review - j-stage home

53

Received; December 26, 2008, Accepted; January 16, 2009*To whom correspondence should be addressed: Professor Amin ROSTAMI-HODJEGAN, Royal Hallamshire Hospital, Room M129-M Floor, TheMedical School, Beech Hill Road, Sheffield, S10 2RX, UK. E-mail: a.rostami＠sheffield.ac.uk

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Drug Metab. Pharmacokinet. 24 (1): 53–75 (2009).

Review

A Framework for Assessing Inter-individual Variability inPharmacokinetics Using Virtual Human Populations andIntegrating General Knowledge of Physical Chemistry,

Biology, Anatomy, Physiology and Genetics:A Tale of `Bottom-Up' vs `Top-Down' Recognition of Covariates

Masoud JAMEI1, Gemma L DICKINSON2 and Amin ROSTAMI-HODJEGAN1,3,*1Simcyp Limited, Sheffield, UK

2Eli Lilly, Windlesham, UK3Academic Unit of Clinical Pharmacology, University of Sheffield, UK

Full text of this paper is available at http://www.jstage.jst.go.jp/browse/dmpk

Summary: An increasing number of failures in clinical stages of drug development have been related to theeffects of candidate drugs in a sub-group of patients rather than the `average' person. Expectation of extremeeffects or lack of therapeutic effects in some subgroups following administration of similar doses requires a fullunderstanding of the issue of variability and the importance of identifying covariates that determine the ex-posure to the drug candidates in each individual. In any drug development program the earlier these covari-ates are known the better. An important component of the drive to decrease this failure rate in drug develop-ment involves attempts to use physiologically-based pharmacokinetics `bottom-up' modeling and simulationto optimize molecular features with respect to the absorption, distribution, metabolism and elimination(ADME) processes. The key element of this approach is the separation of information on the system (i.e. hu-man body) from that of the drug (e.g. physicochemical characteristics determining permeability throughmembranes, partitioning to tissues, binding to plasma proteins or affinities toward certain enzymes and trans-porter proteins) and the study design (e.g. dose, route and frequency of administration, concomitant drugsand food). In this review, the classical `top-down' approach in covariate recognition is compared with the `bot-tom-up' paradigm. The determinants and sources of inter-individual variability in different stages of drug ab-sorption, distribution, metabolism and excretion are discussed in detail. Further, the commonly known toolsfor simulating ADME properties are introduced.

Keywords: ADME; developmental pharmacology; drug development; drug discovery; drug-druginteractions; Monte Carlo simulations; mathematical modeling; pharmacogenetics;physiologically-based pharmacokinetics; pharmacokinetic; pharmacodynamic modeling

Introduction

Individual variability in dose-concentration relation-ships and its impact on dose requirements is of relevanceto clinicians as well as scientists working in drug develop-ment. Regulatory organizations require information oncovariates relevant to patient populations at the point offiling applications for new candidate compounds. Themost relevant information, with implications for dose ad-justment in certain sub-group of patients or avoidingprescribing the drug to some other sub-groups, is then in-

corporated in the labeling for marketed drugs. For manyyears, statistical analyses of data obtained from smallparallel studies, together with population pharmacokinet-ic (POP-PK) assessment of larger Phase II and Phase IIIstudies, have been the two foundations of initial covariaterecognition efforts. These methods require the analysis ofdata from a diverse population of individuals. Classicalpharmacokinetic studies, to assess differences betweengroups, involve intense sampling from individuals butemploy few people in each sub-group of the populationunder investigation. Conversely, POP-PK studies require

5454 Masoud JAMEI, et al.

substantial numbers of individuals who may have pro-vided sparse samples only.

Since the introduction of the POP-PK approach in the1980s, knowledge on mechanistic pharmacology andcovariate analysis has increased significantly. Under-standing the mechanisms by which covariates, such asweight, age, sex, renal impairment, genetic makeup ofdrug metabolizing enzymes and concurrent medication,can influence pharmacokinetics provides the opportunityto pre-select covariates (or anticipate effects in groups ofpatients who could not be studied in an experimentalclinical setting – see later sections). Under the newparadigm of covariate recognition, using the first princi-ples and mechanistic models of physiologically-basedpharmacokinetics (PBPK), any clinical data – even whenthey are obtained for the first time – become `confirma-tory', rather than a step in the `learning' process. An ad-ditional point to consider is the fact that most data analy-sis approaches to studying covariate effects examine onlythe simple relationships where the effects on dependentpharmacokinetic (or pharmacodynamic (PD)) parametersare assumed to be increasing or decreasing monotonical-ly as the independent parameter values change. As shownlater, this may not be the case and some covariate effectsmight be much more complex.

This review presents recent progress in predicting thefate of drugs in the human body and assessing inter-in-dividual variations in pharmacokinetics using modelingand simulation. Such investigations can form the basis fordesigning more efficient clinical trials, with cost-savingimplications. Moreover, these methods might provide theonly means of estimating pharmacokinetic differences incertain sub-populations whose characteristics may not becaptured during typical Phase II studies. These include,but are not restricted to:

(i) subjects with rare combinations of genetic vari-ants of drug metabolizing enzymes and transport-er proteins (whose recruitment into trials in ade-quate numbers is not straight forward);

(ii) patients who receive varying combinations ofdrugs (where testing drug interactions for all pos-sible permutations of co-administrations is notpossible); or

(iii) individuals who cannot be investigated solely forthe purpose of exploring the variations (e.g. drug-drug interactions (DDI) in children – when thenature of interactions may be different inpediatrics than adults, and nonetheless carryingout such drug-drug interaction studies in pediatricvolunteers is fraught with ethical problems).

Characterization of Pharmacokineticsduring Drug Development

Classical Procedure – `Top-Down' Approach:The traditional approach to drug development consists of

four discrete stages: (i) discovery; (ii) pre-clinical researchand development; (iii) clinical research and development;and (iv) `post-marketing' pharmacovigilance.1) Withinclinical research and development, Phase I investigationsare the first point where potential covariates of phar-macokinetics in humans can be investigated, despite thelimitation that these studies only involve small groups ofhealthy (typically young male) volunteers. Phase II stu-dies, which are carried out in patients, provide further in-formation on covariates in addition to their primary aimof providing knowledge on the safety, tolerability, effec-tiveness and appropriate dosage of the drug in the targetpopulation. However, covariates are most commonly in-vestigated in Phase III where the appropriate dosingschemes are evaluated and information to secure drugapproval is gathered. It is now acknowledged that thespeed and cost of drug development can be optimized ifmany of these seemingly separate stages run concurrentlyrather than sequentially. Nonetheless, procedures foreach drug are still being built based on observed data(`top-down') rather than taking advantage of broaderknowledge of the human body and the informationgained from previous cases for drugs which share similarcharacteristics (`bottom-up'). Although the identificationand quantification of covariates, particularly using POP-PK, is now seen as an integral part of drug development,determining covariates using this approach is not straightforward and complications caused by bias and competi-tion between multiple variables are well known and de-scribed in the literature.2)

New Paradigm – `Bottom-Up' Approach: Mod-eling and simulation of the processes that define the plas-ma concentration-time course of a drug – namely, ab-sorption, distribution, metabolism and excretion (ADME)– is an indispensable tool in integrating available prior in-formation and accelerating decision making. The key ele-ment of this approach is the separation of information onthe system (i.e. human body) from that of the drug (e.g.physicochemical characteristics determining permeabil-ity through membranes, partitioning to tissues, binding toplasma proteins, or affinities towards certain enzymesand transporter proteins) and the study design (e.g. dose,route and frequency of administration, concomitantdrugs and food).

