biomolecules

Review
In Silico Approaches to Identify Polyphenol Compounds
as α-Glucosidase and α-Amylase Inhibitors against
Type-II Diabetes
Jirawat Riyaphan 1 , Dinh-Chuong Pham 2 , Max K. Leong 3, * and Ching-Feng Weng 4, *

1 Rubber Authority of Thailand, Bangkok 10700, Thailand; jirawat.riyaphan@gmail.com

2 Biomaterials and Nanotechnology Research Group, Faculty of Applied Sciences, Ton Duc Thang University,
Ho Chi Minh City 700000, Vietnam; phamdinhchuong@tdtu.edu.vn
3 Department of Chemistry, National Dong Hwa University, Hualien 97401, Taiwan
4 Functional Physiology Section, Department of Basic Medical Science, Xiamen Medical College,
Xiamen 361023, China
* Correspondence: leong@gms.ndhu.edu.tw (M.K.L.); cfweng-cfweng@hotmail.com (C.-F.W.);
Tel.: +886-3-890-3609 (M.K.L. & C.-F.W.); Fax: +886-3-890-0163 (C.-F.W.)

Abstract: Type-II diabetes mellitus (T2DM) results from a combination of genetic and lifestyle factors,
and the prevalence of T2DM is increasing worldwide. Clinically, both α-glucosidase and α-amylase
enzymes inhibitors can suppress peaks of postprandial glucose with surplus adverse effects, leading
to efforts devoted to urgently seeking new anti-diabetes drugs from natural sources for delayed starch
digestion. This review attempts to explore 10 families e.g., Bignoniaceae, Ericaceae, Dryopteridaceae,



Campanulaceae, Geraniaceae, Euphorbiaceae, Rubiaceae, Acanthaceae, Rutaceae, and Moraceae as medicinal

 plants, and folk and herb medicines for lowering blood glucose level, or alternative anti-diabetic
Citation: Riyaphan, J.; Pham, D.-C.; natural products. Many natural products have been studied in silico, in vitro, and in vivo assays
Leong, M.K.; Weng, C.-F. In Silico to restrain hyperglycemia. In addition, natural products, and particularly polyphenols, possess
Approaches to Identify Polyphenol diverse structures for exploring them as inhibitors of α-glucosidase and α-amylase. Interestingly, an
Compounds as α-Glucosidase and in silico discovery approach using natural compounds via virtual screening could directly target α-
α-Amylase Inhibitors against Type-II glucosidase and α-amylase enzymes through Monte Carto molecular modeling. Autodock, MOE-Dock,
Diabetes. Biomolecules 2021, 11, 1877. Biovia Discovery Studio, PyMOL, and Accelrys have been used to discover new candidates as inhibitors
https://doi.org/10.3390/biom
or activators. While docking score, binding energy (Kcal/mol), the number of hydrogen bonds,
11121877
or interactions with critical amino acid residues have been taken into concerning the reliability of
software for validation of enzymatic analysis, in vitro cell assay and in vivo animal tests are required
Academic Editor: Ryan Moseley
to obtain leads, hits, and candidates in drug discovery and development.
Received: 17 October 2021
Accepted: 9 December 2021
Keywords: polyphenol; in silico; herb medicine; type II diabetes
Published: 14 December 2021

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in Highlights
published maps and institutional affil- In silico approaches can rapidly provide evolving experimental and analytical tools to
iations. identify polyphenol plant families for treating T2DM.
In silico studies can determine polyphenols as putative inhibitors of α-glucosidase
and α-amylase enzymes regulating blood glucose in T2DM.
In silico modeling accelerates screening of a huge database in a high throughput
Copyright: © 2021 by the authors. fashion to facilitate drug discovery and development.
Licensee MDPI, Basel, Switzerland.
This article is an open access article 1. The Impact of T2DM
distributed under the terms and Diabetes mellitus (DM) is a medical condition characterized by metabolic and chronic
conditions of the Creative Commons disorders with abnormal levels of carbohydrate, protein, lipid, and electrolysis metabolism,
Attribution (CC BY) license (https:// resulting in loss of control over blood glucose level [1,2]. Clinically, DM can be categorized
creativecommons.org/licenses/by/ into four subtypes: type 1 DM (T1DM), which was formerly known as insulin-dependent
4.0/).

Biomolecules 2021, 11, 1877. https://doi.org/10.3390/biom11121877 https://www.mdpi.com/journal/biomolecules

Biomolecules 2021, 11, 1877 2 of 31

DM (IDDM) or juvenile-onset DM and is primarily resulted from pancreatic β-cell de-

struction and diagonized by absolute insulin deficiency; type 2 DM (T2DM), which was
formerly known as noninsulin dependent DM (NIDDM) or adult-onset DM and is pre-
dominantly characterized by insulin resistance with relative insulin deficiency or secretory
defect with insulin resistance; gestational DM (GDM), in which women are diagnosed as
diabetic during pregnancy, and other specific types of diabetes that were not included in
any previous forms according to the American Diabetes Association (ADA) [3].
The prevalence of diabetes in the world was estimated to be 2.8% for all ages in 2000,
and that is expected to increase to approximate 4.4% in 2030 [4]. Diabetes causes of death
will increase to 366 million by 2030 [5]. The World Health Organization (WHO) estimates
that 415 million people will be affected by diabetes in 2015 [6], and this is expected to rise
to 642 million by 2040, worldwide [7]. Currently, the numbers of diabetic patients has
significantly increased in the population between 45 and 64 years of age in many countries,
particularly in China, India, and Southeast Asia [8–10]. Of various DM subtypes, T2DM,
which is characterized by chronic metabolic imbalance [4], beta-cell failure and insulin
resistance, and can be alleviated by changing lifestyle by dietary control and exercise [11],
is the most common type, accounting for more than 90% of all DM patients. The onset
of T2DM can be attributed to behavioral, environmental, and genetic factors, leading to
insulin resistance and deficiency [11–14]. Importantly, the involvements of several factors
in T2DM that cause resistance of target tissues to insulin, usually resulting from abnormal
insulin secretion [15]. T2DM is a common and increasingly prevalent disease and is a major
public health problem worldwide [16].
The clinical diagnosis of T2DM is reliant on one of four plasma glucose (PG) evalua-
tions: (i) fasting plasma glucose (FPG) (>126 mg/dL); (ii) 2-h 75-g oral glucose tolerance test
(OGTT) (>200 mg/dL) [4]; (iii) random PG (>200 mg/dL) with symptoms of hyperglycemia,
or (iv) hemoglobin A1C level >6.5% [17]. Furthermore, human subjects are considered as
prediabetics when their FPG is above the normal value but less than the threshold, namely
110–126 mg/dL, and they are predispose to diabetes, insulin resistance, and a higher risk
of cardiovascular (CV) and neurological pathologies [18,19].

2. T2DM Medicines
A healthy lifestyle and drug treatments are common practices in controlling blood
glucose levels and delaying or preventing the occurrence of complications in T2DM pa-
tients [20]. Insulin treatment is available in clinics as are innovative T2DM therapy agents
that may be applicable for patients based on various molecular targets and pathways.
Specific inhibitors include α-glucosidase, sodium glucose linked transporter-2 (SGLT-2),
dipeptidyl peptidase 4 (DPP-4), peroxisome proliferator activated receptor-γ (PPARγ),
insulin receptor kinase (IRK), and glucose transporter 4 (GLUT4). A G protein-coupled
receptors (GPCR) such as the GLP-1 receptor inhibitor blocks G protein (heterotrimeric)
production (Table 1) [17]. To date, all available T2DM medicines are associated with vari-
ous side effects such as digestion disorder, increased risk of heart failure, infection of the
urinary tract, nerves, kidneys, and eye damage [9]. For instance, metformin, which is the
most prescribed oral therapeutic agent to treat T2DM in western counties and Japan [21],
can cause gastrointestinal side effects, such as loss of appetite, diarrhea, nausea, vomiting,
flatulence, and abdominal pain [22].
Folk or herbal medicines as traditional medicines or traditional Chinese medicines
(TCM) have been used as botanical products or compounds for many years, and some
of them have been derived from crude extraction [23]. Some have shown the potential
as therapeutic agents against T2DM and other disease conditions [18]. The necessity
for developing therapeutic drugs with fewer side effects is still unmet, due to limited
efficacy or unacceptable disadvantages including side effect sand drug resistance in current
available therapeutic agents [24]. Antidiabetic properties of more than 1200 plants have
been asserted and, using these, the adverse effects and inflammation associated with the
most common drugs can be reduced [25].
Biomolecules 2021, 11, 1877 3 of 31

Table 1. Molecular targets of antihyperglycemia therapy drug.

Class Mechanism of Action Generic Name Side Effects

Retards carbohydrate digestion,
Mild stomach pain, gas or
α-Glucosidase and extends overall digestion time and
Acarbose, Miglitol [27] bloating, constipation,
α-amylase inhibitors diminishes glucose level
diarrhea [28].
absorption [26].
Inhibits SGLT2 in proximal convoluted
Sodium glucose linked
tubule (PCT) to block reabsorption of Dapagliflozin, Canagliflozin, Upset stomach, diarrhea,
transporter-2 (SGLT-2)
glucose and facilitate its secretion Sitagliptin [30]. headache [31].
inhibitors
in urine [29].
Blocks DPP-4 activity in peripheral
Sulfonylureas,
Dipeptidyl peptidase 4 plasma, that inhibits the incretin Hunger, weight gain, skin
Thiazolidinediones,
(DPP-4) inhibitors hormone glucagon-like peptide (GLP)-1 reaction [34]
Biguanides [33]
in the peripheral circulation [32].
Peroxisome proliferator PPAR γ agonist, RXR Weight gain, fluid
Diminishes triglyceride level related to
activated receptor-γ (Retinoid X receptors) retention, increased risk of
regulation of energy homeostasis [35].
(PPARγ) agonists (rexinids) [35]. heart failure [36].
Insulin receptor as a tetrameric
glycoprotein and binds to specific cell IRS (1, 2, 3, 4), SHC (Src
Insulin receptor kinase Unclear whether safe or
surface receptors in its target cells homology 2 domain
(IRK) effective treatment [39].
resulting in insulin effects on containing) [38].
phosphorylation [37].
Protein cytoplasmic adaptor that
Insulin receptor functions as a crucial signalling IGF-1 (insulin-like growth Hypotension, fluid
substrate (IRS) intermediates downstream of the factor 1), IGF-2, Insulin [41]. retention, orthostatic [42].
activated cell surface [40].
Expressed in muscle and regulates MET2 (Myocyte enhancer
Glucose transporter 4 Remained largely
insulin-stimulated glucose uptake factor-2), MyoD myogenic
(GLUT4) unknown [44].
within muscle tissue [43]. protein [43].
Insulin secretagogues,
Works with β-cells to inhibit insulin
GLP-1 (glucagon-like Vomit, diarrhea,
G protein-coupled secretion and the number of β-cell
peptide-1), GIP gastrointestinal
receptors (GPCR) GPCRs related to insulin controlling
(glucose-dependent problems [46].
secretion [45].
insulinotropic peptide) [45].

3. Polyphenols & Plant Families

Polyphenols, which are natural compounds and can be extracted from common
plants, have been a subject of considerable research interest in recent years because of their
implications in the treatment of various diseases such as DM and human health-related
disorders [23].
Several plant families have been investigated for their anti-hyperglycemic abilities [47].
Recently, polyphenol-rich functional foods have been proposed as supplementary and
nutraceutical treatments for T2DM [48]. It has been demonstrated that polyphenolic
compounds, which contain multiple phenolic moieties such as lignans, stilbenes, flavonoids,
phenolic acid, hydroxycinnamic acids, hydrobenzoic acids, and olive oil polyphenolics [19],
can result in antioxidation and anti-inflammation, and mediate enzymatic metabolism to
moderate and decrease glucose absorption in the intestine [49]. For various reasons in
recent years, traditional plant and herb therapies prescribed in the indigenous system of
medicine [50], with different mechanisms [51], have commonly been used.

3.1. Euphorbia thymifolia Linn. (E. thymifolia)

Euphorbia thymifolia Linn. (Euphorbiaceae), commonly known as laghududhika or choti-
dudhi, is a prostate annual herb [52]. Their leaves, seeds and fresh juice of the whole plant
are used as a stimulant and astringent in worm infection [53]. It has been reported that
Biomolecules 2021, 11, 1877 4 of 31

plant extracts can be used as traditional medicines to treat various disorders in many parts
of the world [54], using plants such as cassava (Manihot esculenta), castor oil plant (Rici-
nus communis), Barbados nut (Jatropha curcas), and the Para rubber tree (Hevea brasiliensis)
despite the fact that many of them are grown as ornamental plants such as poinsettia (E.
pulcherrima), leafy spurge (E. esula), and Chinese tallow (Triadica sebifera). Euphorbiaceae
species have been used by different populations as folk medicines for remedying a broad
range of diseases and complaints, including cancer, diabetes, diarrhea, heart diseases, hem-
orrhages, hepatitis, jaundice, malaria, ophthalmic diseases, rheumatism, and scabies [55],
with some disadvantages including drug resistance to the plants’ components [56]. The hy-
poglycemic potential of this plant family was mainly identified by virtual screening for high
binding energies (4.8–9.9 Kcal/mol) and strong hydrogen interactions. Moreover, insulin
levels were significantly increased and the lipid profile and body weight were improved
after 20 days when an ethanolic extract of R. communis (Euphorbiaceae) at 500 mg/kg
p.o. was administrated to those diabetic rats [57]. Recently, some herbal medicines have
been reportedly to treat T2DM in worldwide studies, and some of their functions as α-
glucosidase and α-amylase inhibitors to exert their anti-hyperglycemia efficacy have been
identified [58], since inhibition of intestinal α-glucosidases can limit postprandial glucose
levels by delaying the process of carbohydrate hydrolysis and absorption, making such
inhibitors useful for the management of T2DM. Plants and microorganisms are rich sources
of α-glucosidase inhibitors. For example, acarbose, 1-deoxynojirimycin, and genistein
were originally isolated from natural sources [59]. E. hirta L., which is a traditional plant
used for various disease treatments, has been under investigation as an α-glucosidase
inhibitor. Triphala, which is a combination of Terminalia chebula, T. belerica, E. officinalis, is
under in vivo evaluation for antidiabetic potential in relation to antioxidant activity [60]. T.
belerica was found to be most active in reducing serum glucose levels followed by E. offici-
nalis, T. chebula, and Triphala, which is a combination of all the three products, significantly
reducing hyperglycemic effect in alloxan-induced diabetic rats [61]. Aqueous extracts of
E. hirta L. showed inhibition of α-amylase activity compared to acarbose [62]. In contrast,
α-amylase inhibitors from plant sources have a lower effect against α-amylase activity and
stronger inhibition of α-glucosidase activity [63].

3.2. Bignoniaceae
Bignoniaceae are woody, trees, shrubs, and lianas found in all tropical floras of the
world, with lesser representation in temperate regions, and belong to a family of flowering
plants in the order Lamiaes, commonly known as the bignonias [64]. Bignonieae comprise
a major component of neotropical liana flora. Most other species are woody shrubs and
trees including savannah and tropical forest canopy trees, although these three groups
have adopted a herbaceous habit, mostly at high elevations in the Himalaya (Incarvillea)
and the Andes (Arggylia, Tourrettia) [65,66]. Interestingly, Tecona stans (L.) Juss ex Kunth
plants are extensively used for empirical DM treatment, but their antidiabetic mechanisms
remain to be clarified [67]. This family of compounds may show their antidiabetic effect by
stimulating glucose uptake in T2DM [68].

3.3. Ericaceae
The Ericaceae are dominant plants of acid heathlands and upland soils, and include
the genera Calluna, Erica, Vaccinium, Azelea, Rhododendron, and the Epacrids of Australasia,
which grow in dry sandy soils [66]. The Ericaceae are a family of flowering plants, com-
monly known as the heath or heather family, and can be found most commonly in acid
and infertile growing conditions [69]. Wan and Shou (2013) observed that a crude extract
from Vaccinium corymbosum (Ericaceae), including a phenolic compound, shows powerful
α-glucosidase inhibitory activity and it is even more efficacious than the marketed drug
acarbose [70]. Moreover, the α- and β-glucosidases inhibitory activities of Rhododendron ar-
boreum (Ericaceae) have been investigated by various in vitro studies and the results suggest
that it is a potent α-glucosidase inhibitor with an IC50 of 3.3 ± 0.1 µM, many-fold higher
Biomolecules 2021, 11, 1877 5 of 31

than that of acarbose [71]. A wide variety of phenolic compounds, which are the most
abundant secondary metabolites of plants with more 8000 phenolic structures, have great
potential in protecting against cardiovascular diseases, diabetes, cancer, and obesity [72].

3.4. Dryopteridaceae
Many of the Dryopteridaceae, which are a family of leptosporangiate ferns in the order
Polypodiales, are cultivated as ornamental plants. The fern genus Dryopter (Dryopteridaceae)
is among the most common, and includes 225–300 species worldwide in temperate forests
in the northern hemisphere [73]. Dryopteris cycadina is a medicinal plant from the Dry-
opteridaceae family. It has been traditionally used as a folk medicine to treat rheumatism,
epilepsy, and pain and to remedy snake bites and fungal infections [74]. In vitro studies
show that compounds in D. cycadina inhibit α-glucosidase in a concentration-dependent
manner, further validated by in silico studies, and show strong hydrogen bonds interaction.
Four strong interactions between amino acid side chains and hydrogen bonds (Asp215,
Asp352, Arg422, and Gln182) are reported [75].

3.5. Campanulaceae
Codonopsis, belonging to the family Campanulaceae, is a genus including 42 species of
dicotyledonous herbaceous perennial plants predominantly found in central, east and south
Asia. Several Codonopsis species are widely used in traditional medicine and are considered
to have multiple medicinal properties. It has been shown in phytochemical studies that
Codonopsis species, which contain mainly polyacetylenes, phenylpropanoids, alkaloids,
triterpenoids, and polysaccharides, contribute to multiple biological functions [76]. The
less popular Codonopsis species remain to be studied and exploited. One of genus, Lobilia
chinensis has been extracted to obtain two new pyrrolidine alkaloids, radicamines A and B,
that are α-glucosidase inhibitors [77]. In addition, Codonopsis lanceolate Trautvein is a plant
of Campanulaceae family, which is distributed throughout China, Japan, and Korea. The
roots of C. lanceolate have been cultivated and used as a food. Other Codonopsis species
such as C. pilosula and C. tangshen have been used as medicines (Tang-Sam) for ulcers
treatment, memory improvement, and immune stimulation [78,79].Various reports have
indicated that the isolated secondary metabolites of C. lanceolata roots e.g., triterpenoid,
saponin, and alkaloids, show tangshenoside I and β-adenosine with an IC50 of 1.4 and
9.3 mM for α-glucosidase inhibition, respectively [80]. It has been demonstrated previously
that Lobelia sessilifolia can potently inhibit rice α-glucosidase, and crude extracts and coffee
beans can be very specific and potent α-galactosidase inhibitors [81]. Zafar and Khan (2016)
recently reported that the alkaloids isolated from some of Campanulaceae and Lobelia species,
along with standard acarbose, exert significant anti-glucosidase effects. Strong hydrogen
bond binding modes of these inhibitors display four interactions between amino acid side
chain and hydrogen bonds (Lys155, Glu304, Arg312, and Asn153) [82].

