation of the

carbocyclic ring relative to the phenyl group hydroxyl oxygen, work

was begun by considering active, minimally active, and inactive

cannabinoids that all possess identical side chains and free phenyl

group hydroxyls but that differ in the extent and position of unsaturation

in the carbocyclic ring. The compounds chosen for study were the active

cannabinoids -THC and 11 -HHC, the minimally active cannabinoid

11 -HHC, and the inactive cannabinoids -THC and -THC (Reggio

et al. 1989). The activity of each of these compounds had been assayed

in rhesus monkey behavioral tests (Razdan 1986).

Molecular mechanics calculations revealed that only 11 -HHC possessed

more than one accessible minimum energy fused ring conformation. A 3 kcal/

mol cutoff for accessibility was used in this analysis. For the active molecules

in this set, molecular mechanics calculations revealed that, just as in -THC,

the top part of the carbocyclic ring and its C9 substituent clears the bottom

( ) face and protrudes instead into the top ( ) face of each molecule. For the

inactive cannabinoids in this set, molecular mechanics calculations revealed

an obvious shape difference. In inactive cannabinoids, the C9 substituent

at the top of the carbocyclic ring protrudes instead into the bottom ( ) face

of the molecule (see -THC in figure 2, panel B).

To quantitate the orientation of the carbocyclic ring and its C9 substituent in

each molecule, several nonbonded torsion angles were measured. It was

found that the value of one of these angles, (C11-C9—Cl-O), correlates well

214
FIGURE 2. (A) A side view of the conformation of the global minimum

energy conformation of (-)- -THC (with propyl side chain) as

determined by MMP2(85). The perspective is along the vector

from C2 to C10b. The phenyl group hydroxyl oxygen is shown

here as a blackened circle. The carbon of the C9 substituent

(i.e., C11) is shown as a hatched circle. (B) A side view of the

conformation of the global minimum energy conformation of

(-)- -THC (propyl side chain) as determined by MMP2(85). In

this compound the C11 methyl group is axial. See panel A for

other details.

with activity. The -angle measures the inclination of the C9 substituent relative

to the phenyl group hydroxyl oxygen. All active cannabinoids (including -THC)

were found to possess a negative value for this angle. A negative r-value means

that the C9 substituent points into the -face of the molecule or to the left relative

to the phenyl group hydroxyl oxygen in the side view. All inactive molecules in

the set were found to possess positive values of . A positive -value means that

the C9 substituent points into the -face of the molecule or to the right of

the phenyl group hydroxyl oxygen in the side view.

This correlation between shape (as quantitated by ) and activity was taken

to be an indication that there may be a region of steric interference at the site

of action of the cannabinoids. In relation to the structure of the cannabinoids,

215
this region must be located near the top of the carbocyclic ring in the bottom

face of the molecule. Any molecule whose conformation would cause it to

protrude into this region of steric interference may not be shaped properly to

fit at its site of action and thus may be rendered inactive (Reggio et al. 1989).

A recent comparative molecular field analysis study of several cannabinoids

has supplied additional evidence for a region of steric interference near this

portion of the molecule (Thomas et al. 1991).

The idea of a region of steric interference was then extended to suggest why

compounds such as 11 -HHC are minimally active (i.e., only active at higher

doses). Molecular mechanics studies showed that the carbocyclic ring of 11

HHC can exist in two conformations (a CHAIR form and a TWIST form, figure

3). It was found that for the CHAIR form of 11 -HHC (which will be the

predominant form in solution), is positive (figure 3, panel A). Thus, in its

lowest energy form, 11 -HHC resembles the inactive molecules previously

studied. However, the higher energy ( FSE=1.72 kcal/mol) TWIST form of

11 -HHC was found to possess a negative r-value (figure 3, panel B). Thus,

the higher energy form of 11 -HHC resembles the active cannabinoids

previously studied. In the TWIST form the C9 substituent protrudes into the

top ( ) face. Therefore, the diminishment in activity of 11 -HHC was attributed

to the fact that only the higher energy TWIST form of 11 -HHC is shaped

properly to fit at the site of action (Reggio et al. 1989).

These results for the orientation of the C9 substituent suggested one way to

reduce psychoactivity in the cannabinoids, that is, create a molecule in which

the C9 substituent protrudes into the bottom ( ) face of the molecule ( >0).

This idea was incorporated into the design of new cannabinoid analgesics.

Unfortunately, preliminary work has indicated that both psychoactivity and

analgesic activity share a common shape requirement for activity. Thus,

it appears that protrusion of the C9 substituent into the bottom ( ) face of the

molecule is associated with diminishment of analgesic activity as well as with

diminishment of psychoactivity (Reggio et al. 1991).

Lone-Pair Orientation

The second part of the author’s working hypothesis for cannabinoid

psychopharmacological activity concerned the orientation of the lone pairs

of electrons of the phenyl group hydroxyl oxygen. In this part of the work,

studies were conducted on -THC and other cannabinoids that possess

similar shapes but which differ at C-1 (the phenyl group hydroxyl position in

-THC, figure 1, panel A). Both molecular mechanical and ab initio studies

of the rotational behavior of the phenyl group hydroxyl of -THC revealed that,

although the phenyl group hydroxyl is capable of existing in any orientation by

expenditure of a modest amount of energy, it possesses two minimum energy

positions (Reggio and Mazurek 1987; Reggio et al. 1989). In the global

216
A B

FIGURE 3. (A) A side view of the global minimum energy conformation of

11 -HHC. The carbocyclic ring is in a CHAIR conformation.

(B) A side view of the second minimum energy accessible

conformation of 11 -HHC. The carbocyclic ring is in a TWIST

conformation. See figure 2, panel A, for other details.

SOURCE: Reggio, P.H. A search for properties which produce discrimination

between cannabinoid psychoactivity and analgesic activity. Int J

Quantum Chem 44:165-179, 1992. Copyright 1992 by Wiley-Liss, a

division of John Wiley & Sons, Inc.

minimum energy conformer, the phenyl group hydroxyl hydrogen points away

from the carbocyclic ring. In this position the lone pairs of electrons of the

phenyl group hydroxyl oxygen point toward this ring. The C2-C1-O-H torsion

angle is 7° (figure 4, panel A). In the second minimum energy conformer, the

phenyl group hydroxyl hydrogen points essentially in the opposite direction,

toward the carbocyclic ring, and the lone pairs point away from the carbocyclic

ring. The C2-C1-O-H torsion angle is 167° (figure 4, panel B). A comparison

of the MEPs generated by each of these forms revealed that these two different

phenyl group hydroxyl positions generate MEPs that are distinguishable from

one another (Reggio and Mazurek 1987).

217
FIGURE 4. (A) The phenyl group hydroxyl position in the global minimum

energy conformation of (-)- -THC. The C2-C1-O-H torsion

angle, which has a value of 7°—as determined by MMP2(85)—

is indicated by the double-headed arrow. (B) The second

minimum energy phenyl group hydroxyl position in (-)- -THC.

The C2-C1-O-H torsion angle, which has a value of 167°—as

determined by MMP2(85)—is indicated by the double-headed

arrow. (C) A drawing of the structure of O,2-propano- -THC.

(D) A drawing of the structure of O, 10-methano- -THC.

In each of these drawings, the two pairs of dots have been

drawn (according to the Lewis Dot formalism) for each phenyl

group hydroxyl oxygen to symbolize (in two dimensions) the

lone pairs of electrons of this oxygen.

SOURCE: Reggio, P.H. A search for properties which produce discrimination

between cannabinoid psychoactivity and analgesic activity. Int J

Quantum Chem 44:165-179, 1992. Copyright 1992 by Wiley-Liss, a

division of John Wiley & Sons, Inc. (New York).

218
New Compound Design

When the work presented here was begun, one of the traditional conclusions

drawn regarding the structure-activity relationship of the cannabinoids was

that the position of and the environment around the phenyl group hydroxyl

were critical for activity (Razdan 1986). A free phenyl group hydroxyl was

believed to be necessary, presumably for the hydrogen of the phenyl group

hydroxyl to participate as a donor in a hydrogen-bonding interaction at the

site of action. However, it should be noted that the phenyl group hydroxyl

possesses not only a phenolic hydrogen that can participate in a hydrogenbonding interaction as a donor but also possesses lone pairs of electrons

(on the oxygen) that can participate as acceptors in a hydrogen-bonding

interaction. To study the importance to cannabinoid psychoactivity of the

lone pairs and their orientation, two new etherified or oxygen-bridged analogs

were designed. In each analog, the phenyl group hydroxyl oxygen was

incorporated into a new fourth ring (ring D) (Reggio et al. 1990b). In these

new compounds, the orientation of the lone pairs was restricted to one of two

general directions. In O,2-propano- -THC (figure 4, panel C), the lone

pairs point toward the carbocyclic ring, mimicking the lone-pair orientation

in the global minimum energy phenyl group hydroxyl position of -THC.

In O,10-methano- -THC (figure 4, panel D), the lone pairs point away from

the carbocyclic ring, mimicking the lone-pair orientation in the second minimum

energy phenyl group hydroxyl position of -THC.

For O,2-propano- -THC, the compound designed to mimic the first phenyl group

hydroxyl position in -THC, molecular mechanics calculations revealed that

there are two accessible minimum energy fused ring conformers. Conformer

2 is 0.81 kcal/mol higher in steric energy than conformer 1 (figure 5). In both,

the carbocyclic rings exist in a half-chair conformation. The major difference

between the two conformers is in the conformation of the new fourth ring (ring D).

In figure 5, it is clear that in both conformers of O,2-propano- -THC, the C9

substituent of the carbocyclic ring protrudes into the top ( ) face of the molecule

(or to the left of the phenyl group hydroxyl oxygen in the side view), as do the

C9 substituents of active cannabinoids discussed previously.

MEP maps of each conformer were then calculated. The spatial distribution

and relative strength of potential regions in the MEP map form a characteristic

pattern, or “fingerprint,” for a molecule in a given conformation. In the work

reported here, the idea has been employed that molecules that interact at the

same site generate MEP patterns that are similar. Thus, at least in the

recognition stage of the interaction of the drug with its receptor, the receptor

recognizes the drug because each generates an MEP that is complementary

to the other. Therefore, the MEP may be thought of as a recognition pattern

for the site of action. If the MEP of a new analog is similar to that of a known

active compound both in shape and in the position of minimum potential

219
A B

FIGURE 5. Side views of conformer 1 (A) and conformer 2(B) of O,2-propano-

-THC (with methyl side chain). The perspective of ring A is

viewed in the direction from C2 to C10b. Therefore, the side

chain is forward and the carbocyclic ring (ring A) is in the back.

The ring D oxygen is shown as a blackened circle, the C11

carbon (C9 substituent) is shown as a half-blackened circle,

and the C1' carbon of ring D is shown as a hatched circle.

values, then the new compound should be recognized at any site that

recognizes the known active compound. When the MEPs generated by the

conformers of O,2-propano -THC were compared with that generated by

-THC in its global minimum energy phenyl group hydroxyl position, the MEPs

were found to be similar. Figure 6 shows a comparison of the MEP map

generated by conformer 1 of O,2-propane- -THC on the left, with the MEP

map generated by -THC in its global minimum energy phenyl group hydroxyl

position on the right. These MEPs were calculated in a plane parallel to the

aromatic ring using the Gaussian system of programs and the LP-3G basis set

at a distance of 1.5 Å from the aromatic ring in the bottom face of the molecule

(Reggio and Mazurek 1987; Reggio et al. 1990b).

In figure 6, it is clear that the overall shapes of the MEPs generated by

conformer 1 of O,2-propano- -THC and by -THC in its global minimum

energy phenyl group hydroxyl position are similar. In addition, it is clear that the

position and values of analogous minima (labeled here by stars) in the two MEP

maps are also similar. Conformer 2 of O,2-propano- -THC also generated a

220
FIGURE 6. A comparison of the MEPs in a plane parallel to the plane of the

aromatic ring, at a distance of 1.5Å below the aromatic plane,

in (A) conformer 1 of O,2-propano - -THC and (B) the global

minimum energy phenyl group hydroxyl position of - THC. *, the

minimum associated with the ring D oxygen in (A) and with the

phenyl group hydroxyl in (B); **, the minimum associated with the

aromatic ring in each. The potential values are in kcal/mol.

similar MEP map (Reggio et al. 1990b). It was concluded from this comparison

that O,2-propano- -THC should be recognized by any site that recognizes

THC in its global minimum energy phenyl group hydroxyl position,

For O,10-methano- -THC, the compound designed to mimic the second

minimum energy phenyl group hydroxyl position of -THC, molecular mechanics

calculations revealed that there are three accessible minimum energy fused ring

conformers (figure 7). Conformer 2 is 0.40 kcal/mol higher in steric energy than

the global minimum conformer (conformer 1). The third conformer is 1.99 kcal/

mol higher in steric energy than conformer 1 (Reggio et al. 1990b). It is clear in

figure 7 that the C9 substituent in each conformer protrudes into the top ( ) face

(or moves to the left of the phenyl group hydroxyl oxygen in the side view) as do

the C9 substituents of active cannabinoids discussed previously.

