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11.3.8 Urokinase
Urokinase is an activator of plasminogen. Its inhibitors can inhibit tumor metastasis
and slow cancer growth. However, inhibitors of urokinase usually contain a highly
basic amidine or guanidine group (pK
a
> 9), and this positively charged moiety is
unfavorable for bioavailability. A previously discovered inhibitor (81) exhibits a K
i
of
0.03 mM but with no oral bioavailability (Scheme 11.13). X-ray crystallographybased screening (CrystaLEAD) identified 82 with a K
i
of 56 mM(pKa¼ 7.3). An
overlay of the crystal structures of 81 and 82 in complex with urokinase revealed that
82 bound to the same site as the naphthyl moiety of 81. The merging of 82 with 81
76
NN
NH
Cl
77
NN
N
S
Cl
H
IC50=400µM
IC
50
=approximately1mM
78
NN
NN
Cl
IC50=330µM
S
MeO
HN
NH
2
HO
O
O
79
IC50=12µM
S
MeO
HN
NH
HO
O
O
N
Cl
N
N
N
80
IC50=1.4 nM
Scheme 11.12 X-ray crystallography-based fragment screening (pyramid) for thrombin
inhibitors.
Ki=56µM
82
Ki=0.37µM
38% or al biaovailab ility
83
+
K
i
=0.03µM
not orally bioavailable
81
NH
NH
2
NH
N
N
NOHNH
2
N NH
2
NH
N
N
Scheme 11.13 X-ray crystallography-based screening (CrystaLEAD) for urokinase
inhibitors.
CASE STUDIES OF FRAGMENT-BASED SCREENING FOR BETTER BIOAVAILABILITY 441
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generated 83, which exhibited a dramatic improvement in oral bioavailability relative
to 81 [61].
With the same target, an NMR screen (SAR by NMR) of more than 3000
compounds led to the identification of 84 as a weak (IC
50
¼ 200 mM) but competitive
inhibitor that binds to the same site on urokinase as the traditional amidine or
guanidine derivatives (Scheme 11.14). The compound is less ionized at physiological
pH (pK
a
¼ 7.5). A fragment evolution led to 85 with an IC50of 10 mM and a pKaof
7.4 [133].
11.3.9 Cathepsin S
Cathepsin S degrades the major histocompatibility complex class II-associated
invariant chain that is required for productive loading of antigen onto this complex.
Inhibition of cathepsin S can attenuate antigen presentation in autoimmune disease.
Oral bioavailability of antiautoimmune agents is an important consideration in
inhibitor design due to the long-term treatment. SAS was employed to identify
nonpeptidic cathepsin S inhibitors (Scheme 11.15). The N-acyl aminocoumarin
library was designed, and 86 was recognized by the enzyme as a substrate (k
cat
/
K
m
¼ 1). The structural optimization of 86 led to 87 with a much better binding affinity
(k
cat/Km
¼ 8200). The replacement of the aminocoumarin moiety by a hydrogen atom
provided aldehyde 88 that exhibited a K
i
of 9 nM [85a]. Replacement of the aldehyde
with a nitrile moiety led to 89 with a K
i
of 420 nM. The structural optimization of 87
led to a better substrate (90; k
cat/Km
¼ 27,000). The corresponding nitrile derivative
(91) has a K
i
of 15 nM [134].
SAS also identified 92 as a primary hit (Scheme 11.16). The optimization of 92 led
to 93 and 95. The conversion of the substrates into inhibitors by SAS generated 94 and
96. Nonpeptidic 96 has a K
i
of 9.6 nM [135].
11.3.10 Caspase-3
Caspases (cysteinyl aspartate-specific proteases) play key roles in both cytokine
maturation and programmed cell death (apoptosis). Caspase-3 lies at a key junction in
the apoptotic cascade. The inhibition of caspase-3 has been investigated as a valuable
therapeutic approach for the treatment of diseases ranging from Alzheimer’s and
Parkinson’s to myocardial infarction and sepsis. As do all members of the caspase
family, caspase-3 has a linear peptide-binding site that nearly accommodates four
IC50=10µM
pK a=7.4
85
IC50=200µM
pK a=7.5
84
N
H
N
NH
2
N
H
N
NH
2
HO
Scheme 11.14 NMR screen (SAR by NMR) for urokinase inhibitors.
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residues. Not surprisingly, most inhibitors of caspase-3 are linear peptides or
peptidomimetics, which are not bioavailable. In situ fragment assembly using
tethering with extender was conducted to identify reversible small-molecule inhibitors (Scheme 11.17) [105a]. An irreversible tetrapeptide inhibitor 97 was used as
a starting point. A derivative of 97 (98) was treated with the enzyme to generate 99.