3.6. Geraniaceae
The genus, Geranium, which belongs to the Geraniaceae family, is represented by
350 species in the world, of which 38 species include 14 endemic taxons in Turkey [83].
The genus Geranium is known to contain flavonoids, tannins, anthocyanidins, lignans,
sterols, and polyphenolic compounds, as well as essential oils [84]. Geraniaceae are herbs
or subshrubs, and a family of flowering plants in the order Geraniales. The extracts of
Geranium graveolens L. of Geraniaceae are essential oils and act as both α-amylase and α-
glucosidase inhibitors [77]. G. wallichianum has shown very good α-glucosidase inhibition
activity. These observations suggest that the presence of potent compounds can inhibit
these carbohydrate digesting enzymes. A methanolic extract prepared from the aerial parts
of G. wallichianum is the most potent agent for inhibition of α-glucosidase, α-amylase, and
pancreatic lipase with inhibitions of 65.81%, 72.89%, and 52.80%, respectively. Thus, G.
wallichianum is a plausible subject for further studies for the treatment and management
of metabolic syndrome [85]. Some Geranium species have been used to treat diabetes. G.
Biomolecules 2021, 11, 1877 6 of 31

asphodeloides, for instance, showed high α-glucosidase inhibitory effect compared with
acarbose with an IC50 value of 0.85 µM in vitro study [86].

3.7. Rubiaceae
The Rubiaceae are flowering plants, commonly known as the coffer, or bedstraw family.
They consist of terrestrial trees, shrubs, lianas, or herbs that are recognizable by simple,
opposite leaves with interpetiolar stipules. Alkaloids, phytosterols, carbohydrate, and
saponins extracted from many species of the Rubiaceae family, such as Gardenia taitensis,
can reduce blood glucose, total cholesterol, LDL and VLDL cholesterol, and improve HDL
cholesterol associated with T2DM treatment [77]. Ethanol extracts of leaves and twig of
some plants in the Rubiaceae can have 80% inhibitory activity of α-glucosidase [87]. For
example, Xeromphis uliginosa Retz. is found in root extracts and reduces the blood glu-
cose [88]. The extract from Morinda tinctoria fruits shows inhibition of glucose diffusion [89].
The leaves and root extracts of Nauclea latfolia Sm lower fasting blood glucose, increase
MCV and MCH, reduce iWBC and increased lymphocyte levels. A stem bark extract of
Neolamarckia cadamba shows antihyperglycemic activity [90]. An extract of Anthocephalus
indicus leaf can reduce blood glucose and total cholesterol, triglycerides, HDL and LDL [91].
These families may be used as diabetic herbal treatments. Rubia cordfolia Linn. from root
extracts acts as an α-amylase and α-glucosidase inhibitor [92]. Furthermore, oral adminis-
tration of Hamelia patens (Rubiaceae) exhibited the greatest inhibition of α-glucosidase in
an in vivo test. H. patents inhibits α-glucosidase activity as a traditional medicine dur to
its active compounds [93]. Methanol extracts of Hedyotis biflora L. (Rubiaceae) showed 50%
inhibition of α-glucosidase at a concentration of 480.20 ± 2.37 µg/mL in in vitro tests [94].
Nauclllea latifolia also belongs to Rubiaceae family and the root stem is traditionally and
empirically used by diabetic patients in Benin to manage glycemia [95]. In in vivo studies,
N. latifolia (Rubiaceae) was assessed for lowering fasting blood glucose in normoglycaemic
and streptozotocin (STZ)-diabetic rats at the highest administered dose (400 mg/kg) and
lowered the fasting blood glucose of the diabetic rats by 31.7% (aqueous extract) and 36.1%
(ethanolic extract), respectively. Consequently, this plant can be can have traditional use
for treatment of T2DM [96].

3.8. Acanthaceae
Acanthaceae is a family of dicotyledonous flowering plants. Flavonoids, alkaloids,
terpenoids, tannins, and steroids extracted of Acanthus illicfolius reduce blood glucose level
and result in better regeneration of β-cells [77]. Additionally, extracts from Justicia secunda
Vahl. leaves used to treat DM symptoms showed inhibitory effects on α-glucosidase,
and the potential of J. secunda for traditional medicinal use in T2DM treatment was sup-
ported [97]. Justicia is the largest genus of the Acanthaceae family and consists of ap-
proximately 600 species distributed in pantropical and tropical regions. In traditional
medicine, the extracts of leaves are used to treat diabetes and diabetic symptoms [98].
Diterpenoid lactones and andrographoloids, including gibenclamide, glimepiride, glip-
izide, nateglinide, rosiglitazone, pioglitazone, and repaglinide from Andrographis paniculata
Nees are found to inhibit CYP2C9, CYP2C19, CYP2D6, CYP3A4, and glucose transporter
(GLUT4) [99], as well as increasing glucose metabolism and reducing lipid accumulation in
differentiated adipocytes [100–102]. Moreover, A. paniiculata. (Burm.f.) Nees (Acanthaceae)
when applied in oral carbohydrate tolerance tests with starch (3 g/kg), sucrose (4 g/kg),
or glucose (2 g/kg), separately in 18-h fasted rats, resulted in reduced sucrose and starch,
similar to an acarbose effect, while it had no peak blood glucose with a suppressive ef-
fect after an exogenous glucose load in both normal and STZ-induced diabetes rats [103].
Interestingly, Clinacanthus nutans belongs to the Acanthaceae family and is used to treat
diabetes in Malaysia. In vitro, this plant was identified as a potential α-glucosidase in-
hibitor with an IC50 lower than 50 µg/mL. In silico, it showed strong hydrogen bonding
and some hydrophobic interaction between inhibitors and proteins including Asn259,
Hid295, Lys156, Arg335, and Gly209. Additionally, hydrogen bonding is involved in
Biomolecules 2021, 11, 1877 7 of 31

Trp15, Tyr158, Val232, Hie280, Ala292, Pro312, Leu313, Val313, Phe314, Arg315, Try316,
Val319, and Trp343 amino acid residues [104]. Previously, this plant was also identified
as having potential α-glucosidase inhibition properties. Interaction between inhibitors
and protein were predicted involving residues Lys156, Thr310, Pro312, Leu313, Glu411
and Asn415 with hydrogen bonds at Phe314, and at Arg315 with hydrophobic bonding.
Hence, α-glucosidase inhibitor has been identified in C. nutans leaves, indicating the plant’s
therapeutic effect to relieve T2DM [105].

3.9. Rutaceae
Rutaceae, commonly known as citrus family, is a family of flowering plants with ap-
proximatively 160 genera, also having flowering species. The most economically important
genera in the family are Citrus, including the orange (Citrus sinensis), lemon (C. limon),
grapefruit (C. paradisi), and lime (mostly C. aurantifolia) as well as Zanthoxylum or Fagara
and Agathosma. Species of the Fagara genus have been found to have antimicrobial activities.
Fagara leprieurii (Guill and Perr) Engl. is used traditionally in cases of gastritis, diarrhea,
cancer, ulcer, and kidney ache, as well as other infectious diseases [106]. In this term, finger
citron (C. medica L. var.) fruits, widely cultivated in Japan; possess insulin secretagogues
and slimming effects that would be very beneficial to T2DM patients [107]. The extract of
Clauserna anisate Bum. f. root was show to stimulate secretion of insulin [108]. Moreover,
leaf extracts of Murraya koeingii (L.) Spreng can increase glycogenesis, decrease glycogenoly-
sis and gluconeogenesis [109]. After oral administration of pulp extract of Syzygium cumini
fruit to normoglycemic and STZ-induced diabetic rats they showed hypoglycaemic activity
in 30 min, possibly mediated by insulin secretion and inhibited insulin activity of the
pancreas [52]. Interestingly, a flavonoid from Rutaceae aurantiae inhibits advanced glycation
end-products (AGEs) and reduces albumin, that are significantly diminished in flavonoid-
treated diabetic rats [110]. In an in vitro study, terpenoids isolated from stem and bark of
Fagara tessmannii (Rutaceae) showed strong inhibitory activity with an IC50 of 7.6 µmol/L,
which resembled the inhibitory activity of acarbose which was used as a positive con-
trol [111]. Many α-glucosidase inhibitors such as alkaloids, terpenoids, anthocyanin, and
phenolic compounds were found to have α-glucosidase inhibitory potency.

3.10. Moracea
Moraceae, often called the mulberry family of flowering plants, comprises about 40 gen-
era and over 1000 species. The includes Artocarpus heterophyllus (Jackfruit), A. altilis (Bread
fruit), A. camans (Bread nut) and A. integer (cempedak) which possess antibacterial, anti-
inflammation, antioxidant, and antidiabetes properties [112]. Moreover, Dieffenbachia picta
is an herbaceous plant used in southern Cameroon as an antidiabetic and antihypertensive
drug [113]. Leaves of white mulberry (Morus alba, Moraceae) have been used in traditional
medicine to treat diabetes. Recently, leaves and stems were found to inhibit both α-amylase
and α-glucosidase activities by at least 50% [114]. Bark extract of Ficus bengalensis decreased
blood glucose level, restores the levels of serum electrolytes, glycolytic enzymes, and hep-
atic cytochrome P-450 dependent enzyme systems and decreases the formation of liver and
kidney lipid peroxides [115]. Other F. religiosa Linn. can cause rising serum insulin and
initiate insulin release [116]. Leave from Morus alba increased the β-cell number in diabetic
islets reduced levels of glycosylated hemoglobin [117], especially decreasing triglycerides
and VLDL, and restored elevated levels of blood urea. Besides, this species can protect
pancreatic β-cells from degeneration and diminish lipid peroxidation [118]. M. indica L.
leaf extracts increased glucose uptake [119]. M. bomoysis regenerated β-cells of the islets of
Langerhans [77]. The oral administration of the extract of F. bengalensis caused enhanced
serum insulin levels in normoglycaemic and diabetic rats. The increased insulin secretion
was mainly due to inhibition of pancreatic insulin activity from the liver and kidney [120].
The blood glucose lowering activity of a dimethoxy derivative of leucocyandin 3-O-beta-d-
galactosyl cellobioside isolated from the bark of F. bengalensis at a dosage of 250 mg/kg, p.o.
in normal and moderately diabetic rats was mainly due to insulinomimetic activity [121].
Biomolecules 2021, 11, 1877 8 of 31

A glycoside of leucopelargonidin isolated from the bark of F. bengalensis demonstrated

significant hypoglycaemic, hypolipidemic and serum insulin-raising effects in moderately
diabetic rats. Dimethoxy ether of leucopelargonidin-3-O-alpha-L rhamnoside at a dose of
100 mg/kg, p.o. had significant hypoglycaemic and insulinomimetic activity in healthy
and alloxan-induced diabetic dogs during a 2 h test [121].

4. Potential Polyphenols of 10 Plant Families with Regulation of α-Glucosidase and

α-Amylase Activity
α-Glucosidase is located in the brush border of the small intestine and breaks down
starch and disaccharides. α-Amylase breaks internal α-1, 4-glycosidic linkages of starch
into glucose and maltose in the digestive organs [6]. Amylase is found in saliva glands
whereas pancreatic amylase is secreted by the pancreas into the small intestine [7]. However,
blood glucose level can be determined by α-amylase via increasing digestion of starch
and disaccharides [8]. The therapeutic approach to treating T2DM is to delay absorption
of glucose through inhibition of enzymes including α-glucosidase and α-amylase in the
digestive organs [15,122]. The mechanisms and therapeutic potential of polyphenols can
be used for clinical trials and drug discovery in the management of T2DM. Polyphenols
are found mainly in plant-based foods e.g., fruits, vegetables, whole grains, coffee, tea, and
nuts. Polyphenols may affect glycemia and T2DM through different mechanisms, such as
promoting the uptake of glucose in tissues (α-glucosidase and α-amylase) and improving
insulin sensitivity [123]. Besides, polyphenol compounds such as caffeic acid, curcumin,
cyanidin, daidzein, epicatechin, eridyctiol, ferulic acid, hesperetin, narenginin, pinoresinol,
quercetin, resveratrol, and syringic acid can significantly inhibit the α-glucosidase enzyme.
Especially, catechin, hesperetin, kaempferol, silibinin, and pelargonidin are found to be
potent α-amylase inhibitors [49]. The current study aimed to investigate polyphenol
families to discover a new class of α-amylase and α-glucosidase inhibitors to target these
enzymes. Different treatments such as diets and drugs are recommended for α-glucosidase
and α-amylase inhibition. Especially, the primary structure of polyphenols can affect
the inhibition levels of α-glucosidase and α-amylase enzymes [124]. Various families
of polyphenols have beneficial effects and have been shown to suppress α-glucosidase
and α-amylase at a 50% inhibition level and higher [125]. The abundant polyphenols
flavan-3-ol monomers (catechins), were evaluated against the pharmacological glucosidase
inhibitor-acarbose, and catechin 3-galltes strongly inhibited both α-glucosidase and α-
amylase activity [126]. Moreover, positive relationships among α-glucosidase inhibitory
and the polyphenol content of these 28 edible plants were found in both aqueous and
methanolic extracts as well as the fresh juice of the whole plant [127]. Interestingly, some
plants show inhibition of both α-glucosidase and α-amylase against T2DM (Table 2).

Table 2. List of polyphenol plant families that inhibit α-glucosidase and α-amylase.

Family Enzymatic Type Scientific Name

α-Glucosidase inhibitor
Theaceae Camellia sinensis Ktze [128]
Myrtaceae Cleistocalyx operculatus Roxb [129]
Fabaceae Sophora japonica L. [130]
Senna surattensis [11]
Alhagi camelorum [51]
Neptunia oleracea [131]
Peltophorum pterocarpum [132]
Asteraceae Artemisia vulgaris L. [133]
Lecythidaceae Careya arborea Roxb [134]
Apiaceae Centella asiatica (L.) Urb [135]
Biomolecules 2021, 11, 1877 9 of 31

Table 2. Cont.

Family Enzymatic Type Scientific Name

Eryngium foetidum L. [136]
Levisticum officnale [137]
Ligusticum porter [138]
Moraceae Ficus racemosa L. [16]
Artocarpus champeden [139]
Morus alba [140]
Myristicaceae Horsfieldia amygdalina Warb [130]
Saururaceae Houttuynia cordata Thunb [141]
Rubiaceae Paederia lanuginosa Warb [130]
Cinchona succirubra [142]
Hintonia latiflora; H. standleyana [143]
Verbenaceae Premma corymbosa (Burm) [144]
Euphorbiaceae Euphorbia thymifolia [124]
Lamiaceae Perilla frutescens (L.) Britton [145]
Rosmarinus officinalis [146]
Zataria multiflora [147,148]
Zhumeria majdae [51]
Polygonaceae Polygonum odoratum Lour [149]
Clusiaceae Garcinia daedalanthera [87,150]
Scrophulariaceae Verbascum kermanensis [51]
Rosaceae Rosa damascene [151]
Sanguisorba minor [51]
Sarcopotarium spinosum L. [152]
Anacardiaceae Pistacia vera [51]
Ericaceae Vaccinum arctostaphylus [153]
Salvadoracae Salvadora persica [154]
Zingiberaceae Alpinia officinarum [155,156]
Phyllantaceae Antidesma bunius Spreng [157]
Oxalidaceae Averrhoa bilimbi L. [158]
Biophytum sensitivum L. DC [159]
Rhizophoraceae Ceriops tagal Perr. Rob [160]
Rhizophora mucronata Lam [161]
Cyperaceae Kyllinga monocephala Rottb [162]
Asteraceae Brickellia cavanillesii [163]
Blumea lanceolaria Roxb [164]
Celastraceae Salacia oblonga [165]
Lamiaceae Scutellaria baicalensis [166]
Cucurbitaceae Cucurbita pepo L. [167]
Convolvulaceae Ipomoea aquatica Forssk [168]
Ipomoea batatas (L.) Lam [169]
Piperaceae Piper lolot DC [130]
Biomolecules 2021, 11, 1877 10 of 31

Table 2. Cont.

Family Enzymatic Type Scientific Name

Brassicaceae Nasturtium officinale R. Br [170]
Myrtaceae Eucalyptus grandis [171]
E. urophylla [171]
Syzygium aqueum [172]
S. cumini [173]
Meliaceae Azadirachta indica [139]
Clusiaceae Garcinia mangostana [174]
Sapindaceae Nephelium lappaceum [175]
Vitaceae Vitis vinifera [176]
Santalaceae Osyris alba L. [177]
Hypericaceae Hypericum triquetrifolium Turra [178]
Ericaceae Arbutus andrachne L. [179]
Vaccinium oxycoccos [180]
Bignoniaceae Oroxylum indicum [123]
Campanulaceae Codonopsis pilosula [181,182]
Geraniaceae Geranium collinum [183]
Dryopteridaceae Dryopteris cycadina [75,184]
Acanthaceae Clinacanthus nutans [142]
Rutaceae Orixa japonica Thunb [156]
α-Amylase inhibitor
Anacardiaceae Spondias pinnata (Koenig) [185]
Myrtaceae Syzygium cumini L. [186]
Zygophyllaceae Balanites aegyptiaca L. [187]
Amaranthaceae Amaranthus caudatus L. [188]
Theaceae Camellia sinensis L. Del [128]
Fabaceae Galega officinalis L. [189]
Tamarindus indica L. [190]
Cassia auriculata [191]
Apocynaceae Holarrhena floribunda [192]
Melissa officinalis L. [193]
Rubiaceae Mitragyna innermis (Wild.) [189]
Lamiaceae Rosmarinus officinalis L. [193]
Polygalaceae Securidaca longepedunculata [194]
Asparagaceae Polygonatum adoratum [195]
α-Glucosidase and
α-amylase inhibitor
Nelumbonaceae Nelumbo nuciffera Gaertn [47]
Asteraceae Artemisia vulgaris L. [133]
Enydra fluctuans Lour [185]
Araliaceae Polyscias fruticosa (L.) Harms [196]
Myrtaceae Syzygium zeylanicum (L.) DC [186]
Biomolecules 2021, 11, 1877 11 of 31

Table 2. Cont.

