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Anesthetic Pharmacology
Second Edition

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Anesthetic Pharmacology
Second Edition
Edited by
Alex S. Evers
Mervyn Maze
Evan D. Kharasch

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CAMBRIDGE UNIVERSITY PRESS

Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore,
São Paulo, Delhi, Dubai, Tokyo, Mexico City
Cambridge University Press
The Edinburgh Building, Cambridge CB2 8RU, UK
Published in the United States of America by
Cambridge University Press, New York
www.cambridge.org
Information on this title: www.cambridge.org/9780521896665
# Cambridge University Press 2011
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without
the written permission of Cambridge University Press.
First edition published by Churchill Livingstone 2004
Second edition published by Cambridge University Press 2011
Printed in the United Kingdom at the University Press, Cambridge
A catalog record for this publication is available from the British Library
Library of Congress Cataloging in Publication Data
Anesthetic pharmacology / edited by Alex S. Evers, Mervyn Maze, Evan D.
Kharasch. – 2nd ed.
p. ; cm.
Includes bibliographical references and index.
ISBN 978-0-521-89666-5 (hardback)
1. Anesthetics. I. Evers, Alex S. II. Maze, M. (Mervyn) III. Kharasch, Evan D.
[DNLM: 1. Anesthetics–pharmacology. 2. Analgesics–pharmacology. QV 81]
RD82.A687 2011
6150 .781–dc22
2010028676
ISBN 978-0-521-89666-5 Hardback
Cambridge University Press has no responsibility for the persistence or
accuracy of URLs for external or third-party internet websites referred to
in this publication, and does not guarantee that any content on such
websites is, or will remain, accurate or appropriate.
Every effort has been made in preparing this book to provide accurate and
up-to-date information which is in accord with accepted standards and practice
at the time of publication. Although case histories are drawn from actual cases,
every effort has been made to disguise the identities of the individuals involved.
Nevertheless, the authors, editors, and publishers can make no warranties
that the information contained herein is totally free from error, not least because
clinical standards are constantly changing through research and regulation.
The authors, editors, and publishers therefore disclaim all liability for direct or
consequential damages resulting from the use of material contained in this book.
Readers are strongly advised to pay careful attention to information provided
by the manufacturer of any drugs or equipment that they plan to use.

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Contents
List of contributors
Preface xv

viii

Section 1 – Principles of drug action
1 Pharmacodynamic principles of drug action 1
Stuart A. Forman
2 G-protein-coupled receptors
Marcel E. Durieux

17

19 Vascular reactivity 277
Isao Tsuneyoshi, Josephine M. Garcia-Ferrer,
Hung Pin Liu, and Walter A. Boyle

72

7 Drug transport and transporters 90
Roland J. Bainton and Evan D. Kharasch

20 Cardiac rhythm 293
Brian S. Donahue and Jeffrey R. Balser

8 Target-controlled infusions and closed-loop
administration 103
Michel M. R. F. Struys and Tom De Smet

132

11 Pharmacodynamic drug interactions in
anesthesia 147
Talmage D. Egan and Charles F. Minto
12 Pharmacoeconomics
Alex Macario

166

123

21 Myocardial performance 316
Pierre Foëx and Helen Higham
22 Autonomic function 330
Jonathan Moss and David Glick
23 Immunity and inflammation 345
Nigel R. Webster and Helen F. Galley

Section 3 – Essential drugs in anesthetic
practice
24 Mechanisms of anesthetic action 359
C. Michael Crowder and Alex S. Evers

Section 2 – Physiologic substrates
of drug action
13 Sleep and consciousness 177
George A. Mashour and Max Kelz

210

18 Neuromuscular function 261
Joe Henry Steinbach and Ling-Gang Wu

5 Principles of pharmacokinetics 57
Thomas W. Schnider and Charles F. Minto

10 Principles of pharmacogenetics
Kirk Hogan

15 Memory, learning, and cognition
Robert A. Veselis

17 The generation and propagation of
action potentials 248
Gary R. Strichartz

4 Other signaling pathways 47
Istvan Nagy, Isobel Lever, and Mario Cibelli

9 Alternative routes of drug administration
Ola Dale

192

16 Mechanisms of pain transmission and
transduction 227
Robert W. Gereau IV and Laura F. Cavallone

3 Ion channels 28
Thomas McDowell, Misha Perouansky, and
Robert A. Pearce

6 Principles of drug biotransformation
Evan D. Kharasch

14 Synaptic transmission
M. B. MacIver

25 Pharmacokinetics of inhaled anesthetics
Geoff Lockwood

385

26 Clinical pharmacology of inhaled
anesthetics 397
Thomas J. Ebert and Larry Lindenbaum

v
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Contents

44 Positive inotropic drugs 706
Paul S. Pagel and David C. Warltier

27 Pharmacokinetics of intravenous
anesthetics 420
Frédérique S. Servin and John W. Sear

45 Vasodilators 724
Roger A. Johns and Stephen Yang

28 Clinical pharmacology of intravenous
anesthetics 444
John W. Sear
29 Benzodiazepines 466
Uwe Rudolph, M. Frances Davies, and Juliana Barr
30 Alpha2-agonists and other sedatives
and amnestics 478
Robert D. Sanders and Mervyn Maze
31 Mechanisms of action of opioids
Michael Schäfer

47 Bronchodilators 751
Charles W. Emala
48 Pulmonary vasodilators 767
Sunita Sastry and Ronald G. Pearl
49 Renal protection and pharmacology
Dean R. Jones and H. T. Lee

493

50 Fluids and electrolytes
Robert G. Hahn

32 Pharmacokinetics of opioids 509
Dhanesh K. Gupta, Tom C. Krejcie, and
Michael J. Avram

783

800

51 Corticosteroids and anti-inflammatory drugs
Clifford S. Deutschman

33 Clinical pharmacology of opioids 531
Carl E. Rosow and Mark Dershwitz

814

52 Antirejection drugs and immunosuppressants
Nándor Marczin and Kristof Racz

34 Nonsteroidal anti-inflammatory drugs 548
Nilesh Randive and Richard M. Langford

830

53 Antimotility and antisecretory drugs 842
Robert P. Walt and Eugene B. Campbell

35 Other ion-channel and receptor ligands
for analgesia 563
Mark A. Schumacher and Helge Eilers

54 Antiemetics 855
Jens Scholz, Markus Steinfath, and Patrick Meybohm

36 Local anesthetics 574
Francis V. Salinas and David B. Auyong
37 Antiepileptic and antipsychotic drugs
W. Andrew Kofke

741

46 Calcium channel blockers
W. Scott Beattie

55 Insulin and antihyperglycemic drugs 874
Nick Oliver, Martin Smith, and Stephen Robinson
589

56 Nutritional pharmacology
Paul Wischmeyer

890

38 Neuromuscular blocking drugs 608
Heidrun Fink, Manfred Blobner, and
J. A. Jeevendra Martyn

57 Drugs affecting coagulation and
platelet function 912
Troy Wildes, Michael Avidan, and George Despotis

39 Drugs for reversal of neuromuscular
blockade 633
Mohamed Naguib

58 Obstetric pharmacology
Tony Gin

40 Sympathomimetic and sympatholytic drugs
David F. Stowe and Thomas J. Ebert
41 Parasympathomimetic and parasympatholytic
drugs 666
Berend Mets and Imre Redai

648

948

59 Antimicrobial therapy 963
Conan MacDougall, B. Joseph Guglielmo, and
Jeanine Wiener-Kronish

Section 4 – Clinical applications: evidencebased anesthesia practice

42 Beta-blockers and other adrenoceptor
antagonists 676
Andrew J. Patterson

60 Preoperative drug management
Laureen Hill

43 Antiarrhythmic drugs 689
Aman Mahajan and Charles W. Hogue Jr.

61 Induction of anesthesia
T. Andrew Bowdle

vi
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1007

987

Contents

62 Maintenance of and emergence
from anesthesia 1027
J. Lance Lichtor

67 Management of patients with chronic
alcohol or drug use 1106
Howard B. Gutstein

63 Management of sedation, analgesia,
and delirium 1041
Christopher G. Hughes, Stuart McGrane, E. Wesley Ely,
and Pratik P. Pandaharipande

68 Drug allergy and treatment
Jerrold H. Levy

64 Postoperative analgesia 1061
Richard W. Rosenquist and Ellen W. King
65 Control of blood pressure and
vascular tone 1077
Arthur Wallace

1117

69 Pediatric pharmacology 1128
Greg B. Hammer and Brian J. Anderson
70 Geriatric pharmacology
Jeffrey H. Silverstein

1139

71 Emerging concepts of anesthetic
neuroprotection and neurotoxicity
Brian P. Head and Piyush Patel

66 Cardiac protection and pharmacologic
management of myocardial ischemia 1091
Eric Jacobsohn, Waiel Almoustadi, and
Chinniampalayam Rajamohan

Index

1151

1163

vii
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Contributors

Waiel Almoustadi, MBBS
Department of Anesthesiology
University of Manitoba
Winnipeg, Manitoba, Canada
Brian J. Anderson, PhD, FANZCA, FJFICM
Associate Professor of Anesthesiology
University of Auckland
Auckland, New Zealand
David B. Auyong, MD
Staff Anesthesiologist
Department of Anesthesiology
Virginia Mason Medical Center
Seattle, WA, USA
Michael Avidan, MBBCh, FCASA
Associate Professor, Anesthesiology
and Cardiothoracic Surgery
Department of Anesthesiology
Washington University School of Medicine
St. Louis, MO, USA
Michael J. Avram, PhD
Associate Professor of Anesthesiology
Director of the Mary Beth Donnelley
Clinical Pharmacology Core Facility
Northwestern University Feinberg School of Medicine
Chicago, IL, USA
Roland J. Bainton, MD
Associate Professor
Department of Anesthesia and Perioperative Care
University of California
San Francisco, CA, USA
Jeffrey R. Balser, MD, PhD
The James Tayloe Gwathmey Professor of Anesthesiology
and Pharmacology
Vice Chancellor for Health Affairs and Dean
Vanderbilt University School of Medicine
Nashville, TN, USA

Juliana Barr, MD
Associate Professor in Anesthesia
Stanford University School of Medicine
Staff Anesthesiologist and Acting ICU
Medical Director
Veterans Administration Palo Alto
Health Care System
Palo Alto, CA, USA
W. Scott Beattie, MD, PhD, FRCPC
R. Fraser Elliot Chair in Cardiac Anesthesia
Director of Anesthesia Research
University Health Network
Department of Anesthesia
University of Toronto
Toronto, Canada
Manfred Blobner, MD
Klinik für Anaesthesiologie der Technischen
Universität München
Klinikum rechts der Isar
Munich, Germany
T. Andrew Bowdle, MD, PhD
Professor of Anesthesiology and
Adjunct Professor of Pharmaceutics
Chief of the Division of Cardiothoracic
Anesthesiology
University of Washington
Seattle, WA, USA
Walter A. Boyle, MD
Professor of Anesthesiology, Developmental
Biology and Surgery
Washington University School of Medicine
St. Louis, MO, USA
Eugene B. Campbell, MB, ChB
Consultant Gastroenterologist
Erne Hospital
Enniskillen, County Fermanagh, UK

viii
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List of contributors

Laura F. Cavallone, MD
Assistant Professor of Anesthesiology
Washington University School of Medicine
St. Louis, MO, USA
Mario Cibelli, MD
Department of Anaesthesia
St. George’s Healthcare NHS Trust
London, UK
C. Michael Crowder, MD, PhD
Professor of Anesthesiology and Developmental Biology
Washington University School of Medicine
St. Louis, MO, USA
Ola Dale, MD, PhD
Department of Circulation and Medical Imaging
Norwegian University of Science and Technology
Trondheim, Norway

Thomas J. Ebert, MD, PhD
Professor and Program Director
Department of Anesthesiology
Medical College of Wisconsin
Milwaukee, WI, USA
Talmage D. Egan, MD
Professor and Staff Physician
Director of Neuroanesthesia
Department of Anesthesiology
University of Utah School of Medicine
Salt Lake City, UT, USA
Helge Eilers, MD
Associate Professor
Department of Anesthesia and Perioperative Care
University of California
San Francisco, CA, USA

M. Frances Davies, PhD
Anesthesia Service
Stanford University
Palo Alto, CA, USA

E. Wesley Ely, MD, MPH
Department of Medicine
Health Services Research Center
Vanderbilt University School of Medicine
Nashville, TN, USA

Mark Dershwitz, MD, PhD
Professor of Anesthesiology
Department of Anesthesiology
University of Massachusetts Medical School
Worcester, MA, USA

Charles W. Emala, MD
Associate Professor of Anesthesiology
College of Physicians and Surgeons
Columbia University
New York, NY, USA

George Despotis, MD
Associate Professor, Pathology, Immunology
and Anesthesiology
Department of Anesthesiology
Washington University School of Medicine
St. Louis, MO, USA

Alex S. Evers, MD
Henry E. Mallinckrodt Professor of Anesthesiology
Professor of Internal Medicine and
Developmental Biology
Washington University School of Medicine
St. Louis, MO, USA

Clifford S. Deutschman, MD, FCCM
Professor of Anesthesiology and Critical Care
Director, Stavropoulos Sepsis Research Program
University of Pennsylvania School of Medicine
Philadelphia, PA, USA
Brian S. Donahue, MD, PhD
Associate Professor of Anesthesiology
Vanderbilt University School of Medicine
Nashville, TN, USA
Marcel E. Durieux, MD, PhD
Professor of Anesthesiology
and Neurological Surgery
University of Virginia
Charlottesville, VA, USA

Heidrun Fink, MD
Klinik für Anaesthesiologie der Technischen
Universität München
Klinikum rechts der Isar
Munich, Germany
Pierre Foëx, DM, MA
Professor of Anesthetics
Nuffield Department of Anesthetics
University of Oxford
The John Radcliffe Hospital
Oxford, UK
Stuart A. Forman, MD, PhD
Associate Professor of Anesthesiology
Harvard Medical School
Department of Anesthesia & Critical Care

ix
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List of contributors

Massachusetts General Hospital
Boston, MA, USA

University of Linköping
Linköping, Sweden

Helen F. Galley, PhD
Senior Lecturer in Anaesthesia and Intensive Care
University of Aberdeen
Aberdeen, UK

Greg B. Hammer, MD
Professor of Anesthesia and Pediatrics
Lucile Packard Children’s Hospital
Stanford University Medical Center
Stanford, CA, USA

Josephine M. Garcia-Ferrer, PhD
Department of Anesthesiology
Washington University School of Medicine
St. Louis, MO, USA
Robert W. Gereau IV, PhD
Professor of Anesthesiology
Washington University Pain Center and Department
of Anesthesiology
Washington University School of Medicine
St. Louis, MO, USA
Tony Gin, MD, FANZCA, FRCA
Anesthesia and Intensive Care and Paediatrics
The Chinese University of Hong Kong
Prince of Wales Hospital
Shatin, Hong Kong
David Glick, MD
Associate Professor of Anesthesia and Critical Care
Medical Director of the Postanesthesia Care Unit
Anesthesia and Critical Care
The University of Chicago
Chicago, IL, USA
B. Joseph Guglielmo, PharmD
Department of Clinical Pharmacy
University of California
San Francisco, CA, USA
Dhanesh K. Gupta, MD
Associate Professor of Anesthesiology
and Neurological Surgery
Northwestern University Feinberg School of Medicine
Chicago, IL, USA
Howard B. Gutstein, MD
Professor, Department of Anesthesiology
and Pain Management
Department of Biochemistry and Molecular Biology
The University of Texas – MD Anderson Cancer Center
Houston, TX, USA
Robert G. Hahn, MD, PhD
Professor of Anesthesiology
Department of Anaesthesia

Brian P. Head, PhD
Department of Anesthesiology
University of California
San Diego, CA, USA
Helen Higham, MB, ChB
Nuffield Department of Anaesthetics
John Radcliffe Hospital
Oxford, UK
Laureen Hill, MD
Associate Professor and Vice Chair of Anesthesiology
Associate Professor of Surgery
Washington University School of Medicine
St. Louis, MO, USA
Kirk Hogan, MD, JD
Professor of Anesthesiology
Department of Anesthesiology
University of Wisconsin School of Medicine
and Public Health
Madison, WI, USA
Charles W. Hogue Jr., MD
Associate Professor of Anesthesiology
and Critical Care Medicine
The Johns Hopkins Medical Institutions
The Johns Hopkins Hospital
Baltimore, MD, USA
Christopher G. Hughes, MD
Department of Anesthesiology
Vanderbilt University Medical Center
Nashville, TN, USA
Eric Jacobsohn, MBChB, MHPE, FRCPC
Professor and Chairman of Anesthesiology
University of Manitoba
Winnipeg, Manitoba, Canada
Roger A. Johns, MD, MHS
Professor of Anesthesiology and Critical Care Medicine
The John Hopkins Medical Institutions
The John Hopkins Hospital
Baltimore, MD, USA

x
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List of contributors

Dean R. Jones, MD, FRCPC
Assistant Professor of Anesthesiology
Department of Anesthesiology
Columbia University
New York, NY, USA

Jerrold H. Levy, MD, FAHA
Professor and Deputy Chair for Research
Director of Cardiothoracic Anesthesiology
Emory University School of Medicine
Atlanta, GA, USA

Max Kelz, MD, PhD
Assistant Professor of Anesthesiology and Critical Care
University of Pennsylvania
Philadelphia, PA, USA

J. Lance Lichtor, MD
Professor of Anesthesiology
University of Massachusetts Medical School
Worcester, MA, USA

Evan D. Kharasch, MD, PhD
Russell D. and Mary B. Shelden
Professor of Anesthesiology
Vice-Chancellor for Research
Washington University
St. Louis, MO, USA

Larry Lindenbaum, MD
Resident
Department of Anesthesiology
Medical College of Wisconsin
Milwaukee, WI, USA

Ellen W. King, MD
Associate in the Department of Anaesthesia
University of Iowa
Iowa City, IA, USA
W. Andrew Kofke, MD, MBA, FCCM
Professor, Director of Neuroanesthesia
Department of Anesthesiology and Critical Care
University of Pennsylvania
Philadelphia, PA, USA
Tom C. Krejcie, MD
Professor of Anesthesiology and Associate
Chair for Research
Northwestern University Feinberg School of Medicine
Chicago, IL, USA
Richard M. Langford, FRCA, FFPMRCA
Professor of Anaesthesia and Pain Medicine
Pain and Anaesthesia Research Centre
Barts and the London NHS Trust
London, UK
H. T. Lee, MD, PhD
Assistant Professor of Anesthesiology
and Vice-Chair for Laboratory Research
Columbia University
New York, NY, USA
Isobel Lever, PhD
Department of Anaesthetics, Pain Medicine
and Intensive Care
Imperial College London
Chelsea and Westminster Hospital
London, UK

Hung Pin Liu, MD
Department of Anesthesiology
Washington University School of Medicine
St. Louis, MO, USA
Geoff Lockwood, MBBS, PhD
Consultant Anaesthetist
Hammersmith Hospital
London, UK
Alex Macario, MD, MBA
Professor of Anesthesia & Health Research and Policy
Department of Anesthesia
Stanford University School of Medicine
Stanford, CA, USA
Conan MacDougall, PharmD, MAS
Department of Clinical Pharmacy
University of California
San Francisco, CA, USA
M. B. MacIver, MSc, PhD
Professor of Anesthesiology
Department of Anesthesia
Stanford University Medical Center
Stanford, CA, USA
Aman Mahajan, MD, PhD
Chief, Cardiac Anesthesiology
Associate Professor
David Geffen School of Medicine at UCLA
Los Angeles, CA, USA
Nándor Marczin, MD, PhD
Department of Academic Anaesthetics
Imperial College
Chelsea and Westminster Hospital
London, UK

xi
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List of contributors

J. A. Jeevendra Martyn, MD, FRCA, FCCM
Department of Anesthesia, Critical Care and Pain Medicine
Harvard Medical School, Massachusetts General Hospital
and Shriners Hospitals for Children
Boston, MA, USA
George A. Mashour, MD, PhD
University of Michigan Medical School
Ann Arbor, MI, USA
Mervyn Maze, MB, ChB, FRCP, FRCA, FMedSci
Chair, Department of Anesthesia
University of California, San Francisco
San Francisco, CA, USA
Thomas McDowell, MD, PhD
Associate Professor of Anesthesiology
University of Wisconsin School of Medicine and Public Health
Madison, WI, USA
Stuart McGrane, MD
Department of Anesthesiology and Division of Critical Care
Vanderbilt University Medical Center
Nashville, TN, USA
Berend Mets, MB, ChB, PhD
Eric A. Walker Professor and Chair
Department of Anesthesiology
Penn State College of Medicine
Hershey, PA, USA
Patrick Meybohm, MD
University Hospital Schleswig-Holstein
Campus Kiel, Germany
Charles F. Minto, MB, ChB
Staff Anaesthetist
Department of Anaesthesia and Pain Management,
Royal North Shore Hospital
Senior Lecturer, University of Sydney
Sydney, Australia
Jonathan Moss, MD, PhD
Professor and Vice-Chairman of Anesthesia & Critical Care
Chairman of the Institutional Review Board
Anesthesia and Critical Care
University of Chicago
Chicago, IL, USA
Mohamed Naguib, MD
Department of General Anesthesiology
Institute of Anesthesiology
Cleveland Clinic
Cleveland, OH, USA