Figure 8 shows a comparison of the MEP map generated by conformer 1 of

O,10-methano- -THC on the left and the MEP map generated by -THC in

its second minimum energy phenyl group hydroxyl position on the right

(Reggio and Mazurek 1987; Reggio et al. 1990b). It is clear in figure 8 that

the MEP generated by conformer 1 of O,10-methano- -THC is similar to

221
A B C

FIGURE 7. Side views of conformer 1 (A), conformer 2 (B), and conformer 3

(C) of O, 10-methano- -THC (with propyl side chain). See figure

5 for other details.

that of -THC. Again the positions of analogous minima are labeled with

stars. It was concluded from this comparison that O,10-methano- -THC

should be recognized by any site that recognizes -THC in its second

minimum energy phenyl group hydroxyl position.

Synthesis and Pharmacological Evaluation of New Compounds

0,2-propano- -THC and O,10-methano- -THC were synthesized (Seltzman

et al. 1991) and pharmacologically evaluated in a series of tests in mice and

rats, which have been shown to be predictive of cannabinoid effects (Reggio

et al. 1990b). The pharmacological results for both compounds are shown

in comparison to the pharmacological results for active cannabinoids -THC

and -THC in table 1.

Pharmacological evaluation of O,10-methano- -THC revealed that this

compound was inactive in all mouse tests as well as in rat drug discrimination.

Several possible explanations were explored for the inactivity of O,10-methano-

-THC, including possible steric effects. Ultimately, it was concluded that the

inactivity of this compound is caused by the inappropriate directionality of the

lone pairs of electrons of the phenyl group hydroxyl oxygen (Reggio et al.

1990b).

Pharmacological evaluation of O,2-propano- -THC revealed that this

compound possesses a unique activity profile. This compound is similar to

-THC in that it depresses rectal temperature and produces antinociception

222
FIGURE 8. A comparison of the MEPs in a plane parallel to the plane of the

aromatic ring, at a distance of 1.5Å below the aromatic plane, in

conformer 1 of O, 10-methano- -THC (A), and the second

minimum energy phenyl group hydroxyl position of -THC (B).

*, the minimum associated with the ring D oxygen in (A) and

with the phenyl group hydroxyl oxygen in (B); **, the minimum

associated with the aromatic ring in each; ***, the minimum

associated with the pyran oxygen in each. The potential values

are in kcal/mol.

(analgesia) and ring-immobility (catalepsy) in mice. However, it differs from

-THC and -THC in that it only weakly depresses locomotor activity, and

it fails to substitute for -THC in the rat drug discrimination paradigm. Drug

discrimination In animals has been reported to be reflective of or highly

correlated with the subjective “high” in humans (Järbe et al. 1981). Thus,

0,2-propano- -THC appears to produce a separation of cannablnoid

activities, with psychoactivity diminished or absent but with other activities

such as antinociception (analgesia) remaining.

The methyl ether of -THC(O-methyl- -THC) has been reported to be

inactive in the rhesus monkey behavioral assay (Razdan 1986). Because

of etherification, this compound lacks a free phenyl group hydroxyl hydrogen.

A molecular mechanics conformational study of this molecule revealed that

there is only one minimum energy position for the methoxy group in O-methyl-

-THC (Reggio 1987). This minimum energy position is analogous to the

position of the phenyl group hydroxyl in the global minimum energy conformer

o f -THC. Thus, the lone-pair orientation in O-methyl- -THC is analogous

223
TABLE1. Pharmacologica/ activity of THC and O-bridged analogs

Pharmacological activity of THC and O-bridged analogs on mouse locomotion (5 to 15 minutes), tail flick antinociception

(20 minutes), rectal temperature (60 minutes), and ring-immobility (90 to 95 minutes), following tail-vein injection, as well

as activity in the rat discriminative stimulus paradigm following intraperitoneal injection. ED, values are presented (mg/

kg) with their 95-percent confidence limits. The maximum effect produced in each mouse evaluation is indicated in

parentheses below the ED50 (% inhibition vs. control, °C decrease, or %).

Motor Activity Hypothermia % Maximum Discriminative

Analog (% Inhibitor) (°C) % Immobility Possible Effect Stimulus

-THC 1.0 (0.5-1.4) 1.4 (1.2-3.8) 1.5 (0.4-2.7) 1.4 (0.5-3.4) 0.8 (0.4-1.5)

(78%) (4.2) (49%) (100%)

-THC 1.9 (1.2-2.2) 15.5 (8.1-17) 5.2 (3.1-8.9) 1.5 (1.0-2.3) 0.9 (05-1.8)

(79%) (-5.9) (58%) (100%)

O,2-propano- -lHC 5.4 (4.1-7.0) 16.5 (11-26) 2.6 (0.3-23) 3.5 (1.6-7.2) >30

(54%) (-4.9) (44%) (100%)

O,10-methano- -THC >30 >30 >30 >30 >10

SOURCE: Reggio, P.H.; Seltzrman, H.H.; Compton, D.R.; Prescott, W.R., Jr.; and Martin, B.R. An investigation of the rote

of the phenolic hydroxyl in cannabinoid activity. Mol Phamacol 38:854-862,1990b. Copyright 1990

by American Society for Pharmacology and Experimental Therapeutics (Bethesda, MD).
to that in O,2-propano- -THC. Recently, Burstein and coworkers (1988)

repotted that O-methyl- -THC exhibits activity in the mouse tail flick test for

antinociception (analgesia) comparable to the activity of -THC. Therefore,

these pharmacological results for O-methyl- -THC support the results for

O,2-propano- -THC and reinforce the idea that, although a free phenyl group

hydroxyl may be necessary for the production of psychopharmacological

activity in the cannabinoids, lone-pair orientation may be an important

molecular determinant for the production of cannabinoid analgesia.

FUTURE DIRECTIONS

The results for O,2-propano- -THC hold promise in the search for a way to

separate cannabinoid analgesia from cannabinoid psychoactivity. Before

pursuing a new line of compounds based on O,2-propano- -THC, however,

prudence dictates that two crucial questions be answered. First, can the

analgesic effects produced by O,2-propano- -THC be mediated by some

mechanism other than a unique cannabinoid mechanism? Twitch response

tests of the mouse vas deferens and of the guinea pig ileum have revealed

that the effects of O,2-propano- -THC are not blocked by naloxone (R.G.

Pertwee, unpublished data). These results imply no involvement of opiate

receptors in the activity of O,2-propano- -THC, Currently, other possible

mechanisms for the production of antinociception (analgesia) by O,2-propano-

-THC are being pursued. Second, what are the metabolic products of O,2-

propano- -THC? Previous work has indicated that 11-hydroxylation is a

prerequisite for the production of analgesia in compounds such as -THC

(Wilson and May 1975). Currently, the metabolic products of O,2-propano-

-THC are being studied to determine whether 11-hydroxylation is a major

metabolic route for this compound. Analogs of O,2-propano- -THC that

cannot be metabolized at this position are also being studied to see whether

these analogs are still capable of producing analgesia. In addition, the

metabolic studies are probing whether an opening of ring D occurs during

the metabolism of any of the analogs. If the results of these two lines of

inquiry reveal O,2-propano- -THC (or one of its metabolites) to be an

analgesic that acts by a unique cannabinoid mechanism, then the author

intends to design new analogs of this compound that possess enhanced

analgesia.

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ACKNOWLEDGMENTS

The work presented here was supported by National Institute on Drug Abuse

grant DA-03934. Computations were supported by grants of computer time

from the City University of New York and from the Georgia Institute of

Technology School of Chemistry.

AUTHOR

Patricia H. Reggio, Ph.D.

Professor

Department of Chemistry

Kennesaw State College

P.O. Box 444

Marietta, GA 30061

228
Cocaine Receptor: A StructureActivity Relationship Study

F. Ivy Carroll, Anita H. Lewin, Michael J. Kuhar, and

John W. Baja

Abuse of (-)-cocaine (1) an alkaloid obtained from the leaves of E. coca, is

a major U.S. health problem. While (-)-cocaine has many physiological effects,

the most relevant, from the point of view of drug abuse, include its ability

to produce euphoria and its reinforcing properties. The latter are readily

demonstrated in animal models, and the euphorigenic effects of cocaine

are amply documented in human subjects (Johanson and Fischman 1989;

Griffiths et al. 1980). In addition to being a powerful reinforcer, cocaine has

properties common to other drugs of abuse. For example, tolerance develops

to some of its effects, and its psychological withdrawal syndrome takes place

over a long period, which includes periods of craving during which relapse to

drug use may often occur (Johanson and Fischman 1989; Gawin and Kleber

1986).

To understand the biochemical mechanisms of the above processes, it is

necessary to identify the molecular site(s) where the drug interacts to produce

its initial physiological effects. Cocaine has several sites of action in the central

nervous system; however, it is the site associated with the dopamine transporter

that has been implicated in the reinforcing properties of cocaine (Kuhar et al.

1991; Ritz et al. 1987). In fact, the so-called “dopamine hypothesis” (Kuhar et al.

1991; Ritz et al. 1987) (see figure 1 for a pictorial representation of the hypothesis)

stipulates that the reinforcing effects of cocaine are brought about by blockage

of dopamine reuptake, thereby potentiating dopaminergic transmission. Thus,

the cocaine “receptor” (i.e., the dopamine transporter) is ultimately responsible

for cocaine’s reinforcing properties. A goal of the research conducted at the

Research Triangle Institute in collaboration with the Addiction Research Center

of the National Institute on Drug Abuse (NIDA) is to gain information about the

structural features of the cocaine receptor.

To obtain information relating to the structural features of (-)-cocaine that lead to

potent and selective binding at the cocaine site on the dopamine transporter, the

authors and colleagues at the Research Triangle Institute and the Addiction

229
1 Stimulation 2 Normal Dopamine 3 Cocaine Blockage of

Reuptake Reuptake

FIGURE 1. A hypothesis of cocaine’s mechanism of action

Research Center have examined the effects of systematic variations in the

structure of cocaine on binding affinity at the dopamine transporter. Studies

from our laboratories as well as from other laboratories have shown that, in

addition to the overall molecular configuration, the carbomethoxy, the aromatic

ring, and the nitrogen moieties of cocaine are important in receptor interactions

(Carroll et al. 1991 a, 1991 b; Reith et al. 1986; Ritz et al. 1990; Abraham et al.

1991; Lewin et al. 1991).

Prior to the work of Carroll and colleagues (1991 a) and Lewin and colleagues

(1991), it had been pointed out that the presence of a -substituent (Reith et al.

1986; Ritz et al. 1990), preferably a carbomethoxy group (Ritz et al. 1990), at

C-2 contributed substantially to binding affinity. The results of Carroll and

colleagues (1991 a) and Lewin and colleagues (1991) (table 1) confirm these

observations and suggest a role of the 2 -substituent. The substantial

reduction in binding potency observed on replacement of the carbomethoxy

group in cocaine by a hydrogen atom (1a, no hydrogen acceptor capability) or

by a methylenehydroxy group (1d, single hydrogen bond acceptor) and the

partial restoration of binding potency with a methyleneacetoxy group (1e, with

potential for accepting two hydrogen bonds) are consistent with a model in

which the 2 -substituent enhances affinity to the receptor by presenting to it

one and possibly two hydrogen bond acceptor sites (Lewin et al. 1991).

Replacement of the carbomethoxy methyl group in cocaine by bulky and

lipophilic groups (table 1, 1f through 1q) had only minor effects on binding

affinity (Lewin et al. 1991). This tolerance to substitution suggested that the

C-2 site may be amenable to the introduction of chemically or photochemically

active residues, leading to irreversible ligands; thus, analogs 1r through 1t were

230
TABLE 1. Potencies of cocaine and 2-substituted analogs in inhibition

binding of 0.5 nM [

3H]WlN 35,428*

Compound R IC50(nM)

Cocaine

1a

1b

1c

1d

1e

1f

1q

1h

1i

1j

1k

1l

1m

1n

1o

1p

1q

1r

1s

1t

CO2CH3

H

CO2H

CONHCH3

CH2OH

CH2OCOCH3

CO2C2H5

CO2(CH2)2CH3

CO2CH(CH3)2

CO2C6H5

CO2CH2C6H5

CO2(CH2)2C6H5

CO2(CH2)3C6H5

CO2CH2CH = CHC6H5

CO2(CH2)2C6H4NO2

CO2(CH2)2C6H4CI

CO2(CH2)2C6H4NH2

CO2(CH2)2C6H4NHCO(CH2)2CO2Et

CO2(CH2)2C6H44-N3

CO2(CH2)2C6H44-NCS

CO2(CH2)2C6H4 4-NHCOCH2Br

102

5,180

19,500

3,180

561

215

130

191

211

112

257

248

139

371

601

271

71

86

227

196

61

*All values are the mean of four to five experiments performed in triplicate.

231
prepared (Lewin et al. 1991). These could be attached to resins and used in

affinity chromatography; they also could be prepared with radioisotopic labels

and utilized as chemical probes.

