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450 V. C. Santos et al.
Fig. 15.8 Structure-based design of ensitrelvir. Starting from the initial hit 4, obtained from virtual
screening, the cyclization of the P1’ motif led to compound 5, with a 90-fold improvement in IC
An additional cyclization in the P1 motif and a modification in the pattern of fluorine substitution in
the P2 ring yielded ensitrelvir (a). Binding modes of compound 4 (PDB ID: 7VTH) (b) and
ensitrelvir (PDB ID: 7VU6) (c) in the SARS-CoV-2 M
represented as red spheres, and the hydrogen bonds are represented as yellow dashed lines. The
S2–S1′ subsites are labeled. Important residues and compounds are represented as sticks and
colored by atom. Carbons are colored gray in the enzyme and green or cyan in the compounds.
(Figure produced using ChemDraw and PyMOL)
pro
active site. Water molecules are
50
Similar to nirmatrelvir, ensitrelvir showed potential as a pancoronavirus agent, as
it had antiviral activity against several SARS-CoV-2 variants evaluated (Pango
lineages A, B.1.1.7, P.1, B.1.351, B.1.617.2, and B.1.1.529, with EC
50
values
against SARS-CoV-2-infected VeroE6/TMPRSS2 between 0.29 and 0.50 μM) and
other coronaviruses (SARS-CoV-infected VeroE6/TMPRSS2 cells, EC
of
50
0.21 μM; MERS-CoV-infected VeroE6/TMPRSS2 cells, EC50of 1.4 μM; HCoVOC43-infected MRC-5 cells, EC
EC
of 5.5 μM). Additionally, ensitrelvir is selective for coronavirus proteases, with
50
of 0.074 μM; HCoV-229E-infected MRC-5 cells,
90
no inhibitory properties against several host-cell proteases [ 13]. The efficacy of
ensitrelvir in decreasing viral load and ameliorating COVID-19 severity was
shown in hamster models, with good oral bioavailability in this animal model
[84]. Ensitrelvir obtained emergency approval for COVID-19 treatment in
November 2022 in Japan [85]. A recent study indicated ensitrelvir’sefficacy against
the 11 most frequent M
(within two-fold) to the ones obtained against the wild-type protein [86].
The approvals of Paxlovid
therapeutic arsenal for COVID-19 treatment. Nevertheless, numerous studies have
demonstrated the existence of M
pro
mutants circulating globally, with IC50values similar
®
and Xocova®were very important additions to the
pro
mutants resistant to nirmatr elvir and ensitrelvir,
.
15 Molecular Modeling Strategies in Drug Design, Development, and... 451
both in biochemical studies and in antiviral assays [87–89]. Several resistance
mutants identified have been reported in clinical samples, reinforcing their possible
clinical relev ance [87]. Over 30 mutations have been described to reduce inhibition
pro
of M
, the susceptibility of SARS-CoV-2 replication, or both, upon treatment with
nirmatrelvir and/or ensitrevir, and the most frequently reported mutations were G15S
and T21I [90]. Interestingly, mutants resistant to both nirmatrelvir and ensitrelvir
might still be inhibited by M
active site [91]. Structural and kinetic studies have been employed to understand the
mechanisms linking M
pro
inhibitors that show distinct binding modes to the
pro
mutations to drug resistance, revealing two evolutionary
mechanisms. The first group of mutations directly lower drug binding affinity, as
observed for E166V, which disrupts a hydrogen bond interaction betw een
nirmatrelvir and E166 and alters that hydrogen bond network in the S1 pocket.
Frequently, these mutations also alter substrate processing, reducing viral fitness.
The other resistance mechanism is represented by mutations that do not directly
affect drug binding, but increase protease activity, as observed for T21I. The
combination of these two resistance mechanisms, such as variants containing T21I
and E166V, confer resistance to the current drugs while maintaining their viral
fitness [91]. In addition, cross-resistance to nirmatrelvir and ensitrelvir has been
reported, bringing further concerns about the available drug arsenal [90, 92]. Therefore, it is important to continue developing other drug candidates targeting M
pro
which can help to overcome the limitations of the current ones.
,
5 Structure-Based Drug Design of Teneligliptin
as a Dipeptidyl Peptidase IV Inhibitor in the Treatment
of Type 2 Diabetes Mellitus
Another disease in which drug discovery campaigns targeting a protease were
successful is Type 2 diabetes mellitus (T2DM), a metabolic disease characterized
by high glycemia, insulin resistance, and impaired insulin secretion. The symptoms
can include the following: feeling very thirsty, polyuria, blurred vision, tiredness,
and weight loss. If not controlled, T2DM can especially damage nerves and blood
vessels [93]. The dipeptidyl peptidase IV (DPP-4) is a validated drug target for
treating T2DM since its inhibition leads to an increase in insulin levels in response to
hyperglycemia [94]. This therapeutic effect is mediated by glucagon-like peptide
1 (GLP-1), a potent stimulator of insulin secretion, whose activity is abolished by
cleavage by DPP-4. DPP-4 is a serine protease that removes the two last residues
from the N-terminal of peptides with Ala or Pro in P1 [95].
