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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
44. de novo compound 1 45. de novo compound 1
H
2
C
O
O HO
O
O
O
HO
N
NH
CH
3
O
S S
O
H
3
C
CH
3
N
O
NH
3. Enalapril
O
O
CH
3
NH
O
N
OH
HO
1. Lisinopril
4. Enalaprilmaleate
O
NH
2
O
NH
O
N
OH
HO
O
O
CH
3
H
3
C
NH
O
N
OH
O
6. Cilazapril
O
O
O
N
N
OH
H
3
C
NH
O
5. Ramipril
O
O
CH
3
H
3
C
NH
O
N
OH
O
Figure 9.12 Reference structures for ACE inhibitors.
7. Perindopril
O
O
CH
3
H
3
C
NH
O
N
OH
N
O
8. Benazepril
9. Fosinopril
2. Captopril
O
O
CH
3
H
3
C
SH
N
O
OH
NH
N
O
O
CH
3
O
O
O
OH
CH
3
H
3
C
O
H
3
C
O
P OO
Figure 9.13 Reference structures for ACE inhibitors.
9 Scaffold Hopping and De Novo Drug Design214
9.5 Software Tools for SH (Scaffold Hopping) and De Novo
Design Selection
a) SPROUT: The software SPROUT is one of the earliest methods for computer-aided de novo
design of possible ligands. SPROUT’s concept is to split the design process into two phases. The
initial phase creates molecular skeletons that fulfill steric restrictions, also referred to as funda-
mental constraints. Pieces of a template are assembled step-by-step to create skeletons. Since
each template fragment is made up of dummy atoms without element types and solely assigned
hybridization states, it represents a set of molecular fragments. Element types are allocated in
the second stage to satisfy hydrophobic and electrostatic needs (secondary constraints). The
principal and secondary limitations include produced from a ligand binding site’s 3D structure.
As a result, SPROUT has been expanded to include other design goals, such as virtual chemical
synthesis’s ability to facilitate synthetic synthesis (SynSPROUT) [23, 58].
b) FLUX/Topas: One software program for ligand-based de novo design is TOPAS. The construc-
tion of candidate molecules results in a high degree of resemblance to one or more reference
ligands. Measures of similarity include (i) a topological pharmacophore descriptor (CATS
descriptor) and (ii) a structural 2D descriptor. Using a set of bioactive substances and the virtual
retrosynthetic RECAP rules, molecular fragments utilized by TOPAS are produced. Additionally,
the fragment-based assembly of novel ligand candidates follows the same set of guidelines [40].
c) BREED: BREED software takes a very different technique than stochastic optimization while
assembling pieces. Rather, BREED recombines fragments of known ligands in their receptor-
bound state overtop of them to new possible ligands – a process that essentially echoes what
medicinal chemists often do. The process of aligning ligands involves covering the backbone
atoms of protein structures.
d) Skelgen: The Skelgen software is based on an approach for computer-based de novo design that
was proposed by Todorov and Dean in the late 1990s. Like SPROUT (see above), the creation of
novel compounds is divided into two steps: (i) assigning atom types and (ii) creating skeletons.
The process of assembling template fragments randomly results in the creation of molecular
structures. These pieces can be used to create user-defined sets because they are made up of
generic hydrogen and carbon atoms.
9.6 Case Study
9.6.1 De Novo Drug Design
Darolutamide (Nubeqa™) stands as a novel second-generation, nonsteroidal, selective androgen
receptor (AR) inhibitor tailored for treating nonmetastatic castration-resistant prostate cancer
(nmCRPC) [59]. The discovery of Darolutamide began with the identification of a potent non-
steroidal AR antagonist. This compound was initially identified by researchers at Bayer AG using
a structure-guided drug design approach. The team utilized X-ray crystallography to investigate the
structure of the AR ligand-binding domain in complex with various ligands. This analysis led to
the identification of a unique binding pocket on the AR that could be targeted for inhibition.
The researchers then conducted a SAR study to optimize the potency and selectivity of the AR
antagonist. Through the SAR study, they were able to develop a compound with a promising phar-
macological profile. However, this compound had suboptimal pharmacokinetic properties, leading
the researchers to explore additional modifications.
