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PRO_SELECTidentified 118 as the primary hit, which binds to the S1 pocket of factor
Xa (K
i
¼ 200 mM, Scheme 11.20). In silico screening of combinatorial fragment
libraries led to the discovery of 119 and 120. Both of these compounds have a K
i
of 16 nM, which is 10,000 times more potent than the initial fragment [149d]. A
combination of medicinal chemistry and structure-based drug design led to the
replacement of the benzamidine, a moiety that is associated with poor oral bioavailability, and provided LY517717 (in Phase 2 clinical trials). Th is is a remarkable
example demonstrating that fragment-based de novo design and in silico fragment
screening can lead to the discovery of a drug candidate.
11.5.3 X-Linked Inhibitor of Apoptosis Protein
The baculovirus IAP repeat 3 domain of the X-linked inhibitors of apoptosis protein
binds directly to the N-terminus of caspase-9, thus inhibiting programmed cell death.
The blockage of this interaction has an implication in anticancer therapy. It has been
found that in the cell this interaction can be displaced by the protein second
mitochondrial activator of caspases (SMAC) and that N-terminal tetrapeptides (AVPI)
of SMAC are responsible for this binding. The synthetic SMAC-derived peptides or
peptidomimetics as therapeutic compounds suffer from problems of cell permeability,
proteolytic instability, and poor pharmacokinetics. In silico screening of fragment
libraries of alanine derivatives identified 122 as a weak binder (K
d
¼ 200 mM,
Scheme 11.21). Further structural optimization led to 123 with a K
d
of 1.2 mM.
This compound exhibits cellular activity and has better human plasma stability and
metabolic stability compared to AVPI [227].
11.5.4 Activator Protein-1 [196b]
Activator protein-1 (AP-1) is a transcription factor that is responsible for the induction
of a number of genes involved in cell proliferation, differentiation, and immune and
119
Ki= 200µM
118
121
LY517717
Phase IIb
H2N NH
H2N NH
O
N
H
N
O
O
Ki=16nM
O
N
H
N
O
N
NH
N
120
H2N NH
O
N
H
N
O
N
Ki=16nM
Cl
Scheme 11.20 Fragment-based de novo design of factor Xa inhibitors.
CASE STUDIES OF DE NOVO DESIGN FOR BETTER BIOAVAILABILITY 451
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inflammatory responses. Inhibitors of AP-1 have been implicated in the treatment
of rheumatoid arthritis. On the basis of the pharmacophore of cyclic disulfide
decapeptide inhibitor 124 (IC
50
¼ 64 mM), scaffold hopping was used to discover
two nonpepti dic inhibitors 125 and 126 with IC
50
values of 610 and 420 mM,
respectively (Scheme 11.22).
11.6 MINIMAL PHARMACOPHORIC ELEMENTS AND FRAGMENT
HOPPING
11.6.1 Minimal Pharmacophoric Elements
Current fragment-based screening has several internal problems and challenges. First,
a fragment-based strategy can provide a combinatorial advantage relative to preassembled large chemical libraries. A collection of 10
3
fragments can typically probe
the chemical diversity space of 10
9
molecules, a tremendous increase relative to HTS;
however, this is still a small fraction of the total diversity space [228]. Second, most
fragments have low binding affinities as a result of limited interactions with the target.
Although many affinity-based assay techniques have been developed, the identification of relevant fragments and determination of how to link them productively in 3D
Kd=200µM
122
O
Cl
N
H
H2N
O
O
N
H
H
N
O
O
O
H
N
S
N
S
Kd=1.2µM
cell-ba sed a ssay IC
50
=16.4µ M
human plasma stability > 120 min
t
1/2
=14.5min
123
Scheme 11.21 In silico screening fragment libraries for X-linked inhibitors of apoptosis
protein inhibitors.
IC50=64µM
124
125 : R = iBu, IC
50
= 610 µM
126 : R = Bn, IC
50
= 420 µM
O
O
CO2H
O
R
S
Ac-C ys-G ly -Gln -Leu-As p-Le u-A la-A sp-Gly- Cys- NH
2
S
Scheme 11.22 Scaffold hopping for activator protein-1 (AP-1) inhibitors.