The success in this field has been growing in parallel tothe availability of the in vitro systems which act as sur-rogates for in vivo reactions relevant to ADME. In vitro –in vivo extrapolation (IVIVE) has become possible notonly because of advances in the understanding of the ex-trapolation factors (physical chemistry, biology, physiolo-gy and genetics) but because of the ability to `integrate'such information using mechanistic models of the humanbody3) combined with the power of computers.4) Theseefforts also run in parallel to other scientific activities un-der the descriptive umbrella of `Systems Biology' as dis-

5555Virtual Human Populations in Assessing Covariates of ADME

cussed recently in a special issue of the journal Xenobiot-ica.5) Although initiatives for `model based drug develop-ment' in the pharmaceutical industry (e.g. see Lalonde etal.6)) and other pharmaceutical organizations (e.g. EU-FEPS7)) have been strong, a more powerful catalyst for the`systems approach' has been the willingness, and oftenleadership, of drug regulatory bodies in adopting themethodology; this is highlighted in published reportssuch as the FDA's Critical Path Initiative.8)

Requirements to Bridge the `Bottom-Up' and`Top-Down' Approaches: As indicated above, thereare three elements which define the outcome of anADME study and covariates affecting the observations.These are the characteristics of the system (i.e. the attrib-utes of the human body for each subject), the characteris-tics of the drug and the conditions of the study. For asimulation platform to be useful in identifying covariates,these three elements should be adequately separated butinteract with each other during the conduct of a virtualclinical study (Fig. 1). Some of the difficulties and suc-cesses in building such a system have been described re-cently by our group.4)

At the top level are the populations datasets which areindependent of any specific drug or trial design but con-tain all the elements for the compound and trial designdatasets to interact with. These include, but are not res-tricted to: data on enzymes/transporters and their abun-dances, including genotypes, rates of synthesis and degra-dation [mainly in liver and intestine]; intestinal andstomach motility, intestinal surface area and fluid dynam-ics; circulating levels of plasma proteins and red bloodcells; organ size composition and organ blood flow. Eachof these parameters can be determined and stored as aninformation library for various target populations, eachof them having a different composition in regards to sex,age, ethnicity, and genotypic makeup affecting enzymesor transporters. In addition, special populations can bedefined for groups of interest such as obese, cirrhotic andrenally-impaired patients.

As indicated by Jamei et al.,4) the efforts required togather information for each population is much greaterthan those needed for the generation of the basic models;the time and resource implications for creating suchdatabases are often underestimated. However, oncecreated, each population library can be used repeatedlyfor any drug under any study design. Moreover, librariescan be updated and expanded as the knowledge of thesystem improves. Lastly, multiple libraries can be usedsimultaneously creating more divergent populations bymixing patients from different groups.

The second component of any virtual ADME study isdrug-specific information such as affinity to plasma pro-teins, red blood cells or enzymes and the ability to inhibitor induce certain enzymes. These data are usuallygathered during different stages of drug discovery and

development. These compound-specific data can be logi-cally divided into different categories such as those relat-ed to `physical chemistry', òral absorption', òrgan distri-bution', òrgan elimination', ìnhibitory' or ìnduction'potential. Obviously, some parameters may affect morethan one ADME process. The intensity, detail and qualityof the drug-specific data typically increase as a candidatemoves from the selection screens in the discovery stageto the optimization screens in pre-clinical development.Inevitably, many of the data required at earlier stagesmay be calculated from in silico models rather than meas-ured.9,10) Readers are referred to a recent report by Emo-to et al.11) for a typical example of the use of such in silicoalternatives. The study highlighted the issue of measuringnon-specific microsomal partitioning and plasma proteinbinding; where none of the current technologies for mea-surements are compatible with high throughput screen-ing and hence they are difficult to implement at early dis-covery.

Finally, the study design element includes informationsuch as the fluid or food taken with the given dose, dos-ing frequency and administration period (if multiple-dosestudy), duration of blood sampling, makeup of the popu-lation (e.g. sex, genotypes of certain enzymes, age,ethnicity) and concomitant use of other drugs. At anypoint, the ADME in a selected individual can be simulat-ed within the timeframe defined by the trial design; theprocedure is repeated until all randomly selected individ-uals in the study population have been investigated.

Interaction between the above three elements pro-vides a complex covariate matrix (Fig. 2) when all thesmall building blocks of the PBPK models are puttogether (see next sections). This `bottom-up' approachprovides the flexibility to seamlessly change the study de-sign (e.g. the number of individuals, or diversity in thecomposition of the population). Hence, the power of stu-dies to recognize covariates can be investigated a prioriwith the aim of improved decision-making. Examples ofincorporating prior knowledge of the system and in vitrovalues into the design of covariate assessment are shownfor both the classical parallel group studies (effect of en-zyme genetics on PK12,13)) and POP-PK (assessment ofDDI14)).

Compliance (/adherence) to dosage regimen is anotheraspect of the study that can be simulated. However, thecovariates which determine the degree of adherence to agiven dosage regimen in an individual patient are notclearly defined.15) Nonetheless, simulating the conse-quences of non-compliance in virtual clinical studies, as asource of possible variability in exposure to drug andhence the pharmacological and toxicological effects, pro-vides invaluable information on the design of dosage regi-men (e.g. on so-called `forgiveness' of missing doses, orthe strategies to overcome the non-compliance).16–18)


Building Blocks of ADME:Potentials for Propagation of Variability

The exposure of an individual to a certain drug can bemeasured by the area under the concentration timecurve (AUC). The AUC after administration through anynon-parenteral route (such as an oral dose) is dependenton the proportion of the dose that is absorbed and is sub-sequently available in the systemic circulation. In the caseof oral drug administration (the most common route fordrug intake), this involves release of the drug from theformulation, passage through the gut wall and thenthrough the liver. The bioavailability of the drug (F)together with the clearance (CL) and the dose of the drug(D) will determine the overall exposure (AUC) accordingto Equation 1:

AUC＝F･DCL

Equation 1

Total CL is defined as the volume of blood completelycleared of drug per unit time and encompasses clearanceby the liver, the kidneys and biliary excretion (in the ab-sence of re-absorption from the gut). Although exposureto the drug is determined only by the dose, CL andbioavailability, varying shapes of concentration-time pro-file can occur for a given exposure when the rate of entry(absorption rate, infusion rate etc.) and rate of elimina-tion are changed. Elimination rate is a function of CL anddistribution characteristics. A brief description of ADMEprocesses that determine the overall pharmacokinetics isprovided below.

Determinants of Oral Drug Absorption:Bioavailability (F) is a term often used to describe the ab-sorption of a drug. It is defined as the proportion of anoral dose of a drug which reaches the systemic circula-tion in intact form. It is dependent on a number of keyfactors which are described by the following Equation:

F＝fa×FG×FH Equation 2

fa is the fraction of the dose which enters the gut wall(the remaining drug may be lost by decomposition in thegut lumen; it may not be released from the formulationand remain in solid form; or it may become soluble in gutlumen but fail to permeate to the gut wall). FG is the frac-tion of drug which escapes metabolism in the gut walland enters the portal vein, and FH is the fraction of thedrug that enters the liver and escapes metabolism, thusentering systemic circulation. Overall bioavailability canbe assessed by comparing the AUC values following oraland iv administration after correcting for any dose differ-ences. Hepatic first pass effect can be estimated afterdecomposing the systemic clearance (iv administration)to its hepatic and renal components. However, estimat-ing FG and fa from ordinary clinical data is not possibleand many reports in the literature erroneously refer to

the composite function of `FG×fa' as if it represents onlyfa. Obviously, the latter could be true only if there is nogut wall metabolism at all. Current models describing theFG might be less mechanistic than other models howeverthey have been useful in linking some of the in vitro datato clinical observations. Readers are referred to a recentreport where the FG was described using an operationalmodel.19) In this so-called `QGut model' the FG was de-scribed using a flow term (QGut) which is a hybrid of bothpermeability through the enterocytes membrane andremoval from the serosal side by villous blood flow asshown in the following equation:

FG＝QGut

QGut＋fuG･n

Sj＝1

CLuint, Gj

;

«wher QGut＝Qvilli･CLperm

Qvilli＋CLperm$ Equation 3

where CLuint, G is the intrinsic metabolic clearance in thegut by the Jth route based on unbound drug concentra-tion, fuG is the fraction unbound in the gut, CLperm is aclearance term defining permeability through the entero-cytes, and Qvilli is actual villous blood flow.

Determinants of Drug Distribution throughoutthe Body: Distribution refers to the reversible transferof drug from one location to another within the body.Distribution of drugs to and from the blood and other tis-sues occurs at various rates and extents. Several factorsare responsible for the distribution pattern of a drug wi-thin the body over time. Many of these depend on the na-ture of the drug such as its ability to cross membranes,bind to plasma proteins, partition into red blood cells, tis-sues or fat, and its specific affinity to influx or effluxtransporter proteins. Other factors determining the dis-tribution behavior relate to characteristics of the individ-ual such as the perfusion rate of different tissues byblood, the concentration of plasma proteins, hematocrit,body composition, tissue density, and genetic variants oftransporter proteins. If any of the above elements showtime- or dose-dependence then the dosage regimen mayaffect the pattern of the distribution observed at a giventime after drug administration. Typical examples of time-dependence involve the induction of transporter proteinexpression and non-retrievable inhibition (mechanism-based inactivation), whilst dose-dependent non-linearityis often due to saturation.

