Pharmaeodynamics and
`Drug Development
`Perspectives in
`Clinical Pharmacology
`
`Ediiea‘ by
`
`Neal R. Cutler
`Cahfomia Clinical may, Beoei‘e‘y High, Cahfomia, {ISA
`
`John j. Sramek
`Cm‘flromia Ciirzim? fiiais, Bever‘bs E3535, Calsfomio} USA
`
`Prem K. Narang
`PSzmmacia Adria, Chaim! PfiannacoiogyjPharmacokizzesies, Cofiumfms, Ohio, USA
`
`ISBN WILEY 8: SONS
`Chiehester - New York - Brisbane ' Toronto - Singapore
`
`InnoPharma Exhibit 1026.0001
`
`

`

`Copyright
`
`1994 by 30in: Wiley & Sons Leif
`Baffins Lane, Chiohestera
`West Sussex I’Ol‘} lUD, England
`Telephone:
`Chichester {0243} ??9???
`National
`International +44 243 39???
`
`*except Chapter 12 which is in the public domain.
`
`All rights reserved.
`
`No part of this book may be reproduced by 2111;», means.
`or transmitted, or translated into a machine language
`without the written permission of the publisher.
`
`Other Wiley Editorial Offices
`
`john Wiley Ell Sons, Inc.3 605 Third flivenoea
`New York NY 101580012, USA
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`jacaranda Wiley Ltd, 33 Park Road, Milton,
`Queensland 4064. Australia
`
`John Wiley 3: Sons (Canada) Ltd: 22 Worcester Road,
`Rexdale, Ontario M93}? 1L1, Canada
`
`John Wiley :8: Sons (SBA) Pte Ltd, 3'? Jalan Pemimpin #GSMIM
`Block B, Union Industrial Building, Singapore 205?
`
`Library of Cwagress Camiogingwin-Pubfiea55m: Data
`
`Pharmaeodynamies and drug development : perspectives in clinical
`pharmacology! edited by Neal R. Cutler, john I. Sramek and Prem K.
`Narang.
`cm.
`p.
`Includes bibliographical references and index.
`ISBN 0 4’21 95052 1
`
`l. DrugsmPhysiological effect.
`Prem K.
`III. Sramek, John I.
`[DNLM1 l. Phormaeologya Clinical. QV 38 P3318 I994]
`RM300.P48 1994
`615'.?—dc20
`
`I. Cutler> Neal R.
`
`II. Naraog,
`
`DNLMIDLC
`for Library of Congress
`
`94’6103
`{ZIP
`
`British Liilwary Cataloguing in Pubfieotion Date
`
`31 catalogue record for {his book is available from the British. library
`
`ISBN 0 4?} 950 521
`
`Typeset in 10; 12 pt Plantin by
`Mathematical Composition Setters Ltd. Salisbury, Wiltshire
`Printed and bound in Great Britain by
`Bookeraft (Bath) Ltd, Mdsomer Norton, Avon
`
`InnoPharma Exhibit 1026.0002
`
`

`

`Contents
`
`
`A
`
`
`
`List of Contributors
`
`‘
`
`.5
`
`Foreword r
`Lash}? Z. Benet
`
`’
`
`I OVERVIEW OF PHARMACODYNAMICS
`
`1 Basic Pharmacodynamic Concepts and Modem
`Rofiers}. W535
`
`2 Simultaneous l’harmacakineticfl’harmacodynamic Modeling
`Wayne A. {:0is csz Mickaef A. 8355922
`
`3 Factors Influencing Variability in Kinetics and Dynamics
`Pram K. Narmg cam? Ronald C. Li
`
`4 Populatiomfiased Approaches to the Assessment of
`Pharmacokinetics and l’harmacodyflamics
`‘
`30362)}: C. Ffeislzakgr and Edward 5". Anmé
`
`:3 General Perspectives on the Role of Metabolites in
`Pharmacokinetics and Pharmacodynamics
`Rmzdalf D. 56932:?
`
`6 Enantioselectivity in Drug Action and Drug Metabolism:
`Influerzce on Dynamics
`Hey; K. Kroemer, Amaze 8. Grass and Miclzei Eidzeiéaam
`
`? Regulatory Perspective: The Role of Pharmacokinetics and
`Pharmacodynamics
`Lawrenae 1283225: am? Roger L. Wie’fiams
`
`1! APPLICATION OF PHARMACODYNAMICS IN SELECTEI)
`THERAPEUTIC DOMAINS
`
`8 Theoretical Models for Developing Anxiolyfics
`P. V. Nickefi and “flamers W. Ufche
`
`9 Pharmacodynamics of Antidepressants
`Karma Dawkim, Husseini K. Marzji and Wiffiiam Z. Power
`
`2:
`
`xiii
`
`3
`
`19
`
`45
`
`’33
`
`89
`
`103
`
`l 15
`
`133
`
`15?
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`

