Exploration of Belitung Taro Stems (
Xanthosoma
sagittifolium
) as an
Antidiabetic Candidate: An In Silico
and In Vitro Approach
Article
Wahyu Rahmatulloh
, Sugeng Supriyanto
*
, Aditya Mahe Saputra, Nabila Aulia Rahma
1
Department pharmacy, Universitas Muhammadiyah Gombong, 54412 (Indonesia)
A
bstract
Diabetes mellitus is a global metabolic disorder characterized by chronic hyperglycemia. While
conventional oral antidiabetics are effective, their long-term use is associated with adverse side
effects,
necessitating
the
search
for
safer
alternatives
from
natural
sources.
Belitung
taro
(
Xanthosoma
sagittifolium
)
is
a
locally
abundant
tuber
crop
in
Indonesia,
but
its
stems
are
typically discarded as agricultural by-products despite their potential bioactive content. This
study evaluated the antidiabetic potential of
X. sagittifolium
stem extract by investigating its α-
amylase
inhibitory
activity
through
an
integrative
in
silico
and
in
vitro
approach.
In
silico
screening was performed on eight secondary metabolites from the CMAUP database, filtered
based
on
Lipinski's
Rule
of
Five
and
ADMET
profiles
using
the
pkCSM
server.
The
lead
compound, NPC141921, was docked into human pancreatic α-amylase (PDB ID: 2QV4) using
AutoDock,
validated
by
redocking
acarbose.
The
ethanol
extract
was
subjected
to
phytochemical
screening
and
tested
for
α-amylase
inhibition
using
the
iodine-starch
assay.
NPC141921 showed a binding free energy of
-6.13 kcal/mol, comparable to acarbose (-6.66
kcal/mol), forming stable hydrogen bonds with catalytic residues (ASP197, GLU233, ASP300)
and
substrate-binding
residues
(ARG195,
HIS305).
Phytochemical
screening
confirmed
flavonoids, tannins, saponins, and steroids, supporting the
in silico
predictions. In vitro assays
revealed an IC
₅₀
of 5028 ppm for the stem extract versus 1953 ppm for acarbose. Although the
crude extract showed lower potency due to matrix dilution effects, the findings confirm that
X.
sagittifolium
stems contain bioactive metabolites capable of α-amylase inhibition, highlighting
their potential as a natural ingredient for hyperglycemia management
.
Keywords:
Diabetes Mellitus;
In silico
;
In vitro; Xanthosoma sagittifolium
Graphical Abstract
*
Corresponding author
Email addresses:
sugengsupriyanto@unimugo.ac.id
DOI:
https://doi.org/10.22437/chp.v10i1.48655
Received
September 30
th
2025;
Accepted
January 16
th
2026;
Available online
May 18
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
)
38
Introduction
Diabetes mellitus (DM) is a chronic disorder
that occurs when insulin is not released in
sufficient amounts or when insulin activity is
ineffective
due
to
metabolic
resistance
[1].
The International Diabetes Federation (IDF)
reported
a
significant
increase
in
global
diabetes prevalence, with cases rising from
151 million to 537 million people worldwide,
and
the
number
projected
to
reach
783
million by 2045 [2]. The Indonesian Ministry
of Health reported that Indonesia ranks fifth
globally for the highest number of diabetes
cases, with an estimated 21.3 million people
affected by 2030 [3]. In the regional context,
Kebumen Regency in Central Java, Indonesia,
recorded
approximately
12,000
diabetes
cases
in
2022,
distributed
across
all
its
districts [4,5].
Although
diabetes
treatment
with
oral
antidiabetic
agents
has
advanced,
the
outcomes
remain
suboptimal.
Several
undesirable side effects have been reported
with the use of metformin and sulfonylureas,
such
as
diarrhea
and
lactic
acidosis
(metformin),
as
well
as
liver
failure,
weight
gain,
tachycardia,
and
hypothyroidism
(sulfonylureas) [6,7]. In addition, issues such
as drug resistance, adverse effects, and even
toxicity have also been observed [8].
Belitung taro (
Xanthosoma sagittifolium
) is a
traditional carbohydrate staple in Indonesia,
with
stems
and
tubers
containing
approximately
85%
carbohydrate
content
(dry basis) and serving as a primary energy
source for local populations. Belitung taro is
traditionally
consumed
primarily
for
its
carbohydrate-rich
tubers,
while
the
stems
are
typically
discarded
as
agricultural
by-
products.
Paradoxically,
this
high
carbohydrate content provides the scientific
rationale for antidiabetic investigation: as a
widely consumed food source, any inherent
α-amylase
inhibitors within the
plant could
modulate
postprandial
glucose
responses
during
consumption,
offering
a
built-in
glycemic
control
mechanism.
However,
previous
studies
have
highlighted
the
species'
broader
medicinal
potential,
39
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
including folk medicine applications for bone
health, suggesting that non-edible parts may
also
contain
bioactive
compounds.
The
stems
contain
secondary
metabolites
including flavonoids, saponins, and tannins,
compounds
previously
demonstrated
to
inhibit
carbohydrate-metabolizing
enzymes
and improve insulin sensitivity in other plant
species [9–13]
.
Scientific
investigations
into
Xanthosoma
sagittifolium
have
largely
concentrated
on
the pharmacological properties of its leaves
and
tubers
(corms).
Previous
studies
have
documented the antioxidant, antitumor, and
iron-chelating activities of the leaf extracts.
Additionally, the tubers have been evaluated
for
their
nutritional
value,
calcium
content
for
osteoporosis
prevention,
and
potential
as
functional
foods
with
low
glycemic
indices.
However,
the
stems
of
X.
sagittifolium
remain
an
underutilized
agricultural
by-product
with
unexplored
therapeutic potential. Unlike the tubers and
leaves,
scientific
data
specifically
targeting
the antidiabetic mechanisms of the stems is
scarce. To date, there is a notable absence of
studies integrating computational screening
(
in silico
) with enzymatic assays (
in vitro
) to
evaluate how secondary metabolites in the
stems interact with key metabolic enzymes
like α-amylase. This study aims to bridge this
gap by repurposing the stems as a source of
bioactive
compounds
for
hyperglycemia
management [14–17].
The combined in silico and in vitro approach
consistently
demonstrates
effectiveness
in
mapping
bioactive
compound
activity
and
validating
their
function
in
natural
product
research
for
antidiabetic
purposes
[18].
Molecular
docking
predicts
compound
interactions with target proteins such as α-
amylase,
while
in
vitro
assays
provide
empirical evidence of enzyme inhibition and
antihyperglycemic
effects.
This
integrative
method
improves
the
efficiency
of
compound
screening
and
strengthens
the
validity
of
research
findings[18,19].
Therefore,
this
study
aims
to
evaluate
the
effectiveness
of
compounds
found
in
the
stem
of
Xanthosoma
sagittifolium
as
α-
amylase
inhibitors
through
in
silico
and
in
vitro
assays.
