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Discovery of inhibitors of protein
tyrosine phosphatase 1B
contained in a natural products
library from Mexican medicinal
plants and fungi using a
combination of enzymatic and in
silico methods**
Miriam Díaz-Rojas
1
, Martin González-Andrade
2
*,
Rodrigo Aguayo-Ortiz
1
, Rogelio Rodríguez-Sotres
1
,
Araceli Pérez-Vásquez
1
, Abraham Madariaga-Mazón
3
and
Rachel Mata
1
*
1
Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico,
2
Facultad de
Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico,
3
Instituto de Química Unidad
Mérida and Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Unidad Mérida,
Universidad Nacional Autónoma de México, Mexico City, Mexico
This work aimed to discover protein tyrosine phosphatase 1B (PTP1B) inhibitors
from a small molecule library of natural products (NPs) derived from selected
Mexican medicinal plants and fungi to find new hits for developing antidiabetic
drugs. The products showing similar IC
50
values to ursolic acid (UA) (positive
control, IC
50
= 26.5) were considered hits. These compounds were canophyllol
(1), 5-O-(β-D-glucopyranosyl)-7-methoxy-3′,4′-dihydroxy-4-phenylcoumarin
(2), 3,4-dimethoxy-2,5-phenanthrenediol (3), masticadienonic acid (4), 4′,5,6-
trihydroxy-3′,7-dimethoxyflavone (5), E/Zvermelhotin (6), tajixanthone hydrate
(7), quercetin-3-O-(6″-benzoyl)-β-D-galactoside (8), lichexanthone (9),
melianodiol (10), and confusarin (11). According to the double-reciprocal plots,
1was a non-competitive inhibitor, 3a mixed-type, and 6competitive. The
chemical space analysis of the hits (IC
50
<100 μM) and compounds possessing
activity (IC
50
in the range of 100–1,000 μM) with the BIOFACQUIM library
indicated that the active molecules are chemically diverse, covering most of
the known Mexican NPs’chemical space. Finally, a structure–activity similarity
(SAS) map was built using the Tanimoto similarity index and PTP1B absolute
inhibitory activity, which allows the identification of seven scaffold hops,
namely, compounds 3,5,6,7,8,9, and 11. Canophyllol (1), on the other hand,
is a true analog of UA since it is an SAR continuous zone of the SAS map.
KEYWORDS
PTP1B, chemical space, scaffold hops, canophyllol, E/Zvermelhotin
OPEN ACCESS
EDITED BY
Nunziatina De Tommasi,
University of Salerno, Italy
REVIEWED BY
Alessandra Braca,
University of Pisa, Italy
Valeria Iobbi,
University of Genoa, Italy
*CORRESPONDENCE
Rachel Mata,
rachel@unam.mx
Martin González-Andrade,
martin@bq.unam.mx
RECEIVED 21 August 2023
ACCEPTED 13 October 2023
PUBLISHED 31 October 2023
CITATION
Díaz-Rojas M, González-Andrade M,
Aguayo-Ortiz R, Rodríguez-Sotres R,
Pérez-Vásquez A, Madariaga-Mazón A
and Mata R (2023), Discovery of inhibitors
of protein tyrosine phosphatase 1B
contained in a natural products library
from Mexican medicinal plants and fungi
using a combination of enzymatic and in
silico methods**.
Front. Pharmacol. 14:1281045.
doi: 10.3389/fphar.2023.1281045
COPYRIGHT
© 2023 Díaz-Rojas, González-Andrade,
Aguayo-Ortiz, Rodríguez-Sotres, Pérez-
Vásquez, Madariaga-Mazón and Mata.
This is an open-access article distributed
under the terms of the Creative
Commons Attribution License (CC BY).
The use, distribution or reproduction in
other forums is permitted, provided the
original author(s) and the copyright
owner(s) are credited and that the original
publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
Frontiers in Pharmacology frontiersin.org01
TYPE Original Research
PUBLISHED 31 October 2023
DOI 10.3389/fphar.2023.1281045
1 Introduction
More than 474 million people worldwide live with type 2 diabetes
mellitus (T2DM), characterized by chronic hyperglycemia due to
insulin resistance and pancreatic β-cell dysfunction. Furthermore, in
2021, the International Diabetes Federation has estimated that over
6 million deaths yearly are due to T2DM (IDF, 2021). To control high
blood glucose levels in diabetic patients, in addition to a healthy lifestyle,
drug treatments and/or insulin are prescribed. Nonetheless, in some
countries such as in Mexico, a large segment of the population uses
medicinal plants for treating diabetes, alone or in combination with
allopathic medicines.
The global prevalence of diabetes has prompted the search for
novel and more efficacious therapeutic agents. In this regard, a better
understanding of the complicated mechanisms involved in T2DM has
inspired the pursuit of new compounds targeting new receptors and
crucial enzymes involved in glucose homeostasis (ADA, 2022).
Protein tyrosine phosphatase 1B (PTP1B) shows promise among
these enzymes. PTP1B is a critical enzyme in the dephosphorylation
of the insulin receptor and its downstream signaling components. The
relevance of this enzyme in the pathophysiology of diabetes has been
demonstrated by the genetic deletion of PTP1B in mice; PTP1B-
deficient mice remain insulin sensitive on a high-fat diet when
compared with the wild-type (Na et al., 2016;Kanwal et al., 2022).
PTP1B is composed of three domains, an N-terminal catalytic
domain (1–300) that contains the crucial Cys215, Asp181, and
Gln262 residues; a regulatory domain (301–400) that is proline-
rich and confers substrate specificity to PTP1B; and finally, a
C-terminal domain (401–435) that is involved in the binding to
the plasmatic reticulum and is intrinsically disordered (Tonks, 2003;
Song et al., 2017;Liu et al., 2022;Quy et al., 2022). Since the non-
receptor protein tyrosine phosphatase family has a highly conserved
catalytic site (~75% of sequence similarity), searching for selective
allosteric inhibitors is crucial in drug discovery.
To discover new hit compounds that inhibit PTP1B, screening
libraries based on natural products, mostly from antidiabetic
plants, is an excellent and straightforward strategy (Singh et al.,
2022). In addition, natural product libraries occupy a greater area
of chemical space than synthetic compounds, reduce screening
costs, and increase the speed of finding appropriate hits (Lahlou,
2007;Harvey et al., 2010). Finally, it is important to point out that a
few natural products and some chemical derivatives, such as
trodusquemine from the dogfish shark, ertiprotafib, and JTT-
551, have entered clinical trials (Kazakova et al., 2022).
Based on the aforementioned considerations, this work
aimed to 1) identify molecules from a small molecule library
of natural products obtained from Mexican medicinal plants and
fungi that might inhibit PTP1B by in-house enzymatic screening;
2) explore their chemical space; and 3) identify the scaffold hops
and activity with the structure–activity similarity (SAS)
map. Altogether, all these ensure a high success rate of
finding new PTP1B inhibitors with chemically original or
similar structures and increase the probability of identifying
hit compounds.
2 Materials and methods
2.1 PTP1B inhibition assay
A spectrophotometric and colorimetric method was used to
detect the inhibitory activity of 99 in-house compounds on
hPTP1B
1-400
(Rangel-Grimaldo et al., 2020). The analyses were
carried out in triplicate; the test materials and positive control
(ursolic acid) were dissolved in DMSO and buffer solution.