Volume of distribution is the manifestation of drug dis-tribution within the body and it influences the elimina-tion rate and maximum exposure (Cmax). Volume of distri-bution together with clearance determines the rate ofdecline in plasma drug concentrations (elimination rate)– the higher the volume, the longer the residence time inthe body and vice versa. Since the proportion of the drugin different tissues changes with time (and the tissue-drugconcentrations are not necessarily moving in parallel),

57

Fig. 1. Schematic showing the principal elements of a population-based simulation platformInformation on the system (i.e. the human body) is separate and can be used repeatedly for various drugs under different study designs (i.e.trial data). Although the arrows from the system to the central building block of the model (in vitro–in vivo extrapolation [IVIVE] and phys-iologically-based pharmacokinetics [PBPK]) suggest a one-way flow, it should be noted that drugs may well influence the initial (baseline)parameters of the system. This happens through mechanisms such as induction of proteins or enzyme synthesis, and/or interference withdegradation rate (acceleration or stabilization) of proteins/enzymes. Therefore, the level of enzymes and receptors should ideally be definedas a dynamic balance between the synthesis and degradation (rather than fixed static values) so any feedback effect following drug treat-ment can be accommodated in real time (for further details see Jamei et al. 20094)).

Fig. 2. Overview of the relationships between covariates affecting ADMEWhen building virtual human populations for ADME simulation, the composition of the study group is initially considered with respect to age,sex and ethnicity, plus genetic makeup of enzymes and transporter proteins in the target population. However, each of these factors in-fluences multiple elements of ADME creating highly non-linear and non-monotonic relationships. The sensitivity of each pharmacokineticparameter to a potential covariate depends on the nature of drug and the balance of sensitivities to elements within the network. As variousdrugs differ in their sensitivity to these elements, covariates of pharmacokinetics vary and a `one size fits all' solution cannot be assumedSimplistic assumptions for covariate analysis based on purely statistical models are a major shortfall for the current `top-down' data analy-sis in finding covariates. Prior assessment of covariates ensures that the most relevant factors and the most suitable covariate models areconsidered during clinical studies.



volume of distribution is not a fixed term and it alsochanges with time. The Vss (volume of distribution atsteady state) is considered when the ratio of drug in vari-ous tissues has reached equilibrium. It is seen as a `purerdistributional term'20) since other volume terms (such ascentral, Vc, and terminal, Vz, distribution volume) can beaffected by the relative speed of drug elimination and dis-tribution. Traditionally, Vss has been calculated using thefollowing formula after a drug has been administered in-travenously (and assuming AUC is based ON drug con-centrations in plasma):

Vss＝D

AUC×MRT Equation 4

where D＝dose and MRT＝mean residence time.However, this is an over-simplistic view of the processesinvolved.21) Physiologically, volume of distribution is de-termined based on individual characteristics which go be-yond simple links to body size,22) as described by the fol-lowing equation:

Vss＝Vp＋Ve×E:P＋St

Vt×Kp, t Equation 5

where Vp, Ve and Vt are volume of plasma, erythrocyteand tissue, respectively, and E:P and Kp, t are the relativedrug concentrations in erythrocyte and tissue to plasma.It is clear from this equation that the characteristics of in-dividuals (composition and size of tissues) can be separat-ed from those of the drug (affinity to red cells or certaincomponents of tissues).

Determinants of Drug Metabolism: The majori-ty of drugs currently on the market are lipophilic andmetabolism is a major route of elimination from thebody.23) Understanding and fully characterizing metabol-ic routes helps with the early identification of potentialcovariates such as genetic polymorphisms of dugmetabolizing enzymes, effects of environmental inducersor inhibitors, and any metabolic DDIs. However, itshould be noted that overall metabolic clearance is notusually a simple linear function of the organ capacity (i.e.intrinsic clearance) but it is also dependent on the deliv-ery of the free drug to the site of metabolism. Thus,hepatic clearance is determined by hepatic blood flow,plasma protein and red blood cell binding, and the effectsof influx into or efflux from hepatocytes. In vivo intrinsicorgan clearance can be extrapolated from a variety of invitro systems using scaling factors as described by Barteret al.24) and according to the procedure described byRostami-Hodjegan and Tucker3):

(a) using recombinantly expressed systems:

CLuH, int＝« n

Sj＝1 Ø

n

Si＝1

ISEFji

×Vmaxi(rhEnzi)×Enzjabundance

Kmi(rhEnzj) »$×MPPGL×Liver weight Equation 6

Where there are i metabolic pathways for each of j en-zymes; `rh' indicates recombinantly expressed enzyme;Vmax is the maximum rate of metabolism by an individ-ual enzyme; Km is the Michaelis constant; MPPGL is theamount of microsomal protein per gram of liver, andISEF is a scaling factor that compensates for any differ-ence in the activity per unit of enzyme between recom-binant systems and hepatic enzymes.25)

(b) using human liver microsomes:

CLuH, int＝GLuint(per mg Microsomes)×MPPGL×Liver weight Equation 7

(c) using human hepatocytes:

GLuH, int＝GLuint(per millions Hepatocytes)×HPGL×Liver weight Equation 8

where HPGL is hepatocellularity (millions of hepatocytesper gram of liver).

These indicate the possibility of accommodating thecovariate effects on metabolism which stem fromethnic/genetic differences (variability in the expressionand activity of individual enzymes – see Inoue et al.26)),specific diseases (e.g. liver cirrhosis and renal impairment– see Nolin et al.27)), environmental factors (e.g. inductionby smoking28)) and age.29,30) Some of these effects may in-fluence more than one parameter at a time, for example,the ontogeny of enzymes occurs in the form of an age-related change in the expression of enzyme per milligramof microsomal protein31); however age also influencesboth MPPGL29) and liver size.30) This makes the `age-CL'relationship a drug-specific and complex matter with no`one size fits all' simple model (interested readers arereferred to Johnson et al.31) for further information andexamples).

To determine whole organ clearance, estimated intrin-sic clearance is combined with other determinants of CLusing a variety of models.32) The basic models (`well-stirred', `parallel tube', `dispersion') differ with regard toassumptions about the concentration gradient of drug wi-thin the liver, but in their simplest forms they all assumethat the passage of the drug from the blood into the liveris perfusion-rate limited and that only unbound drugcrosses the cell membrane and is available to be metabo-lized. Thus, they identify hepatic blood flow (QH), thefraction of drug unbound in blood (fuB) and intrinsicmetabolic clearance (CLuH, int) as the primary deter-minants of net hepatic blood clearance (CLB, H). On this


basis, the `well-stirred' model, for example, predictshepatic blood clearance and availability (FH) from Equa-tions 9 and 10, respectively33) (note: hepatic plasma clear-ance can be obtained by multiplying blood to plasmadrug concentration ratio to terms in right hand side of E-quation 9; fuB can be expressed in terms of fu divided bythe blood to plasma concentration ratio of the drug):

CLB, H＝QH･fuB･CLuH, int

QH＋fuB･CLuH, intEquation 9

FH＝QH

QH＋fuB･CLuH, intEquation 10

An understanding of the variability in each of the pri-mary determinants is necessary to predict the overallvariability in hepatic drug clearance. Co-variation ofblood flow with body surface area as well as age is wellknown,34) and the effects of additional environmental (de-sign) influences, such as eating,35,36) posture and physicalactivity37,40) can be estimated in these models prior toconducting any clinical studies. Similarly, hemodynamicchanges in disease state (e.g. cirrhosis) may be incorporat-ed into the prediction of variability, although the overallimpact often depends on the multitude of other changesoccurring in plasma proteins and red cells (Johnson et al.,in preparation) as well as intrinsic clearance.

Recent publications have examined the incorporationof the dynamics of binding and uptake (influx) informa-tion41–43) and attempted to incorporate the knowledge oftransporters and their interplay with drug metabolizingenzymes.