The most potent compounds in binding and behavioral studies reported before

the authors’ studies began were 3 -phenyltropan-2 -carboxylic acid methyl

ester (2a, WIN 35,065-2) and 3 -(p-fluorophenyl)-tropan-2 -carboxylic acid

methyl ester (2b, WIN 35,428), the so-called WIN compounds, reported by

Clarke and coworkers (1973). Correlation of the IC50 values of a series of

3 -(p-substituted phenyl)-tropan-2 -carboxylic acid methyl esters (2) for

inhibition of [

3H]WIN 35,428 binding to the dopamine transporter (Carroll

et al. 1991 b) (table 2) with the structural features was carried out by

conducting a quantitative structure-activity relationship (QSAR) study

using the Comparative Molecular Field Analysis (CoMFA) program (Carroll

et al. 1991 b). The substituents (H, F, Cl, CH3, CF3, NO2, NH2, C2H5OCONH,

CH3CONH, C2H5CONH, Br, and I) were selected to represent systematic

variations. The final model, constructed without cross-validation, had good

statistical parameters and a high predictive value. Extension of these studies

has revealed that whereas m-substitution of WIN 35,065-2 had virtually no

effect on binding potency (table 2, 2p through 2r), m-substitution of 2b, 2c,

and 2i enhanced binding potency by factors ranging from 5 to 18 (table 2, 2t

through 2w). These effects are under investigation (Carroll et al. 1992).

The high potency of 2c prompted investigation of analogs with chemically

and photochemically active residues at C-2 (table 3). Highly potent

compounds were obtained; however, no correlation of the effect of the C-2

substituent on the binding potencies of the cocaine analogs and of the

analogs of 2c was observed (Carroll et al., in press). Thus, whereas a pbromoacetamidophenethyl-carboxylate slightly enhanced the potency to

displace [

3H]WIN 35,428 binding in the cocaine series (1t), a fivefold decrease

in potency was observed for the 2c analog 3e (Carroll et al. 1992). Similar

observations were made for other C-2 substituents (see tables 1 and 3),

suggesting that additional studies are needed to determine whether cocaine

and its WIN analogs bind to the same site.

The effects of substitution at the nitrogen atom (Abraham et al. 1991) of

cocaine suggest that decreased electron density is detrimental to binding

potency (table 4). An extreme example is the 111 -fold decrease in potency

observed for cocaine methiodide (4d, full positive charge on nitrogen).

Similarly, the acetamide (4c partial positive charge on nitrogen) is 33

times less active than cocaine. On the other hand, binding potency is only

slightly affected by steric bulk. High binding potency may not require the

nitrogen atom to be at the 8 position of the bicyclic skeleton; similar binding

potencies are exhibited by 6a and 5b (table 4), which are 8-aza and 6-aza

analogs of cocaine, respectively.

232
TABLE 2. Potencies of cocaine and related compounds in inhibiting binding

of 0.5 nM [

3H]WIN 35,428*

Compound X Y IC50(nM)

(-)-Cocaine

2a

2b

2c

2d

2e

2f

2g†

2h

2i

2j

2k

2l

2m

2n

2o

2p

2q

2r

2s

2t

2u

2v

2w

—

H

F

Cl

CH3

CF3

CH3O

H†

NO2

NH2

C2H5OCONH

CH3CONH

C2H5CONH

Br

I

N3

H

H

H

NH2

F

NH2

Cl

Cl

—

H

H

H

H

H

H

H

H

H

H

H

H

H

H

H

Cl

F

I

I

CH3

Br

Cl

CH3

102

23.0

15.7

1.17

1.71

13.1

8.14

257

10.1

24.8

316

64.2

68.0

1.81

1.26

2.12

10.6

23

26.1

1.35

2.95

3.91

0.79

0.81

*All values are the mean of four to five experiments performed in triplicate.

†3 -(2-Thiophenyl)-tropan-2 -carboxylic acid methyl ester.

233
TABLE 3. Potencies of cocaine end analogs in inhibiting binding of 0.5 nM

WIN 35,428*

Compound R2

IC50(nM)

Cocaine CO2CH3 102

2c CO2CH3 1.17

3a CO2

(CH2)2C6H44-NO2 2.71

3b CO2(CH2)2C6H44-NH2 2.16

3c CO2

(CH2)2C6H44-N3

6.17

3d CO2(CH2)2C6H4 4-NCS 5.3

3e CO2(CH2)2C6H4 4-NHCOCH2Br 1.73

3f CO2

(CH2)2C6H3 3-I, 4-NH2 2.51

3g CO2

(CH2)2C6H3 3-I ,4-N3 14.5

3h CO2H 2,070

*All values are the mean of four to five experiments performed in triplicate.

The progress made in the development of highly potent ligands for the

cocaine receptor at the dopamine transporter has enabled the identification

of a neuroaminidase-sensitive polypeptide of approximately 80 kDa by

photoaffinity labeling with [

125I]3g (Patel et al. 1992). Apparently, the same

polypeptide was labeled by [125I]1-[2-(diphenylmethoxy)ethyl]-4-[2-(4-azido3-iodophenyl)ethyl] piperazine (DEEP) (Grigoriadis et al. 1989), providing

strong evidence that WIN analogs and GBR analogs may bind to the same

protein in rat striatal membranes (Patel et al. 1992). Furthermore, [

123I]RTI-55

(compound 2n, table 2) has been used to image the dopamine and serotonin

transporters in vivo (Carroll et al. 1991c).

234
TABLE 4. Potencies of cocaine and analogs in inhibiting binding of [

3H]WIN

35,428*

Compound X R1 R2

IC50 (μM)

Cocaine

4a

4b

4c

4d

5a

5b

(+)-5b

(-)-5b

6a

6b

CH3

H

C6H5CH2

CH3CO

(CH3

)2

+

C6H5CO2

H

H

H

H

C6H5CO2

0.102

0.303

0.676

3.37

10.70

H 19.8

C6H5CO2 4.95

C6H5CO2 2.94

C6H5CO2 2.95

C6H5CO2 5.18

H 21.2

*All values are the mean of four to five experiments performed in triplicate.

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236
ACKNOWLEDGMENT

This chapter was prepared with support from NIDA grant DA-05477.

AUTHORS

F. Ivy Carroll, Ph.D.

Director for Organic and Medicinal Chemistry

Anita H. Lewin, Ph.D.

Senior Research Chemist

Research Triangle Institute

P.O. Box 12194

Research Triangle Park, NC 27709

Michael J. Kuhar, Ph.D.

Chief

Neuroscience Branch

John W. Boja, Ph.D.

Senior Staff Fellow

Addiction Research Center

National Institute on Drug Abuse

P.O. Box 5180

Baltimore, MD 21224

237
The Use of Conformational Restriction

and Molecular Modeling Techniques in

the Development of Receptor-Specific

Opioid Peptide Agonists and

Antagonists

Peter W. Schiller, Thi M.-D. Nguyen, Grazyna Weltrowska,

Nga N. Chung, Carole Lemieux, Brian J. Marsden, and

Brian C. Wilkes

INTRODUCTION

Conformational restriction is now generally recognized as an important

principle in the design of analogs of peptide hormones and neurotransmitters

with certain desired biological activity profiles. Many of the linear, bioactive

peptides are very flexible molecules that exist in a conformational equilibrium

in solution and that are not very selective in their interactions with different

receptor types because conformational adaptation to various receptor

topographies is possible. Obviously, the incorporation of conformational

constraints into such peptides enhances their conformational integrity and,

in case the resulting analogs retain biological activity, conformational studies

aimed at elucidating bioactive conformations become possible. The results

of such conformational studies by either theoretical or experimental techniques

may suggest further structural modifications or rigidifications that ultimately will

lead to the development of peptidomimetics (for a review, see Schiller, in press).

Conformational restriction also may affect both receptor affinity and the intrinsic

activity (“efficacy”) of a peptide. In a situation of receptor heterogeneity,

conformationally restricted peptide analogs often show improved receptor

selectivity because the introduced conformational constraints may be compatible

with the conformational requirements of one preferred receptor class only and no

longer permit significant interaction with other receptor types, as first demonstrated

with conformationally restricted enkephalin analogs (Schiller and DiMaio 1982).

Conformational restriction of a peptide hormone or neurotransmitter may also

decrease its intrinsic activity, resulting in either antagonists or partial agonists.

This was unambiguously demonstrated for the first time with the development

of the -opioid receptor antagonist H-Tyr-Tic-Phe-Phe-NH2 (TIPP-NH2) (Schiller

238
et al., in press) (see below). Conformational restriction of peptides can be

achieved either locally at a particular amino acid residue through incorporation

of backbone or side-chain conformational constraints or more globally through

peptide cyclizations.

Whereas the existence of at least three major opioid receptor classes

) is now well established, the development of potent opioid agonists

and antagonists with high specificity for each receptor type and of ligands

with receptor-specific agonist/antagonist properties continues to be an

important goal in opioid pharmacology. Progress made in the development

of receptor-specific opioid peptide analogs with agonist or antagonist properties

has been described in recent review articles (Hruby and Gehrig 1989;

Renugopalakrishnan et al. 1990; Schiller 1991). Receptor-selective peptide

ligands have been developed by using the classical amino acid substitution/

deletion approach, the bivalent ligand strategy, or the concept of conformational

restriction. The use of these three design principles produced highly selective

μ- and -agonists and antagonists, whereas the development of opioid peptidederived agonists and antagonists with selectivity for -OpiOid receptors has

been less successful to date. Conformationally restricted peptide agonists

and antagonists with high preference for either μ- or -opioid receptors

include both cyclic analogs and analogs in which local conformational

constraints were imposed at a particular amino acid residue.

In this chapter, the authors describe various opioid peptide analogs in

which conformationally restricted analogs of Phe and/or Tyr (figure 1) were

substituted. In the first part of the chapter, we show that replacement of the

Phe3 residue in the relatively nonselective cyclic dermorphin analog H-Tyr-DOrn-Phe-Glu-NH2

 with 2-aminoindan-2-carboxylic acid (Aic) or 2-aminotetralin2-carboxylic acid (Atc) (figure 1) resulted in highly μ-selective compounds.

We then discuss how substitution of the exocyclic Tyr residue in the Aic3

-

analog with 6-hydroxy-2-aminotetralin-2-carboxylic acid (Hat) permitted

further structural rigidification without producing a significant change in the

opioid activity profile. Finally, we describe a new class of peptide analogs

containing the N-terminal sequence H-Tyr-Tic-Phe- that are potent and

highly selective -antagonists (Tic=tetrahydroisoquinoline-3-carboxylic acid).

CYCLIC DERMORPHIN ANALOGS CONTAINING CONFORMATIONALLY

RESTRICTED PHENYLALANINE AND TYROSINE RESIDUES

The cyclic dermorphin tetrapeptide analog H-Tyr-D-Orn-Phe-GIu-NH2

(1) displayed very high affinity for p-receptors but also bound quite strongly

to -receptors and, therefore, was only slightly μ-selective (table 1) (Schiller

et al. 1991). Configurational inversion at the 3-position residue (analog 2)

resulted in a more than 1,000-fold drop in affinity for both μ- and -receptors.

This finding confirms the strict requirement for L-configuration at the 3-position

239
FIGURE 1. Structural formulas of conformationally restricted phenylalanine

residues

240
of both linear and cyclic opioid peptide analogs that are structurally related to

the dermorphin family (Schiller et al. 1987). Methylation of the Phe3-nitrogen

of parent peptide 1 also resulted in a weakly active analog (3). The more than

100-fold decrease in μ-affinity observed with this compound could be due to the

introduced local conformational restriction of the peptide backbone ( -angle),

the unavailability of the amide proton in the 3-position for the formation of an

important hydrogen bond, or the bulk of the N-methyl group that might not be

tolerated at the receptor. In view of the low affinity of analog 3, it was not

surprising that the Tic3

-analog (compound 4) was also very weakly active.

Introduction of a methyl group in the ortho position of the Phe3 aromatlc ring

(analog 5) had little effect on μ-receptor affinity and selectivity. Methylation of

the -carbon of the Phe3 residue (analog 6) produced a sevenfold decrease in

μ-affinity and a similar decrease in -affinity, resulting in a compound with still

only modest preference for μ-receptors over -receptors. -methylation of an

amino acid residue in a peptide drastically reduces the conformational space

available to the peptide backbone around that residue insofar as the torsion

angles at the position of the methylated residue are limited to values around

=-50°, =-50° and =+50°, =+50° (Marshall and Bosshard 1972). The

TABLE 1. Receptor affinities and μ-selectivity of cyclic dermorphin analogs*

No. Compound Ki

μ

 [nM] Ki [nM] K i

/Ki

μ

1

2

3

4

5

6

7

8

9

10

11

12

13

14

H-Tyr-D-Orn-Phe-GluNH2 0.981 3.21 3.27

H-Tyr-D-Orn-D-Phe-Glu-NH, 1,660 14,000 8.43

H-Tyr-D-Orn-N MePhe-Glu-NH2 1,000 12,800 12.8

H-Tyr-D-Orn-Tic-Glu-NH2 2,410 50,100 20.8

H-Tyr-D-Orn-oMePhe-Glu-NH2 1.99 9.22 4.80

H-Tyr-D-Orn-C -MePhe-Glu-NH2 7.17 54.8 7.82

H-Tyr-D-Orn-Aic-Glu-NH2 4.21 209 49.8

H-Tyr-D-Orn-(D or L)-Atc-Glu-NH2 (I) 8.28 1,570 190

H-Tyr-D-Orn-(D or L)-Ate-Glu-NH2 (II) 26.3 3,510 133

H-(D or L)-Htc-D-Orn-Phe-Glu-NH2 (I) 1,680 18,200 10.8

H-(D or L)-Htc-D-Orn-Phe-Glu-NH2 (II) 235 7,050 30.0

H-(D or L)-Hat-D-Orn-Phe-Glu-NH2 (I) 2.91 10.8 3.71

H-(D or L)-Hat-D-Orn-Phe-Glu-NH2 (II) 54.2 74.7 1.38

H-(D,L)-Hat-D-Orn-Aic-Glu-NH2 7.68 119 15.5

[Leu5]enkephalin 9.43 2.53 0.268

*Determinations based on displacement of [

3H]DAMGO (μ-selective) and

[

3H]DSLET ( -selective) from rat brain membrane binding sites

241
slight affinity drop observed with analog 6 could be due either to the backbone

conformational constraints introduced at the 3-position or to steric interference

by the -methyl group.