Ursolic acid was used as a reference since it has been widely
reported as one of the best allosteric PTP1B inhibitor by different
research groups (Liu et al., 2022;Quy et al., 2022). The
compounds were initially tested in concentrations between
20 and 1,000 μM, enzyme (66 nM) and buffer [Tris-HCl
50 mM, pH 6.8] solution, and 0.125 mM of 4-nitrophenyl
phosphate (4-NP, Sigma-Aldrich, St. Louis, MO,
United States) was incubated at room temperature for 20 min
as previously described. At the end of the incubation, the reaction
was terminated by adding 5 μLofNaOH(10M);then,the
absorbance was measured at 405 nm (t20). The inhibitory
activity was determined as a percentage compared to the blank
(Buffer) according to the following equation:
%Inhibit ionPTP1B1−A405 s
()
A405 b
()
×100%,(1)
where A
405(s)
is the ΔAofthesample(A
t20
−A
t0
), A
405(b)
is the
ΔA of the blank obtained in the same manner as that of the
sample. Using the data obtained, the enzyme inhibition curve was
plotted, for which the average percentage of inhibition was
plotted as a function of the inhibitor concentration. To
calculate the IC
50
, non-linear regression was performed using
the Origin 8.0 software (OriginLab Corporation, 2022). For
calculating the IC
50
, the compounds showing a higher percent
of inhibition at 20 μM were tested at different concentrations, as
shown in Supplementary Figure S1.
TABLE 1 In vitro inhibitory activity against PTP1B of most active compounds of the natural product library.
Compound pIC
50
[a]
ΔA
[b]
Compound pIC
50
[a]
ΔA
[b]
1 4.48 0.2 7 4.08 0.6
2 4.39 0.3 8 4.06
3 4.32 9 4.05
4 4.31 0.4 10 4.05
5 4.24 11 4.00 0.7
[a]
Negative log of the medium inhibitory concentration (IC
50
) in Molar.
[b]
Activity difference between UA and the compound.
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2.2 In-house library of natural products
The tested in-house compounds (1–99) were isolated from
medicinal plants and fungi, as mentioned in Supplementary
Table S1, which includes the natural sources and pertinent
references. These compounds belong to a small molecule library
of natural products from the Department of Pharmacy, School of
Chemistry, UNAM.
2.2.1 Preparation of natural products databases
The isomeric SMILES codes of the most active tested
compounds were generated using the Open Babel toolbox
(O’Boyle et al., 2011). For chemical space characterization, we
included our in-house library (most active NPs) and 423 Mexican
natural products from the BIOFACQUIM database (Pilón-Jiménez
et al., 2019).
2.2.2 Molecular fingerprints
Six molecular fingerprints (FPs) were calculated for the natural
products database employing the MayaChemTools (Sud, 2016),
rcdk (Guha, 2007), and RDKit (Landrum, 2022) packages
(Supplementary Figure S2). These FPs were further used to
compute the Tanimoto similarity index from the similarity
matrix analysis. The Tanimoto similarity cumulative
distribution (TSD) plots were generated to select the best FP
representation for the tested compounds and the BIOFACQUIM
library (Supplementary Figure S2). The PubChem bit-string
(881 bits) was computed for the natural products database
chemical space representation using the Rcpi (Cao et al., 2015)
module of the R software (R Core Team, 2020).
In the case of the most active NPs, 25 molecular FPs were
calculated, and the same methodology was followed to select the best
FP representation and plot the SAS map (Supplementary
Figure S14).
The bit-string was dimensionally reduced by employing the
t-distributed stochastic neighbor embedding (t-SNE) projection
computed using the scikit-learn Python library (Pedregosa et al.,
2011) (perplexity: 40; iterations: 3,000). All plots from the
chemoinformatics analysis were generated using Gnuplot 5.0
(Williams et al., 2017).
2.3 Kinetic analysis
The conditions for preparing reagents (enzyme and
substrate), incubation, and analysis of results were the same as
that described in Section 2.1 (Jiménez-Arreola et al., 2020;Díaz-
Rojas et al., 2021). Additional steps in the methodology are
described below.
To characterize the type of inhibition exerted by 1,3,and6on
PTP1B, the hydrolysis of 4-NP at the sub-saturating state and
increasing concentrations of each compound was measured. An
enzyme saturation curve was prepared with a 4-NP stock solution
(10mM);then,aseriesofcurveswithvariable4-NP
concentrations and at least five fixed concentrations of the
inhibitors were built; by considering the IC
50
value as the
FIGURE 1
Chemical space 3D representation: the black spots are the most active compounds tested, and gray spots represent the Mexican natural products
database BIOFACQUIM.
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midpoint, the type of inhibition of 1,3,and6was estimated; each
point of the curves was obtained in triplicate. The compound
concentration yielding 50% activity inhibition was interpolated
from the results by fitting into a general inhibition model. The
resulting set of activities was fitted globally using the non-linear
regression algorithm of Levenberg–Marquardt as implemented in
the Gnuplot (Williams et al., 2017). Each group was fitted to the
kinetic equations for linear competitive, linear uncompetitive,
linear non-competitive (classic), linear mixed type, parabolic
competitive, parabolic uncompetitive, and parabolic non-
competitive inhibition mechanisms. The best fitwas
ascertained by the reduced chi-squared (χ
2
) value, residual
distribution, and uncertainty in the parameter estimates. The
best description of the inhibition data for compounds 1,3,and6
was obtained with Eq. 2,Eq.3,andEq.4, respectively, where
V
MAX
,K
M
,andK
Icu
,K
IC
,andK
IU
are the maximum velocity,
Michaelis constant for the substrate 4-NP, and inhibition
constants for each type of inhibition, respectively.
v0VMAX 4NP
[]
KM1+I
[]
KIcu
+4NP
[]
1+I
[]
KIcu
,(2)
v0VMAX 4NP
[]
KM1+I
[]
KIC
+4NP
[]
1+I
[]
KIU
,(3)
v0VMAX 4NP
[]
KM1+I
[]
KIC +I
[]
2
KIC22
+4NP
[]
.(4)
2.4 Structure–activity similarity
The SAS map of the most active compounds tested against
PTP1B was constructed using the absolute value of the pIC
50
FIGURE 3
Double-reciprocal plots of PTP1B inhibition at different
concentrations of compounds 1and 3.
FIGURE 2
Compounds showing pIC
50
in the same magnitude order as that of ursolic acid (UA).
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difference (|ΔActivity|) and the UA similarity calculated with the
atom type fingerprints (Supplementary Figure S14), following our
previous work methodology (Santiago et al., 2021). The SAS map
can only be constructed using the absolute value of the pIC
50
difference (|ΔActivity|) against ursolic acid and the UA
similarity of Tanimoto (Bajusz et al., 2015).
2.5 Structural model of PTP1B
1-400
A structural model of the hPTP1B
1-400
protein was obtained from
the AlphaFold Protein Structure Database developed by DeepMind
and EMBL-EBI (https://alphafold.ebi.ac.uk/). The UniProt code
P18031 corresponds to the PTPN1 gene, which codes for human
PTP1B that is among the proteins in the database predicted by
AlphaFold (code: Q9PT91). The pdb file was downloaded from
the following link: https://alphafold.ebi.ac.uk/entry/P18031 (David
et al., 2020). The coordinates of this model were prepared to
perform a molecular dynamics (MD) simulation using the LEAP
module from AmberTools 2021. The structure was submitted to the
following procedure: hydrogens were added using the LEAP module
with the leaprc.protein.ff19SB force field; K
+
counter ions were also
included to neutralize the system. The protein was solvated in an
octahedral box of explicit TIP3P model water molecules localizing the
box limits at 12 Å from the protein surface. MD simulations were
performed at 1atm and 315 K, and maintained with the Berendsen
barostat and thermostat, respectively, using periodic boundary
conditions and particle mesh Ewald sums (grid spacing of 1 Å) for
treating long-range electrostatic interactions with a 10-Å cutoff for
computing direct interactions. The SHAKE algorithm was used to
satisfy bond constraints, using a time step of two femtoseconds (fs) to
integrate Newton’s equations as recommended in the Amber package.