Family Enzymatic Type Scientific Name

Phyllanthaceae Phyllanthus amarus [126]
Phyllanthus urinaria [127]
Lamiaceae Ocimum basilicum L. [125]
Thymus serpyllum [197]
Meliaceae Khaya senegalensis [198]
Moraceae Artocarpus altilis [1,199]
Ranunculaceae Aconitum heterophyllum [199]
Acoraceae Acorus calamus [200]
Berberidaceae Berberis aristata [199]
Cyperaceae Cyprus rotundus [201]
Calophyllaceae Mesua ferrea [186]
Plumbaginaceae Plumbago zeylanicum [202]
Combretaceae Terminalia arjuna [203]
Myrtaceae Brazilian cerrado [204]
Eugenia dysenterica [205]
Stryphnodendron adstringens [206]
Pouteria caimito [206]
Pouteria torta [206]
Pouter ramiflora [207]
Psidium guajava L. [2]

DPP-4 inhibitors improve β- and α-cell function and decrease glucagon concentra-
tions [208]. Normally, DPP-4 is widely expressed in numerous tissues including endothelial
cells of multiple vascular beds, rendering the enzyme highly accessible to peptide substrates
circulating through the gut, liver, lung, and kidney [209]. In an in silico study, the DPP-4
active site interacted widely with a hydrophobic pocket via hydrophobic inhibitor moieties
such as Try629 and Try547 [210] and also interacted with other proteins and proline (P)
and alanine (A) residues [211]. GLP-1, which is a physiological incretin hormone from the
lower gastrointestinal (GI) tract [212], is produced from the proglucagon gene in the L cell
of the small intestine and secreted in response to nutrients. GLP-1 exerts its major effects
by stimulating glucose-dependent insulin release from the pancreatic islets. GLP-1 has also
been shown to slow gastric emptying [213] with substantial postprandial GLP-1 release
which, in these conditions, interferes with GLP-1 receptor signaling and has a significant
impact on glucose regulation after eating, including DPP-4 inhibition [214]. The GLP-1
receptor is a member of family B with G protein-coupled receptors and is an important
drug target for T2DM. This hormone docks with high affinity and is a full agonist with
specific amino acid residues namely, Arg131, Lys136, Glu133, and Glu125 within the same
region of the receptor amino termini [215,216]. GLP-1 plays an important physiological
role in maintaining blood glucose homeostasis, and may be a very effective therapeutic
drug for the treatment of T2DM [216,217]. Thus, the basis of molecular docking of ligand
binding and subsequent activation is clinically important for the GLP-1 receptor [215].
Insulin receptor kinase (IRK) is a heterotetrameric receptor composed of two extra-
cellular α-subunits and two transmembrane β-subunits. Insulin is a hormone responsible
for glucose and lipid metabolism. Binding of this hormone to the extracellular domain
of the insulin receptor (IR) induces a conformational change that facilitates ATP bind-
ing and leads to increased autophosphorylation of the receptor [218]. Moreover, IRK is
Biomolecules 2021, 11, 1877 12 of 31

subsequently autophosphorylated and activated to tyrosine-phosphorylated key cellular

substrates that are essential for interacting with the insulin response [219]. IRK activation
occurs at the beginning of insulin signaling in the cell surface, while in silico studies have
shown insulin-sensitive auto-phosphorylation of receptors with mutated glycosylation
sites lacking glycan chains at Asn624-730, Asn730-743 and Asn881, but with a constitutively
active tyrosine kinase [220]. At Asn1234, IRK lacking glycosylation exhibited a threefold
increase of basal autophosphorylation and played a critical role in signal transduction for
IRK activation [221].
Insulin receptor substrate (IRS) molecules are key mediators in insulin signaling.
Several polymorphisms in the IRS gene have been identified; however, only the Gly to Arg
972 substitution of IRS-1 seems to have a pathogenic role in the management of T2DM [222].
In the IRS-1 gene, Gly972 with an Arg substitution has shown to be related to insulin
resistance in T2DM [223]. Therefore, IRS is an important ligand in the insulin response of
human cells; especially, IRS-1 and IRS-2 are ubiquitously expressed and are the primary
mediators of insulin-dependent mitogenesis and regulation of glucose metabolism in most
cell types [224]. IRS-1 was originally identified as the major substrate of insulin receptor and
IGF-1 receptor tyrosine kinase and represents the prototype of the IRS family proteins [225].
IRS-2 contains an additional domain, the KRLB domain, that interacts with the tyrosine
kinase domain of the IR and may function to limit IRS-2 tyrosine phosphorylation [226]. In
addition, both IRS-1 and IRS-2 have been associated with regulating GLUT4-dependent
glucose uptake in the response to insulin [227]. To explore targeting of specific molecules or
genes, and further accelerate the drug discovery and development, computational biology
associated with virtual screens plays a key role.

5. In Silico Approaches
In silico technologies play an increasingly imperative role in drug discovery and
development mainly due to their fast throughput, economical efficiency, and labor saving
characteristics [228], especially compared with their in vitro and in vivo counterparts that
for identifying new natural compounds as drug targets with predicted biological activ-
ity [228]. Basically, in silico approaches can be categorized as structure-based modelling,
in which protein structures, especially cocomplexed structures, are adopted to investigate
protein-ligand interactions normally carried out by docking, and analogue-based modeling
by quantitative structure-activity relationship (QSAR) and pharmacophore, in which the
predictive models are derived based solely on ligand information.
Importantly, in silico methods are a logical extension of controlled in vitro experiments
to shorten massive screens via high throughput methods. There are the natural results
of the explosive increase in computing power available to the research scientist [229].
Several molecular models have selected protein-ligand complexes from the protein data
bank (PDB) database, and the performance of docking has been evaluated by software
including LigandFit, Glide, Gold, MOE Dock, AutoDock, and Surflex-Dock [14]. Recently,
the polyphenol primary structure of catechin, hesperetin, and kaempferol have been found
to express stable chemical characters that can have inhibitory effects on α-glucosidase
and α-amylase enzymes [230]. Interestingly, polyphenol families provide an enormous
resource to explore novel α-glucosidase and α-amylase inhibitors because their biological
properties are abundant in functional foods represent supplementary and nutraceutical
use for DM treatment [230]. The objective of this review is to investigate in silico strategies
to screen polyphenol-containing herb plants that can inhibit α-glucosidase and α-amylase
enzymes. In silico approaches to find novel α-glucosidase and α-amylase inhibitors from
natural compounds to treat T2DM have been demonstrated by Esmail et al. (2019) [231],
in that polyphenols can reduce hyperglycemia and improve acute insulin secretion or
insulin sensitivity [13]. Inhibition of α-glucosidase and α-amylase can reduce the impact
of dietary carbohydrate on blood glucose level [49]. Moreover, it has been observed that
polyphenols play an important role in decreasing insulin resistance in vitro and improving
glucose homeostasis in vivo [51]. Polyphenols such as flavonoids, phenolic acid, and
Biomolecules 2021, 11, 1877 13 of 31

stilbene have been implicated in the treatment of various human disorders [128] including
diabetes [232]. As such, it is plausible to expect that polyphenols can be an important
source of α-glucosidase and α-amylase inhibitors to treat T2DM. Model simulation have
predicted candidates in relation to the binding of polyphenols to the three-dimensional
structure of both enzymes [129]. Inhibition of carbohydrate metabolism during saccharide
digestion, via α-glucosidase and α-amylase inhibition, can play a major role in T2DM
treatment associated with docking studies to determine enzyme inhibition based on the
free energy of binding, since hydrogen bond interactions are when binding α-glucosidase
and α-amylase [233].

5.1. Docking
In general, several elements should be included in a homology template: the nature
of the docked ligands; the docking program; the molecular dynamics (MD) package
(refining the docked poses), and post docking calculations (Force field for MD) in the
procedure of molecular docking. The procedure should involve the following: (i) three-
dimensional structures of target protein are explored in the PDBor “Uniprot” website;
(ii) three-dimensional structure of anticipated small molecules are retrieved from PubChem;
(iii) the H2 O of target proteins and small molecules are removed, and (iv) molecular docking
is conducted by the “GOLD” platform.
A selected target such as α-glucosidase protein structure is constructed by homol-
ogy modeling based on the protein structure of oligo-1,6-glucosidase from B. cereus (PDB
1UOK). Next, various nature of docked ligand derivatives are docked using the program
CDOCKER, followed by molecular dynamics (MD) calculations by GROMACS with AM-
BER03 force field for refinement. The correlation coefficient between the observed Ki
values and calculated interaction energies is 0.89, suggesting that binding modes are
plausible [234]. Various crystal structures of enzymes, whose PDB codes are 2ZJ3 [235],
3TOP [236], 3AJ7 [237], 3A47 [238], 3A4A [239], 3AHX [239], and 3CZJ [240] have been
published. Some protein structures have been adopted to conduct docking studies, as
listed in Table 3, along with a selection of docking packages. PDB is the enzyme from Homo
sapiens but specific proteins are not determined due to functional protein divergence of
gene sets of these protein against α-glucosidase and α-amylase [27]. Inhibiting the activity
of these two enzymes can mediate and control postprandial hyperglycemia and reduce
developing diabetes [241]. The interaction between betulinic acid (BA) and α-glucosidase
is obtained from the oligo-1,6-glucosidase structure (PDB ID: 3AJ7) using the CDOCKER
module of Discovery Studio. The predominate factors in determining BA-α-glucosidase are
hydrophobic interactions and hydrogen bonds [242]. Moreover, the oligo-1,6-glucosidase
structure (PDB ID: 3AJ7) was used as the template by Ding et al. (2018) to build an α-
glucosidase homology model to study the inhibitory mechanisms of oleanolic acid and
ursolic acid using LeDock (available at http://www.lephar.com/software.htm, accessed
on 4 July 2019). Oleanolic acid can form hydrogen bonds with Ser295 and Glu270, whereas
ursolic acid can establish hydrogen bonds with Gln66 andGln67. Zeng et al. (2019) docked
galangin into a homology-built α-glucosidase based on oligo-1,6-glucosidase from S. cere-
visiae (PDB ID: 3A4A) using AutoDock (available at http://autodock.scripps.edu/accessed
on 12 August 2019). It was observed that galangin can form hydrogen bonds with Leu313
and Glu411 of α-glucosidase. Interestingly, it was also found that α-glucosidase can un-
dergo conformation change upon binding with galangin, leading to decreased enzymatic
activity by hindering substrate entrance consistent with the observation made by Ding et al.
(2018) (vide supra) [243]. In silico methods are expanded for predicting potent receptor
targets, including AutoDock Vina, VMD Quantum Chemistry Visualization, Maestro 10.2
software package (Maestro is a front-end GUI), PyMol software (PyMol is a visualizer),
MOE-Dock module (v.2011.10), Model Scoring of GB/VI test, force field AMBER, SCM
model, SiteMap (Schrodinger Release 2018-1: SiteMap), and Pardock (Table 3).
Biomolecules 2021, 11, 1877 14 of 31

Table 3. Natural compounds against α-glucosidase and α-amylase enzymes discovered via in silico approaches, listing the
docking package and scoring function used in the studies.

In Silico Modeling
Binding Hydrophobic &
Natural Compound Plant Family Energy PDB ID Hydrogen-Bond
(Kcal/mol) Interaction
2ZJ3; Homo sapiens, AutoDock
Vina [211], VMD Quantum Ser420, Lys675, Gln421,
Quercetin Euphorbiaceae −7.6
Chemistry Thr375, Ser422
Visualization [228,244]
Quercitrin −9.0
Quercetin-3-O-galactoside −9.1
Cosmosiin −9.9
Kaempferol −7.6
2-(4 methyl-3-cyclohexene-1-
−5.4 Val677, Ala674, Thr375
yl)-2-propanol
B-amyrine −9.0
B-Sitosterol −7.8
Campesterol −8.2
Caryophyllene −7.1
Limonene −4.8
Phytol −5.2
Piperitenone −5.4
Safranal −5.5
Stigmasterol −8.5
Taraxerol −8.9
Euphorbol −8.3
24 methylene cycloartenol −7.9
1-O-Galloyl-beta-D-glucose −8.0
Ser420, Lys675, Gln421,
Corilagin −8.9
Thr375, Ser422
3TOP; Homo sapiens, Schrodinger Pro1327, Glu1284,
Baicalein Bignoniaceae −6.98
Maestro [116] Pro1405, Leu1401
His1584, Asp1279,
Catechin −7.70 Asp1526, Arg1510,
Asp1157
Luteolin −7.52
Quercetin −7.19
3AJ7; Saccharomyces cerevisiae,
Phe177, Asp214,
Quinoline Rubiaceae −8.6 MOE-docking
His279, Phe157
2010.11software [140]
No mentioned for PDB code, 3D
structure: α-glucosidase of
Saccharomyces cerevisiae,
AutodockTools 1.5.6
Benzothiazole Ericaceae −8.08 Phe157, Phe310, Phe311
package [161],
PyMol 1.7.6 software
(http://www.pymol.org/,
accessed on 19 February 2020)
Biomolecules 2021, 11, 1877 15 of 31

Table 3. Cont.

In Silico Modeling
Binding Hydrophobic &
Natural Compound Plant Family Energy PDB ID Hydrogen-Bond
(Kcal/mol) Interaction
The three-dimensional structure
for α-glucosidase of
Asp215, Asp352,
β-Sitosterol Dryopteridaceae −16.097 Saccharomyces cerevisiae has not
Arg442, Gln182
yet been solved, MOE-Dock
(MOE 2010.11) software [165]
β-Sitosterol3-O-β-D-
−7.756 Asn415
glucopyranoside
2, 3, 5, Arg315, Asp307,
7-trihydroxy-2-(p-tolyl) −22.480 His280, Lys156, Ser240,
chorman-4-one Thr310, Tyr158
Quercetin-3-0-β-D-
glucopyranoside
(3/→0-3///)-β-D- −12.931 Arg442, Tyr158
Quercetin-3-0-β-D-
galactopyranoside
5, 7, 4/-Trihydroxyflavon-3- Asp242, Lys156, Pro312,
−15.752
glucopyranoid Tyr158
3A47; Saccharomyces cerevisiae,
2,6-diethylpiiperidine-3,4,5- MOE-Dock module (v.2011.10), Lys155, Glu304, Arg312,
Campanulaceae −6.1790
triol Model Scoring of GB/VI test, Asn153
The force field AMBER99 [143]
2-ethyl-6-methylpiperidine-
−8.8493
3,4,5-triol
6-ethyl-2-
(hydroxymethyl)piperidine- −6.9539
3,4-diol
3AHX; Clostridium cellulovorans,
The SCM model, SiteMap
1,2,4-tri-O-gal-loyl-β-D- Asp232, Ser235,
Geraniaceae −8.7 (Schrodinger Release 2018-1:
glucopyranose Asn314, Glu426
SiteMap, Schrodinger, LLC,
New York, NY, 2018) [163]
Kaempferol-3-O-α-
−9.4 Asp214, Asn241, Val277
rhamnopyranoside
Kaempferol-3-O-α- Asp68, Asp214, Thr215,
−9.2
arabinofuranoside Glu276, Asp408
Quercetin-3-O-β- Asp68, Asp214, Arg312,
−9.8
glucuronopyranoside Asp349, Gln350
Quercetin-3-O-α-
−5.4 Asp232, Asp429
arabinofuranoside
3A4A; Saccharomyces cerevisiae,
Agilent Masshunter software Ver.
Asp 69, Asp215,
Kuwanon L Moraceae −8.4412 B.04.00, The Molecular
Asp352, Asp307
Operating Environment
(MOE.2009.10) software [131]
Mulberrofuran G −8.4634
Asp69, Asp352, Asp215,
Sanggenon C −8.4291
Glu277
Biomolecules 2021, 11, 1877 16 of 31

Table 3. Cont.

In Silico Modeling
Binding Hydrophobic &
Natural Compound Plant Family Energy PDB ID Hydrogen-Bond
(Kcal/mol) Interaction
Asp 69, Asp215,
Moracenin D −8.3188
Asp352, Asp307
Mortatarin C −5.4358 No interaction
Asp69, Asp352, Asp215,
Sanggenon G −9.2855
Glu277, Phe178
Asp69, Asp352, Asp215,
Sanggenon O −8.9427
Glu277
Sanggenol A −7.7639 No interaction
Asp 69, Asp215,
Sanggenon W −8.4194
Asp352, Asp307
50 -Geranyl-5,7,20 ,40 -
tetraphydroxy −8.2431 No interaction
flavone
Asp 69, Asp215,
Nigrasin F −8.0232
Asp352, Asp307
Asp69, Asp352, Asp215,
Sanggenol G −8.7875
Glu277
Asp 69, Asp215,
Mortatarin B −5.9508
Asp352, Asp307
3A4A; Saccharomyces cerevisiae,
2PQR; Saccharomyces cerevisiae,
AutoDock Tools, Biovia
4,6,8-Megastigmatrien-3-one Acanthaceae −7.47 Discovery Studio (San Diego, Asn259, Hid295
CA, USA), PyMOLTM 1.7.4.5
(Schrodinger, LLC, New York,
NY, USA) [245]
N-Isobutyl-2-nonen-6,8-
−5.54 Lys156
diynamide
10 ,20 -bis(acetyloxy)-30 ,40 -
didehydro-20 -hydro-β,ψ- −10.19 Arg335
carotene
22-acetate-3-hydroxy21(6-
methyl-2,4-octadienoate)-
−8.31 Gly209
olean-12-en-28-oic
acid.
3CZJ; Escherichia coli, Pardock
(http://scfbio-iitd.res.in/dock/
paradock.jsp, accessed on 19
Polyhydroxy pyrrolidines Rutaceeae −2.4 February 2020), Accelrys and No interaction
AutoDock software (AutoDock
v.4.2.6, San Diego, CA,
USA) [246,247]
Tosyl −3.1 Asp229, Asp231

More specifically, e.g., AutoDock was used to dock quercetin compounds into the
α-amylase structure extracted from the human glutamine-complexed structure (PDB:
2ZJ3) using the binding scoring function. The binding strength between enzymes hit
compounds were identified. The docking results presented novel inhibitors that were
Biomolecules 2021, 11, 1877 17 of 31

obtained according to the different criteria of docking program. Scoring functions for
docking are potent approximate mathematical protocols applied to predict the strength of
hydrogen interactions and binding affinity [244]. Importantly, scoring can predict robust
intermolecular interactions [248]. In addition, scoring function focuses on nonbonded terms
of a molecular force-field [249]. Recently, Etsassala (2019) found abietane diterpenes from
Salvia Africana-lutea act as novel α-glucosidase and α-amylase inhibitors that exhibit strong
antioxidant and anti-diabetic activities [250]. In vitro methods have characterized their
chemical structure that consists of terpenoids including mangiferolic acid, cycloartenol,
and ambonic acid [250,251]. Moreover, a novel in silico study revealed a new class of
triazoloquinazolines that are potent α-glucosidase inhibitors [252]. Lastly, key residuals
are revealed when Phloretin is docked into the protein using Surflex-Dock (Tripos Inc.,
St. Louis, MO, USA). It is found that phloretin can interact with Asp69, Asp215, Arg442,
Gln353, and Asn350 to form five hydrogen bonds [253]. In another example, the α-amylase
structure was excerpted from RCSB Protein Data Bank (http://www.rcsb.org/pdb accessed
on 4 July 2019) (PDB: 1B2Y), whereas the α-glucosidase structure was built based on the
protein structure of oligo-1,6-glucosidase from S. cerevisiae (PDB ID: 3A4A). The docking
simulations were carried out by the Molecular Operating Environment (MOE, Chemical
Computing Group, Montreal, Canada). It was found that the most potent FG forms
hydrogen bonds with His 201, Glu 233, Asp 197, Gln 63, Trp 59 of α-amylase, and the
most potent α-glucosidase inhibitor interacts with Thr 306, Asp 352, Arg 213, Glu 277,
Asp 215, Arg 442 of the target protein through hydrogen bonds [253]. Quintero-Soto et al.
(2021) selected the most active alcalase hydrolyzate fraction from eight chickpea (Cicer
arietinum L.) samples to align with the complex α-amylase enzyme (PDB code: 1HNY) and α-
glucosidase enzyme (PDB: 5NN8) using GRAMM-XProtein-Protein, followed by molecular
dynamic simulation using the Rosetta FlexPepDock. It was found that inhibitors can bind
to both enzymes by electrostatic interaction, hydrogen bonds, and hydrophobic interactions.
Furthermore, sulfur-X bonds were found in the inhibitor-α-glucosidase interaction [254].
Swaraz et al. (2021) selected the crystal structures of α-amylase (PDB code: 1B2Y) and
α-glucosidase (PDB code: 5NN8) to dock phenolic compounds from Blumea laciniata (Roxb.)
DC. by AutoDoc Vina. Unlike the other molecular docking studies, the conformations
of docked ligands were searched by the Lamarckian genetic algorithm prior to docking.
It was found that Van der Waals interactions, hydrogen bonds, halogen bonds, and π–π
interactions were involved in the interactions between inhibitors and enzymes. The in vitro
results indicated that borassoside E, protodioscin, and diosgenin were the most potent
inhibitors, whereas in silico calculations suggested otherwise [255]. Molecular docking
or virtual screening has demonstrated appealing advantages, including low error level,
greater stabllty and operability, wide application, low-cost and capability to scale up easily.
Several limitations have been found in applications of molecular docking including poor
synergistic computational models, poor quality datasets, and poor standardization, poor
accurate scoring functions, model interpretation issues, issues with multi-domain proteins,
and assessment of multi-drug effects.

