Pages Menu
ISSN (Online): 0975-8232,
ISSN (Print): 2320-5148

International Journal Of
Pharmaceutical Sciences And Research

IJPSR is indexed with Embase

An International Journal published monthly
An Official Publication of Society of Pharmaceutical Sciences and Research

Categories Menu

IN-SILICO DESIGN OF FLUTAMIDE ANALOGUES AS ANDROGEN RECEPTOR ANTAGONIST AND MOLECULAR DOCKING STUDIES

HTML Full Text

IN-SILICO DESIGN OF FLUTAMIDE ANALOGUES AS ANDROGEN RECEPTOR ANTAGONIST AND MOLECULAR DOCKING STUDIES

Ajay Kumar Gupta, Achal Mishra and Sanmati Kumar Jain *

Drug Discovery and Research Laboratory, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India.

ABSTRACT: Androgenic hormones such as testosterone and dihydrotestosterone are essential for the progression of the prostate gland. Overexpression of androgenic receptors is responsible for the proliferation of prostate tumours and androgenic receptors are an essential target in prostate cancer therapy. Flutamide was the first Nonsteroidal androgen receptor antagonist used to treat prostate cancer, but it causes side effects such as hepatotoxicity. This study aims to develop less toxic compounds using a bioisosteric approach by replacing groups such as nitro, trifluoromethyl and aryl of Flutamide drug and to improve pharmacokinetic and toxicity prediction as well as docking studies of newly generated bioisosteres. The Lipinski rule of five was followed. In the docking study, docking scores were obtained in the range of -7.76 to -9.75 Kcal/mol. All ligands docked inside the binding pocket region share a shape that is complementary to the androgen receptor. Among the selected bioisosteres and flutamide, the common amino acid residue 746Val plays a key role in the activity and binding affinity. Based on their QED score, toxicity score, drug likeness, drug score, NR-AR score and binding scores with protein residue, compounds F3, F17, and F39 may be noble antiandrogen agents in the management of prostate cancer.

Keywords: Flutamide, Bioisosteres, ADMET, Prostate Cancer, Molecular docking

INTRODUCTION: Throughout the world, cancer is a major cause of mortality and a leading obstacle to extending life expectancy. Worldwide, approximately 10 million deaths from cancer were reported, compared to 19.3 million cases in 2020. According to estimated data, there are approximately 1.4 million new prostate cancer (PC) patients diagnosed each year and 0.38 million deaths 1.

In men, prostate is a gland which is located in Pelvic region. For normal growth and development of prostate gland, androgenic hormones such as testosterone and dihydrotestosterone (metabolite of testosterone) are required 2-3. Androgenic hormones bind with androgen receptors (AR) and mediate biological effects.

Before puberty, the secretion of androgens are less but in postpuberted male volume of androgens are increased up to ten times, result in continuous growth in prostate glands may lead to benign prostatic hyperplasia (BPH) 4-5. Increased levels of androgens lead to cause proliferation of prostate tumours 6. PC, precious puberty and hair loss are androgen dependent diseases in men, can be treated with antiandrogens.

Antiandrogen agents can be classified into two categories, a) androgen synthetic inhibitors (such as Abiraterone, Seviterone etc.); b) AR antagonist such as steroidal (Cyproterone, Allylestrenol etc.) and non-steroidal (Flutamide, Bicalutamide and Enzalutamide etc.) 7.

Flutamide is chemical propanamide derivative Fig. 1A, used in the treatment of PC and BPH 8. It is an inactive molecule that is activated after going under first pass metabolism (using Cytochrome P450 1A2), resulting in the formation of 2-hydroxyflutamide Fig. 1B 9. Flutamide shows some side effects like drug induced liver injury (DILI) which may lead to liver failure. FDA also reported a warning risk regarding liver nacrosis, jaundice and cholestasis 10-12.

Nitroreduction of Flutamide (formation of N-[4-amino-3-(trifluoromethyl) phenyl] isobutyramide) may cause hepatotoxicity 13. At high doses, flutamide causes some difficulties such as prostatitis, hematuria, hematochezia, anaemia, low libido and elevated methemoglobin in the blood 14.

FIG. 1: STRUCTURE OF FLUTAMIDE, 2-HYDROXY FLUTAMIDE AND BIOISOSTERIC MODIFICATION OF GROUPS (NITRO, TRIFLUOROMETHYL AND ARYL) IN FLUTAMIDE

Bioisosters were compounds that have the same biological activities and are used for modification of potency, efficacy, bioactivitites, pharmacokinetics and toxicological properties 15-16. They can be classified as classical and non-classical bioisosteres.