Istvan Nagy, MD, PhD
Department of Anaesthetics, Pain Medicine
and Intensive Care
Faculty of Medicine
Imperial College
Chelsea and Westminster Hospital
London, UK
Nick Oliver, MBBS, MRCP
Clinical Research Fellow
Faculty of Medicine
Imperial College
London, UK
Paul S. Pagel, MD, PhD
Professor of Anesthesiology
Medical College of Wisconsin
Milwaukee, WI, USA
Pratik P. Pandharipande, MD, MSCI
Department of Anesthesiology and Division of Critical Care
Vanderbilt University Medical Center
Nashville, TN, USA
Piyush Patel, MD, FRCPC
Professor, Department of Anesthesioloogy
University of California
San Diego, CA, USA
Andrew J. Patterson, MD, PhD
Associate Professor
Department of Anesthesia
Stanford University Medical Center
Stanford, CA, USA
Robert A. Pearce, MD, PhD
Professor and Chair of Anesthesiology
University of Wisconsin School of Medicine and Public Health
Madison, WI, USA
Ronald G. Pearl, MD, PhD
Professor of Anesthesiology
Department of Anesthesiology
Stanford University School of Medicine
Stanford, CA, USA
Misha Perouansky, MD
Professor of Anesthesiology
University of Wisconsin School of Medicine
and Public Health
Madison, WI, USA
Kristof Racz, MD
Department of Academic Anaesthetics
Imperial College

xii
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List of contributors

Chelsea and Westminster Hospital
London, UK
Chinniampalayam Rajamohan, MBBS, MD, FRCA, FRCPC
University of Manitoba
Department of Anesthesiology
Winnipeg, Manitoba, Canada
Nilesh Randive, MD, FRCA
Lecturer in Academic Anaesthesia
Pain and Anaesthesia Research Centre
Barts and the London NHS Trust
London, UK

Sunita Sastry, MD
Department of Anesthesia
Stanford University School of Medicine
Stanford, CA, USA
Michael Schäfer, MD
Department of Anesthesiology and Intensive Care Medicine
Charité University
Berlin, Germany
Jens Scholz, MD, PhD
Department of Anesthesiology
University Hospital of Eppendorf
Hamburg, Germany

Imre Redai, MD
Assistant Professor
Department of Anesthesiology
College of Physicians and Surgeons
Columbia University
New York, NY, USA

Thomas W. Schnider, Prof. Dr. med.
Institut for Anasthesiologie
Kantonsspital
St. Gallen, Switzerland

Stephen Robinson, MD, FRCP
Consultant Physician and Endocrinologist
Imperial College NHS Trust
London, UK

Mark A. Schumacher, PhD, MD
Associate Professor
Department of Anesthesia and Perioperative Care
University of California
San Francisco, CA, USA
John W. Sear, MA, PhD, MBBS, FFARCS, FANZCA
Nuffield Department of Anaesthetics
John Radcliffe Hospital, Headington
Oxford, UK

Richard W. Rosenquist, MD
Professor of Anesthesia
Director, Pain Medicine Division
University of Iowa
Iowa City, IA, USA
Carl E. Rosow, MD, PhD
Professor of Anesthesiology
Department of Anesthesia and Critical Care
Massachusetts General Hospital
Boston, MA, USA
Uwe Rudolph, MD
Associate Professor of Psychiatry
Director, Laboratory of Genetic Neuropharmacology
Mailman Research Center
Harvard Medical School
Belmont, MA, USA
Francis V. Salinas, MD
Staff Anesthesiologist
Department of Anesthesiology
Virginia Mason Medical Center
Seattle, WA, USA
Robert D. Sanders, BSc, MBBS, FRCA
Department of Anaesthetics, Pain Medicine
and Intensive Care
Imperial College
London, UK

Frédérique S. Servin, MD
Département d’Anesthésie et de Réanimation Chirurgicale
CHU Bichat Claude Bernard
Paris, France
Jeffrey H. Silverstein, MD
Professor of Anesthesiology, Surgery, and Geriatrics & Adult
Development
Department of Anesthesiology
Mount Sinai School of Medicine
New York, NY, USA
Tom De Smet, MSc
Department of Anesthesia
University Medical Center Groningen
and University of Groningen
Groningen, The Netherlands
Martin Smith, MBBS, FRCP
Consultant Physician and Endocrinologist
Salisbury NHS Foundation Trust
Salisbury, UK
Joe Henry Steinbach, PhD
Russell and Mary Shelden Professor of Anesthesiology
Department of Anesthesiology
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List of contributors

Washington University School of Medicine
St. Louis, MO, USA
Markus Steinfath, MD, PhD
Department of Anaesthesiology and Intensive
Care Medicine
University Hospital Schleswig-Holstein
Campus Kiel, Kiel, Germany
David F. Stowe, MD, PhD
Professor of Anesthesiology
Medical College of Wisconsin
Milwaukee, WI, USA
Gary R. Strichartz, PhD
Professor of Anesthesia (Pharmacology)
Harvard Medical School
Boston, MA, USA
Michel M. R. F. Struys, MD, PhD
Professor and Chairman
Department of Anesthesia
University Medical Center Groningen
and University of Groningen
Groningen, The Netherlands
Isao Tsuneyoshi, MD
Department of Anesthesiology
Faculty of Medicine
University of Miyazaki
Miyazaki-shi, Japan
Robert A. Veselis, MD
Associate Professor
Department of Anesthesiology
Memorial Sloan-Kettering Cancer Center
New York, NY, USA
Arthur Wallace, MD, PhD
Professor of Anesthesiology and
Perioperative Care
University of California, San Francisco
San Francisco, CA, USA

Robert P. Walt, MD, FRCP
Department of Gastroenterology
Queen Elizabeth Hospital
Edgbaston
Birmingham, UK
David C. Warltier, MD, PhD
Chairman, Department of Anesthesiology
Medical College of Wisconsin
Milwaukee, WI, USA
Nigel R. Webster, MD
Anaesthesia and Intensive Care
Institute of Medical Sciences
University of Aberdeen
Aberdeen, UK
Jeanine Wiener-Kronish, MD
Henry Isiah Dorr Professor of Anesthesia
Department of Anesthesia and Critical Care
Massachusetts General Hospital
Boston, MA, USA
Troy Wildes, MD
Assistant Professor of Anesthesiology
Washington University School of Medicine
St. Louis, MO, USA
Paul Wischmeyer, MD
Professor of Anesthesiology
University of Colorado School of Medicine
Denver, CO, USA
Ling-Gang Wu, MD, PhD
Senior Investigator
National Institute of Neurological Disorders and Stroke
Bethesda, MD, USA
Stephen Yang, MD
Department of Anesthesia
Mount Sinai Hospital Medical Center
New York, NY, USA

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Preface

Recent years have seen the beginning of a revolution in our
understanding of how anesthesia is produced and how the drugs
used by perioperative practitioners work at a molecular level.
Concomitantly, the clinical practice of anesthesia has become
increasingly more complex and demanding. As a result of these
developments, there continues to be a growing chasm between
clinically sophisticated anesthesiologists who may be inadequately versed in basic and molecular pharmacology, and anesthetic researchers who are well versed in the mechanistic details
of anesthetic drug action, but inadequately informed about the
clinical context in which these drugs are used. The first edition of
Anesthetic Pharmacology: Physiologic Principles and Clinical
Practice was assembled with the aim of bridging this chasm.
Since then, the understanding of molecular mechanisms of
drug action has grown, mechanisms of interindividual variability in drug response are better understood, and the practice of
anesthesiology has expanded to the preoperative environment,
locations out of the operating room and out of the hospital, and
into various intensive care units. Consequently, Anesthetic
Pharmacology has been significantly revised into a second edition. Significant changes include the addition of a third editor,
expansion from three to four sections, and enhanced organization and readability to make the material accessible to a wide
range of trainees, practitioners, and pharmacologists.
Anesthetic Pharmacology is designed to be a sophisticated,
accessible, reliable, and user-friendly primer of fundamental
and applied pharmacology that is targeted for use by the full
spectrum of those providing care in the perioperative period.

The book is organized into four fully integrated sections.
The first two sections consider the principles and targets of
anesthetic drug action, and the last two sections address the
pharmacology and therapeutic use of the drugs themselves.
Section one, “Principles of drug action,” provides detailed
theoretical and practical information about anesthetic pharmacokinetics and about cell signaling pathways involved in
anesthetic drug action. Section two, “Physiologic substrates
of drug action,” is conveniently arranged by organ systems
and presents the molecular, cellular, and integrated physiology
of the organ or functional system, highlighting targets and
substrates. Section three, “Essential drugs in anesthetic practice,” presents the pharmacology and toxicology of major
classes of drugs that are used perioperatively. A fourth section,
“Clinical applications: evidence-based anesthesia practice,” has
been added to this edition to provide integrated and comparative pharmacology, and the practical therapeutic application of
drugs for specific perioperative indications.
The layout of the chapters accommodates to the varying
needs of the readership. Each chapter contains the fundamental body of knowledge needed by practitioners, as well as more
in-depth information, including basic research directions and
sophisticated clinical applications. The chapters all conclude
with a concise summary of the material deemed to be essential
knowledge for trainees and those seeking recertification.
Through the judicious use of illustrations, boxes, and tables,
information is presented in a comprehensible fashion for all
levels of readership.

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Section 1
Chapter

1

Principles of drug action
Pharmacodynamic principles of drug action
Stuart A. Forman

Introduction
The effects of drugs on patients in the operating room vary
with drug dosage, from patient to patient, and with time.
Different doses of drugs result in different concentrations in
various tissues, producing a range of therapeutic and sometimes undesirable responses. Responses depend on drug pharmacokinetics (the time course of drug concentration in the
body) and drug pharmacodynamics (the relationship between
drug concentration and drug effect). These processes may be
influenced by factors including pre-existing disease, age, and
genetic variability. Patient responses to drugs may also be
dynamically altered by factors such as temperature, pH, circulating ion and protein concentrations, levels of endogenous
signaling molecules, and coadministration of other drugs in
the operating room environment. Pharmacodynamics, the
focus of this chapter, is the study of where and how drugs
act to produce their effects, encompassing drug actions on
biological systems ranging from molecules to organisms and
their responses from conformational changes to behavior and
emotional states [1,2].
Developments in pharmacology have been greatly affected
by the rapid growth in our understanding of biology at the
molecular level. Molecular targets for many drugs used in the
practice of anesthesia are now known in varying degrees of
detail. This knowledge enables development of efficient assays
to identify new potential drugs and, in some cases, structurebased design of improved therapeutic drugs. The practice of
anesthesiology requires an understanding of human pharmacodynamics and pharmacokinetics, but real expertise, and particularly the ability to innovate, demands deeper understanding
of the scientific basis of our practical knowledge. The first and
larger part of this chapter focuses on central concepts of
molecular drug–receptor interactions. In actuality, most drugs
affect more than one molecular target, and the impact of drug
actions at the cellular, tissue, and organism levels are the result
of integrated effects at these higher system levels. The latter part
of the chapter covers pharmacodynamic concepts pertinent to
drug responses in animals and humans. Some of the terms

used, including potency, efficacy, and selectivity, have parallel
meanings at both the molecular and organism levels.
Throughout this chapter, molecular pharmacodynamics concepts are illustrated both with cartoons and with simple chemical
reaction schemes, which lend themselves to quantitative algebraic
analyses. This quantitative formalism is provided to encourage a
deeper understanding of important pharmacodynamic concepts
for those who make the small additional effort.

Drug receptors
Drugs are exogenous chemical substances used to alter a
physiological system. A drug may be identical to an endogenous
compound, such as a peptide, amino acid, nucleotide, carbohydrate, steroid, fatty acid, or gas. Examples of endogenous factors
used in anesthesiology include potassium for diuretic-induced
hypokalemia, insulin for diabetes, clotting factor VIII for hemophilia, and nitric oxide for pulmonary hypertension.

Receptors versus drug targets
Pharmacologic receptors are defined as macromolecular proteins on the cell membrane or within the cytoplasm or cell
nucleus that bind to specific endogenous factors (drugs), such
as neurotransmitters, hormones, or other substances, and initiate cellular responses to these drugs. Protein drug targets also
encompass circulating enzymes, non-chemically stimulated
(e.g., voltage- or mechanically activated) membrane channels,
and membrane transporters. The definition of drug targets can
be further broadened to include DNA, RNA, and epigenetic
control molecules, components of pathogenic or commensal
microbes, toxins, etc. Drug receptor proteins may consist of
one or more peptide chains.
Receptor protein structure can be characterized by
features at multiple levels:
(1) Primary structure – the amino acid sequence.
(2) Secondary structure – the peptide subdomain folding
pattern (e.g., a-helix, b-sheet, random).
(3) Tertiary structure – the entire peptide folding, including
domain–domain interactions and disulfide bridges.

Anesthetic Pharmacology, 2nd edition, ed. Alex S. Evers, Mervyn Maze, Evan D. Kharasch. Published by Cambridge University Press. # Cambridge University Press 2011.

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Section 1: Principles of drug action

(4) Quaternary structure – assembly of multiple peptides,
including peptide–peptide interactions and disulfide
bridges.
(5) Post-translational peptide modifications – including phosphorylation, lipidation, biotinylation, glycosylation, etc.
Physicochemical forces that determine receptor structure are
intrapeptide, interpeptide, and with surrounding water or
lipid. These forces include:
(1) Covalent bonds – sharing of electron pairs between atoms.
(2) Ionic bonds – attraction between oppositely charged ion
pairs (repulsion can also affect structure).
(3) Hydrogen bonds – weak dipole–dipole forces between
electronegative atoms and hydrogen, usually bonded to
oxygen or nitrogen. Solvent water provides many hydrogen bonds for proteins.
(4) Van der Waals interactions – close-range attractive and
repulsive forces between atoms.
(5) Hydrophobic interactions – forces arising from the energetically favorable interaction between nonpolar molecular domains that repel (i.e., do not hydrogen-bond with)
solvent water.
Enzymes (circulating or intracellular) are in an aqueous environment. Hydrophobic interactions tend to make these proteins
have hydrophilic exteriors and hydrophobic interiors.
Transmembrane proteins have at least one hydrophobic
domain that crosses the lipid bilayer [3]. They may have multiple hydrophobic domains within the membrane and hydrophilic domains in the extracellular and intracellular spaces.

Receptor nomenclature and categorization
Classically, drug receptors have been categorized based on
their sensitivity to various drugs (endogenous or otherwise).
For example, nicotinic acetylcholine (nACh) receptors in
muscle, neurons, and glia are strongly activated (agonized)
by acetylcholine and nicotine (an alkaloid from tobacco), and
less so by muscarine (an alkaloid from Amanita muscaria
mushrooms), whereas muscarinic acetylcholine (mACh)
receptors in smooth and cardiac muscle are strongly activated
by acetylcholine and muscarine, but weakly by nicotine. Other
receptors named for drugs widely used in anesthesia include
opioid receptors and adrenergic receptors (adrenoceptors).
Drug receptor categorization by molecular structure –
Analysis of genes and messenger RNA that encode proteins
has provided an enormous quantity of data on protein families
and superfamilies, which represent different classes of drug
receptors. The British Journal of Pharmacology’s “Guide to
Receptors and Channels” [4] lists seven classes of pharmacologic protein targets based upon similar structure and
function: seven-transmembrane (7TM) receptors, ligand
(transmitter)-gated channels, ion channels, catalytic receptors,
nuclear receptors, transporters, and enzymes. Nomenclature
for this ever-growing list is maintained by the International
Union of Basic and Clinical Pharmacology (www.iuphar-db.

org). Building upon the example given for classical receptor
nomenclature, nicotinic acetylcholine receptors are classified
as transmitter-gated channels. More specifically, nicotinic ACh
receptors on fetal muscle consist of five homologous polypeptide subunits, a1/a1/b1/g/d, surrounding a transmembrane
cation channel. The genes for these subunits were first cloned
in the 1980s, providing a complete primary amino acid
sequence [5]. Genetic analysis has subsequently identified
more than a dozen closely related polypeptides (a1–10, b1–4, g,
d, and e) that combine to form a variety of nACh receptors,
constituting a receptor family. The subunit types and stoichiometry for native pentameric nACh receptors in muscle and
neural tissues remains an area of intensive research [6]. In
adult muscle nACh receptors, the e subunit replaces d, but e
may re-emerge in muscle receptors formed during pathological conditions such as after burn or denervation injury.
Neuronal and glial nACh receptors consist mostly of either a7
subunits or a4/b2 combinations, while postsynaptic nACh
receptors in autonomic ganglia consist of a3/b4 and a3/a4/b2/
b4 combinations.
Muscarinic ACh receptors are distinguished from
nicotinic ACh receptors not only by their distinct pharmacology and tissue distribution; they belong to an entirely
separate superfamily of receptors, the seven-transmembrane
G-protein-coupled receptors. Genetic analysis has revealed
five distinct types of muscarinic receptors in a family
(M1 through M5) [7].
Receptor superfamilies of related cellular receptors
have been identified based on structural analyses (mostly
peptide sequence homologies from genetic data, but also
x-ray crystallography) and functional studies. Receptors within
superfamilies are thought to have evolved from common
ancestor receptors. This chapter provides a broad overview of
several chemoreceptor superfamilies (Fig. 1.1). Following
chapters contain detailed discussion of some of these
superfamilies.
(1) The seven-transmembrane receptors, also known as
G-protein-coupled receptors (GPCRs) are the largest
superfamily of drug targets, containing over 60 families
of proteins [8–11]. Some genes encode seven-transmembrane receptors with yet undefined physiological roles,
known as orphan receptors. These are membrane proteins
formed by a single peptide containing seven transmembrane helices with an extracellular N-terminal domain
and an intracellular C-terminal domain. Endogenous
GPCR agonists include neurotransmitters, small peptide
hormones, neurotransmitters, prostanoids, and nucleotides. The intracellular domains of these receptors interact with a heterotrimeric G-protein complex that
includes a GTPase domain. Activation of GPCRs leads
to generation of second messengers such as cAMP,
cGMP, and intracellular calcium. Persistent activation
leads to a drop-off in activity, termed desensitization,
via several mechanisms. Intracellular domains may be

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Chapter 1: Pharmacodynamic principles of drug action

Transmitter-Gated Channel

A

G-Protein-Coupled Receptor

B

K+

Na+

ACh
ACh

Plasma
β
αq
Na+

PIP2

1 2 3 4 5 6 7

Membrane

K+

γ

αq

β γ
αq
GDP

GDP

GTP GDP

P

PLC

DAG

P

PKC

P
P

P

P

IP3

GTP

Second
Messenger
Effects

Ca2+

Intracellular Effectors
(protein kinases)
C

Catalytic Receptor

D

Intracellular Receptor

Binding
domain
Endocytosis = down regulation

Plasma Membrane
P

Y

Y

P

Y

YP

Y

Nuclear Membrane

Y

ATP ADP

P

Catalytic
kinase
domain

Y

P

Y

Intracellular Effectors (protein kinases)

Zn

2+

Zn

2+

Genomic
response
element

Transcription activation
or inhibition
Figure 1.1. Drug receptor superfamilies. Illustrations of different families of receptor proteins, including (A) transmitter-gated ion channels, (B) G-protein-coupled
receptors, (C) catalytic receptors, and (D) intracellular receptors.