Materials and Methods
Materials
The
materials
used
include
the
three-
dimensional structure of α-amylase protein
(PDB
ID:
2QV4)
obtained
from
the
Protein
Data
Bank,
secondary
metabolites
of
Xanthosoma
sagittifolium
stem
retrieved
from CMAUP using the keyword
Xanthosoma
violaceum
,
Xanthosoma
sagittifolium
stem,
96% ethanol, 2N HCl, distilled water, Mayer’s
reagent,
Wagner’s
reagent,
Dragendorff’s
reagent,
70%
ethanol,
magnesium
powder,
concentrated
HCl,
FeCl
₃
,
chloroform,
acetic
anhydride,
concentrated
sulfuric
acid,
100
mg acarbose, PBS (pH 7), α-amylase powder,
starch
powder,
iodine,
and
potassium
iodide.
Protein Receptor Preparation
The crystal structure of human pancreatic α-
amylase (PDB ID: 2QV4) was retrieved from
the
RCSB
Protein
Data
Bank
(
http://www.rcsb.org
).
This
structure
was
selected for its high resolution of 1.97 Å and
its co-crystallization with acarbose, providing
the
experimentally-defined
binding
pose
essential for validating the docking protocol.
.
The
protein
was
prepared
using
BIOVIA
Discovery Studio Visualizer 2021, and saved
in PDB format by removing water molecules,
heteroatoms
and
the
native
acarbose
ligand
[20].
Polar hydrogen atoms were
40
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
subsequently
added
to
ensure
proper
protonation states of amino acid residues. It
was
then
separated
from
water
(solvent),
ligands, and non-standard residues.
Prediction
of
Pharmacokinetics,
Toxicity,
and Lipinski’s Rule of Five
Prediction was conducted using the pkCSM
platform
.
http://structure.bioc.cam.ac.uk/pk
csm
by entering the SMILES codes obtained
from
the
CMAUP
database
[21]
.
The
screening
of
bioactive
candidates
was
performed
in
two
sequential
stages
using
the
pkCSM
web
server
.
(
http://structure.bioc.cam.ac.uk/pkcs
m
)
[22].
First,
the
drug-likeness
of
the
compounds was assessed based on
Lipinski’s
Rule
of
Five
(Ro5).
This
rule
predicts
oral
bioavailability by evaluating physicochemical
parameters:
molecular
weight
(≤
500
Da),
lipophilicity
(LogP
≤
5),
hydrogen
bond
donors (≤ 5), and hydrogen bond acceptors
(≤ 10) [23]. Compounds that failed to meet
any
single
criterion
of
these
rules
were
excluded
from
further
analysis
to
strictly
ensure optimal membrane permeability and
oral
bioavailability.
Second,
compounds
passing
the
Ro5
filter
were
subjected
to
ADMET
prediction
using
pkCSM's
graph-
based
signature
algorithms.
This
step
evaluated pharmacokinetic parameters (e.g.,
intestinal absorption, volume of distribution,
CYP450
inhibition)
and
toxicity
endpoints
(e.g.,
AMES
mutagenicity,
hepatotoxicity,
LD50). The
relationship between these
two
screenings
is
hierarchical:
Lipinski’s
rule
serves as a coarse filter for oral availability,
while pkCSM provides a detailed safety and
metabolic
profile
essential
for
selecting
viable
drug
candidates
[24,25].
ADMET
predictions
were
assessed
based
on
key
pharmacokinetic
indicators
including
intestinal
absorption
(>30%),
predicted
volume of distribution (log VDss between –
0.15 and 0.45), cytochrome
P450 inhibition
(CYP2D6
and
CYP3A4),
total
clearance,
and
toxicity
endpoints
[22].
Compounds
were
classified as
favorable
when they met these
parameters,
and
unfavorable
when
they
exceeded
the
thresholds
or
showed
poor
ADMET profiles.
Test Ligand Preparation
The
test
ligands
were
obtained
from
pharmacokinetic, toxicity, and Lipinski’s rule
predictions,
and
downloaded
from
PubChem by entering the smile code of the
selected
molecules,
then
saved
in
3D
conformer SDF format [14]. The ligands were
subsequently optimized using the Avogadro
application
to
minimize
their
energy
and
saved in PDB format [21].
Redocking (Validation)
AutoDock
was
used
to
re-dock
the
native
ligand
to
the
receptor
protein,
which
generated
grid
box
coordinates.
These
coordinates
were
then
used
both
for
redocking
the
native
ligand
(serving
as
the
reference
ligand)
and
for
docking
the
secondary metabolites. Each redocking and
docking run used 100 ligand conformations,
a
population
size
of
150,
up
to
2,500,000
energy
evaluations,
and
100,000
genetic
generations, with all other parameters set to
AutoDock
defaults.
The
conformation
with
the
lowest
binding
energy
and
an
RMSD
below
2
Å
from
the
redocking
served
as
a
reference
for
subsequent
docking
of
secondary
metabolites
from
Xanthosoma
sagittifolium
[26].
Docking With Secondary Metabolites
AutoDock
was used
to
dock
the secondary
metabolite
compounds
with
the
α-amylase
protein,
employing
the
same
grid
box
settings
as
in
the
redocking/validation
process
to
ensure
consistency
of
results
without
significant
variation
between
docking and redocking
[26].
41
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Interaction
Study
and
Docking
Result
Visualization
The application used was BIOVIA Discovery
Studio
Visualizer
2021.
The
interactions
between ligand and protein were visualized
in both 2D and 3D, including van der Waals
bonds,
hydrophobic
interactions,
hydrogen
bonds, and other interactions [27].
Data Analysis Technique
Data
analysis
included
measurement
of
RMSD
values
and
binding
affinities
from
docking between the reference control and
the test compounds. A T-test was then used
to
assess
significant
differences
between
them.
An
RMSD
value
of
less
than
2
Å
indicates an accurate ligand position, while
values
greater
than
2
Å
suggest
deviation
[28].
Binding affinity measures the strength
of
interaction
between
a
drug
and
its
receptor;
lower
affinity
values
represent
stronger
binding,
while
higher
values
correspond to weaker binding
[29].
Extract Preparation
The extract of
Xanthosoma sagittifolium
stem
was prepared using the maceration method
with 96% ethanol as the solvent. A total of
100 g of powdered simplicia was macerated
at a ratio of 1:10 (w/v) for 3 × 24 h, with three
repetitions. Maceration was carried out in a
glass
container
placed
in
a
location
protected
from
direct
sunlight,
with
occasional stirring. The obtained filtrate was
separated
from
the
simplicia
and
then
concentrated using a rotary evaporator
.
Phytochemical Screening
Alkaloid Screening:
A total of 0.5 g of extract
was added to 2 N HCl and 9 ml of distilled
water, then heated for 2 min. After filtration,
the filtrate was tested with Mayer’s reagent
(white
or
yellow
precipitate
indicates
the
presence of alkaloids), Dragendorff’s reagent
(orange color), and Wagner’s reagent (brown
color)
[30].
Flavonoid Screening:
the extract
was mixed with 3 mL of 70% ethanol, shaken,
heated, shaken again, and then filtered. The
filtrate
obtained
was
added
with
0.1
g
of
magnesium
powder
and
2
drops
of
concentrated
HCl.
The
formation
of
a
red
color
in
the
ethanol
layer
indicated
the
presence of flavonoids
[31]
.
