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`lNS)‘
`NIH Public Access
`Os” %
`5 Author Manuscript
`HEM"
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`0»
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`Published in final edited form as:
`
`DIUgMetab Pbamiacola'net. 2009 ; 24(1): 16—24.
`
`Scaling Pharmacodynamics from In Vitro and Preclinical Animal
`
`Studies to Humans
`
`Donald E. Mager*, Sukyung WooT, and William J. Jusko
`Department of Pharmaceutical Sciences, University at Buffalo, State University of New York, New
`York, USA
`
`Summary
`
`An important feature of mechanism-based pharmacokinetic/pharmacodynamic (PK/PD) models is
`the identification of drug- and system-specific factors that determine the intensity and time-course
`of pharmacological effects. This provides an opportunity to integrate information obtained from 111
`V1'trobioassays and preclinical pharmacological studies in animals to anticipate the clinical and
`adverse responses to drugs in humans. The fact that contemporary PK/PD modeling continues to
`evolve and seeks to emulate systems level properties should provide enhanced capabilities to
`scale-up pharmacodynamic data. Critical steps in drug discovery and development, such as lead
`compound and first in human dose selection, may become more efficient with the implementation
`and further refinement of translational PK/PD modeling. In this review, we highlight fundamental
`principles in pharmacodynamics and the basic expectations for 111 Vitro bioassays and traditional
`allometric scaling in PK/PD modeling. Discussion of PK/PD modeling efforts for recombinant
`human erythropoietin is also included as a case study showing the potential for advanced systems
`analysis to facilitate extrapolations and improve understanding of inter-species differences in drug
`responses.
`
`Keywords
`
`allometric scaling; cell life span models; mechanism-based modeling; pharmacodynamics, PD;
`pharmacokinetics, PK; receptor occupancy; recombinant human erythropoietin, rHuEpo; target-
`mediated drug disposition, TlVlDD
`
`Introduction
`
`The extrapolation of 111 517100, 111 Vitro, and preclinical animal studies to predict the likely
`pharmacokinetic properties of drugs in humans now appears within reach, largely due to
`advancements in physiologically-based pharmacokinetic (PBPK) modeling. 1’2) Whereas
`traditional allometry continues to prove useful under certain conditions for inter-species
`scaling of PK properties, significant progress has been achieved by transitioning from
`models of data (e. g., classic compartmental models) to those of biological systems. The
`PBPK modeling approach provides a framework for integrating drug-specific calculated
`parameters (e. g., octanolzwater and bloodztissue partition coefficients) and 111 Vitro
`measurements (e. g., plasma protein binding and hepatocyte intrinsic clearance) with
`physiological system-specific parameters (e. g., tissue volumes and blood flows). Given the
`
`
`
`*To whom correspondence should be addressed: Dr. Donald E. Mager; Department of Pharmaceutical Sciences; University at Buffalo;
`SUNY; Buffalo; New York 14260; USA. Tel. +1-716-645-2842 (ext. 277); Fax. +1-716-645-3693; dmager@buffalo.edu.
`TPresent address: Center for Cancer Research; National Cancer Institute; 9000 Rockville Pike; Bethesda; Maryland 20892; USA.
`Full text of this paper is available at http://www.jstage.j st.go.jp/browse/dmpk
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`Page 2
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`relative success of anticipating human exposures to drugs and toxicants, 3’4) there is
`considerable interest in the development of techniques for the scaling of pharmacodynamic
`systems. Although drug responses are considerably more complex than processes controlling
`pharmacokinetics, the shift from empirical to mechanism-based PK/PD modeling5’6) should
`provide the best means for translating 1'11 V1110 and ammal data to human clinical
`pharmacology. 7)
`
`In this review, we discuss the basic tenets of pharmacodynamics, namely 1)
`pharmacokinetics or drug exposure as the driving function, 2) capacity-limitation of drug-
`receptor interactions, and 3) physiological turnover processes and functional adaptation or
`homeostatic feedback mechanisms. As with PBPK models, these basic components identify
`drug and system specific properties that might be anticipated using 111 V1110 assays,
`allometry, and/or preclimcal animal experiments. A case study showing how human
`responses to recombinant human erythropoietin (rHuEpo) can be predicted from scaling a
`mathematical model developed in rats is provided as an example of utilizing mechanism-
`based PK/PD models to scale complex pharmacological systems.
