Article

Anticancer Activities of Seven Peronemins (A2, A3, B1, B2, B3, C1, and D1) from

Peronema canescens Jack: A Prediction Studies

Muhammad Fikriansyah¹, Nelson², Madyawati Latief¹, Indra Lasmana Tarigan^1*

¹Department of Chemistry, Faculty of Science and Technology, Universitas Jambi, Muara Jambi 36361,

Jambi, Indonesia

²Department of Chemical Analyst, Faculty of Science and Technology, Universitas Jambi, Muara Jambi

36361, Jambi, Indonesia

Abstract

Cancer is one of the leading causes of human death. In 2019, it was reported that cancer was the second

(22%) cause of death due to non-communicable diseases in the world's population. Research for

alternative anticancer drugs is still being done, including anticancer from plants. One of the plants that

have the potential to be developed as an anticancer alternative is the sungkai plant. Sungkai leaves

contain many bioactive compounds, one of which is the clerodane-type diterpenoids, peronemins, A2

(1), A3 (2), B1 (3), B2 (4), B3 (5), C1 (6), and D1 (7). The aim of this study was to initial screen the potential

of seven Peronemins compounds in Sungkai leaves extract as anticancer candidates. Initial screening

was carried out by predicting in-silico anticancer activity of the seven compounds. Dihydrofolate

reductase inhibitor (DHFR inhibitor) is one of the anticancer activity screening approaches. DHFR

Inhibitor activity from perenomins derivatives with pIC₅₀values of 0.785 (A2), respectively; 0.785 (A3);

0.799 (B2); 0.799 (B3); 0.799 (C1 and D1). In addition, from compounds 1,2,3,4,5 peronemin derivatives

have potential anticancer activity through interaction with the target protein Voltage-gated potassium

channel subunit while compounds 6, 7 also have biological activity potential anticancer on target protein

Dihydrofolate reductase.

Keywords: anticancer, peronemins, Peronema canescens Jack

Graphical Abstract

*

Corresponding author

Email addresses: indratarigan@unja.ac.id

DOI: 10.22437/chp.v7i1.23726

Received 6 February 2023; Accepted 4 June 2023; Available online 30 July 2023

(https://creativecommons.org/licenses/by/4.0 )

54

Chempublish Journal, 7(1) 2023, 54-63

quantitatively. The field of drug design is the

most widely used in this area. The QSAR method

is able to reduce costs and risks in the

pharmaceutical industry. The basic assumption

of QSAR/HKSA is that there is a quantitative

relationship between microscopic (molecular

structure) and macroscopic/empirical (biological

activity) properties of a molecule. The term

structure is not only limited to understanding the

spatial arrangement and the relationships

between atoms in a molecule, but also includes

the physical and chemical properties inherent in

the arrangement.

Introduction

Cancer is one of the main causes of human

death. In 2015 it was reported that cancer was

the second (22%) cause of death due to non-

communicable

diseases

in

the

world's

population. Breast cancer rates are known to

grow faster in Asia than in the West. It was

reported by WHO that nearly 1.38 million cases

of breast cancer were diagnosed in 2008 ^[1], with

a prevalence rate of 23% of all cancer cases in the

world. In addition, it is known that 209,000 new

cases were found, especially in Southeast Asia ^[2]

.

According to the International Agency on

Research in Cancer, breast cancer has become

the most common malignant tumor among

Indonesian women ^[3]. Oral cancer, on the other

hand is one of the most frequently detected

cancers in the world. In several South-Central

Asian countries, the mortality rate caused by this

cancer has become an important public health

problem. Globally, this disease is usually

detected after a late medical diagnosis and

causes a high mortality rate. Squamous cell

carcinoma is the most common malignancy of

the oral cavity. Oral cancer cases are estimated

to be around 275,000 for oral and 130,300 for

pharyngeal cancer per year, excluding the

nasopharynx. Two thirds of these incidents occur

in developed countries ^[4]. Various types of cancer

therapies and complementary agents have been

developed for their treatment.

To study the interaction of a drug molecule with

its receptor and to study the potential of a

molecule as a drug by examining the electronic

structure or quantum chemical aspects of the

molecule, computational chemistry methods are

used. For this reason, it is necessary to have an

initial simulation in the design of new drug

discovery, this initial simulation was carried out

using the pIC50 predictor with the help of the

pChembl website in describing target proteins

(receptors) related to the structure of peronemin

derivative compounds of the sungkai plant,

especially sungkai leaves using the QSAR

machine learning method. The results of the

QSAR Machie learning quantitatively describe the

pIC50 value of the compound and a qualitative

description of the target protein. The pIC50 value

is the same as Log IC50 Peronemine A2 (1) and

B2 (2).