The deprotection of the thioester moiety generated extender 100, which was used to
N
O
N
H
O O
O
OH
N
N
NH
O
S
N
O
N
H
O
O
O
OH
N
N
86
87
k
cat/Km
=1.0
k
cat/Km
=8200
N
O
N
N
NH
O
S
H
88
K
i
=9nM
N
N
N
NH
O
S
N
89
Ki=420nM
N
O
N
H
O O
O
OH
N
N
NH
O
S
90
k
cat/Km
=27000
N
N
N
NH
O
S
N
91
K
i
=15nM
Scheme 11.15 Substrate activity screening (SAS) for cathepsin S inhibitors.
O
N
H
O O
O
OH
O
Cl
Cl
92
k
cat
/Km=1.0
O
N
H
O O
O
OH
O
93
k
cat
/Km= 2700 0
F
F
F
O
O
94
Ki=490nM
F
F
F
H
O
N
H
O O
O
OH
O
F
F
95
k
cat
/Km=288000
O
O
F
F
H
96
K
i
=9.6nM
Scheme 11.16 Substrate activity screening (SAS) for cathepsin S inhibitors.
CASE STUDIES OF FRAGMENT-BASED SCREENING FOR BETTER BIOAVAILABILITY 443
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interrogate fragment libraries (tethering). A salicylic acid was identified as important
fragment hit 101. The resulting molecule 102 exhibited a K
i
of 2.8 mM. Simple
conversion of the flexible linker in 102 to rigid aromatic linker 103 increased the
potency by more than one order of magnitude. The functionalization of 103 afforded
104, which added another order of magnitude in increased affinity [105b]. Compound
105 shows another way to increase the binding affinity (K
i
¼ 70 nM) by addition of
a phenyl group onto the linker [136]. Both 104 and 105 are small-molecule inhibitors
that could be developed into drug candidates with better bioavailability compared
with peptides or peptidomimetics. The simple salicyclic acid fragment 106 does not
inhibit the enzyme demonstrating that this fragment would not be detected by other
fragment-based technologies, supporting the advantages of tethering with extender.
11.3.11 HIV-1 Protease
HIV-1 protease has been recognized as an important target for anti-AIDS treatment.
Although several inhibitors have been approved, the alarming rate at which strains of
HIV-1 are resistant to the currently available drugs underscores the urgent need for
new inhibitors that are effective against the new mutants as well as the wild-type virus.
In situ click chemistry has been applied to discover new nonpeptidic compounds that
inhibit HIV-1 protease (Scheme 11.18) [113]. Azide (107,IC
50
¼ 4.2 mM) was
O
Cl
O
Cl
O
H
N
N
H
O
HO2C
O
H
N
O
N
H
CO2H
HO2C
O
O
Cl
OCl
O
H
N
S
O
CO
2
H
CO
2
H
CO
2
H
CO
2
H
CO
2
H
CO
2
H
O
9897
SEnzyme
O
H
N
S
O
O
CO2H
SEnzyme
O
H
N
SH
O
CO2H
S
Enzyme
O
H
N
S
O
CO
2
H
S
N
H
S
O O
CO2H
OH
SEnzyme
O
N
H
S
O O
CO2H
OH
H
O
H
N
O
N
H
S
O O
OH
OH
H
O
H
N
O
N
H
S
O O
OH
H
O
H
N
O
N
H
S
O O
H
O
H
N
O
CO2H
N
H
S
O O
CO2H
OH
N
N
99
100
extender
exte nder
fragment
101
106
no detectable binding
102
Ki200 µM=
105
K
i
nM70=
103
104
KinM20=
performto
tethering
Cl
Scheme 11.17 In situ fragment assembly using tethering with extender for caspase-3
inhibitors.
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incubated with alkynes (108–112,IC50of all of these alkynes >100 mM) in the
presence of HIV protease. Only 109 was selectively picked up by the enzyme to
react with 107 to generate 113 (IC
50
¼ 6 nM, Ki¼ 1.7 nM). This strategy provides
the possibility to generate other bioavailable nonpeptidic inhibitors for HIV-1
protease.
11.4 DE NOVO DESIGN
In addition to experimental screening approaches, computational methods have also
shown value in fragment-based drug discovery. De novo ligand design is
a computation-based approach to design bioactive compounds that do not exist in
known compound libraries. It, therefore, provides an opportunity to utilize other areas
of chemical space. De novo design has been proposed since the 1980s. To date, 45
computation-based de novo design programs have been reported [137]. Primarily, de
novo design can be divided into receptor- and ligand-based approaches. In the former
case, the 3D structure of the receptor is known or can be modeled by homology
modeling, and the de novo design is based on the structural information of the target.
