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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5319_Библиотеки_им_академика_М_И_Перельмана.pdf
. • D-QSAR: Correlation of Molecular Fields with Biological Properties
. Fig. 18.4 A grid is generated for the calculation of molecular elds
that broadly encompasses amolecule. In this image, the grid points
are color-coded with increasing distance from the ligand (red < yellow < green < blue < gray). The contributions from the chosen elds
are calculated at all points of the lattice, which have agrid spacing of
1–2 Å. The eld contributions at each point in the grid (S1, S2,…Sn,
E1, E2,…En) are entered into aspreadsheet. The analysis is carried
out for all molecules in the dataset. The binding afnities are incorporated into the spreadsheet as, for instance, −log (Ki). The eld
contributions are weighted with appropriate coefcients (a, b,… z)
tion on amolecular scaffold is actually very important
for binding, but remains insignicant in the analysis.
This has to do with the fact that aQSAR analysis only
makes relative comparisons within adataset. In other
words, if aproperty is the same for all ligands in the
dataset, then that property will not be able to explain
any differences in the biological data.
and using aspecial statistical method, the partial least squares (PLS)
analysis, they are related to the afnity. Amodel is obtained in the
form of an equation that indicates at which grid points and with what
weights the different eld contributions explain the biological activity.
(7 https://sn.pub/jndLSI)
These examples are still easy to handle. The question
can be asked whether atedious correlation method with
a“detour” via molecular elds is really necessary. In
practice, the situation is more complicated, especially
when considering molecules with different scaffolds. The
substituents do not fall exactly on top of one another
in the molecular superposition. Their contribution must
18
Chapter • Quantitative Structure–Activity Relationships
. Fig. 18.5 The Lennard-Jones potential (green) is amodel for de-
scribing the intermolecular interactions of two atoms without considering their charge. Negative potential values correspond to mutual
attraction, positive values correspond to arepulsion of the particles.
If areciprocal distance becomes innite, the potential will approach
zero. Upon approach, it goes through ashallow minimum due to alternating polarization. At even shorter distance, it very steeply rises
towards positive innity because of atom–atom repulsions. The Coulomb potential (blue) considers only electrostatic interactions that formally reside as point charges on the atomic nuclei. It also approaches
innity when the distance disappears for like-charged particles. For
oppositely charged atoms, negatively innite values result. The hyperbolic form of the Coulomb potential is considerably less steep, so that
the particles can still “feel” one another at larger distances. Boundary
values are set for potentials in aCoMFA analysis. AGaussian function, which takes the course of abell-shaped curve (red, here only the
right half of the “bell” is shown) describes the distance dependence
of the interaction potential between the particles in the context of the
CoMSIA model. As the distance disappears between the particles, the
curve reaches its maximum value, which remains nite
be described as aeld in space, and only as such can
it be evaluated. In any case, these examples underline
the importance of careful planning of the analysis. The
structures of the dataset must be chosen in such away
that the variation of the substituents and their properties
is maximized.
18.12 Results of aComparative Molecular
Field Analysis and Their Graphical
Interpretation
When the full complexity of the eld contributions is
considered in terms of amultidimensional matrix, asimple regression analysis cannot be applied to extract the
interdependence of the variables, e.g., the binding afnity.
Partial least squares (PLS) analysis is astatistical method
that extracts relevant and explanatory factors, called PLS
vectors, from large amounts of data. In CoMFA analysis,
these vectors describe the range of elds that best correlate with the experimentally determined afnity. The
result is an equation analogous to the results of classical
QSAR methods. This equation shows to what extent par-
ticular grid points in each of the individual elds contribute to the relative differences in the binding afnities.
Depending on how many grid points are to be evaluated
in the analysis, the statistical signicance of the derived
results must be strictly monitored. This signicance is
checked by aspecial test: the cross validation.
This is done by randomly removing one or more
compounds from the dataset. Amodel is constructed
with the remaining compounds of the dataset and the
afnities of the removed compounds are predicted with
this model. The removal of compounds is repeated several times, in the simplest case until all compounds have
been removed consecutively one after the other (leave-
one-out validation). The quality of the overall prediction
obtained is ameasure of the reliability and validity of the
model. The obtained result is expressed by the q2 value,
which can be calculated from the square of the deviation
from the predicted values. It takes values from −∞ to +1.