All calculations were made using the graphics processing unit(GPU)–
accelerated MD engine in Amber (pmemd.cuda), a program package
that runs entirely on CUDA-enabled GPUs (Case et al., 2005, 2012).
The protocol minimized the initial structure, followed by 50-
picosecond (ps) heating and pressure equilibration at 315 K and 1.
0 atm pressure, respectively. Finally, the system was equilibrated with
500 ps before starting the production of MD. The production of the
MD consisted of 100 nanoseconds (ns). The frames were saved at 10-
ps intervals for subsequent analysis. All analyses were done using
CPPTRAJ (Roe and Cheatham, 2013). Root mean square deviations
(RMSDs) and root mean square fluctuations (RMSFs) were
calculated, considering the C, Cα, and N. The charts were built
using OriginPro 9.1, and the trends were adjusted with smooth
function processing (lowess span method).
FIGURE 4
Study of kinetic inhibition for 6.(A) Equilibria for the parabolic competitive inhibition. (B) Inhibition patterns of PTP1B fitted to a global total
competitive parabolic inhibition.
TABLE 2 PTP1B nonlinear fit and inhibitory activity parameters of 1,3,and 6.
Compound Reduce χ
2[a]
V
MAX
[b]
(mM min
−1
) K
I
[c]
(mM)
10.6 44.1 KIcu = 0.009
31.4 56.1 K
IU
= 0.11, K
IC
= 0.04
6E/Z0.08 24.7 K
IC
= 0.031, K
IC2
= 0.020
[a]
Parameter of nonlinear fit. [b] Maximum velocity. [c] Inhibition constant according to the type.
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2.5.1 Molecular docking
The docking analysis was done using the structural model of
hPTP1B
1-400
obtained after MD simulation. Structures 1–11 were
constructed and minimized using the Avogadro software (Hanwell
et al., 2012). The AutoDockTools 1.5.4 was used to prepare the pdb
files of the protein and compounds. Polar hydrogen atoms and
Kollman united-atom partial charges were added to the protein
structures. By contrast, Gasteiger–Marsili charges and rotatable
groups were automatically assigned to the structures of the
ligands. We used AutoDock Vina to carry out the docking,
covering the entire enzyme. The grid box size was 126 Å ×
126 Å × 126 Å in the x, y, and z dimensions and the central
coordinates of 54.94, 68.66, and 63.45 for x, y, and z,
respectively, with exhaustiveness of 8. The best conformational
states were visualized by using PyMOL version 2.4.0 and Maestro
version 5.3.156 (DeLano, 2004).
In the case of compound 6, the geometric isomers and their
more stable conformers (6a1,6a2,6b1, and 6b2) were optimized
using SQM-MP7 methodology with implicit aqueous solvation and
finally converted to PDBQT format. The three-dimensional model
of PTP1B was the same as that used in the aforementioned
compounds. A wide box comprised the whole putative active site
and its neighborhood within 7 Å. Several docking rounds were
performed using AutoDock Vina 1.2.3 (Eberhardt et al., 2021)to
collect at least 160 docking poses for each compound. The
compounds were clustered using the Clustering Tool plugin for
VMD (Humphrey et al., 1996), using a 1-Å cutoff, and the average
Vina docking energy of each cluster was obtained from Vina logs.
Each cluster’s RMSD was calculated using one arbitrary pose as a
reference. Clusters having one or more poses with docking energy
higher than −5.3 kcal/mol were ignored because the more stable
ligand–protein complexes for compound 6presented more negative
values, therefore all values above −5.3 kcal/mol were disregarded.
The docking events with lower energy fell into two neighboring but
well-separated binding pockets designated as I and II. For each
binding pocket, the conformation with lower docking energy and
lower RMSD was considered more likely to represent the real
conformation, although docking poses occupying each pocket
were considered.
2.6 Medicinal chemistry and ADMET
predictions of the most active NPs
The medicinal chemistry (MC) properties analyzed included the
presence of toxicophore groups, undesirable structural fragments
(BRENK alerts), the absence of promiscuous moieties (PAINS), and
the Lipinski and Golden Triangle rules. All these properties were
calculated using the SwissADME or ADMETlab 2.0 (Sander et al.,
2015;Daina et al., 2017;Xiong et al., 2021). Metabolism (inhibition
of CYP1A2, CYP2C19, CYP2C9, CYP2D6, and CYP3A4), passive
human gastrointestinal (GI) absorption, blood–brain barrier (BBB)
permeation (using the BOILED-Egg), and drug-likeness
(bioavailability radar) were predicted using the SwissADME web
platform. Excretion using clearance (CL ≥15, mL/min) and short
half-life (T1/2) by the probability ≤1 and other absorption
parameters such as skin permeability (log Kp, cm/s, positive
value), water solubility (using SILICOS-IT), Caco-2 cell
(permeability ≥−5.15), and distribution (PPB ≤90%; VD,
0.04–20 L/g; Fu ≥20%) were calculated using the ADMETlab
2.0 online tool.
The toxicological properties of the active compounds were
projected using the DataWarrior v.5.5.0 (Sander et al., 2015)and
ICM v. 3.9 (Molsoft). Potential genotoxicity, carcinogenicity, rat
FIGURE 5
(A) SAS map shows the number of compound pairs in each zone. (B) Chemical structures identified as key scaffold hops (ZI). (C) Activity cliff
compounds located in ZIV.
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FIGURE 6
Chemical structure of the 19 pairs of compounds identified as activity cliffs in the SAS map. The compared pairs are represented by a gray line, and
the molecular similarity value is highlighted in yellow.
TABLE 3 Results of molecular docking between PTP1B and compounds 1,3,and 6.
Compound ΔG
T
[a]
(Kcal mol
−1
) Interacting residues
1−6.71 Met1, Glu8, Glu4, Pro241, Gly283, Met282, Lys279, Ala278, Glu276, Ile275, Leu 272
3−5.73 Pro206, Ser205, Leu204, Arg79, Glu200, Arg199, Phe196, Gln288, Phe280, Asp236, Leu233
6a1 EI
[b]
=−5.67 I
[b]
: Ala27, Arg24, Arg254, Gln262, Gly259, Met258, Tyr20
II
[c]
=−6.14 II
[c]
: Ala217, Arg221, Asp181, Asp48, Gln262, Gly220, Ile219, Lys120, Phe182, Tyr46, Val49
6a2 EI: −5.86 I: Ala27, Arg24, Arg254, Asp29, Asp48, Gln262, Gly259, Met258, Phe52, Ser28, Val49
II: −6.02 II: Ala217, Asp181, Asp48, Gln262, Gly220, Ile219, Lys120, Phe182, Tyr46, Val49
6b1 ZI: −5.95 I: Ala27, Arg24, Arg254, Asp29, Gln262, Gly259, Met258, Phe52, Ser28, Tyr20
II: −6.70 II: Ala217, Arg221, Asp181, Cys215, Gln262, Gln266, Gly220, Ile219, Lys120, Phe182, Ser216, Tyr46, Val49
6b2 ZI: −5.78 I: Ala27, Arg24, Arg254, Asp29, Gln262, Gly259, Met258, Phe52, Tyr20
II: −6.64 II: Ala217, Arg221, Asp181, Cys215, Gln262, Gln266, Gly220, Ile219, Lys120, Phe182, Ser216, Tyr46, Val49
UA −6.73 Gln290, Val287, Ser286, Asp284, Gly283, Met282, Lys279
[a]
Theoretical binding energy.