5.2. Pharmacophore Models

A pharmacophore model is derived from the most potent ligand-protein structure by
Discovery Studio to give rise to chemical features including two hydrogen bond donors
(HBDs) and two hydrophobic groups (2 PHs). Pharmacophore modeling that can be
classified as ligand and structure-based approaches has become a major tool in drug dis-
covery [256] and has been extensively used in virtual screening [257]. The objective of
pharmacophore modeling is to find chemical features responsible for a specific biological
activity among potent ligands [258]. Therefore, the use of appropriate modeling for screen-
ing drugs for T2DM is important in evaluating the interaction between the receptor and
ligand, defined as essential geometric arrangement of atoms or functional groups necessary
to produce a given biological response [259]. For instance, Teresa et al. (2015) used Ligand-
scout [260], which is a ligand-based pharmacophore modeling package to identify one of
Biomolecules 2021, 11, 1877 18 of 31

the most important structural properties that can prevent the increase of blood glucose
levels. Thus in modern medicinal chemistry it is necessary to find therapeutic agents for
T2DM treatment [261].
Gerhard et al. (2005) developed multiple pharmacophore models based on different
binding modes using LigandScout [262], and observed different pharmacophore models
comprised of different chemical features. The built models were further adopted to virtually
screen a number of commercially available chemical databases, totaling ca. 1.4 million
compounds. The selected hit compounds were then docked into the α-amylase structure
(PDB code: 3OLE) using Gold. Unlike most docking studies, in which the pose selection
generally relies on scoring function, the docked poses were selected based on the chemical
features derived from the acarviostatin II03 complexed structure in this study. The final
hit compounds showed α-amylase inhibitory activities with assayed IC50 values of tens of
micromoles. This study clearly illustrated the synergy between structure and analogue-
based modeling, as well as in vivo assays and in silico approaches.
Pharmacophore modeling can be used in conjunction with molecular docking and
molecular dynamics (MD) in some cases [263]. They are suitable for identifying the treat-
ment of T2DM as a known anti-pharmacophore was generated to remove all potential
agonists from the screening database [264]. For example, PPAR-α/γ agonists can reg-
ulate glucose metabolism including hyperglycemia and insulin resistance [265]. Thus,
alternative therapeutics for antidiabetic drugs can use this modeling to arrange chemical
features and some elements of drug design such as the absence of structural data for the
target enzyme-linked receptor [266]. Lee et al. (2014) discovered sulfonamide chalcone
derivatives from Saccharomyces cerevisiae as a novel class of non-saccharide compounds
that can potentially inhibit α-glucosidase by molecular docking and MD simulation [267].
Interestingly, oleanonic acid and other components of P. lentiscus oleoresin are new partial
PPARγ agonists unveiled by a pharmacophore hypothesis to treat T2DM [268]. More-
over, pharmacophore models were adopted to identify stilbene derivatives as a class of
α-glucosidase competitive inhibitors [266] and sulphonamide chalcone derivatives as a
new class of compounds to treat T2DM [269]. Pharmacophore also have long been studied
with α-amylase to control diabetes and found to have good binding affinity [269]. Pharma-
cophore packages and computer software include Discovery Studio, LigandScout, Phase,
MOE–Pharmacophore Discovery, ICM-Chemist, ZINCPharmer, and Pharmit model; phar-
macophores are used to determine potent features of one or more molecules with the same
biological activity [270]. Qualitative or quantitative studies can predict qualitative and
quantitative properties and can be used for identification through virtual screening or in sil-
ico models [271]. Findings related to the docking studies, and molecular docking are used
in computer-aided drug design approaches related to structure-based 3-D pharmacophore
because they can predict free energy and scoring schemes to test PDB binding [271].
The major advantages of pharmacophore models are virtual screening of a large
database, no need to know the binding site of the ligands for the target protein, the
design and optimization of drugs, scaffold-hopping, 2-D structural representation, all
with a comprehensive and editable approach. However, some limitations are that 2D
pharmacophore is less accurate than 3D pharmacophore, no interactions with the proteins,
and sensitivity to physicochemical features.

5.3. QSAR Model

The quantitative structure-activity relationship model (QSAR model) is a classification
model used in the chemical and biological sciences and engineering. QSAR shows biologi-
cal activity which can be expressed quantitatively and can be used to predict the model
response of other chemical structures [272]. The QSAR model has function that identifies
chemical structures for drug discovery that could have inhibitory effects on specific targets
with low toxicity (nonspecific activity). Of special interest is the prediction of partition
coefficient log P, which is an important measure used in identifying drug likeness according
to Lipinski’s Role of Five [273]. QSAR schemes, which are mathematically designated to
Biomolecules 2021, 11, 1877 19 of 31

map chemical characteristics with biological activity, have been extensively adopted to
predict α-glucosidase and α-amylase inhibitors [274]. QSAR studies include ligands with
their binding sites, inhibition constants, rate constants and other biological end points, in
addition molecular to properties such as lipophilicity, polarizability, electronic, and steric
properties or with certain structural features [275]. The model attempts to find consistent
relationships between the variations in the values of molecular properties and the biological
activity of a series of compounds which can then be used to evaluate the properties of new
chemical entities [276].
One study used the QSAR model to search natural α-amylase and α-glucosidase
inhibitors of all collected compounds, including active α-amylase and α-glucosidase in-
hibitors from ChEMBl, and inactive α-amylase and α-glucosidase inhibitors from Drug-
Bank; 640 and 214 compounds were divided into the training set and validation set for
the α-amylase inhibition model development, respectively, and 1540 and 515 compounds
into the training set and validation set for the α-glucosidase inhibition model development.
Descriptor enumerations were carried out by Dragon, descriptor selection was done by
linear discriminant analysis (LDA), and the classification models were built by the classifi-
cation tree (CT) algorithm. The best derived model showed a very high level of predictivity
(>95% accuracy in the training set, 86.80% in the test set, and 85.32% by the10-fold cross
validation) [277].As we know, the advantages of predicting biological activity with QSAR
modelling includes a large number of compounds with little to no prior experimental data
on activity, molecular properties that may be worth investigating further, chemical waste is
not generated when performing in silico predictions, the procedure reduces the need for
testing on animals and/or on cell cultures, and saves time. Disadvantages of predicting
biological activity with QSAR modelling include no in-depth insight on the mechanism
of biological action, and some risk of highly inaccurate predictions of pharmacological or
biological activity.

6. Comparing In Vitro (Enzymatic, Cellular) and In Vivo Advantages and

Disadvantages of In Silico Modeling Applications in T2DM)
A variety of in vitro and in vivo assays have been proposed to find novel therapeutic
agents targeting various putative molecular targets (vide supra) for T2DM treatment [278].
Drug discovery processes are important and include variety of activities using assay models
(in vitro, in vivo, and in silico) [279]. In vitro and in vivo tests for T2DM can also evaluate
the toxicity of drugs or compounds in development and the modified activity, and bioavail-
ability of herbal medicine compounds [280]. Importantly, preliminary study includes
protein-ligand interactions comparable to the lock and key principle [281]. The major
potent force for binding is hydrophobic interaction, while in silico modeling can be helpful
to identify drug target via bioinformatics tools [282]. The advantage of in silico methods is
to explore the target structures as possible active sites to generate candidate molecules with
results related to their binding affinity and hydrogen interaction [283]. The disadvantage
of in silico methods is that the binding mode and score function has been extensively
tested with multiple ligands for binding mode prediction, and affinity prediction and
many scoring functions might yield inaccurate predictions with less precision compared
to in vitro and in vivo methods [284]. Hence, enzymatic or cellular assays, animal models,
and in silico modelling are essential for developing new anti-diabetic agents and alternative
therapeutics in the future. Moreover, use of techniques and algorithms “in silico” is a good
way to identify new molecules as subjects for in vitro cell studies, animal in vivo tests for
validation and finally in human clinical trials for filing drug licenses.

7. Perspectives of In Silico Modelling for Discovery and Development of

Anti-Diabetes Drugs
Drug repositioning (DR) is the process of classifying new indications for approved
drugs and can substantially expedite drug discovery and development based on the fact
that their toxicity issues have been evaluated previously [285]. DR can reduce time and cost
because it takes advantage of drugs already in clinical use for other indications, or drugs
Biomolecules 2021, 11, 1877 20 of 31

that have passed phase I safety trials but failed to show efficacy for the intended diseases.
In silico drug discovery methods, and the development of antidiabetic drug repurposing
has become an important factor in new drug discovery. Several computational approaches
that help us to uncover new antidiabetic drug opportunities and discovery process have
been screened or adapted from previous applications [286]. Accordingly, identification of
new drugs is expected to help predict new drug-targets, [228]. In silico approaches are
capable of complement and integrating with each other in drug repurposing and will result
in drugs for the future [287]. Computational (in silico) methods have been developed and
widely applied to pharmacological hypothesis development and tests, including database,
structure-activity relationships (SAR), pharmacophores, molecular docking, and super-
speed computer tools [288]. Drug design and development for T2DM are still in early
stages of management. The conventional target and structure-based methods can be linked
toward therapeutic mechanism of T2DM treatment [257]. In contrast, several approaches in
silico have the advantage of fast speed and low cost, and has been receiving more attention
worldwide; the disadvantage being over-estimation of binding affinity and arbitrarily
choosing non-bonded cut off terms [245]. Currently, we have performed in silico assays to
accelerate new discoveries from herbal or natural compounds and confirmed the validation
of preclinical levels. These include: α-amylase and α-glucosidase activities suppressed by
Garcinia linii extracts, including syringaldehyde, via docking, and further confirmed by
in vitro (cell) and in vivo (diabetic mice) studies [289]; by the mixture of extracts (purple
onion, cinnamon, and tea) via docking, and further confirmed by in vitro (enzyme) and
in vivo (diabetic mice) studies [290]; by γ-mangostin via docking and further confirmed
by in vitro (cell, enzyme) and in vivo (diabetic mice) studies [291]; by syringaldehyde via
docking and further confirmed by in vitro (enzyme, organ culture) and in vivo (diabetic
mice) [292] studies; and by curcumin, antroquinonol, HCD, docosanol, tetracosanol, rutin,
and actinodaphnine via virtual screen and further confirmed by in vitro (cell) and in vivo
(diabetic mice) studies [293]. Remarkably, drug design is a process in which new leads
(efficacy drugs) are discovered which have therapeutic benefits in antidiabetic drugs and
can have potential effects on the management of T2DM in human clinical trials [246,247].

Author Contributions: J.R. and D.-C.P. participated in data analysis and manuscript preparation.
M.K.L. and C.-F.W. critically reviewed the manuscript. All authors have read and agreed to the
published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Sindhu, S.N.; Vaibhavi, K.; Anshu, M. In vitro studies on alpha amylase and alpha glucosidase inhibitory activities of selected
plant extracts. Eur. J. Exp. Biol. 2013, 3, 128–132.
2. Wang, B.; Liu, H.C.; Hong, J.R.; Li, H.G.; Huang, C.Y. Effect of Psidium guajava leaf extract on alpha-glucosidase activity in small
intestine of diabetic mouse. Sichuan Da Xue Xue Bao Yi Xue Ban 2007, 38, 298–301.
3. American Diabetes Association. Standards of Medical Care in Diabetes—2010. Diabetes Care 2010, 33 (Suppl. 1), S11–S61.
[CrossRef] [PubMed]
4. WHO. Global Report on Diabetes; WHO: Geneva, Switzerland, 2016; pp. 1–88.
5. Suganthi, A. Anti Diabetic Plants-Overview. Curr. Res. Diabetes Obes. J. 2018, 7, 555720. [CrossRef]
6. IDF. IDF Diabetes Atlas, 7th ed.; International Diabetes Federation: Brussels, Belgium, 2015.
7. Kerru, N.; Singh-Pillay, A.; Awolade, P.; Singh, P. Current anti-diabetic agents and their molecular targets: A review. Eur. J. Med.
Chem. 2018, 152, 436–488. [CrossRef]
8. Bilal, M.; Iqbal, M.S.; Shah, S.B.; Rasheed, T.; Iqbal, H.M.N. Diabetic Complications and Insight into Antidiabetic Potentialities of
Ethno- Medicinal Plants: A Review. Recent Pat. Inflamm. Allergy Drug Discov. 2018, 12, 7–23. [CrossRef] [PubMed]
9. Hegde, P.K.; Rao, H.A.; Rao, P.N. A review on Insulin plant (Costus igneus Nak). Pharm. Rev. 2014, 8, 67–72. [CrossRef]
Biomolecules 2021, 11, 1877 21 of 31

10. Rask-Madsen, C.; King, G.L. Vascular complications of diabetes: Mechanisms of injury and protective factors. Cell Metab. 2013,
17, 20–33. [CrossRef]
11. Ellappan, T.; Balasubramaian, P.; Chidambaram, K.; Subhash, C.M. α-Glucosidase and α-Amylase Inhibitory Activity of Senna
surattensis. J. Acupunct. Meridian Stud. 2013, 6, 24–30.
12. Ferrannini, E. Insulin resistance versus insulin deficiency in non-insulin-dependent diabetes mellitus: Problems and prospects.
Endocr. Rev. 1998, 19, 477–490. [CrossRef]
13. Olokoba, A.B.; Obateru, O.A.; Olokoba, L.B. Type 2 diabetes mellitus: A review of current trends. Oman Med. J. 2012, 27, 269–273.
[CrossRef] [PubMed]
14. Wilke, T.; Boettger, B.; Berg, B.; Groth, A.; Mueller, S.; Botteman, M.; Yu, S.; Fuchs, A.; Maywald, U. Epidemiology of urinary tract
infections in type 2 diabetes mellitus patients: An analysis based on a large sample of 456,586 German T2DM patients. J. Diabetes
Complicat. 2015, 29, 1015–1023. [CrossRef]
15. Saini, V. Molecular mechanisms of insulin resistance in type 2 diabetes mellitus. World J. Diabetes 2010, 1, 68–75. [CrossRef]
[PubMed]
16. Choo, C.Y.; Sulonga, N.Y.; Mana, F.; Wong, T.W. Vitexin and isovitexin from the Leaves ofFicus deltoideawithin-vivoa-glucosidase
inhibition. J. Ethnopharmacol. 2012, 142, 776–781. [CrossRef] [PubMed]
17. Chaudhury, A.; Duvoor, C.; Reddy Dendi, V.S.; Kraleti, S.; Chada, A.; Ravilla, R.; Marco, A.; Shekhawat, N.S.; Montales, M.T.;
Kuriakose, K.; et al. Clinical Review of Antidiabetic Drugs: Implications for Type 2 Diabetes Mellitus Management. Front.
Endocrinol. 2017, 8, 6. [CrossRef] [PubMed]
18. American Diabetes Association. Standards of medical care in diabetes–2014. Diabetes Care 2014, 37 (Suppl. 1), S14–S80. [CrossRef]
19. American Diabetes Association. Standards of Medical Care in Diabetes-2016: Summary of Revisions. Diabetes Care 2016, 39
(Suppl. 1), S4–S5. [CrossRef]
20. Marin-Penalver, J.J.; Martin-Timon, I.; Sevillano-Collantes, C.; Del Canizo-Gomez, F.J. Update on the treatment of type 2 diabetes
mellitus. World J. Diabetes 2016, 7, 354–395. [CrossRef]
21. Hotta, N. A new perspective on the biguanide, metformin therapy in type 2 diabetes and lactic acidosis. J. Diabetes Investig. 2019,
10, 906–908. [CrossRef]
22. Adak, T.; Samadi, A.; Unal, A.Z.; Sabuncuoglu, S. A reappraisal on metformin. Regul. Toxicol. Pharmacol. 2018, 92, 324–332.
[CrossRef]
23. Pallavi, K.; Antara, B.; Anushka, B.; Ramachandran, M.; Francesca, M.; Surajit, P. An Overview of Dietary Polyphenols and Their
Therapeutic Effects. In Polyphenols: Mechanisms of Action in Human Health and Disease, 2nd ed.; Ronald, R.W., Victor, R.P., Sherma,
Z., Eds.; Elsevier Inc.: London, UK, 2018; Volume 1.
24. Wahab, A. Difficulties in Treatment and Management of Epilepsy and Challenges in New Drug Development. Pharmaceuticals
2010, 3, 2090–2110. [CrossRef]
25. Chang, C.L.; Lin, Y.; Bartolome, A.P.; Chen, Y.C.; Chiu, S.C.; Yang, W.C. Herbal therapies for type 2 diabetes mellitus: Chemistry,
biology, and potential application of selected plants and compounds. Evid.-Based Complement. Altern. Med. 2013, 2013, 378657.
[CrossRef] [PubMed]
26. Tricia, S.C.; Jeremy, H.P. Management of Type 2 Diabetes: Selecting amongst Available Pharmacological Agents; Feingold, K.R., Anawalt,
B., Boyce, A., Eds.; MDText.com, Inc.: South Dartmouth, MA, USA, 2017.
27. Bischoff, H. The mechanism of alpha-glucosidase inhibition in the management of diabetes. Clin. Investig. Med. 1995, 18, 303–311.
28. Lynn, M. What Is Acarbose (Precose)? Everyday Health, Inc.: New York, NY, USA, 2014.
29. Kalra, S. Sodium Glucose Co-Transporter-2 (SGLT2) Inhibitors: A Review of Their Basic and Clinical Pharmacology. Diabetes Ther.
2014, 5, 355–366. [CrossRef]
30. Lavalle-Gonzalez, F.J.; Januszewicz, A.; Davidson, J.; Tong, C.; Qiu, R.; Canovatchel, W.; Meininger, G. Efficacy and safety of
canagliflozin compared with placebo and sitagliptin in patients with type 2 diabetes on background metformin monotherapy: A
randomised trial. Diabetologia 2013, 56, 2582–2592. [CrossRef]
31. Healthline. Sitagliptin, Oral Tablet. 2005. Available online: https://www.healthline.com/health/drugs/sitagliptin-oral-tablet
(accessed on 17 October 2021).
32. Bilal, O.; Bo, A. Pleiotropic Mechanisms for the Glucose-Lowering Action of DPP-4 Inhibitors. Diabetes 2014, 63, 2196–2202.
[CrossRef]
33. Pathak, R.; Bridgeman, M.B. Dipeptidyl Peptidase-4 (DPP-4) inhibitors In the Management of Diabetes. Pharm. Ther. 2010, 35,
509–513.
34. Lynn, M. What Are Sulfonylureas? Everyday Health, Inc.: New York, NY, USA, 2015.
35. Tyagi, S.; Gupta, P.; Saini, A.S.; Kaushal, C.; Sharma, S. The peroxisome proliferator-activated receptor: A family of nuclear
receptors role in various diseases. J. Adv. Pharm. Technol. Res. 2011, 2, 236–240. [CrossRef]
36. Ciudin, A.; Hernandez, C.; Simo, R. Update on cardiovascular safety of PPARgamma agonists and relevance to medicinal
chemistry and clinical pharmacology. Curr. Top. Med. Chem. 2012, 12, 585–604. [CrossRef]
37. Emmanuel, V.O.; Robert, B.; Hélène, G.; Max, F.; Bernard, R.; Steen, G.; Anne, D.; Yannick, L.M.-B.; Aline, K. The insulin receptor
kinase. Biochimie 1985, 67, 1119–1124. [CrossRef]
38. Nolan, M.K.; Jankowska, L.; Prisco, M.; Xu, S.; Guvakova, M.A.; Surmacz, E. Differential roles of IRS-1 and SHC signaling
pathways in breast cancer cells. Int. J. Cancer 1997, 72, 828–834. [CrossRef]
Biomolecules 2021, 11, 1877 22 of 31