`
`Basic Principles of Pharmacodynamics
`
`The basic tenets of pharmacokinetics (PK), pharmacology, and physiology continue to form
`the basis for contemporary pharmacodynamic systems analysis (Fig. l). Pharmacokinetics,
`or the processes controlling the time-course of drug concentrations in relevant biological
`fluids, tissues, and sites of action (biophase), is the driving force for subsequent
`pharmacological and most toxicological effects. Although mammillary plasma clearance
`models (simple linear compartmental models) and area/moment analysis are the most
`commonly applied techniques for characterizing the absorption and disposition (distribution
`and elimination) properties of drugs, PBPK models provide a comprehensive platform for
`describing the major processes influencing the concentration time-course and net exposure
`of drugs in various fluids and tissues (Fig. 1, left panel). Each tissue of interest is
`anatomically arranged and described by a series of mass balance differential equations.
`Fick’s law of perfusion/diffusion and drug partitiomng are featured along with a capacity-
`limited function for various drug binding, transport, and elimination processes. This
`approach provides insights into expected drug concentrations in important tissues, and
`potentially sites of action, and the intrinsic scalability of predictions across species and
`molecular drug properties is unparalleled. Whereas traditional PBPK model development
`has relied on destructive sampling in preclinical studies, advances in nomnvasive imaging
`(such as positron emission tomography and magnetic resonance imaging) and microdialysis
`may eventually provide even finer details of 111 V1'V0 drug disposition”)
`
`At the biophase, the law of mass action and the limited concentration of pharmacological
`targets often manifest as nonlinear, capacity-limited systemslo) The rate of change of a
`dmg-receptor complex (RC) can be defined as:
`
`dRC
`7:1(011 ' (Rtat _
`
`' C _ kofl'
`
`'
`
`(1)
`
`where Rm is the maximum receptor concentration, C is the drug concentration at the site of
`action, and [(0,] and koffare the second-order association and first-order dissociation rate
`constants. Assuming equilibrimn conditions, this equation can be rearranged to yield the
`general binding equation:
`
`RC:
`
`
`Rm: ‘ C
`KD+C (2)
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`Drug Metab Phatmacokjnet. Author manuscript; available in PMC 2013 July 30.
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`Mager et al.
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`Page 3
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`where KD is the equilibrium dissociation constant (km/Iron). Based on Clark’s theory of
`receptor occupancy, the stimulus or drug effect (15) can be directly proportional to the
`fraction of occupied receptors, such that E=cL ~ RC, thus deriving a classic form of the Hill
`equation or sigmoid EmaX model:
`
`_ Ernax ' Cy
`E- —EcyCW (3)
`‘ 50+ ’
`
`where EmaX is the maximum effect, 7 (or Hill coefficient) is a slope term that reflects the
`steepness of the effect-concentration curve, and the EC50 is a sensitivity parameter
`representing the drug concentration producing 50% of Emax. The typical stimulus/effect-log
`concentration relationship is thus curvilinear, and typical profiles for varying values of y are
`shown in the center panel of Figure 1.
