Antidiabetic Activity of
Arcangelisia
flava
Stem Bark from Southeast
Sulawesi Indonesia
In
Vivo
Integrated with Network Pharmacology
Analysis
Article
Nurramadhani A. Sida
*
, Rifa’atul Mahmudah, Hasnawati
, Henny Kasmawati
, Nurull
Hikmah
Department of Pharmacy, Halu Oleo University, Kendari-93232, Indonesia
A
bstract
The antidiabetic activity of
Arcangelisia
flava
stem bark has not been previously reported. This study
evaluated
the
antidiabetic
and
antioxidant
activities
of
its
extract
and
fractions,
identified
bioactive
compounds, and explored potential mechanisms using network pharmacology. Chemical constituents
of the n-hexane fraction were analyzed by gas chromatography–mass spectrometry (GC-MS). In vivo
antidiabetic activity was assessed in streptozotocin/niacinamide-induced Mus musculus using the oral
glucose
tolerance
test
(OGTT),
while
antioxidant
activity
was
determined
by
the
ABTS
assay.
GC-MS
identified
18
compounds
classified
into
aromatic
hydrocarbons,
ketones
and
alcohols,
phenolic
aldehydes, sulfur compounds, terpenes, fatty acid derivatives, and alkaloids. The greatest reductions in
blood
glucose
levels
were
observed
in
groups
treated
with
ethanolic
extract
(500
mg/kgBW),
glibenclamide, and ethyl acetate fraction (125 mg/kgBW), with reductions of 50.25%, 43.53%, and 42.82%,
respectively. ABTS analysis showed IC
₅₀
values of 8.00, 49.80, 110.25, 10.20, and 18.44 µg/mL for vitamin
C, ethanolic extract, n-hexane fraction, ethyl acetate fraction, and butanol fraction, respectively. Network
pharmacology
analysis
indicated
that
compounds
7
and
17
from
the
n-hexane
fraction
may
exert
antidiabetic
effects
through
inhibition
of
the
AGE–RAGE
signaling
pathway
associated
with
diabetic
complications.
Key
target
proteins
included
MAPK1,
MMP2,
MAPK14,
MAPK3,
PIK3CA,
and
TGFBR1.
Overall,
Arcangelisia
flava
extracts
and
fractions
demonstrate
promising
antidiabetic
and
antioxidant
potential, although further in vitro and mechanistic studies are required to validate these findings.
Keywords:
Antioxidant, AGE-RAGE, Mus musculus, Streptozotocin-nicotinamide, GC-MS
*
Corresponding author
Email addresses:
apt.nurramadhani08@uho.ac.id
(NA Sida)
DOI:
https://doi.org/10.22437/chp.v10i1.49750
Received
November 08
th
2025;
Accepted
March 08
th
2026;
Available online
May 26
th
2026
Copyright © 2026 by Authors, Published by Chempublish Journal. This is an open access article under the CC BY License
(
https://creativecommons.org/licenses/by/4.0
)
119
Graphical Abstract
Introduction
Indonesia remains one of the countries with
the
greatest
biodiversity
in
the
world.
In
2017,
it
was
recorded
that
Indonesia
possessed
approximately
31,750
plant
species,
around
15,000
species
were
identified
having
potential
medicinal
properties. Unfortunately, only about 7,000
species have been utilized as raw materials
for
traditional
or
modern
medicines
[1].
Despite this, the use of plants in traditional
medicine has long been an integral part of
Indonesian
culture.
Historical
evidence
shows
that
Indonesians
have
practiced
natural
healing
for
generations,
and
traditional
medicine
continues
to
be
a
preferred choice for maintaining health and
treating
various
diseases
[2].
Originally
based on empirical knowledge, this practice
has
drawn
the
attention
of
many
researchers seeking to scientifically validate
its pharmacological effects and develop it as
a potential source of medicinal compounds.
One plant that has recently gained research
interest is
Arcangelisia flava
, which has been
traditionally
used
across
Southeast
Asia
to
treat
various
ailments,
including
infections,
inflammation, and metabolic disorders[3]. In
Kalimantan, local communities use it as an
antiviral agent and as a remedy for jaundice,
malaria,
and
cancer
[4].
In
other
regions,
decoctions made from the stem and root of
Arcangelisia flava
are traditionally used as a
febrifuge, tonic, abortifacient, and treatment
for
hepatitis,
indigestion,
and
malaria
[5].
These
long-standing
empirical
uses
have
encouraged
researchers
to
further
explore
and
scientifically
substantiate
the
pharmacological potential of this plant.
Recent
studies
on
Arcangelisia
flava
have
revealed
a
wide
range
of
pharmacological
effects,
making
this
plant
a
promising
candidate
for
drug
development.
Several
reports
have
demonstrated
its
activity
against
metabolic
disorders,
including
antihyperuricemic
[6, 7], antihyperlipidemic
[8],
wound
healing
[9],
antioxidant
,
and
antidiabetic
[10]. Its pharmacological activity
as
an
antidiabetic
agent
is
particularly
interesting, as diabetes mellitus remains one
of the most prevalent metabolic diseases in
Indonesia [11]. Therefore, the discovery and
120
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
development of antidiabetic agents whether
as health supplements or as pharmaceutical
raw
materials
are
importance.
However,
studies
investigating
the
antidiabetic
potential
of
this
plant
remain
limited.
The
root
extract
of
Arcangelisia
flava
has
been
reported
to
reduce
blood
glucose
levels
in
experimental
animals
[12],
while
the
leaf
extract
was
found
to
inhibit
α-glucosidase
and amylase enzymes [10]. There have been
no reports on the antidiabetic activity of the
stem bark of
Arcangelisia flava
. Interestingly,
the
stem
bark
is
known
to
contain
the
alkaloid berberine, which has been reported
to inhibit α-glucosidase activity, and in silico
studies have shown its potential interaction
with
dipeptidyl
peptidase-IV
(DPP-IV),
an
enzyme
that
plays
a
critical
role
in
the
pathophysiology of diabetes mellitus. [13]. In
addition,
the
antioxidant
activity
of
Arcangelisia flava stem bark is also believed
to
contribute
to
its
antidiabetic
effect
[14].