Determinants of Drug Excretion: All drugs areultimately removed from the body, either as metabolitesor in their unchanged form. The primary route of secre-tion is through the kidneys and urine, although excretionmay also occur via the biliary route and be considered asa true elimination when there is no re-absorption occur-ring in the intestine. Compromised renal function mayaffect the pharmacokinetics of a drug if urinary excretionis a substantial contributor to overall elimination. Drugcharacteristics which determine the extent of renal elimi-nation include physical chemistry (lipophilicity and ioni-zation),44) plasma protein and erythrocyte binding45) andaffinity to certain transporter proteins in the kidney.46,48)

These mainly affect the fractional tubular re-absorption(FRe-abs), glumerular filtration or active secretion (CLuSec)of the drug which are summarized in Equation 11 [afterLevy45) and Jank ¹u49)]:

CLR＝QR×« fuB×GFRQR

×Ø1－fuB×GFRQR

»×Ø QR･fuB･CLuSec, int

QR＋fuB･CLuSec, int»$×(1－FRe-abs) Equation 11

Some of the `system related' parameters are self-evi-

dent from the Equation 11, such as glumerular filtrationrate (GFR; as assessed by markers such as creatinineclearance) and renal blood flow (QR). However, there areother aspects of the system such as urine pH and urinaryflow that influence the fractional tubular re-absorption(FRe-abs). The equilibrium between the drug residing in theerythrocytes and unbound concentration in plasma maynot be rapid. Moreover, a proportion of erythrocytesmight be separated off by `plasma skimming' and shuntedinto the renal veins without contacting the renal tu-bules50) which may justify replacing QR and fuB with renalplasma flow and fraction unbound of drug in plasma, re-spectively.45) Any renal metabolism might be incorporat-ed in equations such as Equation 11 by adding similarfunctions to that of the active secretion term. However,the sequential or parallel nature of such metabolic elimi-nation relative to the active secretion is difficult to deter-mine experimentally.

An added complexity in assessing covariate effects forrenal impairment is the correlation between kidney im-pairment and the expression of metabolic enzymes andtransporters in the liver.27)

Determinants of Change in ADME with Co-ad-ministration of Different Drugs and Food: Drug-drug interaction has been identified as significant covari-ates of observed pharmacological response for a longtime.51) However, mechanistic understanding of thechanges to drug effects following co-administration ofdrugs, and separation of the type of interactions based onthe pharmacokinetic and pharmacodynamic related na-ture of them has taken place only in recent decades.51)

This mechanistic view has paved the way to using in vitrosystems to understand and anticipate drug-drug interac-tions prior to conducting clinical studies. Attention toco-administration of drugs as a source of variability hasgrown after a number of high-profile problems withmetabolic drug-drug interactions (mDDI). Traditional ap-proaches only involved the assessment of drug combina-tions which were more likely to be co-administered, orcombinations which involved commonly used drugs withnarrow therapeutic index. More recently, a POP-PK ap-proach has been used to determine the effects of co-medication52) although the power of such studies is ques-tioned when negative associations are reported. In otherwords, lack of any statistical significance for a given com-bination could be interpreted either as an indication of`no interaction' or it may indicate the inability of thestudy to identify a difference which stems from a failurein the power of the study.14) Recent publications by theregulatory authorities53,54) guide researchers towards amore mechanistic approach and rationalization of any in-vestigations into the effects of co-administered drugs.This trend has been facilitated by the wider availability ofprograms and databases to assess the likelihood of inter-actions using relevant in vitro data55,59) and help design


the most appropriate study to identify worse case scenar-ios and satisfy regulatory requirements in understandingthe theoretically conceivable effects.60)

Examples of incorporating variability into predictionsof mDDI using first principles are given in our earlierreport55) and point, among other factors, to geneticvariability in non-inhibited pathways. This has since beenreviewed by Collin and Levy61) in relation to approveddrugs on the market. Assessing any changes in drug ex-posure following co-administration of a competitive inhi-bitor of a metabolic pathway relies on understanding theproportional metabolism of the `victim' substrate via theinhibited route (fm) and the potency of the circulatingconcentrations of the inhibitor ('perpetrator') as de-scribed by the following equations:

AUC (inhibited)AUC (uninhibited)

＝

1n

Sj＝1

(fmj×Fold Change in CLuint, j)＋Ø1－ n

Sj＝1

fmj»Equation 12

Fold Change in CLuint, j＝1

1＋p

Sk＝1

[Ik]Kik

Equation 13

where fmj is the fraction of substrate clearance mediatedby the inhibited metabolic pathway `j' and CLuint j is theintrinsic metabolic clearance of the substrate down path-way j. The fold reduction is defined here under multiple(`p') competitive inhibitors acting via the same mechan-ism to inhibit enzyme `j' where [Ik] is the concentration ofinhibitor `k' at the enzyme site, and Ki k is the inhibitionconstant for inhibitor `k' obtained from in vitro studies af-ter accounting for non-specific binding.

If we consider that variation in fm depends on othermetabolic routes, and that many of these routes are in-fluenced by genetic polymorphism, then it is intuitivethat, even at a fixed level of a given inhibitor ([I]), largevariability in the extent of any significant mDDI is likelyto be observed between different individuals. Conversely,the circulating concentration of the inhibitor (and henceconcentration at the active site) is subject to variability inall ADME processes which govern pharmacokinetics.Many of these variations can be predicted.

In the case of induction, the fold change in activity ofthe induced enzyme depends on circulating concentra-tions of the inducer [I] (which could be variable in eachindividual); however the overall impact on AUC is alsodependent on the fractional metabolism by the pathway(fm):

AUC (induced)AUC (un-induced)

＝

1n

Sj＝1

(fmj×Fold Change in CLuint, j)＋Ø1－ n

Sj＝1

fmj»Equation 14

Fold Change in CLuint, j＝1＋Ø Indmax×[I]IndC50＋[I]» Equation 15

where [I] is the concentration of the inducer at the site ofeffect, Indmax is the maximal increase in the level of in-duced enzyme (measured as a fold of the un-inducedvalue) in the presence of a high concentration of inducerand IndC50 is the concentration of inducer associatedwith half maximal induction.

More interesting types of interaction, that influencethe system rather than directly affecting the interplay be-tween the `victim' drug and the enzyme, are mechanism-based (suicidal) inactivation (MBI) of enzymes62) and in-duction.63) In the case of MBI, the fold reduction in clear-ance can be defined as the rate of inhibitor-related degra-dation relative to the natural degradation rate (kdeg) of theenzyme. The latter is an intrinsic parameter of the system(human body), rather than a drug dependentparameter.64) The following equation represents a simplis-tic, pragmatic calculation for assessing the steady-stateimpact of MBI, although a more mechanistic predictionrequires differential equations incorporating degradationrate which determines the dynamics of enzyme synthesisand elimination.65)

Fold Change in CLuint, j＝kdeg

kdeg＋[I]×kinact

[I]＋KI

Equation 16

where kinact is the maximum degradation rate constant inthe presence of a high concentration of inhibitor and KI isthe concentration of inhibitor associated with half max-imal inactivation. One should note that all the above equ-ations are only relevant for drugs undergoing linear `first-pass' and `systemic' hepatic metabolism according to the`well-stirred' model of hepatic elimination as they do notaccount for any inhibition of `first-pass' metabolism inthe gut wall, transient plasma binding displacement dur-ing the absorption phase and its effect on hepatic `first-pass' metabolism or any variation in inhibitor concentra-tion with time. Nonetheless, these, as examples, demon-strate the opportunities where inter-individual variabilitycan be considered in predictions and the likely outcomeof clinical studies. Further, complexities related to gutwall interactions can be added to these predictions55) andsome simpler, more pragmatic approaches involving fullinhibition of gut first pass have also been described.66)

Concomitant food intake can sometimes directly affecta drug (e.g. chelating); however, the effect that food has

61

Fig. 3. Drug dependent propagation of physiological variability in the attributes of GI tract to drug bioavailabilityPart (A) illustrates the reported wide variation in motility of the GI tract (Yu et al. 199868)). This may or may not propagate to the estimatedfractional absorption after oral administration of a drug solution depending on its permeability as shown in part (B) (taken from Jamei et al.200469)). Peff indicates human gut wall permeability of the drug and Tsi refers to the intestinal transit time shown in Part (A). Similarly, formula-tion attributes (slow release) or dissolution characteristics may also determine whether the variability in transit time will affect the variabilityof bioavailability or not. For example, large variation in small intestinal transit time may have very little effect on the variability in bioavailabil-ity of an immediate release formulation of a soluble compound that is permeable through the gut wall. Conversely, clinical study conditionswhich are associated with more variable transit times would be of concern when designing studies for sustained release, sparingly soluble,low permeable drugs.


on the system (human body), can have a significant im-pact on ADME depending on the characteristics of thedrug. For example, changes in stomach pH and gastricemptying rate occur after food intake. These may or maynot affect the bioavailability of drugs depending on thedosage form (solid or solution) and physicochemical char-acteristics (ionization status, lipophilicity and permeabil-ity). Similarly, changes in hemodynamics of intestinalblood flow or gut wall enzymes may affect the gut firstpass of some drugs.