More extensive conformational restriction at the 3-position was achieved

through substitution of the conformationally restricted phenylalanine analog

Aic(figure 1 ). In comparison with parent peptide 1, the cyclic peptide analog

H-Tyr-D-Orn-Phe-Glu-NH2( 7) showed only 4 times lower u-receptor affinity

but 65 times lower affinity for &receptors and, consequently, greatly improved

μ-selectivity (Ki

/Ki

μ

=49.6). Because analogs 5 and 6 are distinguished

from analog 7 merely by the opening of one or the other of two adjacent

bonds in the five-membered ring structure of Aic3 (see figure 1), comparison

of the receptor binding activity profiles of these three analogs permits the

unambiguous conclusion that the high p-receptor selectivity of the

Aic3

-analog is exclusively the consequence of the imposed side-chain

conformational restriction. Replacement of Phe3

 with Aic in the cyclic peptide

structure produces conformational constraints both in the side chain and in

the peptide backbone at the 3-position. Torsional angles of the 3-position

side chain in the Aic3

 analog are limited to values of x1=-80°, x2=-20° and

x1=-160°, x2=+20°, whereas the , -backbone torsional angles around the

3-position are limited to the same values as in the case of the MePhe3

analog. These torsional angles were indeed observed in molecular dynamics

simulations that were carried out at 600 K and lasted for 100 ps. The limited

conformational space available to the Aic3

 side chain and its transitions

between the two conformational states defined by the x1-, x2-angles

indicated above are evident in figure 2, which shows superpositions of

20 snapshots of the Phe3- and Aic3-analogs taken at 5-ps intervals along

the dynamics trajectory.

The two diastereoisomers of the Atc3

-analog (compounds 8 and 9) show

even higher μ-selectivity (Ki/Ki

μ

 values of 190 and 133, respectively) due to

very weak affinity for the b-receptor, while μ-receptor affinity comparable to

that of [Leu5]enkephalin is maintained (table 1). As in the case of the Aic3

analog, it is the conformational restriction per se introduced at the 3-position

that is directly responsible for the drastic selectivity enhancement. Again, the

Atc residue essentially is able to adopt only two side-chain conformations that

are characterized by the torsional angles x1=180° (t), x2=+25° and x1=-60°

(g-), x2=-25° for L-Atc, and x1=180° (t), x2=-25° and x1=+60° (g+), x2=+25°

for D-Atc. Evidently, these side-chain conformational constraints are even

more detrimental to receptor affinity than those present in the Aic3

 analog

that has about 10 times higher -affinity than the Atc3 analogs. Interestingly,

the computer simulations showed that in the cyclic opioid peptide analogs

described here both the L- and the D-Atc side chains underwent fewer

conformational transitions than the Aic side chain (Wilkes and Schiller,

in press).

242
FIGURE 2. Twenty snapshots of (left) and H-Tyr-

(right) taken at 5-ps intervals along the

dynamics trajectory

As indicated above, the μ-receptor affinity of the cyclic dermorphin analog

is more than 1,000 times higher than that of the

corresponding D-Phe3-analog, whereas the μ-receptor affinities of both

diastereoisomers of are high and differ

from one another by a factor of only about 3. This loss of stereospecificity

as a consequence of side-chain conformational restriction may be due to the

fact that the D-Atc3-analog binds to the receptor in a manner different from

that of the D-Phe3

-analog. In the case of the D-Phe3-analog, a stepwise

process of binding according to the “zipper’‘-type model (Burgen et al. 1975)

may occur such that the D-Phe3

 side chain never has a chance to bind to the

hydrophobic receptor subsite with which the L-Phe3

 aromatic ring interacts.

On the other hand, the process of binding of not only the L-Atc3

- but also the

D-Atc3-analog is such that the Atc aromatic ring is forced to interact with this

binding site as a consequence of the conformational constraints existing at

the 3-position of the peptide sequence (Schiller et al. 1991).

As the results of the computer simulations (figure 2) show, the exocyclic

tyrosine residue in the cyclic analogs described above retains considerable

orientational freedom and also needs to be conformationally restricted to

obtain better insight into the μ-receptor-bound conformation of this type

of cyclic analog. This was achieved by replacement of Tyr1 with the

conformationally restricted tyrosine analogs 7-hydroxytetrahydroisoquinoline-3-

carboxylic acid (Htc) or Hat (see figure 1). Both diastereoisomers of H-(D or L)-

( 10, 11) had relatively low receptor affinity, indicating

that the conformational constraints resulting from replacement of Tyr1 with Htc

243
are not well tolerated. On the other hand, one of the diastereoisomers of H-(D

(12, 13) showed an opioid receptor affinity profile

similar to that of 1. High p-receptor affinity was also displayed by H-(D,L)-Hat-

(14) that essentially contains only two freely rotatable

bonds (figure 3) and, therefore, represents the most rigid opioid peptide

analog reported to date. Considering the fact that [Leu5]enkephalin contains

16 freely rotatable bonds, the structural rigidification achieved with analog

14 is indeed quite remarkable. A molecular mechanics study revealed

that the lowest energy conformation of is still

characterized by a close proximity between the two aromatic rings (figure 4),

a structural feature that may be important for high μ-receptor affinity within

this class of cyclic opioid peptide analogs (Schiller and Wilkes 1988).

All the cyclic opioid peptide analogs described above behaved as agonists

in the guinea pig ileum (GPI) and mouse vas deferens (MVD) assays, and none

of them showed significant affinity for -receptors.

FIGURE 3. Structural representation, of the rigidified cyclic opioid peptide

analog

244
FIGURE 4. Lowest energy conformers of (left) and

(right)

THE TIPP PEPTIDE FAMILY: A NEW CLASS OF POTENT AND

HIGHLY SELECTIVE -ANTAGONISTS

The a-position residue in opioid peptides can be replaced with a number of

D-amino acid residues, including aromatic ones, and the resulting compounds

generally not only are resistant to degradation by aminopeptidases but also

often show high opioid activity. For example, the tetrapeptide H-Tyr-D-PhePhe-Phe-NH, is a potent, full agonist with preference for u-receptors over

b-receptors (Schiller et al., in press). The latter peptide consists entirely of

aromatic amino acid residues that can be conformationally restricted in a

number of ways and that provide an opportunity to systematically study the

effect of such conformational constraints on opioid receptor affinities and on

the intrinsic activity (“efficacy”). Conformational restriction at the 2-position

of the peptide sequence through substitution of a D- or L-Tic residue turned

out to be particularly interesting (Schiller et al., in press). The D-Tic2

tetrapeptide analog (15a) was a potent u-receptor ligand with considerable

preference for μ-receptors over -receptors (table 2). On the other hand,

the L-Tic2-analog (15b) showed relatively weak u-affinity and, very surprisingly,

greatly enhanced -affinity. Thus, H-Tyr-D-Tic-Phe-Phe-NH2(15a) turned out

to be μ-selective, whereas its diastereoisomer, H-Tyr-Tic-Phe-Phe-NH2(15b) ,

showed considerable -selectivity (Ki

μ

/Ki =26.3). The opposite receptor

selectivity profiles of analogs 15a and 15b, which contain stereoisomers of

245
TABLE 2. Receptor affinities of opioid peptide analogs containing Tic at

position 2*

No. Compound Ki

μ

 [nM] Ki

[nM] Ki

μ

/Ki

15a

15b

16

17a

17b

18

H-Tyr-D-Tic-Phe-Phe-NH2

H-Tyr-Tic-Phe-Phe-NH2

(TIPP-NH2)

H-Tyr-Tic-Phe-Phe-OH

(TIPP)

H-Tyr-D-Tic-Phe-NH2

H-Tyr-Tic-Phe-NH2

(TIP-NH2)

H-Tyr-Tic-Phe-OH

(TIP)

Naltrindole

[Leu5]enkephalin

7.30 519 0.0141

76.8 3.00 28.3

1,720 1.22 1410

121 396 0.306

624 12.0 52.0

1,280 9.07 141

12.2

9.43

0.687 17.8

2.53 3.73

*Affinities were determined as described in footnote to table 1.

a rigid amino acid at the 2-position, demonstrate that μ- and -receptors have

different stereochemical architectures.

Analogs 15a and 15b were both full agonists in the μ-receptor-representative

GPI assay with potencies that were in agreement with their relative p-receptor

affinities determined in the binding assays. In the -receptor-representative

MVD assay, the D-Tic2-analog was a relatively weak but nearly full agonist,

Thus, H-Tyr-D-Tic-Phe-Phe-NH2 is a potent and selective p-agonist. Most

surprisingly, the L-Tic2

-analog showed no agonist potency in the MVD assay

at concentrations as high as 10 μM despite its demonstrated higH -receptor

affinity, and it turned out to be a potent, selective -antagonist with values

ranging from 14 to 18 nM against various selective -agonists, including

[D-Ala2]deltorphin I and enkephalin (DPDPE) (table 3). The

observation that configurational inversion at a single conformationally restricted

amino acid residue can turn an agonist with selectivity for one receptor type

246
TABLE 3. K, values determined for -antagonists against various -agonists

in the MVD assay

Ke[nm]

Antagonist [Leu5]enkephalin [D-Ala2]deltorphin I DPDPE

TIPP 5.88 2.98 4.80

TIPP-NH2 15.7 14.4 18.0

TIP 11.7 12.6 16.1

TIP-NH2 43.9 58.9 96.8

Naltrindole 0.850 0.632 0.636

into an antagonist with preference for another receptor class is unique and to

the best of our knowledge has not been made previously in the peptide field.

It was interesting to note that the -antagonist H-Tyr-Tic-Phe-Phe-NH2(TIPPNH2

) was a full agonist in the GPI assay with about 15 percent the potency of

[Leu5]enkephalin. The effect of this compound on the ileum was very sensitive

to naloxone inhibition (Ke=1.74±0.45 nM), indicating that It was mediated by

μ-receptors. It thus turns out that TIPP-NH2 is the first known oploid compound

with μ-agonist/ -antagonist properties.

Recently, it has been demonstrated that pretreatment of mice with the

nonpeptlde bantagonist naltrindole (Portoghese et al. 1988) prevented the

development of morphlne tolerance and dependence (Abdelhamld et al.

1991). This important observation led to the suggestion that compounds

with μ-agonist and -antagonist properties might have potential as analgesics

that do not produce tolerance and physical dependence.

Because it has been clearly established that enkephalin analogs with a

free C-terminal carboxylate function are in general more -selective than

corresponding peptlde amides (Paterson et al. 1984), a Tic2-tetrapeptide

analog (16) with a free C-terminal COOH group was synthesized in an attempt

to obtain a more selective -antagonist. This compound, H-Tyr-Tic-Phe-PheOH (TIPP), showed further improved -receptor affinity (Kl =1.22 nM) and

drastically diminished affinity for μ-receptors (table 2). In fact TIPP displayed

extraordinary -receptor selectivity (Ki

μ

/Ki =1,410) and in direct comparisons

turned out to be more selective than any other -agonist or antagonist

described to date. In the MVD assay, TIPP was a potent antagonist (Ke=3

to 6 nM) against various -agonists (table 3) and had no agonist effects at

concentrations as high as 10 μM. As expected on the basis of the receptor

binding data, TIPP was a very weak agonist in the GPI assay (IC50>10 μM)

247
and, most important, produced no antagonist effects at concentrations up to

10 μM in this μ-receptor-representative bioassay system.

In comparison with the nonpeptide antagonist naltrindole, TIPP showed about

twofold lower -receptor affinity in the binding assays (table 2) and fivefold to

sevenfold higher Ke

 values against -agonists in the MVD assay (table 3).

Therefore, TIPP appears to be a somewhat less potent antagonist than

naltrindole but, on the other hand, is about 80 times more -selective than

the nonpeptide -antagonist (table 2). Naltrindole has u-receptor affinity in

the nanomolar range and is a μ-antagonist with a reported Ke value of 29 nM

against morphine in the GPI assay (Portoghese 1991). TIPP is also at least

10 times more potent and 10 times more -selective than the enkephalinderived -antagonist N,N-diallyl-Tyr-Aib-Aib-Phe-Leu-OH (ICI 174864)

(Cotton et al. 1984; Corbett et al. 1984). Because of its high potency,

unprecedented -selectivity, and complete lack of μ-antagonist properties,

TIPP offers several advantages as a -antagonist for use in opioid research.