modified by intracellular enzymes, blocking interactions
with G-protein complexes. In addition, these receptors
may be removed from the cell surface via endocytosis.
This superfamily is described in detail in Chapter 2.
Drugs used in anesthesia that target GPCRs include
atropine and glycopyrollate (muscarinic ACh receptors),
antihistamines (histamine receptors), opioids (opioid
receptors), adrenergic drugs (adrenoceptors), adenosine
(adenosine receptors), and some antiemetics (dopamine
receptors).
(2) The Cys-loop ligand-gated ion channel superfamily
(LGICs) are transmitter-gated channels. This superfamily
includes four families of membrane proteins that are fast
neurotransmitter receptors: nicotinic ACh receptors, gaminobutyric acid type A (GABAA) receptors, glycine
receptors, and serotonin type 3 (5-HT3) receptors
[12,13]. All of these ligand-gated ion channels contain five
subunits arranged around a transmembrane ion pore. All
subunits in this superfamily have structures that include
a large N-terminal extracellular domain containing a
Cys-X13-Cys motif (the Cys-loop), four transmembrane

(TM) helical domains, and a large intracellular domain
between TM3 and TM4. Activating drugs (neurotransmitters) bind to sites formed at the interface between
extracellular domains [14]. These binding events are
coupled to gating of the ion-conductive pore, and
opening of this ion channel leads to altered electrical
potential within cells. Persistent activation of these receptors leads to desensitization via a conformational change
in the receptor that reduces response to neurotransmitter. This superfamily is described in detail in Chapter 3.
Drugs used in anesthesia that target Cys-loop LGICs
include neuromuscular blockers (nicotinic ACh receptors), intravenous and volatile general anesthetics
(GABAA and glycine receptors), and antiemetics (5-HT3
receptors).
(3) Catalytic receptors contain an extracellular drug-binding
domain, one (typically) or more transmembrane
domains, and an intracellular enzyme domain. There
are several classes of these receptors: receptor tyrosine
kinases (RTKs) [15,16], tyrosine kinase associated receptors (TKARs), receptor serine/threonine kinases (RSTKs),
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Section 1: Principles of drug action

receptor guanylate cyclases, and receptor tyrosine phosphatases (RTPs). Drugs include growth factors (e.g., insulin), trophic factors, activins, inhibins, cytokines,
lymphokines such as tumor necrosis factor [17,18], and
natriuretic peptide. Toll-like receptors, which recognize
molecular markers on invasive pathogens and activate
cellular immune defenses, are also in this class. Drug
binding to catalytic receptors usually causes receptor
dimerization with accompanying activation. Intracellular
enzymatic activity triggers a variety of functional changes.
Active dimer forms undergo endocytosis as a mechanism
of desensitization.
(4) Intracellular receptors – Nuclear receptors are a superfamily of intracellular transcription factors that interact
with small hydrophobic molecules such as steroids,
vitamin D, thyroid hormones, and retinoid hormones
(retinoic acid and vitamin A) [19]. Receptor–drug complexes either form in the nucleus or translocate from
cytoplasm to nucleus. Genomic DNA response elements
bind to dimeric receptor–drug complexes at 60-aminoacid domains that also coordinate zinc ions. Nuclear
receptors regulate gene transcription.
(5) Endocytotic receptors are transmembrane receptors
that bind extracellular drugs and then translocate into
the cell by endocytosis, a process of clathrin-coating,
invagination, and vesicle formation. These receptors
take up essential cell nutrients such as cholesterol (bound
to low-density lipoprotein or LDL) and iron (bound to
ferritin). Other cell-surface receptors may undergo endocytosis as a mechanism of receptor downregulation,
usually following persistent activation.
(6) Other protein drug targets. The above list of receptors is
truncated for simplicity. Other drug receptor superfamilies include many ion channels such as transient receptor
potential (TRP) ion channels (important in peripheral
sensory transduction) and voltage-gated ion channels,
including sodium channels, potassium channels, chloride
channels, and calcium channels (important in myocardium, skeletal muscle and nerve excitability, and propagation of electrical signals). Other transmitter-gated ion
channels include N-methyl-D-aspartate (NMDA)-sensitive and kainite-sensitive glutamate receptor ion channels,
purinergic receptors, and zinc-activated channels. Drug
targets also include a variety of transmembrane pumps
and transporters for ions (e.g., the Naþ/Kþ/2Cl cotransporter target of the diuretic furosemide), neurotransmitters, and other molecules. Intracellular and circulating
enzymes represent another large class of drug targets,
including cyclooxygenase, lipoxygenases, phosphodiesterases, and hemostatic factors.
There are several common themes in the physiology of drug
receptor superfamilies. First, receptor–effector coupling is
often a multiple-step process, providing these systems with
both positive (amplification) and negative feedback. Second,

active receptors usually are formed from multiple peptides.
Drug-gated ion channels exist as multimers with multiple sites
for their endogenous drugs, and in most cases more than a
single drug must bind in order to activate these channels.
G-protein-coupled receptors are multimeric complexes that
dissociate upon activation. Both enzyme-linked receptors and
intracellular receptors dimerize as they activate following drug
binding. Third, most receptor molecules undergo desensitization following persistent activation.

Drug–receptor interactions
Drug–receptor binding
The first step in the chain of events leading to a drug effect in a
physiological system is binding to a site on its receptor. Drug
binding sites on receptor molecules are classified as orthosteric (the site where endogenous activators bind) or allosteric.
The term allosteric literally means other place, and was originally applied to modulatory sites on enzymes that are distinct
from active (substrate) sites. When applied to receptors, the
term may have multiple meanings. In particular, the orthosteric sites of chemoreceptors “allosterically” alter activity of the
“active sites,” which may be enzymatic sites where substrates
bind, sites where other proteins (e.g. G proteins) bind, or ion
pores.
Drug binding studies on receptors are used to characterize
their affinities. Measuring binding in tissue, cells, or purified
receptor proteins requires the ability to accurately measure
receptor-bound drug independently from free (unbound)
drug, and correction for nonspecific binding to other components of tissues, cells, and even experimental equipment.
Whereas drug binding to receptors will display saturation as
all of the receptor sites become occupied, nonspecific binding
is characterized by low affinity and is therefore usually linear
and nonsaturable over the drug concentration range relevant
for receptor binding (Fig. 1.2).
Reversible interactions between drugs and their receptor
sites are determined by the same noncovalent biophysical
forces that affect protein structure: ionic bonds, hydrogen
bonds, van der Waals interactions, and the hydrophobic effect.
At the molecular level, initial drug–receptor binding is a
bimolecular association process, and the drug concentration
(in moles/liter, M) is an independent (controllable) variable in
in-vitro experiments. The bimolecular association rate is [D]
kon, where kon is the on-rate in units of M 1 s 1. Drug dissociation is a unimolecular process, characterized by an off-rate,
koff, with units of s 1 (Eq. 1.1). The strength of reversible
interactions between a drug and its site(s) on a receptor is
reflected in its equilibrium binding affinity, which is usually
reported as a dissociation constant, KD, with units in moles/
liter (M). When the drug concentration [D] ¼ KD, association
and dissociation rates are equal. High affinity is associated
with a low KD, and low affinity with a high KD.

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Chapter 1: Pharmacodynamic principles of drug action

A

Specific vs. nonspecific binding

B

Figure 1.2. Drug binding graphical analysis.
(A) Illustration of specific vs. nonspecific binding.
(B) Correcting total binding for nonspecific binding
produces a saturable hyperbolic binding curve on
linear axes (Eq. 1.4). (C) Semilogarithmic plot with
logarithmic concentration axes. (D) Lineweaver–
Burke double-reciprocal plot. (E) Scatchard plot.

Hyperbolic binding curve
1.0

l

Fraction Occupancy

Bound Drug

Tota

Specific

ic

pecif

Nons

0.8
D × kon

0.6
D+R
0.4

RD
koff

0.2
0.0

C

Semi-logarithmic

0

D

Double-reciprocal

0.8
0.6
0.4
0.2

–1/KD

1/[Bound Drug]

Fraction Occupancy

1.0

1/Rtot

0.0
0

0.01 0.1

1

10 100

10
20
30
40
50
Drug Concentration (× KD)
E
[Bound Drug]/[Free Drug]

Free Drug Concentration

60

Scatchard

slope = –1/KD

intercept = Rtot
[Bound Drug]

1/[Free Drug]

Drug Concentration (× KD)

Drug binding
A quantitative treatment of this concept should be familiar
from chemical equilibrium theory. In the simplest case with a
single drug binding site:
½D kon

!
D þ R


RD

ð1:1Þ

koff
where KD

koff ½R ½D
¼
½RD
kon

ð1:2Þ

½D
KD

ð1:3Þ

Thus ½RD ¼ ½R

Assuming the total number of receptors Rtot ¼ R þ RD is
constant (the law of mass action), then the fraction of bound
receptors is:
½R ½KDD
½RD
½RD
½D

¼
¼
¼
½Rtot ½R þ ½RD ½R 1 þ ½D
½D þ KD

ð1:4Þ

KD

Equation 1.4 is a Langmuir isotherm or a hyperbolic binding
curve (Fig. 1.2B). Site occupancy is ~1% at [D] ¼ 0.01 KD,
10% at 0.11 KD, 50% at KD, 90% at 9 KD, and 99% at
99 KD. Because of the wide range (four orders of magnitude)
of drug concentrations needed to span from low occupancy
to nearly saturated, binding curves are frequently plotted
with drug concentration on a logarithmic axis (Fig. 1.2C).

The semilog plot displays a sigmoid shape. The midpoint of
this curve (50% occupancy) corresponds to KD.
Linear transformations of Eq. 1.4 are frequently used to
provide easier graphical analysis (common before computerized
nonlinear regression analysis). The Lineweaver–Burke or
double-reciprocal plot (Fig. 1.2D) is readily derived from Eq. 1.4:
½Rtot ½D þ KD
KD
1
1
1
KD
¼ 1 þ ; thus
þ
¼
¼
½D
½D
½RD
½RD ½Rtot ½D ½Rtot

ð1:5aÞ

Plotting 1/[RD] vs. 1/[D] (i.e., reciprocal of bound drug vs.
reciprocal of free drug) gives a line with slope ¼ KD/[Rtot] and
intercept on the y-axis ¼ 1/[Rtot]. The extrapolated x-axis
intercept is –1/KD.
Equation 1.5a can be rearranged to give:
½RD ½Rtot ½RD

¼
½D
KD
KD

ð1:5bÞ

Equation 1.5b is the basis for another common linear transformation of binding data, the Scatchard plot (Fig. 1.2E).
For Scatchard analysis, the ratio of bound to free drug ([RD]/
[D]) is plotted against bound drug ([RD]), resulting in a line
with slope ¼ –1/KD and x-axis intercept ¼ Rtot.
Stoichiometry of drug binding may be greater than one
site per receptor, especially for multi-subunit receptors. When
more than one site is present, there may be different binding
affinities associated with different receptor subsites. In addition there may be cooperative interactions between different
subsites. Binding cooperativity may be positive or negative.
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Section 1: Principles of drug action

Positive cooperativity is when occupancy of one site enhances
binding at another site. Negative cooperativity is when occupancy of one site reduces affinity at another site.
Selectivity – Drug receptor sites display variable degrees of
selectivity for drugs with slightly different molecular structures
[20–22]. An important example of this concept is the selectivity
for different adrenoceptor subtypes (a1, a2, b1, and b2) to
various derivatives of the endogenous transmitters epinephrine
and norepinephrine (e.g., phenylephrine, dopamine, isopreterenol, terbutaline, etc.) [23]. Another common feature of many
drug sites is stereoselectivity. Drugs often have one or more
chiral centers. A single chiral center means that the drug can
exist as a pair of enantiomers (mirror images, R- or S-, d- or l-),
while multiple chiral centers results in diastereomers. Drug
enantiomers (and diastereomers) may interact differently with
receptor sites and with other sites. If a high-affinity stereoisomer can be isolated, it may act as a more potent, more efficacious, and less toxic drug. Examples used in anesthesia include
etomidate, a general anesthetic used as a pure R(þ) stereoisomer [24], levobupivacaine, the L-isomer of bupivacaine [25],
and cisatracurium, the cis-diastereomer of atracurium.
Specificity – Many drugs bind to more than one molecular
target at clinically relevant concentrations. One receptor may
mediate the desired therapeutic action, while binding to other
targets may be associated with side effects or toxicity. Specificity of binding is therefore usually a desirable feature of drugs.
High specificity means that the drug interacts with only one or
a small number of target sites.
Small hydrophilic drugs can diffuse rapidly and are
exploited for rapid cell-to-cell signaling (e.g., neurotransmission). Small drugs have limited ability to form noncovalent
binding interactions, so are generally lower affinity and lower
specificity than large drugs. In some cases, two or more small
drugs are required for effect (e.g., neurotransmitters). Large
drugs diffuse more slowly, but can generate more binding
affinity and specificity.

Consequences of drug–receptor
interactions
The previous section examined drug binding to receptors.
This section now examines the consequences of drug binding,
that is, drug response or drug effects. Drugs may either
increase or decrease various functions of biological systems.
Drug effects may be studied in molecules, cells, or tissues
under conditions of well-defined free drug concentration,
resulting in concentration–response relationships [26]. Drug
responses are typically graded within an experimentally established minimum to maximum range, and may be mediated
directly by the drug receptor (e.g., an ionic current due to
activation of an ion channel chemoreceptor) or by a second
messenger (e.g., cAMP concentration) or other downstream
cellular processes (e.g., muscle contraction force).

Most drug effects are reversible, ending when drug concentration and occupation of receptor binding sites diminish to
zero. Drug effects may also be irreversible. Irreversible drugs
form covalent bonds with receptors (e.g., aspirin acetylates
cyclooxygenase, irreversibly inactivating the enzyme). Pseudoirreversible drugs are high-affinity noncovalent drugs
that unbind so slowly that they are effectively irreversible.
Antibodies and certain toxins that bind with sub-nanomolar
affinity behave pseudoirreversibly.

Agonists
Agonists are drugs that bind to and activate receptors,
resulting in a biological response. Agonist effects are described
by two fundamental characteristics, efficacy and potency
(Fig. 1.3A) [27]. Efficacy reflects the ability of the agonist to
activate the receptor, and is the maximal response or effect
possible when all receptor sites are fully occupied (sometimes
called Emax). Agonists may be classified as full agonists (high
efficacy) or partial agonists (low efficacy). Full agonists elicit a
maximum possible response from a system, while partial agonists elicit less than a full response, even when all receptors are
occupied. At the receptor level, full agonists activate nearly all
receptors, while partial agonists activate only a fraction of
receptors [28,29]. Partial agonism can be a desirable feature
of drugs, particularly when full agonism is associated with
toxicity. For example, full opioid receptor agonists can cause
profound respiratory depression, whereas partial agonists that
cause less respiratory depression may provide a safety advantage, while also limiting antinociceptive efficacy.
Potency refers to the concentration (or amount) of a
drug needed to produce a defined effect. The most common
measure of agonist potency is the half-maximal effective
concentration (EC50), the concentration at which a drug
produces 50% of its maximal possible response in a molecule,
cell, or tissue [27]. Potency and EC50 are inversely related:
when EC50 is low, potency is high, and vice versa. At the
molecular level, agonist potency is related to its affinity for
the receptor, but is not exactly equal to it, because receptor
activation is not equivalent to agonist binding. Quantitatively, an agonist’s EC50 is a function of both its binding
affinity, KA (the subscript A designates an agonist drug) and
its efficacy, which depends on the series or network of linked
responses that follow binding. A simple example is a two-step
model for activation of a receptor-ion channel target, where
efficacy is represented by a second monomolecular transition
from inactive (nonconductive) to the active (conductive)
state (Eq. 1.6). Agonist (A) binding to the inactive receptor
(R) is defined by the equilibrium binding site affinity KA,
and channel activation is characterized by the equilibrium
between inactive and active drug-bound receptors (RA and
RA , respectively). If the inactive $ active equilibrium
strongly favors the RA state, then the RA state is depopulated, which results in more receptor binding. When this
happens, EC50 is lower than KA (Fig. 1.3B).

6
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Chapter 1: Pharmacodynamic principles of drug action

A × kon

activate

A+R

RA

A

RA*
deactivate

koff
B
Maximal response or
Emax (Efficacy)

Φ = 0.01 Full
Agonism

1.0

Φ = 0.1

80
Fraction Max. Activity

Response (% of maximum)

100

60

40
EC50
(Potency)

20

0.8
0.6
Φ=1

0.4

Φ=3

0.2

Partial
Agonism

Φ = 10

0

0.0
0

0.01

0.1

1

10

100

0

1000

0.01

0.1

1

10

[A] × KA

Drug Concentration (M)

Figure 1.3. Agonist efficacy and apparent potency (EC50). (A) This panel appears similar to Fig. 1.2C, except that the ordinate is a physiological response, rather than
binding-site occupancy. The maximal response is drug efficacy. The concentration producing half-maximal response is the EC50. (B) Lines were generated using
Eq. 1.7. Affinity for inactive receptors, KA, was held constant. f is defined as the equilibrium constant for activation: f ¼ [RA]/[RA ]. Thus, a low f value is associated
with full agonism and a high f is associated with partial agonism. The midpoints of the curves, EC50, are indicated by vertical bars. Note that EC50 approximates
KA only when f is much larger than 1.

½A kon
kactivate





!

!
A þ R RA


RA
koff

ð1:6Þ

kdeactivate

where KA is the dissociation constant for A binding to R and
kdeactivate
½RA
f
. The fraction of active receptors is:
¼
kactivate
½RA
½A
½R
½RA
½ A
fK A

¼
¼
½ A ½ A
½Rtot
½A þf½A þfK A
½R 1þ
þ
K D fK A
!


1
½ A

¼
A
1þf
½A þ fK
1þf

receptor and tightens agonist binding. Agonist binding to active
receptors is characterized by a dissociation constant of fKA.
Multiple agonist sites and the Hill equation – When
occupancy of more than one drug-binding site is required to
activate a receptor, concentration–response curves often display a steeper dependence on drug concentration. The case
with two equivalent sites is:
½A kon
2½A kon





!

!
D þ R RA




RA2



ð1:7Þ

Equation 1.7 has the same form as Eq. 1.4, with a maximum
app
amplitude of (1 þ f) 1 and half-maximal concentration (KA or
1
EC50) ¼ fKA /(1 þ f). The amplitude factor (1 þ f) is agonist
intrinsic efficacy, often designated as e [27]. When f is large
(inactive state favored), efficacy is low (partial agonism) and
when f is small, efficacy is high (full agonism). The EC50 is only
close to KA when f >> 1 (i.e., for weak partial agonists). When
efficacy is high (i.e., f < 1), EC50 is less than KA (Fig. 1.3B).
Note that the serial binding ! activation scheme in Eq. 1.6
does not allow nondrugged receptors to activate. The conformational change triggered by agonist binding is presumed to be due
to “induced fit,” wherein agonist binding to the inactive receptor
induces or allows a conformational change that both activates the

koff

ð1:8Þ

2koff

Dissociation constants at each step reflect the different binding and
unbinding rates depending on the number of binding sites. Thus:
2 ½A
ð1:9Þ
½RA ¼ ½R
KA
and
½RA2 ¼ ½RA

½ A
½ A 2
¼ ½R 2
KA
2 KA

ð1:10Þ

The fraction of activatable RA2 receptors is:
½RA2
½RA2
¼
½Rtot ½R þ ½RA þ ½RA2
½ A 2

2
½R 2
½A
KA
¼
¼


½ A þ K A
2½A ½A 2
½R 1 þ
þ 2
KA
KA

ð1:11Þ

7
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1.0

Figure 1.4. Hill analysis
for multiple agonists. Semilogarithmic logistic dose–
response curves, generated using Eq. 1.13, with
n ¼ 1, 2, and 3. Note that
the midpoint of the curves
(EC50) is not dependent on
the Hill-slope, n.

n=2
n=3

0.8

n=1

0.6
0.4
0.2
0.0
0

0.01 0.1
1
10
100
Drug Concentration (× KD)

The general form of this equation for n equivalent drug (D)
sites is:
RDn
¼
Rtot



D
D þ KD

ð1:14Þ

kI

RI

n
ð1:12Þ

Note that when D ¼ KD, RDn/Rtot ¼ (0.5)n. The half-maximal
occupancy/activity concentration, EC50, is no longer proportional to KD (KA). A closely related equation that is often used
for graphical/parametric analysis of concentration–response
data is the Hill equation [30], also known as the logistic
equation:
Response ¼ ðEmax Emin Þ

display mutually exclusive binding. Binding assays with
increasing concentrations of competitive antagonists result in
reduced agonist binding, and vice versa. Thus, addition of a
reversible competitive antagonist results in a rightward shift of
the agonist dose–response (toward higher doses), decreasing
the apparent potency (increased EC50) of the agonist.
Reversible competitive antagonist binding and effects are
surmountable – increasing the concentration of agonist displaces inhibitor from binding sites and restores full agonist
occupancy and response – and therefore agonist efficacy is
unchanged (Fig. 1.6B).
K A
A þ I þ R
!
RA


!