`
`In contrast to the linear transduction of receptor occupancy (Eq. 3), Black and Leff
`introduced the operational model of agonism to provide a mechanistic interpretation of
`concentration-effect curves“) The stimulus or effect is assumed to be nonlinearly related to
`the drug-receptor complex:
`
`Emax ' RC
`‘ KE+RC
`
`(4)
`
`where KE is the RCvalue producing half-maximal effect. Combining Equations 2 and 4
`yields:
`
`_ Emax ‘T‘C
`_KD+(T+l)-C (5)
`
`where EmaXis a system maximum and ( represents a transducer or efficacy function (Rtoz/
`KE). This model can accommodate complex relationships, such as partial agonism, where
`observed capacity and sensitivity properties are actually hybrid terms composed of drug
`specific (KD and 1:) and system specific (Emax) parameters. Regardless of whether linear or
`nonlinear transduction is operational, capacity-limitation is a hallmark property of
`pharmacology, and consequentially, a suitable range of dose-levels (or concentrations) are
`required to define the parameters of such systems. In addition, the implementation of
`Equation 5 requires pharmacodynamic data, or at least prior information, on the properties
`of a full agonist to identify the maximal system response.
`
`Physiological turnover processes and homeostatic feedback mechanisms represent the third
`major component of pharrnacodynamics (Fig. 1, right panel). An open system for a
`biological substance, R, with zero-order production (km) and first-order removal (1%”) can
`be defined by the following differential equation:
`
`dR/dt=kztn — km -R (6)
`
`Assuming a time-invariant baseline or steady-state, the initial or baseline value (R0) can be
`defined as the ratio of the production and loss terms: R0=k,',/k0ut. A family of basic indirect
`response models apply to many drugs where interaction with the pharmacological target (Eq.
`3) serves to inhibit or stimulate either km or kout. 12) A series of transit compartments can
`also be factored into such models to emulate time-dependent transduction processes that
`often exhibit significant onset delays and exposure-response hysteresis. 13) Knowledge of the
`turnover rates for physiological system components is important for the identification of the
`rate-limiting steps for specific pharmacological responses and might impact study design.
`Such information might also facilitate the characterization of feedback mechanisms that
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`Drug Metab Phatmacokjnet. Author manuscript; available in PMC 2013 July 30.
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`Mager et al.
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`might result in tolerance and/or rebound phenomena.6) As both drugs and diseases often
`interfere with normal physiological processes, the turnover aspect of both indirect response
`models”) and transduction models”) renders them well suited for the simultaneous
`
`consideration of these factors in the time-course of disease progression.
`
`Mechanism-based models seek to integrate these basic components to identify critical
`pharmacological and (patho)-physiological system properties as well as the rate-limiting
`steps in responses to drugs.6’16) Useful models with a potential for translational medicine
`also provide a stmctural framework for incorporating 111 517100, 1'11 V1110, and preclinical PK/
`PD measurements to predict the effects of new drugs in humans and across levels of
`biological organization (Fig. 2). A discussion of all these methods is beyond the scope of
`this review, which will focus on 111 V1110 assays and allometric principles in the context of
`mechanistic PK/PD models. The derivation of quantitative structure-PK/PD relationships (111
`517100 modeling) to predict the exposure-response profiles of new chemical entities has been
`recently reviewedz)
`
`Extrapolation of In Vitro Bioassays
`
`Pharmacodynamic modeling of several systems has revealed that properties of drug
`interactions with pharmacological targets measured 1'11 V1110 may be correlated with specific
`model parameters often reflective of drug potency. Shimada and colleagues developed an
`ion-channel binding model based on 111 V1110 binding data of calcium channel antagonists,
`which demonstrate relatively slow rates of association and dissociation”) The
`pharmacologic effect was assumed to be proportional to the concentration of the drug-
`receptor complex and, as an extension of Equation 1, the rate of change of the effect was
`defined as:
`
`‘L—flkm.
`
`- (Emax — E) - C — keg - E (7)
`
`The inclusion of the binding parameters was sufficient to explain the hysteresis observed
`between the PK and antihypertensive effect of eight calcium channel antagonists in Japanese
`patients. The calculated KD values based on estimates of [(0,] and koffwere shown to be
`significantly correlated with those obtained from 111 V1110 experiments. These results suggest
`that PK and 111 V1110 binding data alone could be used to predict the pharmacodynamic
`profile of future drugs in this class. Kalvass and colleagues18) performed extremely
`insightful PK/PD studies with seven opioids in mice showing the importance of time-course
`of brain distribution and binding in determining their antinociceptive effects. The EC50 of
`unbound drugs in brain showed excellent correlation with 111 V1110 receptor binding affinities
`(KD). From a drug development perspective, these examples demonstrate how 111 V1110
`assays may be coupled with useful PK/PD models to anticipate the outcomes of similar
`compounds and may guide lead compound selection.