Tannin
Screening:
A portion of the extract was placed into a test
tube, added with hot water, and shaken until
homogeneous for 5 min. Then, 3–4 drops of
FeCl
₃
were
added.
A
greenish-blue
(green-
black)
coloration
indicated
the
presence
of
catechol
tannins,
while
a
bluish-black
coloration
indicated
the
presence
of
pyrogallol tannins
[32]
.
Saponin
Screening
:
A
total of 0.5 g of extract was placed into a test
tube. Then, 10 mL of hot water was added,
and
the
mixture
was
cooled
and
shaken
vigorously
until
homogeneous.
The
presence of saponins was indicated by the
formation of a stable froth (1–3 cm height)
that
persisted
for
30
seconds
and
did
not
disappear after the addition of one drop of 2
N
hydrochloric
acid
[33]
.
Steroid
and
triterpenoid
Screening
:
A
total
of
1
g
of
Xanthosoma sagittifolium
stem extract was
added with 20 drops of chloroform and then
shaken
until
homogeneous.
The
filtrate
obtained
was
subsequently
added
with
2
drops
of
acetic
anhydride
and
2
drops
of
concentrated sulfuric acid. A positive result
is indicated by the appearance of a blue or
green
color
for
steroids,
while
a
red
or
purple
color
indicates
the
presence
of
terpenoids [34].
α-Amylase Inhibitory Activity
The α-amylase inhibitory activity assay was
conducted
following
the
method
described
by
Ononamadu
et
al.
[34]
with
slight
modifications.
The
α-amylase
enzyme
solution was prepared by dissolving 20 mg of
α-amylase
powder
in
10
mL
of
phosphate
buffer
saline
(PBS,
pH
7).
A
1%
starch
42
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
solution was prepared by dissolving 250 mg
of starch in 25 mL of distilled water under
heating. The iodine solution was prepared by
dissolving 16.6 mg of potassium iodide (KI) in
a small volume of distilled water, followed by
the
addition
of
25.4
mg
of
iodine
(I
₂
),
and
then diluted to 20 mL with distilled water. A
1 N HCl solution was prepared by diluting 1.7
mL of concentrated HCl (37%) to 20 mL with
distilled water. Acarbose standard solutions
were prepared at concentrations of 30, 60,
120,
and 240 ppm from a 1000
ppm
stock
solution
obtained
by
dissolving
100
mg
of
acarbose in 100 mL distilled water. Similarly,
the
extract
solution
of
Xanthosoma
sagittifolium
stems was prepared at the same
concentrations
from
a
1000
ppm
stock
solution by dissolving 25 mg of extract in 25
mL distilled water and diluting accordingly.
The
maximum
wavelength
(λmax)
was
determined by mixing 3000 μL of 1% starch
solution with 200 μL of iodine solution, and
the absorbance was scanned at 400–800 nm
using a UV–Vis spectrophotometer. For the
control,
2000
μL
of
phosphate-buffered
saline
(PBS)
was
incubated
at
37
°C
for
10
min, followed by the addition of 1000 μL of
1% starch solution and further incubation for
15
min.
The
reaction
was
terminated
by
adding
40
μL
of
HCl
and
200
μL
of
iodine
solution, and the absorbance was measured
at λmax. For the sample treatment, 1000 μL
of PBS was mixed with 1000 μL of α-amylase
solution and incubated at 37 °C for 10 min
prior to the addition of 1000 μL of 1% starch
solution, followed by incubation for 15 min.
The
reaction
was
stopped
using
HCl
and
iodine
solution,
and
the
absorbance
was
subsequently measured at λmax.
For
the
sample
control
(KS),
1000
μL
of
extract
or
acarbose
solution
at
each
concentration
was
mixed
with
1000
μL
of
PBS
and
incubated
at
37
°C
for
10
min,
followed by the addition of 1000 μL of starch
solution and further incubation for 15 min.
The
reaction
was
stopped
with
HCl
and
iodine
solution
before
measuring
the
absorbance. For the sample (S), 1000 μL of
extract or acarbose solution was mixed with
1000 μL of α-amylase solution and incubated
at 37 °C for 10 min, followed by the addition
of
1000
μL
of
starch
solution
and
further
incubation
for
15
min.
The
reaction
was
terminated
by
adding
HCl
and
iodine
solution, and the absorbance was measured
at λmax using a UV–Vis spectrophotometer.
The
inhibitory
activity
of
α-amylase
was
expressed
as
the
percentage
of
inhibition,
which
was
calculated
using
the
standard
equation 1.
% inhibition =
(
K−U
)
−(KS−S)
(K−U)
x100%
(1)
Note:
K = Control
absorbance
U =
Enzyme control absorbance
KS = Sample control absorbance
S = Sample absorbance
To
calculate
IC
₅₀
,
first
plot
sample
concentration on the x-axis and % inhibition
on
the
y-axis.
Use
the
resulting
linear
regression
equation
to
determine
the
concentration
at
which
the
percentage
inhibition equals 50% (equation 2 - 4)
y = bx + a
(2)
50 = bx + a
(3)
x =
(50−𝑎)
𝑏
(4)
Results and Discussion
Plant Determination
The plant determination was carried out at
the
Biology
Laboratory,
Universitas
Ahmad
Dahlan, Yogyakarta. The determination key
is as follows: 1b – 2b – 3b – 4b – 12b – 13b –
14b – 17b – 18b – 19b – 20b – 21b – 22b – 23b
– 24b – 25b – 26b – 27b – 799b – 800b – 801b
– 802a – 803b – 804b – 805b Araceae
43
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
1b
–
2b
–
3b
–
5b
–
8a
–
9a
–
10a
–
11b
Xanthosoma
1
Xanthosoma nigrum
(Vell.) Mansf.
Synonym of:
Xanthosoma atrovirens C. Koch & Bouché
Xanthosoma violaceum Schoot
Xanthosoma nigrum Stellfeld
Xanthosoma
sagittifolium
(L.) Schott
The
plant
material
used
in
this
study
was
taxonomically
identified
at
the
Biology
Laboratory, Universitas Ahmad Dahlan. The
specimen
was
determined
as
Xanthosoma
sagittifolium
(family
Araceae),
which
is
taxonomically synonymous with
Xanthosoma
nigrum
(Vell.)
Mansf.
Identification
was
performed
using
a
standard
taxonomic
identification
key
based
on
morphological
characteristics,
beginning
from
the
family
level
(Araceae),
followed
by
the
genus
Xanthosoma
,
and
subsequently
confirming
the species as
X. sagittifolium
[35–37]
.
This
synonymization
is
important.
Several
references
mention
X. nigrum
(an
alternate
scientific
name
for
X.
sagittifolium)
as
a
synonym of
X. sagittifolium. This strengthens
the validity of the plant material's taxonomic
identification
in
this
research.
These
determination results provide a strong and
valid
basis
for
developing
antidiabetic
phytopharmaca
using
Belitung
Taro
stems
(
Xanthosoma sagittifolium
)
[35–38].