Variability in the System Components(Human Body): Relevance to ADME

The following section describes the available informa-tion on some of the system components which are re-quired for the `bottom-up' approach when attempting toidentify covariates. These are organized according to thebuilding blocks of pharmacokinetics which were de-scribed above.

In general, the information can be divided into`known-knowns' [sets of data which are known to berelevant for identifying covariates of pharmacokineticswhich are available to integrate into models] and `known-unknowns' [sets of data which are known to be relevantbut our current knowledge of them is `sparse' or `non-existent']. It should be noted that there might be otherrelevant variables, of which we currently do not appreci-ate the relevance [i.e. ùnknown-unknowns']. As Benetand colleagues indicated over a quarter of century ago,`àt any point in the history of health care, our knowledgewas considered to be quite extensive; however, in per-spective, the knowledge of yesterday seems to have beenvery limited, just as today's knowledge can be expectedto seem one day as such''.67) Thus, it is of fundamental

importance to note (a) the èvolving' nature of theknowledge that applies to the concepts of physical che-mistry, anatomy, physiology, biology, genetics (proteom-ics) and epidemiology at any given time (our availabledatabank) and (b) the crucial role of the framework (i.e.mathematical models) which integrates the information.

Sources of Variability in Absorption: As de-scribed previously, absorption is affected by threeparameters, fa, FG and FH, all of which are sensitive to in-ter-individual differences.

Formulation-related aspects of drug release can bestudied in vitro. These may include dissolution and solu-bility in aqueous solutions of different pH, simulated gas-tric fluid, fasted simulated intestinal fluid, fed simulatedintestinal fluid; particle size measurements; and disin-tegration time. However, these physicochemical charac-teristics interact with the system-related factors. Hence,variability in fa is determined both by the formulationand physicochemical attributes of the drug and by varia-tions in gastrointestinal (GI) motility and pH. The report-ed large variation in motility and residence time in the in-testine (e.g. by Yu and co-workers68)) (Fig. 3) may or maynot propagate into pharmacokinetic profiles69) dependingon drug and formulation characteristics. Thus, a poorlysoluble and/or a low permeability drug, or a sustainedrelease formulation may lead to more variation inbioavailability.

The residence time of a drug in the stomach is an im-portant factor determining the initiation of oral drug ab-sorption. In some cases it can even determine the rateand extent of absorption. Stomach residence time can beinfluenced by elements of study design such as thevolume of fluid administered with the solid dosage forms,concomitant food intake,70,71) and composition of drug

62

Fig. 4. The inter-individual variation in regional pH values of GIfluidsThe lines indicate 5th, 25th, 50th, 75th and 95th percentile of observedpH values for each segment of the GI tract. These variations mayor may not propagate to the bioavailability of drugs depending onwhether the release, chemical stability, solubility, dissolution orpermeability are pH-dependent or not (key: STO＝stomach; DUO＝

duodenum; PRO＝proximal small intestine; MID＝mid small intes-tine; DIS＝distal small intestine; CAE＝caecum; ASC＝ascendingcolon; TRA＝transverse colon; DES＝descending colon; R/S＝sig-moid colon or rectum; FEC＝feces) [adapted from Fallingborget al.1989 76); courtesy of Dr Sibylle Neuhoff (Simcyp Limited)]


formulation.72) Our knowledge of various factors affect-ing gastric emptying is fairly extensive73) and has beenfurther expanded by advances in the area of imaging.74)

Nonetheless, integration of such information for a prioriidentification of the potential covariates has been lessthan optimal and many pharmaceutical companies gothrough unnecessary cycles of clinical studies involvingformulation optimization without attention to thefeasibility of reducing inter-individual variability and thesource of such variation.

It is known that stomach emptying could be a uniquecharacteristic of each individual. This has been assessedby using repeated bioavailability studies on the same for-mulation under similar conditions (e.g. see Mojaverian etal.75)). However, in contrast to metabolic capacity orrenal function, which can be assessed using biomarkerssuch as genotype or creatinine clearance, respectively,there are no biomarkers to determine the rate of stomachemptying in each individual prior to conducting thebioavailability study.

The pH variation in the stomach and small intestinecan influence the dissolution of solid dosage forms andaffect the release of drugs from enteric coated formula-tions. In addition, pH may influence gut wall permeabil-ity by altering the balance between ionized and non-ionized moieties. There is well-documented evidence forinter-individual76) and inter-occasional77) variation of pHthroughout the GI tract as shown in Figure 4. It is alsoknown that food and its composition,70,78,80) pathophysio-logical conditions,81) and disease (e.g. AIDS)82,83) can alterthe pH in the GI tract.

The large inter-individual variation in residence timein the small intestine can propagate to oral bioavailabilityfor certain groups of drugs (e.g. sparingly soluble, poorpermeability, sustained release formulation). Thus, allconditions influencing intestinal transit times71) may actas a determinant of bioavailability for such drugs. Studydesign (e.g. staggering the dose intake and food intake) isreported to be influential for pre-feed dosing wheresmall-intestinal transit times were significantly shorterthan in the fed or fasted state84); transit times during fast-ed and fed dosing regimens were similar in these studies.

FG is sensitive to the abundance of drug metabolizingenzymes which could be influenced by genetics and diet.Intestinal epithelial cells express a variety of enzymes in-volved in phase I and II reactions85); CYP3A86–90) andUGTs91) are probably the most influential enzymes affect-ing gut wall metabolism and hence FG. Certain pathologi-cal changes to the GI system, e.g. celiac disease, havebeen shown to reduce the expression of CYP3A in thegut wall.92) Inter-individual variation in abundance andregional distribution of intestinal drug metabolizing en-zymes89) can also influence bioavailability of drugs withcertain characteristics (e.g. high enzyme affinity, poor gutwall permeability). The formulation attributes (e.g. slow

rate of drug release) may also affect gut wallmetabolism.19) Moreover, variation in blood flow can in-fluence gut first pass metabolism of highly permeabledrugs, as has been indicated for midazolam in cirrhoticpatients.93,94) Thus, known heterogeneity of blood supplyto different segments of the gut, and its variation underpathophysiological conditions (e.g. food intake),95) shouldbe incorporated in any model that attempts to replicatethe human system. Ideally, dynamic changes with timeand variable effects in certain sub-groups of the popula-tion80) (Fig. 5) should also be considered. These com-plexities are another reminder of the important interplaybetween the system variables and drug attributes in de-termining inter-individual differences in oral drug ab-sorption.

Active secretion of the drug from the gut wall into thegut lumen by the multidrug efflux pump, P-glycoprotein(P-gp), as well as influx and efflux by other transportersmay be subject to inter-individual variations affectingtransporter abundance and/or activity. However,knowledge in this area is sparse. Heterogeneous distribu-tion along the gut, increasing from proximal to distalsmall intestine,96,97) has been suggested for P-gp but thishas not been confirmed uniformly in all studies.98) Thus,these might be specific to each transporter or even eachindividual.

The final element that determines variability inbioavailability is the first pass metabolism of the drugs bythe liver (i.e. FH). Variability in FH is directly related tovariation in the activity of drug metabolizing enzymes in

63

Fig. 5. Some sources of variability in oral drug absorption which are associated with food intakeParts (A) and (B) show the blood flow supply to the GI tract in the human proximal jejunum and ileum, respectively. Regional variations inblood flow may influence gut wall metabolism to a variable degree depending on the affinity of the drug to gut wall enzymes, its permeability,and the luminal location of drug release from its formulation. Food intake can increase these flows by up to 30% in addition to its effects ongastric pH which are shown in Part (C). However, it should be noted that the effects of food are transient, which should be considered whenincorporating the effects into clinical trial simulations. It is also of interest to note that whilst changes in stomach pH are not age dependent,the dynamics of the return to baseline are different between old and young individuals (Part (C)). The data for Part A and B are taken fromDeSesso and Jacobson 200195) and the data for Part (C) are taken from Russell et al. 1993.80)


the liver, where genetic and environmental factors bothplay significant roles. Information on enzyme abundanceand its variation has been gathered recently99); howeverthis may require regular updating with the advent of highthroughput LC-MS assays of proteins.100–103) Some otheraspects of variability in metabolism which influencebioavailability (e.g. inter-ethnic differences, co-variationbetween the enzyme abundance, age, the presence of dis-eases, concomitant drugs) are discussed in the followingsections (under metabolic clearance). However, it shouldbe kept in mind that these are as important in determin-ing variation of absorption as they are in determining theclearance.