Fraction Occupancy or Max. Effect

Section 1: Principles of drug action

½D n
n þ Emin
½D þEC50
n

ð1:13Þ

Emax and Emin are respectively, maximum and minimum
responses. In Eq. 1.13, the half-maximal effect concentration
(EC50) is independent of n (Fig. 1.4). Values of the Hill coefficient (n) larger than 1 indicate more than one drug site and
possible positive cooperativity. Values of n lower than 1 may
also indicate multiple drug sites (heterogeneous binding) with
possible negative cooperativity.
Indirect agonists act through mechanisms that do not
involve binding to the target receptor. A common example in
anesthesia is the use of acetylcholinesterase inhibitors such as
neostigmine and pyridostigmine to reverse neuromuscular
blockade. By slowing the breakdown of acetylcholine (ACh)
in motor synapses, these drugs increase the ACh concentration, increasing the activation of postsynaptic nicotinic ACh
receptors.

Antagonists
Antagonists are drugs that inhibit receptor activity [31].
Receptor antagonists can be classified as competitive or noncompetitive (Fig. 1.5).
Competitive antagonists bind at the orthosteric (agonist)
sites, but do not activate receptors. As a result, they prevent
agonists from occupying those sites and inhibit receptor activation. In other words, competitive antagonists and agonists

In Eq. 1.14, A is an agonist, while I is a reversible competitive
antagonist with dissociation constant KI (the subscript I is for
inhibitor). We eliminate receptor activation for simplicity. The
fraction of activatable RA receptors is:
½RA
½RA
¼
¼
½Rtot ½R þ ½RA þ ½RI

½A
½R
K
A

½A ½I
½R þ ½R
þ
KA KI

ð1:15Þ

½A


¼
½I
½A þ K A 1 þ
KI
This equation again has the general form of a Langmuir
isotherm with a constant maximum occupancy of 1.0 and half
occupancy at [A] ¼ KA (1 þ [I]/KI). Thus, as [I] increases,
agonist concentration–responses shift rightward in a parallel
fashion and EC50 increases as a linear function of [I] (Fig. 1.6).
Schild analysis [32] is based on this relationship: the ratio of
agonist concentrations needed to evoke an equal response (e.g.,
50% of maximum) in the presence vs. absence of a competitive
inhibitor is:
½ECX I
½I
¼1þ
½ECX 0
KI

ð1:16Þ

A similar relationship exists for the competitive inhibitor
when the agonist is varied. The IC50 for inhibitors is the
concentration that inhibits half of the control response with
no inhibitor. Thus:
½ A
1
½A

¼
IC50
2 ½A þK A
½A þK A 1þ
KI
Solving for IC50, one obtains:


½A
IC50 ¼ KI 1 þ
KA

8
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ð1:17Þ

ð1:18Þ

Chapter 1: Pharmacodynamic principles of drug action

Orthosteric
site

Figure 1.5. Model receptor illustration of agonism
and antagonism. Top: A simple catalytic receptor
model is illustrated, depicting agonist (blue triangle)
binding, which induces a conformational change
allowing substrate (green) binding and phosphorylation. Middle: A competitive inhibitor (red triangle)
binds to the agonist (orthosteric) site, preventing
agonist binding and activation of the receptor.
Bottom: A noncompetitive inhibitor (red diamond)
does not block agonist binding, but binds at the
active site, preventing substrate binding and
thereby reducing activity whether or not agonist
binds.

Active Receptorenzyme

agonist

Inactive
Receptorenzyme

P
P
Catalytic
site

ATP

Competitive
antagonist

ADP

agonist

P

substrate

ATP

ADP

agonist

P

ATP

Partial agonists as competitive antagonists – In the presence
of full agonists, partial agonists appear to inhibit receptors like
competitive antagonists. Partial agonists bind at orthosteric
sites, preventing occupancy by full agonists, and reducing
activation. Partial agonists do not produce full inhibition,
because high concentrations activate a fraction of receptors.
Their inhibitory effect is surmountable with increased concentrations of full agonist.
Noncompetitive antagonists bind at sites other than the
orthosteric site (allosteric sites). Thus, noncompetitive antagonists can bind to receptors whether or not orthosteric sites are
occupied by agonist. In the simplest case of noncompetitive
inhibition, agonist binding is unaffected, but receptor activation is blocked. Thus, addition of noncompetitive antagonists
will not alter agonist binding affinity or the number of agonist
sites, but result in a reduced number of activatable receptors. In
the presence of noncompetitive antagonism, agonist concentration–responses display reduced agonist efficacy with
unaltered EC50 (Fig. 1.7). Inhibition by noncompetitive antagonists is not surmountable with high agonist concentrations.
Equation 1.19 and Figure 1.7 illustrate noncompetitive
antagonism when the affinities of agonists and antagonists
are independent:

ADP

K A
A þ I þ R

!
RA
kI



!




substrate



!




Noncompetitive
antagonist

kI

ð1:19Þ

KA

!
RI


RAI

Thus:
½RA
½RA
¼
¼
½R þ ½RA þ kRIk þ ½RAI
Rtot
½ A


½R
KI
½ A
K
A

¼

½A ½I ½A ½I
½I þKI
½A þKA
½R 1þ
þ þ
KA KI
KI

ð1:20Þ

This equation again is a Langmuir isotherm with amplitude ¼
KI/([I] þ KI) and EC50 ¼ KA. (We have again simplified
the math by eliminating receptor activation steps.) In this case,
IC50 ¼ KI. Note that agonist EC50 is independent of inhibitor
concentration and IC50 is independent of agonist concentration.
Irreversible antagonists, whether they act at the orthosteric site (competitive) or not, reduce the number of activatable receptors, while the remaining unbound receptors behave
normally. This is another form of insurmountable inhibition,
9

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Section 1: Principles of drug action

B

A

KA
D+A

RA

KI

RI

Fraction Max Effect or Bound

1.0
0.8

0.4
0.2

C Double-reciprocal

EC50(I = 10 KI)

EC50(0)
[I] = 0

0.1

1
[A] × KA

10

Competitive
Antagonist
added

–1/KA

100

slope = –1/KA

Comp. Antag.
added

1/[Free Agonist]

intercept = Rtot

[Bound Agonist]

A

B
1.0

Emax

KA
RA

KI

KI
KA
RI

RAI

Fraction Maximal Effect

[I] = 0

D+A

Figure 1.6. Competitive inhibition. (A) Mutually
exclusive receptor occupation is depicted schematically. The RA state can activate, but the RI state
cannot. (B) Agonist concentration–response curves
were generated with Eq. 1.13. Addition of a competitive inhibitor reduces agonist binding and
effects at low agonist concentrations, while increasing agonist EC50 (shifting agonist concentration–
response rightward). The inhibition is surmountable,
as Emax remains unchanged. (C) A double-reciprocal
plot for agonist binding experiments in the presence of a reversible competitive inhibitor shows an
altered slope and a change in apparent KA, but the
same number of receptors. (D) A Scatchard plot for
agonist binding depicting the change in slope in the
presence of a competitive inhibitor.

D Scatchard

[Bound Ag.] / [Free Ag.]

1/[Bound Agonist]

Comp.Antag.
added
[I] = 10 KI

0.6

0.0
0.01

1/Rtot

Emax

0.8
Non-comp.
antag. added

EC50
0.6

Emax
0.4

[I] = 1 × KI

0.2

[I] = 3 × KI

0.0
0.01

[I] = 10 × KI
0.1

1

10

Figure 1.7. Noncompetitive inhibition. Left:
A scheme depicting binding of inhibitor (I) to receptors whether or not agonist is bound. Right: The
panel shows the effect of non-competitive inhibitor
on agonist concentration–response curves. Noncompetitive and irreversible antagonists reduce
apparent agonist efficacy (Emax) without changing
apparent KA (EC50), indicated by the vertical bars.
Note that agonist binding studies in the presence
of a noncompetitive inhibitor of this type will not
show any change, because the inhibitor does not
compete with agonist or alter its affinity.

Emax
Emax

100

[A] × KA

and concentration–response data in receptors exposed to irreversible antagonists appear similar to those for noncompetitive
antagonists. Competitive binding studies can reveal whether
an irreversible antagonist binds at the orthosteric site, which
would lead to reduced agonist binding, or allosteric sites,
which would not reduce agonist binding.

Indirect antagonism occurs without receptor binding.
One mechanism of indirect antagonism is direct binding to
agonist (or drug), making it unable to bind to its receptor.
An example is the use of protamine to bind and inactivate
heparin, preventing activation of its molecular target,
antithrombin.

10
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Chapter 1: Pharmacodynamic principles of drug action

R



!






!




L0

L1
K A


!

RA

ð1:21Þ



ð1:22Þ

Thus, this system is defined by only three equilibrium
constants.
The ratio K A /KA c is the allosteric agonist efficacy.
Highly efficacious agonists (c << 1) shift the equilibrium
strongly toward the active state by binding much more tightly
to active than to inactive receptors.
The fraction of active receptors is:
½R þ ½RA
1


¼
Rtot
1 þ L0 1þ½A =KA
1þ½A =KA

ð1:23Þ



!




1=2K A
2K A

!

!
2A þ R

RA


RA2

KA

!
A þ R


RA



!




L1 K A
¼
L0
KA

A more general treatment of drug–receptor interactions
enables formal description of situations that are frequently
observed in molecular pharmacology, but which are poorly
described by serial binding-activation models. These receptor
models, introduced in 1965 by Monod, Wyman, and Changeux, are referred to as allosteric models, based on the fact that
agonist binding sites for receptors are distinct from their
active sites (ion channels or enzyme domains, etc.) [33]. The
major difference between allosteric activation models and the
serial binding-activation models described above is that allosteric models allow for receptor activation in the absence of
agonists. This adds a fourth state, R , and results in a cyclic
scheme (Eq. 1.20). Many receptors, including many GPCRs,
are indeed partially active in the absence of agonists, indicating
a pre-existing equilibrium between active and inactive receptors [34]. Agonist binding shifts this equilibrium further
toward the active state, and, by implication, agonists bind
more tightly to the active state than to the inactive state. The
existence of the R state differs fundamentally from the
induced-fit hypothesis implied by serial binding-activation
models. In practice, serial binding-activation represents a
subset of conditions that can be described by allosteric
models, specifically when the fraction of R is extremely small
relative to R.
The simplest allosteric model for agonism is shown in Eq. 1.21.



!




Note that undrugged R can convert to an active state R
without agonist binding. An equilibrium constant (L0) characterizes this monomolecular transition: L0 ¼ [R]/[R ]. L1 is
equivalent to f in Eqs. 1.6 and 1.7. Equation 1.21 also explicitly shows that agonist binding to active receptors is different
from binding to inactive receptors. Furthermore, because of
the cyclic nature of the scheme, there is a constraint on the
system, KA L1 ¼ L0 K A , so:

When [A] ¼ 0, the minimum fraction of active receptors is
(1þL0) 1 and when [A] is very high (occupying all agonist sites),
the fraction of active receptors approaches (1 þ cL0) 1.
Also implicit in the allosteric gating concept is that
there are conformational changes in the agonist binding site
that are coupled to activation of the receptor. These conformational changes are associated with tighter binding to agonist
at the orthosteric site, but are not necessarily “induced” by
agonist binding, because they may occur in the absence of
agonists.
Using the formalism of allosteric gating, full agonists and
partial agonists are redefined (Fig. 1.8B) [35]. Agonists shift the
equilibrium toward active states, so for all agonists, c < 1. Full
agonists are those for which c L0 << 1, and partial agonists are
those for which c L0 > 1 (i.e., less than 50% activation is
induced). An interesting feature of this concept is that agonist
efficacy is dependent on L0, so when L0 is small, an agonist
that only modestly shifts the activation equilibrium can
stimulate a large fraction of receptors to activate. Conversely, if
L0 is extremely large (i.e., the receptor has extremely low spontaneous activity), agonists need to shift the activation equilibrium a great deal to activate a significant fraction of receptors
(Fig. 1.8C).
The concept of inverse agonism can be understood in the
context of allosteric activation schemes. Inverse agonists are
drugs that bind to the orthosteric site, and where c > 1 (Fig.
1.8D) [36]. Thus, inverse agonists stabilize the inactive state
relative to the active state by binding to inactive receptors
more strongly than to active receptors. Indeed, upon careful
study, many drugs that are categorized as competitive antagonists are found to be inverse agonists or extremely weak partial
agonists. In the context of allosteric schemes, a truly competitive antagonist binds both active and inactive receptors with
equal affinity and has no impact on the activation equilibrium;
that is, c ¼ 1.
Allosteric gating schemes are useful for modeling multisubunit receptors with multiple agonist sites. For example,
with two homologous subunits and only two receptor states
(inactive R and active R ), agonist sites are all identical and
couple equally to activation:

Allosteric receptor activation models

cL0
c2 L0

1=2K A
2K A


!
2A þ R


RA
!
RA2
L0

The fraction of active receptors is:


½R þ ½RA þ RA 2
1
¼


Rtot
1 þ ½A =KA 2
1 þ L0
1 þ ½A =cKA

ð1:24Þ

ð1:25Þ

Allosteric concepts are also useful for modeling interactions
between different drugs on a single receptor. Some drugs are
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Section 1: Principles of drug action

A

B

Figure 1.8. Allosteric models: agonism and
inverse agonism. (A) A simple allosteric activation
scheme is depicted showing inactive (R) and active
(R ) receptor forms with different affinities for agonist. Agonist–response curves in panels B–D were
generated using Eq. 1.23. (B) L0 is held constant,
c (efficacy) varies. Note how this panel looks just like
Fig. 1.3B, because when L0 is large the allosteric
model behaves like a serial binding-activation
model. (C) Efficacy (c) is constant, L0 varies. Note
spontaneous activity and higher apparent efficacy
and potency of agonist as L0 decreases. (D) Inverse
agonism (reduced activity) is observed when spontaneous activity is present and c > 1. Pure competitive antagonism is present when c ¼ 1.

Constant L, varying c

1.0

L = 1000; c = 10–4

Allosteric Agonism

0.8
Fraction Max. Effect

KA
RA

A+R

L

cL
K*A

A + R*

0.6

L = 1000 c = 10–3

0.4

0.2

RA*

L = 1000; c = 10–2

c = K*A/KA

0.0
0

C

0.8

10

100

L=1
c = 10–3

0.8

L = 100; c = 10–3

0.6

1
[A] × KA

1.0

L = 10; c = 10–3

Fraction Max.Effect

Fraction Max. Effect

1.0

0.1

Agonism vs. Inverse Agonism

D

Constant c,varying L
L = 1; c = 10–3

0.01

L = 1000; c = 10–3

0.4

L = 1;
c=1

0.6

0.4
L=1
c = 10

0.2

0.2

L=1
c = 100

0.0

0.0
0

0.01

1
0.1
[A] × KA

10

100

0

0.01

1
0.1
[A] × KA

allosteric enhancers. Examples include classic benzodiazepines like diazepam and midazolam that sensitize GABAA
receptors to GABA, shifting GABA concentration–responses
for GABAA receptors leftward. This effect could be due to
allosteric effects on the GABA binding site (reducing KA)
or due to effects on receptor activation (i.e., reducing L0).
Studies on spontaneously active mutant GABAA receptors
demonstrate that benzodiazepines directly enhance receptor
activation in the absence of GABA, indicating that benzodiazepines are in fact weak allosteric agonists [37].

Spare receptors
Spare receptors exist if a maximal cellular or tissue response is
elicited when receptors are not fully occupied by agonist. Formally, the presence of spare receptors is equivalent to very high
drug efficacy for agonists, while reducing the apparent effect of
inhibitors (Fig. 1.9). Neuromuscular transmission is characterized by spare receptors. Critical neuromuscular junctions, such
as those in the diaphragm and major muscle groups, have an
extremely high density of nicotinic ACh receptors. In most
cases, activation of a small fraction of nACh receptors is
adequate to fully activate postsynaptic muscle fascicles. With
administration of nondepolarizing muscle relaxants that competitively block ACh receptor activation, symptoms of weakness
typically are first seen in ocular and pharyngeal muscles, which

10

100

1000

have smaller degrees of spareness for neurotransmission.
Weakness in trunk muscles typically occurs when over 80% of
receptors are blocked. Agonist concentration–responses in
tissues or cells with spare receptors are shifted toward lower
concentrations relative to molecular responses (Fig. 1.9A). In
contrast, antagonist concentration–responses are shifted
toward higher concentrations, because a maximal response
may be present until a large fraction of receptors are inhibited
(Fig. 1.9B). When noncompetitive inhibitors are studied in
experimental systems with spare receptors, they may produce
rightward shifts in agonist concentration–response without
decreasing apparent efficacy. This occurs because they reduce
the degree of spareness. Thus, noncompetitive inhibitors may
appear to act competitively in systems exhibiting spare receptors, whereas binding studies can reveal the underlying noncompetitive interaction with agonists.

Signal amplification
Signal amplification is typical of receptors that are coupled to
enzymes. Amplification is another mechanism that mimics spare
receptors. Drug binding to GPCRs triggers G-protein activation
that persists much longer than drug binding at the receptor. Each
G protein can catalyze the production of many second-messenger molecules before deactivation. In turn, second messengers
can trigger additional cascades of intracellular signal activation.

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Chapter 1: Pharmacodynamic principles of drug action

A

B

Fraction Max. Response

1.0

1.0
1000

0.8

100

0.8

30

30

100
10

0.6

0.6

0.4

10

1000

0.4
3

0.2

3

0.2

0.0

0.0
0.01

0.1

1

10

[Agonist] × KA

100

1000

0.01

0.1

1

10

100

[Inhibitor] × KI

Thus, GPCR activation is amplified in both space and time and
occupation of a small fraction of receptors can result in a maximal cellular or tissue response that outlasts drug binding to the
receptor. Similarly, the enzyme-linked surface receptors initiate
cascades of phosphorylation or dephosphorylation which amplify the initiating receptor activation.

Signal damping
Signal damping or negative feedback is often present to limit
physiological drug responses. This is usually observed as a
diminishing response to equal drug doses over time. The term
for rapidly (hours) diminishing responses to repeated drug
administration is tachyphylaxis. Resistance to drug effects that
develops over longer periods (days to months) is termed tolerance. Tachyphylaxis may be linked to receptor desensitization in some cases, such as the phase II neuromuscular block
associated with prolonged succinylcholine administration.
Ligand-gated ion channels, when persistently exposed to high
agonist concentrations, go through a monomolecular conformational change that reduces channel opening even while
agonist is bound. Many voltage-gated ion channels go through
a similar process (inactivation). Mechanisms that involve other
molecules can also damp responses. Synapses such as the
neuromuscular junction show altered structure and activity
within hours after physiological changes such as reduced presynaptic motor neuron activity or profound blockade of postsynaptic activity [38]. Some downstream proteins activated
following GPCR agonism (receptor kinases) are feedback
inhibitors that phosphorylate GPCRs and reduce their activity.
Similarly, protein phosphatases (both surface receptor-linked
and cytoplasmic) can be mechanisms that oppose various
protein kinase enzymes. Other, slower negative feedback
mechanisms include depletion of neurotransmitters or metabolites, expression of regulatory factors, receptor downregulation, and transcriptional changes.