Avalue of +1 indicates aperfect model. All predictions
exactly match the measured binding afnities. There is no
deviation. Avalue of q2 = 0 indicates that the predictions
of the model are no better than no model at all; they are
as good as the average of all afnities. If q2 takes negative
values, the model is worse than the average, which means
it is worse than no model. Therefore, amodel can only be
trusted if q2 is above 0.4–0.5.
Afurther step is required to check the predictive power
of atrained model (one speaks of a “trained” model
and, therefore, one refers to the dataset as a“training
dataset”). This validation step requires atest dataset of
molecules that are similar to the molecules in the training
dataset, but were not used for the initial training. Binding
afnities are predicted for these molecules. Only if acorrelation coefcient, calculated analogously to q2 with the
training dataset, is of similar magnitude will the derived
model have sufcient predictive power. This procedure is,
therefore, atest of the model on independent data that
were not used to derive the model.
The derived model can then be used to estimate the
afnity of new compounds that have not yet been synthesized. The conformations of these compounds are
generated and superimposed on the other structures.
They must fall within the grid dimensions dened in the
training set. Then their eld contributions are calculated.
Using the correlation derived by CoMFA for the training
set, it is possible to calculate which grid points are predictive of the binding afnity of new compounds.
CoMFA techniques establish acorrelation between
activity data and molecular properties. From the relative
comparison within atraining set, amodel can be derived
that encompasses the properties of new molecules. Relevant predictions will only be obtained if the structural
variations in the new molecule remain within the scope
of the model. In other words, the model cannot make
predictions about the inuence of substituents that occur
in regions where there were no structural variations in the
. • Scope, Limitations, and Possible Expansions of the CoMFA Analysis
training set. CoMFA models interpolate between the eld
contributions of molecules. Extrapolation to regions not
covered by the dataset is not possible.
The results of aCoMFA analysis can be evaluated
graphically, which is probably the most powerful aspect
of the method for the medicinal chemist. From the model
and the derived equation, it is known at which grid points
eld contributions are obtained that provide asignicant
explanation for the binding afnity. These contributions
can be outlined for the different elds according to their
importance. They indicate volume regions around the
molecules in which changes in the eld contributions run
parallel or opposite to the afnity changes in the dataset. These contour maps are an important support in the
design of new compounds (Sect.18.14). They indicate
where the properties of alead structure need to be varied
to increase afnity.
18.13 Scope, Limitations, and Possible
Expansions of the CoMFA Analysis
Typically, only steric and electrostatic eld contributions
are evaluated in CoMFA analyses. Ahydrophobic eld
can quantify the size of the hydrophobic surfaces and,
therefore, partially accounts for the entropic contribution
to afnity. Since CoMFA evaluations yield relevant models without the explicit use of hydrophobic elds, these
eld contributions must be at least partially included in
the Lennard-Jones and Coulomb elds. The lipophilicity of amolecule increases when an uncharged, sterically
demanding group is enlarged, e.g., from methyl to butyl. Here the changes in the steric eld contributions can
correctly reect the lipophilic surface. Acorrelation with
electrostatic properties is also possible. Hydrophobic moieties usually carry only small partial charges. Positively or
negatively charged groups represent hydrophilic regions.
In this way, the lipophilic and hydrophilic regions of the
surface can be quantied by differences in charge.
The deviation that cannot be explained by aCoMFA
model also includes, apart from experimental errors, all
inadequately described binding contributions. These include structural adaptations of the protein that are not
identical for all compounds in the dataset. Entropic contributions resulting from (i)conformational xation of
the drug molecules in the binding pocket, (ii)residual
mobility of the ligands in the binding pocket, (iii)con-
formational changes on the side of the protein, (iv)or
signicant differences in the solvate structure remaining on the protein are all not considered in the elds. It
should be noted, however, that such deviations will only
be signicant if they differ from one ligand to another
in the dataset.
In addition to these shortcomings, the elds themselves cause some problems. Because of their mathematical functional form, they reach very large or very
small values near the surface or inside the molecules
(. Fig. 18.5). Because the Lennard-Jones potential
grows faster than the Coulomb potential as it approaches the atoms, they both reach an arbitrarily set
cutoff (Sect.18.10) at different distances from the molecules. The extremely steep Lennard-Jones potential can
change its functional values from practically zero to the
cut-off value within adistance of 2 Å, the commonly
used grid spacing! These discontinuities and accordingly
the lack of any variation within the cut-off regions near
the ligand surfaces cause considerable problems in the
evaluation. Moreover, they often produce “disrupted”
and, therefore, difcult to interpret contour maps of the
different elds.
