Predictive ADMET studies, the challenges and the opportunities

Predictive ADMET studies, the challenges and the opportunitiesAndrew M Davis� and Robert J Riley

Predictive ADMET is the new ‘hip’ area in drug discovery.

The aim is to use large databases of ADMET data associated with

structures to build computational models that link structural

changes with changes in response, from which compounds with

improved properties can be designed and predicted. These

databases also provide the means to enable predictions of

human ADMET properties to be made from human in vitro and

animal in vivo ADMET measurements. Both methods are limited

by the amount of data available to build such predictive models,

the limitations of modelling methods and our understanding of

the systems we wish to model. The current failures, successes

and opportunities are reviewed.

AddressesDepartment of Physical and Metabolic Science, AstraZeneca R&D

Charnwood, Bakewell Road, Loughborough, Leicestershire,

LE11 5RH, UK�e-mail: [email protected]

Current Opinion in Chemical Biology 2004, 8:378–386

This review comes from a themed issue on

Next-generation therapeutics

Edited by Tudor Oprea and John Tallarico

1367-5931/$ – see front matter

� 2004 Elsevier Ltd. All rights reserved.

DOI 10.1016/j.cbpa.2004.06.005

AbbreviationsADMET absorption, distribution, metabolism elimination and toxicology

PK pharmacokinetic

QSAR quantitative structure–activity relationship

RMSE root mean square error

Vss volume of distribution

IntroductionThe pharmaceutical industry is facing a problem. While

the costs of research and development continue to rise,

the output measured in terms of new medical entities

reaching the market is dropping. Once on the market,

many compounds fail to recover their research and devel-

opment costs. Market withdrawals due to adverse effects

further add to the industry’s problems. Even worse, the

attrition of compounds through clinical development

means only 1 in 10 compounds entering development

will ever make it to the marketplace. Contrast this against

the limited patent lifetimes and the global focus on

pharmaceutical prices, then it is clear to see that the

problems are real. All this, and the industry operates in

a highly aggressive competitive environment. This is why

speed, efficiency and reducing attrition through pharma-

ceutical drug discovery and development are a major

focus of the whole industry. Some of the reasons for

failure during and after development are multifactorial,

including lack of efficacy, which may relate to the valida-

tion of the biological target, or even ‘portfolio reasons’.

However, many of the reasons for failure can be traced

back to the chemical structure itself. For instance, phar-

macokinetics, animal toxicity and observation of adverse

effects in humans are all inextricably linked to the chem-

ical structure of the drug. Hence, the appropriate choice

of a quality compound to enter clinical development is a

key decision.

Drug discovery has reacted to this pressure towards

higher quality by embracing absorption, distribution,

metabolism and elimination studies and, increasingly,

safety data into the discovery process. But although

increasing the number of progression criteria on discovery

projects may aid in the identification of quality com-

pounds, it can also increase the complexity of the dis-

covery process, potentially slowing it down, increasing

costs, reducing patent lifetimes, and decreasing competi-

tiveness. Speed comes from the synthesis of compounds

with preferred properties early, and deselecting com-

pounds likely to have undesirable properties. Hence,

application of traditional approaches of structure–activity

analysis and quantitative structure–activity relationship

(QSAR) analysis to absorption, distribution, metabolism

elimination and toxicology (ADMET) data has become

the new ‘hip’ area and has spawned the new discipline of

‘predictive ADMET’.

Human ADMET predictions can be attempted at several

levels:

1. In silico or computational predictions from QSAR

models to project in vitro or in vivo data.

2. Inter-species, in vivo–in vivo (including allometry)

using data from pre-clinical species.

3. In vitro–in vivo using data obtained from tissue or

recombinant material from human and pre-clinical

species.

QSAR models to predict ADMETFrom their origins in physical organic chemistry, to

Hansch analysis and their application in medicinal chem-

istry, QSAR methods were largely limited to prediction of

potency or selectivity in homologous series of chemical

structures. Prediction outside the model space was often

not necessary or not investigated. With ADMET pro-

blems being generic across chemical series, predictive

Current Opinion in Chemical Biology 2004, 8:378–386 www.sciencedirect.com

ADMET may offer the techniques of QSAR both their

greatest opportunity but also their greatest challenge. To

this end, many pharmaceutical companies, commercial

software vendors and academic groups have been devel-

oping QSAR models for ADMET properties based on

their large corporate databases, or from compilations of

published data, and the literature is replete with exam-

ples of such models. For instance, a large number of

publications describe models for the identification of oral

absorption, bioavailability [1–3], pgp binding [4], meta-

bolism, volume of distribution [5,6] and p450 inhibition

[7] for 3A4, 2C9, 2C19 [8], 3A4 and 2D6 [9,10] and even

CYP induction [11]. (Recent examples are cited for

illustration.) Developed models may be made accessible

to large numbers of scientists through commercial code,

through corporate intranet or other distributed means.