In Silico Investigation
In
this
study,
the
selection
of
ligand
compounds
was
performed
using
a
knowledge-based screening
approach utilizing
the CMAUP (Collective Molecular Activities of
Useful
Plants)
database.
While
direct
metabolite
profiling
(e.g.,
via
LC-MS)
provides
sample-specific
data,
database
mining
serves
as
a
widely
accepted
preliminary
strategy
to
predict
potential
bioactivity based on chemotaxonomic data.
The
eight
metabolites
selected
(Table
1)
were
retrieved
using
the
keyword
Xanthosoma
violaceum
(a
synonym
for
X.
sagittifolium
)
and
have
been
previously
documented in the genus
Xanthosoma
. This
approach allows for the screening of "likely"
bioactive
candidates
to
narrow
down
molecular
targets
before
costly
extraction
and isolation procedures. Ideally, this
in silico
prediction serves as a hypothesis-generating
step
to
guide
future
targeted
isolation,
rather
than
a
confirmation
of
the
extract's
exact
composition.
To
mitigate
this
limitation,
standard
qualitative
phytochemical screening was performed in
parallel (Section 3.3) to confirm the presence
of
major
metabolite
classes
(alkaloids,
flavonoids,
tannins)
predicted
by
the
database[39–41].
Eight
secondary
metabolites
were
identified
in
the
CMAUP
database from Belitung taro stems using the
keyword
Xanthosoma
violaceum
(Table
1)
[42].
All
identified
secondary
metabolites
were
subsequently
evaluated
for
their
drug-
likeness
properties
using
Lipinski’s
Rule
of
Five (Table 2). According to Lipinski’s criteria,
a
compound
is
considered
to
possess
favorable
oral
bioavailability
when
it
fulfills
the following parameters: molecular weight
(MW) < 500 Da, partition coefficient (log P) <
5,
hydrogen
bond
donors
(HBD)
<
5,
and
hydrogen
bond
acceptors
(HBA)
<
10.
Compounds
that
satisfy
these
criteria
are
generally
predicted
to
exhibit
good
membrane
permeability
and
absorption
characteristics [1].
44
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Table 1.
Secondary Metabolites of Belitung Taro Stems (Source: CMAUP)
No.
NP_ID
Metabolite Name
Smiles Code
Structure
1.
NPC78263
Trifolin
c1cc(ccc1c1c(c(=O)c2c(cc(cc
2o1)O)O)O[C@H]1[C@@H]([
C@H]([C@H]([C@@H](CO)O
1)O)O)O)O
2.
NPC76315
(R)-5-phenyl-3-
(((2R,3R,4S,5S,6R)-3,4,5-
trihydroxy-6-
(hydroxymethyl)tetrahy
dro-2H-pyran-2-
yl)oxy)pentanoic acid
c1ccc(cc1)CC[C@H](CC(=O)
O)O[C@H]1[C@@H]([C@H](
[C@@H]([C@@H](CO)O1)O)
O)O
3.
NPC281131
Hyperoside
c1cc(c(cc1c1c(c(=O)c2c(cc(cc
2o1)O)O)O[C@H]1[C@@H]([
C@H]([C@H]([C@@H](CO)O
1)O)O)O)O)O
4.
NPC235260
Quercetin Glucuronide
c1cc(c(cc1c1c(c(=O)c2c(cc(cc
2o1)O)O)O[C@H]1[C@@H]([
C@H]([C@@H]([C@@H](C(=
O)O)O1)O)O)O)O)O
5.
NPC179950
Isoquercetin
c1cc(c(cc1c1c(c(=O)c2c(cc(cc
2o1)O)O)O[C@H]1[C@@H]([
C@H]([C@@H]([C@@H](CO)
O1)O)O)O)O)O
6.
NPC150546
1-(2-hydroxy-3,6-
dimethoxy-4-
(((2S,3R,4S,5S,6R)-3,4,5-
trihydroxy-6-
((((2R,3R,4S,5S,6R)-
3,4,5-trihydroxy-6-
(hydroxymethyl)tetrahy
dro-2H-pyran-2-
yl)oxy)methyl)tetrahydr
o-2H-pyran-2-
COc1cc(c(c(c1C(=O)CCc1ccc
cc1)O)OC)O[C@H]1[C@@H]
([C@H]([C@@H]([C@@H](C
O[C@H]2[C@@H]([C@H]([C
@@H]([C@@H](CO)O2)O)O)
O)O1)O)O)O
45
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
No.
NP_ID
Metabolite Name
Smiles Code
Structure
yl)oxy)phenyl)-3-
phenylpropan-1-one.
7.
NPC141921
butyl (R)-5-phenyl-3-
(((2R,3R,4S,5S,6R)-3,4,5-
trihydroxy-6-
(hydroxymethyl)tetrahy
dro-2H-pyran-2-
yl)oxy)pentanoate
CCCCOC(=O)C[C@@H](CCc1
ccccc1)O[C@H]1[C@@H]([C
@H]([C@@H]([C@@H](CO)
O1)O)O)O
8.
NPC113836
Isoquercitin 2''-O-
Gallate
c1cc(c(cc1c1c(c(=O)c2c(cc(cc
2o1)O)O)O[C@H]1[C@@H]([
C@H]([C@@H]([C@@H](CO)
O1)O)O)OC(=O)c1cc(c(c(c1)
O)O)O)O)O
A
molecular
weight
greater
than
500
Da
indicates
that
a
compound
is
unlikely
to
penetrate the cell membrane; therefore, the
molecular weight of secondary metabolites
should be less than 500 Da. The higher the
log
P
value,
the
more
hydrophobic
the
molecule becomes. Excessively hydrophobic
molecules
tend
to
exhibit
a
higher
level
of
toxicity.
The
number
of
hydrogen
bond
donors
and
acceptors
describes
the
hydrogen
bonding
capacity,
where
an
increased
number
corresponds
to
greater
energy
requirements
for
the
absorption
process to occur [2].
Table 2.
Lipinski’s Rule of Five Screening Results (Source: pkCSM)
Secondary Metabolites
Lipinski’s Rule
BM (≤500)
HBD (≤5)
HBA (≤10)
Log P (≤5)
NPC78263
448.1
7.0
11.0
0.828
NPC76315
356.15
5.0
8.0
-0.112
NPC281131
464.1
8.0
12.0
0.462
NPC235260
478.07
8.0
13.0
0.688
NPC179950
464.1
8.0
12.0
0.225
NPC150546
626.608
8
15
-1.7722
NPC141921
412.21
4.0
8.0
1.866
NPC113836
616.11
10.0
16.0
1.297
46
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Secondary
metabolites
that
pass
Lipinski’s
rule of five screening are those that meet all
of its criteria. From the Lipinski screening of
eight
secondary
metabolites,
two
compounds
were
found
to
satisfy
the
requirements,
namely
NPC76315
and
NPC141921 (Table 2).
The
compounds
that
passed
the
Lipinski’s
rule
of
five
screening
were
subsequently
subjected
to
pharmacokinetic
and
toxicity
prediction
to
determine
their
pharmacokinetic
profiles,
including
Absorption,
Distribution,
Metabolism,
Excretion,
and
Toxicity
(ADMET)
(Table
3).