The
use
of
antioxidants
has
long
been
associated
with
the
prevention
of
diabetes
mellitus
through
several
mechanisms
[15].
Nevertheless,
the
precise
mechanisms
underlying
the
antidiabetic
effects
of
Arcangelisia
flava
remain
to
be
fully
elucidated.
Exploration of the antidiabetic mechanism of
Arcangelisia
flava
requires
further
analysis
using
complementary
methods
to
strengthen the existing findings. The in silico
network pharmacology approach serves as a
powerful tool to predict the mechanisms of
drug
compounds
against
pharmacological
conditions
through
complex
interactions
involving multiple components and multiple
targets
[16].
Therefore,
this
study
was
conducted
to
evaluate
the
antidiabetic
activity
of
Arcangelisia
flava
extract
and
fractions,
identify
their
bioactive
compounds, and investigate the
mechanistic
pathways
of
these
compounds
using
the
network pharmacology approach
Materials and Methods
Materials
Mice (
Mus
musculus
) were obtained from the
experimental
mouse
breeding
facility
E
Mentik Kendari, glucose strips (Autocheck®),
ethanol 96%, butanol (technical grade), ethyl
acetate
(technical
grade),
n
-hexane
(technical
grade),
concentrated
HCl
(technical
grade),
sodium
carboxymethyl
cellulose
(Na
CMC),
streptozotocin
(Sigma
Aldrich®),
nicotinamide
(Sigma
Aldrich®),
glibenclamide
(First
Medifarma®),
glucose
(Merck®),
(2,2-Azinobis(3-
ethylbenzothiazoline)-6-sulfonic
acid)
ABTS
(Sigma®),
potassium
persulfate
(Nitra
Kimia®),
vitamin
C
(Sigma
Aldrich®),
and
ammonium acetate buffer (Merck®). A set of
equipment
including
a
glucometer
(Autocheck®),
rotary
vacuum
evaporator
(Buchi®),
oven
(Stuart®),
water
bath
(B-
ONC®),
oral
gavage
(Govage®),
and
pH
meter.
Extraction and Fractionation
Stem bark of
kayu kuning
stems (
Arcangelisia
flava
(L.) Merr.) or Akar kuning plant [4], were
collected
from
Kapontori
District,
Buton
Regency,
Southeast
Sulawesi
Province
(-
5.144479, 122.787238). The
Arcangelisia flava
stem
were
identified
by
Laboratory
of
the
Department
of
Biology,
Faculty
of
Mathematics
and
Natural
Sciences,
Halu
Oleo
University.
The
stem
bark
were
processed
by
washing,
cutting,
drying
at
50°C,
and
grounding
into
a
fine
simplicia
powder.
The
simplicia
powder
(500
g)
was
macerated with 96% ethanol, the filtrate was
concentrated using a rotary evaporator until
a crude ethanolic extract was obtained. The
extract
(200
g)
was
subsequently
fractionated
using
the
trituration
method
with a gradient polarity solvent system. The
extract was placed in a mortar, and
n
-hexane
was gradually added while grinding until the
121
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
solvent
became
colorless.
The
filtrate
was
collected
and
concentrated
using
a
rotary
evaporator to obtain the
n
-hexane fraction.
The
remaining
residue
was
then
triturated
successively with ethyl acetate and butanol.
The ethyl acetate and butanol filtrates were
collected
and
concentrated
to
obtain
the
concentrated
ethyl
acetate
and
butanol
fractions.
Extract,
and
the
fractions
were
used for further analysis [17].
Identification of Chemical Compound using
GC-MS/MS
The
n
-hexane
fraction
of
Arcangelisia
flava
was
subjected
to
GC–MS/MS
analysis
to
identify
its
volatile
constituents.
The
n
-
hexane
fraction
(1
g)
was
dissolved
with
distilled
water
(2
mL),
homogenized
for
10
min.
Then,
the
ethyl
acetate
(10
mL)
and
anhydrous
sodium
sulfate
(10
g)
(Merck®,
Germany)
were
added
to
remove
residual
moisture.
The
mixture
was
then
sonicated
for 10 minutes, and the sample was filtered
through
Whatman
filter.
The
filtrate
was
evaporated
with
ultrasonic
conditions
to
remove
the
ethyl
acetate,
and
the
crude
residue
was
obtained.
The
residue
was
re-
dissolved
with
n
-hexane
(1
mL)
(Merck®,
Germany), and put into tightly sealed GC vial.
GC–MS/MS analysis was carried out using an
Agilent
8890
gas
chromatograph
coupled
with
a
Xevo®
TQ-GC
mass
spectrometer
(UK).
Separation
was
achieved
using
a
DB-
5MS column (30 m × 250 μm × 0.25 μm), with
helium as the carrier gas at a constant flow
rate
of
1.0
mL/min.
The
oven
temperature
program was set as follows: 110°C (held for
3.5 min), ramped at 10°C/min to 200°C (held
for
1
min),
then
increased
at
5°C/min
to
280°C and maintained for 12 min, yielding a
total
runtime
of
41.5
min.
The
injector
operated in splitless mode at 280°C, with a 1
μL
sample
injection
volume.
The
mass
spectrometer
was
operated
in
electron
ionization mode (EI+), scanning in the range
of
50–500
m/z,
with
the
ion
source
temperature
set
at
200°C
and
the
transfer
line at 250°C. Compound identification was
achieved by comparing the acquired spectra
with the NIST library (version 2011) [18]. No
authentic
reference
standards
were
used,
and retention indices were not determined.
Preparation
of
Streptozotocin–
Nicotinamide-Induced
Diabetes
Mellitus
Solution
Nicotinamide
was
dissolved
in
sodium
chloride
solution
0.9%
and
administered
intraperitoneally
(i.p.)
15
min
prior
to
the
administration of streptozotocin at a dose of
110 mg/kgBW. Streptozotocin was dissolved
in 50 mM citrate buffer (pH 4.5), stored in a
glass bottle, and wrapped in aluminum foil
to
protect
it
from
light.
The
streptozotocin
solution was administered immediately after
homogenization (within 5 min) at a dose of
45
mg/kg
body
weight
via
intraperitoneal
injection [19].