[b]
Pocket I where 6interacts.
[c]
Pocket II where 6interacts.
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oral acute (ROA) toxicity, inhibition of the cardiac potassium
channel (hERG), human hepatotoxicity (H-HT), drug-induced
liver injury (DILI), renal toxicity, and reproductive effects were
analyzed (probability ≤1). In addition, the prediction of the
medium lethal dose (DL50, mg/kg) in rats (acute oral toxicity)
was achieved using the Toxicity Estimation Software Tool (TEST,
EPA, version 5.1).
The properties calculated were compared to those known
inhibitors of PTP1B, which included UA (pIC
50
= 4.66),
trodusquemine (T, pIC
50
= 6.0), ertiprotafib (E, pIC
50
= 4.70),
and metformin (M) (Lantz et al., 2010;Kumar et al., 2020).
2.7 Acute oral toxicity in mice
The potential acute toxicity of compound 6was determined
according to the Lorke’s method (Lorke, 1983). The evaluation was
performed following the Official Mexican Norm for Laboratory
Animal Care and Use (NOM-062-ZOO-1999), with international
conventional codes for laboratory animal use, and approved by the
Institutional Committee for Care and Use of Laboratory Animals
(CICUAL-FQ), Facultad de Química, UNAM (FQ/CICUAL/440/
21). Eight-week-old male ICR mice (25–36 g) were adjusted to
laboratory conditions. At the end of the experiments, the mice
were euthanized by hypoxia in a CO
2
chamber.
Compound 6was fed by an intragastric route in two
independent phases. In each case, 12 mice were divided into four
groups (n = 3). In the first stage, the animals were treated with 10,
100, and 1,000 mg/kg; in the second phase, 1,600, 2,900, and
5,000 mg/kg were administered. The control animals were fed
0.05% Tween 80
®
in saline solution. The weight of the animals
was measured daily for 14 days at each stage. At the end of the test,
all animals were euthanized to obtain the lungs, heart, kidneys, and
liver to detect macroscopic organ injury. The mice were observed to
identify acute toxic effects, changes in behavioral patterns, or
mortality.
3 Results and discussion
3.1 Inhibition of PTP1B and chemical space
of NPs
A screening using hPTP1B
1-400
of a small customized (in-
house) NP library containing 99 compounds, mostly from
medicinal plants, was performed. The molecules were selected
by the following criteria: ethnopharmacological background of
the plant source, good physicochemical properties such as
solubility, and the availability of the compounds for future
research. The preliminary screening process at two different
FIGURE 7
Predicted interactions between ligands and PTP1B (AlphaFold code: Q9PT91). The protein is displayed in blue (cartoon), and active sites are shown as
yellow spheres and compounds as sticks: red (1), green (3), and UA (orange).
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concentrations (20 and 1,000 μM) allowed the identification of
those molecules that showed 90% inhibition or more at 20 μM.
Thereafter, the IC
50
values of these molecules were calculated.
The results allowed us to identify 47 inhibitory NPs, with IC
50
values ranging from 30 to 1,000 μM, and the most active were
compounds 1–11, whose activity gap ranged between 30 and
100 μM. Ursolic acid was used as the reference since it has been
widely reported as one of the best allosteric PTP1B inhibitor by
different research groups (Liu et al., 2022;Quy et al., 2022). Next,
their pIC
50
values [the negative log of the medium inhibitory
concentration (IC
50
)] (Table 1,Supplementary Table S2, and
Supplementary Figure S1) were calculated; the pIC
50
value is a
more reliable parameter to compare the potency of the different
compounds tested at the same molar levels, and it is commonly
used in chemoinformatics studies; furthermore, the pIC
50
value
covers both micro- and millimolar potencies (Kalliokoski et al.,
2013;Thakur et al., 2022).
We use the same fingerprints (NCBI, 2009) to represent the
active NPs (1–11,14,15,18,25,32,34,40,41,43,46,47,49–51,53,
54,56,58,63,64,67,73,77,82,84–86, and 90–94), UA and the
Mexican NPs database (BIOFACQUIM https://zinc15.docking.org/
catalogs/biofacquimnp/)(Pilón-Jiménez et al., 2019). Then, the 3D
chemical space projection obtained from the t-SNE analysis of the
computed FP demonstrated that the compounds with the best
inhibitory activity are chemically diverse. Furthermore, the active
compounds of the in-house library were distributed entirely in the
chemical space of BIOFACQUIM (Figure 1 and Supplementary
Figure S2).
FIGURE 8
Predicted interactions between PTP1B (AlphaFold code: Q9PT91) and the four more stable conformational isomer complexes of E/Zvermelhotin
(6). In the center, the protein is displayed in cyan (cartoon); pocket I residues are shown as pink spheres and pocket II residues are represented as yellow
spheres. The conformational entities in both pockets are shown as sticks: green (6a1), pink (6a2), gray (6b1), slate (6b2), cyan (6a1), yellow (6a2), salmon
(6b1), and orange (6b2).
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Among the 47 most active NPs, compounds 1–11 (Figure 2;Table 1
and Supplementary Figure S2) presented similar pIC
50
values to UA
(pIC
50
= 4.66 ± 0.004), therefore they were considered hits (Sharma
et al., 2020). These compounds included canophyllol (1), a friedelane
triterpenoid (Calzada et al., 1991); 5-O-(β-D-glucopyranosyl)-7-
methoxy-3′,4′-dihydroxy-4-phenylcoumarin (2)(Guerrero-Analco
et al., 2005); 3,4-dimethoxy-2,5-phenanthrenediol (3)(Estrada et al.,
1999); masticadienonic acid (4); a tirucallane (Rivero-Cruz et al., 2010);
FIGURE 9
Visual representation of the optimal range for each property presented in axes. Trodusquemine (T), ertiprotafib (E), and metformin (M).
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Díaz-Rojas et al. 10.3389/fphar.2023.1281045
4′,5,6-trihydroxy-3′,7-dimethoxyflavone (5)(Salinas-Arellano et al.,
2020); E/Z-vermelhotin (6)(Leyte-Lugo et al., 2012); tajixanthone
hydrate (7)(Figueroa et al., 2009); quercetin-3-O-(6″-benzoyl)-β-D-
galactoside (8)(Flores-Bocanegra et al., 2015); lichexanthone (9)(Rojas
et al., 2000); the protolimonoid melianodiol (10)(Jimenez et al., 1998);
and the phenanthrene confusarin (11)(Morales-Sánchez et al., 2014).
All but the fungal compounds 6,7,and9were isolated from plants with
reputed antidiabetic properties. The natural sources of these
compounds are given in Supplementary Table S1.Allcompounds
but 5were not previously evaluated against PTP1B.
3.2 Kinetic analysis of selected compounds
Compounds 1,3,and6were selected for kinetic analysis due
to their sufficient availability and good inhibitory activity against
PTP1B (Table 1;Table 2;Figure 3;Figure 4). Compound 1(the
most active compound identified, according to pIC
50
and ΔA)
behaved as a non-competitive inhibitor and 3as a mixed
inhibitor (with the second-best value of pIC
50
and ΔA). In
both cases, when the concentration of the compounds
increased, the slope and intersection with the ordinate axis
changed, but the intersection with the abscissae did not. In
addition, the K
IC
and K
IU
values changed for compound 3
(Spector and Cleland, 1981;Copeland, 2000). In both cases,
the inhibition mechanism discovered was relevant because
they interacted with a distinct area from the active site,
guaranteeing a selective interaction with PTP1B.