39. Dool, C.J.; Mashhedi, H.; Zakikhani, M.; David, S.; Zhao, Y.; Birman, E.; Carboni, J.M.; Gottardis, M.; Blouin, M.J.; Pollak, M.
IGF1/insulin receptor kinase inhibition by BMS-536924 is better tolerated than alloxan-induced hypoinsulinemia and more
effective than metformin in the treatment of experimental insulin-responsive breast cancer. Endocr. Relat. Cancer 2011, 18, 699–709.
[CrossRef]
40. Katerina, M.; Shannon, L.P.; Leslie, M.S. Expression and function of the insulin receptor substrate proteins in cancer. Cell Commun.
Signal. 2009, 7, 14.
41. Fagerberg, L.; Hallstrom, B.M.; Oksvold, P.; Kampf, C.; Djureinovic, D.; Odeberg, J.; Habuka, M.; Tahmasebpoor, S.; Danielsson,
A.; Edlund, K.; et al. Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and
antibody-based proteomics. Mol. Cell. Proteom. 2014, 13, 397–406. [CrossRef] [PubMed]
42. Sullivan, D.H.; Carter, W.J.; Warr, W.R.; Williams, L.H. Side effects resulting from the use of growth hormone and insulin-like
growth factor-I as combined therapy to frail elderly patients. J. Gerontol. A Biol. Sci. Med. Sci. 1998, 53, M183–M187. [CrossRef]
[PubMed]
43. Zorzano, A.; Palacin, M.; Guma, A. Mechanisms regulating GLUT4 glucose transporter expression and glucose transport in
skeletal muscle. Acta Physiol. Scand. 2005, 183, 43–58. [CrossRef] [PubMed]
44. Pulipparacharuvil, S.; Renthal, W.; Hale, C.F.; Taniguchi, M.; Xiao, G.; Kumar, A.; Russo, S.J.; Sikder, D.; Dewey, C.M.; Davis,
M.M.; et al. Cocaine regulates MEF2 to control synaptic and behavioral plasticity. Neuron 2008, 59, 621–633. [CrossRef]
45. Madiraju, S.R.; Poitout, V. G protein-coupled receptors and insulin secretion: 119 and counting. Endocrinology 2007, 148, 2598–2600.
[CrossRef]
46. Deborah, H. Glucagon-Like Peptide 1 Receptor Agonists for Type 2 Diabetes. Diabetes Spectr. 2017, 30, 202–210. [CrossRef]
47. Liu, S.; Li, D.; Huang, B.; Chen, Y.; Lu, X.; Wang, Y. Inhibition of pancreatic lipase, alpha-glucosidase, alpha-amylase, and
hypolipidemic effects of the total flavonoids from Nelumbo nucifera leaves. J. Ethnopharmacol. 2013, 149, 263–269. [CrossRef]
48. Grosso, G. Effects of Polyphenol-Rich Foods on Human Health. Nutrients 2018, 10, 1089. [CrossRef]
49. Aryaeian, N.; Sedehi, S.K.; Arablou, T. Polyphenols and their effects on diabetes management: A review. Med. J. Islamic Repub.
Iran 2017, 31, 134. [CrossRef]
50. Tundis, R.; Loizzo, M.R.; Menichini, F. Natural products as alpha-amylase and alpha-glucosidase inhibitors and their hypogly-
caemic potential in the treatment of diabetes: An update. Mini Rev. Med. Chem. 2010, 10, 315–331. [CrossRef]
51. Ahmad, G.; Hossein, F.; Fariba, S.-f.; Mansour, M. The inhibitory effect of some Iranian plants extracts on the alpha glucosidase.
Iran. J. Basic Med. Sci. 2008, 11, 1–9.
52. Mali, P.Y.; Panchal, S.S. A review on phyto-pharmacological potentials of Euphorbia thymifolia L. Anc. Sci. Life 2013, 32, 165–172.
[CrossRef] [PubMed]
53. Deepti, S.; Pragya, S. A Review on Pharmacognostical and pharmacological prospective Euphorbia thymifolia L. Asian J. Pharmaceut.
Edu. Res. 2017, 6, 31–39.
54. Muthumani, D.; Hedina, A.; Kausar, J.; Anand, V. Phytopharmacological activities of Euphorbia thymifolia Linn. Syst. Rev. Pharm.
2016, 7, 30–34. [CrossRef]
55. Phani, G.K.; Alka, C. Ethnobotanical Observations of Euphorbiaceae Species from Vidarbha region, Maharashtra, India. Ethnobot.
Leafl. 2010, 14, 674–680.
56. Kooti, W.; Farokhipour, M.; Asadzadeh, Z.; Ashtary-Larky, D.; Asadi-Samani, M. The role of medicinal plants in the treatment of
diabetes: A systematic review. Electron. Physician 2016, 8, 1832–1842. [CrossRef]
57. Lin, C.C.; Cheng, H.Y.; Yang, C.M.; Lin, T.C. Antioxidant and antiviral activities of Euphorbia thymifolia L. J. Biomed. Sci. 2002, 9,
656–664. [CrossRef] [PubMed]
58. Malviya, N.; Jain, S.; Malviya, S. Antidiabetic potential of medicinal plants. Acta Pol. Pharm. 2010, 67, 113–118.
59. Franco, R.R.; Ribeiro Zabisky, L.F.; Pires de Lima Júnior, J.; Mota Alves, V.H.; Justino, A.B.; Saraiva, A.L.; Goulart, L.R.; Espindola,
F.S. Antidiabetic effects of Syzygium cumini leaves: A non-hemolytic plant with potential against process of oxidation, glycation,
inflammation and digestive enzymes catalysis. J. Ethnopharmacol. 2020, 261, 113132. [CrossRef]
60. Sabu, M.C.; Kuttan, R. Anti-diabetic activity of medicinal plants and its relationship with their antioxidant property. J.
Ethanopharmacol. 2002, 81, 155–160. [CrossRef]
61. Widharna, R.M.; Soemardji, A.A.; Wirasutisna, K.R.; Kardono, L.B.S. Anti Diabetes Mellitus Activity in vivo of Ethanolic Extract
and Ethyl Acetate Fraction of Euphorbia hirta L. Herb. Int. J. Pharm. 2010, 6, 231–240. [CrossRef]
62. Manjur, A.S.; Rayhana, B.; Kolappa, K.P.; Vidhu, A.; Showkat, R.M.; Abuzer, A.; Manju, S. In vitro α-glucosidase and α-amylase
inhibition by aqueous, hydroalcoholic, and alcoholic extract of Euphorbia hirta L. Drug Dev. Ther. 2016, 7, 26–30.
63. Mahomoodally, M.F.; Dall’Acqua, S.; Sinan, K.I.; Sut, S.; Ferrarese, I.; Etienne, O.K.; Sadeer, N.B.; Ak, G.; Zengin, G. Phenolic
compounds analysis of three Euphorbia species by LC-DAD-MSn and their biological properties. J. Pharm. Biomed. Anal. 2020, 189,
113477. [CrossRef] [PubMed]
64. Richard, G.O.; Michelle, L.Z.; Lúcia, G.L.; Susan, O.G.; Andrew, J.E. A molecular phylogeny and classification of bignoniaceae.
Am. J. Bot. 2009, 96, 1731–1743. [CrossRef]
65. Gentry, A.H. Relationships of the Malagasy Bignoniaceae: A striking case of convergent evolution. Plant Syst. Evol. 1976, 126,
255–256. [CrossRef]
66. The Angiosperm Phylogeny Group. An update of the Angiosperm Phylogeny Group classification for the orders and families of
flowering plants: APG II. Bot. J. Linn. Soc. 2003, 141, 399–436.
Biomolecules 2021, 11, 1877 23 of 31

67. Alonso-Castro, A.J.; Zapata-Bustos, R.; Romo-Yanez, J.; Camarillo-Ledesma, P.; Gomez-Sanchez, M.; Salazar-Olivo, L.A. The
antidiabetic plants Tecoma stans (L.) Juss. ex Kunth (Bignoniaceae) and Teucrium cubense Jacq (Lamiaceae) induce the incorporation of
glucose in insulin-sensitive and insulin-resistant murine and human adipocytes. J. Ethnopharmacol. 2010, 127, 1–6. [CrossRef]
68. JOSE, A.P.; JES, U. Health Care Affordability and Complementary and Alternative Medicine Utilization by Adults with Diabetes.
Diabetes Care 2007, 30, 2030–2031.
69. Thomas, J. Elpel’s Web World Pages, Wildflowers-and-Weeds.com, Nov. 2021. Available online: http://www.wildflowers-and-
weeds.com/ (accessed on 17 October 2021).
70. Chunpeng, W.; Shouran, Z. Acylated Flavonoid from Vaccinium corymbosum (Ericaceae) Flowers with Yeast α-Glucosidase
Inhibitory Activity. Trop. J. Pharm. Res. 2013, 12, 549–552. [CrossRef]
71. Rabia, R.; Zaitoon, I.; Sajid, A.; Muhammad, N.; Muhammad, Y.K.; Jamshed, I. Identification of Highly Potent and Selective
α-Glucosidase Inhibitors with Antiglycation Potential, Isolated from Rhododendron arboreum. Rec. Nat. Prod. 2015, 9, 262–266.
72. Javanmardi, J.; Stushnoff, C.; Locke, E.; Vivanco, J.M. Antioxidant activity and total phenolic content of Iranian Ocimum
accessions. Food Chem. 2003, 83, 547–550. [CrossRef]
73. Zhang, L.B.; Zhang, L.; Dong, S.Y.; Sessa, E.B.; Gao, X.F.; Ebihara, A. Molecular circumscription and major evolutionary lineages
of the fern genus Dryopteris (Dryopteridaceae). BMC Evol. Biol. 2012, 12, 180. [CrossRef]
74. Gao, Z.; Ali, Z.; Zhao, J.; Qiao, L.; Lei, H.; Lu, Y.; Khan, L.A. Phytochemical investigation of the rhizomes of Dryopteris
crassirhizoma. Phytochem. Lett. 2008, 1, 188–190. [CrossRef]
75. Proenca, C.; Freitas, M.; Ribeiro, D.; Oliveira, E.F.T.; Sousa, J.L.C.; Tome, S.M.; Ramos, M.J.; Silva, A.M.S.; Fernandes, P.A.;
Fernandes, E. alpha-Glucosidase inhibition by flavonoids: An in vitro and in silico structure-activity relationship study. J. Enzyme
Inhib. Med. Chem. 2017, 32, 1216–1228. [CrossRef]
76. He, J.Y.; Ma, N.; Zhu, S.; Komatsu, K.; Li, Z.Y.; Fu, W.M. The genus Codonopsis (Campanulaceae): A review of phytochemistry,
bioactivity and quality control. J. Nat. Med. 2015, 69, 1–21. [CrossRef]
77. Govindappa, M. A Review on Role of Plant(s) Extracts and its Phytochemicals for the Management of Diabetes. J. Diabetes Metab.
2015, 6, 565. [CrossRef]
78. Xu, L.; Wang, W. Chinese Materia Medica: Combination and Application; Fielding, C., Ed.; Donica Publishing: St. Albans, UK, 2002.
79. Bensky, D.; Clavey, S.; Stoger, E. Chinese Herbal Medicine; Materia Medica; Eastland Press: Seattle, WA, USA, 2004.
80. Suk, W.J.; Ae, J.H.; Hae, J.H.; Myoung, G.C.; Kwan, S.K.; Si, H.P. α-Glucosidase Inhibitors from the Roots of Codonopsis lanceolata
Trautv. Agric. Chem. Biotechnol. 2006, 49, 162–164.
81. Ikeda, K.; Takahashi, M.; Nishida, M.; Miyauchi, M.; Kizu, H.; Kameda, Y.; Arisawa, M.; Watson, A.A.; Nash, R.J.; Fleet, G.W.;
et al. Homonojirimycin analogues and their glucosides from Lobelia sessilifolia and Adenophora spp. (Campanulaceae). Carbohydr.
Res. 2000, 323, 73–80. [CrossRef]
82. Zafar, M.; Khan, H.; Rauf, A.; Khan, A.; Lodhi, M.A. In Silico study of alkaloids as α-glucosidase inhibitors: Hopefor the discovery
of effective lead compounds. Front. Endocrinol. 2016, 7, 153. [CrossRef] [PubMed]
83. Oner, H.H.; Yildirim, H.; Pirhan, A.F.; Gemici, Y. A new record for the flora of Turkey: Geranium macrorrhizum L., (Geraniaceae).
Biol. Div. Conserv. 2010, 3, 151–154.
84. Didem, Ş.; Mahmut, K.S.; Suna, A.S.; Hilal, Ö.; Hayri, D.; Olov, S. Antioxidant secondary metabolites from Geranium lasiopus
Boiss. & Heldr. Nat. Prod. Res. 2012, 26, 1261–1264. [CrossRef]
85. Hitender, S.; Sunil, K. Management of metabolic syndrome by some herbs ethnic to western Himalayan region of Himachal
Pradesh. J. Pharmacogn. Phytochem. 2016, 5, 192–195.
86. Renda, G.; Sari, S.; Barut, B.; Soral, M.; Liptaj, T.; Korkmaz, B.; Ozel, A.; Erik, I.; Sohretoglu, D. α-Glucosidase inhibitory effects of
polyphenols from Geranium asphodeloides: Inhibition kinetics and mechanistic insights through in vitro and in silico studies.
Bioorg. Chem. 2018, 81, 545–552. [CrossRef]
87. Berna, E.; Katrin, B.; Abdul, M.i.; Wulan, Y.; Anastasia, B.; Eva, K.S. Screening of α-Glucosidase Inhibitory Activity from Some
Plants of Apocynaceae, Clusiaceae, Euphorbiaceae, and Rubiaceae. J. Biomed. Biotechnol. 2011, 2012, 281078. [CrossRef]
88. Maheswari, J.U.; Gandhimathi, R. Hypoglycemic and hyporlipidemic activity of leaves of Gardenia taitensis on streptozotocin
induced diabetic rats. Indian J. Pharmaceut. Sci. Res. 2011, 1, 10–14.
89. Khan, M.F.; Khan, Z.I.; Uddin, M.R.; Rahman, M.S.; Rashid, M.A. In vivo hypoglycemic and alloxan induced antidiabetic activity
of Xeromphis uliginosa Retz. Afr. J. Pharm. Pharmacol. 2015, 9, 363–366.
90. Edet, A.E.; Patrick, E.E.; Olorunfemi, E.A. Hematological parameters of alloxan-induced diabetic rats treated with ethanol extracts
and fractions of Nauclea latifolia leaf. Eur. Sci. J. 2015, 9, 203–210.
91. Sanadhya, I.; Lobo, V.; Bhot, M.; Varghese, J.; Chandra, N. Antidiabetic activity of Anthocephalus indicus A. Rich. fruits in alloxan
induced diabetic rats. Int. J. Pharm. Sci. 2013, 5, 519–523.
92. Rani, S.; Mandave, P.; Khadke, S.; Jagtap, S.; Patil, S.; Kuvalekar, A. Antiglycation, antioxidant and antidiabetic activity of
traditional medicinal plant: Rubia cordifolia Linn. for management of hyperglycemic effect. Int. J. Plant Anim. Environ. Sci. 2013,
3, 42–49.
93. Verónica, J.-S.; Antonio, N.-C.; Manuel, J.-E.; Brenda, A.S. Anti-inflammatory, free radical scavenging and alpha-glucosidase
inhibitory activities of Hamelia patens and its chemical constituents. Pharm. Biol. 2016, 54, 1822–1830.
Biomolecules 2021, 11, 1877 24 of 31