Molecular
Ligand binding
Ion current
Enzyme activity

1000

Figure 1.9. Spare receptors. (A) Spare receptors
cause an apparent increase in potency and efficacy
of agonists. In this model, approximately 35 receptors must be activated for a maximal cellular
response and the number of receptors per cell is
labeled on each curve. If there are less than
35 receptors in the cell, a sub-maximal response is
observed and apparent KA (EC50) is fairly constant.
When there are spare receptors (i.e., more than 35
receptors per cell), low agonist concentrations are
needed to achieve the maximum response (whatever concentration activates 35 receptors). EC50 is
therefore significantly reduced. The dotted line represents fractional agonist occupation (binding) of
receptors. (B) Spare receptors cause an apparent
decrease in the potency of antagonists. In this case,
no inhibition occurs until noncompetitive inhibitor
binding has reduced the number of active receptors
to less than 35. Thus, higher fractional antagonist
occupancy is required as the number of receptors
per cell increases. The dotted line represents fractional antagonist occupation (binding).

Cellular
Membrane voltage
[Metabolite]
[mRNA]
[Protein]
Mitotic activity
Apoptosis
Trophism

Tissue
Tissue/Organ
activity
Toxicity

Organism
Physiology
Behavior
Toxicity
Lethality

Figure 1.10. Integration of drug responses from molecules to organism.

Drug effects on organisms
Integration of drug effects – Drug responses show different
concentration-dependent patterns in molecules vs. cells vs.
tissues vs. animals, because the spatial and temporal integration of effects is altered at different system levels (Fig. 1.10). As
a result, small changes in occupancy or efficacy at one step in a
signal transduction cascade may have large effects on the
overall system. Moreover, variability in individual responses
at different system levels, due to genetics, environment, drug
interactions, and other factors, can lead to significant interindividual differences in response to drugs. Therefore, assessing dose–response relationships in individuals provides
different information than studies in populations.
Drug–response analysis in individual organisms is analogous to that in molecular pharmacology, because organism
responses are the integration of multiple molecular events,
governed by similar underlying relationships, and thus molecular and organism concentration–response curves have
similar sigmoidal shapes. However, analysis of drug effects
in organisms is usually parametric (descriptive), and difficult
to relate quantitatively to underlying mechanisms [27]. Clinical
concentration–response determinations most commonly
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Section 1: Principles of drug action

A
1.0

Response

Drug B
Low efficacy
Low potency

0.2

Drug B

0.4

Low efficacy
High potency

0.0
0.1

1

10
Dose

100

1000

C

0.01

0.1

1
Dose

10

100

D
1.0
Therapeutic Effect
ED99

1.0
0.8

Drug A

Fraction Responding

Fract. Individuals Responding

0.6

0.2

0.0
0.01

High efficacy
Low potency

0.8

0.6
0.4

Drug A

1.0

High efficacy
High potency

0.8
Response

B

Drug A

Drug B

0.6
0.4
0.2
0.0
0.01

0.1

1

10
Dose

100

1000

0.8

Lethal
Effect

0.6
ED50

0.4

Figure 1.11. Clinical dose–response concepts.
(A, B) These panels illustrate the concepts of efficacy
and absolute potency in comparing drugs with
similar effects. Efficacy is the maximum response.
Absolute potency is the inverse of ED50. A drug with
low absolute potency but high efficacy may be
more effective than an equal dose of a drug with
higher absolute potency but lower efficacy.
(C) Population dose–response studies are based on
quantal (yes/no) outcomes at different drug doses.
Quantal responses are often distributed in a classical
bell-shaped probability curve on semilogarithmic
axes (filled curves). Cumulative response curves
(lines) generated from the quantal response data
are sigmoidal and can be described using the Hill
equation. Note that the slopes of the cumulative
curves are determined by the variability (width) of
the population response distributions. (D) Toxicity
ratios are usually reported as the ratio of the midpoints of toxicity vs. therapeutic dose–responses.
Here, the 50% lethal dose in a group of animals
(LD50) is 60 times higher than the half-effect dose
(ED50). However, the dose that is effective for 99%
(ED99) is also lethal for about 10%. The certain safety
factor (ED99/LD1) for this drug is low, about 0.1.

LD50

0.2
0.0
0.01

LD1
0.1

measure drug concentrations in blood (plasma or serum)
because of its accessibility, although certain exceptions are
well-known (e.g., end-tidal volatile anesthetic concentrations).
Recognizing that blood is not the real site of drug action, the
concept of effect-site concentration has been applied in pharmacokinetic/pharmacodynamic (PK/PD) models (see Chapter 5).
Frequently, dosage rather than concentration is the independent
variable used for pharmacological studies in organisms when
biophase drug concentrations are not measured.
Pharmacodynamic responses in animals can be therapeutic, toxic, or lethal. Graded log drug concentration–
response or dose–response curves for an individual subject
measure responses on a continuous scale from minimum to
maximum. Examples of graded responses include pupil size,
blood pressure, heart rate, temperature, intracranial pressure,
or pain relief. Pharmacodynamic analysis of graded concentration–response curves in individual subjects therefore parallels that in molecular, cellular, and tissue studies. The
independent concepts of drug potency and efficacy are applied
similarly (Fig. 1.11A–B). Efficacy is the maximal response in
an individual achievable at the highest drug dose and absolute
drug potency for graded responses is defined as the reciprocal
of the ED50 (the dose producing half-maximal response).
Figure 1.11A and B illustrate the comparison of drug pairs
with respect to their potency and efficacy for the same effect.
Drug–response analysis in populations – In anesthesiology, it is easy to observe that responses to the same drug
dose vary widely among patients. Part of the art of delivering

1

10
Dose

100

1000

anesthesia is titrating drug doses to provide optimal therapy
for a specific patient, particularly when the drug has significant
toxicities. At the same time, anesthesiologists must know
dosing ranges that are appropriate for broad populations of
patients, to provide dosing guidelines. For populations of
patients, what matters is determining drug doses that result
in important clinical (therapeutic or toxic) endpoints. As a
result, characterizing dose–response relationships in populations is based on quantal responses and quantal dose–
response curves. Quantal responses are either/or outcomes in
individuals, such as awake/asleep, stroke/no stroke, alive after
five years, etc. Graded responses may also be quantized. For
example, a 20 mmHg decrease in blood pressure (yes/no), or a
50% decrease in pain (yes/no). Thus the y-axis on a quantal
dose–response curve is the fraction (or percentage) of the
population that exhibits the defined response (Fig. 1.11C–D).
The minimum dose required to achieve the specified quantal
response in a population of study subjects is usually distributed in a bell-shaped probability curve (Fig. 1.11C). The
cumulative fraction of subjects that respond at a given dose
(i.e., responding at that dose or lower) appears as a sigmoid
curve on semilogarithmic axes. It is important to note that the
shape, particularly the slope, of cumulative dose–response
relationships derived from quantal data reflects the heterogeneity of the population studied rather than the underlying
physiology of drug action (Fig. 1.11C).
Parameters used to describe graded dose–response curves,
such as potency and efficacy, have analogs for quantal

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Chapter 1: Pharmacodynamic principles of drug action

dose–response curves. Thus the dose producing a therapeutic
effect in 50% of the population studied is the ED50, and the
maximum fraction of the population displaying the specified
response at high drug doses represents population efficacy.
Note that the nomenclature for quantal (ED50) and graded
(ED50) dose–response curves differs, with the subscript
numeral used only in the latter case.
In an organism or patient, a single drug may have (in fact
usually has) multiple effects, and each effect may have a different dose–response relationship, depending on the mechanisms
underlying each effect. Quantal dose–response curves can be
used to describe multiple drug effects, such as therapeutic
response, drug toxicity, or even drug lethality (more commonly
in animals, for toxicity and safety testing). For example, the dose
producing a particular toxicity in half the population is
the TD50, and that causing lethality in half the population is
the LD50. A more useful measure of drug safety than just the
LD50 or TD50 is the distance between the concentration–
response curves (Fig. 1.11D). The therapeutic index is defined
as the LD50/ED50 (or sometimes TD50/ED50). Another, more
stringent measure of drug safety is the certain safety factor,
defined as the LD1/ED99 (or TD1/LD99). Compared with the
therapeutic index, certain safety factor is less dependent on
assuming similar slopes of the therapeutic and toxic effect curves.

Summary
Pharmacodynamics is the study of how drugs alter physiological functions, which is initiated by drug interactions with
molecular targets. Drugs used in anesthesiology interact with a
variety of receptors, which are proteins within or on the
surface of cells that are activated by endogenous drugs,
resulting in altered cell function. Receptors are categorized
both by the drugs that activate them and by their structure
and function. Families and superfamilies of receptors (e.g.,
G-protein-coupled receptors and ligand-gated ion channels)
are defined by the degree of structural and functional similarity among groups of receptors. Receptors display varying
degrees of selectivity for drugs with similar chemical structures.
Conversely, drugs show varying degrees of specificity for the
plethora of receptors in organisms. Drug–receptor binding can
be understood as a simple bimolecular interaction process, which
is characterized by an equilibrium dissociation constant, KD.
Saturable drug binding to receptors with one site appears
graphically as a hyperbolic Langmuir isotherm. Graphs
of bound drug against log[free drug] appears as a sigmoidal
(s-shaped) curve. Drug effects are classified as agonism

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Section 1
Chapter

2

Principles of drug action
G-protein-coupled receptors
Marcel E. Durieux

Introduction
G-protein-coupled receptors (GPCRs) are involved in the
transduction of signals from a variety of extracellular signaling
molecules, including hormones, neurotransmitters, and cytokines. The diversity of the effector pathways which may
couple to GPCRs gives rise to considerable signaling flexibility.
Furthermore, tissue-specific expression of various downstream
targets allows for specificity, and signaling regulation may
occur at multiple levels. Consequently, there is a range of
potential opportunities for pharmacologic intervention, and
GPCRs are extremely important in anesthesiology; for
example, α- and β-adrenergic agonists and opiates all act on
GPCRs. Tolerance to drugs like opiates poses a significant
clinical challenge, and an understanding of the mechanisms
underlying desensitization may help identify future targets for
intervention. This chapter deals with the general principles of
signal transduction and the specifics of GPCR signaling pathways and common second-messenger systems, as well as
exploring the mechanisms for receptor desensitization.

General principles
Signal transduction
The terms signal transduction and cell signaling refer to the
mechanisms by which biologic information is transferred
between cells. Intercellular and intracellular signaling pathways
are essential to the growth, development, metabolism, and
behavior of the organism [1,2], which helps explain why the
human genome includes at least 3775 genes (or 14.3% of
genes) involved in signal transduction [3]. More than 2% of
genes encode GPCRs.
The cellular response to an extracellular signaling molecule
requires its binding to a specific receptor (Table 2.1), which
then transduces this information to changes in the functional
properties of the target cell. The particular receptors expressed
by the target cell determine its sensitivity to various signaling
molecules and determine the specificity involved in cellular
responses to various signals. Receptors can be classified by their

cellular localization (Fig. 2.1). The majority of hormones and
neurotransmitters, including peptides, catecholamines, amino
acids, and their derivatives, are water-soluble (hydrophilic)
signaling molecules that interact with cell-surface receptors.
Prostaglandins are an exception, in that they are lipid-soluble
(hydrophobic) signaling molecules that interact with cell-surface receptors. Most hydrophobic signaling molecules diffuse
across the plasma membrane and interact with intracellular
receptors. Steroid hormones, retinoids, vitamin D, and thyroxine are examples. These molecules are transported in the blood
bound to specific transporter proteins, from which they dissociate in order to diffuse across cell membranes to bind to specific
receptors in the nucleus or cytosol. The hormone–receptor
complex then acts as a transcription factor to modulate gene
expression. However, recent evidence suggests that receptors
for the steroid estrogen also act at the plasma membrane,
modulating intracellular Ca2þ and cyclic adenosine 30 -50 monophosphate (cAMP) levels through G-protein interactions.
Nitric oxide (NO), and possibly carbon monoxide (CO), are
members of a class of gaseous signaling molecules that readily
diffuse across cell membranes to affect neighboring cells. NO,
which is unstable and has a short half-life (5–10 seconds), is able
to diffuse only a short distance before breaking down, and
therefore acts as a paracrine signal only. Cell-surface receptors
can also bind to insoluble ligands, such as the extracellular
matrix of cell adhesion molecules, interactions which are crucial
to cell development and migration.

Properties of signal transduction pathways
Signal transduction pathways have a number of common
properties with important functional implications [4]. Signal
amplification occurs as a result of sequential activation of
catalytic signaling molecules. This enables sensitive physiologic
responses to small physical (several photons) or chemical
(a few molecules of an odorant) stimuli, as well as graded
responses to increasingly larger stimuli. Specificity is imparted
by specific receptor proteins and their association with celltype-specific signaling pathways and effector mechanisms.
Additional specificity is imparted by the existence of distinct

Anesthetic Pharmacology, 2nd edition, ed. Alex S. Evers, Mervyn Maze, Evan D. Kharasch. Published by Cambridge University Press. # Cambridge University Press 2011.

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Section 1: Principles of drug action

Table 2.1. Receptor classification

Cell-surface receptors
G-protein-coupled

Receptors for hormones, neurotransmitters (biogenic amines, amino acids), and neuropeptides
Activate/inhibit adenylate cyclase
Activate phospholipase C
Modulate ion channels

Ligand-gated ion channels

Receptors for neurotransmitters (biogenic amines, amino acids, peptides)
Mediate fast synaptic transmission

Enzyme-linked cell-surface receptors
Receptor guanylate cyclases

Receptors for atrial natriuretic peptide, Escherichia coli heat-stable enterotoxin

Receptor serine/threonine kinases

Receptors for activin, inhibin, transforming growth factor β (TGFβ)

Receptor tyrosine kinases

Receptors for peptide growth factors

Tyrosine kinase-associated

Receptors for cytokines, growth hormone, prolactin

Receptor tyrosine phosphatases

Ligands unknown in most cases

Intracellular receptors
Steroid receptor superfamily

Receptors for steroids, sterols, thyroxine (T3), retinoic acid, and vitamin D

Figure 2.1. Extracellular signaling. Ligands bind to
either cell-surface receptors or intracellular receptors. Most signaling molecules are hydrophilic and
therefore unable to cross the plasma membrane.
They bind to cell-surface receptors, which in turn
generate one or more intracellular signals (second
messengers) inside the target cell or change the
activity of effector proteins (e.g., G proteins, protein
kinases, ion channels) through their intracellular
effector domains. Receptor activation can result in
direct changes in the intrinsic enzymatic activities of
the receptor intracellular domain, or it can work
indirectly through association of the receptor with
intracellular mediators, which in turn regulate the
activity of effector proteins. Some effectors translocate to the nucleus to control gene expression (e.g.,
transcription factors) or to other subcellular compartments. Some small signaling molecules, by contrast, diffuse across the plasma membrane and bind
to receptors inside the target cell, either in the
cytosol (as shown) or in the nucleus. Many of these
small signaling molecules are hydrophobic and
nearly insoluble in aqueous solutions; therefore,
they are transported in the bloodstream and other
extracellular fluids bound to carrier proteins, from
which they dissociate before entering the target
cell. HSP-90, heat shock protein-90.

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Chapter 2: G-protein-coupled receptors

receptors coupled to different intracellular signaling pathways
that respond to the same extracellular signal. Thus a single
extracellular signal can elicit different effects on different target
cells depending on the receptor subtype and the signaling
mechanisms present. A good example is the neurotransmitter
acetylcholine, which stimulates contraction of skeletal muscle,
but relaxation of smooth muscle. Differences in the intracellular signaling mechanisms also allow the same receptor to
produce different responses in different target cells. Pleiotropy
results from the ability of a single extracellular signal to generate multiple responses in a target cell: for example, the opening
of some ion channels, the closing of others, activation or
inhibition of many enzymes, modification of the cytoskeleton,
or changes in gene expression.

G-protein-coupled receptors
A variety of signals (hormones, neurotransmitters, cytokines,
pheromones, odorants, photons) produce their intracellular
actions by a pathway that involves interaction with receptors
that activate G proteins [5,6]. G proteins act as molecular
switches to relay information from activated receptors to the
appropriate effectors [7,8]. An agonist-stimulated receptor can
activate several hundred G proteins, which in turn activate a
variety of downstream effectors [9]. GPCRs have a particularly
important role in pharmacology – more than two-thirds of all
nonantibiotic drugs target GPCRs – and are thus critical to
anesthesiology [10]. Genetical disruption in their function is
involved in a number of disease states [11].

The GPCR signaling pathway
G-protein-coupled signal transduction begins with receptor
proteins in the plasma membrane, which sense changes in
the extracellular environment. As a result of the interactions
between these receptors and their ligands, signals are transduced across the plasma membrane (Fig. 2.1). Ligand binding
to a GPCR causes a change in the shape (conformation) of the
receptor, which is transmitted to the cell interior. This results
in a change in the activity of a coupled intracellular guanine
nucleotide (GTP)-binding protein (G protein), which subsequently activates or inhibits intracellular enzymes or ion channels. Through this mechanism, the activation of many GPCRs
leads to changes in the concentration of intracellular signaling
molecules, termed second messengers. These changes are usually transient, a result of the tight regulation of the synthesis
and degradation (or release and reuptake) of these intracellular
signals. Important second messengers include cAMP, cyclic
guanosine 30 -50 -monophosphate (cGMP), 1,2-diacylglycerol,
inositol 1,4,5-trisphosphate (IP3), and Ca2þ. Changes in the
concentrations of these second messengers following receptor
activation modulate activities of important regulatory enzymes
and effector proteins. The most important second-messengerregulated enzymes are protein kinases and phosphatases,
which catalyze the phosphorylation and dephosphorylation,

respectively, of key enzymes and proteins in target cells. Reversible phosphorylation alters the function or localization of specific
proteins. It is the predominant effector mechanism involved in
mediating cellular responses to almost all extracellular signals.

GPCR structure and function
GPCRs form a large and functionally diverse receptor superfamily; more than 500 (more than 2% of total genes) members
have been identified, and a large number of orphan receptors
(receptors identified as GPCRs by amino acid structure, for
which the ligand is not known) brings the total of GPCRs over
a thousand.
The G proteins, coupled to by the receptors, are heterotrimeric structures, that is, they consist of three distinct
protein subunits: a large α subunit and a smaller βg subunit
dimer. The βg complex is so tightly bound that it is usually
considered a single unit. The binding of extracellular signals to
their specific receptors on the cell surface initiates a cycle of
reactions to promote guanine nucleotide exchange on the
G-protein α subunit. This involves three major steps: (1) the
signal (ligand) activates the receptor and induces a conformational change in the receptor; (2) the activated receptor “turns
on” a heterotrimeric G protein in the cell membrane by
forming a high-affinity ligand–receptor–G-protein complex,
which promotes exchange of guanosine triphosphate (GTP)
for guanosine diphosphate (GDP) on the α subunit of the
G protein, followed by dissociation of the α subunit and
the βg subunit dimer from the receptor and each other;
and (3) the appropriate effector protein(s) is then regulated
by the dissociated G-protein α or βg (or both) subunits, which
thereby transduces the signal. The dissociation of the G protein
from the receptor reduces the affinity of the receptor for the
agonist. The system returns to its basal state as the GTP bound
to the α subunit is hydrolyzed to GDP by a catalytic activity (or
GTPase) inherent in the α subunit, and the trimeric G-protein
complex reassociates and turns off the signal.
A number of different isoforms of G-protein α, β, and g
subunits have been identified that mediate the stimulation or
inhibition of functionally diverse effector enzymes and ion
channels (Table 2.2). Among the effector molecules regulated
by G proteins are adenylate cyclase, phospholipase C, phospholipase A2, cGMP phosphodiesterase, and Ca2þ and Kþ
channels. These effectors then produce changes in the concentrations of a variety of second-messenger molecules or in the
membrane potential of the target cell.
Despite the diversity in the extracellular signals that stimulate the various effector pathways activated by G-proteincoupled receptors, these receptors are structurally homologous,
which is consistent with their common mechanism of action.
Molecular cloning and sequencing have shown that these receptors are characterized by seven hydrophobic transmembrane α
helical segments of 20–25 amino acids connected by alternating intracellular and extracellular loops. Therefore, GPCRs
cross the membrane seven times (hence the alternative terms

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Section 1: Principles of drug action

Table 2.2. Diversity of G-protein-coupled receptor signal transduction pathways: G proteins and their associated receptors and effectors

G protein

Representative receptors

Effectors

Effect

Gs

β1, β2, β3 adrenergic; D1, D5 dopamine

Adenylate cyclase Ca2þ channels

Increased cAMP; increased Ca2þ influx

Gi

α2 adrenergic; D2; M2, M4 muscarinic;
μ, δ, κ opioid

Adenylate cyclase Phospholipase
A2 Kþ channels

Decreased cAMP; eicosanoid release;
hyperpolarization

Gk

Atrial muscarinic

Kþ channel

Hyperpolarization

Gq

M1, M3 muscarinic; α1 adrenergic

Phospholipase C β

Increased IP3, DG, Ca2þ

Golf

Odorants

Adenylate cyclase

Increased cAMP (olfactory)

Gt

Photons

cGMP phosphodiesterase

Go

?