`

`10
`
`11
`
`12
`
`13
`
`14
`
`15
`
`16
`
`I?
`
`18
`
`19
`
`20
`
`Antihypertensive Drugs
`L. Micfiznei Prisms: and A551??? 53. Cam
`
`Pharmacodynamics of Calcium Antagonist Drugs
`Darrefi R. Aber‘neflzy and Nabii S. Andr‘awis
`
`Agents in Congestive Heart Failure
`Edmund V. Cappczrez’li
`
`Antiarrhythmic Drugs
`Ennice B. Sclzwm‘iz
`
`Antibiotic Pharmacodynamics
`303m C. Roiscfzafer, Km‘fn 3. Waiker, Km? 3’. K353}: and
`Ckrz'szopker ff. Suiiiwn
`
`Pharmacodynamics of Antineoplastic Agents
`Gm}! L. Renter and Maria 3'. Ramin
`
`Controlling the Systemic Exposure of Anticancer Drugs:
`The Dose Regimen Design Problem
`Dania! Z. D’Argenio and 30;“; H. 806mm:
`
`Vimlogy and Antiviral Drug Bevelnpment
`Michele? A. Amanzea, games R. Minor and Stngfzen ES Swans
`
`Ill
`
`FRONTIERS IN PHARMACGDYNEXMICS: INSIGHT FROM
`MOLECULAR APPROACHES
`
`21
`
`aafidreneceptors and their Subtypes: Pharmacological
`Aspects
`P. A. man Zwieten
`
`vi
`
`CONTENTS
`
`Pharmacodynamics of Antipsychotic Drugs in
`Schizophrenia
`3033?: 3’. Smmefia and George M. Simpson
`
`Pharmacodynamic Modeis Useful in the Evaluation of
`Drugs for Cognitive Impairment
`Me’ckaefi F. Murphy, Kiazedizes R. Siegfl‘ied, F. 35:50.5 Huffand
`Neal? R. Cage?
`
`181
`
`201
`
`Alzheimer’s Disease: Assessment of Cholinamimetic Agents
`850:? A. Reines
`
`225
`
`241
`
`26?
`
`291
`
`315
`
`363
`
`339
`
`409
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`CONTENTS
`
`22 Mumarinic Receptors: Pharmacologicai Subtypes,
`Structure.) Function and Regulation
`Lira Mei, Wifiz'am R. Roesé‘ee and Henry I. Yamamm‘a
`
`23 Serotonin Receptor Subtypes
`503m: B. Pritchest
`‘
`
`Index
`
`Vii
`
`433
`
`4-5?
`
`475
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`
` This material may be protected by Copyright law (Title 17 U.S. Code)
`
`1 Basic Pharmacodynarnic Concepts
`and Models
`
`ROBERT j. WILLS
`
`Drug therapy is intended to produce and maintain an efficacious pharmaco»
`logical response(s). The closing regimen for most drugs has evolved from a
`combination of pharmacokinetics and dose-response. However, there is strong
`evidence that pharmacological response correlates better with plasma concen»
`trations. if concentrations can be related to pharmacological response, then the
`optimization of therapyr stands to be improved.
`Pharmacodynaniics is a measure of the time course of pharmacological
`response to the presence of a given drug. Understanding the pharmacodynarnics
`of a drug is a key step towards understanding the relationship between concen—
`tration and effect. The value of this understanding is evidenced by the
`resurgence of scientific and regulatory emphasis being placed on the use of
`pharmacokinetics/pharmacodynarnics in optimizing therapeutics, in particular
`during drug development. The recent literature is replete with new-method
`development for pharmacological endpoints, delineation of the pharmaco-
`dynamics of many drugs and application of kinetics and dynamics to therapeutic
`utility. It is clear that the principles of pharmacodynamics combined with
`pharmacokinetics has utility to clinicians and to drug developers alike.
`This chapter will
`review the basic pharmacodgnamic models} provide
`guidance to model selection, and highlight some considerations for evaluating
