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
Current Opinion in Chemical Biology 2004, 8:378–386 www.sciencedirect.com
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.
Predictive ADMET studies, the challenges and opportunities Davis and Riley 381
www.sciencedirect.com Current Opinion in Chemical Biology 2004, 8:378–386
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
Current Opinion in Chemical Biology
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
Current Opinion in Chemical Biology
-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
Current Opinion in Chemical Biology
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.
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Current Opinion in Chemical Biology 2004, 8:378–386 www.sciencedirect.com
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
Current Opinion in Chemical Biology
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.
Predictive ADMET studies, the challenges and opportunities Davis and Riley 383
www.sciencedirect.com Current Opinion in Chemical Biology 2004, 8:378–386
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.
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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.
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Associative libraries may extend the longevity of QSAR models to takeadvantage of new data without having to manually and continually rebuildQSAR models.
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16.�
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A nice example, we modestly believe, of a small QSAR model highlightingstructural features that qualitatively may be more generalisable.
17.��
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21.�
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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.
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29.��
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First human cytochrome P450 X-ray crystal structure.
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An up-to-date overview and appraisal of an in-vogue approach.
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A useful database for clinical drug–drug interactions ascribed to humanCYPs.
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