Phospholipase C Ca

channels

Decreased cGMP (vision)
Increased IP3, DG, Ca2þ; decreased
Ca2þ influx

cAMP, adenosine 30 -50 -monophosphate; cGMP, guanosine 30 -50 -monophosphate; DG, 1,2,-diacylglycerol; Gs, stimulation; Gi, inhibition; Gk, potassium regulation;
Gq, phospholipase C regulation; Golf, olfactory; Gt, transducin; Go, other; IP3, inositol trisphosphate.

seven-transmembrane domain, heptahelical, or serpentine
receptors; Fig. 2.2). The structural domains of G-protein-coupled
receptors involved in ligand binding and in interactions with
G proteins have been analyzed by deletion analysis (in which
segments of the receptor are sequentially deleted), by sitedirected mutagenesis (in which specific single amino acid
residues are deleted or mutated), and by constructing chimeric
receptor molecules (in which recombinant chimeras are formed
by splicing together complementary segments of two related
receptors). For example, the agonist isoproterenol binds among
the seven transmembrane α helices of the β2-adrenoceptor near
the extracellular surface of the membrane. The intracellular
loop between α helices 5 and 6 and the C-terminal segments is
important for specific G-protein interactions.
Heterogeneity within the GPCR signaling pathway exists
both at the level of the receptors and at the level of the
G proteins. A single extracellular signal may activate several
closely related receptor subtypes. For example, six genes for
α-adrenoceptors and three genes for β-adrenoceptors have
been identified, all of which can be activated by the ligand
norepinephrine. Likewise, G proteins consist of multiple subtypes. Sixteen homologous α-subunit genes are classified as
subtypes (Gs, Gi, Gk, Gq, and so on) based on structural
similarities. The different α subunits have distinct functions,
coupling with different effector pathways. The different
β- and g-subunit isoforms may also couple with distinct
signaling pathways. Heterogeneity in effector pathways makes
divergence possible within GPCR-activated pathways. This
effector pleiotropy can arise from two distinct mechanisms:
(1) a single receptor can activate multiple G-protein types,
and/or (2) a single G-protein type can activate more than one
second-messenger pathway. Thus a single type of GPCR can
activate several different effector pathways within a given cell,
whereas the predominant pathway may vary between cell
types. All together, this ability of a single agonist to activate
multiple receptor subtypes, which in turn can interact with

multiple G-protein subtypes and thereby activate various
effectors, allows a tremendous amount of flexibility in signaling, as well as many opportunities for regulation.
The structure and function of the α- and β-adrenoceptors
for epinephrine and norepinephrine and their associated
G proteins exemplify some of these principles (Fig. 2.2).
β-adrenoceptors are coupled to the activation of adenylate
cyclase, a plasma-membrane-associated enzyme that catalyzes
the synthesis of cAMP. cAMP was the first second messenger
identified and has been found to exist in all prokaryotes and
animals. The G protein that couples β-adrenoceptor stimulation to adenylate cyclase activation is known as Gs, for stimulatory G protein. Epinephrine-stimulated cAMP synthesis can
be reconstituted in phospholipid vesicles using purified
β-adrenoceptors, Gs, and adenylate cyclase, which demonstrates that no other molecules are required for the initial steps
of this signal transduction mechanism. In the resting state,
Gs exists as a heterotrimer consisting of αs and βg subunits,
with GDP bound to αs. Agonist binding to the β-adrenoceptor
alters the conformation of the receptor and exposes a binding
site for Gs. The GDP-Gs complex binds to the agonist-activated
receptor, thereby reducing the affinity of αs for GDP, which
dissociates, allowing GTP to bind. The αs subunit bound to
GTP then dissociates from the G-protein complex, and binds
to and activates adenylate cyclase. The affinity of the receptor
for agonist is reduced following dissociation of the G-protein
complex, leading to agonist dissociation and a return of the
receptor to its inactive state. Activation of adenylate cyclase is
rapidly reversed following agonist dissociation from the receptor because the lifetime of active αs is limited by its intrinsic
GTPase activity. The bound GTP is thereby hydrolyzed to
GDP, which returns the α subunit to its inactive conformation.
The αs subunit then dissociates from adenylate cyclase,
rendering it inactive, and reassociates with βg to reform Gs.
Nonhydrolyzable analogs of GTP, such as GTPgS or
GMPPNP, prolong agonist-induced adenylate cyclase activation

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Figure 2.2. G-protein-coupled receptors. (A) General features. Many receptors belong to this class, including those for neurotransmitters, hormones, odorants,
light, and Ca2þ. These receptors associate with heterotrimeric G proteins composed of three subunits: α, β, and g. They are not transmembrane proteins but are

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Section 1: Principles of drug action

by preventing inactivation of active αs. Such compounds are
important research tools, but the mechanism also has clinical
implications. Cholera toxin and pertussis toxin are adenosine
diphosphate (ADP)-ribosyltransferases, and induce selective
ADP ribosylation of αs or αi, respectively, which inhibits its
GTPase activity and results in prolonged Gsα activation or
Giα inactivation.
The activity of adenylate cyclase can be negatively regulated
by receptors coupled to the inhibitory G protein, Gi. An
example is the α2-adrenoceptor, which is coupled to inhibition
of adenylate cyclase through Gi. Thus the same extracellular
signal, epinephrine in this example, can either stimulate or
inhibit the formation of the second messenger cAMP,
depending on the particular G protein that couples the receptor
to the cyclase. Gi, like Gs, is a heterotrimeric protein consisting
of an αi subunit and a βg subunit. Activated α2 receptors bind to
Gi and lead to GDP dissociation, GTP binding, and complex
dissociation, as occurs with Gs. Both the released αi and the βg
complex are thought to contribute to adenylate cyclase inhibition, αi by direct inhibition, and βg by direct inhibition and
indirectly, by binding to and inactivating any free αs subunits.
Activated Gi can also open Kþ channels, an example of how a
single G protein can regulate multiple effector molecules.

Receptor desensitization
The number and function of cell-surface receptors are subject
to regulation by several mechanisms [9]. Many receptors
undergo receptor desensitization in response to prolonged
exposure to a high concentration of ligand, a process by
which the number or function of receptors is reduced, so
that the physiologic response to the ligand is attenuated
(tachyphylaxis). This process is often responsible for
decreased response to administered drugs (such as adrenergic
agonists or opiates). At times, however, it can have beneficial
consequences: for example, it appears that the analgesic
action of cannabinoids may result in part from their ability

to desensitize transient receptor potential vanilloid 1 (TRPV1)
receptors [12], and the prolonged effect of the antiemetic
palonosetron may be explained in part by its ability to desensitize 5-HT3 receptors.
Receptor desensitization can occur by several mechanisms,
including receptor internalization, downregulation, and
modulation (Fig. 2.3). Receptor internalization by endocytosis
is a common mechanism for desensitization of hormone
receptors (e.g., insulin, glucagon, epidermal growth factor),
and may be the manner in which palonosetron desensitizes
5-HT3 receptors. The agonist–receptor complex is sequestered
by receptor-mediated endocytosis, which results in translocation of the receptor to intracellular compartments (endosomes) that are inaccessible to ligand. This is a relatively slow
process. Cessation of agonist stimulation allows the receptor to
recycle to the cell surface by exocytosis. In other cases the
internalized receptors are degraded and are no longer available
for recycling, a process known as receptor downregulation.
Receptors must then be replenished by protein synthesis.
Receptor downregulation in response to prolonged agonist
stimulation can also occur at the level of receptor protein
synthesis or of receptor mRNA regulation caused by changes
in gene transcription, mRNA stability, or both. These processes are of great importance in modulating the effects of
drugs, e.g., opiates [13].
A more rapid and transient form of receptor desensitization
involves receptor modulation by phosphorylation, which can
rapidly change receptor affinity, signaling efficiency, or both.
For example, the β-adrenoceptor is desensitized as a result of
phosphorylation of a number of sites in its intracellular
carboxy-terminal domain by cAMP-dependent protein kinase,
protein kinase C (PKC), and β-adrenergic receptor kinase
(βARK), a G-protein-coupled receptor kinase (GRK). The
former kinase is activated as a result of β-receptor stimulation
of adenylate cyclase and results in homologous or heterologous
desensitization, whereas the latter kinase is active only on

Caption for Figure 2.2. (cont.) associated with the membrane by covalently bound fatty acid molecules. In the resting state, GDP is bound to the α subunit, which is
closely attached to the βg complex. When the neurotransmitter binds to the receptor, the conformation of the receptor changes, inducing a change in the
conformation of the α subunit, which expels GDP and replaces it by GTP. The GTP-bound α subunit is no longer capable of interacting with the receptor or g. GTPbound α and g interact with specific targets that differ for each isoform α or g subunits. After a short time GTP is hydrolyzed to GDP and α-GDP reassociates with g. At
about the same time, the neurotransmitter leaves its receptor, which returns to its resting state. G protein, guanine nucleotide-binding protein; Pi, inorganic
phosphate. (B) The adenylate cyclase/protein kinase A (PKA) pathway. cAMP is formed from ATP by a class of transmembrane enzymes, adenylate cyclases. A cytosolic
form of adenylate cyclase has also been described recently. Transmembrane adenylate cyclases are activated by two related subtypes of G-protein α subunits, αs
(stimulatory, which is ubiquitous) and αoff (olfactory, which is found in olfactory epithelium and a subset of neurons). Adenylate cyclases are inhibited by αi
(inhibitory). In addition, some adenylate cyclases can be stimulated or inhibited by βg, or Ca2þ combined with calmodulin. Cyclic adenosine 30 -50 -monophosphate
(cAMP) is inactivated by hydrolysis into AMP by phosphodiesterases, a family of enzymes that is inhibited by theophylline and related methylxanthines. cAMP has
only two known targets in vertebrates: one is a cAMP-gated ion channel that is most prominently found in olfactory neurons, and the other is cAMP-dependent
protein kinase that is present in all cells. cAMP-dependent protein kinase is a tetramer composed of two catalytic subunits and two regulatory subunits (only one of
each is shown). When cAMP binds to the regulatory subunits (two molecules of cAMP bind to each regulatory subunit), they dissociate from the catalytic subunits.
The free active catalytic subunit phosphorylates numerous specific substrates including ion channels, receptors, and enzymes. In addition, the catalytic subunit can
enter the nucleus, where it phosphorylates transcription factors. One well-characterized transcription factor phosphorylated in response to cAMP is cAMP response
element-binding protein (CREB). In the basal state, CREB forms a dimer that binds to a specific DNA sequence in the promoter region of cAMP-responsive genes,
called CRE (cAMP-responsive element). CREB is unable to promote transcription when it is not phosphorylated, whereas phospho-CREB strongly stimulates
transcription. Genes regulated by CREB include immediate-early genes c-Fos and c-Jun. CREB is also activated by Ca2þ calmodulin-dependent protein kinase.

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Chapter 2: G-protein-coupled receptors

downregulation
modulation
of synthesis

phosphorylation
receptor
internalization

effector
regulation

ion
channels

enzymes

GPCR
6

P
P P


6

+

6

AC


arrestin

3

ATP

kinase

2

7

cAMP

5

4

1

adrenergic
agonist

extra cellular
adenylate
cyclase

β-adrenergic
receptor

βr
αs

+



+

G protein

ATP

β
arrestin

P

cAMP

BARK

+
P
PKA

β receptors occupied by ligand and therefore results in only
homologous desensitization. Phosphorylation by βARK leads
to the binding of β-arrestin to the receptor [14]. Arrestins are
a group of proteins that sterically hinder the coupling between
a GPCR and its associated G protein [15]. These processes
both serve to uncouple the active ligand–receptor complex
from interacting with the Gs protein, creating a negative feedback loop for modulation of β-receptor activity (Fig. 2.4). In
other instances, receptor phosphorylation can affect ligand
affinity or associated ion-channel kinetics rather than G-protein
coupling.
Signaling regulation is an area of very active research, and a
variety of molecular systems involved in this process have been
identified. In addition to those mentioned above, these include
systems that affect ligand binding specificity and affinity, and
coupling between receptors, G proteins, and effectors [9].

intra cellular

Figure 2.3. Receptor desensitization. A multitude
or systems regulates GPCR signaling. Synthesis and
expression of receptors can be regulated at the
DNA (1) and RNA (2) level, i.e., at the levels of gene
transcription as well as translation, post-translational
modification, and trafficking to the membrane.
Expressed receptors can be removed from the
membrane by internalization (3); internalized receptors can either be recycled to the membrane or
degraded (4). All the processes are relatively slow,
and referred to as receptor downregulation. Faster
modulation of receptor functioning often involves
phosphorylation of the receptor by one of a variety
of kinases (5). Commonly, this phosphorylation
allows interaction between the receptor and a
member of the arrestin family, thereby blocking
receptor functioning. Additional possibilities for
regulation exist downstream of the receptor itself.
Both ion channels and enzymes such as adenylate
cyclase (AC) can be modulated to counteract the
effects of GPCR on their signaling. For exampale,
whereas opioid receptor signaling normally results
in a decrease in cAMP levels because of inhibition
of AC, modulating AC into a hyperactive state
can induce cellular tolerance to opiate signaling.
G, G protein; þ, activates; –, inhibits; P, phosphorylates.

Figure 2.4. Modulation of β-adrenoceptor functioning by β-adrenergic receptor kinase (βARK)
and β-arrestin. Ligand binding to the β-adrenoceptor results in activation of its associated G protein.
The αs G-protein subunit in turn activates adenylate
cyclase, which converts ATP to cAMP, resulting in
increased intracellular levels of this second messenger. One of the enzymes regulated by cAMP is
protein kinase A (PKA), and one of its phosphorylation targets is βARK. Once phosphorylated, βARK in
turn selectively phosphorylates serine and threonine residues on activated β-adrenoceptor molecules. As a result, these phosphorylated receptors
become accessible for interaction with β-arrestin,
which binds to the receptor and blocks further
signaling. Together, this system forms a feedback
loop that prevents over-activated β-adrenoceptor
signaling. Similar systems exist for other GPCRs.
þ, activates; –, inhibits; P, phosphorylates.

These systems all provide potential drug targets, many of
which could be of use in anesthesiology. A particularly relevant, yet complex, example is the regulation of opiate signaling. All anesthesiologists are familiar with the profound
tolerance that can occur during chronic opiate treatment, at
times requiring more than 100 times normal doses for a
clinically relevant effect [16]. On the other hand, a degree of
tolerance develops to opiates administered during the course
of a single anesthetic, and results in increased analgesic
requirements postoperatively, an effect most clearly demonstrated with use of remifentanil [17]. The latter primarily
results from desensitization and internalization of the μ-opioid
receptor itself. Desensitization occurs by a mechanism similar
to that described above for adrenoceptors: the opioid receptor
is phosphorylated by a G-protein-coupled receptor kinase,
increasing the affinity for an arrestin. This subsequently leads
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Section 1: Principles of drug action

to decreased effector coupling, and induces internalization of
the receptor. Chronic tolerance to opiates results from desensitizing effects throughout the various opiate signaling pathways. Opioid receptors couple to Gi and Go proteins, with
the main effectors being an inwardly rectifying potassium
channel, voltage-gated calcium channels, and adenylate cyclase
(see also Chapters 3 and 4). Each of these effector systems can
be regulated to counteract the effects of opiates. Prolonged
opiate exposure induces changes in the coupling between the
receptor and the coupled potassium channel, resulting in less
effect of the drug. However, the magnitude of this effect is
insufficient to explain clinically observed increases in tolerance, and indeed other parts of the signaling pathway are
affected as well. The intracellular result of opioid receptor
signaling is a decrease in cAMP levels (see next section), and
cellular tolerance can develop by hyperactivation of adenylate
cyclase (possibly induced by G-protein βg units) that counteracts this opiate-induced decrease. Yet further desensitization of
the system can occur because of changes in feedback from
other cells in the network, which functionally counteract the
opiate effect. All of these desensitizing actions are in principle
amenable to modulation by drugs.
An area of particular interest is the synaptic plasticity
induced by opiates, which in essence makes the nervous system
“learn” to be less responsive to the drugs. This process, which
occurs after both short-term and long-term opiate administration, is highly dependent on changes in glutamate receptor
expression, and therefore opiate tolerance can be modulated
significantly by drugs that affect glutamate signaling. In the
context of anesthesiology, ketamine (an NMDA receptor
antagonist) has found a place in preventing or even reversing
opiate tolerance. For example, in rats, fentanyl administration
reduces the effectiveness of a subsequent morphine bolus. This
desensitizing effect can be completely prevented by ketamine
pretreatment. In addition, fentanyl induces a long-term hyperalgesia of several days’ duration. This hyperalgesia is similarly
prevented by ketamine. Hence, it appears the drug has beneficial effects on both short-term and long-term desensitizing
processes [18]. However, it is not clear if these findings always
translate to the clinical setting, as ketamine did not affect
opiate requirements after remifentanil-based anesthesia for
major spine surgery [19].
The desensitizing processes mentioned here do not exhaust
the list. A number of other systems change their functioning in
response to opioid receptor signaling: protein kinase A (PKA),
adrenergic systems in the locus coeruleus, g-aminobutyric acid
(GABA) signaling, MAP kinases, and phosphoinositide-3
kinases have all been shown to be affected, although their role
in the clinical symptomatology of opiate tolerance remains to
be determined. In addition, there are well-described roles of
opiates that are not directly associated with pain pathways, but
are similarly modulated by desensitization. μ-Opioid receptor
activation induces a proinflammatory response, in part by
modulating cytokine and chemokine receptors. In contrast,

activation of κ-opioid receptors is able to reverse this effect
by downregulating these receptor systems [20].
So, even for a single pharmacologic class such as the
opiates, we already find a remarkably large number of potential targets for interference with desensitization processes.
Only very few of these have been explored outside the
cellular laboratory. We may expect, however, that the future
will bring us novel classes of drugs, specifically targeted to
modulating desensitization of G-protein-coupled receptor
systems.

Second messengers
Cyclic adenosine 30 -50 -monophosphate
cAMP, the first intracellular messenger identified, operates as a
signaling molecule in all eukaryotic and prokaryotic cells.
A variety of hormones and neurotransmitters have been
found to regulate the levels of cAMP. Adenylate cyclases form
a class of membrane-bound enzymes that catalyze the formation of cAMP, usually under the control of receptor-mediated
G-protein-coupled stimulation (by αs and αolf) and inhibition
(by α). The rapid degradation of cAMP to adenosine
50 -monophosphate by one of several isoforms of cAMP phosphodiesterase provides the potential for rapid reversibility and
responsiveness of this signaling mechanisms. Most of the
actions of cAMP are mediated through the activation of
cAMP-dependent protein kinase (PKA) and the concomitant
phosphorylation of substrate protein effectors on specific
serine or threonine residues.
Substrates for cAMP-dependent protein kinase are characterized by two or more basic amino acid residues on the
amino-terminal side of the phosphorylated residue. The various substrates for cAMP-dependent protein kinase present in
different cell types explain the diverse tissue-specific effects of
cAMP. They include ion channels, receptors, enzymes, cytoskeletal proteins, and transcription factors (e.g., cAMP
response element-binding protein [CREB]).