For E/Zvermelhotin (6), the kinetic analysis data fit for a
competitive parabolic inhibition; the statistical analysis of this fit
predicted that two molecules of 6bound to the active site (Table 2),
which is possible due to the small molecular size. Figure 4A presents
the mechanism of inhibition for 6, implying mutually exclusive
binding of the inhibitor and the substrate, allowing for two
molecules of the inhibitor to bind to the enzyme. In the double-
reciprocal plot, the V
MAX
value did not show any change, but the K
M
value changed significantly (Figure 4B). The modification of the
structure of E/Zvermelhotin could improve its primary site of
interaction.
The classic Michaelis–Menten plots were also convenient for
defining the mode of inhibition of compounds 1,3, and 6and have
been included in Supplementary Figure S3.
3.3 SAS map
A SAS map (Medina-Franco, 2012) was created to identify
scaffold hopes (compounds with low structural similarity but low
activity difference) by plotting the absolute value of the PTP1B pIC
50
difference (|ΔActivity|) against ursolic acid structural similarity
FIGURE 10
BOILED-Egg model for the prediction of passive absorption in GI and BBB using SwissADME.
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Díaz-Rojas et al. 10.3389/fphar.2023.1281045
(Tanimoto similarity indexes) (Figure 1 and Figure 5) employing
atom-type fingerprints. The resulting map was divided into four
zones (ZI–ZIV), comprising the scaffold hopping region (ZI),
smooth or continuous SAR (ZII), a non-descript region (ZIII),
and activity cliffs (ZIV) (Naveja et al., 2018).
The analysis of the ZI region (818 pairs) revealed that NPs 3,
5,6(Figure 5B), 7,8,9,11,34,82,and99 (Supplementary Figure
S15) possessed low structural similarity to the reference
compound (UA) and good PTP1B inhibitory potency,
therefore they are considered scaffold hops. In this group, it is
not possible to make conclusions regarding structural activity
relationships due to the diversity of structures. The structural
simplicity of compounds 3and 6makes them hit compounds,
likely to be optimized and used for developing potential drugs
with novel scaffolds.
TheSASmapallowstheidentification of canophyllol (1),
also a pentacyclic triterpenoid, as the only true analog of UA
(located in ZII), justified as the smallest ΔA obtained (0.2); the
results suggest that the pentacyclic structure and oxygenated
functionalities at C-3 and C-28 make similar compounds more
active. This information is consistent with other UA
derivatives possessing alcohol and carboxyl functionality at
C-3 and C-28, respectively (Guzmán-Ávila et al., 2018;Khwaza
et al., 2020).
This research also examined several triterpenoids that included
4,10,56,64,85,86, and 89, with a lower activity level than UA.
None of them had the same pentacyclic core as UA and lacked the
alcohol or carboxylic functionality at C-3 and C-28, respectively,
suggesting that these structural features are important for activity
(Figure 6).
3.4 Molecular docking analysis of hit
compounds
To predict the binding modes between compounds 1–11 and
PTP1B, a docking analysis was performed using the AlphaFold
enzyme (code: Q9PT91), which was first subjected to molecular
dynamics simulation (MDS) (Supplementary Figure S5A,B).
After 100 ns, the region between amino acids 300 and 400 of
FIGURE 11
Graphic representation of some of the estimated toxicological properties for compounds 1–11. Correlation plot of compounds according to their
estimated LD
50
and toxicity class. Marker size indicates the probability for a compound to be a PGP inhibi tor (the larger, the more probable). Marker color
refers to the toxicity score (chemical alerts found in the structure; values >1 indicate unf avorable substructures). Marker shape indicates high (circle), low
(square), or negligible (triangle) mutagenicity. Marker background color correlates with the probability of being a PAINS.
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Díaz-Rojas et al. 10.3389/fphar.2023.1281045
PTP1B (Supplementary Figure S6A), whichisanintrinsically
unstructured site, was more stable (Supplementary Figure S6B).
The robustness of the structural model was supported by
Ramachandran plots and quality scores, before and after the
MDS. Therefore, the model shown in Figure F6B was used for
docking studies. This section shows only the results of 1,3,and6
(Table 3;Figures 7,8) to correlate the findings with those of the
kinetic analysis. Compounds 1and 3bind in a pocket formed by
the 3α,6α,and7αhelices near the C-terminal region of PTP1B, as
previously reported for UA (Liu et al., 2022); this outcome is in
agreement with the non-competitive mechanism found in the
kinetic studies. Compounds 1and 3interacted with different
amino acids, but the nature of the interactions was
predominantly hydrophobic. Compound 1had lower energy
binding.
Since compound 6undergoes interconversion between the E
(6a1,6b1) and Z(6a2,6b2) isomers (Supplementary Figure S4),
forming an equilibrium E/Zmixture with a ratio of 6.4:3.6 (Leyte-
Lugo et al., 2012), the docking analysis was undertaken with the four
more stable conformational isomers. For all conformers, 6a1,6a2,
6b1, and 6b2, the low-energy docking events fell in two contiguous
relatively separate pockets designated as I and II (Figure 8 and
Supplementary Figure S7–S10), where simultaneous binding of two
molecules might occur. Conformers 6b1 and 6b2 predominantly
bind to pocket II, which includes active site residues supporting the
parabolic competitive inhibition kinetic mechanism found for NP 6.
Pocket I, where 6a1 and 6a2 might attach, contains secondary
residues different from the active site (Liu et al., 2022). The
lowest energy pose was in pocket II (active site), followed by
theposeatsiteI,whichmaybeconsideredanallostericspot
(Table 3;Figure 8,Supplementary Figure S7–S10). This outcome
is relevant because the bidentate PTP1B inhibitors bind
simultaneously to the catalytic and allosteric sites, conferring
higher specificity and potency to these inhibitors (Chen et al.,
2018;Akyol and Kilic, 2021).
The docking studies for compounds 2,4,5, and 7–11 are given
in Supplementary Figure S11, S12 and Supplementary Table S3, S4.
All compounds bound to an allosteric area in the enzyme; 2,4,5, and
7–11 presented ΔG between −7.2 and −5.6 kcal mol
−1
, and their
interactions were hydrophobic; metabolites 4,7,9, and 10 attached
in the region formed by helices 3α,6α, and 7α, the same as UA. In the
case of 2,8, and 11, they were positioned near the 3αand 9αhelices
and two βstrands. Finally, among all hits, flavonoid 8showed the
best binding energy to the protein.
FIGURE 12
Graphical representation of some of the estimated toxicological properties for compounds 1–11. Correlation plot of compounds accordin g to their
estimated LD
50
and toxicity class. Marker size indicates the Caco-2 cell permeability related to in vivo absorption. Marker color refers to low or non-
tumorigenic effect. The marker background color indicates teratogenicity (reproductive effect).
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3.5 Medicinal chemistry and ADMET
predictions of most active NPs
To estimate the safety and efficacyofthe11activeNPs,their
physicochemical, pharmacokinetic, and pharmacodynamic
profiles were analyzed to predict their potential use in drug
development (Supplementary Figure S13,Supplementary Table
S5–S11). All the results predicted were only taken as an indicator of
passive absorption properties because NPs or NP-derived drugs
have more than two violations of the Lipinski’sruleandtheir
physicochemical, pharmacokinetic, and pharmacodynamic
profiles are different from those of the approved drugs
(Boufridi and Quinn, 2018).
The bioavailability radar (Figure 9) predicted that
compounds 1–11 possess good physicochemical properties.