94. Nimal, I.V.S.C.; Praveen, P.K.; Christudas, S.; Vajravijayana, S.; Lakshmi, R.S.; Jenifer, S.S.; Agastiana, P. In vitro studies on
α-glucosidase inhibition, antioxidant and free radical scavenging activities of Hedyotis biflora L. Food Chem. 2013, 138, 1689–1695.
[CrossRef] [PubMed]
95. Yessoufou, A.; Gbenou, J.; Grissa, O.; Hichami, A.; Simonin, A.M.; Tabka, Z.; Moudachirou, M.; Moutairou, K.; Khan, N.A.
Anti-hyperglycemic effects of three medicinal plants in diabetic pregnancy: Modulation of T cell proliferation. BMC Complement.
Altern. Med. 2013, 13, 77. [CrossRef]
96. Gidado, A.; Ameh, D.A.; Atawodi, S.E.; Ibrahim, S. Hypoglycaemic activity of Nauclea latifolia Sm. (Rubiaceae) in experimental
animals. Afr. J. Tradit. Complement. Altern. Med. 2008, 5, 201–208. [CrossRef] [PubMed]
97. Theiler, B.A.; Istvanits, S.; Zehl, M.; Marcourt, L.; Urban, E.; Caisa, L.O.; Glasl, S. HPTLC Bioautography Guided Isolation of
alpha-Glucosidase Inhibiting Compounds from Justicia secunda Vahl (Acanthaceae). Phytochem. Anal. 2017, 28, 87–92. [CrossRef]
98. Otaiza, G.R.; Arzola, C.J.; Rodríguez, A.M.C. Plantas Medicinales de la Mesa de Los Indios, Municipio Campo Elías (Estado
Mérida, Venezuela). Plantula 2006, 4, 55–67.
99. Pan, Y.; Abd-Rashid, B.A.; Ismail, Z.; Ismail, R.; Mak, J.W. In vitro modulatory effects of Andrographis paniculata, Centella
asiatica and Orthosiphon stamineus on cytochrome P450 2C19 (CYP2C19). J. Ethnopharmacol. 2011, 133, 881–887. [CrossRef]
100. Venkataiah, G.; Ahmed, M.I.; Reddy, D.S.; Rejeena, M. Anti-diabetic activity of Acanthus ilicifolius root extract in alloxan induced
diabetic rats. Indo Am. J. Pharm. Sci. 2013, 3, 9007–9012.
101. Yu, B.C.; Hung, C.R.; Chen, W.C.; Cheng, J.T. Antihyperglycemic effect of andrographolide in streptozotocin-induced diabetic
rats. Planta. Med. 2003, 69, 1075–1079.
102. Gulati, V.; Gulati, P.; Harding, I.H.; Palombo, E.A. Exploring the anti-diabetic potential of Australian Aboriginal and Indian
Ayurvedic plant extracts using cellbased assays. BMC Complement. Altern. Med. 2015, 15, 8. [CrossRef] [PubMed]
103. Rammohan, S.; Asmawi, M.Z. Inhibition of α-Glucosidase by Andrographis paniculata. Ethanol Extract in Rats. Pharm. Biol.
2006, 44, 600–606. [CrossRef]
104. Suganya, M.; Zalikha, I.; Qamar, U.A.; Bisha, F.U.; Nik, I.N.Y.; Vikneswari, P.; Faridah, A.; Khozirah, S.; Alfi, K. Identification ofα-
glucosidase inhibitors fromClinacanthus nutansleafextract using liquid chromatography-mass spectrometry-basedmetabolomics
and protein-ligand interaction with molecular docking. J. Pharm. Anal. 2019, 9, 91–99.
105. Murugesu, S.; Ibrahim, Z.; Ahmed, Q.U.; Nik Yusoff, N.I.; Uzir, B.F.; Perumal, V.; Abas, F.; Saari, K.; El-Seedi, H.; Khatib,
A. Characterization of alpha-Glucosidase Inhibitors from Clinacanthus nutans Lindau Leaves by Gas Chromatography-Mass
Spectrometry-Based Metabolomics and Molecular Docking Simulation. Molecules 2018, 23, 2402. [CrossRef] [PubMed]
106. Tamokou, J.D.D.; Kuete, V. Rutaceae. Antimicrobial Activities of African Medicinal Spices and Vegetables. In Medicinal Spices
and Vegetables from Africa: Therapeutic Potential against Metabolic Inflammatory Infectious and Systemic Diseases, Medicinal Spices and
Vegetables from Africa; Academic Press: Cambridge, UK, 2017.
107. Peng, C.H.; Ker, Y.B.; Weng, C.F.; Peng, C.C.; Huang, C.N.; Lin, L.Y.; Peng, R.Y. Insulin secretagogue bioactivity of finger citron
fruit (Citrus medica L. var. Sarcodactylis Hort, Rutaceae). J. Agric. Food Chem. 2009, 57, 8812–8819. [CrossRef] [PubMed]
108. Dilipkumar, E.K.; Janardhana, G.R. Antidiabetic and Regenerative effects of alcoholic corm extract of Nervilia aragoana Gaud. in
NIDDM rats. Int. J. Phytomed. 2013, 5, 207–210.
109. Ojewole, J.A.O. The hypoglycaemic effect of Clausena anisata (Willd) Hook methanolic root extracts in rats. J. Ethnopharmacol.
2002, 81, 231–237. [CrossRef]
110. Urios, P.; Kassab, I.; Grigorova-Borsos, A.M.; Guillot, R.; Jacolot, P.; Tessier, F.; Peyroux, J.; Sternberg, M. A flavonoid frac-
tion purified from Rutaceae aurantiae (Daflon(R)) inhibiting AGE formation, reduces urinary albumin clearance and corrects
hypoalbuminemia in normotensive and hypertensive diabetic rats. Diabetes Res. Clin. Pract. 2014, 105, 373–381. [CrossRef]
111. Kumar, S.; Narwal, S.; Kumar, V.; Prakash, O. alpha-glucosidase inhibitors from plants: A natural approach to treat diabetes.
Pharm. Rev. 2011, 5, 19–29. [CrossRef]
112. Somashekhar, M.; Naira, N.; Basavraj, S. A review on family Moraceae (Mulberry) with a focus on Artocarpus species. World J.
Pharm. Pharm. Sci. 2019, 2, 2614–2626.
113. Mikhail, O.N.; Mutiu, I.K. Antioxidant activity of African Medicinal plants. In Medicinal Plant Research in Africa; Elsevier Inc.:
Amsterdam, The Netherlands, 2013; pp. 787–803. [CrossRef]
114. Paramita, B.; Charitha, T.; Camelia, M. In vitro antidiabetic activities of dioecious White mulberry (Morus alba, Moraceae). FASEB
J. 2017, 31, 974.
115. Gayathri, M.; Kannabiran, K. Antimicrobial activity of Hemidesmus indicus, Ficus bengalensis and Pterocarpus marsupium roxb.
Indian J. Pharm. Sci. 2009, 71, 578–581.
116. Wadood, N.; Wadood, A.; Nisar, M. Effect of Ficus relegiosa on blood glucose and total lipid levels of normal and alloxan diabetic
rabbits. J. Ayub. Med. Coll. Abbottabad 2003, 15, 40–42.
117. Mohammadi, J.; Naik, P.R. The histopathologic effects of Morus alba leaf extract on the pancreas of diabetic rats. Turk. J. Biol.
2012, 36, 211–216.
118. Tripathi, A.K.; Bhoyar, P.K.; Baheti, J.R. Herbal antidiabetics: A review. Int. J. Res. Pharm. Sci. 2011, 2, 30–37.
119. Andallu, B.; Suryakantham, V.L.; Srikanthi, B.; Reddy, G.K. Effect of mulberry (Morus indica L.) therapy on plasma and erythrocyte
membrane lipids in patients with type 2 diabetes. Clin. Chim. Acta 2001, 314, 47–53. [CrossRef]
120. Khare, C.P. Indian Herbal Remedies; Springer: Berlin/Heideberg, Germany; New York, NY, USA, 2004.
Biomolecules 2021, 11, 1877 25 of 31

121. Ayodhya, S.; Kusum, S.; Anjali, S. Hypoglycaemic activity of different extracts of various herbal plants. Int. J. Res. Ayurveda
Pharm. 2010, 1, 212–224.
122. Zhang, B.-w.; Xia Li, W.-l.S.; Yan Xing, Z.-l.X.; Chun-lin, Z.; Yue-sheng, D. Dietary Flavonoids and Acarbose Synergistically Inhibit
α-Glucosidase and Lower Postprandial Blood Glucose. J. Agric. Food Chem. 2017, 65, 8319–8330. [CrossRef]
123. Guasch-Ferre, M.; Merino, J.; Sun, Q.; Fito, M.; Salas-Salvado, J. Dietary Polyphenols, Mediterranean Diet, Prediabetes, and Type
2 Diabetes: A Narrative Review of the Evidence. Oxid. Med. Cell. Longev. 2017, 2017, 6723931. [CrossRef] [PubMed]
124. Hoang, T.N.V.; Ngan, T.; Dat, N.; Ly, L. An in silico study on antidiabetic activity of bioactive compounds in Euphorbia thymifolia
Linn. SpringerPlus 2016, 5, 1359. [CrossRef]
125. El-Beshbishy, H.A.; Bahashwan, S.A. Hypoglycemic effect of basil (Ocimum basilicum) aqueous extractis mediated through
inhibition of α-glucosidase and α-amylase activities: An in vitro study. Toxicol. Ind. Health 2012, 28, 42–50. [CrossRef] [PubMed]
126. Nazir, N.; Zahoor, M.; Ullah, R.; Ezzeldin, E.; Mostafa, G.A.E. Curative Effect of Catechin Isolated from Elaeagnus Umbellata
Thunb. Berries for Diabetes and Related Complications in Streptozotocin-Induced Diabetic Rats Model. Molecules 2020, 26, 137.
[CrossRef] [PubMed]
127. Maria, D.G.-P.; Eisuke, K.; Jun, K. α-Amylase inhibitors from an Indonesian medicinal herb, Phyllanthus urinaria. J. Sci. Food
Agric. 2012, 92, 606–609.
128. Vinholes, J.; Vizzotto, M. Synergisms in Alpha-glucosidase Inhibition and Antioxidant Activity of Camellia sinensis L. Kuntze
and Eugenia uniflora L. Ethanolic Extracts. Pharmacogn. Res. 2017, 9, 101–107. [CrossRef]
129. Mai, T.T.; Chuyen, N.V. Anti-hyperglycemic activity of an aqueous extract from flower buds of Cleistocalyx operculatus (Roxb.)
Merr and Perry. Biosci. Biotechnol. Biochem. 2007, 71, 69–76. [CrossRef]
130. Mai, T.T.; Thu, N.N.; Tien, P.G.; Van Chuyen, N. Alpha-glucosidase inhibitory and antioxidant activities of Vietnamese edible
plants and their relationships with polyphenol contents. J. Nutr. Sci. Vitaminol. 2007, 53, 267–276. [CrossRef] [PubMed]
131. Lee, S.Y.; Mediani, A.; Nur, A.A.H.; Azliana, A.B.S.; Abas, F. Antioxidant and α-glucosidase inhibitory activities of the leaf and
stem of selected traditional medicinal plants. Int. Food Res. J. 2014, 21, 379–386.
132. Thamilvaani, M.; Ling, L.T.; David, A.; Cheng, H.M.; Theanmalar, M.; Uma, D.P. Antioxidant and antiglycemic potential of
Peltophorum pterocarpum plant parts. Food Chem. 2011, 129, 1355–1361.
133. Olennikov, D.N.; Chirikova, N.K.; Kashchenko, N.I.; Nikolaev, V.M.; Kim, S.W.; Vennos, C. Bioactive Phenolics of the Genus
Artemisia (Asteraceae): HPLC-DAD-ESI-TQ-MS/MS Profile of the Siberian Species and Their Inhibitory Potential against
alpha-Amylase and alpha-Glucosidase. Front. Pharmacol. 2018, 9, 756. [CrossRef]
134. Hafiz, A.K. Pharmacognostic, physicochemical, phytochemical and pharmacological studies on Careya arborea Roxb.; A review. J.
Phytopharm. 2016, 5, 27–34.
135. Supkamonseni, N.; Thinkratok, A.; Meksuriyen, D.; Srisawat, R. Hypolipidemic and hypoglycemic effects of Centella asiatica (L.)
extract in vitro and in vivo. Indian J. Exp. Biol. 2014, 52, 965–971. [PubMed]
136. Tabarak, M.; Devendra, K.P.; Priyanka, R.; Annie, O. Evaluation of Phytochemicals, Antioxidant, Antibacterial and Antidiabetic
Potential of Alpinia galanga and Eryngium foetidum Plants of Manipur (India). Pharmacogn. J. 2016, 8, 459–464.
137. Mohamed, S.; Hanen, L.; Jannet, K.; Ali, K.; Mohamed, D. Inhibition of pancreatic lipase and amylase by extracts of different
spices and plants. Int. J. Food Sci. Nutr. 2017, 68, 313–320.
138. Brindis, F.; Rodriguez, R.; Bye, R.; Gonzalez-Andrade, M.; Mata, R. (Z)-3-butylidenephthalide from Ligusticum porteri, an
alpha-glucosidase inhibitor. J. Nat. Prod. 2011, 74, 314–320. [CrossRef]
139. Manaharan, T.; Palanisamy, U.D.; Ming, C.H. Tropical plant extracts as potential antihyperglycemic agents. Molecules 2012, 17,
5915–5923. [CrossRef]
140. Wang, Z.; Li, X.; Chen, M.; Liu, F.; Han, C.; Kong, L.; Luo, J. A strategy for screening of alpha-glucosidase inhibitors from Morus
alba root bark based on the ligand fishing combined with high-performance liquid chromatography mass spectrometer and
molecular docking. Talanta 2018, 180, 337–345. [CrossRef]
141. Shizuo, T. Antioxidative Effects of Polyphenols in Leaves of Houttuynia cordata on Protein Fragmentation by Copper–Hydrogen
Peroxide In Vitro. J. Med. Food 2005, 8, 266–268. [CrossRef]
142. Muhammad, T.; Nor, H.I.; Syahrul, I.; Abdul, W.; Fazal, R.; Muhammad, A.; Ashfaq, U.R. Novel quinoline derivatives as
potentin vitro α-glucosidase inhibitors: In silico studies and SAR predictions. Med. Chem. Commun. 2015, 6, 1826–1836. [CrossRef]
143. Mata, R.; Cristians, S.; Escandon-Rivera, S.; Juarez-Reyes, K.; Rivero-Cruz, I. Mexican antidiabetic herbs: Valuable sources of
inhibitors of alpha-glucosidases. J. Nat. Prod. 2013, 76, 468–483. [CrossRef]
144. Sharma, C.; Rokana, N.; Chandra, M.; Singh, B.P.; Gulhane, R.D.; Gill, J.P.S.; Ray, P.; Puniya, A.K.; Panwar, H. Antimicrobial
Resistance: Its Surveillance, Impact, and Alternative Management Strategies in Dairy Animals. Front Vet. Sci. 2017, 4, 237.
[CrossRef]
145. Tae, J.H.; Jin, H.L.; Myoung-Hee, L.; Byeong, W.L.; Hyun, S.K.; Chang-Hwan, P.; Kang-Bo, S.; Hyun-Tae, K.; In-Youl, B.; Dae,
S.J. Isolation and identification of phenolic compounds from the seeds of Perilla frutescens (L.) and their inhibitory activities
againsta-glucosidaseand aldose reductase. Food Chem. 2012, 135, 1397–1403.
146. Koga, K.; Shibata, H.; Yoshino, K.; Nomoto, K. Effects of 50% Ethanol Extract from Rosemary (Rosmarinus officinalis) on α-
Glucosidase Inhibitory Activity and the Elevation of Plasma Glucose Level in Rats, and Its Active Compound. J. Food Sci. 2006,
71, S507–S512. [CrossRef]
Biomolecules 2021, 11, 1877 26 of 31

147. Gholamhoseinian, N.A.; Fallah, H.; Sharififar, F. Anti-hyperglycemic Activity of Four Plants Extracts Effective against Alpha
Glucosidase in Normal and Diabetic Rats. J. Kerman Univ. Med Sci. 2015, 15, 35–44.
148. Kamrani, Y.Y.; Amanlou, M.; Yazdanyar, A.; Adli, A.; Moghaddam, A.; Ebrahimi, S.N. Potential anti-diabetic and anti-oxidant
activity of essential oil of Zataria multiflora leaves. Planta Med. 2008, 74, PA172. [CrossRef]
149. Xiaoling, Z.; Junsheng, L.; Yi, Z.; Huading, Z.; Ying, G.; Shuyun, S. Separation and purification of α-glucosidase inhibitors from
Polygonatum odoratum by stepwise high-speed counter-current chromatography combined with Sephadex LH-20 chromatogra-
phy target-guided by ultrafiltration–HPLC screening. J. Chromatogr. B 2015, 985, 149–154.
150. Sarah, Z.N.; Meiliza, E.; Lia, A.; Rani, S.; Berna, E. Pharmacognostical and Phytochemical Evaluation Leaves Extract of Garcinia
daedalanthera Pierre. J. Young Pharm. 2017, 9, S60–S64. [CrossRef]
151. Boskabady, M.H.; Shafei, M.N.; Saberi, Z.; Amini, S. Pharmacological effects of rosa damascena. Iran J. Basic Med. Sci. 2011, 14,
295–307. [PubMed]
152. Kasabri, V.; Afifi, F.U.; Hamdan, I. In vitro and in vivo acute antihyperglycemic effects of five selected indigenous plants from
Jordan used in traditional medicine. J. Ethnopharmacol. 2011, 133, 888–896. [CrossRef]
153. Aboozar, M.F.; Shideh, M.K.; Saeed, M. Vaccinium arctostaphylos, a common herbal medicine in Iran: Molecular andbiochemical
study of its antidiabetic effects on alloxan-diabetic Wistar rats. J. Ethnopharmacol. 2011, 133, 67–74.
154. Khan, M.; Ali, M.; Ali, A.; Mir, S.R. Hypoglycemic and hypolipidemic activities of Arabic and Indian origin Salvadora persica
root extract on diabetic rats with histopathology of their pancreas. Int. J. Health Sci. 2014, 8, 45–56. [CrossRef] [PubMed]
155. Srividya, A.R.; Dhanabal, S.P.; Kumar, M.N.S.; Bavadia, P.k.H. Antioxidant and Antidiabetic Activity of Alpinia Galanga. Int. J.
Pharmacogn. Phytochem. Res. 2010, 3, 6–12.
156. Liu, X.C.; Lai, D.; Liu, Q.Z.; Zhou, L.; Liu, Q.; Liu, Z.L. Bioactivities of a New Pyrrolidine Alkaloid from the Root Barks of Orixa
japonica. Molecules 2016, 21, 1665. [CrossRef]
157. Mauldina, M.G.; Sauriasari, R.; Elya, B. α-Glucosidase Inhibitory Activity from Ethyl Acetate Extract of Antidesma bunius (L.)
Spreng Stem Bark Containing Triterpenoids. Pharmacogn. Mag. 2017, 13, 590–594. [CrossRef]
158. Ali, H.; Houghton, P.J.; Soumyanath, A. alpha-Amylase inhibitory activity of some Malaysian plants used to treat diabetes; with
particular reference to Phyllanthus amarus. J. Ethnopharmacol. 2006, 107, 449–455. [CrossRef]
159. Lawag, I.L.; Aguinaldo, A.M.; Naheed, S.; Mosihuzzaman, M. Alpha-Glucosidase inhibitory activity of selected Philippine plants.
J. Ethnopharmacol. 2012, 144, 217–219. [CrossRef]
160. Zhang, B.; Wu, J.T.; Zheng, C.J.; Zhou, X.; Yu, Z.X.; Li, W.S.; Chen, G.Y.; Zhu, G.Y. Bioactive cyclohexene derivatives from a
mangrove-derived fungus Cladosporium sp. JJM22. Fitoterapia 2021, 149, 104823. [CrossRef] [PubMed]
161. Rege, A.A.; Chowdhary, A.S. Evaluation of alpha-Amylase and alpha-Glucosidase Inhibitory Activities of Rhizophora Mucronata.
IJPSR 2014, 5, 2261–2265.
162. Chai, T.-T.C.; Loo-Yew, Y.; Nor, I.; Mohd, I.; Hean-Chooi, O.; Fazilah, A.M.; Fai-Chu, W. Evaluation of Glucosidase Inhibitory and
Cytotoxic Potential of Five Selected Edible and Medicinal Ferns. Trop. J. Pharm. Res. 2015, 14, 449–454. [CrossRef]
163. Escandon-Rivera, S.; Gonzalez-Andrade, M.; Bye, R.; Linares, E.; Navarrete, A.; Mata, R. alpha-glucosidase inhibitors from
Brickellia cavanillesii. J. Nat. Prod. 2012, 75, 968–974. [CrossRef]
164. Yan, X.; Jian, Z.; Xiang, L.; Jian-wei, C. Antihyperglycemic Effect of Various Fractions from Residues of Blumea balsamifera. CHM
2014, 6, 136–139.
165. Gladis, R.; Malar, C.; Chellaram, C. Alpha amylase and Alpha glucosidase inhibitory effects of aqueous stem extract of Salacia
oblonga and its GC-MS analysis. Braz. J. Pharm. Sci. 2018, 54, 1–10. [CrossRef]
166. Lu, Q.Y.; Zhang, L.; Moro, A.; Chen, M.C.; Harris, D.M.; Eibl, G.; Go, V.L. Detection of baicalin metabolites baicalein and
oroxylin-a in mouse pancreas and pancreatic xenografts. Pancreas 2012, 41, 571–576. [CrossRef] [PubMed]
167. Yi, S.; Yuyu, Z.; Tingting, Z.; Hua, Z.; Xiaosong, H.; Quanhong, L. A preliminary study of monosaccharide composition and
a-glucosidase inhibitory effect of polysaccharides from pumpkin (Cucurbita moschata) fruit. Int. J. Food Sci. Technol. 2011, 47,
357–361. [CrossRef]
168. Umar, L.; Khozirah, S.; Intan, S.I.; Alfi, K.; Faridah, A. Antioxidant and α-Glucosidase Inhibitory Activities of Isolated Compounds
from Ipomoea aquatica Rec. Nat. Prod. 2016, 10, 701–707.
169. Zhang, L.; Tu, Z.C.; Yuan, T.; Wang, H.; Xie, X.; Fu, Z.F. Antioxidants and alpha-glucosidase inhibitors from Ipomoea batatas
leaves identified by bioassay-guided approach and structure-activity relationships. Food Chem. 2016, 208, 61–67. [CrossRef]
170. Marta, K.-S.; Agnieszka, S.; Halina, E. Chemical composition, traditional and professional use in medicine, application in
environmental protection, position in food and cosmetics industries, and biotechnological studies of Nasturtium officinale
(watercress). Fitoterapia 2018, 129, 283–292.
171. Ping, J.; Jia, X.; Fei, W.; Mary, H.G.; Mary, A.; Rui, X. α-Amylase and α-Glucosidase Inhibitory Activities of Phenolic Extracts from
Eucalyptus grandis × E. urophylla Bark. J. Chem. 2017, 2017, 8516964. [CrossRef]
172. Thamilvaani, M.; Cheng, H.M.; Uma, D.P. Syzygium aqueum leaf extract and its bioactive compounds enhances pre-adipocyte
differentiation and 2-NBDG uptake in 3T3-L1 cells. Food Chem. 2013, 136, 354–363.
173. Shinde, J.; Taldone, T.; Barletta, M.; Kunaparaju, N.; Hu, B.; Kumar, S.; Placido, J.; William Zito, S. α-Glucosidase inhibitory activity
of Syzygium cumini (Linn.) Skeels seed kenel in vitro and in Goto-Kakizaki (GK) rats. Carbohydr. Res. 2008, 343, 1278–1281.
[CrossRef]
Biomolecules 2021, 11, 1877 27 of 31