These models have the potential to significantly influ-

ence the future synthetic direction of whole organisa-

tions, for better — or possibly for worse, depending on

the predictive value of the model. Hence, computa-

tional chemists are, maybe for the first time, taking

seriously the predictive ability of their models. Rigorous

validation procedures are for the first time investigating

and uncovering the true predictive power of these

models.

Stouch has recently described the experiences of Bristol-

Myers Squibb in developing models for the prediction of

blocking hERG ion channel, caco-2 permeability and

solubility, and CYP450 2D6 inhibition [12��]. The models

were developed in collaboration with an external infor-

matics vendor, using commercial, literature or in-house

data for training, and in-house data for validation. Their

experiences are instructive to all.

The data for the model should be generated by a screen

that is representative of the screen that will be used to

validate the predictions. This was suggested to be a

particular problem for the hERG model, which was

trained on data collected from diverse literature sources,

but was also a factor in the poor predictive power of their

solubility model. It is known that data generated from

patch-clamp electrophysiology experiments is notoriously

sensitive to the conditions of the experiment, and it is

unrealistic to expect cell-lines containing non-human

hERG channel to yield data representative to human

cells. Hence the model trained on 76 compounds gave

essentially a random prediction of the 116 compounds

measured in the internal BMS screen (R2 ¼ 0.01).

The caco-2 model was based on 800 marketed drugs, and

was a multiple linear regression model using proprietary

pharmacophores. The model was applied to 76 com-

pounds measured in an internal screen with poor pre-

dictive performance. A retrospective analysis highlighted

that few of the test set had a paired similarity with a

member of the training set >0.3 based on DAYLIGHT

fingerprints, where a similarity <0.5 show little structure

similarity.

The solubility model also showed poor predictive power,

but of more concern was the utility of the model, as the

range of solubility covered by the training set was inade-

quate considering the solubility range of the compounds

being predicted. This raises questions again about the

representation of required property and solubility space

of the training set.

Finally the 2D6 model was a classification model, based

on 250 drugs and drug-like compounds assayed by

the vendor, predicting potency ><10 mM, a common

watershed for concern over CYP inhibition in discovery

projects. This model had modest predictive power, cor-

rectly classifying 60% of compounds with IC50 <10 mM.

(Random prediction would give 50% success). Although

this model was judged a success by the creators within the

context of the resolution it was built on, for most users at

BMS this resolution was not good enough, with 40% of

compounds predicted to be benign but in reality being

potent 2D6 inhibitors.

The prediction of solubility had been extensively

reviewed over many years. Large databases are available

in the public domain, and these have been a rich source

for academic QSAR studies, and also exploitation by

commercial software vendors selling predictive packages.

A recent study has highlighted the deficiencies of using

such databases to predict corporate drug-like databases

[13��]. Using large literature and proprietary databases of

intrinsic solubility, 2-D and 3-D physicochemical descrip-

tors, and a Bayesian neural network approach with vari-

able selection, Bruneau was able to build models with root

mean square error (RMSE) in prediction of log sol (loga-

rithmn10 of the measured solubility in mM) at best around

0.8. Importantly, he also highlighted several important

considerations in generating generic QSAR models that

apply to all ADMET QSAR approaches. Although the

models reported were compared with the performance of

literature models reporting RMSEs in log sol from 0.23 to

0.56, the comparison of the predictive power of a training

set to predict a test set was shown to be a misleading and

erroneous. Models trained on literature datasets did well

at predicting literature data, but not the proprietary

database, and the contrary was also found to be true.

The differences in predictive ability were quite startling.

A mixed literature and proprietary training set was able to

successfully model both literature and proprietary valida-

tion sets. The distance of compounds to be predicted

from the training set model space was found to be strongly

related to the error in prediction for all models. This

diagnostic is rarely considered in the majority of QSAR

studies. Hence, the ability of a model to predict a com-

pound depended strongly on representation of closely

related compounds in the training set and is a key

Predictive ADMET studies, the challenges and opportunities Davis and Riley 379

www.sciencedirect.com Current Opinion in Chemical Biology 2004, 8:378–386

determinant of successful prediction forms the model.

Hence, representation of the compounds to be predicted

in the training set is an important prerequisite for good

predictive power. One concern to the academic world is

the paucity of data available in the public domain from

which to build QSAR ADMET models. Pharmaceutical

companies could help by releasing data to broaden the

range of chemotypes with data available. From the

experiences of Stouch and Bruneau it would appear that

the best models are those where your own chemotypes are

well represented in the training set space, and from

experimental measurements from screens representative

of one’s own. Literature data may hold only limited value

in building models for true predictive use within a com-

pany or project area, and there is commercial advantage in

maintaining experimental data on chemotypes out of the

public domain.