Classical bioisosters are atoms or molecules having the same valence electrons but share different numbers of atoms. Non-classical bioisosters differ in valance electron but similarity in some important parameters such as liophilicity, pKa, chemical reactivity etc 17-18. For the design of novel and potent molecules chemists or scientists can use rational drug design approaches, among them molecular docking which is used to predict the ligand-protein interaction in three-dimentional mode. Also, docking gives the information about the binding score of the ligand-protein complex may help in lead optimisation 19-20.

The aim of the investigation is to develop a less toxic compound than flutamide through a bioisosteric approach, ADMET properties prediction, drug likeness (DL), drug score (DS) and moleculer docking studies. In Flutamide, three groups such as nitro, trifluromethyl and aryl are modified using the bioisosteric approach is shown in Fig. 1C.

MATERIALS AND METHODS:

Designing of Flutamide Bioisosteres: Flutamide is a pure androgen antagonist used to treat PC. However, in the course of medication, patients were suffering from hepatotoxicity, which led to liver damage. Therefore, it is necessary to modify the flutamide structure in order to reduce toxicity like hepatotoxicity. Various bioisosteres of groups such as nitro, trifluoromethyl, and aryl in Flutamide were generated using the MolOpt online tool. MolOpt, online software used for in-silico design of bioisosteres that uses deep generative models, data mining, and similarity comparisons as bioisosteric transformation rules. A useful feature of MolOpt is that it can navigate historical bioisosteric group space and identify new bioisosteric transformation ideas. The purpose of MolOpt is to assist the medicinal chemist in finding what to make next 21. Types of newly generated analogues of nitro, trifluoromethyl and aryl groups in flutamide are shown in Fig. 2A, 2B and 2C, respectively. The structures of newly generated analogues are shown in Table 1.

FIG. 2: TYPE OF BIOISOSTERES OF NITRO GROUP, TRIFLUOROMETHYL GROUP, ARYL GROUP IN FLUTAMIDE

Pharmacokinetic and Toxicity (ADMET) Properties Prediction: Absorption, distribution, metabolism and excretion and toxicological (ADMET) properties of newly generated analogues of flutamide were calculated using ADMET lab 2.0. It is an integrated online platform with eighty-four quantitative and four qualitative regression models with authentic and extensive predictions of ADMET properties for novel ligands that mimic mammalian ADMET properties tool 22-25.

Drug Likeness and Drug Score Prediction: OSIRIS property explorer (PEO) was employed for DS and DL calculations. PEO includes the processing of all information related to compound synthesis, biological testing, and preclinical development. PEO Online platform with six quantitative and four qualitative regression models with comprehensive prediction of toxicity risk, DL and DS 26.

DL and DS properties determine whether a drug has the physicochemical and biological properties required to be successful and safe for use 27.

Molecular Docking Study: Molecular docking is used in uderstanding molecular biology, determining interactions between targets (macromolecules such as DNA, RNA and Proteins) and small molecules (ligands). Docking softwares is used to understand the recognition of binding affinity, binding score etc 28-29. A molecular docking study of analogues was done using the crystal structure of the androgen receptor (PDB ID: 2AM9) and this study involved a number of steps like preparation of the ligand structure preparation of the protein structures and protein-ligand docking using Argus Lab 4.0 software30-32.

RESULTS AND DISCUSSION:

Bioisosteres of Trifluoromethyl, Nitro and Aryl Groups in Flutamide: As a drug discovery technique, bioisosteric replacement is widely used to improve potency and selectivity, address pharmacokinetic problems and remove unwanted side effects such as toxicity.

MolOpt was used to generate eighty-six, ninety-two and seventy-seven bioisosteres of trifluoromethyl, nitro and aryl groups, in flutamide, respectively. Among these, fifty analogues Table 1 were chosen for further evaluation based on their QED value, DILI score, NR-AR score, DL and DS.