9.7 Con clusion 215
At this stage, Bayer AG entered into a collaboration with Orion Corporation, a Finnish pharmaceutical
company with expertise in the development of compounds with improved pharmacokinetic prop-
erties. The collaboration aimed to optimize the pharmacokinetic profile of the AR antagonist while
retaining its pharmacological properties. The model of the antagonistic AR conformer was devel-
oped with SwissModel19, drawing on the antagonistic conformation of the progesterone receptor
(PDB entry 2ovh,20). A 3D low-energy conformation of Darolutamide was formed using the
Discovery Studio Suite 2017. Coot version 0.8.8 was employed for receptor-ligand modeling, and
PyMOL (The PyMOL Molecular Graphics System, Version 2.0 Schrödinger, LLC, Cambridge, MA)
was used to prepare figures [60]. The collaborative effort of medicinal chemists, pharmacologists,
and drug metabolism experts led to the development of Darolutamide, which exhibited potent and
selective AR antagonism and had an improved pharmacokinetic profile compared to the initial
compound.
9.6.2 Scaffold Hopping
Ledipasvir (Harvoni) is an antiviral medication used in combination with sofosbuvir for the treat-
ment of hepatitis C virus (HCV) infection. HCV, an RNA virus belonging to the Flaviviridae family,
penetrates the cell and its viral genome is translated into a polyprotein consisting of 3000 amino
acids. This polyprotein is then cleaved by both host and viral proteases into several proteins. These
proteins are categorized into two main types: structural proteins (such as the core, E1, and E2 pro-
teins), which are a part of the virions, and nonstructural proteins that play a role in the replication
cycle of the virus (including proteins NS2, NS3, NS4A, NS4B, NS5A, and NS5B) [61].
One of the key targets for direct-acting antivirals (DAAs) development in HCV was the NS5A
protein. The NS5A protein has multiple functions in the HCV life cycle, including RNA replica-
tion, modulation of host cell signaling pathways, and assembly of new viral particles [61]. A lead
compound, known as ACH-2928, was identified as a potent inhibitor of the NS5A protein.
ACH-2928 exhibited favorable antiviral activity in vitro, but its pharmacokinetic properties were
suboptimal for oral administration. To overcome this limitation, medicinal chemists at AbbVie
(formerly Abbott Laboratories) focused on optimizing the structure of ACH-2928 to improve its
pharmacokinetic profile, including bioavailability, metabolic stability, and safety [62]. The lead
optimization process resulted in the discovery of ledipasvir, a potent and selective NS5A inhibitor
with improved pharmacokinetic properties. Ledipasvir demonstrated robust antiviral activity in
preclinical studies and was subsequently evaluated in clinical trials.
9.7 Conclusion
The integration of de novo drug design techniques into the molecular designer’s toolbox represents
a significant advancement in early-phase hit and lead discovery. The inherent multiobjective
nature of optimizing drug molecules from atomic or fragment sets necessitates sophisticated com-
putational methodologies. Many de novo drug design tools employ evolutionary techniques to
simultaneously optimize multiple objectives, facilitating the identification of promising drug can-
didates. Furthermore, the incorporation of retrosynthetic rules and fragment-based construction
ensures that the molecules generated by de novo drug design tools are chemically synthesizable,
enhancing their translational potential. These novel drug molecules, absent from existing data-
bases, offer unique opportunities for drug development and innovation.
9 Scaffold Hopping and De Novo Drug Design216
In parallel, scaffold hopping has emerged as a widely accepted method in the arsenal of medici-
nal chemists and molecular modelers. The last decade has witnessed numerous successful scaffold
hops facilitated by ligand-based virtual screening methods. The introduction of diverse ligand-
based virtual screening techniques with scaffold hopping capabilities has further expanded the
toolbox available to researchers, enabling the exploration of novel chemical space and the identifi-
cation of promising lead compounds.
In essence, the convergence of de novo drug design and scaffold hopping represents a paradigm
shift in drug discovery, offering unprecedented opportunities for the rapid and cost-effective iden-
tification of innovative therapeutics. Moving forward, continued advancements in computational
methodologies and virtual screening techniques will further propel drug development efforts, ulti-
mately improving patient outcomes and advancing healthcare worldwide.
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10.1 Introduction
Computational methods have been used in the drug discovery process for decades. In the early
days, computers were used to calculate the properties of molecules and to screen large libraries of
compounds for potential drug candidates. As computers have become more powerful, computa-
tional methods have become more sophisticated and have been used to address a wider range of
drug discovery problems [1]. One of the most important uses of computational methods in drug
discovery is to identify potential drug targets. Drug targets are proteins or other molecules that are
involved in the disease process. By understanding the structure and function of drug targets, scien-
tists can design drugs that will interact with these targets and inhibit their activity.
Molecular modeling can be used to identify potential drug targets in a number of ways. One
common approach is to use molecular docking simulations, which are used to predict the binding
interactions between a drug molecule and a drug target [2]. If the drug molecule can bind to the
drug target in a way that inhibits its activity, then it is considered to be a potential drug candidate.