Calcium ion and inositol
trisphosphate


Along with cAMP, Ca controls a wide variety of intracellular processes [21]. Ca2þ entry through Ca2þ channels or its
release from intracellular stores triggers hormone and neurotransmitter secretion, initiates muscle contraction, and activates many protein kinases and other enzymes. The
concentration of free Ca2þ is normally maintained at a very
low level in the cytosol of most cells (< 10–6 M) compared
with the extracellular fluid (~ 10–3 M) by a number of homeostatic mechanisms. A Ca2þ ATPase in the plasma membrane
pumps Ca2þ from the cytosol to the cell exterior at the
expense of adenosine triphosphate (ATP) hydrolysis, a Ca2þ
ATPase in the endoplasmic and sarcoplasmic reticulum concentrates Ca2þ from the cytosol into intracellular storage
organelles, and a Naþ/Ca2þ exchanger, which is particularly

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Chapter 2: G-protein-coupled receptors

Figure 2.5. Pathways by which Ca2þ can enter the cytosol as a second messenger in response to extracellular signals. Ca2þ enters a nerve terminal from the
extracellular fluid through voltage-gated Ca2þ channels when the nerve terminal membrane is depolarized by an action potential. Binding of an extracellular
signaling molecule to a cell-surface receptor generates inositol 1,4,5-trisphosphate (IP3), which stimulates the release of Ca2þ from the endoplasmic reticulum. Ca2þ is
a divalent cation whose concentrations are relatively high in the extracellular space (approximately 1.2 mM) and more than 10 000 times lower within the cytosol
(approximately 100 nM). In resting conditions, the plasma membrane is impermeable to Ca2þ. In neurons, it can penetrate through specific channels that include
voltage-gated Ca2þ channels (VGCC) and glutamate receptors of the N-methyl-D-aspartate (NMDA) subtype. When these channels are open, in response to
depolarization in the case of VGCC or in the presence of glutamate in the case of NMDA receptor, Ca2þ flows readily into the cytosol following both its concentration
gradient and the electrical potential. Ca2þ can also be released into the cytosol from internal stores (the endoplasmic reticulum). Two types of Ca2þ channels are
responsible for the release of Ca2þ from internal stores: one is the IP3 receptor, the opening of which is triggered by IP3, a second messenger generated by
phospholipase C from phosphatidylinositol 4,5-bisphosphate; and the other is the ryanodine receptor, named after ryanodine, a drug that triggers its opening.
Opening of ryanodine receptors is triggered by Ca2þ itself by a mechanism called Ca2þ-induced Ca2þ release, which can give rise to propagation of waves of Ca2þ
release along the endoplasmic reticulum. In the cytosol, Ca2þ is mostly bound to specific binding proteins. Some of them function as buffering proteins, preventing
excessive increases in cytosolic free Ca2þ. Others are the actual targets of Ca2þ, which account for the potent biologic effects of this cation. Among the bestcharacterized targets are calmodulin and calmodulin-related proteins, which undergo a conformational change enabling them to interact with, and activate, a
number of enzymes. Ca2þ can also bind to another type of protein domain called C2. Free Ca2þ in the cytosol is maintained at very low levels by several highly active
processes that include Ca2þ pumps and Ca2þ exchangers. The Ca2þ pumps have a high affinity but a low capacity for Ca2þ and are used for fine tuning of Ca2þ levels.
They are located on the plasma membrane and the membrane of the endoplasmic reticulum, and their energy is provided by adenosine triphosphate (ATP)
hydrolysis. Naþ/Ca2þ exchangers, whose driving force is provided by the Naþ gradient, have a large capacity, but a low affinity for Ca2þ. DAG, diacylglycerol; ER,
endoplasmic reticulum; GPCR, G-protein-coupled receptor; NMDA-R, N-methyl-D-aspartate subtype of glutamate receptor; PLC, phospholipase C.

active in excitable plasma membranes, couples the electrochemical potential of Naþ influx to the efflux of Ca2þ (Naþdriven Ca2þ antiport). Although mitochondria have the ability to take up and release Ca2þ, they are not widely believed
to play a major role in cytosolic Ca2þ homeostasis during
normal conditions.
Changes in intracellular free Ca2þ concentration can be
induced directly by depolarization-evoked Ca2þ entry down
its electrochemical gradient through voltage-gated Ca2þ channels (as in neurons and muscle), by extracellular signals that
activate Ca2þ-permeable ligand-gated ion channels (e.g., the
NMDA glutamate receptor), or directly by extracellular signals
coupled to the formation of IP3 (Fig. 2.5). IP3 is formed in
response to a number of extracellular signals that interact with

G-protein-coupled cell-surface receptors (Gq, G11) coupled to
the activation of phospholipase C [22].
Phospholipase C hydrolyzes phosphatidylinositol 4,5bisphosphate to IP3 and diacylglycerol; further degradation
of diacylglycerol by phospholipase A2 can result in the release
of arachidonic acid. All three of these receptor-regulated
metabolites are important second messengers. IP3 increases
intracellular Ca2þ by binding to specific IP3 receptors on the
endoplasmic reticulum, which are coupled to a Ca2þ channel
that allows Ca2þ efflux into the cytosol. IP3 receptors are
similar to the Ca2þ release channels (ryanodine receptors) of
muscle sarcoplasmic reticulum that release Ca2þ in response to
excitation. Diacylglycerol remains in the plasma membrane
where it activates PKC, whereas arachidonic acid, in addition
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Section 1: Principles of drug action

to its metabolism to biologically active prostaglandins and
leukotrienes, can also activate PKC. The Ca2þ signal is terminated by hydrolysis of IP3 and by the rapid reuptake or extrusion of Ca2þ.
Ca2þ carries out its second-messenger functions primarily
after binding to intracellular Ca2þ binding proteins, of which
calmodulin is the most important. Calmodulin is a ubiquitous
multifunctional Ca2þ binding protein, highly conserved
between species, which binds four atoms of Ca2þ with high
affinity. Ca2þ can also bind to C2 domains found in several
proteins (PKC, phospholipase A2, synaptotagmin).
PKC is a family of serine/threonine protein kinases consisting of 12 structurally homologous phospholipid-dependent isoforms with conserved catalytic domains, which are
distinguished by their variable N-terminal regulatory
domains and cofactor dependence [23]. The Ca2þ-dependent
or conventional isoforms of PKC (cPKC) are components of
the phospholipase C/diacylglycerol signaling pathway. They
are regulated by the lipid second messenger 1,2-diacylglycerol, by phospholipids such as phosphatidylserine, and by
Ca2þ through specific interactions with the regulatory
region. Binding of diacylglycerol to the C1 domain of cPKC
isoforms (α, β1, β2, g) increases their affinity for Ca2þ and
phosphatidylserine, facilitates PKC translocation and binding to cell membranes, and increases catalytic activity. The
novel PKC isoforms (nPKC; δ, e, Z, y, μ) are similar to
cPKCs, but lack the C2 domain and do not require Ca2þ.
The atypical isoforms (aPKC; z, l) differ considerably in the
regulatory region, and do not require Ca2þ or diacylglycerol
for activity.

Summary
Cell signaling pathways are important for multiple biological
functions. The role of the receptor is to bind signaling molecules and transduce this information into a functional
response. Receptor expression determines tissue and cell sensitivity to a variety of signaling molecules. Receptors may be
expressed at the cell surface, where they typically interact with
insoluble ligands or hydrophilic molecules – an exception
being prostaglandins, which are hydrophobic. Intracellular
receptors interact with hydrophobic signaling molecules which
cross the plasma membrane by diffusion. Signal transduction
pathway organization can facilitate signal amplification, specificity, and pleiotropy.
The G-protein-coupled receptor (GPCR) superfamily is
large and functionally diverse, and constitutes an important
pharmacologic target. These receptors have seven transmembrane domains, and are coupled to an intracellular
heterotrimeric guanine nucleotide (GTP)-binding protein
(G protein). Ligand binding induces a conformational change
in the GPCR, and GTP replaces GDP on the G protein. The

βg subunit and the GTP-bound α subunit of the G protein
then dissociate from the receptor. Different isoforms of
the G-protein subunits are coupled to diverse effector pathways which alter the concentration of second-messenger molecules or produce changes in the membrane potential of the
cell. Effectors activated or inhibited by G-protein subunits
include adenylate cyclase, phospholipase C, phospholipase
A2, cyclic GMP (cGMP) phosphodiesterase, and calcium and
potassium ion channels. Pleiotropy at multiple stages in GPCR
signal transduction gives rise to flexibility in signaling and
regulation.
Common second-messenger molecules include cyclic
adenosine 30 -50 -monophosphate (cAMP), inositol 1,4,5trisphosphate (IP3), 1,2-diacylglycerol (DAG), and calcium
ions (Ca2þ). Certain G-protein subunits can stimulate or
inhibit membrane-bound adenylate cyclase, which catalyzes
cAMP formation. cAMP activates cAMP-dependent protein
kinase, which in turn phosphorylates a variety of targets
such as ion channels, enzymes, and transcription factors,
which are present in different cell types. Some G-protein
subunits activate phospholipase C to hydrolyze phosphatidylinositol 4,5-bisphosphate to IP3 and DAG. IP3 binds
receptors on the endoplasmic reticulum, causing release of
Ca2þ into the cytosol. Calcium signaling pathways are mediated by Ca2þ binding proteins such as calmodulin. DAG and
its metabolite, arachidonic acid, activate protein kinase C.
Second-messenger-regulated enzymes such as kinases and
phosphatases modulate the function and localization of
downstream proteins to produce a cellular response. Changes
in second-messenger concentration are usually transient by
virtue of tightly regulated degradation, synthesis, release, and
reuptake.
Dissociation of the G protein from the receptor reduces
receptor–ligand affinity, and the agonist is released. The α
subunit of the G protein has intrinsic GTPase activity, which
hydrolyses the bound GTP to GDP. The GDP-bound α subunit
rejoins the βg subunit, returning the G protein to its resting
state.
Receptor desensitization following prolonged exposure to
high-concentration agonist is one mechanism of regulation,
which can cause decreased responses to drugs, including
adrenoceptor agonists and opiates. Receptors may be internalized by endocytosis, their expression may be downregulated
by increased degradation or decreased synthesis, or the receptor may be modulated by phosphorylation to achieve rapid,
transient desensitization. Receptor modulation can lead to the
binding of arrestins, which hinder coupling to the associated
G protein, as occurs in the desensitization of the β-adrenoceptor;
modulation can also affect ligand affinity or ion channel kinetics. Pharmacologic intervention in the processes underlying
desensitization has the potential to reduce tolerance to drugs
such as opiates.

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Chapter 2: G-protein-coupled receptors

References
1. Alberts B, Bray D, Lewis J, et al. Cell
signaling. In: Molecular Biology of the
Cell, 3rd edn. New York, NY: Garland,
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2. Lodish H, Baltimore D, Berk A, et al.
Cell-to-cell signaling: hormones and
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Berk A, et al., eds., Molecular Cell Biology,
3rd edn. New York, NY: Scientific
American Books, 1995:
853–924.
3. Hemmings HC. Cell signaling. In:
Hemmings HC, Hopkins PM, eds.,
Foundations of Anesthesia: Basic and
Clinical Sciences. London: Mosby, 2000:
21–36.
4. Venter JC, Adams MD, Myers EW, et al.
The sequence of the human genome.
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5. Carroll RC, Beattie EC, von Zastrow M,
Malenka RC. Role of AMPA receptor
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6. Hepler JR, Gilman AG. G proteins.
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7. Exton JH. Cell signalling through
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10–20.
8. Hamm HE, Gilchrist A. Heterotrimeric
G proteins. Curr Opin Cell Biol 1996; 8:
189–96.

9. Luttrell LM. Transmembrane signaling
by G protein-coupled receptors.
Methods Mol Biol 2006; 332: 3–49.
10. Coleman DE, Sprang SR. How
G proteins work: a continuing
story. Trends Biochem Sci 1996;
21: 41–4.
11. Thompson MD, Percy ME, McIntyre
Burnham W, Cole DE. G proteincoupled receptors disrupted in human
genetic disease. Methods Mol Biol 2008;
448: 109–37.

Br J Pharmacol 2008; 154:
384–96.
17. Joly V, Richebe P, Guignard B, et al.
Remifentanil-induced postoperative
hyperalgesia and its prevention with
small-dose ketamine. Anesthesiology
2005; 103: 147–55.
18. Laulin JP, Maurette P, Corcuff JB, et al.
The role of ketamine in preventing
fentanyl-induced hyperalgesia and
subsequent acute morphine
tolerance. Anesth Analg 2002; 94:
1263–9.

12. Patwardhan AM, Jeske NA, Price TJ,
et al. The cannabinoid WIN 55,212–2
inhibits transient receptor potential
vanilloid 1 (TRPV1) and evokes
peripheral antihyperalgesia via
calcineurin. Proc Natl Acad Sci U S A
2006; 103: 11393–8.

19. Engelhardt T, Zaarour C, Naser B, et al.
Intraoperative low-dose ketamine does
not prevent a remifentanil-induced
increase in morphine requirement after
pediatric scoliosis surgery. Anesth Analg
2008; 107: 1170–5.

13. Martini L, Whistler JL. The role of mu
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17: 556–64.

20. Finley MJ, Happel CM, Kaminsky DE,
Rogers TJ. Opioid and nociceptin
receptors regulate cytokine and cytokine
receptor expression. Cell Immunol 2008;
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14. Barki-Harrington L, Rockman HA.
Beta-arrestins: multifunctional cellular
mediators. Physiology 2008; 23: 17–22.

21. Moncada S, Higgs A. The L-argininenitric oxide pathway. N Engl J Med
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15. DeWire SM, Ahn S, Lefkowitz RJ,
Shenoy SK. Beta-arrestins and cell
signaling. Annu Rev Physiol 2007; 69:
483–510.

22. Berridge MJ, Lipp P, Bootman MD. The
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16. Christie MJ. Cellular
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Section 1
Chapter

3

Principles of drug action
Ion channels
Thomas McDowell, Misha Perouansky, and Robert A. Pearce

Introduction

Eion ¼

Ion channels are integral membrane proteins that form an
aqueous channel in the lipid bilayer through which charged
particles can pass. There are many different types of ion
channels, and they may be classified according to the factors
that regulate channel opening and closing (gating), as well as
the types of ions allowed to traverse the pore (selectivity). This
chapter reviews the structure and function of the major classes
of channels, focusing on those that are essential to neuronal
and cardiac function and signaling. These include the voltagegated ion channels, which open and close in response to
changes in the voltage across the cell membrane, and the
ligand-gated ion channels, which open in the presence of
extracellular ligands (e.g., neurotransmitters). Included in the
discussion of voltage-gated ion channels are the background or
baseline Kþ channels, some of which are activated by anesthetics and thus may contribute to the anesthetic state.

Basic membrane electrophysiology
Membrane potential is determined
by ionic conductances
Whether ions go into or out of the cell when a channel opens
depends on both the membrane potential and the concentration
gradient for that ion at the time the channel is open. Under
physiologic conditions, Naþ, Ca2þ, and Kþ ions generally flow
down their respective concentration gradients. Thus when their
respective channels are opened, Naþ and Ca2þ ions flow into the
cell, whereas Kþ ions flow out of the cell. However, Naþ and Ca2þ
ions will be repelled from entering the cell if the interior of the cell
is very positively charged, whereas Kþ ions tend to be retained in
the cell if it is very negatively charged. The membrane potential at
which net flow for a particular ion through its channel is zero, and
beyond which the direction of flow reverses, can be calculated
using the Nernst equation [1], which is based on thermodynamic
principles and is shown in a simplified form as:

60 mV
½ion extracellular
log
zion
½ion intracellular

ð3:1Þ

In this equation, Eion is the Nernst potential or reversal potential
for the ion of interest, zion is the charge number for the ion, and
the log term is the ratio of extracellular to intracellular concentrations of the ion. For the Kþ ion, for example, the ratio of
extracellular to intracellular concentrations is approximately 5
mM/150 mM (¼ 0.033), making EK about –90 mV. This means
that at membrane potentials more positive than –90 mV, Kþ
ions will flow out of the cell, whereas at potentials more negative
than –90 mV, Kþ ions will flow into the cell. Conversely, the
reversal potentials for Naþ and Ca2þ are about þ60 mV and
þ200 mV, respectively, because the concentrations of these ions
are greater outside than inside the cell (especially Ca2þ, which
has a resting intracellular concentration of about 100 nM).
The Nernst equation is used to determine the membrane
potential at which no current will flow when the membrane is
permeable to only one ion. Excitable cell membranes, however,
are permeable to several different ions, mainly Naþ, Ca2þ, Kþ,
and Cl–. In cells, the membrane potential at which no current
flows is the resting membrane potential, and it can be
estimated if the concentration gradients and resting conductances of the major permeant ions are known by using the
following equation [2]:

Em ¼








gNa
gCa
gk
gCl
ENa þ
ECa þ
EK þ
ECl
gtotal
gtotal
gtotal
gtotal

ð3:2Þ

Em is the resting potential of the membrane, g stands for
conductance (the reciprocal of resistance), gtotal is the sum of
all individual ionic conductances, and ENa, ECa, and so on are
the Nernst potentials for each permeant ion. The resting membrane potential is determined by the weighted sum of the
Nernst potentials for all permeant ions, the weighting term
being the conductance of each ion relative to the total conductance. Therefore, it is easy to see that the membrane potential
will trend toward the Nernst potential for a particular ion

Anesthetic Pharmacology, 2nd edition, ed. Alex S. Evers, Mervyn Maze, Evan D. Kharasch. Published by Cambridge University Press. # Cambridge University Press 2011.

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Chapter 3: Ion channels

when the conductance for that ion is large relative to other
ionic conductances in the membrane. In normal neurons at
rest, Em is dominated by EK and ECl because of the relatively
large resting conductances for these ions, and the membrane is
hyperpolarized at rest. When Naþ and Ca2þ channels open,
however, the membrane depolarizes toward the positive
Nernst potentials for these ions.

Voltage-gated ion channels
Three types of voltage-gated ion channels
Voltage-gated channels are found in neurons, muscle, and
endocrine cells. At normal resting membrane potentials (usually –60 to –80 mV), these channels are closed. When the
membrane is depolarized (becomes less negative), the channels
undergo a conformational change, which opens the pore of the
channel allowing ions to pass through. The type of ion that is
allowed to traverse the channel is determined by the structure
of the pore and is used to classify the channel. The three main
classes of voltage-gated channels are the Naþ, Ca2þ, and Kþ
channels. Although they share some physical characteristics
that determine their voltage sensitivity, other differences in
their structures and ionic selectivities contribute to their
unique physiologic functions.
Opening and closing of individual ion channels are modeled as nearly instantaneous state transitions of the channel
protein that are both voltage-dependent and time-dependent.
In the simplest model, the channel can switch from the closed,
nonconducting state (C) to the open, conducting state (O), and
then transition either back to the closed state or to an inactivated, nonconducting state (I).
C$O$I

ð3:3Þ

After the channel reaches the inactivated state, it cannot open
again until the membrane is hyperpolarized, allowing a transition back to the closed state from which it can once again
open. This is referred to as recovery from inactivation.

How do voltage-gated channels affect
neuronal activity and signaling?
When a channel opens, ions flow passively according to the
driving electrical and chemical gradients as described earlier.
For Naþ and Kþ channels, approximately 104 to 105 ions pass
through a single channel each millisecond, and during an
action potential, thousands of channels may open. This massive flux of ions, however, may represent only about 0.1% of
the total number of ions inside a cell, so the concentration
gradients for Naþ and Kþ do not change much during periods
of normal neuronal activity. Conversely, because of the ability
of the cell membrane to separate and store electrical charge
(the capacitance of the lipid bilayer is about 1 mF/cm2), ionic
shifts of this magnitude produce enormous changes in

membrane potential. It is through these changes in membrane
potential that information is coded and rapidly transferred
from one part of the cell to another.
For example, consider a peripheral sensory neuron that
responds to mechanical deformation of the skin. When the
sensory terminal associated with the Ruffini ending in the skin
is deformed, a generator potential is evoked in the nerve
ending. This is a passive electrical response, in that the ionic
shifts that produced the depolarization are short-lived and the
depolarization will dissipate as the ions travel through their
aqueous environment to areas of lower potential energy. If,
however, the depolarization reaches a critical threshold level, it
will trigger an action potential, a series of complex voltagedependent and time-dependent changes in ionic conductances.
Voltage-gated Naþ channels in the membrane open, which
then produce a rapid depolarization that reaches a peak near
the Nernst potential for Naþ. Ca2þ and Kþ channels may also
be activated at this time. As Naþ channels inactivate, the
membrane potential returns to its resting level and other
voltage-gated channels close or inactivate. The action potential
is an active, regenerative, all-or-none response of constant
magnitude and duration that does not dissipate over space or
time. Deformation of the distal sensory nerve terminal, originally sensed as a generator potential, is converted by voltagegated ion channels to an action potential, which can be
propagated from the skin to the spinal cord, and from there
to higher brain centers (via chemical synapses) where the skin
deformation is sensed.
As described for Naþ and Kþ channels, opening of voltagegated Ca2þ channels also produces changes in membrane
potential as charged Ca2þ ions enter the cell. However, Ca2þ
channels also signal through a different mechanism. Because
the intracellular Ca2þ concentration is normally maintained at
very low levels (about 100 nM) compared with Naþ (about
10 mM) and Kþ (about 150 mM) ions, the influx of even a
small number of Ca2þ ions can transiently increase the intracellular Ca2þ concentration by several fold. This is particularly
true in neuronal presynaptic terminals, because of their small
volume and high density of Ca2þ channels. Increases in
intracellular Ca2þ can cause neurotransmitter release, open
Ca2þ-activated ion channels, and regulate Ca2þ-dependent
kinases and phosphatases.