In Vivo Antihyperglycemic Assay
The
experimental
animals
used
were
male
white mice (
Mus musculus
), aged 2–3 months
and weighing 20–30 g. The ethical clearance
for
the
use
of
experimental
animals
was
issued
by
the
Research
and
Community
Service
Institute
of
Halu
Oleo
University
under
approval
number
182/UN29.20.1.2/PG/2025.
The
animals
were
acclimatized
for
seven
days
under
controlled
conditions
(12-h
light
and
12-h
dark
cycles)
and
provided
with
a
standard
diet [20]. The initial blood glucose levels of
the
animals
were
measured
prior
to
streptozotocin–nicotinamide induction. Each
mouse
was
then
injected
intraperitoneally
with nicotinamide solution (110 mg/kg body
weight),
followed
15
min
later
by
an
intraperitoneal
injection
of
streptozotocin
solution
(45
mg/kg
body
weight).
The
animals
were
left
for
72–120
h
after
induction,
and
their
blood
glucose
levels
122
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
were
measured
on
the
third
day
post-
induction
as
the
blood
glucose
level
t0
[21,22].
Animals
with
blood
glucose
levels
greater
than
176
mg/dL
were
considered
hyperglycemic
and
subsequently
divided
into
groups
according
to
the
experimental
design [23].
The diabetic animals were divided into nine
groups, with four mice in each group (n = 4).
The normal control group (KN) (normal mice
that
received
0.5%
Na-CMC),
the
negative
control group (K–) (diabetic mice + 0.5% Na-
CMC),
the
positive
control
group
(K+)
(diabetic mice + glibenclamide at a dose of
0.013
mg/20
gBW),
the
treatment
groups
were divided based on the type and dose of
extract
administered.
Dose
group
1
(KD1),
dose group 2 (KD2), and dose group 3 (KD3)
received
the
ethanolic
extract
at
doses
of
125 mg/kg, 250 mg/kg, and 500 mg/kg body
weight,
respectively.
Dose
group
4
(KD4)
received the
n
-hexane fraction at a dose of
125 mg/kgBW, dose group 5 (KD5) received
the
ethyl
acetate
fraction
at
a
dose
of
125
mg/kgBW, and dose group 6 (KD6) received
the
butanol
fraction
at
a
dose
of
125
mg/kgBW. The diabetic animals were treated
according
to
their
respective
group
assignments.
The
treatments
were
administered 30 min prior to glucose loading
as
a
single
oral
dose.
Thirty
minutes
after
treatment,
each
experimental
animal,
except
those
in
the
normal
control
group,
was orally administered a glucose solution at
a dose of 2 g/kg body weight. Blood glucose
levels were measured at 30, 60, 90, and 120
min
after
glucose
administration.
Blood
samples were collected from the lateral tail
vein
and
analyzed
using
a
glucometer
(Autocheck®)
[24].
The
blood
glucose
level
data
were
subsequently
used
to
calculate
the
AUC
and
the
percentage
reduction
in
blood
glucose
levels.The
animals
that
had
been used were subsequently euthanized by
placement into a killing jar containing cotton
saturated
with
chloroform.
The
animals
were monitored and remained in the jar until
death was confirmed [25].
The
area
under
the
curve
(AUC)
value
was
determined
to
evaluate
changes
in
blood
glucose levels for each treatment group. The
AUC was calculated using the formula 1
[26]
.
AUC
=
KGDt1
+
KGDt2
2
×
(
t2
−
t1
)
(1)
Where,
AUC
: Area under the curve;
KGDt
₁
: Initial blood glucose level (mg/dL)
KGDt
₂
: Final blood glucose level (mg/dL);
t
₁
:Time of initial blood glucose
measurement (min);
t
₂
:Time of final blood glucose
measurement (min)
The
percentage
reduction in blood glucose
levels
was
calculated
using
the
formula
2
[26].
%
PKGD
=
AUC
KN
−
AUC
P
AUC
KN
×
100%
(2)
Where,
%PKGD
:
Percentage
reduction
in
blood
glucose
level;
AUC
(KN)
:
AUC
of
the
negative control group; AUC (P) : AUC of the
treatment group.
In Vitro Antioxidant Assay
The
antioxidant
activity
in
this
study
was
evaluated
using
the
ABTS
radical
method.
The ABTS stock solution was mixed with 5 mL
of potassium persulfate solution to generate
ABTS
radical
cations.
The
resulting
mixture
was then kept in the dark for 12–16 h at a
temperature
of
22–24°C,
producing
a
dark
blue ABTS radical solution
[27].
Sample stock
solutions
(1000
ppm)
were
prepared
by
weighing 10 mg each of the ethanolic extract
and
concentrated
fractions
of
Arcangelisia
flava
stem, which were then dissolved in 10
mL
of
96%
ethanol.
From
these
stock
solutions,
a
series
of
concentrations
were
prepared
as
follows:
Arcangelisia
flava
123
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
ethanolic
extract
at
10,
20,
30,
40,
and
50
ppm;
n
-hexane
fraction
at
40,
60,
80,
100,
and 120 ppm; ethyl acetate fraction at 5, 7.5,
10, 12.5, and 15 ppm; and butanol fraction at
5, 10, 15, 20, and 25 ppm [28]. The vitamin C
stock solution was prepared by dissolving 10
mg
of
vitamin
C
in
10
mL
of
96%
ethanol.
From
this
stock
solution,
a
series
of
concentrations were prepared at 2.5, 5, 7.5,
10, and 12.5 ppm [28]. A total of 1 mL from
each sample concentration series and 1 mL
from
each
standard
concentration
series
were mixed with 1 mL of ABTS solution.
The
mixture
was
then
adjusted
to
a
final
volume
of
5
mL
using
96%
ethanol,
homogenized,
and
incubated
at
room
temperature for 10 min (operating time). The
absorbance was measured at a wavelength
of
745
nm
using
a
UV–Vis
spectrophotometer
[28].
The
inhibitory
concentration
(IC
₅₀
)
was
determined
by
analyzing
the
relationship
between
the
percentage
of
inhibition
and
the
sample
concentration, represented as a calibration
curve.
The
percentage
of
inhibition
(%
inhibition) was calculated using the formula
3.