`pharmacological response and the relationship to drug concentrations.
`
`PI—IARMACOLOGICAL RESPONSE
`
`‘ The pharmacological response is any physiological action attributable to the
`presence of drug. The response can be desired in the case of a pharmacological
`action which is a measure of or is a surrogate to therapeutic effectiveness. The
`response can also be undesired in the case of a toxicological response. Altering
`the dose of a drug to effect a change in the clinical response was the historical
`first step towards optimising drug therapy. However, this empirical approach
`did not always produce the expected response. The limitations associated with
`
`Pitamzacodynamics and Drag Deoeiopnmzt: Properties: in Chaim? Phamzamlngy
`Edited by N. R. Cutler, I. It Sramck and P. K. Narang
`1994 John Wiley <3: Sons Ltd
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`InnoPharma Exhibit 1026.0006
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`4-
`
`OVERVIEW OF PHARMACODYNAMICS
`
`defining} quantifying and interpreting the dosewresponse gave rise to the
`concentration-effect
`relationship. The basic principle assumes that drug
`concentrations circulating in plasma are more reflective of drug concentrations
`at the site of action (receptor) than dose, because the dose-response relationship
`is confounded by pharmacokinetic variability associated with the absorption>
`distribution and elimination of a given drug.
`There are two key components of the pharmacological response that are
`critical to the successful understanding and utility of the pharmacodynamics of
`a drug. The response should have clinical meaning or the establishment of the
`concentration—effect relationship ins},t simply be an exercise of little value. The
`response should be reliably measurable. Developing validated methodologies
`for quantifying pharmacological response continues to he a major impediment
`limiting the utility of pharmacodynamics. In the simplest case, monitoring
`changes in blood pressure is both clinically meaningful and reliable as a
`pharmacodynarnic measure of the therapeutic effectiveness for antihypertensive
`drugs. However, can this measure be related to drug concentration?
`This is the right question to ask but often difficult to answer. If the response
`changes immediately with a change in concentration) establishing a con—
`centration-effect relationship is highly probable. However,
`the response is
`often delayed in relation to appearance of drug since the site of pharmacological
`action is generally outside of the vasculature. The concentration-effect profile
`takes on the shape of an anticlockwise hysteresis loop (Figure 1). Here,
`it
`becomes somewhat difficult to establish a concentration—effect relationship.
`One approach is to measure drug at the site of action, but this is rarely possible
`for a number of scientific, technical and ethical reasons. Another approach is
`to measure the response at steady state. The approach which will he discussed
`
`8C?
`
`60
`Response4;<3
`
`
`20
`
`(l
`
`l .0
`0.5
`Concentration
`
`1‘5
`
`Figure 1. Response as a function of concentration shows a time dependence relative to
`the rise and decline in concentration. The arrows indicate the direction in time
`
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`