General structure of voltage-gated
ion channels
The voltage-gated ion channels are protein complexes formed
by the association of several individual subunits. The largest
subunit of each channel is termed the α subunit (α1 for the Ca2þ
channel) (Fig. 3.1). The three-dimensional structure and function of the α subunits of the voltage-gated channels are strikingly similar, reflecting similarities in their voltage-dependent
gating and high ionic conductance. If the α subunit of a voltagegated ion channel is expressed in the absence of other subunits,
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Section 1: Principles of drug action

A

Voltage-gated Na+ and Ca2+ channels

COOH
NH2

Voltage-gated
K+ channel
4 3

2 1
(x4)

I

6 5

3
1 6

II

B

5 4
3 III

5 6
3 4

COOH

NH2

2

2 IV
6 1

4 5

1 2

C

Isoflurane
Pulse 20

Desflurane 2.6 mM

Control

Pulse 1

300 pA

Recovery

Pulse 1 and 20 control

500 pA

0.5 ms
200 ms
D

2

μA

Control
1.35 mM halothane

1000 ms

2.7 mM halothane

it forms a channel with ionic selectivity and voltage-dependent
behaviors that are similar to those of the native channel. The
other subunits that associate with α subunits to form functional
channels in vivo are much smaller proteins and are thought to
stabilize the α subunit and modulate its function.

Figure 3.1. Structure of voltage-gated ion channels
and the effects of anesthetics. (A) The main subunits of
the voltage-gated ion channels. The α subunit of the
Naþ channel and the α1 subunit of the Ca2þ channel
have similar structures, shown schematically by the
drawing at the top. A single protein is segregated into
four repeating homologous domains (I–IV), each containing six membrane-spanning segments (S1–S6). The
α subunit of the voltage gated Kþ channel (A, bottom
left) contains only one domain of six membranespanning segments. In each domain of all three types
of channels, the fourth transmembrane segment
(shown in red) is highly charged and is the voltage
sensor. The long extracellular loop between the fifth
and sixth transmembrane segments (shown in yellow)
forms the outer vestibule of the pore of the channel. In
the membrane, four domains aggregate to form the
main structure of the channel, as shown in an “end-on”
view (A, bottom right). (B) Currents through voltagegated Naþ channels recorded from a Chinese hamster
ovary cell stably transfected with the gene coding for
rat brain IIA (Nav1.2) sodium channels [3]. Twenty
depolarizing voltage steps from –85 to 0 mV were
applied at a frequency of 5 Hz to elicit Naþ currents.
The large downward deflections represent inward ionic
currents. In control conditions, Naþ current activated
and inactivated rapidly during the short depolarizations. The magnitude of the Naþ current did not
change after repetitive stimulation (compare pulse 1
and pulse 20). After exposing the cell to a solution
containing desflurane (2.6 mM), the Naþ current was
immediately reduced (pulse 1) and decreased even
more after repetitive stimulation (compare pulse 1 to
pulse 20 in the presence of desflurane), indicating both
tonic and phasic block of Naþ channels. (C) Currents
through voltage-gated Ca2þ channels recorded from
an isolated rat hippocampal pyramidal neuron in
response to a depolarizing voltage step from –90
to –10 mV [4]. Downward deflections in the traces
indicate inward currents. The control current increased
rapidly after the voltage step and decayed over time,
representing inactivation of Ca2þ channels. Open channels closed rapidly at the end of the voltage step.
Exposure of the cell to extracellular solution equilibrated with isoflurane 2.5% in the gas phase reduced
the magnitude of the Ca2þ current evoked by the same
voltage step. This inhibition was reversed after washout
of anesthetic-containing solution (recovery). (D) Currents through voltage-gated Kþ channels recorded
from Xenopus oocytes injected with cRNA for the voltage gated Kþ channel Kv 2.1 [5]. The membrane potential was rapidly stepped from –50mV to þ50 mV at
the beginning of the trace and held at þ50mV for
10 seconds. Upward deflections indicate outward
membrane current. In control conditions, the Kþ
current increased rapidly and showed a slow
decay over the long depolarization, representing
slow inactivation of these Kþ channels. Exposure
of the cell to extracellular solution containing
increasing concentrations of halothane (1.35 and
2.7 mM) markedly accelerated the current inactivation.
The current traces are normalized to emphasize inactivation rates.

The α subunits of the Naþ and Ca2þ channels were the first to
be cloned, and their primary amino acid sequences were the first
to be deduced. These α subunits are large proteins containing
four repeating homologous domains (I–IV) separated by long
cytoplasmic loops, which allow the four domains to aggregate in

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Chapter 3: Ion channels

the membrane, forming a central pore through which the ions
pass (Fig. 3.1). Each domain consists of six membrane-spanning
α-helical segments (S1–S6) that anchor the protein in the membrane. The fourth transmembrane segment (S4) of each domain
is considered to be the voltage sensor because it contains several
positively charged amino acids in an otherwise hydrophobic
α-helix. Movement of these positive charges during changes in
transmembrane potential produces conformational changes in
the protein that ultimately lead to voltage-dependent gating of
the channel. The segment of the protein that connects the fifth
and sixth transmembrane segments, sometimes called the P loop,
forms part of the outer portion, or outer vestibule, of the pore.
The amino acids within the P loop are important in determining
the ionic selectivity of the channel. The overall structure of the
pore-forming part of the voltage-gated Kþ channel is almost
identical to that of the Naþ and Ca2þ channels, the only exception
being that each of the four domains is a separate protein.
Kþ channels are thus formed by the aggregation of four individual α subunits into either a homotetramer or, with different
α subunits, a heterotetramer.

Individual
voltage-gated ion channels
þ
Na channels þ

The voltage-gated Na channel is composed of an α subunit
and one or more auxiliary b subunits. The α subunit is the
largest subunit and contains both the pore region of the
channel and the voltage sensor. Nine different genes encoding
functional Naþ channel α subunit have been identified
(Nav1.1–1.9), as well as splice variants. These different isoforms are preferentially expressed in different tissues or at
different times of development. The cytoplasmic linker
between domains III and IV of the α subunit is responsible
for inactivation of the Naþ channel. After the channel opens,
this segment of the protein is drawn to the inner surface of the
channel like a “hinged lid,” where it is thought to physically
block ions from passing through the pore [6,7].
Four different protein isoforms comprise the family of Naþ
channel b subunits. These b subunits are each about one-tenth
of the mass of the α subunit and are anchored in the cell
membrane through one transmembrane segment. Their extracellular domains are large and have structures similar to those
of immunoglobulins. Beta subunits regulate the expression
and subcellular targeting of Naþ channels, and modulate the
function of the α subunit [6,8].
The effects of local anesthetics on Naþ channel function are
described in Chapter 36. Unlike local anesthetics, general
anesthetics were initially thought to have little effect on voltage-gated Naþ channels. More recent evidence has revealed
important effects of anesthetics on central nervous system
(CNS) Naþ channels at concentrations similar to those required
for clinical anesthesia [3, 9–11]. Both volatile and intravenous
anesthetics decrease Naþ currents by enhancing voltagedependent inactivation of the channel. Volatile anesthetics

inhibit resting Naþ channels but actually bind more strongly
to the inactivated state of the channel, leading to a use-dependent
block of Naþ currents similar to that described for local anesthetics (Fig. 3.1B; see also Chapter 36). This Naþ channel inhibition does not alter axonal conduction, but inhibition of Naþ
currents in central glutamatergic presynaptic terminals may
lead to changes in the action potential waveform and thereby
reduce excitatory neurotransmitter release [11,12].

Ca2þ channels

The voltage-gated Ca2þ channel is composed of 4–5 different
subunits. The pore-forming and voltage-sensing portions of
the voltage-gated Ca2þ channel are contained in the largest
subunit, the α1 subunit. Ten isoforms of the α1 subunit exist,
and according to current nomenclature are arranged into three
subfamilies based on amino acid sequence homology [13,14].
Cav1.1–1.4 include all the dihydropyridine-sensitive highvoltage-activated L-type Ca2þ channels expressed in muscle,
heart, endocrine cells, and neuronal tissue. Cav2.1–2.3 include
the N-type, P/Q-type, and R-type high-voltage-activated Ca2þ
channels found primarily in neurons and neuroendocrine cells.
Cav3.1–3.3 include the three types of low-voltage-activated
rapidly inactivating T-type channels. Each subfamily, and in
some cases the isoforms within a subfamily, can be identified
by its biophysical properties and sensitivities to blockers and
toxins. Splice variants of these subunits also exist [13].
Four accessory Ca2þ channel subunits that interact with
the α1 subunit have been described [6,14,15]. The b subunit of
the Ca2þ channel, unlike that of the Naþ channel, is a cytoplasmic protein. There are four gene products and several
splice variants, all with similar structure and function. The
b subunit associates with the cytoplasmic linker between
domains I and II of the α1 subunit and regulates both the
trafficking of the channel to the membrane and the kinetics
of channel activation and inactivation. The α2 and d subunits
are derived from a single preprotein that is post-translationally
divided into the two subunit proteins, which are then linked
together by a disulfide bond. The α2 subunit is entirely extracellular, whereas the d subunit contains a transmembrane
segment that anchors the α2d complex in the membrane. Four
different genes encode α2d subunits, and splice variants of
these exist. The α2d subunits regulate expression of Ca2þ
channels, and may modulate channel gating [16]. Finally, eight
types of g subunit have been discovered, as well as splice
variants. All have four transmembrane segments and intracellular amino and carboxy termini. The first g subunit was
found to associate with Ca2þ channel α1 subunits in skeletal
muscle, and others have since been shown to associate with
different α1 subunits in other tissues. Interestingly, some g
subunits associate with and regulate the trafficking and gating
of AMPA-type glutamate receptors instead of Ca2þ channels.
When associated with Ca2þ channel subunits, g subunits have
variable effects on gating [15,17].
31

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Section 1: Principles of drug action

The α1 subunits of Cav2 channels display two interesting
structural features not found in other α1 subunits. The first is in
the cytoplasmic loop linking domains I and II, which contains a
site that interacts with the bg subunits of inhibitory GTPbinding proteins (G proteins) [14]. This molecular interaction
is responsible for the well-known inhibition of these Ca2þ
channels by agonists of G-protein-coupled receptors (e.g.,
opioids, norepinephrine, baclofen). The second interesting feature of Cav2 α1 subunits is that the cytoplasmic linker between
domains II and III interacts with the soluble N-ethylmaleimidesensitive attachment factor receptor (SNARE) proteins syntaxin, SNAP-25, and synaptotagmin, which are part of the
cellular machinery involved in synaptic vesicle exocytosis and
neurotransmitter release [18–20]. This interaction segregates
Ca2þ channels to parts of the membrane where vesicle docking
and release occur, allowing Ca2þ entry to be localized to these
areas where it can initiate rapid neurotransmitter release.
Most Ca2þ channels gate over the same range of voltages as
þ
Na channels, although the rate of inactivation is much
slower. Ca2þ channels prolong the duration of the action
potential and provide Ca2þ entry during depolarization, an
effect seen particularly in cardiac ventricular myocytes. Cav3
channels are somewhat unique in that they activate at more
hyperpolarized membrane potentials and inactivate more
quickly than other types of Ca2þ channels. These distinct
biophysical properties allow Cav3 channels to provide different
functions, such as increasing the magnitude of low threshold
potentials, neuronal pacemaking, and burst firing [21].
The effects of general anesthetics on voltage-gated Ca2þ
channels have been studied in a variety of tissues. Almost
all anesthetics tested inhibit Ca2þ channels to some extent
(Fig. 3.1C). The volatile anesthetics reduce native Ca2þ currents
in many types of neuronal tissue [4,22–29], although the magnitude and concentration dependence of the effects are somewhat variable. All families of voltage-gated Ca2þ channels have
been shown to be sensitive to anesthetics. As with voltage-gated
Naþ channels, anesthetics appear to stabilize the inactivated
state of Ca2þ channels. For some Cav1 and 2 channels, volatile
anesthetics increase the rate of Ca2þ current inactivation, slow
recovery from inactivation, and cause hyperpolarizing shifts in
the voltage dependence of inactivation [28]. Further evidence
comes from single channel recordings, in which open channel
lifetimes were decreased and closed channel lifetimes were
increased by halothane, suggesting stabilization of nonconducting (closed, inactivated, or both) states of the channel [27].

Kþ channels

Four individual α-subunit proteins aggregate in the cell membrane to create the pore-forming segment of the voltage-gated
Kþ channel (Fig. 3.1A). Forty genes encode voltage-gated Kþ
channel α subunits, which are divided into 12 families (Kv1–12)
each with between one and eight members [30]. Additional
variability is produced by alternative splicing of many of these
subunit transcripts. Individual α subunits form either

homotetramers or heterotetramers, which then associate with
at least three different types of accessory proteins.
Three intracellular Kvb subunit isoforms exist, which pair
with the α subunits of Kv1 channels in a 1:1 stoichiometry and
regulate membrane expression and channel gating [6,30]. In
addition, the N-terminus of the b1 subunit forms the inactivation gate for the pore formed by Kv1 α subunits [31]. Recent
studies have shown that b subunits also act as aldo-keto reductases, thus coupling the redox state of the cell to Kþ channel
activity [32]. Four isoforms of the Kþ channel interacting protein (KChIP) regulate Kv4 channels. Five types of minK-like
subunits or minK-related peptides (MiRPs) associate with and
regulate Kv7, Kv10, Kv11, and perhaps other Kv families [6].
The structure of several types of Kþ channels, including a
mammalian Kv1.2, have been deduced using x-ray crystallography [33,34]. The shapes of the conducting portions of the
channels are similar, shaped like an “inverted teepee” with
the narrowest external diameter on the cytoplasmic side.
Conversely, the internal pore region is widest at the cytoplasmic
end, forming a large water-filled cavity. The gate is formed from
the inner helices of the four S6 segments of Kv, which bend
near the inner surface of the membrane to allow channel
opening. The selectivity filter is the narrowest region of the pore,
formed by the P loops from the S5–6 linker at the extracellular
end of the channel. The highly conserved amino acids in this
region, known as the “signature sequence,” provide rapid and
selective passage to Kþ ions but exclude other ions [1,34,35].
The N-terminal portion of some Kv α and b subunits is mobile
in the cytoplasm and is responsible for rapid “N-type” inactivation by blocking the pore in a “ball and chain” type of model. The
N-terminal “ball” interacts with the small cytoplasmic linker
between transmembrane segments 5 and 6 of the α subunit, where
it prevents flow of Kþ ions through the pore of the channel [31].
Only one of the four N-terminal regions is required to produce
inactivation, although the rate of inactivation is four times slower
if only one subunit retains its N-terminus.
As with Naþ and Ca2þ channels, voltage-gated Kþ channels
open in response to depolarization of the cell membrane.
Historically, Kþ channel currents have been classified
according to their rates and voltage dependences of activation
and inactivation. These biophysical attributes (i.e., how the
channel responds to voltage over time) define the role that
each Kþ channel has in the cell. For example, delayed rectifiertype Kþ channels activate rapidly and show little or no inactivation, whereas fast transient, or A-type Kþ channels,
inactivate rapidly during a depolarization. Delayed rectifiers
from the Kv1 family are found on nerve axons, where they
keep axonal action potentials short so Naþ channels can
recover from inactivation and initiate the next action potential
as quickly as possible. Conversely, A-type Kþ channels, such as
those from the Kv4 family, are located at axon hillocks, dendrites, and other regions of the neuron where action potentials
are generated. In these areas, passive membrane depolarizations produced by synaptic potentials and receptor potentials

32
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Chapter 3: Ion channels

A Different classes of K+ channels

NH2

COOH
B

NH2
C

1.25 nA

NH2

COOH

TASK

I (nA)
6

(Halothane)
1 mM
0.3 mM
0.1 mM
Control

4
2
0.75 nA

1 min
Halothane

D

–100

COOH

V (mV)

100

pH 6.5
Halothane

wash
- 7 min 1 min

20 mV

Figure 3.2. Structures of the main classes of Kþ channels and the effects of anesthetics. (A) The three main classes of Kþ selective channels, differing in the number
of transmembrane (TM) segments and the number of P loop (P) segments in each subunit. Left to right: the 6TM/1P class of voltage-gated Kþ channels (reproduced
from Fig. 3.1A to facilitate comparison among the Kþ channel classes); the 2TM/1P class (including Kir and KATP channels); and the 4TM/2P class, also known as the K2P
or tandem-pore-domain Kþ channels. (B) Membrane current measured at a holding potential of 0 mV in a COS cell transfected with DNA containing the human TASK
gene [38]. Application of halothane (1 mM) rapidly and reversibly increased outward currents, indicating activation of the TASK channel by halothane. (C) Membrane
current-voltage relations in a COS cell transfected with DNA containing the human TREK-1 gene [38]. Current was recorded during a rapid linear increase in
membrane potential in the absence (control) and the presence of various concentrations of halothane in solution. Outward current is positive. Halothane increased
currents through the TREK-1 channel. (D) Membrane potential recorded from a rat locus coeruleus neuron in the presence of bicuculline and strychnine to block
g-aminobutyric acid type A (GABAA) receptors and glycine receptors, respectively, in a brain slice preparation [39]. These neurons contain mRNA for TASK-1 [40].
Spontaneous action potential firing is seen at the beginning of the trace. Halothane (0.3 mM) hyperpolarized the membrane and inhibited action potential firing.
Acidifying the extracellular solution (pH 6.5) reversed the hyperpolarization, as would be expected if the effect of halothane was caused by activation of the TASK-1
channel, which is inhibited by acidic solutions.

are converted to action potentials in a graded fashion, such that
larger depolarizations are coded as a higher frequency of action
potential firing. A-type Kþ channels contribute to this frequency
modulation of passive electrical potentials. If A-type Kþ channels
were not present, all depolarizations above the threshold for action
potential generation would elicit the same high frequency of action
potential firing, regardless of magnitude [1].
Voltage-gated Kþ channels are generally insensitive to
anesthetics. Delayed rectifier-type Kþ channels, from squid
giant axon [36] and a Kv2.1 channel cloned from mammalian
brain [35] are inhibited by relatively high concentrations of
anesthetics. Interestingly, these normally sustained currents
displayed rapid inactivation in the presence of anesthetics
(Fig. 3.1D). A-type Kþ currents recorded from ventricular
myocytes also show enhanced inactivation, a prolongation of
recovery from inactivation, and a hyperpolarizing shift in the
inactivation curve [37]. These effects are all consistent with
anesthetics stabilizing the inactivated state of Kþ channels.
Voltage-gated Kþ channels account for about half of all
known mammalian Kþ selective ion channels [30]. Both

voltage-gated and non-voltage-gated Kþ channels share the
highly conserved “signature sequence” within the pore region
that defines the Kþ selectivity of the channel. Large differences
in channel structure (Fig. 3.2) and gating define three major
classes of mammalian Kþ channels [35].

Channels with six transmembrane segments
and one pore (6TM/1P)

Kþ channels in this family include the voltage-gated channels
Kv1–12 and most Ca2þ-activated Kþ channels (KCa) [30,35,41].
There are eight KCa channels organized into five families, each
with unique conductances and Ca2þ sensitivity [41]. The α
subunits of 6TM/1P channels form tetramers, and may associate with various auxilliary subunits to form a native channel [6].
Kv channels were described in detail earlier in this chapter.

Channels with two transmembrane segments
and one pore (2TM/1P)

There are 15 known Kþ channels with this simple structure,
including the inwardly rectifying Kþ channels (Kir1–5; Kir7)
33

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