`
`Relative receptor affinity has been shown to be correlated with 111 V1'V0 estimates of drug
`potency for several drugs, and 111 V1110 measurements could be used in scaling of EC50
`values across species. In a 5-way randomized placebo-controlled crossover study aimed at
`evaluating the dosing equivalency of four systemically administered corticosteroids,
`mechanism-based PK/PD models were used to estimate EC50 values for several
`immunomodulatory effects, including cortisol suppression, lymphocyte and neutrophil
`trafficking, and ex V1'V0 inhibition of lymphocyte proliferation. 19:20) The estimated potencies
`for all of these responses were highly correlated with relative receptor affinity (1'11 V1110 KD
`values normalized to dexamethasone). Differences in protein homology and other genetic
`sources of variability may result in altered drug binding affinity among species. Chien and
`colleagues corrected an EC50 value for a competitor drug measured in humans, using several
`
`Drug Metab Phatmacokjnet. Author manuscript; available in PMC 2013 July 30.
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`Mager et al.
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`factors including receptor binding, to predict the 111 V1'V0 human EC50 for a new chemical
`entity (NCE):21>
`
`EC
`
`50.NCE,hurndu—
`
`-J:)C
`
`50.CulupeLiLoi',l111111a1|
`
`'
`
`E(15031013
`50,(:ompctit0r
`
`)
`
`rat
`
`‘ 5f” ' 6K”
`
`(8)
`
`where Sfu and 8K1) are correction factors for differences in the free fraction in plasma (fa)
`and binding affimty (KD). For example,
`
`‘
`OK 2
`D
`
`
`Kahuman
`KDJM
`-
`K
`K
`
`D,rat
`
`NOE
`
`DJuuu-dn conlpetitor
`
`9
`
`(
`
`)
`
`The scaled EC50 from ammal and 111 who data (Eq. 8) was coupled with other projected
`parameters to simulate a dose-response curve (Eq. 3 with an added baseline) for a new
`antihypertensive agent, relative to a competitor, in the preclimcal phase of development.
`Monte Carlo simulations included a relatively large confidence interval about expected
`outcomes; however, data from clinical studies would eventually be used to confirm and
`update the model.
`
`Traditional Allometric Scaling in PK/PD
`
`Although the structural nature of physiologically-based models makes them uniquely suited
`for scaling and predicting human drug exposures, the extrapolation of PK-PD models from
`animals to humans is primarily based on classic allometric principleszz) There is a general
`expectation that many physiological processes and organ sizes (9) tend to obey a power
`law223)
`
`6:11- Wb
`
`(10)
`
`with Wrepresenting body weight and a and b as dmg/process coefficients. The exponent, 1),
`tends to be around 0.75 for clearance processes, 1.0 for organ sizes or physiological
`volumes, and 0.25 for physiological times or the duration of physiological events (e. g.,
`heart-beat and breath duration, cell lifespans, and turnover times of endogenous substances
`or processes).24) A theoretical basis for allometric scaling has been proposed by West and
`colleagues based on the fractal nature of biological systems and energy conservation
`principles.25) Empirical models have also been coupled with allometric relationships and 111
`V1110 metabolism experiments using nonlinear mixed effects modeling to improve the
`scalability of such models.26’27)
`
`The basic expectations in pharmacodynamics are that physiological turnover rate constants
`of most general structures and functions should be predictable among species based on
`allometric principles, whereas capacity (Emax) and sensitivity (EC50) parameters tend to be
`similar across species. Brodie and colleagues were the first to examine some PK-PD
`properties across species, revealing inter-species differences in duration of action and
`biological half-life, but similarity in plasma concentrations on awakemng (i.e., concentration
`producing a standard response analogous to an EC50), following hexobarbital
`administration”) There has long been a general belief that the plasma drug concentration
`required to elicit a certain (intensity of) action is often similar in experimental animals and
`humans”) While interspecies differences in relative receptor affimty and plasma protein
`binding occur (Eqs. 8 and 9),21) there are several examples that show reasonable agreement
`of such properties between rats and humans for chemically-related series of drugs. Ito and
`colleagues demonstrated a linear correlation between the logarithm of KD values of
`benzodiazepines in rat and human cerebral cortex tissue over several orders of magnitude.30)
`
`Drug Metab Phatmacokjnet. Author manuscript; available in PMC 2013 July 30.