Sources of Variability in Distribution: Theparameters determining inter-individual variability indrug distribution have been outlined in earlier sections.Binding of a drug to plasma proteins and erythrocytes isone factor that influences distribution. However, despitelarge variation in the binding affinity of different drugs,inter-individual variability tends to be less than that inother pharmacokinetic parameters under non-pathophysiological conditions.104) The fraction of drugwhich is unbound in blood is determined by the affinityof the drug to plasma proteins and red blood cells as wellas levels of circulating plasma proteins and hematocrit inthe individual. The levels of protein in the plasma varywith age, diseases (e.g. liver cirrhosis), pregnancy, traumaand stress. Similarly, age, sex and environmental factors

affect the hematocrit (see Fig. 6 for a summary scheme).Models for tissue distribution such as those developed

by Poulin and Theil22) (with later corrections byBerezhkovskiy21)) or those by Rodgers et al.20,105,106) all relyon our knowledge of tissue composition and its covari-ates. At present such information for humans is sparseand it appears that sex, age, and in some instances ethnic-ity, are the only known covariates for the size and com-position of some, but not all, tissues. Differences in bodyfat content, in particular between males and females,may lead to differences in the volume of distribution andsubsequent variations in the elimination rate and troughconcentrations after multiple doses at steady-state. Thesedifferences are observed in routine samples taken fortherapeutic monitoring (e.g. clozapine; see Rostami-Hod-jegan et al.55)) and should not be confused with variableexposure that is determined only by the clearance. Forexample, a highly lipid soluble drug may distribute intothe adipose tissue of a female with a high proportion ofbody fat and become less available to the eliminating or-gans; so for a given clearance, the half-life would be lon-ger in female and trough concentrations would behigher. A mechanistic description of partitioning to vari-ous tissues (Fig. 7) is essential to help determine simplecovariates of distribution such as body size, sex and age.

Although it is claimed that the use of physiologically-based models has essentially been limited by their com-plexity,107), increasing computer power and reduced run-

64

Fig. 6. Steps in propagating individual variability in fraction of unbound drug in plasmaThe scheme above indicates the steps for building inter-individual variability in fraction unbound plasma drug concentration with theknowledge of fraction unbound in a test sample. Fraction unbound in plasma (fu) is often seen as a drug-related parameter. However, this istrue only if the plasma protein concentration was not variable; which is not true. Knowing the concentration of proteins in the test samplebinding affinity constant can be estimated [Step 1] and used to recalculate fu values in a group of individuals with diverse concentration ofplasma proteins [Step 2]. Thus, any in silico model that attempts to incorporate the effects of variable plasma proteins in different individuals(e.g. various pathophysiological conditions that influence the plasma proteins) [Step 3] must separate the protein binding affinity (a purercharacteristic of the drug binding than fu) from the `system-related' value of protein concentration. Under such a scheme, known covariatesassociated with plasma protein concentrations can be mirrored on fu with implications for pharmacokinetic parameters such as CL, FH, andVss (see Table 1) [Key: AAG＝Alpha-Acid Glycoprotein; [P]＝concentrations of the protein in plasma, fu＝fraction of unbound drug in plasma]

Fig. 7. The multi-compartment mammillary model of drug distributionThe models such as those suggested by Rodgers et al.105,106,142) give the ability to separate drug attributes from those of the system and to in-corporate inter-individual variability in partitioning of various drugs to different tissues in each individual if the individual tissue compositionsare known. [K values are `on' and `off' binding rate constants]. See the text for information on availability of parameter values (or otherwise)in human populations.


times have removed this restriction. Nonetheless, thelimited availability of information regarding tissue sizeand composition in certain patient groups and individualscan be considered as a hurdle in using the models for de-

termining clinically relevant covariates of drug distribu-tion. The only exceptions relate to extreme pathophysio-logical conditions such as morbid obesity, very lowhematocrit (transplant patients) and severe cirrhosis


(leading to ascites and very low levels of circulating plas-ma proteins).

Although the effects of influx or efflux transporterproteins and its inter-individual variability on distributionof drugs into organs may not have significant influenceon concentration-time profiles, there is now a greater un-derstanding of how these influence the local kinetics inthe organ and the pharmacological or toxicological con-sequences. These can be simulated; however, mechanis-tic scaling factors to link the in vitro affinities to in vivoare not currently available.

Sources of Variability in Metabolism: Many ofthe sources of variability in intrinsic metabolic clearancewere described before, as part of factors affectingvariability in FH. Hepatic clearance of drugs which are in-efficiently extracted from the blood is sensitive tochanges in the activity of drug metabolizing enzymes inthe liver. Also, variation in the activity of drug metaboliz-ing enzymes can affect oral clearance of all drugs regard-less of the efficiency of extraction (i.e. by affecting thehepatic first pass clearance or the systemic clearance). In-duction and inhibition of enzymes by environmental sub-stances or toxins contributes to inter-individual differ-ences in drug metabolism as much as the genetic makeupof the individual. Variants of genes coding for drugmetabolizing enzymes result in enzymes with higher,lower or no activity, or they may lead to a complete lossof expression.108) Such genetic variation is termed a`polymorphism' when the monogenic trait occurs at asingle gene locus with a frequency of more than (ar-bitrarily) 1%.109) The potential consequences of geneticpolymorphisms for pharmacokinetic processes in individ-uals who lack activity in an enzyme include:

(i) either unwanted responses (toxic effects) or sub-therapeutic responses associated with the average,`normal' doses,

(ii) a lack of pro-drug activation, or(iii) a dependence on alternative routes of elimination.

This may be a problem if these routes are alsocompromised (e.g. by renal impairment, drug-drug or drug-food interactions).110)

Consideration of polymorphisms of drug metabolizingenzymes is of central importance during drug develop-ment. However, the seriousness of the consequences ofsuch polymorphisms for the PK of a drug depends on anumber of factors110):

(i) whether the polymorphic enzyme metabolizes theparent drug, the metabolite(s) or both,

(ii) the contribution of the polymorphic enzyme tooverall elimination of the drug,

(iii) the potency of any active metabolites, and(iv) the viability of the other competing pathways of

drug elimination.The frequency of various phenotypes varies in differentethnic groups. Inter-ethnic differences in pharmacokinet-

ics have been reviewed recently, particularly in relationto submissions to drug regulatory organizations.111) Theexisting information on variation in enzyme abundancemay help with the design of bridging studies if these arerequired to support applications to certain regulatoryauthorities.26)

One area that requires further attention is the co-regu-lation of abundance for various enzymes (particularly forenzymes sharing a common regulatory protein such asPXR, CAR, and FXR). There are some indications thatabundance correlates with age for CYP2C9 andCYP2C19112) and that ethnicity might influence the cor-relation between the abundance of CYP3A4 andCYP3A5113,114); however, there is a paucity of data con-cerning all other inter-correlations of enzyme abun-dance. Variations in the amount of liver microsomes andhepatocytes per gram of liver are reported.24,29) However,with the exception of age, potential covariates which de-termine such variability are unknown. Similarly, the cur-rent models defining the size of liver (and hence the over-all level of the enzymes contributing to first pass hepaticmetabolism) are solely based on body size30) althoughbody size may not explain all the observed variation inliver size.

Age-related changes are of particular interest, as theontogeny of various metabolic routes115–117) relative toeach other and to the maturation of renal function118)

leads to a variable contribution of different routes ofelimination at each age group, particularly in the firsttwo years of life.

The majority of system parameters discussed abovecan vary in the presence of diseases or under the phar-macological influence of concomitant drugs.