`rasanaconmnmc MODELS
`
`’5
`
`throughout the remainder of this chapter is a modeling approach that takes
`into account
`the time course of drug concentrations and pharmacological
`response.
`V
`
`Prior to discussing pharmacodynamic models. it is worthwhile mentioning the
`many factors which influence the pharmacological response and by doing so
`confound the delineation of the pharmacodynamics even though the response
`may have clinical meaning and may be reliably measurable.
`Interwsu‘oiect
`variability to pharmacological response is the most influential factor that affects
`the pharmacodynamics of ansr drug. The disease state and the pharmacokinetic
`state {organ function) of the patient,
`the pharmacohinetic behavior of the
`drug (metabolism, protein binding, etc.) and the hiochemical/physiological
`mechanisms involved contribute to inter-subject variability of pharmacological
`response.
`
`Further examples of influential factors that confound pharmacodynamics
`include indistinct pharmacological measures such as sedation or pain, which are
`highly variable among patients and therefore difficult to quantitate reliably.
`Responses that occur after the drug has been eliminated or that last well beyond
`the presence of a drug are difficult
`to relate to concentration since these
`situations often represent a cascade of biochemical or physiological events.
`Tolerance to the continued presence of a drug alters the concentration—effect
`relationship as a function of time and adds complexity to the pharmacodynamic
`understanding of a drug. Diurnal variation: compensatory responses and
`responses (desired or undesired) sensitive to the rate of appearance of plasma
`concentrations require design and data analysis modifications to gain an under»
`standing of the concentrationweffect relationship. There is a considerable body
`of literature that discusses the pharmacological response in relation to concen-
`trations of drug (1—5). The point here is that pharmacological response is a
`complex measure that needs to he understood and reliably measurable to he
`meaningful and useful.
`
`PHARMACODYNAMIC MODELS
`
`The use of mathematical models to aid in testing hypotheses that would not
`otherwise he experimentally feasible has proven to he a useful developmental
`tool. Pharmacodynamic models are no exception. In experimental medicine,
`modeling can be a valuable surrogate where experiments are difficult or
`impossible to conduct because of practical
`limitations or ethical concerns.
`Pharmacodynamic models were born out of existing chemical models. The Sum
`model is an adaptation of the Michaclis~hlenten {6} equation, which describes
`the kinetics of enzymatic reactions. The sigmoid 133nm model is a derivation
`of the Hill (.3) equation, describing the mass action of chemical dissociation,
`and the Langmuir {8,9} equation. describing the phenomena of physical
`adsorption. It is believed that Iangmuir’s work seeded the development of the
`
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`6
`
`OVERVIEW OF E’HARMACODYNAMICS
`
`drug—receptor theories advanced by Clark (l0) and serve as the basis for
`concentrationwffect relationships.
`
`THE LINEAR MODEL
`
`The simplest of all pharmacodynamic models is the linear model. Here concen-
`trations of drug are proportionally related to a pharmacological response. The
`model takes the form of the equation for a straight line where E is the effect,
`which is usually expressed as a fraction of maximal response, C is the drug
`concentration, 5 is the slope or the rate of change in response with a given
`change in concentration, and EU is the effect when no drug is present. Even
`though this model can account for a baseline effect,
`
`E=SXC+E9
`
`this model cannot predict a maximal response3 since this model predicts a
`continuous increase in effect with increasing concentrations. Unfortunately, the
`absence of a maximal response is inconsistent with most physiological stimuli.
`Another deficiency of this model is the limited usefulness since for most drugs
`the relationship between effect and concentration is not linear.
`tin attempt to adjust for the non—linearityr gave rise to the log~linear model,
`which, through log transformation of concentrations, provides a linear approxi-
`mation of a non—linear relationship. In the equation, the baseline effect is
`replaced by I) an empirical constant of no physiological meaning. This model
`cannot predict the effect
`
`EmSXlogC-i-I
`
`{2)
`
`in the absence of drag;3 nor a maximal effect. The model can only predict effects
`which fall between 20% and 30% of the maximal response {1 1). This model can
`be useful if the 20—80% range of maximal response can be ascertained, and if
`this occurs over a wide range of concentrations. An example of the application
`of the legaliiiear model is depicted in Figure 2.
`
`THE Enm MODEL
`
`This model most often describes the concentrationweffect relatiOnship. The
`equation for the model describes a hyperbolic relationship Where E and C are
`as defined shove3 Em“ is the maximum effect attributable to the drug, and the
`EC“,
`is the concentration that elicits 50% of the maximal effect. Unlike the
`linear model, this model predicts a maximal response:
`
`_ Emax X C
`” ECSO + C:
`
`{3}
`
`An alternative to transforming the data when the response is inhibitor}? is to use
`the inhibitor}; Sum model. In this model Es is the effect when no drug is
`
`InnoPharma Exhibit 1026.0009
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`PMARMACODYNAMIC MQDELS
`
`7
`
`CI
`
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`2.0
`
`Drug in central compartment (mg 1 m?)
`
`Figure 2. Log-linear fit of neuromuaeular blocking effect (% paralysis) of tubocnrarine