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`Cox and coworkers also showed good agreement for the EC50 values of four synthetic
`opioids between these same species.31) Mechanistic modeling was applied to PK/PD data
`for S(+)—ketoprofen obtained from several species, and estimated parameters further support
`these basic expectations. Pharmacokinetic parameters were shown to scale proportionally to
`body weight (albeit with unusual power coefficients), and anti-inflammatory PD parameters
`exhibited limited ranges that were essentially independent of body weight.32)
`
`Mechanism-Based PK/PD Modeling of rHuEpo
`
`To demonstrate the use of scaling principles within mechanism-based PK/PD models, we
`present here scaled phannacodynamic responses using a rat model of rHuEpo PK/PD to
`anticipate the time-course of several biomarkers in humans. This drug is clinically indicated
`for the treatment of specific types of anemia, and binding of this endogenous protein to its
`biological receptor (EPOR) expressed by progenitor cells in bone marrow elicits
`proliferation and differentiation of erythroid cells, thereby increasing reticulocytes, red
`blood cells, and hemoglobin concentrations in blood. Erythropoietin exhibits a high degree
`of homology among mammals, which explains the conserved biological activity of rHuEpo
`in various species.
`
`The disposition of rHuEpo in several species is polyexponential and nonlinear, and typical
`PK profiles have been described using a two-compartment model with a concentration-
`dependent Michaelis-Menten elimination function operating in parallel with a linear
`nonsaturable clearance pathway.33’35) Target-mediated drug disposition (TMDD) represents
`a likely explanation for the capacity-limited elimination of erythropoietin; a condition where
`a significant proportion of the drug (relative to dose) is bound to its pharmacological target,
`such that this interaction influences the PK properties of the dmg.36’37) Receptor-mediated
`endocytosis is a major clearance mechanism for many protein dmgs,38) and this saturable
`process can result in nonlinear drug disposition”) The binding of erythropoietin to EPOR is
`specific and results in saturable internalization of the dmg—receptor complex. 40) Chapel and
`colleagues demonstrated that bone marrow ablation in sheep produced a significant decrease
`in erythropoietin clearance, providing experimental evidence that target binding and
`transport plays a major role in the 1'11 V1'V0 disposition of erythropoietin“) Interestingly, the
`simultaneous modeling of PK profiles of rHuEpo from a wide-range of intravenous dose
`levels in rats, monkeys, and humans revealed that full and reduced TMDD models42’43) well
`characterized rHuEpo disposition and provided a basis for linking an established
`phannacodynamic model.44)
`
`Woo and Jusko have provided a comparison of interspecies PK/PD properties of rHuEpo.45)
`Although the prospective use of allometric scaling can be limited,46) it is generally
`considered that peptide and protein drugs are more likely to exhibit allometric PK
`relationships than small molecules owing to the relative species conservation of mechanisms
`that control the biodistribution and elimination of such compounds/“’49) Despite the non-
`linear disposition of rHuEpo, total systemic clearance and the steady-state volume of
`distribution show good correlation with body weight. The exponent for clearance (0.708)
`was close to the expected value of 0.75; however, the exponent for the volume of
`distribution (0.853) was slightly lower than the expected value (1.0). Pharmacokinetic model
`specific parameters, such as Michaelis-Menten capacity or VmaX, the central volume of
`distribution, and a first-order rate constant of absorption, also scaled to body weight with
`exponents of 0.504, 0.983, and -0.349, respectively (based on rat, monkey, and human
`data). As anticipated, the pharmacological capacity and sensitivity parameters were
`essentially species-independent.