%Inhibition = [A
control
-A
sample
]
/
A
control
x
100
(3)
The
percentage
of
inhibition
values
were
used
to
calculate
the
IC
₅₀
value
using
formula 4
.
IC
50
(ppm) =
50−𝑎
𝑏
(4)
where (x) is the sample concentration (ppm
or
(µg/mL),
(a)
is
the
intercept
of
the
regression equation, and (b) is the slope of
the regression line.
The strength of antioxidant activity based on
the IC
₅₀
value is generally classified into four
categories:
very
strong
(IC
₅₀
<
50
ppm),
strong (IC
₅₀
= 50
–
100 ppm), moderate (IC
₅₀
=
100
–
150
ppm),
and
weak
(IC
₅₀
>
150
ppm)
[29]
.
Network pharmacology
The
bioinformatics
analysis
was
conducted
in silico using an ASUS laptop with an Intel
Core
i3
7th
Generation
processor.
In
this
study,
compounds
numbered
7
and
17
identified through GC–MS/MS analysis were
selected
for
network
pharmacology
evaluation
due
to
their
chemical
classification
as
phenolic
derivatives
and
alkaloid
compounds.
These
classes
of
secondary
metabolites
are
widely
reported
to
exhibit
antidiabetic
properties
through
multiple mechanisms. The compound 7 and
17
canonical
SMILES
were
obtained
from
PubChem
(
https://pubchem.ncbi.nlm.nih.gov/
)
(accessed
on
October
30,
2025).
Target
proteins of these bioactive small molecules
were predicted using SwissTargetPrediction
(
http://www.swisstargetprediction.ch/
)
(accessed
on
October
30,
2025),
while
diabetes
mellitus–related
target
proteins
were
retrieved
from
GeneCards
(
https://www.genecards.org/
)
(accessed
on
October
30,
2025).
The
results
from
both
databases
were
combined
and
analyzed
using
a
Venn
diagram
generated
through
Venny
(
https://bioinfogp.cnb.csic.es/tools/venny/
)
(accessed
on
October
30,
2025)
to
identify
overlapping
proteins
associated
with
both
the
metabolites
and
diabetes
mellitus.
The
intersected
proteins
were
then
analyzed
using
ShinyGO
(
http://bioinformatics.sdstate.edu/go74/
)
(accessed on October 30, 2025) focusing on
Kyoto Encyclopedia of Genes and Genomes
(KEGG)
pathways
to
obtain
the
top
50
enriched
biological
mechanisms,
and
the
Gene
Ontology
(GO)
terms
were
visualized
through
an
enrichment
bubble
plot.
Furthermore,
the
target
proteins
were
imported
into
STRING-DB
(
https://string-
db.org/
) (accessed on October 30, 2025) to
construct a protein–protein interaction (PPI)
network
related
to
diabetes
mellitus,
and
124
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
subsequently analyzed using the CytoHubba
plugin in Cytoscape version 3.10 to identify
the
genes
based
on
the
Maximal
Clique
Centrality (MCC) algorithm [30].
Data Analysis
The
blood
glucose
levels
were
statistically
analyzed using the ANOVA, and posthoc LSD.
The
analysis
was
conducted
at
a
95%
confidence
level
(α
=
0.05)
using
the
IBM
Statistical
Package
for
the
Social
Sciences
(SPSS®) software version 21.
Result and Discussion
Chemical compound from Arcangelisia flava
Fraction using GC-MS
The
chemical
constituents
of
the
n-hexane
fraction of Arcangelisia flava were analyzed
using
Gas
Chromatography–Mass
Spectrometry (GC–MS). The GC–MS analysis
identified
18
volatile
compounds
(Table
1),
with
the
corresponding
chromatogram
presented in Figure 1. The major classes of
compounds
in
the
n-hexane
fraction
of
Arcangelisia flava were presumed to be fatty
acid derivatives (6 compounds), followed by
aromatic
hydrocarbons
(5
compounds),
alcohols (2 compounds), and one compound
each
from
the
terpenoid,
phenolic,
and
alkaloid groups (Table 1). In a separate study,
the
chemical
composition
of
Arcangelisia
flava
wood
collected
from
the
Philippines
was also analyzed, identifying a total of 64
compounds. Among them, two compounds
that
are
ethylbenzene
and
o-xylene,
were
found
to
be
consistent
with
the
GC–MS
results obtained in the present study [31].
The
volatile
compounds
present
in
the
n
-
hexane
fraction
of
Arcangelisia
flava
are
presumed
to
possess
pharmacological
activity.
The
fatty
acid
derivative
with
the
highest probability (prob%) was identified as
Hexadecanoic acid ethyl ester (16.9%). This
compound has also been detected in
Nigella
sativa
seeds
and
has
been
reported
to
exhibit
pharmacological
potential,
particularly
by
playing
a
role
in
neuroprotective
effects
through
the
inhibition of EGR1[32].
Figure 1.
GC–MS Spectrum of the n-Hexane Fraction of Arcangelisia flava from Southeast Sulawesi
In
addition,
in
a
separate
study,
hexadecanoic
acid
was
reported
to
be
present
in
Codiaeum
variegatum
and
is
suggested
to
exert
antidiabetic
effects
through
the
inhibition
of
glucosidase
and
amylase
enzymes
[33].
The
alkaloid
compound Strychane, 1-acetyl-20α-hydroxy-
16-methylene- was
identified in the fruit of
125
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
Cassia fistula
and is presumed to contribute
to
the
plant’s
antibacterial
and
antifungal
effects [34]. In a separate study,
Strychane, 1-
acetyl-20α-hydroxy-16-methylene-
,
has
been
reported
to
be
present
in
Strychnos
nux-
vomica
[35],
and is suggested to contribute
to its antidiabetic effects [36]. The similarity
in active compounds, namely Hexadecanoic
acid ethyl ester and Strychane, 1-acetyl-20α-
hydroxy-16-methylene-,
identified
in
Arcangelisia flava
, is presumed to contribute
to
other
potential
pharmacological
effects,
including antidiabetic activity.
Table 1.