`and the amount of tuhocurarine in the body. The solid circles are the actual data, which
`Show a traditional S~shape. The open circles and the line represent the log—linear fit over
`the 20—80% effect range (Reprinted with permission from reference ll}
`
`present, and Enm is the maximum reduction in response. If a drug is capable
`of completely abolishing an effeet3 then Emax becomes 50, and Equation 3
`reduces to a fractional Emax model:
`
`3-341 Mm)
`
`C
`
`<4)
`
`An example is illustrated in Figure 3, where the relationship between
`trimoprostil, a PGE; analogue> plasma concentrations and inhibition of meal‘
`induced gastric acid secretion is best described by an inhibitory Emmi model
`{12).
`
`THE SIGMOID Emax MODEL
`
`Oftentimes the concentration-effect curve takes on a more pronounced S—shape,
`which is not adequately described by the Ema; model. Wagner (1) and later
`Holfotd and Sheiner (3) adapted the Hill ('2’) equation to the Emu model as a
`means of improving the fit. The difference involves the use of an exponent, n:
`which determines the slope of the curve and has no physiological meaning
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`InnoPharma Exhibit 1026.0010
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`8
`
`OVERVIEW OF PHARMRCODYNAMICS
`
`I00
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`
`Mean plasma concentration (ng! ml)
`
`fit of meal-induced gastric acid secretion and
`Figure 3. An inhibimry 5",.“ model
`trimspmstil plasma concentrations. The solid circles are the actual data and the lines
`mpresent the best fit. Model 1 shows the parameters for the fit; 1C50=1.1 ngfml and
`Enmx953% inhibitien. Minimal 2 refers to a sigmoid Em“ model linked to a kinetic
`model where :2 =08? iReprimed with permission from reference 12)
`
`attributed to it; This model Callapses to an Em“ model when the expenent has
`a value of 1 {sec Figure 3}:
`
`E
`
`__ Emax X C?"
`E650» + Ct:
`
`{5)
`
`Similar to the Enm, an inhibitory respanm can be incorporated into this model.
`The influence: of the exponent) a, on the: shape of :he concentration—effect
`profile is shown in Figure 4.
`
`InnoPharma Exhibit 1026.0011
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`snasnacoornasnc MODELS
`
`9
`
`$00
`
`80
`
`response
`Percentageofmaximal
`
`60
`
`£10
`
`20
`
`Concentration
`
`Figure 4. Response as a function of concentration conforming to the sigmoid Em“
`model with the same ECso, the same me and varying exponent values
`
`THE PROBABILITY MODEL
`
`If the pharmacological response is subjective and difficult to grade, or when the
`importance of a pharmacological response is whether it occurs or not and not
`the clegree of the response} it may be useful to determine the probability of
`achieving such an effect as a function of concentration. There are several
`statistical approaches that can be used, such as the Kaplan—‘Meier analysis (13)
`and the Cox regression analysis (14). As an example, Antal ct o2. (15) applied
`this model to the reduction in the number of panic attacks during the final week
`of therapy of alprazolam compared with the number of panic attacks at baseline
`using the Cox regression analysis. They determined that a 73% chance of being
`a major responder (5% reduction in panic attacks compared to baseline) is
`associated with an alprazolam steady—state plasma concentration of 48 rig/ml.
`Likewise, fmral at
`:12. (15) determined that there was a 50% probability of
`sedation emergence associated with an alprasolam plasma concentration of
`40 nglml. A comparison of the probability profiles for these two different
`outcomes appears in Figure 5. The authors concluded that at an slprasolam
`concentration necessary to elicit a ’35% probability of a major response there was
`< 50% chance of the emergence of sedation. In general, this model is well suited
`to therapeutics Where response is often su‘oiective, a large placebo effect
`generally exists and the inter-subject variability is usually large.
`
`MODEL r‘xDAPTATIONS
`
`There are many literature examples of where the standard models are adapted
`to meet the specific needs of a particular drug. For example, Smith at al. (16)
`
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`:0
`
`OVERVIEW OF PHARMACODYNAMICS
`
`Probability
`
`0
`
`25
`
`50
`
`125
`100
`3’5
`Alprazolam concentration (no I m!)
`
`150
`
`175
`
`20!}
`
`Figure 5. Probability of being a maior responder (solid line} or for the emergence of
`sedation {dashed line} as a function of alptazolam plasma concentrations in patents
`suffering from panic attacks (Reprinted with permission from reference 15}
`
`to account for
`incorporated an exponential function into the Em“ model
`pharmacoiogical toierance. The deveiopment of tolerance could he represented
`by an exponential decay in the maximum effect as shown in Equation 6.
`
`m Bum x e "K‘ x C"
`E —
`E050" + C”
`
`The tolerance function can easily be incorporated into the EC” term, resulting
`in an exponential increase. Another adaptation involves the dynamic modeling
`of loop diuretics where C and Eng are replaced by ER and ERso—the urinary
`excretion rate, and that which is associated with an increase in the sodium
`excretion rate which is 50% of maximal, respectively (137,18).
`
`THE LINK MODEL
`
`A more elaborate approach to understanding and predicting pharmacologicai
`response as a function of concentration when the response is delayed in relation
`to the appearance of drug (hysteresis) is to use the link model.
`
`(foo Zf V1) "
`E 2 (time; V1)" + tom)"
`
`i?)
`
`This model, first proposed by Sheiner at oi. (19)> links a pharmaookinetie model
`to a pharmacodynamie model? and was applied to the sinmitaneoua fitting of
`
`InnoPharma Exhibit 10260013
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`