Efforts to find alternatives to treat cancer are still

being carried out, but there are still very few

alternative drug candidates for the disease. One

of the plants that has the potential to be explored

and developed as raw material for anticancer

Experimental Section

Compound Structure

A total of seven peronemin compounds used as

research materials were made into two-

drugs

is

the

Sungkai

plant

(Peronema

cannescens Jack) ^[5]. Sungkai Acetone Extract has

seven peronemins compounds (A2, A3, B1, B2,

B3, C1 and D1). The results of the isolation of the

seven compounds have not been tested for their

activities. This study is an initial screening of the

anticancer activity of the peronemin compound

of sungkai extract, using an in-silico approach.

dimensional

(2D)

structures

using

the

Hyperchem® 7.0 program, then the structures

were equipped with hydrogen atoms to obtain a

complete structure as well as its three-

dimensional (3D) shape. The structure of the 3D

shape is formed by a molecular model (model

build) to obtain a structure that is close to the

most stable state. The next step is geometry

optimization, which is to find the most stable

molecular structure. Furthermore, compound

SMILES will be generated to be used in the

prediction of pIC₅₀.

One of the widely used areas of computational

chemistry is the Quantitative Structure-Activity

Relationship (QSAR). QSAR can be used to study

the relationship between molecular structure

and

its

biological

activity

expressed

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Chempublish Journal, 7(1) 2023, 54-63

Predictor Calculation

Results and Discussions

A single point calculation was performed using

the pChEMBL® program package on an

optimized structure to obtain the electronic

parameter (σ) in the form of the net charge of the

atom (q) contained in the molecule, as well as

selecting the R2test value from 0.8 to 1.00 with a

Sungkai is a novel compound belonging to the

clerodane-type

diterpenoids,

pronemins.

Peronemins is reported to have seven types of

peronemins compounds, A2(1), A3(2), B1(3),

B2(4), B3(5), C1(6), and D1(7) (Figure 1).

pIC50 value

ranging from 0, 6-0.9 for

enzymes/precursors involved in anticancer

activity.

(1)

(2)

(4)

(3)

(5)

(6)

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Chempublish Journal, 7(1) 2023, 54-63

(7)

Figure 1. Peronemins, A2(1), A3(2), B1(3), B2(4), B3(5), C1(6), dan D1(7).

Figure 2. Protein target voltage-gated potassium channel subunit Kv1.3.

Figure 3. Protein target Dihydrofolate reductase.

Based on the pCHEMBL Machine Learning

analysis, the pIC₅₀value of peronemin derivative

compounds contained 2 target proteins that

were active as receptors for anticancer drugs

from peronemine derivatives. The 2 target

proteins are Voltage-gated potassium channel

subunit Kv1.3 (Figure 2) and Dihydrofolate

reductase (Figure 3). Voltage-gated potassium

channel Kv1.3 is an integral membrane protein,

which is selectively permeable for potassium

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Chempublish Journal, 7(1) 2023, 54-63

ions and is activated upon a change of

primary astrocytes ^[9]. Activity of Kv1.3 channel

plays an important role in cell proliferation and

apoptosis ^[10–13]. The channel activity is inhibited

by many chemically unrelated compounds:

heavy-metal cations, smallmolecule organic

compounds and venom-isolated oligopeptides.

membrane

potential.

Kv1.3

channel

is

expressed in human T and B lymphocytes,

macrophages,

fibroblast,

osteoclasts,

platelets,

microglia,

oligodendrocytes, brain (e.g., olfactory bulb,

hippocampus, and cerebral cortex), lung, islets,

thymus, spleen, lymph nodes, and testis ^[6]. Kv1.3

channel is also expressed in the inner

mitochondrial membrane (mito Kv1.3) of normal

human T lymphocytes and cancer cells, such as

human leukemic T cell line Jurkat, prostate

cancer PC-3 cells and breast cancer MCF-7 cells

The most potent specific inhibitors inhibit the

channel

at

subnanomolar concentrations.