However, the degree of unsaturation and solubility values were
out of the optimal range in almost all the molecules but 7.
Regarding the medicinal chemistry rules, only 3,6,7,9,and
10 fulfilled the Lipinski and Golden Triangle rules
(Supplementary Table S6); compounds 2,5,and8are pan-
assay interference substances (PAINS) due to the catechol
fragment motifs; in consequence, they could show false
positive results in biological assays. Finally, all compounds,
excluding 1, showed one or two BRENK alerts, so they possess
unwanted fragments (Supplementary Table S6).
The predictions related to passive permeability in the GI tract
and BBB, as well as interaction with the glycoprotein P (PGP), are
presented in the BOILED-Egg (Figure 10); the visual analysis of
the BOILED-Egg graphic shows that 3,9,and11 could passively
cross the BBB, therefore they could be neurotoxic. It is possible
that 5–7and 10 may experience passive gastrointestinal
absorption because they share similar physicochemical
properties with oral medications, such as M. T and E fell
outside the graphic, precluding any prediction regarding their
absorption type. Finally, only the prenylated xanthone 7and
triterpenoid 10 could be a substrate of PGP, indicating low
possibility for unsafe drug–drug interaction and problems with
their excretion.
In addition, the medium lethal concentration doses (LD
50
s) were
estimated using TEST v.5.1; all calculations are given
in Supplementary Table S10, S11. The toxicity conclusion
must be interpreted cautiously; no compound is free of toxic
effects. According to these results, compounds 1,6, and 10 were
the NPs with fewer toxicological alerts. The remaining compounds
presented one or two alerts. The most relevant toxicological alerts
were for compounds 9(high mutagenic and teratogenic effects), 5,
and 7(high mutagenicity), and compound 2(high teratogenic
action). Regarding the LD
50
s, compounds 1,3, and 6showed
LD
50
s≥1,000 mg/kg.
Figure 11 and Figure 12 display six toxicological properties on
a graph to better understand potentially toxic molecules. On
average, NPs 1and 6showed the lowest number of alerts
(three and six, respectively) (Supplementary Table S11).
Experimental Lorke toxicity evaluation of compound 6
revealed no acute toxic effect on mice; the experimental LC
50
value was estimated to be higher than 5,000 mg/kg, in agreement
with the predicted results.
4 Conclusion
From a small and diverse NP library selected mostly on the
basis of an ethnopharmacological criterion, 11 hit compounds
(1–11)wereidentified, seven of which were scaffold hops (3,5,6,
7,8,9,and11). NPs 1–5,8,10,and11 are used in different
traditional preparations for treating diabetes or its
complications. Therefore, this work provides information
supporting the rational use of some Mexican medicinal plants
employed for treating diabetes.
The 47 active compounds detected after the preliminary screen
are chemically diverse, covering most of the explored space of
Mexican NPs. The Tanimoto similarity indexes and the absolute
value of the PTP1B inhibitory activity differences of all pairs of
compounds allowed identifying 10 hit molecules (3,5,6,7,8,9,11,
34,82, and 99) with low structural similarity to the reference
compound (UA) but good PTP1B inhibitory potency (scaffold
hops). The structural simplicity of 3and 6pinpoint them as hits
for developing potential drugs with novel scaffolds.
After thoroughly analyzing the medicinal chemistry and
physicochemical, pharmacokinetic, and toxicological properties of
various NPs, 1,6, and 10 seem to be the most promising hit
compounds as PTP1B inhibitors. According to the Lorke
analysis, compound 6is not toxic to mice, with an LC
50
value
higher than 5,000 mg/kg. The three compounds warrant drug
optimization analysis to develop new leads for drug development.
Data availability statement
The original contributions presented in the study are included in
the article/Supplementary Material; further inquiries can be directed
to the corresponding authors.
Ethics statement
The animal study was approved by the Institutional Committee
for Care and Use of Laboratory Animals (CICUAL-FQ), Facultad de
Química, UNAM (FQ/CICUAL/440/21). The study was conducted
in accordance with the local legislation and institutional
requirements.
Author contributions
MD-R: conceptualization, methodology, and manuscript
writing–original draft and review and editing. MG-A:
conceptualization, project administration, manuscript
writing–original draft and review and editing. RA-O:
conceptualization, formal analysis, methodology, and manuscript
writing–original draft and review and editing. RR-S:
conceptualization, formal analysis, methodology, and manuscript
writing–original draft and review and editing. AP-V:
conceptualization, project administration, and manuscript
writing–original draft and review and editing. AM-M: formal
analysis, methodology, and manuscript writing–original draft and
Frontiers in Pharmacology frontiersin.org14
Díaz-Rojas et al. 10.3389/fphar.2023.1281045
review and editing. RM: conceptualization, project administration,
and manuscript writing–original draft and review and editing.
Funding
The authors declare financial support was received for the
research, authorship, and/or publication of this article. This work
was supported by grants from DGAPA-UNAM (PAPIIT-IN203523;
PAPIIT-IN203222; PAPIIT-IN217320); CONAHCYT A1_S_11226
(CY011226); DGTIC-UNAM (LANCAD-UNAM-DGTIC-313 and
LANCAD-UNAM-DGTIC-218); PAIP-5000-9140 from FQ-UNAM;
and the Research Division of the Medical School, UNAM. MD-R
acknowledges a fellowship from CONAHCYT (604010).
Acknowledgments
The authors thank Ramiro del Carmen for computational
support and Isabel Rivero-Cruz for technical assistance from
FQ-UNAM and Luz Xochiquetzalli Vásquez–Bochm for technical
assistance from FM-UNAM.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, editors, and reviewers.
Any product that may be evaluated in this article, or claim that
may be made by its manufacturer, is not guaranteed or endorsed by
the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fphar.2023.1281045/
full#supplementary-material
References
ADA (2022). 9. Pharmacologic approaches to glycemic treatment: standards of
medical Care in diabetes—2022. Diabetes Care 45, S125–S143. doi:10.2337/dc22-S009
Akyol, K., and Kilic, D. (2021). Discovery of novel and selective inhibitors
targeting protein tyrosine phosphatase 1B (PTP1B): virtual screening and
molecular dynamic simulation. Comput. Biol. Med. 139, 104959. doi:10.1016/j.
compbiomed.2021.104959
Bajusz, D., Rácz, A., and Héberger, K. (2015). Why is Tanimoto index an appropriate
choice for fingerprint-based similarity calculations? J. Cheminformatics 7 (1), 20–13.
doi:10.1186/s13321-015-0069-3
Boufridi, A., and Quinn, R. J. (2018). Harnessing the properties of natural products.
Annu. Rev. Pharmacol. Toxicol. 58, 451–470. doi:10.1146/annurev-pharmtox-010716-
105029
Calzada, F., Navarrete, A., del Rio, F., and Delgado, G. (1991). Long-chain phenols
from the bark of Amphypterygium adstringens. J. Ethnopharmacol. 34 (2–3), 147–154.
doi:10.1016/0378-8741(91)90032-9
Cao, D.-S., Xiao, N., Xu, Q. S., and Chen, A. F. (2015). Rcpi: R/Bioconductor package
to generate various descriptors of proteins, compounds and their interactions.