The shortcomings of these elds have stimulated the
search for alternative solutions. One method is to determine the similarity of molecules by their steric and physicochemical properties in space. These are then correlated
with binding afnities. This is done in the CoMSIA
method (comparative molecular similarity indices anal-
ysis). The molecules are superimposed similarly to the
CoMFA method. How similar they are to one another is
then measured in relation to aprobe, for example, acarbon atom. For each molecule, the similarity to this probe
is sampled at the intersections of asurrounding grid. The
similarity measure between the probe and the molecule
is dened as adistance-dependent function. AGaussian
function is chosen for this purpose (. Fig.18.5). Unlike
the hyperbolic course of the potentials described above,
the Gaussian bell curve does not tend to innity for decreasing distance values. Therefore, no cutoff values need
to be set. At any grid point, asimilarity measure can be
determined for alarge number of properties. Aprerequisite for the CoMSIA method is the description of the
properties in terms of atomic values (e.g., partial charges,
atomic volumes, lipophilicity, H-bonding properties).
The same distance dependence is used for all properties. Property-specic similarity elds are obtained and
correlated with the binding afnities. The interpretation
of the eld contributions is analogous to the CoMFA
method. The main advantage of this method is the ease
of interpretation of the resulting contour maps. If aparticular property in aregion of the superimposed molecules
correlates signicantly with binding afnity, that region
will be highlighted and this information can be easily
translated into the design of anew molecule. In contrast,
the CoMFA method contours only regions outside the
molecules where aproperty reveals changes in eld contributions that positively or negatively affect afnity.
However, by setting cut-off values, entire regions of these
eld contributions are hidden, especially near ligand surfaces (. Fig.18.5). Instead, the CoMSIA approach also
contours at the atomic positions of the molecules that
are responsible for the trends in the afnity changes. This
gives medicinal chemists amuch more intuitive picture
of where to modify molecules in an optimization process.
Chapter • Quantitative Structure–Activity Relationships
18
3D-QSAR analyses were originally designed to establish structure–activity relationships in cases where
the structure of the target protein was not available as
areference. Today, as more and more crystal structures
of target proteins become available, the technique is increasingly used for cases where the protein reference is in
deed known. The protein structure then helps to generate
areasonable and relevant superposition of the training
compounds to be compared in their biologically active
conformations. It seems paradoxical to use the information about the protein environment only to superimpose
the molecules and then to disregard this valuable data in
the comparative eld analysis. Therefore, methods have
been developed that take this information into account.
Rebecca Wade’s group at the EMBL and HITS in Heidelberg, Germany, has developed the COMBINE method.
It uses aset of modeled protein–ligand complexes to calculate adata table. It contains the interaction energies
between individual ligand atoms in the test molecules
of the dataset and the amino acid residues and water
molecules in the surrounding protein. The interpretation
of this huge data table is achieved using atechnique similar to CoMFA methods. The graphical interpretation of
the correlation model obtained by COMBINE indicates
which regions of the protein are critical in explaining the
afnity differences in the ligand dataset. These are very
valuable details, but they are of limited help for the direct
design of improved molecules with higher afnity.
The variant AFMoC (adaptation of fields for mo-
lecular comparison), developed by Holger Gohlke in
Marburg, allows the integration of information about
the protein environment into the eld-based model.
The advantages of the intuitive evaluation of the eld
contributions with regard to the structural optimization
of the ligands are not lost. To this end, the empirical
scoring function DrugScore (Sect.17.10) is rst used to
map onto the intersections of aCoMFA-like grid the
values that aprotein environment would sense as an
interaction at each of the grid points if sampled with
aparticular atomic probe according to the functional
form in DrugScore. The lattice is effectively “prepolarized” by the protein environment. The ligands of the
training set are then placed on this lattice (using either
adocking program or one of the superposition methods
described). Whenever aligand places an atom type on
aregion of the lattice where the protein considers that
atom type to be advantageous, the eld contribution is
increased. Otherwise, the interaction contribution on the
lattice is reduced. In this way, adata table analogous to
the CoMFA method is created for the entire training
dataset. This table is then evaluated to generate aQSAR
equation. The individual contributions can be graphically visualized on the grid. They illustrate where certain
atom types cause an increase or decrease in afnity, but
now taking into account simultaneous information from
the surrounding protein.