In the latter case, the structure of the target is unknown, and the pharmacophore
information of ligands is used to guide the design of new structures.
Five different approaches have been developed for the receptor-based de novo
design according to the method of structure sampling [137a]: (1) plan ar structure
107
IC50=4.2µM
S
N
N
3
OH
O
O
O
O
O O
O N
H
O
OH
O N
H
O
O
O
OH
N
H
O
O
N
N
O
109
IC50>100µM
108
IC50>100µM
110
IC50>100µM
111
IC50>100µM
112
IC50>100µM
+
S
N
N
OH
O
O
O
N
N
O
O
HN
HO
113
IC50=6nM
K
i
=1.7nM
Scheme 11.18 In situ click chemistry for HIV-1 protease inhibitors.
DE NOVO DESIGN 445
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fitting, (2) atom or fragment growing, (3) fragment linking, (4) target protein latticebased sampling, and (5) molecular dynamics simulation-based sampling. The
corresponding de novo design programs are shown in Table 11.1.
Two approaches are used in ligand-based de novo design according to the method
of structure sampling: (1) topological molecular graphs-based sampling and (2)
molecular physicochemical properties-based sampling. The corresponding de novo
design programs are shown in Table 11.2.
In conventional computation-based de novo design strategies, the output structures
obtained from computer programs can be problematic with regard to synthetic
accessibility [183] and binding affinity predictions [184]. Analyses of conventional
computation-based de novo design techniques indicate that it is rare to generate
novel lead structu res with nanomolar activity initially [137b]. Synthetic feasibility
problems can be controlled during the buildup of ligands. This can be done by using
commercially available or easy to synthesize building blocks or by connecting them
via common reaction schemes. This feature has already been incorporated into more
TABLE 11.1. Receptor-Based De Novo Design Programs
Structure Sampling De Novo Design Programs
Planar structure fitting HSITE/2D [138]
Atom or fragment growing 3D Skeletons [139], LEGEND [140], LUDI [141],
GenStar [142], GroupBuild [143], SPROUT [144],
GrowMol [145], PRO_LIGAND [146], SMoG [147],
RASSE [148], PRO_SELECT [149], LigBuilder [150],
BREED [151], GROW [152], LeapFrog [153], Pellegrini
& Field [154], and BOMB [155]
Fragment linking LUDI, NEWLEAD [156], SPLICE [157], SPROUT,
MCSS/HOOK [158], PRO_LIGAND, LigBuilder,
BREED, CAVEAT [159], COREGEN [160], MCSS/
SEED/CCLD [161], FlexNovo [162], Flux/CATS [163],
and GRANDI [164]
Target protein
lattice-based sampling
Diamond Lattice [165], BUILDER [166], MCDNLG [167],
MCSS/DLD [168], SkelGen [169], ADAPT [170],
CLIX [171], and Chemical Genesis [172]
Molecular dynamics
simulation-based sampling
CONCEPTS [173], CONCERTS [174], DycoBlock [175],
and F-DycoBlock [176]
TABLE 11.2. Ligand-Based De Novo Design Programs
Structure Sampling De Novo Design Programs
Topological molecular
graphs-based sampling
Chemical Genesis, SkelGen, Nachbar [177],
Globus [178], TOPAS [179], CoG [180], and
BREED
Molecular physicochemical
properties-based sampling
LEA [181], Pellegrini & Field, SYNOPSIS [182],
and PRO_LIGAND
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recent de novo design programs such as CAESA for SPOUT [185], SEEDS for
LEGEND [186], RECAP [187], SYNOPSIS [182], and Flux [163a, 163b].
The prediction of binding affinity is a real problem [188]. De novo design utilizes
various scoring functions to evaluate each step of the design process. Unfortunately,
available scoring functions are limited in their abilities to accurately predict experimental binding affinities. Rigorous approaches based on free energy perturbation and
thermodynamic integration have been applied to predict binding energies [189].
However, the sampling and convergence problems prevent them from being used
routinely in ligand screening. Moreover, it is very difficult to handle large structural
diversity between ligands during screening, for example, in the case of completely
different core structures. Currently, the best that can be done is to model the
complexes in the presence of hundreds or thousands of explicit water molecules
using Monte Carlo (MC) statistical mechanics or molecular dynamics [190]. Several
semiempirical methods based on linear approximations to the free energy such as
linear response theory [191] and molecular mechanics/Poisson–Boltzmann surface
area [192] have been introduced and used with success.