The
results
of
the
pharmacokinetic
and
toxicity
screening
of
the
two
secondary
metabolites
showed
that
only
one
compound
met
the
requirements,
namely
NPC141921.
Based
on
pharmacokinetic
and
toxicity
screening,
the
first
parameter
used
was
Absorption.
The
compound
demonstrated
good intestinal Absorption with an Intestinal
Absorption value of 47.902%. A compound is
considered
to
have
good
intestinal
Absorption if its Intestinal Absorption value
exceeds
30%.
Intestinal
Absorption
is
directly related to the ability of the intestine
to absorb a drug administered orally [3].
The
following
pharmacokinetic
prediction
parameter is distribution. Distribution refers
to
the
process
by
which
a
drug
enters
the
systemic circulation. The greater the extent
of
distribution
throughout
the
body,
the
faster the drug can reach its target site and
exert its therapeutic effect. The distribution
parameter
assessed
was
VDss
(volume
of
distribution at steady state). A compound is
considered
to
have
a
low
volume
of
distribution when the Log VDss value is < –
0.15, and high when it is > 0.45. The volume
of
distribution
(VDss)
represents
a
theoretical
volume
required
for
the
total
administered dose of a drug to be uniformly
distributed,
achieving
the
same
concentration
as
in
plasma.
A
higher
VD
value
indicates
a
greater
proportion
of
the
drug
is
distributed into
tissues
rather than
remaining in the plasma [4].
The volume of distribution (VD) of acarbose
is
relatively
low,
approximately
0.32
L/kg,
based
on
data
from
studies
in
healthy
volunteers
that
calculated
VD
from
plasma
concentrations
following
intravenous
administration. This low VD value indicates
that
acarbose
is
primarily
retained
in
the
plasma
and
gastrointestinal
tract,
with
minimal
distribution
into
tissues.
This
finding is consistent with the mechanism of
action of acarbose, which mainly inhibits the
α-glucosidase enzyme in the intestine rather
than exerting effects in other tissues of the
body [6,8,43].
The
third
pharmacokinetic
parameter
is
metabolism. In general, metabolism occurs
in the liver and is facilitated by cytochrome
P450 enzymes. Cytochrome P450 (CYP) plays
a
crucial
role
in
detoxification
by
oxidising
xenobiotics
for
excretion.
This
enzyme
family is responsible for the metabolism of
many
drugs,
including
CYP1A2,
CYP2C9,
CYP2D6,
and
CYP3A4/5,
which
together
account
for
approximately
72%
of
overall
drug metabolism
[44]
. Based on the results of
metabolism
screening,
represented
by
the
cytochrome
isoforms
CYP2D6
and
CYP3A4,
one compound was found not to inhibit the
metabolism
of
CYP2D6
or
CYP3A4.
Therefore,
the
compound
can
be
metabolised by these enzymes.
The
final
pharmacokinetic
parameter
is
excretion.
The
excretion
process
of
a
compound
can
be
assessed
by
measuring
the
Total
Clearance
(CLTOT)
value.
CLTOT
represents
the
combination
of
hepatic
clearance (metabolism in the liver and bile)
and
renal
clearance
(excretion
via
the
kidneys)
[4]
. This parameter is closely related
47
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
to
bioavailability
and
is
critical
for
determining
the
appropriate
dosage
to
achieve
a
steady-state
concentration.
The
CLTOT value can be used to predict the rate
of
compound
elimination.
Based
on
the
excretion
screening
results,
the
compound
showed
a
CLTOT
value
of
1.438.
Another
important
parameter
is
toxicity,
which
is
evaluated
through
the
LD
50
value.
LD
50
is
defined
as
the
single
dose
of
a
test
compound,
calculated
statistically,
that
causes the death of 50% of the test animals
following oral administration [18].
The LD
50
screening results showed that the
compound
had
an
LD
50
value
of
2.148
mol/kg.
The
compound
that
passed
the
pharmacokinetic and toxicity screening was
then
prepared
for
the
next
step,
namely,
molecular
docking.
The
docking
process
began
with
validation,
which
involved
separating the protein from its native ligand,
followed
by
re-docking
the
separated
protein
and
ligand.
The
validation
stage
began
with
protein
preparation,
which
involved
removing
water
molecules,
native
ligands,
and
other
complex
compounds
contained within the structure [19,45]
The
prepared
protein
was
then
used
for
validation through a redocking process with
its native ligand. Prior to
redocking
, the native
ligand
was
prepared
by
removing
the
protein
and
other
associated
complex
molecules,
leaving
only
the
ligand.
The
results
of
the
protein
and
native
ligand
preparation are presented in the figure 1.
Table 3.
Pharmacokinetic and Toxicity Screening Results (Source: pkCSM)
Parameter
NPC76315
NPC141921
Intestinal Absorption (human) (% Absorbed)
21.616
47.902
VDss (human) (log L/kg)
-0.783
-0.636
CYP2D6 substrate
No
No
CYP3A4 substrate
No
No
CYP2D6 inhibitor
No
No
CYP3A4 inhibitor
No
No
Total clerance (log ml/min/kg)
1.247
1.438
LD
50
(mol/kg)
2.311
2.148
The
compound
that
passed
the
pharmacokinetic and toxicity screening was
subsequently
subjected
to
molecular
docking analysis. This process began with a
validation
step,
in
which
the
protein
was
separated from its native ligand and then re-
docked
to
ensure
the
accuracy
of
the
docking
method.
Protein
preparation
was
carried
out
by
removing
water
molecules,
the
native
ligand,
and
other
complex
molecules
[6,8]
Subsequently,
the
native
ligand was also prepared by eliminating the
protein
and
other
associated
molecules,
leaving
only
the
ligand,
so
that
both
were
ready for the redocking stage.
The prepared protein and native ligand were
subsequently
validated
by
re-docking
the
ligand into the binding site of the separated
protein. The validation results showed that
the
ligand
successfully
returned
to
its
original
position with high
accuracy
(Figure
1),
as
indicated
by
a
Root
Mean
Square
Deviation (RMSD) value of 1.51 Å. Validation
is
considered
successful
when
the
RMSD
value
is
less
than
2
Å
[1].
An
RMSD
value
closer to 0 indicates that the ligand can be
precisely restored to its native binding pose.
48
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Therefore, the validation process requires an
RMSD
value
of
<
2
Å
to
be
deemed
valid,
ensuring
that
the
docking
protocol
can
be
reliably applied for screening the candidate
compounds [46].
To
assess
the
accuracy
of
the
docking
protocol,
the
redocked
acarbose
conformation (blue) was superimposed onto
the native crystallographic ligand (red) within
the
active
site
of
2QV4.
As
illustrated
in
Figure 1, the two conformations exhibited a
high
degree
of
overlap,
particularly
in
the
core
hexasaccharide
rings
involved
in
catalytic
interactions.
The
calculated
RMSD
of 1.51 Å confirms that the docking algorithm
successfully
reproduced
the
bioactive pose
of the inhibitor with minimal deviation from
the
experimental
structure.
This
structural
alignment validates the grid box parameters
and
scoring
function
used
for
subsequent
screening of candidate compounds [47].