Although we may hope to develop generic global

ADMET models, the model space limits the generalisa-

bility of the model to predict, as Bruneau demonstrated.

This may mean that models require constant updating to

ensure their applicability to developing chemistry. Tetko

has examined the use of errors in predictions of measured

‘libraries’ of compounds as a way of adjusting the pre-

dictions of unknown compounds that have a near struc-

ture in the library [14��,15]. These so-called associative

libraries were shown to make significant improvements in

neural net models predicting logP. Both the use of distance

to model metrics to assess predictive performance, and the

study of errors in prediction of near neighbours in either

the training set or an associated library may offer improved

and realistic predictions for generic QSAR models.

Many of the literature models are characterised by small

training and test sets, which, on the basis of work by

Bruneau and Stouch, may be a concern for the generali-

sability of any predictions made from such models. If

qualitative SAR can be extracted from such small quan-

titative models, rules or guidelines may be generated that

might actually be more useful than the small fitted model

would suggest. A study of a diverse set of 3A4 inhibitors

highlighted two such useful design guidelines for CYP-

3A4 [16�]. It appears that compounds containing nitrogen

acceptors (e.g. pyridine, imidazoles triazole, etc.) are more

potent CYP450 3A4 inhibitors than compounds of similar

logD7.4 where these features are absent. Both sets of

compounds follow an underlying dependence of 3A4

inhibition on logD7.4. Predictions based on such a small

model are unlikely to be quantitatively useful. But these

SAR features qualitatively might be true across many

chemotypes. They may or may not be important in any

particular project chemistry – but at least they may

indicate first points of examination in problematic com-

pound series. The appeal of such simple guidelines is that

they are memorable, do not require a complex in silicostrategy to apply, and rely rather on an in cerebro approach.

Their potential power is exemplified by influential effects

of the role of hydrogen bonding in blood–brain barrier

penetration [17,18], Lipinski’s Rules-of-5 guidelines for

oral absorption [19], Veber’s subsequent analysis of

GSK’s database of bioavailability data [20], Wenlock

analysis of properties of drugs in different development

phases [21�] and the leadlike concept for lead generation

chemistry [22]. Such ‘rules’ are also the basis of the

compact model of Lewis [23]. Some of these guidelines

are already part of the dogma of predictive ADMET. For

instance, CYP 1A2 likes small flat molecules, 2D6 binds

and metabolises bases with aromatic groups 5–7 A from

the basic centre, 2C9 and 2C19 favour acids and 3A4 has a

large flexible active site, which binds hydrophobic com-

pounds. These CYP alerts, although valuable, alone are

probably not useful enough to guide medicinal chemistry

lead optimisation projects, as they essentially flag most of

the history and future of medicinal chemistry as potential

CYP substrates or inhibitors.

Influence of structure-based designingpredictive ADMETAlthough QSAR approaches are useful in interpolating

important structure–activity relationships, to many chem-

ists an X-ray crystal structure (even with all its caveats

and ambiguities [24]) with a representative ligand bound

is an intuitively more appealing tool for overcoming

problems of potency and selectivity. Structural informa-

tion on proteins important to ADMET is beginning to

grow, including the ligand-binding domain of PXR [25],

transcriptional regulator of CYP3A4, structures for human

serum albumin [26,27], and the structure of the KCSa

potassium channel, which has provided the start point for

homology models for the hERG ion-channel. As far as

CYP-P450s are concerned, rabbit 2C5 structure became

available in 2000 [28], but the first human cytochrome

P450 X-ray crystal structure, CYP2C9, has just been made

available [29��]. In order to produce a structure, the 2C9

molecule was protein engineered at both the C- and

N-terminal ends, together with seven mutations in the

F-G loop to promote solubilisation and crystallisation.

The molecular engineering did not apparently affect the

turnover of common substrates, or the selectivity towards

a panel of ligands.

The reported structures ask as many questions as they

answer. Firstly, although 2C9 is thought to show a pre-

ference for acidic substrates, no basic residue is found in

the active site. Arg105 and 108, both residues implicated

as important in the putative anionic binding site in

mutagenesis experiments, point away from the active

site cavity, whereas two acidic residues are present within

the cavity. The X-ray structure with (S)-warfarin bound

locates the site of hydroxylation of the ligand appro-

ximately 10 A from the haem iron, and the ligand sits

in a region of the protein not previously identified as

a ligand binding site. Although this is consistent with

380 Next-generation therapeutics


spectroscopic data showing only a small increase in high

spin iron when warfarin is added to oxidised protein, it is

not consistent with common sense docking and informa-

tion from bacterial structures, which would place substrates

proximally above the haem. It is possible that this first X-

ray has identified an auxiliary activation site supporting

kinetic evidence that S-warfarin increases the turnover of

7-methoxy-4-trifluromethylcooumarin and cooperativity

often seen in CYP-450 3A4. It will be fascinating to see

further structures with inhibitors bound, and to see how

this first structure influences predictive ADMET work.