`

`niiARMACODYNAMIC MODELS
`
`l l
`
`
`
`he pharmacoltinetics and pharmacodynamics of d—tuhocurarine. The phar-
`1acodynamic model has the look of the sigmoid 333nm model, where {38850 is the
`steadyotate concentration corresponding to 50% maximal effect3 less is a first-
`order rate constant for removal of drug from the site of action, a is the exponent
`hat determines the shape of the curve, and 2/ V1 represents the sum of
`exponentials
`for
`the appropriate pharmacoltinetic model. This modeling
`cchniqne collapses the hysteresis, allowing for a true concentrationweffect
`characterization. it is the most commonly used of the link models.
`Two other link approaches have been described and applied. A non-
`oarametric approach was proposed by Fuseau and Sheiner (28) and later
`odiiied (2i). This approach makes fewer assumptions about the specificity of
`the kineticX dynamic models and the underlying physiological mechanisms. A
`systems approach proposed by Veng—Pcdersen (22) also makes fewer assump—
`ions about the specificity of the kinetic/dynamic models where linear and
`on—linear systems are described using operators.
`These link-model approaches are an effective way to adjust for hysteresis, and
`has describe and understand the concentration—effect relationship. The theory,
`advantages, limitations and applications are discussed in more detail in the next
`chapter and elsewhere {23).
`
`MODEL SELECTION
`
`SCIENTIFIC BASIS
`
`Model selection begins with the understanding of the pharmacological response.
`Is the response the result: of inhibition or stimulation? Is the response a direct
`action of the drug or a cascade of events? is there diurnal variation in response?
`Does the response have a baseline or a maximum? What is the biochemistry?
`Is the duration of the response important to clinical outcome? The answers to
`these kinds of questions, as well as others: lead to the understanding of the
`pharmacological response, yet are often not answerable. The challenge facing
`experts working in this area is to gain as much of an understanding as possible
`to best select a model.
`
`DESIGN CONSIDERATIONS
`
`Study design is a key component toward obtaining meaningful data. When
`conducting pharmacodynamic studies, it is imperative to examine the placebo
`response, since this will define the baseline pharmacological response. It is also
`strongly recommended that
`these studies be conducted in a double-blind
`fashion to protect against bias and to lend validity to the dynamic measure(s}.
`It has to be decided whether to conduct a single—close or a multipledose study.
`This choice is dependent on what is known about the study drug. The choice
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`