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FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
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space are still quite intractable problems, Third, ligand specificity for its targets
is a particularly important goal of drug discovery in the postgenomic era because
a myriad of functional proteins has been characterized. The enzymatic pockets within
a target family/or superfamily, which execute the same or similar metabolic reactions
and functions, are often quite similar. An important challenge in modern medicine is
how to design compounds that can modulate a specific enzyme while leaving related
isozymes unaffected. Known fragment-based approaches, however, are only able to
identify and characterize fragment-binding sites of the target protein (often called
“hot spots,” that is, the regions of a protein surface that are major contributors to the
ligand-binding free energy [63, 229]). In fact, many binding sites in the active site
that are responsible for target selectivity are not included in these “hot spots.”
In de novo ligand design, the accurate prediction of binding free energy is a major
problem. In in silico fragment screening how to effectively identify fragment hits is
still a concern. In scaffold hopping, the skeleton of the newly designed molecules
is confined to the basic architecture of the template structure. Scaffold rigidities
sometimes prevent an optimal binding event.
The concept of minimal pharmacophoric elements has recently been proposed [230]. The minimal pharmacophoric element is smaller than a fragment. It
can be an atom, a cluster of atoms, a virtual graph or vector(s). The focused (or
targeted) fragment libraries that match the requirement of minimal pharmacophoric
elements are generated on the basis of various fragment libraries. Various fragments
with different chemotypes, but containing the same minimal pharmacophoric elements, can be derived and a wider chemical space can be explored. Conversely, in
many cases, the region in the active site responsible for ligand selectivity is rather
delicate. Although conventional fragment-based approaches are able to identify and
characterize those fragments located in the “hot spot” of the active site [231], the
fragments that are responsible for isozyme selectivity are generally not located in
these “hot spots.” The mapping of minimal pharmacophoric elements can preferentially consider the regions in the active site responsible for isozyme selectivity; thus,
better isozyme selectivity can be incorporated in the inhibitor design.
11.6.2 Fragment Hopping
Fragment hopping, a pharmacophore-driven strategy for fragment-based inhibitor
design, is represented in Figure 11.6. The first step of the strategy is to determine the
pharmacophoric sites of a specific drug target. If the target structure can be determined
by X-ray crystallography or NMR spectroscopy, various experimental approaches can
be used to determine the potential pharmacophoric sites. The multiple solvent crystal
structures method [57a, 232] and various affinity-based biophysical techniques
mentioned above are efficacious tools for understanding how small molecules bind
to the active site of the enzymes. The energetic hot spots of enzymes for ligand binding
can be elucidated in combination with alanine scanning [233]. The computati onal
methods for active site analysis are useful when the receptor structure is known or, if
unknown, the structure can be constructed by homology modeling [234]. Two of
the most popular and venerable algorithms are GRID [235], and multiple copy
MINIMAL PHARMACOPHORIC ELEMENTS AND FRAGMENT HOPPING 453
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simultaneous search [236] (MCSS). GRID calculates 3D energy maps around proteinbinding sites, thus highlighting favorable sites for small functional groups. MCSS
randomly places thousands of copies of small functional groups into the binding site
and subjects them to energy minimization. The copies with the lowest energies
highlight hot spots of ligand binding. Many other computational methods such as the
knowledge-based equivalents of GRID (X-SITE [237] and SuperStar [238]) and
energy-based approaches (PocketFinder [239], Q-SiteFinder [240]) also can be
used to explore sensitive and specific hot spots in the active site. Computational
solvent mapping [241] and binding site determination technology (Lotus)
[190b, 229a, 242], can be regarded as an important new breakthrough in this field.
Figure 11.6 Schematic flow diagram for fragment hopping, the pharmacophore-driven
strategy for fragment-based de novo design.
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FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
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GRID/CPCA is an excellent tool for understanding the selectivity of inhibitors for
a specific target over the other structure-related enzymes [243]. If the structure of the
receptor is unknown, the pharmacophoric sites can be identified by structure–activity
analysis of ligands, various pharmacophoric, molecular shape, or field descriptors,
or by various computational methods, such as Catalyst, DISCO, and GASP [244].
Self-organizing maps can be used as a ligand-based approach to predict compound
selectivity [245].
Three- or four-point pharmacophore models can be generated from the above
analyses [246]. However, the key essential for the above pharmacophore investigation
is to derive the minimal pharmacophoric elements for each pharmacophore, which
requires a combinatorial application of different pharmacophore identification methods to provide as much information as possible. On the basis of the derived minimal
pharmacophoric elements , the second step of this approach is to query two main
general-purpose libraries: (1) a basic fragment library, constructed from fragments
extracted directly from known drugs and/or drug candidates. The fragments are either
from well-known libraries, such as the MDL CMC database, the WDI, the MDDR, or
from the literature [65, 81b, 247]; (2) a bioisostere library, constructed from
known bioisosteric principles reported in the literature [248]. The basic fragment
library is initially searched to find all of the possible fragments that are able to match
the requirements of the minimal pharmacophoric elements for each pharmacophore.