The Phytochemical compound Profile of
Arcangelisia
flava
n-hexane Fraction
Compoun
d number
RT
MW
Match
Prob
(%)
Formula
Chemical
compound name
Classes of
compounds
1
3.516
92
843
13.5
C
₇
H
₈
Spiro[2,4]hepta-4,6-
diene
Aromatic
hydrocarbons
2
4.702
104
557
6.89
C
₅
H
₁₂
O
₂
2-Methylbutane-
1,4-diol
Aromatic
hydrocarbons
3
6.227
106
870
41.9
C
₈
H
₁₀
Ethylbenzene
Aromatic
hydrocarbons
4
6.502
106
839
13.2
C
₈
H
₁₀
o-Xylene
Aromatic
hydrocarbons
5
7.149
106
770
12.8
C
₈
H
₁₀
p-Xylene
Aromatic
hydrocarbons
6
7.535
142
606
4.11
C
₈
H
₁₄
O
₂
6-Hepten-3-one,
5-
hydroxy-4-methyl-
Ketones
and
alcohols
7
17.781
206
729
10.8
C
₁₃
H
₁₈
O
₂
4-Hydroxy-3,5-
diisopropylbenzalde
hyde
Phenolic
aldehyde
8
18.804
204
634
5.68
C
₁₀
H
₂₀
O
₂
S
3-n-Hexylthiolane,
S,S-dioxide
Sulfur
compounds
9
19.973
252
617
3.06
C
₁₅
H
₂₄
O
₃
Ageratriol
Terpenes
10
21.073
214
616
3.81
C
₁₄
H
₃₀
O
7-Tetradecanol
alcohols
11
21.947
280
535
4.94
C
₁₈
H
₃₂
O
₂
17-Octadecynoic
acid
Non-aromatic
hydrocarbons
12
22.102
200
533
4.24
C
₁₂
H
₂₄
O
₂
8-Methylnonanoic
acid, ethyl ester
Fatty
acids
derivate
13
23.12
284
724
16.9
C
₁₈
H
₃₆
O
₂
Hexadecanoic
acid,
ethyl ester
Fatty
acids
derivate
14
24.711
308
821
6.71
C
₂₀
H
₃₆
O
₂
Ethyl 9.cis.,11.trans.-
octadecadienoate
Fatty
acids
derivate
15
24.764
282
748
5.07
C
₁₈
H
₃₄
O
₂
Cis-accenic acid
Fatty
acids
derivate
16
24.999
298
682
12.6
C
₁₉
H
₃₈
O
₂
Ethyl
14-methyl-
hexadecanoate
Fatty
acids
derivate
17
28.335
338
521
10.6
C
₂₁
H
₂₆
N
₂
O
₂
Strychane,
1-acetyl-
20α-hydroxy-16-
methylene-
Alkaloid
18
30.251
312
472
3.17
C
₂₀
H
₄₀
O
₂
n-Propyl
14-methyl-
hexadecanoate
Fatty
acids
derivate
126
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
Antidiabetic activity
In this study, a total of 36 mice
were used
and
induced
to
develop
diabetes
mellitus
using
the
STZ-NA
model.
Streptozotocin
(STZ)
serves
as
a
diabetogenic
agent
that
generates
free
radicals
and
is
toxic
to
pancreatic
β-cells,
leading
to
impaired
insulin
secretion
and
hyperglycemia
[37].
However,
the
cytotoxic
effect
of
STZ
on
β-
cells
is
highly
destructive
and
closely
resembles the pathological features of type
1 diabetes in humans [38]. This condition is
undesirable
for
modeling
type
2
diabetes
mellitus;
therefore,
a
combination
with
nicotinamide (NA) is required to mitigate the
cytotoxic effects of STZ. Nicotinamide (NA), a
derivative
of
vitamin
B3,
possesses
antioxidant
properties
and
can
reduce
the
cytotoxic action of STZ. The STZ-NA–induced
type 2 diabetes model allows pancreatic β-
cells
to
avoid
extensive
destruction
and
produces a diabetic condition characterized
by
insulin
deficiency
without
insulin
resistance. This model is marked by stable,
moderate
hyperglycemia
associated
with
approximately
60%
loss
of
β-cell
function
[19],
[38].
In
this
study,
the
dose
of
streptozotocin (STZ) used ranged from 45 to
65 mg/kgBW, while the dose of nicotinamide
(NA) ranged from 60 to 290 mg/kgBW. [19].
The blood glucose profile obtained from the
experimental results is presented in
Table 2
.
Table 2.
Profile of Blood Glucose Levels of Experimental Animals During the OGTT
Group
Average Blood Glucose Levels of Test Animals at Each Time Point (t) ± SD (n = 4)
t
0
t
30
t
60
t
90
t
120
KN
167.3
±3
b
95.0
±28.6
112.6
±7.3
79.6
±9.6
81.0
±5
K-
194.0
±6
a
193.3
±4.1
194.6
±9.2
204.6
±15.8
218.6
±15.5
K+
184.3
±5
112.0
±7
116.6
±10.7
95.0
±18.3
70.6
±14.5
KD1
202.0
±22.3
147.3
±45.5
c
99.0
±15.1
b,c
79.6
±7.5
b,c
77.6
±9.6
b,c
KD2
191
±3.4
151.0
±34.2
c
127.3
±21.9
c
115.0
±15.1
b,c
74.6
±12.5
b,c
KD3
202.6
±8.9
107.6
±30
c
101
±36.8
c
74
±4.5
b,c
67
±2.6
b,c
KD4
208
±25.16
a
151
±16.77
a,c
137.7
±12.89
b,c
82.7
±9.01
b,c
76
±11.50
b,c
KD5
117.3
±38.97
a,b
102.7
±16.50
b,c
82
±16.50
b,c
72
±15.13
b,c
121
±16.09
b,c
KD6
224.3
±16.17
a
140
±29.51
b,c
131
±29.51
b,c
96
±2
b,c
81.7
±4.50
b,c
Where, KN
: Normal Control; K– : Negative Control; K+
: Positive Control; KD 1, 2, and 3
: Treatment groups administered the
extract at doses of 125, 250, and 500 mg/kgBW, respectively; KD 4, 5, and 6
: Treatment groups administered the
n
-hexane, ethyl
acetate, and butanol fractions, respectively, at a dose of 125 mg/kgBW; a : Significantly different from the normal control group (
p
< 0.05); b : Significantly different from the negative control group (
p
< 0.05); c : Not significantly different from the positive control
group (
p
> 0.05)
Administration
of
the
Arcangelisia
flava
extract
and
its
fractions
demonstrated
a
pharmacological effect in reducing the blood
glucose
levels
(BGL)
of
the
animals.