The role of the Phe4 residue in defining the opioid activity profiles of

compounds 15a, 15b, and 16 was assessed by preparing and characterizing

the corresponding des-Phe4-tripeptide analogs (17a, 17b, 18). Like the

D-Tic2-tetrapeptide analog(15a),H-Tyr-D-Tic-Phe-NH2(17a) was a

μ-selective agonist, but its p-receptor affinity was lower, and it was less

μ-selective (table 2). Its diastereoisomer H-Tyr-Tic-Phe-NH2

 (TIP-NH2) (17b)

was about twice as -selective as its tetrapeptide parent (TIPP-NH2), but it

had about four times weaker -antagonist potency. As in the case of the

tetrapeptides, the corresponding tripeptide with a free C-terminal carboxyl

group, H-Tyr-Tic-Phe-OH (TIP) (18), showed higher -affinity, was more

selective, and was a more potent -antagonist against various -agonists in

the MVD assay with values ranging from 12 to 16 nM. In comparison with

its tetrapeptide parent (TIPP), TIP was 10 times less -selective and had

about 4 to 7 times lower -antagonist potency. It can be concluded that

deletion of the C-terminal Phe residue in compounds 15a, 15b, and 16

produced tripeptide analogs with opioid activity profiles that were qualitatively

similar to those of the corresponding parent tetrapeptides.

None of the D- or L-Tic analogs described above showed significant affinity

for -OpiOid receptors, as determined by displacement of [

3H]U69,593 from

guinea pig brain membranes. It is highly unlikely that the activity profiles

determined with these analogs were distorted by enzymatic degradation,

because incubations in the receptor binding experiments were performed

at 0°C (DiMaio and Schiller 1980). Furthermore, nearly all the Tic2-analogs

described here should be quite enzyme-resistant even at higher temperatures

because of their structural characteristics.

248
A molecular mechanics study based on a systematic energy minimization

approach was performed with the tripeptides H-Tyr-D-Tic-Phe-NH2

 (17a)

and H-Tyr-Tic-Phe-NH2 (17b). The resulting lowest energy conformations

of the two compounds were compared by superimposing their Tyr and Tic

residues (figure 5). Most strikingly, this spatial superimposition revealed

that the Phe3 side chains of these diastereoisomeric peptides are located

on opposite sides of the plane defined by the Tic residue as a consequence

of the conformational constraints existing at the Tic2 residue. This difference

in the orientation of the Phe3 residue may explain the different opioid activity

profiles of the two diastereoisomers in structural terms.

FIGURE 5. Superimposition of lowest energy conformers of H-Tyr-Tic-PheNH, (heavy lines) and H-Tyr-D-Tic-Phe-NH2

 (light lines). An

inverse c-turn stabilized by a Tyr1-CO HN-Phe3 hydrogen

bond is observed in the case of the L-Tic2-analog.

249
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Schiller, P.W.; Nguyen, T.M.-D.; Weltrowska, G.; Wilkes, B.C.; Marsden, B.J.;

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ACKNOWLEDGMENTS

The authors’ work described in this chapter was supported by operating grants

from the Medical Research Council of Canada (MT-10131) the Quebec Heart

and Stroke Foundation, and the U.S. National Institute on Drug Abuse

(DA-06252).

AUTHORS

Peter W. Schiller, Ph.D.

Director

Thi M.-D. Nguyen

Research Assistant

Grazyna Weltrowska

Research Associate

Nga N. Chung

Research Assistant

Carole Lemieux, D.E.C.

Research Assistant

Brian J. Marsden, Ph.D.

Senior Investigator

251
Brian C. Wilkes, Ph.D.

Senior Investigator

Laboratory of Chemical Biology and Peptide Research

Clinical Research Institute of Montreal

110 Pine Avenue West

Montreal, Quebec

CANADA H2W 1R7

252
Probes for the Cannabinoid

Sites of Action

Alexandros Makriyannis

INTRODUCTION

Cannabinoids produce a complex pattern of pharmacological actions, some of

which are believed to be related to their effects on cellular membranes (Martin

1986; Hillard et al. 1990; Leuschner et al. 1984); others are thought to be

produced through an interaction with one or more cannabinoid receptors

(Devane et al. 1989, 1988; Herkenham et al. 1990; Bidaut-Russel et al. 1990).

Existing evidence indicates that the membrane effects involve cannabinoid

interactions with noncatalytic amphipathic sites, resulting in perturbation of the

membrane and a modification of the functions of membrane-associated proteins.

There is also evidence that the cannabinoid:membrane interactions are

governed by stereoelectronic requirements of a varied degree of specificity.

Regarding the receptor-mediated effects, cannabinoids are believed to first

partition in the membrane where they assume a specific orientation and

location and then laterally diffuse across the bilayer to reach their receptor

site(s). Thus, for a productive interaction with its active site(s), a cannabinoid

must fulfill two criteria. First, it must possess certain pharmacophores in a

specific stereochemical arrangement (stereoelectronic requirements). Second,

it must assume an appropriate orientation and location in the bilayer that would

allow an optimal alignment of its pharmacophores with the corresponding binding

components at the active site (drug:membrane interaction requirements).

CANNABINOID RECEPTORS

Although it had been generally recognized that at least some of the cannabinoid

effects are due to interactions with specific receptor sites, direct evidence for a

cannabinoid receptor has become available only recently. This evidence comes

from experiments in which the tritiated cannabinoid-like ligand [

3H]CP-55940 was

shown to bind stereospecifically to a brain membrane preparation (Devane et al.

1989). The relative abilities of cannabinoid analogs to inhibit [

3H]CP-55940

binding were found to parallel their potencies for producing behavioral effects

in animals (Devane et al. 1988) and for regulating adenylate cyclase in vitro

(Hewlett 1985). The same tritiated ligand was also used in autoradiographic

253
experiments for cannabinoid receptor distribution in brain sections from several

mammalian species, including humans (Herkenham et al. 1990). The study

revealed a unique and conserved distribution, with the highest “receptor”

density in the basal ganglia, hippocampus, and cerebellum. More recently,

the receptor was cloned. The cDNA was then injected in CHO-K4 cells, and

a cannabinoid-responsive G protein-coupled receptor was expressed (Matsuda

et al. 1990).

CANNABINOID:LIPID INTERACTIONS

Interactions of drug molecules (including cannabinoids) with membrane lipids

are generally discussed in terms of changes in the bulk properties of the lipid

component, which are often described as changes in membrane fluidity.

However, there is increasing evidence from a variety of sources for specific

drug:membrane interactions. For example, evidence has been presented for

the formation of -trahydrocannabinol ( -THC):phospholipid complexes,

the stoichiometry and stability of which varied depending on the nature of the

phospholipids in the membrane (Bruggemann and Melchior 1983). More

detailed evidence comes from the work of the author and colleagues showing

that cannabinoids assume specific orientation in the phospholipid bilayers

(Makriyannis et al. 1989).

Because of their high lipid solubility, cannabinoids partition preferentially in

the lipid component of the membrane. The cannabinoid:lipid interactions

can play two roles in determining drug activity: (1) Cannabinoids may affect

the functions of membrane-associated proteins by perturbing their lipid

environment, and (2) cannabinoids may diffuse across the membrane lipid

bilayer (lateral diffusion) to reach a specific site of action on the cannabinoid

receptor. It has been argued for other groups of lipophilic drugs (Herbette

et al. 1986), and evidence was provided, that the location and orientation of

the drug molecule in the membrane is critical in determining its ability to

reach its site(s) of action in the proper orientation and/or conformation for a

productive interaction. The author and coworkers have studied the location

and orientation of several cannabinoids in model and biological membranes

and have developed arguments correlating these properties with their abilities

to interact with the receptor (Makriyannis et al. 1991; Mavromoustakos et al.

1990) .

DESIGNING PROBES FOR CANNABINOID RECEPTORS

To better understand the molecular basis of cannabinoid activity, the author

and colleagues have sought to study in detail the interactions of some important

cannabinoid analogs with their respective sites of action. Such studies have

involved the combined use of a variety of biophysical and biochemical methods

that could only be realized with the successful design and synthesis of

254
appropriate molecular probes. This chapter’s goal is to describe some of these

molecular probes, their design, and their applications.

The various methods used are aimed at answering the following questions:

What are the conformational properties of the drug molecule in solution and

in the membrane?

What orientation does the drug molecule assume when it partitions in the

membrane?

What is the location of the drug molecule in the membrane bilayer?

What is the distribution of cannabinoid receptors in mammalian brains? How

can one follow the effects of a cannabinoid on the biochemistry of brain

function?

How can the cannabinoid receptor be covalently labeled? Can this approach

be used for receptor isolation and characterization?

Cannablnold Conformations

To explore the conformational requirements for interaction with their sites

of action on the cannabinoid receptor, pairs of cannabinoid analogs are

compared that differ only in one structural feature but exhibit widely different

pharmacological potencies and affinities for the receptor. The conformational

properties of the drug molecules in solution and in membrane-like environments

can be studied using multinuclear one- and two-dimensional high-resolution

nuclear magnetic resonance (NMR). For example, the results of comparing

the two isomeric cannabinoids ( -THC and -THC) (Kriwacki and

Makriyannis 1989) led to the suggestion that a slight deviation from planarity

in the carbocyclic ring C of the cannabinoid, as is the case with the

pharmacologically potent -THC, is a requirement for activity (figure 1).

Conversely, the much less potent -analog has all three rings coplanar.

The same conclusions were reached by Reggio and colleagues (1989), who

compared the theoretically obtained conformations of the above two analogs.

Currently, the author’s research group is synthesizing suitably designed

cannabinoids to test the hypothesis regarding the role of ring C conformation

in pharmacological activity.

Cannabinoid Orientation in the Membrane

As with many other membrane-active lipophilic molecules, cannabinoids

preferentially partition in the membrane where they assume a thermodynamically

favorable orientation and location. In this preferred orientation and location, the

255
FIGURE 1. Left: Structures of -tetrahydrocannabinol and

tetrahydrocannabinol. Right: Drawings showing the

experimentally determined conformations of -THC and

-THC.

SOURCE: Kriwacki, R.W., and Makriyannis, A. The conformational analysis

of - and -tetrahydrocannabinols in solution using high

resolution nuclear magnetic resonance spectroscopy. Molecular

Pharmacology 35:495-503, © American Society for Pharmacology

and Experimental Therapy, 1989.

cannabinoid experiences fast lateral diffusion within the membrane bilayer. As

mentioned earlier, these features of the drug:membrane interaction are believed

to play an important role in determining the drug’s ability to interact with its site

of action on the receptor.

The orientation of the cannabinoid molecule in the membrane can be

calculated from the solid-state [

2H]-NMR spectra of membrane preparations

into which the appropriate [

2H]-labeled analog is introduced. The [2H]-labels are

introduced in strategic positions of the tricyclic ring system. Each experiment

involves the singly [

2H]-labeled drug molecule. However, preparations in which

the drug molecule is labeled in more than one position can be used if each

256
[

2H]-subspectrum in the composite [2H]-spectrum can be positively assigned

to an individual label, The approach was initially used to study the orientation

of cholesterol in the membrane and subsequently expanded in the author’s

laboratory into a more general method (Makriyannis et al. 1989).

Studies with -THC required the stereospecific introduction of six individual

deuterium atoms in the tricyclic component of the molecule (figure 2). The author

and coworkers found that the cannabinoid assumes an awkward orientation in

the bilayer, with the long axis of its tricyclic system perpendicular to the bilayer

chains (Makriyannis et al. 1989, 1991). This can be accounted for by assuming

that this amphipathic drug molecule uses its phenolic hydroxyl group to anchor

itself at the interface of the amphipathic membrane bilayer; small-angle x-ray

results confirmed this interpretation. In addition, it was discovered that

cannabinoids with two hydroxyl groups orient in a manner that allows both

hydroxyls to be near the interface facing the polar surface of the membrane

(Makriyannis and Yang 1989; Yang et al. 1991). However, if the phenolic

hydrogen of THC is replaced with a methyl group, the cannabinoid orients

with its natural long axis parallel to the chains (Yang et al. 1992).

Cannabinoid Topography in the Membrane

Information on the location of the drug in the bilayer can be obtained from smallangle x-ray diffraction experiments using carefully hydrated membrane samples

into which the cannabinoid analog is introduced (Mavromoustakos et al. 1990).

The well-layered membrane preparation is exposed to a small-angle x-ray beam,

from which several diffraction orders are obtained. After transformation these

orders can provide an electron density profile of the membrane bilayer (figure 3)

in which the highest electron density corresponds to the phosphate headgroups

and the lowest to the ends of the chains in the center of the bilayer. In such an

experiment, three membrane preparations are compared. The first of these

contains no drug molecule. The second preparation contains the cannabinoid

molecule under study, and the third contains the same cannabinoid analog

into which a high electron density substituent (e.g., Br, I) is introduced as a

marker. The differences among the electron density profiles of the above three

preparations reveal the positions of the cannabinoid tricyclic ring system and

that of the heavy atom and thus provide direct information on the location of the

drug in the membrane. If the high electron density atom is placed at the end of

the cannabinoid chain, then the experiment also provides information on the

conformation of this flexible portion of the drug molecule in the membrane.

Experiments with -THC, -THC, and their inactive O-methyl analog provided

data that complemented the [

2H]-NMR results. Such experiments required the

synthesis of these cannabinoids as well as analogs having a halogen (I)

substituent in the 5'-position of the cannabinoid side chain (figure 4).