`l2
`
`OVERVIEW OF PHARMACODYNAMICS
`
`of study population, healthy subjects or patients, has to be made. The
`meaningful choice is to use patients, but the decision needs to be based on
`risk/benefit, on probability of compliance for patients, since these studies are
`often rigorous, and on the relevance of healthy subjects to the patient population
`(2.4}. It is a good idea to test the study population for poor responders. A
`prostudy rnn~in should also be considered to properly acclimate the study
`population. Consider excluding as many external factors as possible that can
`introduce variability across the study population, such as concomitant medi~
`cation, smoking and other controllable factors. Lastly, ample and appropriate
`sample times are critical to ensure that the model will be meaningful, hut care
`has to be taken so as not to affect the outcome of the dynamic rcsponsels).
`The goal here is to reduce variability, enhance validity and optimize clinical
`utility.
`
`THE DATA
`
`Once data have been collected, they should he visually inspected. This step is
`often taken too lightly as predetermined expectations sway the experimentalist.
`The data themselves are generally the best indicator of the appropriate dynamic
`model. Examine the changes in plasma concentrations of drug and changes in
`response to doses of drug and placebo over time. fire the implicit assumptions
`of dose response in play, are there anomalies, etc“? Ask yourself, what is
`maximally or minimally expected in the presence and absence of drug? What
`makes sense pharmacologically? For example, one would not expect a calcium
`channel blocker to drop blood pressure to zero.
`Based on this data overview, decisions as to the handling of the data need to
`be made.
`Ideally, evaluating each individual separately is the most robust
`approach, since individual treatment provides intra- as well as interwsubiect
`variability. Pooling of data becomes necessary when the data density from
`individuals is not sufficient for analysis or the data are highly variable. Pooling
`has the risk of masking individual anomalies that may be indicative of a small
`subset of responders. An example might be a small subset of hypersensitive
`responders whose shift to the left on the concentration—response curve could be
`lost when data are pooled.
`‘
`The next step is to decide whether to use the raw data or to transform the
`data. This is critical because transforming the data carries assumptions about
`the data themselves and the pharmacological mechanism.
`In particular,
`transformed data alter the distribution of the data and can conceal baseline
`
`variability. Transformation also determines which equations may he used with
`a given dynamic model. In general, data should not be transformed Without a
`theoretical basis, but in practice it is heat to incorporate the choice of raw versus
`transformed data as part of the selection of a model.
`
`InnoPharma Exhibit 1026.0015
`
`

`

`PHARMACODYNAMIC MODELS
`
`l3
`
`MODEL SELECTION
`
`Choosing a pharmacodynamie model comes from the data analysis and
`knowledge of the pharmacological mechanism. At this point there is some trial
`and error necessary prior to the final selection. Initial parameter estimates such
`as ECgo or Ems for any model can usually be obtained from inspection of the
`concentration—effect plot of raw or transformed data. These estimates should be
`used in a nonlinear regression analysis of the concentration—effect data>
`employing whatever model/(s) have been initially selected. Overlaying the
`predicted data on top of the actual data will preside guidance as to the next
`steps. thentimes, the results of these regressions will eliminate poor models,
`because a predicted parameter violates what is already known to be. This point
`is best made by using the example of Kroboth st of. (25).
`The example involves
`the inhibition of gastric acid secretion by the
`ngblocker, nizatidine. The percentage inhibition (transformed) of gastric
`acid secretion as a function of nizatidine plasma concentrations (includes
`placebo response) were fit using the Em}; and the sigmoid Emax models. The
`predicted regressions are shown in Figure 6(a) and (b), respectively. Here the
`choice is obvious in favor of the sigmoid Emax model when the data were
`transformed.
`
`The authors also modeled the raw data using the inhibitory sigmoid Emax
`modelt The net
`inhibition of gastric acid secretion (placebo minus drug
`response) was fit as a function of concentration. The regression is shown in
`Figure pic). Here the model predicted a maximal inhibition of as mEq relative
`to baseline. The actual data showed the potentiai for greater suppression. In this
`case, transforming the data was more appropriate. The reader is referred to
`Kroboth er ad. (25) for the details surrounding this example.
`
`MODEL JUSTIFICATION
`
`Good science dictates that statistical justification be used to discern between
`models even in the obvious example given above. The better model is based on
`a smaller residual sum of squares, smaller 95% confidence intervals for
`parameter estimation and smaller standard deviations around the parameter
`estimates. The distribution of the residuals can be used to discern between the
`
`'fits that models predict at the extremes. The application of the F-test (26} and
`the Akaike Information Criterion (2?) can also be used to distinguish the better
`model when the difference between the models is an additional parameter.
`Testing for the robustness of the model is equally encouraged, since an
`obvious goal of modeling is to predict events or outcomes for a larger popula-
`tion. The more perturbations that can be explained by the model, the greater
`the predictability to a real system. As a simple example, the robustness of a
`kinetic/dynamic model developed for oral administration may be tested by
`
`InnoPharma Exhibit 1026.0016
`
`