174. Ryu, H.W.; Cho, J.K.; Curtis-Long, M.J.; Yuk, H.J.; Kim, Y.S.; Jung, S.; Kim, Y.S.; Lee, B.W.; Park, K.H. alpha-Glucosidase
inhibition and antihyperglycemic activity of prenylated xanthones from Garcinia mangostana. Phytochemistry 2011, 72, 2148–2154.
[CrossRef] [PubMed]
175. Uma, D.P.; Lai, T.L.; Thamilvaani, M.; David, A. Rapid isolation of geraniin fromNephelium lappaceumrind wasteand its
anti-hyperglycemic activity. Food Chem. 2011, 127, 21–27.
176. Hoda, K. Isolation and Characterization of Natural α-Glucosidase Inhibitors from Antioxidant Rich Red Wine Grapes (Vitis vinifera);
Wayne State University: Detroit, MI, USA, 2014.
177. Imad, I.H.; Fatma, U.A. Screening of Jordanian Flora for α-Amylase Inhibitory Acivity. Pharm. Biol. 2008, 46, 746–750. [CrossRef]
178. Manuela, M.; Monica, S.; Antonio, F.; Cinzia, S.; Luca, C.; Fabiana, A.; Francesca, B.; Ferruccio, P. Phytochemical profile and
α-glucosidase inhibitory activity of Sardinian Hypericum scruglii and Hypericum hircinum. Fitoterapia 2017, 120, 184–193.
179. Mrabti, H.N.; Jaradat, N.; Fichtali, I.; Ouedrhiri, W.; Jodeh, S.; Ayesh, S.; Cherrah, Y.; Faouzi, M.E.A. Separation, Identification,
and Antidiabetic Activity of Catechin Isolated from Arbutus unedo L. Plant Roots. Plants 2018, 7, 31. [CrossRef]
180. Gong, Z.; Peng, Y.; Qiu, J.; Cao, A.; Wang, G.; Peng, Z. Synthesis, In Vitro alpha-Glucosidase Inhibitory Activity and Molecular
Docking Studies of Novel Benzothiazole-Triazole Derivatives. Molecules 2017, 22, 1555. [CrossRef]
181. Wang, R.Y.; Su, P.J.; Li, B.; Zhan, X.Q.; Qi, F.M.; Lv, C.W.; Hu, F.D.; Gao, K.; Zhang, Z.X.; Fei, D.Q. Two new aromatic derivatives
from Codonopsis pilosula and their α-glucosidase inhibitory activities. Nat. Prod. Res. 2021, 16, 1–8. [CrossRef] [PubMed]
182. He, K.; Li, X.; Chen, X.; Ye, X.; Huang, J.; Jin, Y.; Li, P.; Deng, Y.; Jin, Q.; Shi, Q.; et al. Evaluation of antidiabetic potential of
selected traditional Chinese medicines in STZ-induced diabetic mice. J. Ethnopharmacol. 2011, 137, 1135–1142. [CrossRef]
183. Numonov, S.; Edirs, S.; Bobakulov, K.; Qureshi, M.N.; Bozorov, K.; Sharopov, F.; Setzer, W.N.; Zhao, H.; Habasi, M.; Sharofova, M.;
et al. Evaluation of the antidiabetic activity and chemical composition of Geranium collinum root extracts-computational and
experimental investigations. Molecules 2017, 22, 983. [CrossRef]
184. Amin, S.; Ullah, B.; Ali, M.; Rauf, A.; Khan, H.; Uriarte, E.; Sobarzo-Sanchez, E. Potent in Vitro alpha-Glucosidase Inhibition of
Secondary Metabolites Derived from Dryopteris cycadina. Molecules 2019, 24, 427. [CrossRef] [PubMed]
185. Rahmatullah, M.; Azam, N.K.; Khatun, Z.; Seraj, S.; Islam, F.; Rahman, A.; Jahan, S.; Aziz, S. Medicinal plants used for treatment
of diabetes by the marakh sect of the Garo tribe living in Mymensingh district, Bangladesh. Afr. J. Tradit. Complement. Altern. Med.
2012, 9, 380–385. [CrossRef] [PubMed]
186. Berna, E.; Rosita, H.; Rani, S.; Azizahwati, U.S.H.; Idam, T.P.; Yunita, I.P. Antidiabetic Activity and Phytochemical Screening of
Extracts from Indonesian Plants by Inhibition of Alpha Amylase, Alpha Glucosidase and Dipeptidyl Peptidase IV. Pak. J. Biol. Sci.
2015, 18, 279–284.
187. Maha, M.E.D.; Haytham, A.A. Molecular Investigation of anti-diabetic effect of balanites aegyptiaca fruits in streptozotocin-
induced diabetic rats. Slov. Vet. Res. 2018, 55, 137–145. [CrossRef]
188. Sales, P.M.; Souza, P.M.; Simeoni, L.A.; Silveira, D. alpha-Amylase inhibitors: A review of raw material and isolated compounds
from plant source. J. Pharm. Pharm. Sci. 2012, 15, 141–183. [CrossRef] [PubMed]
189. Ingrid, F.; Matthias, F.M.F. Traditionally used plants in diabetes therapy—Phytotherapeutics as inhibitors of α-amylase activity. J.
Pharmacogn. 2006, 16, 1–5.
190. Bhutkar, M.A.; Bhise, S.B. In vitro assay of alpha amylase inhibitory activity of some indigenous plants. Int. J. Chem. Sci. 2012, 10,
457–462.
191. Muthukumar, A.; Mohan, S.; Sundharaganapathy, R.; Nagaraja, G.P. Anti-diabetic activity of Cassia auriculata flowers in α-
amylase inhibition and glucose uptake by isolated rat hemi-diaphragm. Sch. Res. Libr. Pharm. Lett. 2016, 8, 101–105.
192. Omonike, O.O.; Latifat, O.A.; Oyindamola, O.A.; Edith, O.A. Alpha-amylase Inhibition and Brine Shrimp Lethality Activities of
Nine Medicinal Plant Extracts from South-West Nigerian Ethnomedicine. J. Herbs Spices Med. Plants 2016, 22, 319–326. [CrossRef]
193. Patrick, P.M.; Kalidas, S. Inhibitory effects of rosmarinic acid extracts on porcine pancreatic amylase in vitro. Asia Pac. J. Clin.
Nutr. 2004, 13, 101–106.
194. Najafian, M. A review ofα-amylase inhibitors on weight loss and glycemiccontrol in pathological state such as obesity and
diabetes. Comp. Clin. Phothol. 2016, 25, 1253–1264.
195. Usune, E.; Ana, L.d.l.G.; Javier, C.; Alfredo, J.M.; Fermin, I.M. Antidiabetic effects of natural plant extracts via inhibition of
carbohydrate hydrolysis enzymes with emphasis on pancreatic alpha amylase. Expert Opin. Ther. Targets 2012, 16, 269–297.
196. Tran, T.H.H.; Nguyen, H.D.; Nguyen, T.D. α-Amylase and α-Glucosidase Inhibitory Saponins from Polyscias fruticosa Leaves. J.
Chem. 2016, 2016, 2082946. [CrossRef]
197. Ali, K.A.; Imad, H.H. In Vitro Anti-diabetic Properties of Methanolic Extract of Thymus vulgaris Using α-glucosidase and
α-amylase Inhibition Assay and Determination of its Bioactive Chemical Compounds. Indian J. Public Health Res. Dev. 2018, 9,
388–392. [CrossRef]
198. Mohammed, A.I.; Neil, A.K.; Shahidul, I. Antioxidative activity and inhibition of key enzymes linked to type-2 diabetes
(a-glucosidase and a-amylase) by Khaya senegalensis. Acta Pharm. 2014, 64, 311–324. [CrossRef]
199. Rituparna, C.; Bhavtaran, S.; Prakrith, V.; Lalthanzama, V.; Kavitha, T. Screening of nine herbal plants for in vitroa-amylase
inhibition. Asian J. Pharm. Clin. Res. 2014, 7, 84–89.
200. Arpagaus, S.; Braendle, R. The significance of α-amylase under anoxia stress intolerant rhizomes (Acorus calamus L.) and
non-toleranttubers (Solanum tuberosum L., var. Désiree). J. Exp. Bot. 2000, 51, 1475–1477. [PubMed]
Biomolecules 2021, 11, 1877 28 of 31

201. Tran, H.H.; Nguyen, M.C.; Le, H.T.; Nguyen, T.L.; Pham, T.B.; Chau, V.M.; Nguyen, H.N.; Nguyen, T.D. Inhibitors of alpha-
glucosidase and alpha-amylase from Cyperus rotundus. Pharm. Biol. 2014, 52, 74–77. [CrossRef]
202. Dhiraj, A.J.; Dishantsingh, R.; Komal, A.J.; Rohini, K.; Anuja, S.K.; Vaishali, S.S.; Jayesh, B.; Kaushik, R.B.; Sougata, G. Gnidia
glauca- and Plumbago zeylanica-Mediated Synthesis of Novel Copper Nanoparticles as Promising Antidiabetic Agents. Adv.
Pharmacol. Sci. 2019, 2019, 9080279. [CrossRef]
203. Anam, K.; Widharna, R.M.; Kusrini, D. α-Glucosidase inhibitor activity of Terminalia species. Int. J. Pharm. 2009, 5, 277–280.
[CrossRef]
204. Souza, P.M.; Sales, P.M.; Simeoni, L.A.; Silva, E.C.; Silveira, D.; Magalhaes Pde, O. Inhibitory activity of alpha-amylase and
alpha-glucosidase by plant extracts from the Brazilian cerrado. Planta Med. 2012, 78, 393–399. [CrossRef]
205. El-Manawaty, M.; Gohar, L. In vitro alpha-glucosidase inhibitory activity of egyptian plant extracts as an indication for their
antidiabetic activity. Asian J. Pharm. Clin. Res. 2018, 11, 360–367. [CrossRef]
206. Paloma, M.d.S.; Paula, M.S.; Mariana, D.; Inês, S.R.; Luiz, A.S.; Yris, M.F.-B.; Pérolade, O.M.; Dâmaris, S. Pouteria torta epicarp as
a useful source of α-amylase inhibitor in the control of type 2 diabetes. Food Chem. Toxicol. 2017, 109, 962–969. [CrossRef]
207. De Gouveia, N.M.; De Albuquerque, C.L.; Espindola, L.S.; Espindola, F.S. Pouteria ramiflora extract inhibits salivary amylolytic
activity and decreases glycemic level in mice. An. Acad. Bras. Cienc. 2013, 85, 1141–1148. [CrossRef] [PubMed]
208. Nauck, M.A.; El-Ouaghlidi, A. The therapeutic actions of DPP-IV inhibition are not mediated by glucagon-like peptide-1.
Diabetologia 2005, 48, 608–611. [CrossRef]
209. Erin, E.M.; Daniel, J.D. Pharmacology, Physiology, and Mechanisms of Action of Dipeptidyl Peptidase-4 Inhibitors. Endocr. Rev.
2014, 35, 992–1019.
210. Arulmozhiraja, S.; Matsuo, N.; Ishitsubo, E.; Okazaki, S.; Shimano, H.; Tokiwa, H. Comparative Binding Analysis of Dipeptidyl
Peptidase IV (DPP-4) with Antidiabetic Drugs—An Ab Initio Fragment Molecular Orbital Study. PLoS ONE 2016, 11, e0166275.
[CrossRef]
211. Jixin, Z.; Xiaoquan, R.; Sanjay, R. An emerging role of dipeptidyl peptidase 4 (DPP4) beyond glucose control: Potential implications
in cardiovascular disease. Atherosclerosis 2013, 226, 305–314.
212. Nauck, M.A. Glucagon-like peptide 1 (GLP-1): A potent gut hormone with a possible therapeutic perspective. Acta Diabetol. 1998,
35, 117–129. [CrossRef]
213. Nauck, M.A.; Niedreichholz, U.; Ettler, U. Glucagon-like peptide 1 inhibition of gastric emptying outweight its insulinotropic
effects in health human. Am. J. Physiol. 1997, 273, E981–E988. [CrossRef]
214. D’Alessio, D. Is GLP-1 a hormone: Whether and When? J. Diabetes Investig. 2016, 7 (Suppl. 1), 50–55. [CrossRef]
215. Chen, Q.; Pinon, D.I.; Miller, L.J.; Dong, M. Molecular basis of glucagon-like peptide 1 docking to its intact receptor studied with
carboxyl-terminal photolabile probes. J. Biol. Chem. 2009, 284, 34135–34144. [CrossRef]
216. Weis, W.I.; Kobilka, B.K. Structural insights into G-protein-coupled receptor activation. Curr. Opin. Struct. Biol. 2008, 18, 734–740.
[CrossRef] [PubMed]
217. Cherezov, V.; Rosenbaum, D.M.; Hanson, M.A.; Rasmussen, S.G.; Thian, F.S.; Kobilka, T.S.; Choi, H.J.; Kuhn, P.; Weis, W.I.;
Kobilka, B.K.; et al. High-resolution crystal structure of an engineered human beta2-adrenergic G protein-coupled receptor.
Science 2007, 318, 1258–1265. [CrossRef]
218. Lawrence, M.C. Understanding insulin and its receptor from their three-dimensional structures. Mol. Metab. 2021, 52, 101255.
[CrossRef] [PubMed]
219. Posner, B.I. Insulin Signalling: The Inside Story. Can. J. Diabetes 2017, 41, 108–113. [CrossRef] [PubMed]
220. Dridi, L.; Seyrantepe, V.; Fougerat, A.; Pan, X.; Bonneil, E.; Thibault, P.; Moreau, A.; Mitchell, G.A.; Heveker, N.; Cairo, C.W.; et al.
Positive regulation of insulin signaling by neuraminidase 1. Diabetes 2013, 62, 2338–2346. [CrossRef] [PubMed]
221. Leconte, I.; Auzan, C.; Debant, A.; Rossi, B.; Clauser, E. N-linked oligosaccharide chains of the insulin receptor beta subunit are
essential for transmembrane signaling. J. Biol. Chem. 1992, 267, 17415–17423. [CrossRef]
222. Skrgatic, L.; Baldani, D.P.; Gersak, K.; Cerne, J.Z.; Ferk, P.; Coric, M. Genetic polymorphisms of INS, INSR and IRS-1 genes are not
associated with polycystic ovary syndrome in Croatian women. Coll. Antropol. 2013, 37, 141–146.
223. Yousef, A.A.; Behiry, E.G.; Abd, A.W.M.; Hussien, A.M.; Abdelmoneam, A.A.; Imam, M.H.; Hikal, D.M. IRS-1 genetic polymor-
phism (r.2963G>A) in type 2 diabetes mellitus patients associated with insulin resistance. Appli. Clin. Genet. 2018, 11, 99–106.
[CrossRef]
224. Shaw, L.M. The insulin receptor substrate (IRS) proteins: At the intersection of metabolism and cancer. Cell Cycle 2011, 10,
1750–1756. [CrossRef]
225. Kubota, N.; Tobe, K.; Terauchi, Y.; Eto, K.; Yamauchi, T.; Suzuki, R.; Tsubamoto, Y.; Komeda, K.; Nakano, R.; Miki, H.; et al.
Disruption of insulin receptor substrate 2 causes type 2 diabetes because of liver insulin resistance and lack of compensatory
beta-cell hyperplasia. Diabetes 2000, 49, 1880–1889. [CrossRef]
226. Deborah, P.L.; Morris, F.W.; Derek, P.B. IRS proteins and diabetic complications. Diabetologia 2016, 59, 2280–2291. [CrossRef]
227. Stuart, C.A.; Howell, M.E.; Cartwright, B.M.; McCurry, M.P.; Lee, M.L.; Ramsey, M.W.; Stone, M.H. Insulin resistance and muscle
insulin receptor substrate-1 serine hyperphosphorylation. Physiol. Rep. 2014, 2, e12236. [CrossRef]
228. Ekins, S.; Mestres, J.; Testa, B. In silico pharmacology for drug discovery: Applications to targets and beyond. Br. J. Pharmacol.
2007, 152, 21–37. [CrossRef] [PubMed]
Biomolecules 2021, 11, 1877 29 of 31