257
FIGURE 2. Left: Solid-state [

2H]-NMR spectra of model membrane samples

containing specifically [

2H]-labeled -THC. Four deuterated

-THC analogs have [2H]-labels in the 2, 4, 8 , 8 ,10, and 10a

positions. Right: Orientation of -THC in the membrane as

determined from the solid-state [

2H]-NMR results. The dashed

line represents the direction of the lipid acyl chains in the

membrane.

SOURCE: Reprinted from Pharmacology, Biochemistry and Behavior, Volume

40. Yang, D.P.; Banijamali, A.; Charalambous, A.; Marciniak, G.;

and Makriyannis, A. Solid State 2H-NMR as a method for

determining the orientation of cannabinoid analogs in membranes,

553-557, Copyright (1991), with permission from Pergamon Press

Ltd., Headington Hill Hall, Oxford, OX3 OBW, UK.

The study, which made use of membrane preparations from partially hydrated

phosphatidylcholine, showed that the biologically active -THC and -THC

analogs are located in the upper portion of the phospholipid bilayer, with the

phenol hydroxyl anchored at the bilayer interface (Yang et al. 1992). However,

258
FIGURE 3. Electron density profile obtained by Fourier transformation of smallangle diffraction intensities from a dimyrisroylphosphatdylcholine

(DMPC) model membrane. A molecular graphic representation

above the profile shows the space correlation in the dimension

across the bilayer.

SOURCE: Reprinted from Pharmacology, Biochemistry and Behavior, Volume

40. Mavromoustakos, T.; Yang, D.P.; Broderick, W.; Fournier, D.;

and Makriyannis, A. Small angle x-ray diffraction studies on the

topography of cannabinoids in synaptic plasma membranes, 547-

552, Copyright (1991), with permission from Pergamon Press Ltd.,

Headington Hill Hall, Oxford, OX3 OBW, UK.

259
FIGURE 4. Structures of (-)- -tetrahydrocannabinol ( -THC) and (-)-O-methyl-

-tetrahydrocannabinol (Me- -THC), along with the respective

iodinated analogs 5'-I- -THC and 5'-I-Me- -THC.

when the polar phenoiic hydroxyi is replaced with the more lipophilic methoxyl

group, the cannabinoid sinks deeper into the biiayer. The study also revealed

that the iodine atom’s location in the biiayer requires that the cannabinoid side

chain be parallel with the biiayer chains in a fully extended all-trans conformation.

When studies were extended from model to biological membranes obtained from

rat or calf brain synaptosomes, there was no significant change in the manner

in which the cannabinoid analogs orient in the membrane (Mavromoustakos

et al. 1991). However, the x-ray data revealed a small but significant variation

in the location of the drug in the biiayer. It was found that -THC and its

haiogenated analog are located 2 to 3 Å deeper in the membrane preparations

obtained from calf brain synaptosomes compared with the model membrane

preparations. The data from the synaptosomai plasma membrane preparations

also showed that the THC side chain assumes a more compact, gauche-transgauche conformation, unlike the fully extended conformation observed in the

phosphatidylchoiine preparations. When molecular modeling was used to

represent data from the biological membranes, the phenoiic hydroxyi in the

cannabinoid could still interact with the biiayer interface through hydrogen

bonding with either one of the phospholipid carbonyis or with a water molecule

located slightly below the interface (figure 5).

A biophysical method that can be used in an analogous manner with the x-ray

method described above is neutron scattering. in this method, the high electron

density halogen marker used in the x-ray experiment is now replaced with

260
FIGURE 5. Left: Graphical representation of the locations and orientations

of 5'-I- -THC and 5'-I-Me- -THC In a DMPC model membrane

based on results from a small-angle x-ray diffraction and solidstare [

2H]-NMR. Right: Graphical representation of a -THC

molecule in a synaptosomal plasma membrane.

SOURCE: Reprinted from Pharmacology, Biochemistry and Behavior, Volume

40. Mavromoustakos, T.; Yang, D.P.; Broderick, W.; Fournier, D.;

and Makriyannis, A. Small angle x-ray diffraction studies on the

topography of cannabinoids in synaptic plasma membranes, 547-

552, Copyright (1991), with permission from Pergamon Press Ltd.,

Headington Hill Hall, Oxford, OX3 OBW, UK.

deuterium atoms. The two membrane preparations compared in this method

contain, respectively, unlabeled and specifically [

2H]-labeled cannabinoids.

The location of the [2H]-label is then revealed by subtracting the data from the

two preparations. One advantage of this method is that the two membrane

samples being compared are almost identical. This avoids the potential pitfall

encountered in the x-ray experiment where the samples being compared are

not totally identical and may have somewhat different phase properties, thus

requiring rigorous controls. Recent data using [

2H]-labeled -THC in neutron

diffraction experiments (P. Martel and A. Makriyannis, unpublished results)

have confirmed, to a large extent, the earlier x-ray data described above.

The combined data from the solid-state [

2H]-NMR and small-angle x-ray

diffraction experiments support the hypothesis that the ability of a cannabinoid

to orient “properly” in the amphipathic membrane system can seriously influence

its ability to bind to its active site(s) on the receptor. This “proper” orientation is

261
one in which all the cannabinoid’s polar hydroxyl groups face the polar side of

the membrane bilayer and, in turn, is determined by the position and relative

stereochemistry of these hydroxyi groups in the cannabinoid molecule.

Cannabinoid Binding Sites in Mammalian Brains

The distribution of a cannabinoid in the brains of live animals can be studied

using positron emission tomography (PET). The method requires the labeling

of the drug probe with a short-lived positron-emitting isotope. To carry out such

an experiment, the author and coworkers synthesized an 18F-containing analog

of the parent -THC, namely (-)-5'-[18F]- -THC. The probe was administered

to live baboons, and the presence and distribution of the drug in the brain was

followed using computerized tomography. Among the findings (Charaiambous

et al. 1991) was that the drug reaches its maximum concentration in the brain

(approximately .02 percent dose/cc) during the first 5 minutes and persists

(.005 percent dose/cc) up to 2 hours afler injection (figure 6). High drug

concentrations were found in the striatum, thaiamus, and cerebellum. A

shortcoming in these early experiments was the high iipophilicity of the probe

as well as its relatively low affinity for the receptor sites. Both factors resulted

in relatively high levels of nonspecific binding by the ligand in the brain.

Currently under way is the synthesis of novel cannabinoid PET probes

possessing higher affinities for the receptor and more favorable partitioning

properties,

Cannabinoid Photoaffinity Probes

The task of obtaining direct information on the molecular features of the

cannabinoid receptor binding sites can be successfully pursued with the help

of cannabinoid affinity labels so that, after interacting with the cannabinoid

binding sites, they covalently attach themselves to the receptor. The affinity

probes should be especially useful for the isolation and characterization of the

cannabinoid receptor. The most successful of the first-generation cannabinoid

probes was (-)-5'-azido- -THC (figure 7), the first photoaffinity label for the

cannabinoid receptor. This molecule was shown to specifically bind and

inactivate the receptor after ultraviolet irradiation (Charaiambous et al. 1992).

Furthermore its 125I-radiodinated analog, when incubated with two different

membrane preparations, was shown to covaientiy label the receptor (Burstein

et al. 1991). The use of an aliphatic azide as a photaffinity label represents a

departure from the generally used aromatic azides, which upon photoactivation

are transformed into aromatic nitrenes as the reactive species and are known

to be considerably more stable than their aiiphatic counterparts. in this

photoaffinity probe, the aiiphatic azido group is expected to be transformed to

an aiiphatic nitrene when subjected to ultraviolet irradiation. However, it is still

unclear whether the originating highly unstable aliphatic nitrene directly reacts

with the receptor or whether it first rearranges to a more stable intermediate

262
FIGURE 6. Time courses for (-)-5'-[18F]- -THC distribution in baboon brain,

showing the rates of accumulation and clearance of the

radioactivity in the various areas of the brain.

SOURCE: Reprinted from Pharmacology, Biochemistry and Behavior, Volume

40. Charalambous, A.; Marciniak, G.; Shiue, C.-Y.; Dewey, S.L.;

Schlyer, D.J.; Wolf, A.P.; and Makriyannis, A, PET studies in the

primate brain and biodistribution in mice using (-)-5'-18F- -THC,

503-507, Copyright (1991), with permission from Pergamon Press

Ltd., Headington Hill Hall, Oxford, OX3 OBW, UK.

that, in turn, covalently attaches itself at or near the active sites on the receptor.

Currently, the author’s group is investigating the mechanism of this photoactivation

reaction.

Recently, we have developed second-generation photoaffinity probes that possess

considerably higher affinities for the cannabinoid receptor, as exemplified by the

one (IC50~0.25 nM) depicted in figure 8 (Charalambous et al., in press).

These novel probes should be ideally suited for pursuing the important but

difficult task of labeling and characterizing the cannabinoid receptor.

263
FIGURE 7. Photoirradiation of (-)-5'-azido- -THC with forebrain membranes

inhibits specific binding of [

3H]CP-55940 to the cannabinoid

receptor. Heterologous displacement by levonantradol is shown

for membranes irradiated in the absence or after equilibration with

5'-azido- -THC.

SOURCE: Reprinted with permission from Charalambous, A.; Guo, Y.;

Houston, D.B.; Hewlett, A.C.; Compton, D.R.; Martin, B.R.; and

Makriyannis, A. (-)-5'-azido- -THC; a novel photoaffinity label for

the cannabinoid receptor. J Med Chem 35:3076-3079, 1992.

Copyright (1992), American Chemical Society.

264
FIGURE 8. Structure of (-)-1',1'-dimethyl-5'-N3

- -THC

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ACKNOWLEDGMENTS

Professor D.P. Yang provided useful discussions concerning the small-angle

x-ray work. This work was supported by National Institute on Drug Abuse

grants DA-3801 and DA-00152.

AUTHOR

Alexandros Makriyannis, Ph.D.

Professor

Section of Medicinal Chemistry

University of Connecticut School of Pharmacy

Institute of Materials Science

U-92

Storrs, CT 06269-2092

F. Bitter National Magnet Laboratory

Massachusetts Institute of Technology

Cambridge, MA 02139

267
A Model for the Structural Basis of

2

-Opioid Receptor Selectivity

Henry I. Mosberg and Frank Porreca

Although opioid receptor heterogeneity has long been established, and the

existence of distinct μ-, -, and -types of opioid receptors has been widely

accepted since the late 1970s convincing evidence of subtypes of these

major categories of opioid receptors is of more recent origin. In particular,

the clear demonstration of -receptor subtypes in vivo has evolved only

during the past 2 years. As is often the case, this demonstration of receptor

subtypes followed from the observation of disparate effects mediated by

compounds considered to be selective for the same receptor, in this case

the -opioid receptor. Several of these observations have uncovered

pharmacological differences between Tyr-c[D-Pen-Gly-Phe-D-Pen]-OH

(DPDPE) (Mosberg et al. 1983) where penicillamine (Pen) is , -dimethyl

cysteine, and deltorphin II (DELT II, Tyr-D-Ala-Phe-Glu-Val-Val-GlyNH2)

(Erspamer et al. 1989), a naturally occurring opioid peptide originally isolated

from frog skin. The earliest such observation was that, although both DPDPE

and DELT II displayed b-receptor-mediated antinociceptive activity in mice

following intracerebroventricular (ICV) administration, pretreatment with two

-selective, irreversible antagonists, Tyr-D-Ala-Gly-Phe-Leu-Cys (DALCE)

(Bowen et al. 1987) and natrindole-5'-isothiocyanate (5'-NTII) (Portoghese

et al. 1990), resulted in differential effects: the former antagonizing DPDPE

but not DELT II, the latter antagonist blocking the antinociceptive effect of

DELT II, but not DPDPE (Jiang et al. 1991). This result was interpreted as

evidence that the antinociceptive actions of DPDPE and DELT II were affected

by interaction with different subtypes of -receptor. This interpretation was

supported by a later series of experiments that demonstrated a three-way

lack of antinociceptive cross-tolerance between DPDPE, DELT II, and the

μ-selective agonist Tyr-D-Ala-Gly-NMePhe-Gly-ol (DAMGO) (Mattia et al.

1991), indicative of mediation by three different receptors or receptor subtypes,

Similar conclusions regarding -receptor subtypes in mouse brain have been

reached by Takemori and coworkers from in vivo studies employing the

-selective antagonists naltrindole and naltriben (Sofuoglu et al. 1991).

Although considerable evidence that DPDPE and DELT II act via distinct

-receptor subtypes has been compiled, the structural basis for the disparate

actions of these peptides has not yet been addressed. In what follows, the

288
authors propose ahypothesis implies insufficient evidence hypothesis for the

structural basis of the subtype selectivities of DPDPE and DELT II and examine

the structural features that endow DELT II with - vs. μ- and -selectivity. We

then describe how this analysis has led us to reexamine a series of tetrapeptide

opioids that we have developed over the past 5 years in the new context of

receptor subtype selectivity.

The structures of DPDPE and DELT II are shown in figure 1. Two significant

structural differences between the peptides stand out. First, whereas DPDPE

has a standard enkephalin-like pentapeptide structure, DELT II has an extended

C-terminus culminating in a carboxamide function. As discussed below, this

C-terminal extension is important for conferring b-receptor affinity to DELT II.