A
decrease in BGL was observed in the positive
control group and in the treatment groups
receiving
Arcangelisia flava
samples, starting
from
the
30
th
to
the
120
th
min.
The
BGL
values of the treatment groups between 60
and 120 min showed a significant difference
(
p
< 0.05) compared to the negative control
group
(Table
2).
This
finding
indicates
that
the
administration
of
the
extract
and
fractions effectively reduced the BGL of the
animals.
The
percentage
of
BGL
reduction
was
subsequently
calculated,
showing
the
127
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
highest decrease in the group treated with
ethanolic extract at a dose of 500 mg/kgBW,
followed by the ethyl acetate,
n
-hexane, and
butanol
fractions
at
125
mg/kgBW,
respectively (Table 3) [40]. In this study, we
also
calculated
the
Area
Under
the
Curve
(AUC) using the blood glucose level data. The
AUC
value
is
inversely
proportional
to
the
antidiabetic activity of a drug, where a lower
AUC
indicates
more
effective
utilization
of
the
compound
in
reducing
blood
glucose
levels. Thus, the smaller the AUC value, the
greater
the
efficacy
of
the
substance
in
lowering
blood
glucose
concentration
[41].
In
this
study,
the
lowest
AUC
value
was
observed
in
the
group
treated
with
the
ethanolic extract at a dose of 500 mg/kgBW,
followed by the
positive control group and
the groups treated with the ethyl acetate,
n
-
hexane,
and
butanol
fraction,
respectively
(Table 3). These results indicate that a lower
AUC
value
corresponds
to
better
blood
glucose control in the animals, which can be
attributed
to
the
administration
of
the
extract, glibenklamid, and the fractions.
Table 3.
Area Under the Curve (AUC) and Percentage Reduction in Blood Glucose Levels of Test Animals
After Treatment
Treatment group
Area under curve
(AUC)
Percentage Reduction in Blood Glucose
Levels
(%)
KN
3086
48.49
K-
5992
0.00
K+
3383
43.53
KD1
3482
41.88
KD2
3
946
34.14
KD3
2981
50.25
KD4
3856
34.96
KD5
3394
42.82
KD6
3903
34.28
Where, KN
: Normal Control; K– : Negative Control; K+ : Positive Control; KD 1, 2, and 3
: treatment groups administered the
extract at doses of 125, 250, and 500 mg/kgBW, respectively; KD 4, 5, and 6 : treatment groups administered the
n
-hexane, ethyl
acetate, and butanol fractions, respectively, at a dose of 125 mg/kgBW
The efficacy of
Arcangelisia flava
to reduce
the
BGL
can
be
observed
from
the
differences
in
BGL
between
the
positive
control
and
the
treatment
groups
with
extract and fractions. All samples exhibited a
percentage reduction in BGL comparable to
the
positive
control.
Notably,
the
group
tre
ated
with
the
ethanolic
extract
at
500
mg/kgBW showed a lower AUC and a higher
percentage
reduction
in
blood
glucose
compared
to
the
positive
control (Table
3).
Statistical
analysis
also
indicated
no
significant
difference
(
p
>
0.05)
in
blood
glucose level
s between the positive control
and
the
treatment
groups.
These
findings
suggest
that
the
administration
of
the
extract
and
fractions
effectively
lowers
blood
glucose
levels
in
the
animals,
with
a
hypoglycemic
effect
produced
comparable
glucose-lowering
effect
to
glibenclamide
in
this OGTT model.
The antihyperglycemic activity of
Arcangelisia
flava
stems is closely related to the bioactive
compounds. The stems of
Arcangelisia flava
have
been
reported
to
contain
berberine
(Karim et al., 2020), which is known to inhibit
the
enzyme
α
-
glucosidase,
and
reduces
glucose
absorption
in
the
intestine.
Molecular docking studies have also shown
128
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
that berberine exhibits a low binding affinity
value
toward
dipeptidyl
peptidase
-
IV
(DPP
-
IV),
indicating
strong
interaction
with
this
receptor
[13]
.
In
a
separate
study,
Arcangelisia
flava
was
also
reported
to
contain
palmatine,
another
bioactive
alkaloid
[42]
.
This
compound
is
known
to
exhibit
strong
inhibitory
activity
against
α
-
amylase,
while
showing
weaker
inhibition
toward
α
-
glucosidase
and
DPP
-
4
[43]
.
The
GC
–
MS analysis of the
n
-
hexane fraction in
this
study,
we
identified
the
compound
hexadecanoic
acid,
ethyl
ester,
which
is
known to possess antidiabetic effects.
[44]
.
The
presence
of
this
compound
in
Arcangelisia
flava
is presumed to play a key
role in reducing blood glucose levels
of the
animals in this study.
The
antidiabetic
potential
of
Arcangelisia
flava
is also associated with its antioxidant
effects.
The
relationship
between
free
radicals
and
the
development
of
diabetes
mellitus
continues
to
be
explored,
particularly at the molecular level. One study
described
several
mechanisms
linking
free
radicals to
diabetes, including mitochondrial
ROS;
non
-
mitochondrial
ROS
such
as
NAD(P)H
oxidase
(NOX),
xanthine
oxidase
(XO), uncoupled nitric oxide synthase (NOS),
cyclooxygenase
-
2 (COX
-
2), and endoplasmic
reticulum
(ER) stress; carbonyl stress; as well
as
the
Warburg
effect
(aerobic
glycolysis)
and cell proliferation
[15]
. Therefore, the use
of
antioxidants
is
associated
with
the
prevention of diabetes mellitus.
Arcangelisia
flava
contains multiple chemical compounds
that
may
act
as
both
antidiabetic
and
antioxidant agents. Among them, palmatine,
reported
in
Arcangelisia
flava
,
has
been
shown
to
effectively
neutralize
DPPH
free
radicals
and
exhibit
strong
activity
in
non
-
enzymatic
SOD
mimic
systems
[45]
.