Sources of Variability in Excretion: Renal drugclearance, as the major excretion route, is influenced todifferent extents by urine flow, urine pH and plasma pro-tein binding. The extent of sensitivity to these parametersdepends on the nature of the compound and the mechan-isms involved in its renal elimination (e.g. glumerularfiltration, active secretion, passive re-absorption). Thus,inter-individual variation in these parameters, togetherwith renal function, determines the overall renal clear-ance. Urine flow is sensitive to individual fluid intake andadministration of diuretic drugs. Inter-individual differ-ences in urine pH are mainly related to differences indiet and physical activity. Many of the parameters in-fluencing the renal clearance were studied in the mid1960s and early 1970s and interested readers arereferred to review articles such as those by Tucker44) andGarrett.119)

Biliary excretion plays a smaller role in elimination formost drugs. However, pathophysiological conditions thatcause cholestasis120) or genetic differences in transporters(e.g. OATP 1B1121)) may influence pharmacokinetics if ac-tive uptake to hepatocytes43) or biliary excretion are sig-


nificant components of their ADME.Sources of Variability in the Effects of Co-ad-

ministered Drugs: In assessing any DDI and itsvariability it is important to realize the significance of theexposure level to the interacting (`perpetrator') drug.Although the dose of a co-administered medication is amain factor determining such exposure and its variabilityamongst a population of individuals who receive variousdoses, it is not the only variable affecting exposure. Sofar, we have discussed all the parameters that influencethe inter- and intra-individual variability of exposure to`victim' drug at a given dose; however, it is obvious thatall sources of variability affecting ADME of a `victim'drug can influence any co-administered `perpetrator'drug too. Therefore, similar doses of any co-administereddrug could lead to variable concentration-profiles of in-teracting drugs and hence a variable level of interaction.It is important to note that even similar concentration-time profiles of co-administered drugs can be associatedwith a variable impact on the ADME of a `victim' drug.For instance, fractional metabolism, fm, has often beenignored, despite the its major role as a source of variationin mDDI.55)

The mechanisms of renal elimination were describedin earlier sections. Some of these, particularly those in-volving active transport, might be subject of potentialdrug interactions. However, a review conducted byBonate et al. in 1998122) concluded that clinically sig-nificant drug interactions resulting in toxicity related torenal excretion appear to be relatively rare. The authorsrecommended that in vitro screening for potential DDI atthe renal level is not required for all drugs during drugdevelopment. The progress with identification of individ-ual transporter proteins involved in renal elimination ofcertain drugs46–48) makes such decisions even more specif-ic. Nonetheless, other potential mechanisms for drug in-teractions at the renal level and its variability (e.g. dis-placement of bound drug leading to increased glumeru-lar filtration; and change in urinary pH and/or flow thatmay change renal clearance depending on the ionizationand lipophilicity status of the drug) should be kept inmind if renal clearance is a major route of elimination.

Virtual Human Populations:Applications for Assessing Covariates of ADME

A number of examples have been used to illustrate thevarious implications of building virtual human popula-tions to improve drug development practices. A summaryof the parameters and the sources of their variability areoutlined in Table 1 and many examples can be foundwhich demonstrate the value of integrating such priorknowledge into the design of studies. Here we have onlyprovided a brief description of some selected studies andtheir findings.

Perhaps the most appreciated applications of assessing

population variability in the safety of a computer are theincorporation of age-related changes in drug eliminationand investigation of genetic variability in drugmetabolism to the pharmacological effects. The generaluse of whole body physiologically-based pharmacokinet-ics for the purpose of pediatric dose selection has beenreviewed very recently by Bouzum and Walther.123) Thetake-home messages resulting from the simulations car-ried out for 11 drugs by Johnson et al. are (a) the large in-terplay of various factors in determining age-relatedpharmacokinetics, and (b) the absence of a `one modelfits all' idea for analyzing the clinical data. Although bodysize may be the most important determinant of phar-macokinetics after maturation of elimination routes,124) itwould be naive to assume that variable maturation ratesof different elimination routes115,125,126) and other factorssuch as age-related protein binding127) can be summarizedin simple forms that can be applied to `all' drugs regard-less of their nature. A clear example where the outcomesof the `bottom-up' and `top-down' approaches were com-pared is the recent report by Fanta et al.128) who investi-gated the population pharmacokinetics of cyclosporine inchildren of varying age. The authors indicated that theirsemi-empirical body-size-related function of `weight0.75'as a covariate for clearance is comparable to the metabol-ic elimination of the compound that is to be expectedfrom the liver size according to Johnson et al.30) (Fig. 8).Moreover, the effect of hematocrit in the study could berecovered by applying the knowledge of blood bindingand the impact of red blood cell volumes on circulatingconcentrations of the drugs (Fig. 9).

Incorporating in vitro information to assess the powerof data in pharmacogenetic-related clinical studies hasbeen another success story regarding the application of a`bottom-up' approach. Dickinson et al.12,13) have recentlyused (S)-warfarin and dextromethorphan as examples todemonstrate the value of incorporating mechanisticIVIVE into a population PK-PD model. Based on in vitrodrug metabolism data and information on the frequencyand activity of the different allelic forms of relevantCYPs, they estimated the statistical power of in vivo stu-dies needed to discern the effect of genotype on PK andPD. Their approach represents a paradigm for assessingthe impact of genetic polymorphisms on the PK and PDof new drugs prior to costly population studies.

Another recent application relates to assessing the ef-ficiency of mixed effects modeling in quantifyingmetabolism based drug-drug interactions. The study in-volved the use of in vitro data as an aid to assess the studypower of population-based pharmacokinetic trials.14) TheNONMEM operator, who was blind to the nature of theinhibitory effect of six possible co-medications, did notpick up any false positive cases using an overall popula-tion study size of 2000. More importantly, no false nega-tive cases were identified (cut off point of two-fold true

67

Table 1. Summary of the variables determining ADME and sources of their variability

Main ADME Parameter List of Potential Sources of Variability

Absorption:

fa, FG (Qvilli, CLperm, CLuint-Gut), Qvilli, FH (fuB, QH, CLuint), QH

– Parameters affecting pH in GI tract (e.g. food, age, ethnicity)– Fluid taken with the dose– Factors affecting stomach and intestinal residence time– Factors affecting enzyme levels and activity in gut wall– Factors affecting transporter levels and activity in gut wall– Factors affecting enzyme levels and activity in liver– Factors affecting transporter levels and activity in liver– Factors affecting blood flow to gut and liver– Factors affecting plasma protein binding

Distribution:

Vp, Ve, E:P, Vt, Kp, t (fuB, fu)

– Body size and composition– Factors affecting plasma protein binding– Factors affecting hematocrit– Factors affecting the level and activity of transporters

Metabolism (hepatic):

FH (QH, fuB, CLuint), QH

– Factors affecting enzyme levels and activity in liver– Factors affecting transporter levels and activity in liver– Factors affecting blood flow to liver– Factors affecting plasma protein binding

Excretion (renal):

GFR, fuB, QR, Fre-abs, CLuSec

– General covariates of renal function– Factors affecting the transporter level and activity in kidney– Factors affecting blood flow to kidney– Factors affecting plasma protein binding– Factors affecting pH and flow

DDI:DoseInhibitor, fm (frenal), Ki, KI, kinact, kdeg, IndC50, Indmax

– Factors affecting the circulating level of the interacting drug– Factors determining fractional elimination by metabolic or renal routes (see above)

Note: frenal defines the fractional elimination via renal excretion; for all other symbols refer to the text.

Fig. 8. Case example for compatibility of `bottom-up' vs `top-down' approaches to covariate recognitionThe graph shows the relationship between liver volume and body size (BW0.75). The data were obtained from a population pharmacokineticstudy in pediatric transplant patients and provide a covariate link with body size (BW0.75) which is consistent with the growth of liver with age;confirming the relevance of covariate recognition from the first principles and providing mechanistic justification for the observation.128)


mDDI on average) when À2.5% of patients had receivedthe true interacting co-medications. The results in-creased confidence in the ability of mixed effects model-

ing approaches to appropriately quantify `true' interac-tions. However, it also highlighted the importance ofstudy design.

68

Fig. 9. Case example for compatibility of `bottom-up' vs `top-down' approaches to covariate recognitionA comparison of the covariate effect of hematocrit [HC] on cyclosporine clearance obtained from concentration-time data analysis indi-cates that the POP-PK-derived model ([1–0.00732×100×(HC-0.31)]128)) is seemingly different from the equation that was derived based onthe knowledge of PBPK, and in vitro values of fuB, fu and the average value of HC in patients. However, the relative changes in CL predictedusing each of the models fully overlaps for the entire range of HC values observed in the study (i.e. HC from 0.05 to 0.50).