The second obvious structural difference is in the location within the primary

sequence of the important Phe residue, as residue 4 in DPDPE and as residue

3 in DELT II. Our working hypothesis, for which there is as yet, to our

knowledge, no confirming or refuting evidence, is that this structural difference

is a key element underlying the -subtype selectivity differences in DPDPE

and DELT II. Results presented below, although not sufficient to prove this

hypothesis, are at least consistent with it.

Structural features of DELT II that confer - vs. μ-selectivity ( -affinities of

DPDPE and the deltorphins are very low and are ignored here) can be deduced

from the increasingly well-established structure-activity relationships for the

deltorphins. Table 1 summarizes some of these data. As can be seen

in table 1, deltorphin I (DELT I), which has been shown, like DELT II, to be

selective for the 2

-opioid receptor (Porreca, unpublished observations), and

FIGURE 1. Structures of DPDPE and DELT II

269
TABLE 1. Role of residue 4 side chain in DELTI and II; opioid receptor

binding in membrane homogenates from guinea pig brain

Ki(nM)

Peptide [

3H]DAMG0 [3H]DPDPE Ki

 (μ)/Ki ( )

Tyr-D-Ala-Phe-Glu-Val-Val-GlyNH2 1,310 2.69 487

Tyr-D-Ala-Phe-Asp-Val-Val-GlyNH2 677 1.73 391

Tyr-D-Ala-Phe-Asn-Val-Val-GlyNH2 69.7 3.66 19

Tyr-D-Ala-Phe-Gly-Val-Val-GlyNH2 4.7 2.60 1.81

DELT II differ only in residue 4, which in both cases has a negatively charged

carboxylate side chain. The importance of this side chain is apparent by

comparing the receptor-binding results obtained for these opioids with those

for the corresponding amidated side chain in [Asn4]deltorphin. As shown in

table 1, this latter modification has only a minor effect on -receptor affinity

but increases μ-affinity by approximately an order of magnitude, thus effecting

a reduction in -selectivity. Removing all steric effects of the side chain by

substitution with Gly in residue 4 again has no significant effect on -affinity

but leads to a further order of magnitude increase in μ-affinity. These results

strongly support the conclusion that the negatively charged side chain in residue

4 of DELT I and DELT II is critical for the -receptor selectivity displayed by these

compounds but has no significant effect on -receptor-binding affinity. Rather,

the -selectivity arises from adverse interactions at the μ-receptor attributable

to both steric and charge effects of the residue 4 side chain. This same

interpretation of similar results was proposed by Lazarus and coworkers (1991).

The role of the tripeptide C-terminal tail of DELT I has been examined

independently by Melchiorri and colleagues (1991) and Salvadori and

coworkers (1991). Both studies revealed that, whereas truncation to the

hexapeptide, Tyr-D-Ala-Phe-Asp-Val-ValNH2, resulted in only modest

(threefold to fivefold) losses in b-binding affinity, further truncation led to

increasingly pronounced reductions in -affinity. Affinity for μ-receptors was

relatively unaffected, although a slight increase in affinity was observed for

the shorter fragments. Consequently, -selectivity decreases with decreasing

peptide length with the tetrapeptide, Tyr-D-Ala-Phe-AspNH2

, displaying slight

μ-selectivity. The results clearly indicate the importance of the complete

heptapeptide sequence for optimal -binding affinity and selectivity. A possible

270
explanation of the role of the tripeptide C-terminal tail may lie in the lipophilicity

of this segment, in particular the valine residues. These lipophilic side chains

may, for example, play a direct binding role with a complementary lipophilic

subsite on the -receptor. This might account for the relatively high -affinity

of the hexapeptide that retains both valine residues. Alternatively, the tripeptide

tail may play a conformational role by energetically facilitating a relative orientation

of other portions of the peptide that is required for -receptor recognition.

Possible support for this latter explanation comes from conformational studies

that suggest that the C-terminal tail of DELT I is folded on the N-terminal portion

via a p-turn involving residues 3 through 6 (Balboni et al. 1990). In summary then,

we can identify three structural features that may be critical for the 2-receptor

selectivity of the deltorphins: (1) a negative charge in the side chain of residue 4

that reduces p-receptor affinity; (2) the tripeptide C-terminal tail that enhances

affinity, perhaps via a conformational role; and (3) the presence of a Phe3

residue rather than the enkephalin-like Phe4 residue that may underlie the

2

-subtype selectivity of the deltorphins.

Support for the above structural basis for the selective opioid actions of deltorphin

comes indirectly from attempts to determine the bioactive conformation of

DPDPE, an area of interest in our laboratories as well as several others.

Our approach for the elucidation of this bioactive conformation has combined

experimental evidence of the solution conformation of DPDPE gained from

extensive nuclear magnetic resonance (NMR) studies with computational

methods (Mosberg et al. 1990). In particular, interproton distances calculated

from nuclear Overhauser effect buildup rates were used as constraints for

distance geometry calculations of conformers consistent with these constraints.

Each of the conformers generated was then energy minimized, and those

conformers of lowest energy were examined further for consistency with

additional conformation-dependent NMR parameters. In this manner three

low-energy conformers, I through III in table 2, were identified. Of these,

conformer III was most consistent with all experimental evidence and was

further modified to take into account calculated major conformations of the

Tyr and Phe side chains. Further energy minimization led to a significantly

lower energy conformer, conformer Ill’, that was consistent with all experimental

data, This conformer was then proposed as the active conformation of DPDPE

(Mosberg et al. 1990). Many other conformational models, based primarily on

computational studies, have also been proposed for DPDPE (e.g., Hruby et al.

1988; Froimowitz 1990; Chew et al. 1991; Nikiforovich et al. 1991; Wilkes and

Schiller 1991). In general, however, agreement among these models is lacking.

Among other interpretations, this lack of agreement raises the possibility that

DPDPE may sample many low-energy conformations in solution; that is, it is

more flexible than is usually assumed. Consequently, conformation-dependent

NMR parameters interpreted in terms of a single conformation may reflect some

significant degree of conformational averaging and thus may result in erroneous

conclusions regarding “the” solution conformation. Naturally, extrapolations

271
TABLE 2. Low-energy conformers of DPDPE

Model

Dihedral

Residue Angle I II III Ill'

– Tyr1 – –

Pen2

Gly3

Phe4

Pen5

82 -80 96 78

-66 49 -130 -111

-171 176 -168 -164

-105 -153 -79 -85

-53 -81 27 38

-174 178 -178 172

-60 -54 63 -64

-73 -86 93 105

c-s-s-c 109 110 102 110

Energy (Kcal/mol) -43.7 -43.8 -44.3 -51.5

-36 150

178 -176

-62 -179

96 -125

155

-173

69

-69

163

-177

-173

-115

-79 135 142 149

-129 -29 -153 -153

171 180 -173 -175

-88 -69 -82 -78

-175 -168 176 178

140 131 76 61

– – – –

– – – –

-85 -69 -91 -87

62 72 61 60

SOURCE: Adapted with permission from Mosberg, H.I.; Sobczyk-Kojiro, K.;

Subramanian, P.; Crippen, G.M.; Ramalingam, K.; and Woodard,

R.W. Combined use of stereospecific deuteration, NMR, distance

geometry and energy minimization for the conformational analysis

of the delta opioid receptor selective peptide, [D-Pen2

, DPen5

]enkephalin. J Am Chem Soc 112:822-829, 1990. Copyright

1990 by American Chemical Society, Washington, D.C.

272
to the receptor-bound, active conformation would in this case be unwarranted.

Similarly, residual flexibility in DPDPE would devalue the significance of

computationally based conformational models because the existence and

calculation of many low-energy conformers does not allow the convincing

identification of the pharmacologically relevant conformation.

Some degree of flexibility in DPDPE can be expected in the side chains,

especially those of the Tyr and Phe residues. Such side-chain flexibility is

ubiquitous in small peptides containing natural amino acids and can be

addressed by replacement with conformationally restricted analogs of these

residues. Correlation of biological activity with side-chain conformational features

of the corresponding peptides allows deductions regarding side-chain geometry

in the bioactive conformation of the parent peptide. The backbone variability In

the conformational models for DPDPE suggests that residual flexibility also

exists here. As depicted in figure 2, the likely source of such flexibility is the

Gly3

 residue of DPDPE, which, lacking a side chain, has many low-energy

conformations available. In fact, we had earlier proposed, based on the similar

-activity but dissimilar Gly3

 NMR parameters of a series of DPDPE-like analogs

with various combinations of Cys and Pen in residues 2 and 5, that the Gly

residue acts as a “hinge” allowing the receptor-required relative orientation of

key structural elements throughout the series (Mosberg 1987).

FIGURE 2. Origin of backbone flexibility in DPDPE

273
To examine the role of the Gly residue in conferring &selectivity in DPDPE

and as an approach to increase the rigidity in this series, several years ago

we began to Investigate the effect of eliminating the Gly residue in analogs

otherwise similar to DPDPE. At that time, the deltorphins were still unknown;

however, the p-selective frog skin heptapeptide dermorphin, Tyr-D-Ala-Phe-Gly

Tyr-Pro-SerNH2, had been described (Montecucchi et al. 1981) as had several

cyclic, lactam-containing tetrapeptides of the general form, Tyr-c[D-Xxx-PheYyy]-NH2

 (Schiller et al. 1985), that also displayed μ-selectivity. Our initial study

(Mosberg et al. 1988), in addition to identifying several relatively nonselective

tetrapeptide carboxamides, also reported the tetrapeptide Tyr-c[D-Cys-Phe-DPen]-OH (JOM-13) (figure 3) that was shown to exhibit similar -binding affinity

and selectivity as DPDPE. Further study of this series revealed that, unlike the

DPDPE series in which -affinity is maintained with various combinations of

D-Pen or D-Cys in residue 2 and D- or L-Pen, or D- or L-Cys as the C-terminal,

any such variation in JOM-13 greatly reduces -affinity (Heyl et al. 1991).

This is consistent with the view, expressed above, that the flexibility of the Gly

residue in the DPDPE series allows all members to interact favorably with the

-receptor, whereas the increased rigidity of the des-Gly3 tetrapeptide series

results in a much greater dependence on the nature of the residues involved

in the cyclization.

Although the tetrapeptide series that resulted in JOM-13 was designed as a desGly3

 DPDPE set of compounds, the subsequent discovery of the deltorphins

and the elucidation of the structural features underlying the -selectivity of the

deltorphins lead to an alternate view—JOM-13 as a truncated, conformationally

FIGURE 3. Structure of JOM- 13

274
restricted analog of DELT I and DELT II, which is represented in figure 4. As

depicted in this figure, the side-chain to side-chain cyclization in JOM-13, which

restricts this tetrapeptide to compact, folded conformations, serves the same

structural function as the proposed turn-inducing C-terminal tail of DELT I and

II, discussed above. In both cases then, relative orientations of key structural

elements in the N-terminal tetrapeptide segment, which are required for

-receptor recognition, are stabilized. Also, as shown in figure 4, the C-terminal

carboxyl function of JOM-13 can be seen as corresponding to the residue 4

side-chain carboxyl group of DELT I (as shown) or DELT II. This correspondence

between -carboxyl in the former and side-chain carboxyl in the latter results

only because of the chirality difference (D-stereochemistry for JOM-13,

L-stereochemistry for the deltorphins) at residue 4 between the peptides.

In addition to the similarities pointed out above, JOM-13 shares with DELT I

and II an additional structural feature, the presence of a Phe3 (rather than an

enkephalin-like Phe4

) residue. Because we have proposed that this structural

feature may be crucial for the 2

-subtype selectivity displayed by the deltorphins,

a critical test of the view of JOM-13 as a deltorphin analog is the in vivo evaluation

of its actions. This evaluation is presented in figures 5 and 6. Figure 5 displays

the antinociceptive activity of JOM-13, following ICV injection, in the mouse tailflick model under control conditions as well as following pretreatment with the

μ-selective antagonist -FNA or the -selective antagonist ICI 174,864. As can

be seen from figure 5, the antinociceptive dose-response line of JOM-13

is unaffected by -FNA but is shifted to the right by ICI 174,864, a behavior

indicative of -receptor mediation of the antinociceptive action of JOM-13.

The control antinociceptive potency of JOM-13 (A50=1.4 nmol) is comparable

FIGURE 4. JOM-13 viewed as a conformationally restricted deltorphin analog

275
FIGURE 5. Antinociceptive potency of JOM-13 alone (O), in the presence

of coadministered ICI 174,864 ( ), or after pretreatment with

-FNA (•),

to that found for DELT II (Jiang et al. 1991). Figure 6 further examines the

effect on this antinociceptive action of pretreatment with either the -selective

antagonist, DALCE, or the -selective antagonist, 5'-NTII. As earlier

demonstrated for DELT II (Jiang et al. 1991), the antinociceptive action of

JOM-13 is blocked by 5'-NTII, but not by DALCE, a profile expected for a

-selective agonist. Clearly then, JOM-13 can best be viewed as a truncated,

conformationally restricted deltorphin analog. Furthermore, this demonstration

of -selectivity for JOM-13 lends support for the structural and conformational

model of deltorphin selectivity presented above.

Compared with the native deltorphins, JOM-13 presents several advantages.