Inter-species scaling to predict humanADMET parametersIt is generally accepted that inter-species predictions are

most successful for ADME parameters, which rely on

passive processes such as absorption, renal clearance and

distribution volume.

Absorption

Interrogation of the literature has shown that the absorp-

tion of many drugs relying on passive, transcellular perme-

ability is similar across pre-clinical species (rat, dog and

primate) and human [30–32] (Figure 1). Within the dataset

shown, a subset of small MW (�300) compounds appears

to be better absorbed in the dog, probably as a function of

established differences in gastro-intestinal physiology and

perhaps inter-species differences in uptake/efflux trans-

porter activity [31]. Such compounds unfortunately are

rarely encountered early in modern drug discovery!

These findings suggest that human absorption may be

predictable from knowledge of the physico-chemical

properties, passive permeability potential, (gastrointest-

inal) solubility and an understanding of absorption in

animal models. Indeed, some laboratories claim to have

made sufficient progress with computational models to

replace their cellular permeability screens (mostly Caco-2

or MDCK cells) as a filter for absorption potential analysis

[33]. Indeed, preliminary evaluation of commercial tools

such as IDEATM and GastroPlusTM suggest that in silicodata may provide appropriate absorption classification in

�70% cases with additional cellular permeability data

providing minimal improvement [34��]. Although, robust

large-scale validation, as described for QSAR models

above, has yet to stress these theoretically based math-

ematical modelling approaches.

Clearance and volume of distribution

Allometry relates in vivo ADME parameters across spe-

cies as power function to changes in body weight. On log-

log scales, these relationships are linear and enable pre-

dictions, which are usually extrapolations, of that para-

meter in man. Allometric relationships for clearance tend

to be most successful for compounds undergoing renal

clearance or high hepatic extraction where clearance

approaches liver blood flow (Cl ) Qh) [35]. A recent

analysis of 68 drugs has shown that predictions from simpleallometry (without Mahmood and Balian’s ‘rule of expo-

nents’ correction) yielded a q2 of only 0.174 with RMSE ¼0.564 [36]. Interestingly, a multiple linear regression

method combining clearance data from two species and

readily calculated structural parameters (MW, clogP and

number of hydrogen bond acceptors) predicted human

clearance much better (q2 ¼ 0.682, RMSE ¼ 0.35).

Renal clearance in humans may be predictable from rat

renal clearance that has been corrected for species differ-

ences in glomerular filtration rate [37]. Volume of dis-

tribution (Vss) may also be predicted successfully by

allometry since the basic tenet for this parameter is that

unbound volume of distribution is consistent across spe-

cies. Once again, armed with this knowledge, more sim-

ple relationships relating Vss in pre-clinical species to that

in humans through species differences in plasma protein

binding may be adopted [38]. Such a relationship is shown

in Figure 2. Projecting Vss for compounds subject to

active transport (e.g. hepatic uptake via OATPs) may

pose a different challenge.

In vitro–in vivo approachesMetabolic clearance

In general, marked differences in processes dependent on

the activity of specific enzymes or proteins confound or

even preclude direct inter-species extrapolation. The

literature is rife with examples for metabolic clearance,

active renal or hepatobiliary elimination and active tissue

uptake/exclusion. In these cases, in vitro–in vivo relation-

ships are often sought and if a successful correlation can

be established for pre-clinical species, this should provide

Figure 1

Current Opinion in Chemical Biology

0

20

40

60

80

100

120

0 20 40 60 80 100 120

Other species Fabs (%)

Hum

an F

abs

(%)

Rat

Dog

Primate

MW 309

A comparison of the fraction absorbed (Fabs) for several drugs in rat,

dog, primate and human.



confidence for any prediction for humans. This assumes

that the dominant processes have been adequately

defined in the pre-clinical species of interest and is likely

to occur in humans. For metabolic clearance, several

laboratories have reported successes in establishing rela-

tionships between in vitro intrinsic clearance (Clint) and

metabolic clearance in vivo in pre-clinical species [39,40]

and humans [41,42]. Figure 3 shows data obtained in the

authors’ laboratory for three mature lead optimisation

projects. These data show that metabolic clearance in

vivo could be projected from in vitro microsomal or

hepatocyte Clint data for mouse, rat and dog using the

well-stirred liver model. Perhaps, as anticipated, signifi-

cant variability is seen with low extraction compounds

(Cl � 20% Qh).