In concomitance with the development of experimentally fragment-based screening, fragment-based de novo design has been an active field in de novo ligand design in
recent years. Two computation-based strategies for lead identification have been
extensively reported: one is in silico fragment screening and assembly, and the other
one is scaffold hopping.
11.4.1 In Silico Fragment Screening
In silico fragment screening is conceptually related to the experimental fragmentbased screening methodology. A typical protocol for in silico fragment screening
consists of docking a fragment into the binding site, choosing a best orientation, and
using it as the starting point for fragment evolution and fragment linking. The in silico
fragment screening has obvious advantages over experimental methods because they
are very fast, cost efficient, and much more applicable across a wide range of targets.
However, to reliably calculate the affinity of a fragment, its binding site and
binding mode need to be predicted correctly. Furthermore, a reliable scoring function
is required to score the protein–ligand complex. Both tasks are particularly challenging for fragments for two concerns (1) a weakly binding small fragment has more
possibilities of multiple binding modes to the receptor than a tightly binding ligand
that fits well in the binding pocket; (2) the existing scoring functions have been
elaborately calibrated with data from tightly binding ligands; therefore, the range of
prediction is outside the typical fragment-binding affinities, that is, in the range from
K
d
¼ 100 mMtoKd¼ 100 mM [193]. Shoichet and coworkers recently reported an in
silico screen of 300,000 fragments using DOCK3.5.54 to test the concerns of this
approach [164]. Among them, 69 top-ranking fragments were tested, and 10 inhibitors
in the millimolar range were successfully identified [194a]. An X-ray crystallographic
study showed that the docking poses matched very well with the experimentally
determined structures, which highlights the liability of the DOCK program in in silico
fragment screening.
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An integrated approach of experimental fragment-based screening and in silico
fragment-based screening offers huge advantages. An experimental investigation of
the affinity and the binding mode of a fragment with biophysics- or bioassay-based
methods can substantially increase the reliability of in silico design. These validated
binders can be used as the starting points for in silico de novo design. In silico design
can give quick feedback about the possible binding modes of fragments in the active
site, which helps to evaluate the proposed ligands and guide the hit-to-lead process.
Furthermore, the new design ligands can be verified experimentally.
Two different scenarios for the integrated in silico fragment design process and
experimental fragment-based screening are briefly outlined. The design cycle could
start either from an existing fragment library or from an in silico fragment screen,
which needs to be synthesized initially. The latter approach has the advantage that
novel starting points can be utilized, whereas the first approach relies on existing
molecules. The first approach is beneficial since experimental data (affinity and
structure) can be incorporated from the beginning, therefore increasing the reliability of the method. In both cases, the crucial step is the biological screening of
fragments, which will give direct input to the design cycle. These data not only allow
one to focus on the most promising fragments on which to follow up but also aid in
the calibration of the scoring functions. Alternatively, constraints derived from the
experimental data reduce the number of (possibly false) solutions generated by the
algorithm.
11.4.2 Scaffold Hopping
In drug design programs, biological active molecules often show severe limitations,
and this prevents these compounds from moving toward clinical development. Such
limitations can include an inherently low solubility, metabolic instability, or the
absence of binding selectivity. On the basis of the concept that biologically active
compounds for a specific target are discontinuous points in the vast chemical space,
scaffold hopping (also termed lead hopping, leapfrogging, scaffold searching, and
chemotype switching) has been proposed to identify those compounds that have
similar biological activities, but totally different scaffolds [195].
The starting point of scaffold hopping is the selection of a template structure
followed by hopping isofunctional, but structurally dissimilar, substructures (scaffolds) into different parts of the template structure. In contrast to de novo design,
which aims to generate entire ligands, scaffold hopping is an attempt to replace only
the core motif of a known ligand, while conserving key substituen ts. Scaffold hopping
can be viewed as a special case of de novo design.
Three different approaches for scaffold hopping have been proposed (1) pharmacophore-driven approach [196], (2) the use of reduced graphs for the identification of
scaffold [197], and (3) shape-based similarity searching [198]. The programs for
different categories of scaffold hopping are shown in Table 11.3.
In pharmacophore-driven scaffold hopping, various pharmacophore descriptors
have been proposed to determine the correlation between the newly generated
scaffold and the original scaffold. Beside the common 2D and 3D pharmacophoric
448 FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
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fingerprints [222], specific pharmacophore descriptors for scaffold hopping have been
designed, as shown in Table 11.3.