First, its smaller molecular weight and compact size may result in improved

absorption or blood-brain barrier penetration via passive diffusion mechanisms.

Second, both the conformationally constrained nature and the alternating D- and

L-amino acid sequence should render JOM-13 highly resistant to metabolism by

peptidases. Finally, as a tool for the elucidation of the bioactive conformation at

the -receptor, JOM-13, which was initially designed as a more rigid analog of

276
FIGURE 6. Antinociceptive potency of JOM-13 alone (O), or after

pretreatment with 5'-NTll ( ), or DALCE ( ).

DPDPE, is ideal because conformational averaging should not significantly

influence experimental measurements.

Despite the potential advantages of JOM-13, a significant disadvantage is

apparent from the comparison of μ- and -binding affinities shown in table 3.

Although JOM-13 displays high -binding affinity, comparable to DELT I and

DELT II, its -selectivity is severalfold lower. This reduced -selectivity,

however, is not a limitation, because rather straightforward analogs of JOM-13

display highly improved selectivity. This, too, can be seen in table 3 where

binding data for two examples, [2-Nal3]JOM-13 (2-Nal=2-naphthylalanine) (Heyl

and Mosberg 1992a) and [p-CIPhe3]JOM-13 (Heyl and Mosberg 1992b), are

presented. Both of these analogs display -selectivity comparable to that of

DELT I and DELT II with [p-CIPhe3

]JOM-13 also showing significantly higher

-affinity.

In summary, the - vs. μ-selectivity of JOM-13 supports the view that the

- (vs.μ-) selectivity of DELT I and DELT II results from the negatively charged

side chain in residue 4 (which lowers μ-affinity) and a conformational effect of

277
TABLE 3. Binding affinities and selectivities of JOM- 13 analogs

Ki (nM)

Peptide [

3H]DAMG0 [3H]DPDPE Ki

(μ)/K( )

DELT I 677 1.73 391

DELT II 1,310 2.69 487

DPDPE 810 3.98 204

JOM-13 107 1.79 60

[2-Nal3]JOM-13 590 2.23 265

[p-CIPhe3]JOM-13 327 0.98 334

the C-terminus, which stabilizes a reverse-turn structure favoring -receptor

recognition. The -subtype selectivity displayed by JOM-13 is consistent

with the proposal that the Phe3 residue in DELT I and DELT II is important

for such selectivity. The results presented clearly establish that JOM-13,

originally developed prior to the discovery of the deltorphins as a more

conformationally restricted analog of DPDPE, can more correctly be viewed

as a conformationally restricted, truncated deltorphin analog. No other class

of small, potent, and selective deltorphin analogs has been reported.

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, D-Pen5]enkephalin in aqueous

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ACKNOWLEDGMENTS

These studies were supported by National Institute on Drug Abuse grants

DA-03910 and DA-00118 (Research Scientist Development Award).

AUTHORS

Henry I. Mosberg, Ph.D.

Associate Professor of Medicinal Chemistry

University of Michigan College of Pharmacy

Ann Arbor, Ml 48109

Frank Porreca, Ph.D.

Professor

Department of Pharmacology

University of Arizona

Tucson, AZ 85719

280
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283
97 NEUROBIOLOGY OF DRUG ABUSE: LEARNING AND MEMORY. Lynda

Erinoff, Ph.D., ed.

NTIS PB #90-237686/AS $31

NCADI #M97

98 THE COLLECTION AND INTERPRETATION OF DATA FROM HIDDEN

POPULATIONS. Elizabeth Y. Lambert, M.S., ed.

NTIS PB #90-237694/AS $23

NCADI #M98

99 RESEARCH FINDINGS ON SMOKING OF ABUSED SUBSTANCES. C.

Nora Chiang, Ph.D., and Richard L. Hawks, Ph.D., eds.

NTIS PB #91-141119/AS $23

NCADI #M99

100 DRUGS IN THE WORKPLACE: RESEARCH AND EVALUATION DATA.

VOLUME II. Steven W. Gust, Ph.D.: J. Michael Walsh, Ph.D.; Linda B. Thomas,

B.S.; and Dennis J. Crouch, M.B.A., eds.

GPO Stock #017-024-01458-3 $8 NCADI #Ml 00

101 RESIDUAL EFFECTS OF ABUSED DRUGS ON BEHAVIOR. John W.

Spencer, Ph.D., and John J. Boren, Ph.D., eds.

GPO Stock #017-024-01426-7 $6 NTIS PB #91-172858/AS $31

NCADI #M101

102 ANABOLIC STEROID ABUSE. Geraline C. Lin, Ph.D., and Lynda Erinoff,

Ph.D., eds.

GPO Stock #017-024-01425-7 $8 NTIS PB #91-172866/AS $31

NCADI #M102

103 DRUGS AND VIOLENCE: CAUSES, CORRELATES, AND

CONSEQUENCES. Mario De La Rosa, Ph.D.; Elizabeth Y. Lambert, M.S.;

and Bernard Gropper, Ph.D., eds.

GPO Stock #017-024-01427-3 $9 NTIS PB #91-172841/AS $31

NCADI #M103

104 PSYCHOTHERAPY AND COUNSELING IN THE TREATMENT OF DRUG

ABUSE. Lisa Simon Onken, Ph.D., and Jack D. Blaine, M.D., eds.

GPO Stock #017-024-01429-0 $4 NTIS PB #91-172874/AS $23

NCADI #M104

284
106 IMPROVING DRUG ABUSE TREATMENT. Roy W. Pickens, Ph.D.;

Carl G. Leukefeld, D.S.W.; and Charles R. Schuster, Ph.D., eds.

GPO Stock #017-024-01439-7 $12

Paperback $50 Microfiche $19

NCADI #M106

107 DRUG ABUSE PREVENTION INTERVENTION RESEARCH:

METHODOLOGICAL ISSUES. Carl G. Leukefeld, D.S.W., and William J.

Bukoski, Ph.D., eds.

GPO Stock #017-024-01441-9 $9 NTIS PB #92-160985

Paperback $35 Microfiche $17

NCADI #M107

108 CARDIOVASCULAR TOXICITY OF COCAINE: UNDERLYING

MECHANISMS. Pushpa Thadani, Ph.D., ed.

GPO Stock #017-024-01446-0 $7 NTIS PB #92-106608 P

Paperback $35 Microfiche $17

NCADI #M108

109 LONGITUDINAL STUDIES OF HIV INFECTION IN INTRAVENOUS DRUG

USERS: METHODOLOGICAL ISSUES IN NATURAL HISTORY RESEARCH.

Peter Hartsock, Dr.P.H., and Sander G. Genser, M.D., M.P.H., eds.

GPO Stock #017-024-01445-1 $4.50 NTIS PB #92-106616

Paperback $26 Microfiche $12.50

NCADI #M109

111 MOLECULAR APPROACHES TO DRUG ABUSE RESEARCH.

VOLUME I: RECEPTOR CLONING, NEUROTRANSMITTER EXPRESSION,

AND MOLECULAR GENETICS. Theresa N.H. Lee, Ph.D., ed.

NTIS PB #92-135743

Paperback $35 Microfiche $17

NCADI #M111

112 EMERGING TECHNOLOGIES AND NEW DIRECTIONS IN DRUG

ABUSE RESEARCH. Rao S. Rapaka, Ph.D.: Alexandros Makriyannis, Ph.D.;

and Michael J. Kuhar, Ph.D., eds.

GPO Stock #017-024-01455-9 $11 NTIS PB #92-155449

Paperback $44 Microfiche $17.50

NCADI #M112

285
113 ECONOMIC COSTS, COST-EFFECTIVENESS, FINANCING, AND

COMMUNITY-BASED DRUG TREATMENT. William S. Cartwright, Ph.D., and

James M. Kaple, Ph.D., eds.

NTIS PB #92-155795

Paperback $35 Microfiche $17

NCADI #M113

114 METHODOLOGICAL ISSUES IN CONTROLLED STUDIES ON EFFECTS

OF PRENATAL EXPOSURE TO DRUG ABUSE. M. Marlyne Kilbey, Ph.D., and

Khursheed Asghar, Ph.D., eds.

GPO Stock #017-024-01459-1 $12 NTIS PB #92-146216

Paperback $43 Microfiche $17

NCADI #M114

115 METHAMPHETAMINE ABUSE: EPIDEMIOLOGIC ISSUES AND

IMPLICATIONS. Marissa A. Miller, D.V.M., M.P.H., and Nicholas J. Kozel,

M.S., eds.

GPO Stock #017-024-01460-5 $4 NCADI #M115

116 DRUG DISCRIMINATION: APPLICATIONS TO DRUG ABUSE

RESEARCH. Richard A. Glennon, Ph.D.; Torbjörn U.C. Jörbe, Ph.D.; and

Jerry Frankenheim, Ph.D., eds.

GPO Stock #017-024-01470-2 $13 NCADI #M116

117 METHODOLOGICAL ISSUES IN EPIDEMIOLOGICAL, PREVENTION,

AND TREATMENT RESEARCH ON DRUG-EXPOSED WOMEN AND THEIR

CHILDREN. M. Marlyne Kilbey, Ph.D., and Khursheed Asghar, Ph.D., eds.

GPO Stock #017-024-01472-9 $12 NCADI #M117

118 DRUG ABUSE TREATMENT IN PRISONS AND JAILS. Carl G. Leukefeld,

D.S.W., and Frank M. Tims, Ph.D., eds.

GPO Stock #017-024-01473-7 $16 NCADI #M118

119 PROBLEMS OF DRUG DEPENDENCE 1991: PROCEEDING OF THE

53RD ANNUAL SCIENTIFIC MEETING, THE COMMITTEE ON PROBLEMS

OF DRUG DEPENDENCE, INC. Louis Harris, Ph.D., ed.

GPO Stock #017-024-01474-5 $22 NCADI #M119

120 BIOAVAILABILITY OF DRUGS TO THE BRAIN AND THE BLOOD-BRAIN

BARRIER. Jerry Frankenheim, Ph.D., and Roger M. Brown, Ph.D., eds.

GPO Stock #017-024-01481-8 $10 NCADI #M120

121 BUPRENORPHINE: AN ALTERNATIVE TREATMENT FOR OPIOID

DEPENDENCE. Jack D. Blaine, Ph.D., ed.

GPO Stock #017-024-01482-6 $5 NCADI #M121

286
123 ACUTE COCAINE INTOXICATION: CURRENT METHODS OF

TREATMENT. Heinz Sorer, Ph.D., ed.

GPO #017-024-01501-6 $6.50 NCADI #M123

124 NEUROBIOLOGICAL APPROACHES TO BRAIN-BEHAVIOR

INTERACTION. Joseph Frascella, Ph.D., and Roger M. Brown, Ph.D., eds.

GPO Stock #017-024-01492-3 $9 NCADI #M124

125 ACTIVATION OF IMMEDIATE EARLY GENES BY DRUGS OF ABUSE.

Reinhard Grzanna, Ph.D., and Roger M. Brown, Ph.D., eds.

GPO #017-024-01503-2 $7.50 NCADI #M125

126 MOLECULAR APPROACHES TO DRUG ABUSE RESEARCH.

VOLUME II: STRUCTURE, FUNCTION, AND EXPRESSION. Theresa N.H.

Lee, Ph.D., ed.

NCADI #M126

127 PROGRESS AND ISSUES IN CASE MANAGEMENT. Rebecca Sager

Ashery, D.S.W., ed.

GPO Stock #017-024-01490-7 $14 NCADI #M127

128 STATISTICAL ISSUES IN CLINICAL TRIALS FOR TREATMENT OF

OPIATE DEPENDENCE. Ram B. Jain, Ph.D., ed.

GPO Stock #017-024-01491-5 $6.50 NCADI #M128

129 INHALANT ABUSE: A VOLATILE RESEARCH AGENDA. Charles W.

Sharp, Ph.D.; Fred Beauvais, Ph.D.; and Richard Spence, Ph.D., eds.

GPO Stock #017-024-01496-6 $12 NCADI #M129

130 DRUG ABUSE AMONG MINORITY YOUTH: ADVANCES IN RESEARCH

AND METHODOLOGY. Mario R. De La Rosa, Ph.D., and Juan-Luis Recio

Adrados, Ph.D., eds.

NCADI #M130

131 IMPACT OF PRESCRIPTION DRUG DIVERSION CONTROL SYSTEMS

ON MEDICAL PRACTICE AND PATIENT CARE. James R. Cooper, Ph.D.:

Dorynne J. Czechowicz, M.D.; Stephen P. Molinari, J.D., R.Ph.; and Robert C.

Peterson, Ph.D., eds.

NCADI #M131

132 PROBLEMS OF DRUG DEPENDENCE, 1992: PROCEEDING OF THE

54TH ANNUAL SCIENTIFIC MEETING, THE COLLEGE ON PROBLEMS OF

DRUG DEPENDENCE, INC. Louis Harris, Ph.D., ed.

GPO Stock #017-024-01502-4 $23 NCADI #M132

287
133 SIGMA, PCP, AND NMDA RECEPTORS. Errol B. De Souza, Ph.D.; Doris

Clouet, Ph.D.: and Edythe D. Condon, Ph.D., eds.

NCADI #M133

*U.S. GOVERNMENT PRINTING OFFICE: 1993 342-283/80002

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NIH Publication No. 93-3638

Printed 1993