Many of the first DPP-4 inhibitors had substituted pyrrolidines or thiazolidines to
mimic proline in P1 and an electrophilic warhead such as nitr iles to bind to the
catalytic Ser630 covalently [16]. However, they were chemically unstable and also
inhibited DPP-8 and DPP-9, causing multiorgan toxicities and mortality in rats,
gastrointestinal toxicity in dogs, and inhibition of T cell activation/proliferation in
452 V. C. Santos et al.
both animal model s [16, 96]. Therefore, researchers searched for inhibitors without
the nitrile moiety and with an increased affinity for the S2 site. The interest in
optimizing P2 was based on a previously observed ten-fold increase in the inhibitory
activity of an arylamino group in P2, compared with a prolyl-(S)-2-cyanopyrrolidine
[97]. Compound 6, a phenyl analog in which the (S)-2-cyanopyrrolidine moiety was
converted to a thiazolidine structure, showed potent DPP-4 inhibitory activity (IC
50
of 1.6 nM) and moderate selectivity against DPP-8 and DPP-9 (Fig. 15.9a).
Substituting the aryl group with a quinolyl ring with a trifluoromethyl group at S2
increased potency (compound 7,IC
of 0.4 nM) and selectivity [98]. The crystal
50
structure of compound 7 bound to DPP-4 (PDB ID 3VJM) revealed the thiazolidine
group of the molecule occupying the S1 site and the proline group forming salt
bridges with the active site’s glutamate residues 205 and 206; moreover, it weakly
interacts with Tyr585 and Arg358, residues located in an extension of the S2 site
(Fig. 15.7b)[98]. Since the S1 site is identical in DPP-4, DPP-8, and DPP-9 but there
are substitutions in the extensive S2 site (R358D and Y385H/N), the S2 site seemed
important to be considered to achieve selectivity. Docking studies guided further
synthesis of linkers to improve interactions in S2, leading to teneligliptin
(Fig. 15.9a), which presented a threefold increase in DPP-4 inhibitory activity
(IC
of 0.4 nM) and having its ex vivo activity sustained. The ex vivo activity
50
consisted of orally administrating the compounds to rats and evaluating the plasma
Fig. 15.9 Structure-based development of teneligliptin. Chemical representation of compounds
6, 7, and teneligliptin (a); Crystal structure of 7 complexed with DPP-4 (PDB ID: 3VJM); (c)
Crystal structure of teneligliptin complexed with DPP-4 (PDB ID: 3VJK) (b). Water molecules are
represented as red spheres, and the polar contacts are shown in yellow dashed lines. The P2–P1’
positions of each compound are labeled. Important residues and compounds are represented as
sticks and colored by atom. Carbons are colored gray in the enzyme and green in the compounds.
(Figure produced using ChemDraw and PyMOL)
15 Molecular Modeling Strategies in Drug Design, Development, and... 453
DPP-4 activity at different time points. Teneligliptin also had about 700- and 1500fold selectivity over DPP-8 (IC
of 260 nM) and DPP-9 (IC50of 340 nM), respec-
50
tively. The crystal structure of teneligliptin complexed with DPP-4 shows the
thiazolidine moiety occupying the S1 site and the pyrazole with the aryl group
occupying the S2 site (PDB ID 3VJK) (Fig. 15.9c)[16]. Teneligliptin was approved
in Japan in 2012 under the trade name Tenelia
®
as a once-a-day oral treatment for
patients with T2DM that is not controlle d by diet or exercise [99].
6 Conclusions
In this chapter, we highlight successful examples of drug design strategies targeting
aspartic, cysteine, and serine proteases. The importance of structure-based drug
design techniques clearly demonstrates how these strategies allow quick and efficient lead optimization. In addition to designing first-in-class compounds, SBDD
approaches have shown the potential to overcome drug resistance or design compounds for which resistance is less likely to arise. SBDD was also valuable in aiding
the design of the selective DPP-4 inhibitor teneligliptin.
Examples from several decades reveal that, with time, the optimization of potency
and pharmacokinetic properties becomes increasingly more connected. A good
example is the development of ensitrelvir, in which the lead compound was not
the most potent hit obtained against the enzyme, but it was chosen due to its
favorable pharmacokinetics profile. This illustrates how, despite the frequent focus
on potency agains t target proteins, successful drug discovery projects need to
account for numerous compound characteristics, such as activity in cellular assays,
low cytotoxicity, and an overall favorable ADMET profile.
Another tendency observed is the diversification of strategies for inhibitor development. In the development of the first inhibitors of HIV and HCV proteases, we
find mostly strategies based on molecular modeling of peptidic compounds. On the
other hand, more recent examples include the discovery of novel nonpeptidic scaffolds from virtual screening of chemically diverse libraries and the exploration of
abundant structural information to derive pharmacophore models. Additionally,
computational techniques allow researchers to access virtually a chemical space
not yet synthesized. The computational cost varies widely depending on the techniques employed, as discussed in the M
virtually screen millions or billions of compounds than to perform high throughput
screening of this magnitude. Therefore, molecular modeling strategies can be pivotal
do drug design, development, and discovery of drugs targeting proteases.
pro
section. However, it is still cheaper to
454 V. C. Santos et al.
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