Reduced graph representation has been employed to identify related structural
classes, rather than related compounds. In this method, entire substructures of
a molecule, according to the defined scheme, are collapsed into single nodes resulting
in a reduced graph that is smaller and less complex, and only the important features of
a molecule are retained. This allows the identification of a diverse set of structures
starting from a single reference structure. There have been several programs proposed
to implement this general concept (Table 11.3). Among them, Recore is a reduced
graph-based method for identifying suitable scaffold based on the X-ray crystal
structure conformations that emphasize ligand similarity in the 3D space. Some
approaches that are at the interface of reduced graph and pharmacophore-driven
methods have also been proposed, including feature tree algorithm [223] and MEDSuMoLig [224].
Shape-based scaffold hopping has been intensively addressed recently. It has been
demonstrated that the use of shape and electrostatics for similarity searching is
superior to the traditional 2D fingerprints [225]. The important programs in this field
are summarized in Table 11.3. The important approaches in this field including
SQUIRREL take into account both molecular shape and potential pharmacophore
points [226].
These methods can decrease the risks of molecular construction or synthetic
accessibility, increase the hit rates for lead generation, and offer certain structural
diversity. However, the skeleton of the newly designed molecules is confined to the
basic architecture of the template structure, which usually comes from a known
drug or drug candidate. Moreover, mimicking the different parts of the template
structure with scaffolds often does not optimize the interaction between ligand and
receptor to the maximal extent, because the scaffold is quite large and rigidity of the
template structure sometimes does not allow an optimal match between ligand and
receptor.
TABLE 11.3 Scaffold Hopping Programs
Scaffold Hopping Programs
Pharmacophore-driven
approaches
CATS descriptors (CATS, CATS3D, and SURFCATS) [199],
Charge3D and TripleCharge3D [200], FEPOPS [201], the
Similog pharmacophoric keys [202], SQUID [199c, 203],
and LIQUID [204]
Reduced graph-based
approaches
Clique detection [205], ErG [206], centroid connecting
path [207], structural unit analysis [208], Recore [209]
Shape-based approaches GRID molecular interaction field-based approaches [210]
(such as MOLPRINT3D [211], FLAP [212], and
SHOP [213]), ROCS [214], FieldScreen [215], extended
electric distribution [216], field-based similarity
search [217], Surflex-Sim [218], Topomer [219],
KIN [220], and ParaFrag [221]
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11.5 CASE STUDIES OF DE NOVO DESIGN FOR BETTER
BIOAVAILABILITY
11.5.1 DNA Gyrase
DNA gyrase is a prokaryotic type-II DNA topoisomerase that is involved in the vital
processes of DNA replication, transcription, and recombination. Because there is no
direct mammalian counterpart, DNA gyrase is an attractive and well-established
antibacterial target. The enzyme consists of two subunits, A and B; subunit B catalyzes
the hydrolysis of ATP. The known classes of DNA inhibitors for the ATP recognition
site include coumarins and cyclothialidines. However, the coumarins suffer from
low membrane permeability, high toxicity, and a rapidly developing resistance. The
limitation on cyclothialidines is their rapid and extensive glucuronidation of the
essential phenol moiety. In silico fragment screening of an initial pool of 350,000
fragmentsrevealed600 compounds aspotential hits. High concentration assays of these
fragments were carried out, and analytical ultracentrifuge, surface plasmon resonance,
NMR,andX-ray crystallography wereusedto discard nonspecificinhibitorsand further
characterize useful fragments. Compounds 114 and 115 were identified as the primary
fragment hits (Scheme 11.19). The maximal noneffective concentration (MNEC) of
115 is 41 mM. Structural optimization, guided by X-ray crystallography, led to the
discovery of 117 with an MNEC of 62 nM [81b]. This study is one of the earliest
examples using in silico fragment screening prior to an enzyme assay. This strategy
allowsan early focuson themost promising candidates in a compound library,increases
the hit rate, and is highly time- and cost-efficient. It has also been noted that fragmentbased drug discovery provides chemical starting points that do not have unnecessary
structural elements, and therefore reduces the risk of toxicity or metabolic instability.
11.5.2 Factor Xa
Factor Xa lies at the junction of the intrinsic and extrinsic pathways of the coagulation
cascade. It converts prothrombin into thrombin. The inhibition of factor Xa can offer
antithrombotic treatment. It has been claimed that factor Xa is a better antithrombotic
target than thrombin because there is evidence that factor Xa inhibitors may have less
propensity to cause bleeding side effects. Fragment-based de novo design by
115
MNEC = 41µM
Kd=10mM
N
H
N
N
H
N
S
O
O
N
H
N
S
O
O
N
H
N
114
116
117
MNEC = 25µM
MNEC = 62 nM
MNEC > 2 mM
Scheme 11.19 In silico fragment screening for DNA gyrase inhibitors.
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