Tools for Simulating ADME:Incorporating Population Variability

A number of reports have recently reviewed the availa-ble tools for modeling and simulation of ADME as well asother aspects of in silico drug discovery and development.Interested readers are referred to these reviews, whichare listed in Table 2 along with comments on the scopeof each review. Although this covers the majority of re-cent reviews, this is by no means a comprehensive list.Interestingly, very few of these tools include databasesrepresenting true population variability in the system ele-ments of ADME which were described in previous sec-tions of this review. Thus, for many of these tools,simulating different individuals requires either repeatedsimulations for each individual, which can be done bychanging the parameter values for the system, or ar-bitrarily incorporating some variability in each parametervalue regardless of the inter-correlation betweenparameters. The latter may produce varying concen-tration-time profiles in each simulated population;however, it does not necessarily reproduce a realisticcovariate matrix that is needed for optimal clinical studydesign and assessment of study power for recognition of

potentially influential covariates. The tools most relevantto the subject of this review are listed below.

Intellipharm} PK SoftwareIntellipharm}PK (Intellipharm, LCC, Niantic, CT,

USA, www.intellipharm.com) has been developed ascommercial software to predict oral absorption based onthe one mixing tank model of Johnson.129) Considerationsfor the variable physiology of the GI tract have been limit-ed by the original model; however, the ability to definetime-varying solubility, absorption constant and volume,makes it amenable to mimicking true movement alongthe gut. The original absorption model is coupled to clas-sical one or two compartment pharmacokinetic models,which cannot address sources of inter-individual variabil-ity in elements of ADME (other than absorption) in theabsence of clinical data.

GastroPlusTM SoftwareThis is one of the most commonly used platforms

(http://www.simulations-plus.com). It was built by in-troducing several modifications and improvements to the`Compartmental Absorption and Transit (CAT)' model.CAT has also been the basis for many other compartmen-tal transit models (see later sections). Different segments

69

Table 2. Some recent papers reviewing tools for simulatingADME or its components

Reference Description of the Review's Key Features

Ekins et al.144)Recent advances in databases, data mining andpredictive modeling, algorithms, visualization toolsand high-throughput data analysis solutions

Van de Waterbeemedand Gifford9)

Focuses on QSAR models and prediction ofparameters at early discovery

Kulkarni et al.145) andLangowski and Long146)

Specific QSAR models concerning prediction ofmetabolic routes

Wishart147)A comprehensive list of models, software and data-bases used in various stages of drug developmentprior to clinical investigations

Ruiz-Garcia et al.148)

The present status of PK in drug discovery and therelated computational and in silico tools with limitedand selected description of tools available to andused by the industry

Dokoumetzidis et al.134) Focuses on predictive models for oral drug absorp-tion

Nestorov149) Summarizes the most recent developments and ap-plications of PBPK modeling

Dong et al.150)A comprehensive review of applications of com-puter-aided PK and PK tools with descriptions ofthe unique attributes of each tool

Bouzom andWalther123)

Focuses on available tools for PK predictions in chil-dren by using PBPK modeling


(or so called tanks or compartments) acted as delay ele-ments in CAT without necessarily having specific physio-logical meaning. GastroPlus(tm) (Simulations Plus, Inc.Lancaster, CA, USA) was introduced in the mid 1990s130)

and further developed later68,130,132) based on `AdvancedCompartmental Absorption and Transit (ACAT)'133)

where transit rate constants were assigned according toactual compartments of the intestinal tube.133) This modelputs more emphasis on formulation and drug relatedprocesses than CAT including release, dissolution,precipitation and degradation. It also has optimizationmodels for gut metabolism, and influx and efflux trans-port in the enterocytes134) to match the simulated ob-served data. Considering the major strength of the plat-form in handling the pharmaceutical issues, and the ab-sence of stochastic inter-correlated system parameters, itis of little surprise that applications have been limited tostudying formulation effects, with the exception of inves-tigating the effects of food as covariate.135) GastroPlusTM

benefits from a PBPK module for distribution kineticsand is able to extrapolate clearance values from in vitrodata using typical average values for scaling factors. Someresearchers have combined the deterministic features ofGastroPlusTM in predicting oral absorption and averagepharmacokinetic profiles with stochastic predictions ofpopulation variability in other tools (e.g. see Allan etal.136)).

iDEA SoftwareThe iDEATM software was a physiologically-based oral

absorption model developed at Lion Biosciences (LIONBiosciences, Heidelberg, Germany) which was not devel-oped further. The model is based on the work ofGrass137,138) and is very similar to the CAT model. TheiDEATM model did not incorporate a GI stability factor ormetabolism. Therefore, in the presence of significant gutlumen metabolism, or chemical instability in gastric acid,the bioavailability predicted with the system could beoverestimated.139)

PK-Sim} SoftwarePK-Sim} (Bayer Technology Services www.pk-sim.

com) is a software tool for whole-body PBPK modelingand simulation. A validated PK model allows extrapola-tion from preclinical data to clinical study design and in-corporation of models into PK-Sim} add-on modulessuch as `Clearance Scaling' for children and `PK-Pop' formodeling of virtual populations. A unique feature of thissoftware is that unlike other models that use a multi-com-partmental approach, the GI tract is modeled as a con-tinuous tube with spatially varying properties. In themodel, it is assumed that dissolution occurs instantane-ously, but solubility in the intestinal fluids can limit theluminal concentration of drug. It is also assumed that ifthe calculated luminal concentration is above the satura-tion solubility of the compound then the luminal concen-tration should be the saturation solubility of the drug. Inthis model, the intestinal permeability coefficient is cal-culated using a mechanistic equation incorporating thecompound's membrane affinity and molecular weight;however it can also use the experimental permeabilitycoefficient. The software also includes a module of en-terohepatic cycling and supports several species (human,dog, rat, mouse) in physiological conditions under fastedand fed state. Recently, it was shown that by changing thePBPK model based on the physiological changes associ-ated with liver cirrhosis it is possible to predict the drugpharmacokinetics in patients with liver cirrhosis.140)

Cloe} PK ModelCloe} PK (http://www.cyprotex.com/) is a commercial

simulation tool capable of predicting oral absorption aswell as distribution and elimination. This modelrepresents the lumen of the GI tract as a series of fivecompartments, namely stomach, duodenum, jejunum, il-eum and colon. There are not many published reports onapplications of this model to covariate recognitionalthough it has been used in some of the Frame Work 7research projects in Europe.

Simcyp Population-Based ADME SimulatorInter-individual variability has been an intrinsic char-

acteristic of the models with in the Simcyp Simulator(Simcyp Ltd, Sheffield, UK, http://www.simcyp.com). The


platform encompasses all the known variability sourcesof ADME which are covered in this review. The `Ad-vanced Dissolution, Absorption and Metabolism (ADAM)Model', accommodated within Simcyp V7 onwards, con-tains the physiological covariates of oral drug absorption.It belongs to the family of compartmental transit modelsof the GI tract. The interplay between drug characteris-tics and anatomical and physiological changes, food in-take etc., provides a true representation of inter-individ-ual variability. The prediction of variability in absorptioncan be valuable in power calculations for lowbioavailability drugs.141) A full account of the ADAMmodel is given in a report by Jamei et al. (AAPSJ 2009,submitted). The distribution simulations in Simcyp in-clude both Rodgers and Rowland models20,106,142) andthose of Poulin and Thiel.22,143) The elimination modelsare fully mechanistic (dividing clearance between itscomponents) which gives the platform a unique ability toassess the effects of multiple concomitantly administereddrugs (as well as the impact of drug metabolites) via com-petitive inhibition, mechanism-based inhibition and in-duction, or combination of all three. A full account of al-gorithms for building the CL and assessing DDI are al-ready given in the literature and on the website(www.simcyp.com) and some applications of the systemhave been described previously.

Acknowledgement: The authors would like to thankDr Elizabeth Kingsley [Simcyp Limited] for her assistancein preparing the manuscript. GLD was formerly spon-sored for her PhD studentship by the Simcyp Consorti-um. Financial assistance of all the Consortium membercompanies (www.simcyp.com) and European Framework7 (Biosim Network of Excellence) is appreciated.

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