Similar eld analyses can also be used to correlate
and predict selectivity differences between ligands. Many
enzymes exist as isoforms. They therefore have similarities in their binding pockets. As aconsequence, ligands
show graduated afnities or “selectivity proles” for
-
these isoforms. If aligand is to be optimized to improve
selectivity, the positions at which achange in aproperty leads to an improved prole must be known. A3D
QSAR model is constructed for each isoenzyme. Either
the difference of the afnity values can be calculated and
used for the model as the values to be predicted, or alternatively two correlation models can be constructed
and the eld contributions at each grid point are sub-
tracted from one another. The models obtained from
either approach can be interpreted graphically. Contour
plots show where and how molecules should be modied to improve their selectivity for one or the other
isoenzyme.
18.14 A Glimpse Behind the Scenes:
Comparative Molecular Field
Analysis of Carbonic Anhydrase
Inhibitors
Today, comparative eld analyses are part of the standard repertoire in drug discovery. As an example, the
binding of inhibitors to carbonic anhydraseI (CAI)
andII (CAII) will be examined. The biological function of these enzymes is described in detail in Sect.25.7.
The sequence identity of the two isoforms is 60%. The
ligands in the training dataset are derived from the parent
structures shown in . Fig.18.6. First, asuperposition
model is created by docking the ligands into the protein (. Fig.18.7). The funnel-shaped binding pocket of
the enzyme is occupied by ligands in avariety of ways.
A good correlation model is obtained with the three
methods, CoMFA, CoMSIA, and AFMoC. The models
also achieve convincing predictive power on atest dataset
independent of the training set.
The contours for the acceptor properties with respect
to the inhibition of carbonic anhydraseII are shown in
. Fig.18.8. Molecules in the dataset that exhibit an ac-
ceptor function in the areas marked in red have lower
potency. On the other hand, an acceptor function in the
blue area improves potency. Compound 18.2, which has
both acceptor functions of an SO2 group oriented in the
detrimental red area, is aweak CAII inhibitor. Moreover
its NH group is in the blue region, which should be occupied by an acceptor. Compound 18.3, which is about
four orders of magnitude more potent, leaves the area
that was occupied by the oxygen atoms in 18.2 empty,
and orients its thiadiazole ring in the direction of the desirable acceptor function. It achieves considerably better
inhibition of the target enzyme.
. • A Glimpse Behind the Scenes: Comparative Molecular Field Analysis of Carbonic Anhydrase Inhibitors
. Fig. 18.6 The scaffolds of inhibitors that were used in different eld analyses to establish afnity (pKi[CAII]) and selectivity models (pKi[-
CAII] − pKi[CAI] = ∆pKi[CAII − CAI]) to describe the inhibition of the carbonic anhydrase CAI and CAII. Different substituents were varied
at the positions that are marked as R1 and R2
. Fig. 18.7 The superposition of inhibitors from the dataset in the
funnel-shaped binding pocket of carbonic anhydraseII; the zinc ion is
shown as the blue-gray sphere, carbon atoms are light yellow, oxygen
red, nitrogen blue, sulfur orange, and hydrogen light cyan
As with acceptor properties, contour maps can be
generated for steric, electrostatic, hydrophobic, and hydrogen-bond donor properties. Their evaluation helps
to identify where certain properties improve or decrease
binding afnity. Such correlation analyses help the synthetic chemist plan the optimization of lead structures.
Contour maps based on a CoMFA analysis for steric
properties that cause adifference in selectivity between
CAI and CAII are shown in . Fig.18.9. Placing an inhibitor next to the green areas improves selectivity for
. Fig. 18.8 Contour map (CoMSIA) for the description of the bind-
ing contributions of H-bond acceptor properties. Inhibitors that occupy the red-contoured areas with H-bond acceptor groups do not
inhibit carbonic anhydraseII (CAII) well; however, the occupancy of
the blue areas with acceptor groups leads to increasing values. Both
oxygen atoms of the sulfonamide group of 18.2 occupy the red-contoured area, which is unfavorable for acceptor properties. On the other
hand, 18.3 leaves these areas unoccupied and places its basic nitrogen
in the vicinity of the blue-contoured region, which is favorable for the
occupancy by acceptor groups. This explains the markedly better inhibition of CAII by 18.3
CAI. On the other hand, occupancy next to the yellow
areas improves the selectivity for CAII. Compound
18.4 binds unselectively with the same afnity to both
isoforms, but 18.5 can clearly discriminate between the
two. The model shown is derived purely from the correlation of ligand-binding data. The relative alignment of
the molecules in the dataset is achieved by docking into
Chapter • Quantitative Structure–Activity Relationships
18
. Fig. 18.9 Top left The selectivity can be improved with regard to
carbonic anhydraseII (CAII) inhibition by sterically lling the region
next to the yellow-contoured area. Filling the green area with sterically
demanding group causes an increase in selectivity with regard to CAI.