Bioinformatics 31 (2), 279–281. doi:10.1093/bioinformatics/btu624
Chen, X., Gan, Q., Feng, C., Liu, X., and Zhang, Q. (2018). Virtual screening of novel
and selective inhibitors of protein tyrosine phosphatase 1B over T-cell protein tyrosine
phosphatase using a bidentate inhibition strategy. J. Chem. Inf. Model. 58 (4), 837–847.
doi:10.1021/acs.jcim.8b00040
Copeland, R. A. (2000). Enzymes. A practical introduction to structure, mechanism,
and data analysis. Second. New York: Wiley-CVH. doi:10.1006/abio.2001.5023
Daina, A., Michielin, O., and Zoete, V. (2017). SwissADME: a free web tool to evaluate
pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small
molecules. Sci. Rep. 7, 42717. doi:10.1038/srep42717
David, L., Thakkar, A., Mercado, R., and Engkvist, O. (2020). Molecular
representations in AI-driven drug discovery: a review and practical guide.
J. Cheminformatics 12 (1), 56–22. doi:10.1186/s13321-020-00460-5
DeLano, W. L. (2004). Use of PYMOL as a communications tool for molecular
science. Abstr. Pap. Am. Chem. Soc. 228, U313–U314.
Díaz-Rojas, M., Raja, H., González-Andrade, M., Rivera-Chávez, J., Rangel-Grimaldo,
M., Rivero-Cruz, I., et al. (2021). Protein tyrosine phosphatase 1B inhibitors from the
fungus Malbranchea albolutea. Phytochemistry 184, 112664. doi:10.1016/j.phytochem.
2021.112664
EberhardtSantos-MartinsTillack, J. D. A. F., and Forli, S. (2021). AutoDock Vina
1.2.0: new docking methods, expanded force field, and Python bindings. J. Chem. Inf.
Model. 61 (8), 3891–3898. doi:10.1021/acs.jcim.1c00203
Estrada, S., Toscano, R. A., and Mata, R. (1999). New phenanthrene derivatives from
Maxillaria densa. J. Nat. Prod. 62 (8), 1175–1178. doi:10.1021/np990061e
Figueroa, M., González, M. d. C., Rodríguez-Sotres, R., Sosa-Peinado, A., González-
Andrade, M., Cerda-García-Rojas, C. M., et al. (2009). Calmodulin inhibitors from the
fungus Emericella sp. Bioorg.Med.Chem.17 (6), 2167–2174.doi:10.1016/j.bmc.2008.10.079
Flores-Bocanegra, L., Pérez-Vásquez, A., Torres-Piedra, M., Bye, R., Linares, E., and
Mata, R. (2015). α-Glucosidase inhibitors from vauquelinia corymbosa. Molecules 20
(8), 15330–15342. doi:10.3390/molecules200815330
Guerrero-Analco, J. A., Hersch-Martínez, P., Pedraza-Chaverri, J., Navarrete, A., and
Mata, R. (2005). Antihyperglycemic effect of constituents from Hinto nia standleyana in
streptozotocin-induced diabetic rats. Planta Medica 71 (12), 1099–1105. doi:10.1055/s-
2005-873137
Guha, R. (2007). Chemical informatics functionality in R. J. Stat. Softw. 18 (5). doi:10.
18637/jss.v018.i05
Guzmán-Ávila, R., Flores-Morales, V., Paoli, P., Camici, G., Ramírez-Espinosa, J. J.,
Cerón-Romero, L., et al. (2018). Ursolic acid derivatives as potential antidiabetic agents:
in vitro, in vivo,andin silico studies. Drug Dev. Res. 79 (2), 70–80. doi:10.1002/ddr.
21422
Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., and
Hutchison, G. R. (2012). Avogadro: an advanced semantic chemical editor,
visualization, and analysis platform. J. ofCheminformatics 4 (17), 17. doi:10.1186/
1758-2946-4-17
Harvey, A. L., Clark, R. L., Mackay, S. P., and Johnston, B. F. (2010). Current strategies
for drug discovery through natural products. Expert Opin. Drug Discov. 5 (6), 559–568.
doi:10.1517/17460441.2010.488263
HumphreyDalke, W. A., and Schulten, K. (1996). VMD: visual molecular dynamics.
J. Mol. Graph. 14 (1), 33–38. doi:10.1016/0263-7855(96)00018-5
IDF (2021). “IDF diabetes atlas,”in Diabetes research and clinical practice. 10th edn.
doi:10.1016/j.diabres.2013.10.013
Jimenez, A., Villarreal, C., Toscano, R. A., Cook, M., Arnason, J. T., Bye, R., et al.
(1998). Limonoids from swietenia humilis and guarea grandiflora (Meliaceae)Taken in
part from the PhD and MS theses of C. Villarreal and M. A. Jiménez, respectively.
Phytochemistry 49 (7), 1981–1988. doi:10.1016/s0031-9422(98)00364-1
Jiménez-Arreola, B. S., Aguilar-Ramírez, E., Cano-Sánchez, P., Morales-Jiménez, J.,
González-Andrade, M., Medina-Franco, J. L., et al. (2020). Dimeric phenalenones from
Talaromyces sp. (IQ-313) inhibit hPTP1B1-400: insights into mechanistic kinetics from
in vitro and in silico studies. Bioorg. Chem. 101, 103893. doi:10.1016/j.bioorg.2020.
103893
Kalliokoski, T., Kramer, C., Vulpetti, A., and Gedeck, P. (2013). Comparability of
mixed IC₅₀ data - a statistical analysis. PLoS ONE 8 (4), e61007. doi:10.1371/journal.
pone.0061007
Kanwal, A., Kanwar, N., Bharati, S., Srivastava, P., Singh, S. P., and Amar, S. (2022).
Exploring new drug targets for type 2 diabetes: success, challenges and opportunities.
Biomedicines 10 (2), 331. doi:10.3390/biomedicines10020331
Frontiers in Pharmacology frontiersin.org15
Díaz-Rojas et al. 10.3389/fphar.2023.1281045
Kazakova, O., Giniyatullina, G., Babkov, D., and Wimmer, Z. (2022). From marine
metabolites to the drugs of the future: squalamine, trodusquemine, their steroid and
triterpene analogues. Int. J. Mol. Sci. 23 (3), 1075. doi:10.3390/ijms23031075
Khwaza, V., Oyedeji, O. O., and Aderibigbe, B. A. (2020). Ursolic acid-based
derivatives as potential anti-cancer agents: an update. Int. J. Mol. Sci. 21 (16),
5920–5927. doi:10.3390/ijms21165920
Kumar, G. S., Page, R., and Peti, W. (2020). The mode of action of the Protein tyrosine
phosphatase 1B inhibitor Ertiprotafib. PLOS ONES 15 (10), e0240044. doi:10.1371/
journal.pone.0240044
Lahlou, M. (2007). Screening of natural products for drug discovery. Expert Opin.
Drug Discov. 2 (5), 697–705. doi:10.1517/17460441.2.5.697
Landrum, G. (2022). RDKit: open-source cheminformatics.
Lantz, K. A., Hart,S. G. E., Planey, S. L., Roitman, M. F.,Ruiz-White, I. A., Wolfe, H. R.,
et al. (2010).Inhibition of PTP1B by trodusquemine (MSI-1436) causes fat-specific weight
loss in diet-induced obese mice. Obesity 18 (8), 1516–1523. doi:10.1038/oby.2009.444
Leyte-Lugo, M., González-Andrade, M., González, M. d. C., Glenn, A. E., Cerda-
García-Rojas, C. M., and Mata, R. (2012). (+)-Ascosalitoxin and vermelhotin, a
calmodulin inhibitor, from an endophytic fungus isolated from Hintonia latiflora.
J. Nat. Prod. 75 (9), 1571–1577. doi:10.1021/np300327y
Liu, R., Mathieu, C., Berthelet, J., Zhang, W., Dupret, J. M., and Rodrigues Lima, F.