The bioisostere library is then utilized to generate a focused fragment library with
diverse structures.
The generated focused fragment library is then interrogated with the rules
for metabolic stability (Figure 11.7) [79a, 249] and a toxicophore library (Figure 11.8) [250] to provide a focused library for a specific pharmacophore. The focused
library is then converted into a LUDI fragme nt library, and the LUDI program is used
to search the optimal binding position for each fragment of each pharmacophore
[141a, 251].
The third step of this approach is to link these fragments. A constructed side-chain
library is used for this purpose, in which the synthetic accessibility is considered
[187, 252]. SciFinder Scholar [253], in conjunction with the bioisostere library, also
plays a key role in securing the synthetic accessibility of the formed chemical bond.
The bioisostere library plays an assistant role in enhancing the binding capabilities
and optimizing the chemical properties of the generated ligands. The generated
ligands are interrogated again with the rules for metabolism stability and the
toxicophore library.
The ligands generated by this iterative process are then docked into the active
site (using AutoDock3.0 [254]), scored with consensus scoring functions [255], and
filtered with absorption, distribution, metabolism, excretion, and toxicity (ADME/
Tox) considerations [256]. If the ligands generated are not satisfactory,the molecule is
reconstructed using the generated focused fragment libraries, the side chain library,
and the bioisostere library (Figure 11.6).
Fragment hopping determines the minimal pharmac ophoric elements for each
pharmacophore that is important for ligand selectivity. Fragments are generated
to match the requirement of minimal pharmacophoric elements based on the basic
MINIMAL PHARMACOPHORIC ELEMENTS AND FRAGMENT HOPPING 455
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Figure 11.7 An example of the rules for metabolic stability.
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FRAGMENT-BASED DRUG DESIGN: CONSIDERATIONS FOR GOOD ADME PROPERTIES
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fragment and bioisostere libraries. After the focused fragment library is generated for
each pharmacophore, other fragment-based approaches, such as NMR-based and/or
X-ray crystallography-based fragment screening techniques, click chemistry, and
dynamic combinatorial chemistry, can be utilized to investigate the binding mode of
the above-generated fragments within the active site of the enzyme. The interaction of
the generated fragments with the selective regions of the active site can be analyzed
further by tethering or tethering with an extender. Therefore, fragment hopping as
a pharmacophore-driven strategy is an open system that can incorporate other
techniques and provide a more efficient pathway to generate more potent and more
selective inhibitors.
11.6.3 Case Study: Nitric Oxide Synthase
Nitric oxide synthase (NOS) catalyzes the five-electron, two-step oxidation of
L-arginine to produce L-citrulline and NO. NOS consists of three isozymes: neuronal
NOS (nNOS), endothelial NOS (eNOS), and inducible NOS (iNOS). Overproduction
of NO from nNOS has been associated with harmful effects in the central nervous
system, including stroke, various neurodegenerative diseases, and cerebral palsy. The
minimal pharmacophoric elements were extracted according to the binding mode of
peptidic inhibitor 127 (K
i
¼ 130 nM) with nNOS (Scheme 11.23). Fragment hopping
led to the generation of 128 with a K
i
of 388 nM [230]. The structural optimization of
Figure 11.7 (Continued)
MINIMAL PHARMACOPHORIC ELEMENTS AND FRAGMENT HOPPING 457
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Figure 11.8 An example of toxicophores.
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128 using fragment hopping led to 129 and 130 with Kivalues of 85 and 14 nM,
respectively [257]. These compounds were then tested in a rabbit model for cerebral
palsy and were found to prevent hypoxiaischemia-induced deaths of the fetuses and to
reduce the number of newborn kits exhibiting symptoms of cerebra l palsy. Following
maternal administration of 129 and 130 in a rabbit model, the compounds were found
to distribute to fetal brain, to be nontoxic, without cardiovascular effects, and inhibit
fetal brain NOS activity in vivo [258].