229. Colquitt, R.B.; Colquhoun, D.A.; Thiele, R.H. In silico modelling of physiologic systems. Best Pract. Res. Clin. Anaesthesiol. 2011,
25, 499–510. [CrossRef]
230. Rasouli, H.; Hosseini-Ghazvini, S.M.; Adibi, H.; Khodarahmi, R. Differential alpha-amylase/alpha-glucosidase inhibitory
activities of plant-derived phenolic compounds: A virtual screening perspective for the treatment of obesity and diabetes. Food
Funct. 2017, 8, 1942–1954. [CrossRef] [PubMed]
231. Ng, K.C.S.; Ngabonziza, J.C.S.; Lempens, P.; de Jong, B.C.; van Leth, F.; Meehan, C.J. Bridging the TB data gap: In silico extraction
of rifampicin-resistant tuberculosis diagnostic test results from whole genome sequence data. PeerJ 2019, 7, e7564. [CrossRef]
232. Kabir, M.T.; Tabassum, N.; Uddin, M.S.; Aziz, F.; Behl, T.; Mathew, B.; Rahman, M.H.; Akter, R.; Rauf, A.; Aleya, L. Therapeutic
potential of polyphenols in the management of diabetic neuropathy. Evid.-Based Complement. Alternat. Med. 2021, 9940169.
[CrossRef]
233. Ibrahim, M.A.; Bester, M.J.; Neitz, A.W.; Gaspar, A.R.M. Rational in silico design of novel alpha-glucosidase inhibitory peptides
and in vitro evaluation of promising candidates. Biomed. Pharmacother. 2018, 107, 234–242. [CrossRef] [PubMed]
234. Lee, Y.; Kim, S.; Kim, J.Y.; Arooj, M.; Kim, S.; Hwang, S.; Kim, B.W.; Park, K.H.; Lee, K.W. Binding mode analyses and
pharmacophore model development for stilbene derivatives as a novel and competitive class of α-glucosidase inhibitors. PLoS
ONE 2014, 9, e85827. [CrossRef]
235. Nakaishi, Y.; Bando, M.; Shimizu, H.; Watanabe, K.; Goto, F.; Tsuge, H.; Kondo, K.; Komatsu, M. Structural analysis of human
glutamine:fructose-6-phosphate amidotransferase, a key regulator in type 2 diabetes. FEBS Lett. 2009, 583, 163–167. [CrossRef]
236. Ren, L.; Qin, X.; Cao, X.; Wang, L.; Bai, F.; Bai, G.; Shen, Y. Structural insight into substrate specificity of human intestinal
maltase-glucoamylase. Protein Cell 2011, 2, 827–836. [CrossRef]
237. Yamamoto, K.; Miyake, H.; Kusunoki, M.; Osaki, S. Crystal structures of isomaltase from Saccharomyces cerevisiae and in
complex with its competitive inhibitor maltose. FEBS J. 2010, 277, 4205–4214. [CrossRef]
238. Madej, T.; Lanczycki, C.J.; Zhang, D.; Thiessen, P.A.; Geer, R.C.; Marchler-Bauer, A.; Bryant, S.H. MMDB and VAST+: Tracking
structural similarities between macromolecular complexes. Nucleic Acids Res. 2014, 42, D297–D303. [CrossRef]
239. Jeng, W.Y.; Wang, N.C.; Lin, M.H.; Lin, C.T.; Liaw, Y.C.; Chang, W.J.; Liu, C.I.; Liang, P.H.; Wang, A.H. Structural and functional
analysis of three beta-glucosidases from bacterium Clostridium cellulovorans, fungus Trichoderma reesei and termite Neotermes
koshunensis. J. Struct. Biol. 2011, 173, 46–56. [CrossRef] [PubMed]
240. Kappelhoff, J.C.; Liu, S.Y.; Dugdale, M.L.; Dymianiw, D.L.; Linton, L.R.; Huber, R.E. Practical considerations when using
temperature to obtain rate constants and activation thermodynamics of enzymes with two catalytic steps: Native and N460T-
beta-galactosidase (E. coli) as examples. Protein J. 2009, 28, 96–103. [CrossRef] [PubMed]
241. Sundar, P.; Madasamy, P. In vitro and in vivo α-amylase and αglucosidase inhibiting activities of the protein extracts from two
varieties of bitter gourd (Momordica charantia L.). BMC Complement. Altern. Med. 2016, 16, 185. [CrossRef]
242. Ding, H.; Wu, X.; Pan, J.; Hu, X.; Gong, D.; Zhang, G. New Insights into the Inhibition Mechanism of Betulinic Acid on
α-Glucosidase. J. Agric. Food Chem. 2018, 66, 7065–7075. [CrossRef]
243. Zeng, L.; Ding, H.; Hu, X.; Zhang, G.; Gong, D. Galangin inhibits α-glucosidase activity and formation of non-enzymatic glycation
products. Food Chem. 2019, 271, 70–79. [CrossRef] [PubMed]
244. Eric, M.-V.; Luca, P.; Noé, S.; Annachiara, T.; Ola, E.; Hongming, C.; Giulio, R. On the Integration of In Silico Drug Design Methods
for Drug Repurposing. Front. Pharmacol. 2017, 8, 298. [CrossRef]
245. Yuhei, N.; Hideaki, H. Drug Repositioning: Current Advances and Future Perspectives. Front. Pharmacol. 2018, 9, 1068. [CrossRef]
246. Weng, L.; Chen, T.H.; Zheng, Q.; Weng, W.H.; Huang, L.; Lai, D.; Fu, Y.S.; Weng, C.F. Syringaldehyde promoting intestinal motility
with suppressing alpha-amylase hinders starch digestion in diabetic mice. Biomed. Pharm. 2021, 141, 111865. [CrossRef] [PubMed]
247. Riyaphan, J.; Jhong, C.H.; Lin, S.R.; Chang, C.H.; Tsai, M.J.; Lee, D.N.; Sung, P.J.; Leong, M.K.; Weng, C.F. Hypoglycemic Efficacy
of Docking Selected Natural Compounds against alpha-Glucosidase and alpha-Amylase. Molecules 2018, 23, 2260. [CrossRef]
248. Le, A.V.; Phan, T.C.Q.; Nguyen, T.H. In silico Drug Design: Prospective for Drug Lead Discovery. Intern. J. Eng. Sci. Invent. 2015,
4, 2319–6734.
249. Hsin, J.; Arkhipov, A.; Yin, Y.; Stone, J.E.; Schulten, K. Using VMD: An introductory tutorial. Curr. Protoc. Bioinform. 2008, 24, 5–7.
[CrossRef]
250. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 33–38. [CrossRef]
251. Pham, T.A.; Jain, A.N. Customizing scoring functions for docking. J. Comput.-Aided Mol. Des. 2008, 22, 269–286. [CrossRef]
252. Sheng-You, H.; Grinter, S.Z.; Xiaogin, Z. Scoring functions and their evaluation methods for protein-ligand docking: Recent
advances and future directions. Phys. Chem. Chem. Phys. 2010, 12, 12899–12908. [CrossRef]
253. Kuntz, I.D.; Blaney, J.M.; Oatley, S.J.; Langridge, R.; Ferrin, T.E. A geometric approach to macromolecule-ligand interactions. J.
Mol. Biol. 1982, 161, 269–288. [CrossRef]
254. Ninon, G.E.R.E.; Jelili, A.B.; Tesfaye, T.W.; Jeanine, L.M.; Christopher, N.C.; Ahmed, A.H.; Emmanuel, I.I. Alpha-Glucosidase
and Alpha-Amylase Inhibitory Activities of Novel Abietane Diterpenes from Salvia africana-lutea. Antioxidants 2019, 8, 421.
[CrossRef]
255. Arief, M.M.H.; Hussein, A.A.F.; Mohammed, A.; ElMwafy, H.M. Chemical and Bioactivity Studies on Salvia Africana-Lutea:
Cytotoxicity and Apoptosis Induction by Abietane Diterpenes Isolated from Salvia Africana-Lutea. J. Basic Environ. Sci. 2018, 5,
72–79.
Biomolecules 2021, 11, 1877 30 of 31

256. Abuelizz, H.A.; Anouar, E.H.; Ahmad, R.; Azman, N.; Marzouk, M.; Al-Salahi, R. Triazoloquinazolines as a new class of potent
alpha-glucosidase inhibitors: In vitro evaluation and docking study. PLoS ONE 2019, 14, e0220379. [CrossRef] [PubMed]
257. Hua, F.; Zhou, P.; Wu, H.Y.; Chu, G.X.; Xie, Z.W.; Bao, G.H. Inhibition of α-glucosidase and α-amylase by flavonoid glycosides
from Lu’an GuaPian tea: Molecular docking and interaction mechanism. Food Funct. 2018, 9, 4173–4183. [CrossRef] [PubMed]
258. Quintero-Soto, M.F.; Chavez-Ontiveros, J.; Garzon-Tiznado, J.A.; Salazar-Salas, N.Y.; Pineda-Hidalgo, K.V.; Delgado-Vargas, F.;
Lopez-Valenzuela, J.A. Characterization of peptides with antioxidant activity and antidiabetic potential obtained from chickpea
(Cicer arietinum L.) protein hydrolyzates. J. Food Sci. 2021, 86, 2962–2977. [CrossRef] [PubMed]
259. Swaraz, A.M.; Sultana, F.; Bari, M.W.; Ahmed, K.S.; Hasan, M.; Islam, M.M.; Islam, M.A.; Satter, M.A.; Hossain, M.H.; Islam, M.S.;
et al. Phytochemical profiling of Blumea laciniata (Roxb.) DC. and its phytopharmaceutical potential against diabetic, obesity,
and Alzheimer’s. Biomed. Pharm. 2021, 141, 111859. [CrossRef]
260. Sheng-Yong, Y. Pharmacophore modeling and applications in drug discovery: Challenges and recent advances. Drug Discov.
Today 2010, 15, 444–450.
261. Shabana, B.; Katsumi, S. Current Status of Computer-Aided Drug Design for Type 2 Diabetes. Curr. Comput.-Aided Drug Des.
2016, 12, 167–177.
262. Sangeetha, K.; Sasikala, R.P.; Meena, K.S. Pharmacophore modeling, virtual screening and molecular docking of ATPase inhibitors
of HSP70. Comput. Biol. Chem. 2017, 70, 164–174. [CrossRef] [PubMed]
263. Khedkar, S.A.; Malde, A.K.; Coutinho, E.C.; Srivastava, S. Pharmacophore modeling in drug discovery and development: An
overview. Med. Chem. 2007, 3, 187–197. [CrossRef]
264. Teresa, K.; Katharina, R.B.; Muhammad, A.; Alex, O.; Daniela, S. Pharmacophore Models and Pharmacophore-Based Virtual
Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases. Molecules 2015, 20, 9880. [CrossRef]
265. Mohammad, U.H.; Arif, M.K.; Rakib-Uz-Zaman, S.M.; Mohammad, T.A.; Saidul, M.I.; Chaman, A.K.; Salimullah, M. Treating
Diabetes Mellitus: Pharmacophore Based Designing of Potential Drugs from Gymnema sylvestre against Insulin Receptor Protein.
BioMed Res. Intern. 2016, 2016, 3187647. [CrossRef]
266. Gerhard, W.; Thierry, L. LigandScout: 3-D Pharmacophores Derived from Protein-Bound Ligands and Their Use as Virtual
Screening Filters. J. Chem. Inf. Model. 2005, 45, 160–169.
267. Hu, B.; Lill, M.A. PharmDock: A pharmacophore-based docking program. J. Cheminform. 2014, 6, 14. [CrossRef] [PubMed]
268. Kaushik, P.; Lal Khokra, S.; Rana, A.C.; Kaushik, D. Pharmacophore modeling and molecular docking studies on Pinus roxburghii
as a target for diabetes mellitus. Adv. Bioinform. 2014, 2014, 903246. [CrossRef] [PubMed]
269. Bernard, C. PPAR-α and PPAR-γ agonists for type 2 diabetes. Lancet 2009, 374, 96–98. [CrossRef]
270. Adelusi, T.I.; Du, L.; Chowdhury, A.; Xiaoke, G.; Lu, Q.; Yin, X. Signaling pathways and proteins targeted by antidiabetic
chalcones. Life Sci. 2021, 284, 118982. [CrossRef] [PubMed]
271. Petersen, R.K.; Christensen, K.B.; Assimopoulou, A.N.; Frette, X.; Papageorgiou, V.P.; Kristiansen, K.; Kouskoumvekaki, I.
Pharmacophore-driven identification of PPARgamma agonists from natural sources. J. Comput.-Aided Mol. Des. 2011, 25, 107–116.
[CrossRef]
272. Bharatham, K.; Bharatham, N.; Park, K.H.; Lee, K.W. Binding mode analyses and pharmacophore model development for
sulfonamide chalcone derivatives, a new class of alpha-glucosidase inhibitors. J. Mol. Graph. Model. 2008, 26, 1202–1212.
[CrossRef]
273. Santhosh, S. Pharmacophore Mapping And Virtual Screening on Human Alpha Amylase Inhibitors. Intern. J. Pharma. Sci. Res.
2015, 6, 2127–2132.
274. National Research Council. Opportunities in Biology; National Academies Press: Washington, DC, USA, 1989.
275. Kumar, S.P.; Rawal, R.M.; Pandya, H.A.; Jasrai, Y.T. Qualitative and quantitative pharmacophore-similarity assessment of
anthranilamide-based factor Xa inhibitors: Applications on similar molecules with identical biological endpoints. J. Recept. Signal
Transduct. Res. 2016, 36, 189–206. [CrossRef]
276. Tropsha, A. Best practices for QSAR model development, validation, and exploitation. Mol. Inform. 2010, 29, 476–488. [CrossRef]
277. Freyhult, E.K.; Andersson, K.; Gustafsson, M.G. Structural modeling extends QSAR analysis of antibody-lysozyme interactions to
3D-QSAR. Biophys. J. 2003, 84, 2264–2272. [CrossRef]
278. Karel, D.-S.; Hai, P.-T.; Oscar, M.R.-B.; Amilkar, P.; Huong, L.-T.; Gerardo, M.C.-M. A Two QSAR Way for Antidiabetic Agents
Targeting Using α-Amylase and α-Glucosidase Inhibitors: Model Parameters Settings in Artificial Intelligence Techniques. Lett.
Drug Des. Discov. 2017, 14, 862–868. [CrossRef]
279. Neeraja, D.; Bhartenda, N.M.; Vishwa, M.K. 2D-QSAR model development and analysis on variant groups of anti-tuberlosis
drugs. Bioinformation 2011, 7, 82–90.
280. Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and computational approaches to estimate solubility
and permeability in drug discovery and development settings (Lipinski’s Rule of Five). Adv. Drug Deliv. Rev. 2001, 46, 3–26.
[CrossRef]
281. Dieguez-Santana, K.; Rivera-Borroto, O.M.; Puris, A.; Pham-The, H.; Le-Thi-Thu, H.; Rasulev, B.; Casanola-Martin, G.M. Beyond
model interpretability using LDA and decision trees for alpha-amylase and alpha-glucosidase inhibitor classification studies.
Chem. Biol. Drug Des. 2019, 94, 1414–1421. [CrossRef] [PubMed]
282. Gollucke, A.P.; Aguiar, O., Jr.; Barbisan, L.F.; Ribeiro, D.A. Use of grape polyphenols against carcinogenesis: Putative molecular
mechanisms of action using in vitro and in vivo test systems. J. Med. Food 2013, 16, 199–205. [CrossRef]
Biomolecules 2021, 11, 1877 31 of 31

283. Reed, M.J.; Scribner, K.A. In-vivo and in-vitro models of type 2 diabetes in pharmaceutical drug discovery. Diabetes Obes. Metab.
1999, 1, 75–86. [CrossRef]
284. Ruiyi, Y.; Lu, W.; Jie, X.; Xiang, L.; Shan, L.; Shengxiang, Q.; Yingjie, H.; Xiaoling, S. Treatment of type 2 diabetes mellitus via
reversing insulin resistance and regulating lipid homeostasis in vitro and in vivo using cajanonic acid A. Int. J. Mol. Med. 2018,
42, 2329–2342.
285. Tripathi, A.; Bankaitis, V.A. Molecular Docking: From Lock and Key to Combination Lock. J. Mol. Med. Clin. Appl. 2017, 2.
[CrossRef]
286. Bharathi, A.; Selvaraj, M.R.; Vasavi, C.S.; Punnagai, M.; Gayathri, G.A.; Gayathri, M. In Silico Molecular Docking and In Vitro
Antidiabetic Studies of Dihydropyrimido[4,5-a]acridin-2-amines. BioMed Res. Intern. 2014, 2014, 971569. [CrossRef] [PubMed]
287. Damian, B.; Agnieszka, A.K.; Katarzyna, M.T.-D.; Dariusz, M. Recent Advances and Applications of MolecularDocking to G
Protein-Coupled Receptors. Molecules 2017, 22, 340. [CrossRef]
288. Sheng-You, H.; Xiaoqin, Z. Advances and Challenges in Protein-Ligand Docking. Int. J. Mol. Sci. 2010, 11, 3016–3034. [CrossRef]
289. Kunal, R. Silico Drug Design; Roy, K., Ed.; Academic Press: London, UK, 2019; p. 886.
290. Wadood, A.; Ahmed, N.; Shah, L.; Ahmad, A.; Hassan, H.; Shams, S. In-silico drug design: An approach which revolutionised the
drug discovery process. OA Drug Des. Deliv. 2013, 1, 3.
291. Chen, T.H.; Fu, Y.S.; Chen, S.P.; Fuh, Y.M.; Chang, C.; Weng, C.F. Garcinia linii extracts exert the mediation of anti-diabetic
molecular targets on anti-hyperglycemia. Biomed. Pharm. 2021, 134, 111151. [CrossRef]
292. Weng, L.; Chen, T.H.; Huang, L.; Lai, D.; Kang, N.; Fu, Y.S.; Weng, C.F. A nutraceutical combination of cinnamon, purple onion,
and tea linked with key enzymes on treatment of type 2 diabetes. J. Food Biochem. 2021, 45, e13971. [CrossRef]
293. Chen, S.P.; Lin, S.R.; Chen, T.H.; Ng, H.S.; Yim, H.S.; Leong, M.K.; Weng, C.F. Mangosteen xanthone gamma-mangostin exerts
lowering blood glucose effect with potentiating insulin sensitivity through the mediation of AMPK/PPARgamma. Biomed. Pharm.
2021, 144, 112333. [CrossRef] [PubMed]