More recently, several laboratories have attempted to

improve the precision of such predictions by comparing

the more fundamental Clint, in vivo with predicted or scaled

Clint [39,40,43–45]. Figure 4 shows a summary for rat

Figure 2


0.01

0.1

1

10

100

0.01 0.1 1 10 100

Pre-clinical species Vss xfu (Man)/ fu (species) (l/kg)

Hum

an V

ss (

l/kg)

Prediction of human Vss from pre-clinical PK data using species

differences in plasma protein binding. The green dot represents a

prediction of human volume incorporating error bars for its error in

prediction.

Figure 3


-0.5

0

0.5

1

1.5

2

-0.5 0 0.5 1 1.5 2

Log Cl(predicted)

Log

Cl(o

bser

ved)

Mouse

Dog

Rat

Prediction of in vivo metabolic clearance for a selection of lipophilic

neutral compounds from AZ projects using microsomes from

mouse (N ¼ 15) and rat (N ¼ 27) and dog hepatocytes (N ¼ 25).

Figure 4


y=0.9199x+0.8092R2=0.8216

-1

0

1

2

3

4

-1 0 1 2 3 4

Log (ScaledHepsClint, ml/min/kg) Log (ScaledHepsClint, ml/min/kg)

Log

(InV

ivo

Clin

t, ub

ml/m

in/k

g)

y=1.1 119x+0.5057R2=0.6235

-1

0

1

2

3

4

5

-1 0 1 2 3 4 5Log(

InV

ivo

Clin

t, ub

ml/m

in/k

g)

(a) (b)

Analysis of Cl int, in vivo versus scaled Clint, in vitro from two independent laboratories using rat PK data and rat hepatocyte metabolic stability:

(a) Houston; (b) Sugiyama.



hepatocyte data from two such laboratories and demon-

strates that Clint, in vivo may be some 3–6-fold higher than

that predicted from in vitro data. It is important to note

the considerable variability in these factors for individual

compounds. Further confidence may be gained by

confirming the empirical scaling factors for individual

compounds in several species before applying with con-

fidence for humans [44,45].

Inhibition of drug metabolising enzymes

Given the prominence of pharmacokinetic (PK) drug–

drug interactions involving the CYP family of drug meta-

bolising enzymes, most drug discovery programmes now

include screening for at least competitive human CYP

inhibition. Confidence in our ability to model and project

the likelihood of clinically significant CYP drug–drug

interactions through a simple competitive mechanism

has been provided by studies in rats and primates.

Key to such predictions is an estimate of the concentra-

tion of inhibitor (I) operating in vivo (at the level of the

hepatic enzyme). Recent efforts by Ito et al. [46�] have

compared the value of different estimates of I for a range

of reported clinical drug–drug interactions involving the

major hepatic human CYPs. This group concluded that

the use of unbound I,in,max (maximum concentration at the

inlet to the liver) as described below resulted in fewer

false positives than I,max (maximum concentration in the

circulation):

Iin;max ¼ Imax þKa:Dose:Fa

Qh

Analysis of these data using AUC ratio ¼ 1 þ Iin,max,u/Ki is

shown in Figure 5.

Further analyses provide additional information of value

for this dataset (Figure 5):

1. Independent of dose and enzyme, a Ki � 10 mM should

be targeted to avoid significant CYP interactions.

2. Iin,max can be markedly higher than Imax and an average

ratio of 5–10-fold should be adopted in the absence of

detailed information.

Using an analogous approach, Dayer’s group [47] has used

a computer programme (QDIPs) to assess drug–drug

interaction potential based on in vitro data and clinical

PK with a success rate of 79%. The remaining risks are

then understanding substrate/inhibitor differences for

CYP3A4, mechanism-based inhibition and gaining an

understanding of the false negatives from such assays.

The recent resurgence of interest in mechanism-based

CYP inhibition is perhaps then not surprising. Sugiyama’s

group has also shown that these drug interactions may also

be predicted from a consideration of the PK of substrate

and inhibitor together with in vitro data, including tissue

partitioning [48]. However, these studies need to be

validated and extended further to include a wider range

of inhibitors and would benefit from the inclusion of inter-

subject variation in key parameters before they can be

applied prospectively with confidence.

For compounds such as fluconazole, the in vivo Ki esti-

mated from clinical PK studies with CYP2C9 substrates

Figure 5


Ki(uM)

100

200

400600800

1000

2000

40006000

0.01 0.1 1 10 100 1000

Imax(uM)

0.001

0.01

0.1

1

10

100

1000

10000

0.001 0.01 0.1 1 10 100 1000 10000

I in, m

ax(u

M)

Iin,max,u/Ki

0

200

400

600

800

1000

1200

1400

0.0001 0.001 0.01 0.1 1 10 100

AU

C (

%C

ontr

ol)

(a)

AU

C (

%C

ontr

ol)

(b)

(c)

Analysis of human CYP inhibition data. Relationship between in vivo

inhibitor concentration (Iin,max), in vitro Ki and change in AUC.