Figure 1.
Validation Results. Native ligand (blue) and re-docked ligand (red)
The validation parameters were then used to
perform
the
docking
process
with
the
candidate
compounds.
The
parameter
applied
was
the
grid
parameter
file
(GPF),
obtained from the validation results, which
contains
information
on
the
grid
box
size
and grid box position (Table 4)
[46]. The grid
box
size
represents
the
dimensions
of
the
grid
box,
while
the
grid
box
position
describes
the
three-dimensional
coordinates of the ligand in terms of X, Y, and
Z points.
The
candidate
drug
compound
was
first
prepared
prior
to
the
docking
process
(Figure
4).
Preparation
was
carried
out
by
optimising
the
molecule
through
energy
minimisation using Avogadro software. This
step
aimed
to
obtain
a
stable
three-
dimensional
molecular
structure
[19].
The
prepared
candidate
drug
compound
was
then
subjected
to
docking
with
the
target
protein.
Table
4.
Validation
Results
of
the
Docking
Process
Parameter
Value
Grid Box Size (Å)
44 x 40 x 40
Grid Box Position
X : 13.378
Y : 47.46
Z : 26.277
Spacing
0.375
RMSD
1.51 Å
Binding energy
-6.66 kcal/mol
Figure
4.
Optimized
Candidate
Drug
Compound 3D view (NPC141921).
49
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
The molecular docking simulation aimed to
evaluate the binding affinity and interaction
modes of the selected secondary metabolite,
NPC141921, within the active site of human
pancreatic
α-amylase
(PDB
ID:
2QV4).
The
docking
results
revealed
that
NPC141921
possesses
a
binding
free
energy
of
-6.13
kcal/mol. While this affinity is slightly lower
than that of the standard inhibitor acarbose
(-6.66
kcal/mol)
(Table
X),
the
difference
is
marginal
(<
1.0
kcal/mol).
According
to
thermodynamic principles, a more negative
binding
free
energy
(ΔG)
correlates
with
a
more stable ligand-receptor complex. Thus,
although
acarbose
demonstrates
a
theoretically
stronger
interaction,
the
comparable
binding
energy
of
NPC1421
suggests it can still spontaneously and stably
occupy the enzyme's active pocket, acting as
a competitive inhibitor potentially capable of
blocking substrate access to the catalytic site
[14,16,48].
Table 5.
The binding interactions between the active compounds, and α-amylase (2QV4)
Protein
Compound
Binding
Energy
(kcal/mol)
Protein
Residue
Type
of
Interaction
3D
Interaction
and
2D
Interaction
2QV4
Native
Ligand
(Acarbose)
-6.66
ALA106
Hydrogen
Bond
3D View
ARG195
Hydrogen
Bond
2D View
ASP300
Hydrogen
Bond (3)
GLU233
Hydrogen
Bond (2)
HIS201
Hydrogen
Bond
HIS305
Hydrogen
Bond (2)
THR163
Hydrogen
Bond
TRP59
Hydrogen
Bond
50
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Protein
Compound
Binding
Energy
(kcal/mol)
Protein
Residue
Type
of
Interaction
3D
Interaction
and
2D
Interaction
NPC141921
-6.13
GLN63
Hydrogen
Bond (2)
3D View
ARG195
Hydrogen
Bond
2D View
ASP197
Hydrogen
Bond (3)
GLU233
Hydrogen
Bond
HIS101
Hydrogen
Bond
ASP300
Hydrogen
Bond
HIS305
Hydrophobic
TRP59
Hydrophobic
(3)
LEU165
Hydrophobic
TRP58
Hydrophobic
The
catalytic
site
of
human
pancreatic
α-
amylase
is
mainly
composed
of
three
essential
amino
acid
residues,
namely
ASP197,
GLU233,
and
ASP300,
which
are
directly
involved
in
the
hydrolysis
of
α-1,4-
glycosidic
linkages
in
starch
substrates.
ASP197
acts
as
the
nucleophilic
residue
during
catalysis,
GLU233
functions
as
a
proton donor and acceptor in the acid–base
reaction
mechanism,
whereas
ASP300
contributes
to
substrate
orientation
and
stabilization of the transition state through
hydrogen-bond
interactions.
Therefore,
effective
α-amylase
inhibitors
are
generally
51
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
characterized by their ability to interact with
one
or
more
of
these
catalytic
residues,
thereby
interfering
with
substrate
binding
and enzymatic activity [49–51].
Our
analysis
of
the
ligand-protein
interactions revealed that NPC141921 forms
hydrogen
bonds
with
two
of
the
three
catalytic triad residues: ASP300 and GLU233
(Table 5). The formation of these hydrogen
bonds is critical as it likely interferes with the
proton
transfer
required
for
catalysis
or
stabilizes a non-productive conformation of
the
enzyme.
Additionally,
NPC141921
interacts
with
ARG195
and
HIS305
via
hydrogen
bonds.
Previous
studies
have
highlighted
that
ARG195
and
HIS305
are
essential
for
substrate
recognition
and
transition-state
stabilization;
inhibitors
like
acarbose are known to bind tightly to these
residues to exert their inhibitory effect. The
fact
that
NPC141921
shares
these
specific
interaction
partners
(ARG195,
GLU233,
ASP300, HIS305 and TRP59) with the native
ligand acarbose (Table 5) strongly supports a
competitive inhibition mechanism [52,53]. It
is
well-established
in
rational
drug
design
that the greater the similarity in key amino
acid
residues
bound
between
a
reference
drug and a candidate compound, the higher
the probability that the candidate will exhibit
comparable biological activity [2,45].
Furthermore,
NPC141921
exhibited
hydrophobic interactions with TRP58, TRP59,
and LEU165 within the active site cavity of α-
amylase.
Aromatic
residues,
particularly
TRP59,
are
known
to
contribute
to
ligand
stabilization
through
π–π
stacking
and
hydrophobic
interactions
with
carbohydrate-like
moieties,
thereby
enhancing
ligand
binding
stability.
The
simultaneous formation of hydrogen bonds
with the catalytic residues and hydrophobic
interactions within the binding pocket likely
contributes to the strong binding affinity of
NPC141921
toward
α-amylase.
These
molecular
docking
results
support
the
potential antidiabetic activity of Xanthosoma
sagittifolium
stem-derived
compounds
by
suggesting
their
capability
to
inhibit
α-
amylase
activity,
which
may
subsequently
delay
carbohydrate
hydrolysis
and
reduce
postprandial glucose elevation [53].
Phytochemical Screening
Based
on
the
research
findings,
it
was
confirmed that the stem extract of Belitung
taro
contains
secondary
metabolites,
including
alkaloids,
flavonoids,
steroids,
saponins,
and
tannins.
The
results
of
the
phytochemical
screening
of
Belitung
taro
stem extract are presented in Table 6.
Phytochemical
screening
of
Belitung
taro
stems
(
Xanthosoma
sagittifolium
)
showed
positive
results
for
several
secondary
metabolites.