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`Drug Metab Phatmacokjnet. Author manuscript; available in PMC 2013 July 30.
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`We sought to predict the time-course of reticulocytes, red blood cells, and hemoglobin
`concentrations in humans after rHuEpo administration from an established PK/PD model
`developed in rodents. Pharmacodynamic data were extracted from a clinical study in which
`healthy male volunteers were given 150 IU/kg subcutaneously (SC) three times weekly for 4
`weeks.34) The general structure of the PK/PD model for rHuEpo developed from rat
`preclinical data is shown in Figure 3.35) The PK component of the model can be described
`by:
`
`
`max
`dAP .
`dt =1np11t(l)= m+kel+k17t 'A12+kt17'AI
`p
`m
`P
`
`(11)
`
`dA
`d—tt2kp,-A,,—k,p-A,
`
`(12)
`
`where Ap and At represent the amounts of rHuEpo in the central and tissue compartments,
`Vmax and Km are Michaelis-Menten parameters, Vp is the volume of the central rHuEpo
`compartment, [(61 is first-order elimination rate constant, and km and ktp are first-order
`distribution rate constants. The initial conditions of Equations 11 and 12 are zero, and the
`input function after SC drug administration is defined as:
`
`Input(t)= {
`
`F~(l—fr)~Dose
`r
`,
`T
`ka -F -fr - Dose - (MU—T)
`
`;0<t S T
`;I>7'
`
`13
`
`)
`
`(
`
`where F is bioavailability, firis the fraction of the dose undergoing first-order absorption
`(19), and 1: is the time period of zero-order input. This input function is based on the
`complex absorption profile due in part to the significant role of the lymphatics in the uptake
`of proteins administered subcutaneously.50:51)
`
`The catenary PD model (Fig. 3) contains two precursor compartments (P1 and P2) linked to
`reticulocyte (REY), red blood cell (RBC), and hemoglobin (Hb) compartments. This model
`mimics the process of erythropoiesis from bone marrow to blood, and is based on cell life
`span concepts integrated into indirect response models for drugs that alter the generation of
`natural cells.52) Cells are assumed to be produced at a constant rate, circulate for a specific
`duration of time (Ti), and are then eliminated from the system not by a first-order process,
`but at the same rate as the input, delayed by the cell life span (senescence or conversion to
`another cell type). The precursor compartments represent early progenitor cells and
`erythroblasts, and TH and Tp2 are the average times taken for cells to differentiate. The
`rates of change of the reticulocyte (RE?) and mature RBC (RBCM) counts are described by:
`
`
`(IRE T
`:kin'S (t Tpl T112)'S (1 Tp2)'1(t Tpl T112) kin'S (t Tpl TpZ
`
`(t_Tp2_TRET)'1(I_Tpl_T}12_TRET)
`
`(14)
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`Drug Metab Phatmacokjnet. Author manuscript; available in PMC 2013 July 30.
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`
`dRBCM
`d:
`
`=19, . s (t
`
`_ P1 _ TPZ _ TRET) ' SO"
`
`_ TP2 _ TRET) ' [(t
`— T,,1 — T,2 — Tm) — km .50
`
`(15)
`
`— pl — TpZ _ TRET _ TRBC) ' SO
`
`_ Tl"!l _ THET _ REC) ' [(1
`
`— pl — T122 _ TRET _ TRBC)
`
`with TRETand TRBC as the average life span of these cells, and km is the zero-order
`production rate constant. The initial conditions for Equations 14 and 15 are RE72) and
`RBCO—RETO, where RE72) and RBCO are baseline measurements. The stimulation function
`S(t) was defined as:
`
`50):] +Sma‘x A[1(t)/Vp
`S Cso+AP(l‘)/Vp
`
`(16)
`
`where SmaX is the maximal stimulation factor and SC50 is the rHuEpo concentration
`resulting in 50% of Smax. Hemoglobin concentrations were calculated as the product of the
`mean corpuscular hemoglobin (MCH; measured) and sum of RET and RBCM (Eqs. 14, 15).