Top left and top right Compound 18.4 occupies virtually no area that
is particularly selectivity discriminating; the compound is not isoen-
the binding pocket of the protein. Therefore, the protein
environment around this binding pocket should be examined more closely to see whether the derived contours
are reasonable. Comparing the amino acid replacements
between the two isoforms, it is apparent that CAI has
two large residues, Phe91 and Leu 131, which restrict
the lower left portion of the binding pocket more than in
CAII. The inhibitors have less space in CAI than in CAII.
In fact, the comparative eld analysis generates ayellow
contour in this region (near position91), the occupancy
of which should be favorable for potent inhibition of
CAII. CAII also provides alarge space for inhibitors
near position 204, which is occupied by the less crowded
Leu 204 in CAII instead of Tyr 204 in CAI. Ayellow
contour is visible, indicating a favorable occupancy of
this site. Inhibitor 18.5, which is much more potent at
zyme specic. On the other hand, 18.5 occupies ayellow-contoured
area neighboring position 204 (bottom left), which causes aselectivity
enhancement for CAII. Compound 18.5 inhibits CAII decidedly more
potently than CAI. All contour maps shown are based on data analysis by CoMFA; the adjacent protein residues were added to the images
from corresponding crystal structures
CAII, orients its pentauorophenyl group exactly in this
region (. Fig.18.9, right). In the vicinity of position 131
(Leu 131/Phe 131), ayellow and agreen region appear
directly adjacent to each other but spatially separated, the
occupancy of which is favorable for either CAI or CAII
inhibitors. Compound 18.4, which can hardly distinguish
between the two isoforms, occupies the upper edge of
both regions equally well. Moreover, it leaves virtually all
regions unoccupied which, for steric reasons, should lead
to abetter inhibition of either CAI or CAII. This explains
why this compound shows no particular selectivity.
Finally, the binding of the well-discriminating compound 18.6 should be considered (. Fig.18.10). The
evaluation of the acceptor properties of the ligands in
the training dataset shows that the occupancy of the red
contour regions with H-bond acceptor groups shifts the
. • Synopsis
. Fig. 18.10 Compound 18.6 inhibits carbonic anhydraseI (CAI;
left, green) signicantly less potently than CAII (right, yellow). The
sulfone oxygen atom on the left hand side (circled in magenta) falls
close to ared contoured area (occupancy with H-bond acceptor group
favorable), the lling of which causes an increase in the selectivity for
CAII binding. Interestingly, Gln 92 is found in this region in both
isoforms. However, it is only in CAII that this group is available to
selectivity in favor of CAII. Filling the blue contours with
this property results in an increase in potency with respect
to CAI. Compound 18.6 places its oxygen atoms of the
endocyclic SO2 group near the red CAII selective regions.
Again, it is important to note that the model shown is derived purely from the correlation of ligand-binding data,
and that in the following the information from the protein
isoforms is used only to understand the obtained correlation model. The protein structures show aglutamine residue adjacent to position92 in both CAI and CAII. This
amino acid can accept an H-bond from the inhibitor via
the NH2 group of its carboxamide group. However, only
CAII allows these structural conditions. Gln92 is adjacent
to Asn69 and Glu58 in CAI. The carboxamide group of
Glu92 forms acontinuous H-bonding network with these
residues and with His94. Therefore, the NH group is no
longer available for interactions with abound inhibitor.
This is reected in the lower binding afnity of inhibitors
that have an acceptor function at this position, such as
18.6. The situation is completely different for CAII. The
adjacent functional groups of Glu69 and Arg58 form an
internal salt bridge. Therefore, they are not available as
accept an H-bond from the inhibitor that will contribute to binding
afnity (right, black dotted line). The comparable residue Gln92 in
CAI is involved in anetwork of H-bonds to neighboring amino acids
Asn69 and Glu58. Therefore, it is not available as abinding partner,
and adecrease in the afnity for CAI is the consequence. The shown
contour maps are based aCoMSIA analysis and superimposed onto
crystal structures of CAI and CAII
H-bonding partners for Gln92. The carboxamide group
of Gln92 involves His94 in an H-bond via its carboxamide CO group, and its NH2 group is now available as
an acceptor functionality to interact with abound ligand.