(2022). Human protein tyrosine phosphatase 1B (PTP1B): from structure to clinical
inhibitor perspectives. Int. J. Mol. Sci. 23 (13), 7027. doi:10.3390/ijms23137027
Lorke, D. (1983). A new approach to practical acute toxicity testing. Archives Toxicol.
54 (4), 275–287. doi:10.1007/BF01234480
Medina-Franco, J. L. (2012). Scanning structure-activity relationships with structure-
activity similarity and related maps: from consensus activity cliffs to selectivity switches.
J. Chem. Inf. Model. 52 (10), 2485–2493. doi:10.1021/ci300362x
Morales-Sánchez, V., Rivero-Cruz, I., Laguna-Hernández, G., Salazar-Chávez, G., and
Mata, R. (2014). Chemical composition, potential toxicity, and quality control
procedures of the crude drug of Cyrtopodium macrobulbon. J. Ethnopharmacol. 154
(3), 790–797. doi:10.1016/j.jep.2014.05.006
Na, B., Nguyen, P. H., Zhao, B. T., Vo, Q. H., Min, B. S., and Woo, M. H. (2016).
Protein tyrosine phosphatase 1B (PTP1B) inhibitory activity and glucosidase inhibitory
activity of compounds isolated from Agrimonia pilosa. Pharm. Biol. 54 (3), 474–480.
doi:10.3109/13880209.2015.1048372
Naveja, J. J., Oviedo-Osornio, C. I., and Medina-Franco, J. L. (2018). Computational
methods for epigenetic drug discovery: a focus on activity landscape modeling. Adv.
Protein Chem. Struct. Biol. 113, 65–83. doi:10.1016/bs.apcsb.2018.01.001
NCBI (2009). PubChem subgraph fingerprint. Available at: https://ftp.ncbi.nlm.nih.
gov/pubchem/specifications/pubchem_fingerprints.pdf.
O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., and
Hutchison, G. R. (2011). Open Babel: an open chemical toolbox. J. Cheminformatics
3 (1), 33. doi:10.1186/1758-2946-3-33
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., and Grisel, O.
(2011). Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830.
doi:10.48550/arXiv.1201.0490
Pilón-Jiménez, B. A., Saldívar-González, F. I., Díaz-Eufracio, B. I., and Medina-
Franco, J. L. (2019). BIOFACQUIM: a Mexican compound database of natural
products. Biomolecules 9 (1), 31. doi:10.3390/biom9010031
Quy, P. T., Van Hue, N., Bui, T. Q., Triet, N. T., Van Chen, T., and Van Long, N.
(2022). Inhibitory, biocompatible, and pharmacological potentiality of dammarenolic-
acid derivatives towards α-glucosidase (3W37) and tyrosine phosphatase 1B (PTP1B).
Vietnam J. Chem. 60 (2), 223–237. doi:10.1002/vjch.202100189
R Core Team (2020). R: a language and environment for statistical computing. Vienna,
Austria: R Foundation for Statistical Computing.
Rangel-Grimaldo, M., Macías-Rubalcava, M. L., González-Andrade, M., Raja, H.,
Figueroa, M., and Mata, R. (2020). α-Glucosidase and protein tyrosine phosphatase 1B
inhibitors from malbranchea circinata. J. Nat. Prod. 83 (3), 675–683. doi:10.1021/acs.
jnatprod.9b01108
Rivero-Cruz, I., Acevedo, L., Guerrero, J. A., Martínez, S., Bye, R., Pereda-Miranda, R.,
et al. (2010). Antimycobacterial agents from selected Mexican medicinal plants.
J. Pharm. Pharmacol. 57 (9), 1117–1126. doi:10.1211/jpp.57.9.0007
Roe, D. R., and Cheatham, T. E. (2013). PTRAJ and CPPTRAJ: software for
processing and analysis of molecular dynamics trajectory data. J. Chem. Theory
Comput. 9 (7), 3084–3095. doi:10.1021/ct400341p
Rojas, I. S., Lotina-Hennsen, B., and Mata, R. (2000). Effect of lichen metabolites on
thylakoid electron transport and photophosphorylation in isolated spinach
chloroplasts. J. Nat. Prod. 63 (10), 1396–1399. doi:10.1021/np0001326
Salinas-Arellano, E., Pérez-Vásquez, A., Rivero-Cruz, I., Torres-Colin, R., González-
Andrade, M., Rangel-Grimaldo, M., et al. (2020). Flavonoids and terpenoids with PTP-
1B inhibitory properties from the infusion of salvia amarissima ortega. Molecules 25
(15), 3530. doi:10.3390/molecules25153530
Sander, T., Freyss, J., von Korff, M., and Rufener, C. (2015). DataWarrior: an open-
source program for chemistry aware data visualization and analysis. J. Chem. Inf. Model.
55 (2), 460–473. doi:10.1021/ci500588j
Santiago, Á., Guzmán-Ocampo, D. C., Aguayo-Ortiz, R., and Dominguez, L. (2021).
Characterizing the chemical space of γ-secretase inhibitors and modulators. ACS Chem.
Neurosci. 12 (15), 2765–2775. doi:10.1021/acschemneuro.1c00313
Sharma, B., Xie, L., Yang, F., Wang, W., Zhou, Q., Xiang, M., et al. (2020). Recent
advance on PTP1B inhibitors and their biomedical applications. Eur. J. Med. Chem. 199,
112376. doi:10.1016/j.ejmech.2020.112376
Singh, S., Singh Grewal, A., Grover, R., Sharma, N., Chopra, B., Kumar Dhingra, A.,
et al. (2022). Recent updates on development of protein-tyrosine phosphatase 1B
inhibitors for treatment of diabetes, obesity and related disorders. Bioorg. Chem. 121,
105626. doi:10.1016/j.bioorg.2022.105626
Song, Y. H., Uddin, Z., Jin, Y. M., Li, Z., Curtis-Long, M. J., Kim, K. D., et al. (2017).
Inhibition of protein tyrosine phosphatase (PTP1B) and α-glucosidase by geranylated
flavonoids from Paulownia tomentosa. J. Enzyme Inhibition Med. Chem. 32 (1),
1195–1202. doi:10.1080/14756366.2017.1368502
Spector, T., and Cleland, W. W. (1981). Meanings of Ki for conventional and
alternate-substrate inhibitors. Biochem. Pharmacol. 30 (1), 1–7. doi:10.1016/0006-
2952(81)90277-X
Sud, M. (2016). MayaChemTools: an open source package for computational
drug discovery. J.Chem.Inf.Model.56 (12), 2292–2297. doi:10.1021/acs.jcim.
6b00505
Thakur, A., Kumar, A., Sharma, V., and Mehta, V. (2022). PIC50: an open source tool
for interconversion of PIC50 values and IC50 for efficient data representation and
analysis. bioRxiv.
Tonks, N. K. (2003). PTP1B: from the sidelines to the front lines!. FEBS Lett. 546 (1),
140–148. doi:10.1016/S0014-5793(03)00603-3
Williams, T., Kelley, C., Bersch, C., Bröker, H. B., Campbell, J., Cunningham, R., et al.
(2017). gnuplot 5.2. An interactive plotting program. Available at: http://www.gnuplot.
info/docs_5,2.
Xiong,G.,Wu,Z.,Yi,J.,Fu,L.,Yang,Z.,Hsieh,C.,etal.(2021).ADMETlab2.0:an
integrated online platform for accurate and comprehensive predictions of ADMET
properties. Nucleic Acids Res. 49 (W1), W5–W14. doi:10.1093/nar/gkab255
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