`A counter regulation feedback loop is also included, I(t), driven by the difference in Hb from
`baseline values, and was defined as:
`
`[max '
`I =1——
`IC50+AHb(z)
`(1)
`
`(17)
`
`where the maximal inhibition factor (Imax) was fixed to 1, and [€50 is the Hb difference
`from baseline producing 50% feedback inhibition.
`
`The parameter values, their sources, and scaled-up values in humans used for the PK/PD
`model simulations are listed in Table 1. Inter-individual variability (IIV) for each parameter
`used in the Monte Carlo simulations is also reported. Volume, clearance, and first-order rate
`constants were scaled with allometric exponents of 1, 0.75, and -0.25. The baseline values
`for RET, RBC, and MCH (and their respective IIV) were considered drug and species
`independent and were set to literature values for humans.53) Life span parameters were
`scaled using an allometric exponent of 0.124 which was previously estimated using RBC
`data obtained from over 20 species.45) Only nominal variability was assigned to PK terms
`(10% CV%), whereas CV% values were set to 20% for SmaX and 30% for sensitivity
`parameters (SC50 and IC50).
`
`Monte Carlo simulations were conducted using ADAPT II (Biomedical Simulation
`Resource, USC, Los Angeles), and mean observed data and model predicted profiles are
`shown in Figure 4. The predicted values of the three biomarkers are in good agreement with
`observed data, which fall well within the 90% prediction interval (gray areas). The
`successful scaling of the rat PK/PD model of rHuEpo to human responses demonstrates how
`basic allometric principles and preclinical data may be integrated using mechanism-based
`models to make useful predictions. It is important to recognize that the biomarkers of drug
`activity and preclinical PK/PD models must be meaningful across species. The likelihood of
`these appears to be greater for macromolecules as compared to small molecules;54) however,
`a similar interspecies scaling approach was shown to apply to two 5-HT1A receptor
`agonists.55)
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`Drug Metab Phatmacokjnet. Author manuscript; available in PMC 2013 July 30.
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`InnoPharma Exhibit 1028.0008
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`In summary, the scaling of pharmacodynamic data relies heavily on the ability to predict and
`integrate the fundamental processes controlling drug exposure (pharmacokinetics), drug
`action (pharmacology), and interactions with physiological systems. Preclinical data and 111
`V1110 bioassays can provide important insights into these properties, especially as
`pharmacodynamic parameters tend to be species independent; however, it is important to
`verify whether measurements of drug effects are meaningful across species.
`Notwithstanding the limitations of prospective allometry, such power law relation-ships
`have proven useful in scaling-up physiological turnover processes and PK properties for
`many drugs. New techniques are needed to identify conditions under which allometric
`scaling may or may not be appropriate in PK/PD models. Physiologically-based PK models
`will likely become commonplace given their intrinsic potential for projecting human PK
`properties from 111 who and 111 517100 measurements and data obtained in other species.
`Animal studies can provide preliminary data for the development of mechanism-based PK/
`PD models, which will continue to evolve toward efficient descriptions of pharmacological
`systems. Such models offer the best approach toward effectively combining and interpreting
`the major determinants of drug action across species.
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`Acknowledgments
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`This research was supported in part by Grant GM 57980 from the National Institutes of Health (to W.J. J.), and a
`New Investigator Grant from the American Association of Pharmaceutical Scientists (to D.E.M.).
`
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`InnoPharma Exhibit 1028.0009
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