Inhibition of Kv1.3 channel by specific inhibitors

may be beneficial in therapy of T-lymphocyte-

mediated autoimmune diseases (e.g., sclerosis

multiplex, type I diabetes mellitus, rheumatoid

arthritis, psoriasis), chronic renal failure,

asthma, obesity, type II diabetes mellitus,

cognitive disabilities, and some cancer disorders

[6,7]

.

Recently published data demonstrated that

Kv1.3 channel is also expressed in the nuclei of

cancer cells, such as Jurkat T cells, breast cancer

MCF-7, lung cancer A549 and gastric cancer

SNU-484 cells as well as in human brain tissues

^[8]. Moreover, Kv1.3 channel was also discovered

in the cis-Golgi apparatus membrane in rat

cancer astrocytoma C6 cells as well as in non-

cancerous CTX TNA2 astrocyte cell line and in rat

[9]

.

A good pIC₅₀value has a range of 6-8. The

smaller the IC₅₀value of a compound in relation

to biological activity, the potential for biological

activity increases, meaning that the reactivity of

a compound increases when associated with

drug candidates (Table 1 – Table 4).

Table 1. Prediction of pIC₅₀based on machine learning (pchembl) QSAR of peronemin compounds A2

and B2.

Target Report

Protein Target

pIC₅₀

∑ Dataset

R²test

Drug/Clinical Candidates

Card

Peronemins A2 and B2

Tissue factor pathway

inhibitor

7.69

7.13

7.04

3382

578

0.83

0.91

0.85

CHEMBL371306

2

Anticoagulation potential

(blood clotting)

Serine/threonine-

protein kinase WEE1

CHEMBL5491

Adavosertib (8.28)

Voltage-gated

801

CHEMBL4633

Cancer therapy potential (+)

potassium

channel

subunit Kv1.3

Angiotensin II type 2

(AT-2) receptor

6.93

6.47

690

670

0.83

0.87

CHEMBL257

Anti-inflammatory potential

Serine/threonine-

protein

CHEMBL116310

1

Endoplasmic reticulum (ES)

stress (heart drug potential)

kinase/endoribonucle

ase IRE1

Phosphodiesterase 7A

6.43

621

0.8

58

CHEMBL3012

Potential chronic obstructive

pulmonary disease, erectile

dysfunction, pulmonary

arterial hypertension, benign

prostatic hyperplasia, acute

Chempublish Journal, 7(1) 2023, 54-63

decompensated heart

disease, psoriasis, arthritis,

psoriatic arthritis, atopic

dermatitis, neonatal apnea

Calcitonin

related peptide type 1

receptor

gene-

6.31

744

0.83

CHEMBL3798

Olcegepant (10.70),

Telcagepant (8.70)

Apoptosis

Bcl-2

regulator

6.17

6.15

888

0.87

0.83

CHEMBL4860

CHEMBL1827

Venetoclax (8.13), Navitoclax

(8.70)

Phosphodiesterase 5A

1987

Vardenafil Hydrochloride

(9.15), Sildenafil Citrate (8.66),

Gisadenafil (8.91), Avanafil

(8.15), Pf-00489791(9.30),

Tadalafil (8.92)

Dihydrofolate

reductase

6.1

1442

0.83

CHEMBL2425

Potential anticancer (+),

antimalarial, antifungal,

antibacterial

Table 2. Prediction of pIC₅₀based on machine learning (pchembl) QSAR of peronemin compounds A3,

B3 and B1.

Target Report

Protein Target

pIC₅₀

∑Dataset

R²test

Drug/Clinical Candidates

Card

Peronemin compounds A3, B3 and B1

Calcitonin

related peptide type

1 receptor

gene-

9.65

744

0.83

CHEMBL3798

Olcegepant (10.70), telcagepant

(8.70)

Tissue

pathway inhibitor

factor

8.28

6.63

3382

801

0.83

0.85

CHEMBL371306 Anticoagulant potential (blood

2

clotting)

Voltage-gated

CHEMBL4633

Cancer therapy potential (+)

potassium

channel

subunit Kv1.3

Phosphodiesterase

5A

6.51

1987

0.83

CHEMBL1827

Vardenafil Hydrochloride (9.15),

Sildenafil Citrate (8.66),

Gisadenafil (8.91), Avanafil

(8.15), Pf-00489791(9.30),

Tadalafil (8.92)

Phosphodiesterase

7A

6.34

6.3

621

0.8

CHEMBL3012

CHEMBL2425

Anti-inflammatory potential

Dihydrofolate

reductase

1442

0.83

Potential

anticancer

(+),

antimalarial,

antibacterial

antifungal,

Angiotensin II type 2

(AT-2) receptor

6.25

690

0.83

CHEMBL257

Anti-inflammatory potential

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Table 3. Prediction of pIC₅₀based on machine learning (pchembl) QSAR of peronemin C1 compound.