(a) y-axis truncated for clarity. (b) Ki versus AUC. (c) Comparison of

Iin,max and Imax. The different colours of the graphs represent different

cytochrome P450 isoforms.



approximates that determined in vitro (�10–20 mM) [49].

Theoretically, tissue distribution and active uptake

may also influence the intra-hepatic free concentration.

Studies with fluvoxamine have suggested that the appar-

ent in vivo Ki towards CYP1A2 and CYP2C19 may be up

to 40-fold lower than that estimated in vitro, even when

Ki,unbound has been considered [50,51]. Similar effects

have also been reported in the rat for statins [52].

Collectively, such data suggest that some compounds

may accumulate within hepatocytes as a result of active

uptake or that additional mechanisms such as down-

regulation of CYP activity occur. Identification of the

role of drug transport in such phenomena and their

incorporation into in vitro screens should permit a more

accurate prediction of in vivo CYP inhibition. This

learning should then be applied to the extrapolation of

interactions involving CYP induction and transporter

proteins.

ConclusionsThe opportunities and needs for predictive ADMET

have never been greater, but will they be able to

deliver? This will, to some extent, depend upon the

expectations placed on such models. Global QSAR

models that attempt ADMET predictions direct from

chemical structure are most applicable to predicting

libraries of compounds, rather than individual com-

pounds, in accord with the statistical nature of such

models, and hence are particularly suited to early

chemistry, such as lead generation. QSAR models

are most likely to give useful predictive power when

there is good representation of the chemical classes

being predicted in the training sets of such models.

There are many pitfalls to developing such models if

useful predictivity is to be obtained. While generic

global models have their utility in lead generation,

and projects where not enough measured data exists,

the benefits of training set representation would mean a

local project model would often be preferable. Distance

to the training set model space also modulates the

predictive power of such models and should always

be considered. Qualitative guidelines and alerts, which

may be deduced from quantitative models, may be

even more useful than absolute numerical predictions,

although may prove too simplistic to solve subtleties of

lead optimisation where the core pharmacophore may

be invariant. X-ray crystal structures are beginning to

become available to aid predictive ADMET, although

they are presently too few and it is too soon to know

to what extent. The recent human 2C9 structures

have arguably created more questions than they have

answered.

Relative to QSAR model development, scaling tools are

still in their infancy. Even so, long-term expensive deci-

sions, such as the potential of compounds to enter clinical

development, are often placed on scaling predictions.

Their success can only be adjudged some years after

the prediction has been made when the human pharma-

cokinetics, of one or at best a very small number of

compounds are ultimately determined. The small data-

base of human pharmacokinetics available to validate

such approaches is a major limitation, and this dataset

is not going to grow very rapidly. Hence, the challenges

and opportunities for predictive ADMET have never

been greater.

References and recommended readingPapers of particular interest, published within the annual period ofreview, have been highlighted as:

� of special interest��of outstanding interest

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2. Yoshida F, Topliss JG: QSAR model for drug human oralbioavailability. J Med Chem 2000, 43:2575-2585.

3. Turner JV, Glass BD, Agatonovic-Kustrin S: Prediction of drugbioavailablity based on molecular structure. Anal Chim Acta2003, 485:89-102.

4. Ekins S, Kim RB, Leake BF, Danzig AH, Schuetz EG, Lan L-B,Yasuda K, Shepard RL, Winter MA, Schuetz JD et al.:Three-dimensional quantitative structure-activityrelationships of inhibitors of P-glycoprotein. Mol Pharmacol2002, 61:974-981.

5. Lombardo F, Obach RS, Shalaeva MY, Gao F: Prediction ofhuman volume of distribution values for neutral and basicdrugs. 2. Extended data set and leave-class-out statistics.J Med Chem 2004, 47:1242-1250.

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7. de Groot MJ, Ekins S: Pharmacophore modelling of cytochromeP450. Adv Drug Del Reviews 2002, 54:367-383.

8. Afzelius L, Zamora I, Ridderstrom M, Andersson TB, Karlen A,Masimirembwa CM: Competitive CYP2C9 inhibitors: enzymeinhibition studies, protein homology modeling, and three-dimensional quantitative structure-activity relationshipanalysis. Mol Pharmacol 2001, 59:909-919.