The
alkaloid
test
produced
a
white
precipitate
with
Mayer’s
reagent,
a
brown
precipitate
with
Wagner’s
reagent,
and
an
orange
colour
with
Dragendorff’s
reagent,
indicating
the
presence
of
coordinate covalent bonds between alkaloid
nitrogen
and
metal
ions
[20,27].
The
flavonoid
test
using
the
Wilstater
Cyanidin
method showed a red colouration after the
addition of Mg metal and concentrated HCl,
confirming the presence of flavonoids [26].
The tannin test with FeCl
₃
produced a bluish-
green
colour, indicating
the
formation
of
a
tannin–FeCl
₃
complex
[29]. The saponin test
showed positive results with the
formation
of
stable
foam
due
to
the
amphipathic
structure
of
saponins
forming
micelles,
particularly after the addition of HCl [26,27].
Finally,
the
steroid
test
showed
a
bluish-
green
colour
after
the
addition
of
chloroform,
acetic
anhydride,
and
concentrated H
₂
SO
₄
, indicating the presence
of
steroids/triterpenoids
through
dehydration reactions and the formation of
conjugated double bonds [31]
52
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Table 6.
Identification Results of the Extract Using Color Reagents
Compound
Class
Reagent
Result
Note
Documentation
Alkaloids
Extract + 2N HCl + distilled
water + Mayer’s reagent
White
precipitate
+
Extract + 2N HCl + distilled
water + Wagner’s reagent
Brown
precipitate
+
Extract + 2N HCl + distilled
water + Dragendorff’s
reagent
Orange
color
+
Flavonoids
Extract + 70% ethanol + Mg
powder + concentrated HCl
Yellow color
+
Tannins
Extract + 5% FeCl
₃
Black color
+
Saponins
Extract + hot water + 2N HCl
Foam
formed &
foam
persists
+
53
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Compound
Class
Reagent
Result
Note
Documentation
Steroids and
Triterpenoids
Extract + acetic anhydride +
concentrated H
₂
SO
₄
Greenish-
blue color
+
In Vitro Antidiabetic Assay
Determination
of
Maximum
Wavelength
(λ
max).
This process is necessary because this
stage
has
high
sensitivity,
so
changes
in
absorbance
at
each
concentration
are
the
greatest
and
are
measured
within
the
wavelength range of 400–800 nm [34]. The
maximum wavelength was obtained at 584
nm with an absorbance value of 1.0705. The
results
of
the
maximum
wavelength
determination
are
presented
in
Figure
6.
Iodine solution is generally yellowish-brown
in
colour
and
will
form
a
blue-colored
complex
when
reacting
with
starch
(amylose)
[54].
Therefore,
the
absorbance
measured by the UV-Vis spectrophotometer
at
the
maximum
wavelength
of
584
nm
indicates
the
amount
of
starch
that
is
not
hydrolysed by the enzyme.
α-Amylase
Inhibition
Assay.
The
α-amylase
inhibition
activity
was
evaluated
using
control,
test,
sample
control,
and
sample
solutions.
The
samples
used
in
this
study
were
the
ethanol
extract
of
Belitung
taro
stems and acarbose. Acarbose was used as
a reference compound because it is an oral
antidiabetic medication that inhibits glucose
absorption in the intestine [55]. In principle,
the more active the extract, the less starch is
hydrolysed,
resulting
in
lower
glucose
production, as the extract inhibits α-amylase
activity. When the extract inhibits α-amylase,
it
cannot
react
with
the
starch
substrate,
producing a higher colour intensity [54,56].
The
assay
began
by
calculating
the
%
inhibition
for
each
concentration
of
the
Belitung taro stem extract and acarbose. The
concentrations tested were 30, 60, 120, and
240
ppm.
The
%
inhibition
value
indicates
the
ability
of
the
extract
or
acarbose
at
a
specific
concentration
to
inhibit
α-amylase
activity.
The
observed
data
for
each
concentration
of
the
extract
and
acarbose
are presented in Table 7.
Based
on
these
results,
the
%
inhibition
values
of
the
extract
were
very
low
at
all
tested
concentrations,
even
at
240
ppm,
where the inhibition did not exceed 11%. In
contrast,
acarbose
showed
a
much
higher
percentage
of
inhibition
at
the
same
concentrations, consistent with its role as a
standard α-amylase inhibitor. This difference
is also reflected in the IC
₅₀
values, where the
extract reached 5,028.111 ppm, significantly
higher
than
that
of
acarbose
at
1,953.478
ppm.
A
high
IC
₅₀
value
indicates
that
the
extract
has
very
weak
inhibitory
potential
and requires a much higher concentration to
achieve a meaningful effect. These findings
suggest that the active compound content in
the
extract
is
likely
low
or
insufficiently
concentrated to produce a strong inhibitory
effect.
54
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Figure 6.
Results of Maximum Wavelength Determination
IC
₅₀
is the inhibitor concentration required to
inhibit
50%
of
α-amylase
enzyme
activity
[54]. The IC
₅₀
value can be determined using
a
linear
regression
equation,
where
the
sample concentration is plotted on the x-axis
and the % inhibition on the y-axis (Figure 7).
Using the equation y=bx+ay = bx + ay=bx+a,
the
IC
₅₀
value
can
be
calculated.
The
inhibitory
activity
of
the
Xanthosoma
sagittifolium
stem
extract
was
quantified
using a four-point concentration series (30–
240 ppm), yielding an IC
50
value of 5,028.11
ppm.
While
pharmaceutical
method
validation
guidelines (e.g.,
ICH Q2)
typically
recommend
a
minimum
of
five
to
six
concentration
points
for
analytical
assays,
this
study
employed
a
streamlined
four-
point
model
suitable
for
preliminary
bioactivity screening.
Table 6.
Results of α-Amylase Inhibition by
Belitung Taro Stem Extract and Acarbose
Concentration
% inhibition
Extract
Acarbose
30 ppm
5.05
5.248
60 ppm
5.399
6.912
120 ppm
5.616
8.121
240 ppm
6.996
10.499
The
results
of
testing
Belitung
taro
stem
extract
for
α-amylase
inhibitory
activity
showed an IC
50
value of 5,028.111 ppm, with
a
correlation
coefficient
(r)
of
0.984
and
a
linear
regression
equation
of
y
=
0.009x
+
4.7477.
Meanwhile,
acarbose,
used
as
a
control, exhibited an IC
50
value of 1,953.478
ppm,
with
a
correlation
coefficient
(r)
of
0.980 and a linear regression equation of y =
0.0233x
+
5.0703.
The
use
of
acarbose
served
to
determine
its
IC
50
as
a
positive
control and to provide a comparison with the
IC
50
value of the tested extract.
Table 7.
IC
50
Values for α-Amylase Inhibition
by the Extract and Acarbose
Sample
IC
50
(ppm)
Extract
5,028.111
Acarbose
1,953.478
In our analysis, the linear regression models
for
both
the
extract
and
the
acarbose
standard
demonstrated
high
linearity,
with
correlation
coefficients
(r)
of
0.984
and
0.980, respectively. These values exceed the
conventional
acceptance
threshold
of
r
>
0.95
for
exploratory
biological
assays,
confirming
that
the
four-point
calibration
curve
provides
a
reliable
and
statistically
valid
estimation
of
potency
for
this
investigative
stage.