Other
studies
have
also
demonstrated
that
palmatine
possesses
antioxidant
and
antiglycation properties, with its mechanism
of action believed to involve the inhibition of
free radical formation and the reduction of
reactive
carbonyl
compounds
[46]
.
Berberine has been shown to enhance
the
body’s
natural
antioxidant
defenses,
including
superoxide
dismutase
(SOD),
catalase
(CAT),
and
glutathione
peroxidase
(GPx)
[47]
. To support these findings, in the
present
study,
the
antioxidant
activity
of
Arcangelisia
flava
from
Southeast
Sulawesi
was evaluated using the ABTS assay.
Antioxidant Activity
The antioxidant activity was analyzed using
both the extract and its fractions. The assay
employed
was
the
2,2′-azino-bis(3-
ethylbenzothiazoline-6-sulfonic
acid)
(ABTS)
method.
Antioxidants
react
with
ABTS
through
electron
transfer,
converting
the
colored
radical
form
into
its
colorless
reduced form. The degree of this reduction
correlates
with
the
antioxidant
capacity
of
the samples. As commonly noted, the ABTS
assay
is
considered
more
sensitive
than
DPPH
and
FRAP
assays
due
to
its
reaction
kinetics and the nature of the radical species
involved [48].
Table
4.
IC
₅₀
values
of
ethanolic
extract
and
Fractions of
Arcangelisia flava
based on the ABTS
Method
Samples
IC
50
(ppm)
Vitamin C
8.00
Ethanol extract
49.80
n
-hexane fraction
110.25
Ethyl acetate fraction
10.20
Butanol fraction
18.44
Based on the results of the ABTS antioxidant
assay,
the
ethyl
acetate
fraction
of
Arcangelisia flava
stems exhibited the highest
antioxidant
potential,
with
an
IC
₅₀
of
10.20
ppm, which is close to the IC
₅₀
of Vitamin C
(8.00 ppm) used as the positive control. The
butanol
fraction
also
demonstrated
strong
antioxidant
activity,
with
an
IC
₅₀
of
18.44
ppm.
In
contrast,
the
n
-hexane
fraction
129
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
showed moderate antioxidant activity, with
an IC
₅₀
greater than 100 ppm. These results
indicate
that
the
active
antioxidant
compounds are more soluble in semi-polar
solvents,
which
are
capable
of
extracting
aglycone
flavonoids,
such
as
polyphenols
present in the extract. Phenolic compounds
function as antioxidants due to their ability
to
regenerate
reactive
oxygen
species,
as
their aromatic rings contain hydroxyl groups
that
act
as
electron
donors, stabilizing
and
neutralizing free radicals. Flavonoids, being
derivatives of polyphenols, also play a crucial
role
in
determining
antioxidant
activity.
Together,
these
compounds
contribute
significantly
to
the
overall
antioxidant
potential of the extract and its fractions.
This finding also suggests that the
n
-hexane
fraction contains relatively low levels of polar
antioxidant compounds, which are typically
responsible
for
radical-scavenging
activity.
However,
despite
its
limited
antioxidant
potential, the
n
-hexane fraction was selected
for
further
GC–MS/MS
analysis
due
to
its
ability
to
extract
nonpolar
and
semi-polar
constituents,
including
fatty
acids,
terpenoids, and alkaloid-related compounds
[49],
which
may
exert
biological
activities
through
mechanisms
independent
of
antioxidant
effects.
Moreover,
antidiabetic
activity
is
not
exclusively
mediated
by
antioxidant
capacity
but
may
involve
alternative
pathways
such
as
enzyme
inhibition, insulin signaling modulation, and
glucose
metabolism
regulation
[50].
Therefore,
the
chemical
constituents
identified
in
the
n
-hexane
fraction
were
prioritized
for
network
pharmacology
analysis
in
silico
to
explore
their
potential
interactions with diabetes-related molecular
targets.
This
approach
allows
the
identification
of
multi-target
mechanisms
that may underlie the antidiabetic potential
of nonpolar bioactive compounds present in
the extract.
Network Pharmacology
The
compounds
identified
by
GC–MS,
specifically
compounds
7
and
17,
were
further
analyzed
for
target
protein
identification
(Figure
2).
The
selection
of
compounds
7
and
17
was
based
on
their
classification
as
phenolic
and
alkaloid
derivatives,
respectively.
Compounds
from
the phenolic and alkaloid classes have been
widely
reported
to
exhibit
antidiabetic
effects. Compound 7 is a phenolic aldehyde
derivative, a group of compounds known to
reduce
blood
glucose
levels
and
enhance
insulin
secretion.
Collectively,
these
pharmacological properties provide a strong
rationale for selecting compounds 7 and 17
for
further
investigation
of
their
potential
antidiabetic activity [51].
H
3
C
CH
3
O
H
CH
3
CH
3
OH
N
N
H
3
C
O
OH
CH
3
CH
2
(a)
(b)
Figure
2.
The
two
compounds
used
in
the
pharmacological
analysis.
(a)
compound
7,
4-
Hydroxy-3,5-bis(isopropyl)benzaldehyde, and (b)
compound 17, Strychane, 1-acetyl-20α-hydroxy-
16-methylene-. The chemical structures of these
compounds
were
drawn
using
MarvinSketch
®
5.2.5.1.
In
addition,
compound
17,
identified
as
Strychane, 1-acetyl-20α-hydroxy-16-methylene
,
has been reported to be present in
Strychnos
nux-vomica
[35],
and
is
suggested
to
contribute
to
its
antidiabetic
effects
[36].
Data
on
the
target
proteins
of
these
compounds
were
obtained
from
SwissTarget,
while
disease-related
target
proteins
were
retrieved
from
GeneCards.
130
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
Both datasets were then analyzed using the
Draw
Venn
Diagram
tool.
The
analysis
revealed
80
overlapping
target
proteins
between the targets of compounds in the
n
-
hexane
fraction
of
Arcangelisia
flava
and
proteins
associated
with
type
2
diabetes
mellitus (Figure 3).
Figure 3.
Overlay Analysis Using a Venn Diagram.