`

`14
`
`OVERVIEW OF PHARMACODYNPKMICS
`
`100
`
`
`
`
`
` 03O Acid00000050300(96)
`
`{a}
`
` Q 7
`
`o
`200 400 000 800 100012001400100010002000
`Prasma ooncentrafion (ng I mi}
`
`100
`
`o
`
`200 400 000 30010001200140010001000200
`
`Plasma concentration (mg I mi}
`
`o
`
`m0“w
`
`(%)
`
`Acidsuppression
`Mambo—«treatment(mEq) 0
`
`(C)
`-
`
`0
`
`000 100015002000250030003000400040005000
`Conwntration (ng Imi)
`
`{a} Gastric acid suppression as a function of nazatidine plasma concentrations
`Figaro 6.
`{solid Circles) along with a regression fit (solid line) using the Emax model. in) Same data
`along with a regression fit {snlid line) using the sigmoid Enm model. (0} Inhibition of
`gastric acid secretion relative: to placebo as a function of nazatidine plasma concentrations
`(solid ciz‘cles) along with the regression fit {solid line) using the inhibitory Emax modei
`(Reprinted with permission from reference 25}
`
`InnoPharma Exhibit 1026.001?
`
`

`

`Pl—IARMrXCODYNAMiC MODELS
`
`15
`
`comparing the simulated data for intravenous administration with actual data
`collected after intravenous administration.
`
`SUMMARY
`
`If a goal of health care professionals is to provide meaningful therapeutics, then
`an action to this goal would he the development of a rational process for
`optimising drug therapy.
`Integration of pharmacoldnetics and pharmaco-
`dynamics is one such process that has been applied successfully to a number of
`therapeutics. In the case Where there is a wide enough separation between
`concentrations that produce the desired response and concentrations that
`produce the undesired response (applicable to most drugs}, the utilization of
`pharmacokinetics and pharmacodynamics may help establish an optimal dose
`and regimen during drug development, but will not be needed to individualize
`patient therapy since it is more practical to prescribe doses of drug that ensure
`therapeutic effectiveness in most patients where the occurrence of toxicity is of
`little or no consequence. For drugs where the separation is narrower, pharmaco~
`kinetics and pharmacodynamics can be utilized to individualize therapy to
`ensure a separation of desired from undesired responses. Applicable to either
`situation, understanding the concentrationweffect
`relationship can aid in
`optimizing drug therapy.
`The intent of this chapter was to review pharmacodynamic concepts and
`models as they apply to the concentration—effect relationship. Progress has been
`made towards utilizing kinetics and dynamics in understanding the variability
`of pharmacological response. The challenge that continues to face researchers
`working in this area is to develop more sophisticated technology for measuring
`pharmacological response and to increase the knowledge of the underlying
`biochemical and physiological mechanisms associated with a given disease. The
`regulatory, economic and social expectations being placed on health care world-
`wide provide the necessary impetus.
`
`REFERENCES
`
`H
`
`. Wagner JG. Kinetics of pharmacologic response. 5". Timer Biol. 1968; 20: 13’3w201.
`2. Holford NEG, Sheiner LB. Kinetics of pharmacologic response. Pizornzacol liter.
`1982; 16: 143—166.
`3. Holford NHG, Sheiner LB. Understanding the dose—effect relationship: clinical
`application of pharmacol<inetic—pharmacodynamic models. Clio. Phonetic-oldest.
`198i; 6: 429—453.
`4. Smith RB, Kroboth PD, Iuhl RP. Plzonnacoéinetics and pltomzdcodynemics: research
`design and catalysis) 1st edn. Harvey Whitney, Cincinnati, 1986.
`S. Kroboth PD, Smith RB, 31.1111 RP. Phomzacoe‘einetics and phermacodynamics: amen?
`proclaim-potential solutions, Vol.