Consequently,
the
calculated
IC
50
values
serve
as
robust
indicators
of
the
extract's
relative
antidiabetic
potential
compared
to
the
standard drug.
55
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Figure 7.
Linear regression between sample concentration and % inhibition of α-amylase
Based
on
the
comparison
between
the
Belitung taro stem extract and acarbose, it
can be concluded that the ethanol extract of
Belitung
taro
stems
exhibits
inhibitory
activity
against
the
α-amylase
enzyme,
thereby
reducing
its
enzymatic
action.
The
antidiabetic potential of this extract lies in its
ability
to
inhibit
α-amylase
activity,
which
slows
the
conversion
of
complex
carbohydrates
into
glucose,
increases
glycogen
content
in
the
liver,
stimulates
insulin
secretion,
and
improves
pancreatic
beta-cell
function
[34].
However,
the
α-
amylase
inhibitory
activity
of
acarbose
remains
more
potent
compared
to
the
tested
ethanol
extract
of
Belitung
taro
stems.
Integrative Analysis of In Silico and In Vitro
Findings
This
study
integrates
computational
predictions
with
experimental
enzymatic
assays to elucidate the antidiabetic potential
of
X.
sagittifolium
stems.
A
qualitative
correlation was established between the two
approaches:
the
in
silico
model
identified
flavonoid
glycosides
(e.g.,
NPC141921)
as
high-affinity
ligands
for
α-amylase,
which
aligns
with
the
phytochemical
screening
results
confirming
the
presence
of
flavonoids in the extract. This suggests that
the
predicted
bioactive
ligands
are
likely
constituents
of
the
tested
extract
matrix
[58].
Regarding
the
potency,
the
in
vitro
assay
revealed
that
the
crude
stem
extract
exhibited an IC
50
value of 5,028 ppm, which
is higher than the standard acarbose (1,953
ppm).
This
difference
in
potency
is
characteristic
of
crude
natural
products,
where the concentration of specific bioactive
secondary metabolites is naturally diluted by
non-active
structural
components
such
as
cellulose and starch. Previous studies have
consistently
demonstrated
that
crude
extracts
display
higher
IC
50
values
comparedo their isolated pure compounds
due
to
this
matrix
dilution
effect.
Consequently,
while
the
docking
results
provide a mechanistic understanding of how
specific
flavonoids
interact
with
the
enzyme's
active
site,
the
in
vitro
results
reflect the aggregate biological activity of the
whole stem extract. These findings support
the
rationale
for
future
bioassay-guided
fractionation
to
isolate
and
validate
the
specific
high-affinity
compounds
predicted
by our computational model [14,16,17].
While
X.
sagittifolium
tubers
have
been
recognized
for
their
nutritional
value,
the
current
findings
demonstrate
that
the
56
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
y = 0.0233x + 5.0703
R² = 0.9618
y = 0.009x + 4.7477
R² = 0.9698
0
2
4
6
8
10
12
0
50
100
150
200
250
300
%Inhibition
Concentration (ppm)
Acarbose
Extract
stems—typically
considered
agricultural
waste—possess
significant
α-amylase
inhibitory activity. This expands the utility of
the
species
beyond
its
conventional
edible
parts and aligns with reports of its broader
ethnomedicinal applications [15,59].
Study Limitations and Future Directions.
The current
in silico
investigation focused on
molecular
docking
to
predict
the
binding
affinity
and
interaction
modes
of
the
lead
compound,
NPC141921.
While
molecular
docking
provides
a
static
snapshot
of
the
ligand-receptor interaction, we acknowledge
that it does not account for the flexibility and
stability
of
the
complex
over
time.
Future
studies
incorporating
Molecular
Dynamics
(MD) simulations will be valuable to assess
the
thermodynamic
stability
and
conformational changes of the NPC141921-
α-amylase
complex
in
a
solvated
physiological environment, providing a more
comprehensive understanding of its binding
mechanism [60].
Furthermore, regarding the ligand selection,
this study employed a hierarchical screening
strategy where the eight initial metabolites
were
first
filtered
based
on
drug-likeness
(Lipinski’s
Rule
of
Five)
and
ADMET
safety
profiles. Only NPC141921 satisfied all safety
and
pharmacokinetic
criteria
to
justify
its
progression to the molecular docking stage.
This
funneling
approach
ensures
that
computational resources are focused on the
most viable drug candidates with the highest
probability of clinical translation, rather than
screening
compounds that
would
likely
fail
due to poor bioavailability or toxicity.
Conclusion
This
study
successfully
evaluated
the
antidiabetic
potential
of
Xanthosoma
sagittifolium stems through an integrative in
silico
and
in
vitro
approach.
The
computational
screening
identified
NPC141921 as a promising lead compound,
exhibiting a favorable ADMET profile and a
strong binding affinity to human pancreatic
α-amylase
(PDB
ID:
2QV4)
with
a
binding
energy of -6.13 kcal/mol. Molecular docking
analysis revealed that NPC141921 interacts
with key catalytic residues (ASP197, GLU233,
ASP300)
and
substrate-binding
residues
(ARG195,
HIS305)
via
hydrogen
bonds,
suggesting
a
competitive
inhibition
mechanism
comparable
to
the
standard
drug acarbose. Experimentally, the
ethanol
extract
of
X.
sagittifolium
stems
demonstrated
α-amylase
inhibitory
activity
with an IC
50
value of 5,028 ppm. While this
potency is lower than that of acarbose (1,953
ppm), likely due to the crude nature of the
extract, the results confirm the presence of
bioactive
constituents
capable
of
modulating
carbohydrate
metabolism.
Collectively,
these
findings
validate
the
traditional
use
of
Belitung
taro
as
a
functional
food
resource
and
highlight
the
stem's
potential
as
a
raw
material
for
developing
natural
antidiabetic
agents.
Further research focusing on the isolation of
NPC141921
and
in
vivo
efficacy
studies
is
recommended
to
advance
its
pharmaceutical application.
Acknowledgement
This research was funded by the Ministry of
Higher
Education,
Science,
and
Technology
of
the
Republic
of
Indonesia
under
the
2024/2025
Beginner
Lecturer
Research
(Penelitian Dosen Pemula) scheme.
Author Contributions
For research articles with several authors, a
short
paragraph
specifying
their
individual
contributions
must
be
provided.
The
following
statements
should
be
used
"Conceptualization,
WR.
and
SS.;
Methodology,
WR.
And
SS.;
Software,
AM.;
57
W. Rahmatulloh et al.,
Chempublish Journal, 10(1) 2026, 38-62
Validation,
WR.,
SS.,
AM
and
NA.;
Formal
Analysis, SS.; Investigation, WR and SS.; Data
Curation,
WR.,
SS.,
AM
and
NA;
Writing
–
Original
Draft
Preparation,
WR
and
SS.;
Writing
–
Review
&
Editing,
WR
and
SS;
Visualization,
WR
and
SS.;
Supervision,
WR
and SS.; Project Administration, WR and SS.;
Funding Acquisition, WR and SS.”.
Conflict of Interest
The authors declare no conflict of interest.
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