Target Report

Protein Target

Calcitonin gene-

related peptide type

1 receptor

pIC₅₀

∑ Dataset

R²test

Drug/Clinical Candidates

Card

9.43

744

0.83

CHEMBL3798

Olcegepant (10.70), Telcagepant

(8.70)

Angiotensin II type 2

(AT-2) receptor

6.79

6.31

6.25

690

1248

621

0.83

0.8

CHEMBL257

CHEMBL1906

CHEMBL3012

CHEMBL2425

Anti-inflammatory

Regorafenib (8.82)

Anti-inflammatory

Serine/threonine-

protein kinase RAF

Phosphodiesterase

7A

Dihydrofolate

reductase

1442

0.83

Potential anticancer (+),

antimalarial, antifungal,

antibacterial

Apoptosis regulator

Bcl-2

6.2

888

638

0.87

0.88

CHEMBL4860

CHEMBL3979

Venetoclax (8.13), Navitoclax

(8.70)

Peroxisome

proliferator-

activated receptor

delta

6.03

GW501516 (9.00)

Table 4. Prediction of pIC₅₀based on machine learning (pCHEMBL) QSAR Peronemin D1 Compound.

Target Report

Protein Target

Calcitonin gene-

related peptide type

1 receptor

pIC₅₀

∑ Dataset

R₂test

Drug/Clinical Candidates

Card

7.66

744

0.83

CHEMBL3798

Olcegepant (10.70),

Telcagepant (8.70)

Apoptosis regulator

Bcl-2

6.38

6.34

6.07

6.06

6.04

6.03

888

621

578

627

690

1248

0.87

0.8

CHEMBL4860

CHEMBL3012

CHEMBL5491

CHEMBL2789

CHEMBL257

CHEMBL1906

Venetoclax (8.13), Navitoclax

(8.70)

Phosphodiesterase

7A

Anti-inflammatory potential

Serine/threonine-

protein kinase WEE1

0.91

0.84

0.83

Adavosertib (8.28)

Estradiol

17-beta-

Antiosteoporosis Potential

Anti-Inflammatory Potential

Regorafenib (8.82)

dehydrogenase 2

Angiotensin II type 2

(AT-2) receptor

Serine/threonine-

protein kinase RAF

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Figure 4. A scheme of the “membrane potential model” for the contribution of Kv1.3 and K(Ca) channels

to proliferation of T lymphocytes ^[14]

.

Table 5. IC₅₀prediction value of peronemine derivative compounds.

No

Compound

pIC₅₀/Log IC₅₀

A

pIC₅₀/ Log IC₅₀

B

Nilai Log IC₅₀

A

Nilai Log IC₅₀

B

1

2

3

4

5

6

1

2

3

4

5

6

7.04

6.63

6.25

6.1

6.3

-

0.847

0.821

0.795

0.785

0.799

-

A = Voltage-gated potassium channel subunit Kv1.3

B = Dihydrofolate reductase

Compounds 1,2,3,4,5 peronemin derivatives

have potential for biological activity, namely

anticancer potential on the target protein

Voltage-gated potassium channel subunit Kv1.3

and Dihydrofolate reductase, while compound 6

the IC₅₀value, the greater the potential for

biological activity, which means that it is possible

to find new drug compounds, but compound 7

does not appear to have potential anticancer

activity, it is possible to change the structure of

peromemin derivatives. due to the loss of one of

the bonds in one of the bonds that bind the furan

ring, this difference is shown in compounds 6 and

7 (Figure 5).

also has potential biological

activity for

anticancer on target protein Dihydrofolate

reductase (Figure 4). This is evidenced by the IC₅₀

value of each compound in Table 5. The smaller

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Chempublish Journal, 7(1) 2023, 54-63

Figure 5. Differences in bonds in the Furan ring of compound 6 and compound 7

The decrease in the inductive effect of the

structural change is possible because the oxygen

atom has a greater electronegativity than carbon

based on the Pauling scale this is possible the

flow of electrons through the sigma bond (σ)

decreases when viewed from the biological

activity of anticancer potential.

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