9. Snyder R, Sangar R, Wang J, Ekins S: 3-Dimensional quantitativestructure activity relationship for Cyp2D6 substrates.Quant Struct-Act Relatsh 2002, 21:357-365.

10. Susnow RG, Dixon SL: Use of robust classification techniquesfor the prediction of human cytochrome P450 2D6 inhibition.J Chem Inf Comput Sci 2003, 43:1308-1315.

11. Lewis DF: Quantitative structure-activity relationships (QSARs)within the cytochrome P450 system: QSARs describingsubstrate binding, inhibition and induction of P450s.Inflammopharmacology 2003, 11:43-73.

12.��

Stouch TR, Kenyon JR, Johnson SR, Chen X-Q, Doweyko A,Li Y: In silico ADME/Tox: why models fail. J Comput AidedMol Des 2003, 17:83-92.

A beautifully honest paper that should be a lesson to all of us, as we havedescribed.

13.��

Bruneau P: Search for predictive generic model of aqueoussolubility using Bayesian neural networks. J Chem InfComput Sci 2001, 41:1605-1616.

A must-read paper that highlights the importance of structural represen-tation in the training set for optimum predictivity. The implications of thispaper to predictive ADMET are enormous.

14.��

Tetko I: Neural network studies: 4. Introduction to associativeneural networks. J Chem Inf Comput Sci 2002, 42:717-728.



Associative libraries may extend the longevity of QSAR models to takeadvantage of new data without having to manually and continually rebuildQSAR models.

15. Tetko I, Tanchuk Yu: Application of associative neural networksfor prediction of lipophilicity in ALOGPS 2.1 Program.J Chem Inf Comput Sci 2002, 42:1136-1145.

16.�

Riley RJ, Parker AJ, Trigg S, Manners CN: Development if ageneralised quantitative physicochemical model of CYP314inhibition for use in early drug discovery. Pharm Res 2001,18:652-655.

A nice example, we modestly believe, of a small QSAR model highlightingstructural features that qualitatively may be more generalisable.

17.��

Young RC, Mitchell RC, Brown TH, Ganellin CR, Griffiths R,Jones M, Rana KK, Saunders D, Smith I et al.: Development of anew physicochemical model for brain penetration and itsapplication to the design of centrally acting H2 receptorhistamine antagonists. J Med Chem 1988, 31:656-671.

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21.�

Wenlock MC, Austin RP, Barton P, Davis AM, Leeson PD:A comparison of physicochemical property profiles fordevelopment and marketed oral drugs. J Med Chem 2003,46:1250-1256.

This paper has already stimulated a lot of interest, and is being widelycited on the conference circuit. As a survey of development successand failure with respect to physical properties, its conclusions may befar-reaching.

22. Teague SJ, Davis AM, Leeson PD, Oprea TI: The design of leadlikecombinatorial libraries. Angew Chem Intl Ed Engl 1999,38:3743-3748.

23. Lewis DFV: COMPACT: a structural approach to the modeling ofcytochromes P450 and their interactions with xenobiotics.J Chem Technol Biotechnol 2001, 76:237-244.

24. Davis AM, Kleywegt GJ, Teague SJ: Applications and limitationsof the use of X-ray crystallographic data in structure-basedligand and drug design. Angew Chem Int Ed Engl 2003,42:2718-2736.

25. Watkins RE, Wisely GB, Moore LB, Collins JL, Lambert MH,Williams SP, Wilson TM, Kleiwer SA, Redimbo MR: The humannuclear xenobiotic receptor PXR: structural determinants ofdirected promiscuity. Science 2001, 292:2329-2333.

26. Petitpas I, Gruene T, Bhattacharya AA, Curry S: Crystal structuresof human serum albumin complexed with monounsaturatedand polyunsaturated fatty acids. J Mol Biol 2001,314:955-960.

27. Petitpas I, Bhattacharya AA, Twine S, East M, Curry S: Crystalstructure analysis of warfarin binding to human serum albumin:anatomy of drug site I. J Biol Chem 2001, 276:22804-22809.

28. Williams PA, Cosme J, Sridhar V, Johnson EF, McRae DE:Mammalian microsomal cytochrome P450 monooxygenase:structural adaptations for membrane binding and functionaldiversity. Mol Cell 2000, 5:121-131.

29.��

Williams PA, Cosme J, Ward A, Angove HC, Vinkovic DM,Jhoti H: Crystal structure of human cytochrome P450 2C9with bound warfarin. Nature 2003, 424:464-468.

First human cytochrome P450 X-ray crystal structure.

30. Chiou WL, Barve A: Linear correlation of the fraction of oraldose absorbed of 64 drugs between humans and rats.Pharm Res 1998, 15:1792-1795.