A
total
of
100
target
proteins
were
associated
with
compounds
from
Arcangelisia
flava
,
while
17,745 proteins are involved in diabetes mellitus.
The
intersection
revealed
80
target
proteins
of
Arcangelisia
flava
compounds
that
are
also
implicated as targets in type 2 diabetes mellitus.
These
80
proteins
were
subsequently
analyzed
using
CytoHubba
to
identify
the
central proteins with the greatest influence
on the therapeutic effects of the compounds
in the
n
-hexane fraction of
Arcangelisia flava
.
The proteins were ranked based on Maximal
Clique Centrality (MCC). MCC analysis helps
determine
the
most
critical
proteins
within
the pharmacological network by identifying
groups of proteins that form closed network
clusters. Proteins within the MCC were then
evaluated
according
to
their
centrality
in
subnetworks,
which
measures
the
importance
of
a
protein
in
maintaining
or
influencing connections within the network
[52].
Figure 4.
Visualization of protein–protein interactions (PPI) targeting the bioactive compounds of the
n
-
hexane fraction of
Arcangelisia flava
against type 2 diabetes mellitus (T2DM). The analysis was performed
using Cytoscape 3.10 with a confidence value of 0.8 to identify the ten key proteins based on Maximal
Clique Centrality (MCC) using the CytoHubba plugin.
Pharmacological
analysis
identified
ten
central
proteins
with
the
highest
scores,
namely
ALB,
MAPK3,
PTGS2,
SRC,
MMP2,
MAPK1, MAP2K1, MAPK14, PGR, and MMP7
(Figure
4).
These
findings
were
further
validated
through
KEGG
pathway
analysis
.
The
80
target
proteins
were
analyzed
to
determine
their
mechanistic
pathways
131
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
based on KEGG data. The analysis revealed
that the most significant pathway associated
with type 2 diabetes mellitus (T2DM
)
for the
n-hexane fraction of
Arcangelisia
flava
is the
AGE-RAGE
signaling
pathway
in
diabetic
complications
(Figure
5).
Other
signaling
pathways not included in the
top 50 KEGG
pathways
included
the
insulin
signaling
pathway.
The
proteins
responsible
for
the
AGE-RAGE signaling pathway were identified
using STRING-DB (Table 5). Six proteins were
found to play key roles in this pathway are
MAPK1,
MMP2,
MAPK14,
MAPK3,
PIK3CA,
and TGFBR1.
Figure 5.
Annotation of the top 50 KEGG pathways targeted by
Arcangelisia flava
, performed using the
ShinyGO web platform
The AGE-RAGE signaling pathway has been
extensively
studied.
Patients
with
type
2
diabetes mellitus (T2DM) have been shown
to exhibit significantly higher concentrations
of Advanced Glycation End Products (AGEs)
compared to non-diabetic populations. AGEs
are formed throughout life due to elevated
circulating glucose levels and other reducing
sugars,
such
as
galactose
and
fructose,
which react with amino groups on proteins
to
form
Schiff
bases.
These
intermediates
then enter
the
polyol
pathway
to
generate
AGEs or undergo degradation. The resulting
AGEs
interact with Receptors for Advanced
Glycation
End
Products
(RAGEs),
transmembrane
proteins
belonging
to
the
immunoglobulin
family.
RAGE
expression
increases in response to elevated circulating
AGE
levels.
Upon
AGE-RAGE
binding,
RAGE
activates PKC-ζ (protein kinase C zeta), which
subsequently triggers downstream signaling
cascades via p38 mitogen-activated protein
kinase (MAPK), transforming growth factor-β
(TGF-β), and nuclear factor κB (NF-κB). This
activation stimulates signaling pathways that
can
induce
diabetes-related
complications,
particularly in the vascular system [53].
Conclusion
In
conclusion,
the
extract
and
fractions
of
Arcangelisia flava
stem bark has the potential
to lower blood glucose levels and scavenge
free
radicals.
The
n
-hexane
fraction
is
presumed
to
exert
its
antidiabetic
effect
through
the
inhibition
of
the
AGE–RAGE
signaling pathway, which plays a critical role
in
diabetic
complications.
This
study
is
limited
by
its
reliance
on
in
silico
network
132
NA Sida et al.,
Chempublish Journal, 10(1) 2026, 119-137
pharmacology
approaches,
which
are
inherently
dependent
on
predictive
databases
and
computational
algorithms.
Consequently,
the
identified
compound–
target
interactions
may
not
fully
represent
biological
complexity
under
physiological
conditions.
In
addition,
only
selected
compounds
identified
by
GC–MS/MS
were
included,
potentially
overlooking
other
bioactive constituents and synergistic effects
within
the
extract.
Future
studies
should
prioritize
experimental
validation
of
the
predicted targets and pathways through in
vitro
and
in
vivo
assays
to
confirm
antidiabetic
efficacy
and
mechanistic
relevance.
Expanding
phytochemical
coverage
and
integrating
pharmacokinetic
and toxicity evaluations will be essential to
advance the extract toward translational and
clinical
applications.
Further
studies
are
required
to
isolate
the
active
compounds
and
validate
their
mechanisms
of
action
using in vitro and in vivo models.
Acknowledgement
This research was supported by Universitas
Halu
Oleo
and
funded
through
the
2025
fiscal
year
DIPA
Universitas
Halu
Oleo
allocation.
Author Contributions
Conceptualization,
Nurramadhani
A.
Sida
and
Henny
Kasmawati.;
Methodology,
Nurramadhani
A.
Sida.;
Software,
;
Validation,
Henny
Kasmawati,
Hasnawati.;
Formal
Analysis,
Rifa’atul
Mahmudah;
Investigation,
Nurramadhani
A.
Sida.;
Resources,
Nurramadhani
A.
Sida;
Data
Curation,
Rifa’atul
Mahmudah;
Writing
–
Original Draft Preparation, Nurramadhani A.
Sida,
Hasnwati;
Writing
–
Review
&
Editing,
Nurramadhani
A.
Sida.;
Visualization,
Nurramadhani A. Sida.; Supervision, Henny
Kasmawati.; Project Administration, Rifa’atul
Mahmudah.;
Funding
Acquisition,
Nurramadhani A. Sida.
Conflict of Interest
The authors declare no conflict of interest.
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