This results in signicantly enhanced binding to CAII and
is expressed as aselectivity advantage. It is important to
remember that the applied CoMSIA analysis was trained
only on ligand-binding data. Therefore, it did not know
anything about the properties of the neighboring amino
acids. Nevertheless, it produces acorrelation model that
can be easily explained by the unexpected binding behavior of the adjacent amino acids in the two isoforms.
18.15 Synopsis
The concept of quantitative structure–activity re-
-
lationships is not new. It was rst described in the
nineteenth century qualitatively, and later more quan-
titatively by Hansch and Fujita. It is an attempt to
describe structure–activity relationships with mathe-
matical models.
Chapter • Quantitative Structure–Activity Relationships
18
Across aseries of structurally closely related test com-
-
pounds, the equieffective dose that induces aparticular biological effect is related in alinear or squared
dependence on the logarithm of the octanol/water
partition coefcient and the Hammett constant,
which describes the electronic properties of substituents at agiven scaffold. Amathematical correlation
model is computed by regression analysis.
3D QSAR methods have been developed to consider
-
and correlate the spatial structure of active substances beyond molecular topology.
The mutually aligned test molecules are embedded
-
in aregularly spaced grid and their properties are explored with an interaction probe. The probe is placed
systematically at all grid points and amolecular interaction eld is computed around the aligned molecules
by using adistance-dependent property potential.
Usually, Lennard-Jones and Coulomb potentials are
-
evaluated, and the generated data table for all molecules of the training dataset is correlated by the partial least squares technique.
The derived CoMFA correlation model can be used
-
to predict the biological properties of novel ligands
not included in the training dataset. Strict criteria to
monitor the statistical signicance of the derived correlations must be met.
Other property elds beyond Lennard-Jones and
-
Coulomb potentials with mathematically different
functional forms can be applied. With respect to the
prediction of binding afnity, it has to be regarded
that hydrophobic properties also implicitly reect an
entropic contribution to binding that is particularly
difcult to regard in property elds.
QSAR analysis only performs arelative comparison
-
of molecules with regard to the considered biological
property. Any dependence on aparticular descriptor across acompound series can only be expected
if the property related to this descriptor is varied in
the series. QSAR methods only interpolate and never
extrapolate beyond the scope of molecular properties
reected by the training set.
Anumber of alternative 3D QSAR approaches have
-
been developed. They use different types of elds
(e.g., for similarity analysis, CoMSIA) or try to incorporate protein information into the analysis (COMBINE and AFMoC).
Comparative molecular eld analyses can be evaluated
-
graphically. Results are displayed as contours around
the molecules and indicate where the change of aparticular property runs either parallel or opposite to the
changes in the biological property in the dataset.
The graphical information can be directly translated
-
into the design of modied molecules and, thus, support the medicinal chemist in optimizing agiven lead
structure in asystematic fashion.