31. Chiou WL, Jeong HY, Chung SM, Wu TC: Evaluation of using dogas an animal model to study the fraction of oral dose absorbedof 43 drugs in humans. Pharm Res 2000, 17:135-140.

32. Chiou WL, Buehler PW: Comparison of oral absorptionand bioavailability of drugs between monkey and human.Pharm Res 2002, 19:868-874.

33. Hodgson J: ADMET- turning chemicals into drugs.Nat Biotechnol 2001, 19:722-726.

34.��

Theil F-P, Guentert TW, Haddad S, Poulin P: Utility ofphysiologically based pharmacokinetic models to drugdevelopment and rational drug discovery candidate selection.Toxicol Lett 2003, 138:29-49.

An up-to-date overview and appraisal of an in-vogue approach.

35. Mahmood I, Balian JD: The pharmacokinetic principles behindscaling from preclinical results to phase I protocols.Clin Pharmacokinet 1999, 36:1-11.

36. Wajima T, Fukumura K, Yano Y, Oguma T: Prediction of humanclearance from animal data and molecular structuralparameters using multivariate regression analysis. J Pharm Sci2002, 91:2489-2499.

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40. Houston JB, Carlile DJ: Prediction of hepatic clearance frommicrosomes, hepatocytes and liver slices. Drug Metab Rev1997, 29:891-922.

41. Obach RS: Prediction of human clearance of twenty-nine drugsfrom hepatic microsomal intrinsic clearance data: anexamination of in vitro half-life approach and nonspecificbinding to microsomes. Drug Metab Dispos 1999, 27:1350-1359.

42. Lau YY, Sapidou E, Cui X, White RE, Cheng KC: Development of anovel in vitro model to predict hepatic clearance using fresh,cryopreserved and sandwich-cultured hepatocytes.Drug Metab Dispos 2002, 30:1446-1454.

43. Shibata Y, Takahashi H, Ishii Y: A convenient in vitroscreening method for predicting in vivo drug metabolicclearance using isolated hepatocytes suspended in serum.Drug Metab Dispos 2000, 28:1518-1523.

44. Naritomi Y, Terashita S, Kagayama A, Sugiyama Y: Utility ofhepatocytes in predicting drug metabolism: comparison ofhepatic intrinsic clearance in rats and humans in vivo andin vitro. Drug Metab Dispos 2003, 31:580-588.

45. Naritomi Y, Terashita S, Kimura S, Suzuki A, Kagayama A,Sugiyama Y: Prediction of human hepatic clearance from in vivoanimal experiments and in vitro metabolic studies with livermicrosomes from animals and humans. Drug Metab Dispos2001, 29:1316-1324.

46.�

Ito K, Chiba K, Horikawa M, Ishigama M, Mizuno N, Aoki J, Gotoh Y,Iwatsubo T, Kanamitsu S, Kato M et al.: Which concentration ofthe inhibitor should be used to predict in vivo drug interactionsfrom in vitro data? AAPS PharmSci 2002, 4:1-8.

A useful database for clinical drug–drug interactions ascribed to humanCYPs.

47. Bonnabry P, Sievering J, Leemann T, Dayer P: Predictivemodelling of in vivo drug interaction from in vitro data:from theory to a computer-based workbench and itsexperimental validation. In Interindividual Variability in DrugMetabolism. Edited by Pacifici GM, Pelkonen O: Taylor & Francis;2001:240-268.

48. Kanamitsu S-I, Ito K, Green CE, Tyson CA, Shimada N, Sugiyama Y:Prediction of in vivo interaction between triazolam anderythromycin based on in vitro studies using human livermicrosomes and recombinant human CYP3A4. Pharm Res2000, 17:419-426.

49. Black DJ, Kunze KL, Weinkers LC, Gidal BE, Seaton TL, McDonnellND, Evans JS, Bauwens JE, Trager WF: Warfarin-fluconazole II.



A metabolically based drug interaction: in vivo studies.Drug Metab Disp 1996, 24:422-428.

50. Yao C, Kunze KL, Kharasch ED, Wang Y, Trager WF, Ragueneau I,Levy RH: Fluvoxamine-theophylline interaction: gap betweenin vitro and in vivo inhibition constants toward cytochromeP4501A2. Clin Pharmacol Ther 2001, 70:415-424.

51. Yao C, Kunze KL, Trager WF, Kharasch ED, Levy RH: Comparisonof in vitro and in vivo inhibition potencies of fluvoxaminetoward CYP2C19. Drug Metab Dispos 2003, 31:565-571.

52. Ubeaud G, Hagenbach J, Vandenschrieck S, Jung L, Koffel JC:In vitro inhibition of simvastatin metabolism in rat and humanliver by naringenin. Life Sci 1999, 65:1403-1412.



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Predictive ADMET studies, the challenges and the opportunities