Another approach to identifying potential drug targets is to use protein structure prediction.
Protein structure prediction is the process of using computational methods to predict the three-
dimensional structure of a protein from its amino acid sequence. Once the structure of a protein is
known, it can be used to design drugs that will interact with the protein and inhibit its activity [3].
In addition to identifying potential drug targets, computational chemistry can also be used to
design new drugs. Drug design is the process of developing new molecules that have the desired
pharmacological properties. One common approach is to use computer-aided drug design (CADD).
CADD is a process that uses computational methods to identify new molecules that have the
desired properties, such as binding affinity, selectivity, and solubility [4].
Another approach to drug design is to use artificial intelligence (AI), which deals with the crea-
tion of intelligent agents, computer programs that can learn and adapt to their environment [5]. AI
can be used to design new drugs by automating the drug design process and by identifying new
drug targets. Some examples of successful drugs that were developed using computational meth-
ods include Gleevec, Herceptin, and Sovaldi. Gleevec is a drug that is used to treat chronic myeloid
10
Fragment-based Drug Design and Drug Discovery
André M. Oliveira
1
and Mithun Rudrapal
2
1
Department of Environment Studies, Federal Centre of Technological Education of Minas Gerais, Contagem, Minas Gerais, Brazil
2
Department of Pharmaceutical Sciences, School of Biotechnology and Pharmaceutical Sciences, Vignan’s Foundation for Science,
Technology & Research, Guntur, Andhra Pradesh, India
222
leukemia [6]. Herceptin is a drug that is used to treat breast cancer [7]. Sovaldi is a drug that is used
to treat hepatitis C [8].
In the context of computational chemistry, the set of techniques and strategies known as
fragment-based drug design (FBDD), widely used in the modern development of new drugs, is
based on the construction of new ligands for molecular targets from the combination of structural
fragments chosen according to some criteria. This choice can be directed based on the structure of
the ligand, the molecular target, or both. FBDD has benefited from the development of databases
of structures of molecules of different sizes and statistical techniques that make it possible to
explore their structural universe.
Historically, FBDD was proposed in the 1980s by Jencks (1981) and Nakamura and Abeles
(1985). The methods serve as an alternative or support to high-throughput screening (HTS), which
is a more expensive and exhaustive process.
The reduction of time (and consequently of costs) is a great achievement in the new pharmaceu-
tical industry, which deals with increasing demand due to population growth and the emergence
of new diseases or the resurgence of old teeth that were already believed to be eliminated.
The use of these tools rivals more traditional resources such as Combinatorial Chemistry, an
object of interest to many chemists and biologists [9]. A large part of the objections to FBDD is its
possible circular nature, in which a new hit obtained generates new candidates that do not neces-
sarily become consolidated as viable drugs.
In this scenario, the development of FBDD follows the evolution of AI, machine learning, genom-
ics, transcriptomics, proteomics, metabolomics, microbiome, and pharmacogenomics tools [10].
The process involves libraries of fragments and links obtained by X-ray crystallography, and the
construction process is mediated by calculations of affinity constants (Figure 10.1). Among the
most commonly used structure databases Table 10.1 presents some examples.
10.2 The Process of Finding Fragments
Finding the fragments that will serve as the basis for proposing new ligands to a molecular target
is not a simple task. It is necessary to have a wide variety of fragments and connections between
them (functionalized or not) and a suitable metric to evaluate the best constructions, such as inter-
action energies or inhibition constants obtained by theoretical means. We can summarize the main
steps that guide the FBDD process: obtaining the fragment libraries, protein hot-spots identifica-
tion, and computational calculation for the fragments fitting into the sites.
The first step is to obtain the fragment library. There must be a compromise between structural
diversity and library complexity, and for this purpose, certain criteria are used, known as the “rule
of three” (RO3) [11] (Jacquemard and Kellenberger, 2019; Brown, 2016; Kirsch et al., 2019). The
rule of three is similar in design and purpose to Lipinski’s rule of five, which has application in the
design of drugs with good oral bioavailability (Table 10.2).
Another important factor is synthetic feasibility, which can be measured by indicators such as
the number and complexity of functional groups, and the presence of fused rings and chiral
centers [11].
Finding the fragments is only part of the challenge of conducting a FBDD study. It is also neces-
sary to find suitable molecular targets and to find appropriate binding sites on those targets. The
search for molecular targets is usually done by similarity to natural ligands. An example of the
application of this resource is OpenTargets [12], which brings together molecular targets that can
be searched according to the associated disease or genetic profile.