Bibliography and Further Reading
General Literature
C. A. Ramsden, Eds., Quantitative Drug Design, Vol. 4: Comprehen-
sive Medicinal Chemistry, C. Hansch, P. G. Sammes and J. B. Tay-
lor, Eds., Pergamon Press, Oxford (1990)
H. Kubinyi, QSAR: Hansch Analysis and Related Approaches, VCH,
Weinheim (1993)
H. van de Waterbeemd, Chemometric Methods in Molecular Design,
VCH, Weinheim (1995)
H. van de Waterbeemd, Advanced Computer-Assisted Techniques in
Drug Discovery, VCH, Weinheim (1995)
C. Hansch and A. Leo, Exploring QSAR. Fundamentals and Appli-
cations in Chemistry and Biology, 2 Volumes, American Chemical
Society, Washington (1995)
H. Kubinyi, Ed., 3D-QSAR in Drug Design: Theory, Methods, and
Applications, ESCOM, Leiden (1993)
H. Kubinyi, G. Folkers and Y.C. Martin, 3D QSAR in Drug Design,
Vol. 1–3, Kluwer/ESCOM, Dordrecht, Boston, London (1998)
Special Literature
S. H. Unger and C. Hansch, On Model Building in Structure-Activity
Relationships. A Reexamination of Adrenergic Blocking Activity
of β-Halo-β-arylalkylamines, J. Med. Chem., 16, 745–749 (1973)
J. M. Blaney, C. Hansch, C. Silipo and A. Vittoria, Structure-Activity
Relationships of Dihydrofolate Reductase Inhibitors, Chem. Rev.,
84, 333–407 (1984)
C. Hansch and T. E. Klein, Quantitative Structure-Activity Relation-
ships and Molecular Graphics in Evaluation of Enzyme-Ligand
Interactions, Methods Enzymol.,202, 512–543 (1991)
H.-D. Höltje and L. B. Kier, Nature of Anionic or α-Site of Cholines-
terase, J. Pharm. Sci., 64, 418–420 (1975)
R. D. Cramer and M. Milne, Abstracts of the ACS Meeting, April
1979, COMP 44
R. D. Cramer, D. E. Patterson, and J. D. Bunce, Comparative Molecu-
lar Field Analysis (CoMFA). 1. Effect of Shape on Binding of Ste-
roids to Carrier Proteins, J. Am. Chem. Soc., 110, 5959–5967 (1988)
S. A. DePriest, D. Mayer, C. B. Naylor and G. R. Marshall, 3D-QSAR
of Angiotensin-Converting Enzyme and Thermolysin Inhibitors:
A Comparison of CoMFA Models Based on Deduced and Exper-
imentally Determined Active Site Geometries, J. Am. Chem. Soc.,
115, 5372–5384 (1993)
P. J. Goodford, A Computational Procedure of Determining Energet-
ically Favorable Binding Sites on Biologically Important Macro-
molecules, J. Med. Chem., 28, 849–857 (1985)
G. E. Kellogg and D. J. Abraham, Key, Lock and Locksmith: Comple-
mentary Hydropathic Map Predictions of Drug Structure from a
Known Receptor-Receptor Structure from Known Drugs, J. Mol.
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T. Hüfner-Wulsdorf and G. Klebe, Protein-Ligand Complex Solva-
tion Thermodynamics: Development, Parameterization and Test-
ing of GIST-based Solvent Functionals, J. Chem. Inf. Model., 60,
1409–1423 (2020)
G. Klebe, U. Abraham and T. Mietzner, Molecular Similarity Indices
in a Comparative Analysis (CoMSIA) of Drug Molecules to Cor-
relate and Predict Their Biological Activity, J. Med. Chem., 37,
4130–4146 (1994)
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. • Bibliography and Further Reading
A. Weber, M. Böhm, C. T. Supuran, A. Scozzafava, C. A. Sotriffer and
G.Klebe, 3D QSAR Selectivity Analyses of Carbonic Anhydrase
Inhibitors: Insights for the Design of Isozyme Selective Inhibitors,
J. Chem. Inf. Model., 46, 2737–2760 (2006)
A. Hillebrecht, C. T. Supuran and G. Klebe. Integrated Approach Us-
ing Protein and Ligand Information to Analyze Afnity and Selectivity Determining Features of Carbonic Anhydrase Isozymes,
ChemMedChem, 1, 839–853 (2006)
From In Vitro to In Vivo:
Optimization of ADME and
Toxicology Properties
Contents
19.1 Rate Constants of Compound Transport – 292
19.2 Absorption of Organic Molecules: Model
and Experimental Data – 294
19.3 The Role of Hydrogen Bonds – 294
19.4 Distribution Equilibria of Acids and Bases – 295
19.5
19.6 What Is the Optimal Lipophilicity of aDrug? – 298
19.7 Computer Models and Rules to Predict ADME
19.8 From In Vitro to In Vivo Activity – 300
19.9 Compartmentalization: Natural Ligands
19.10 Specicity and Selectivity of Drug Interactions – 301
19.11 Of Mice and Men: The Value of Animal Models – 302
19.12 Toxicity and Adverse Eects – 304
19.13 Animal Protection and Alternative Test Models – 306
19.14 Synopsis – 306
Absorption Proles of Acids and Bases – 296
Parameters – 299
Are Often Unspecic – 300
Bibliography and Further Reading – 308
© The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature 2024
G. Klebe, Drug Design, https://doi.org/10.1007/978-3-662-68998-1_19