11.7 CONCLUSIONS AND FUTURE PERSPECTIVES
The techniques of fragment-based drug discovery are orthogonal to those of HTS on
the basis of screening techniques, binding affinities, compound size and weight, size
of compound libraries, and the strategies for hit-to-lead optimization. It has been
widely accepted in large pharmaceutical companies that fragment-based drug design
is an effective complement to HTS. In academic and biotechnological laboratories,
these methods have emerged as the major approaches to obtain new chemical
structures. Fragment-based drug discovery has successfully identified new hits when
an HTS campaign has failed to yield useful results with difficult targets.
Compared with HTS, fragment-based drug disc overy can generate new leads with
better physicochemical properties that are more amenable to structural optimization
for the generation of compounds with better drug- like properties. Fragment-based
methods also provide chemical starting points that have no or fewer unnecessary
structural elements, and therefore leave more chemical space to reduce the risk of
toxicity or metabolic instability. In most current cases, the integration of early ADME
considerations in fragment-based design is dependent on random ideas with a lack of
systematic approaches. Fragment hopping utilizes the concept of minimal pharmacophoric elements to provide an open system that can efficiently incorporate early
Scheme 11.23 Fragment hopping for nitric oxide synthase (NOS) inhibitors.
CONCLUSIONS AND FUTURE PERSPECTIVES 459
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“ADME/Tox” considerations. However, to draw a complete picture for incorporation
of early ADME considerations into fragment-based drug discovery, a comprehensive
survey and analysis of metabolic stability and the bioactivation and biotransformation
of fragments in vivo are urgently needed.
Similar to structure-based drug design, computational chemistry and computer
modeling, and combinatorial chemistry and HTS, fragment-based drug design is and
will not be a panacea. It is only one of many key weapons in the drug design arsenal
that can be used to discover new ligands for a particular target. However, it is also
noteworthy that with continuing breakthroughs in structural biology, particularly
a better understanding of the active site of receptors, further developments in
molecular modeling and computational chemistry might be able to place fragment-based drug discovery into a priority position for the practice of standard
medicinal chemistry.
ACKNOWLEDGMENTS
The author would like to thank Professor Richard B. Silverman of the Department of
Chemistry, Northwestern University for support and encouragement in this work, and
also for his critical reading of the manuscript and thoughtful discussions.
REFERENCES
1. (a) Abou-Gharbia, M. Discovery of innovative small molecule therapeutics. J. Med.
Chem. 2009, 52(1), 2–9; (b) Davis, A. M., Keeling, D. J., Steele, J., Tomkinson, N. P.,
and Tinker, A. C. Components of successful lead generation. Curr. Top. Med. Chem.
2005, 5(4), 421–439; (c) Lombardino, J. G. and Lowe, J. A. III., The role of the
medicinal chemist in drug discovery—then and now. Nat. Rev. Drug Discov. 2004, 3
(10), 853–862.
2. (a) Snowden, M. and Green, D. V. The impact of diversity-based, high-throughput
screening on drug discovery: “chance favours the prepared mind”. Curr. Opin. Drug
Discov. Devel. 2008, 11(4), 553–558; (b) Golebiowski, A., Klopfenstein, S. R., and
Portlock, D. E. Lead compounds discovered from libraries. Curr. Opin. Chem. Biol. 2001,
5, 273–284; (c) Golebiowski, A., Klopfenstein, S. R., and Portlock, D. E. Lead
compounds discovered from libraries: Part 2. Curr. Opin. Chem. Biol. 2003, 7,
308–325; (d) Fox, S., Farr-Jones, S., Sopchak, L., Boggs, A., Nicely, H. W., Khoury,
R., and Biros, M. High-throughput screening: update on practices and success. J. Biomol.
Screen. 2006, 11(7), 864–869; (e) Posner, B. A. High-throughput screening-driven lead
discovery: meeting the challenges of finding new therapeutics. Curr. Opin. Drug Discov.
Devel. 2005, 8(4), 487–494.
3. (a) Bender, A., Bojanic, D., Davies, J. W., Crisman, T. J., Mikhailov, D., Scheiber, J.,
Jenkins, J. L., Deng, Z., Hill, W.A., Popov,M., Jacoby,E., and Glick, M. Which aspects of
HTS are empirically correlated with downstream success? Curr. Opin. Drug Discov.
Devel. 2008, 11(3), 327–337; (b) Review Lahana, R. How many leads from HTS? Drug
Discov. Today 1999, 4(10), 447–448; (c) Shelat, A. A. and Guy, R. K. The interdepen-
dence between screening methods and screening libraries. Curr. Opin. Chem. Biol. 2007,
460
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