TABLE 1: STRUCTURE AND MOLECULAR PROPERTIES OF THE ANALOGUES


S. no.		 Entry no. Structure MW nHA nHD TPSA nRot LogS LogP
Aryl Group Bioisosteres
1 F1 306.08 6 1 81.47 6 -4.499 3.114
2 F2 292.07 6 2 92.47 5 -3.76 3.104
3 F3 306.08 6 1 81.47 6 -3.656 3.121
4 F4 290.09 5 1 72.24 5 -3.598 2.866
5 F5 282.12 5 1 72.24 5 -2.563 2.019
6 F6 290.09 5 1 72.24 6 -2.967 2.849
7 F7 290.09 5 1 72.24 6 -3.546 2.886
8 F8 304.1 5 1 72.24 6 -5.093 3.228
9 F9 290.09 5 1 72.24 6 -2.665 2.829
10 F10 276.07 5 1 72.24 5 -3.879 2.941
11 F11 276.07 5 1 72.24 5 -3.882 3.23
12 F12 276.07 5 1 72.24 5 -4.661 3.323
13 F13 290.09 5 1 72.24 6 -3.417 3.044
14 F14 294.06 5 1 72.24 5 -4.243 3.041
15 F15 353.98 5 1 72.24 5 -5.264 3.529
Nitro Group Bioisosteres
16 F16 246.1 3 3 55.12 4 -3.253 2.677
17 F17 259.08 3 1 46.17 5 -3.267 2.928
18 F18 272.09 5 1 77.86 5 -5.569 3.706
19 F19 260.11 3 3 55.12 5 -1.478 2.504
20 F20 274.13 3 2 41.13 6 -4.084 3.479
21 F21 275.11 3 2 49.33 5 -2.254 2.818
22 F22 274.09 4 2 58.2 6 -3.165 2.558
23 F23 289.1 5 4 84.22 6 -3.147 2.207
24 F24 288.14 3 2 41.13 6 -4.904 3.852
25 F25 303.13 2 1 29.1 5 -5.98 4.56
26 F26 271.12 2 1 29.1 6 -4.889 4.105
27 F27 261.1 3 1 38.33 5 -3.663 3.146
28 F28 256.08 3 1 52.89 4 -4.125 3.044
29 F29 289.09 4 1 55.4 6 -3.144 2.819
30 F30 300.14 3 1 32.34 5 -5.124 4.001
31 F31 314.16 3 1 32.34 5 -5.558 4.386
32 F32 275.08 4 2 66.4 5 -2.859 2.958
Trifluoromethyl Group Bioisosteres
33 F33 238.1 6 2 92.47 5 -2.53 1.804
34 F34 236.08 6 1 89.31 5 -3.098 2.058
35 F35 265.11 7 2 101.34 6 -2.944 1.456
36 F36 262.13 5 1 72.24 5 -5.529 3.616
37 F37 266.13 6 2 92.47 5 -2.693 2.408
38 F38 323.98 5 1 72.24 5 -4.892 3.764
39 F39 252.11 6 2 92.47 5 -2.307 1.927
40 F40 278.13 6 1 81.47 5 -3.769 2.549
41 F41 290.02 5 1 72.24 5 -4.282 3.172
42 F42 342.19 5 1 72.24 5 -6.466 5.128
43 F43 248.12 5 1 72.24 6 -4.379 3.216
44 F44 252.11 6 1 81.47 6 -2.901 2.159
45 F45 276.15 5 1 72.24 5 -5.973 4.056
46 F46 291.16 6 1 75.48 5 -5.14 3.366
47 F47 291.16 6 1 75.48 5 -5.509 3.8
48 F48 262.13 5 1 72.24 6 -5.314 3.626
49 F49 292.14 6 1 81.47 5 -4.48 2.905
50 F50 292.14 6 1 81.47 6 -5.32 3.634
Std. Flutamide 276.07 5 1 72.24 5 -3.842 3.243

MW; molecular weight, nHA; number of hydrogen bond acceptor, nHD; number of hydrogen bond donor, nRot; number of rotatable bonds, TPSA; topological polar surface area, logP; the logarithm of partition coefficient value, logs; the logarithm of aqueous solubility value.

Screening of Molecular Properties: The molecular properties of the newer analogue were calculated using the ADMETlab2.0 online tool and the results are shown in Table 2. Lipinski's rule of five promotes the bioavailability of the drug candidates. Lipinski's rule of five also predicts the absorption or permeation of the drug candidates 33. The result indicates that all analogues met the acceptance criteria with flutamide as the standard.

TABLE 2: MEDICINAL PROPERTIES OF THE ANALOGUES

Entry no. QED Synth Fsp3 MCE-18 Lipinski Pfizer GSK GT
Aryl Group Bioisosteres
F1 0.684 2.276 0.417 13 Accepted Accepted Accepted Accepted
F2 0.509 2.318 0.364 13 Accepted Accepted Accepted Accepted
F3 0.684 2.324 0.417 13 Accepted Accepted Accepted Accepted
F4 0.683 2.308 0.417 13 Accepted Accepted Accepted Accepted
F5 0.636 3.858 0.909 31 Accepted Accepted Accepted Accepted
F6 0.684 2.162 0.417 12 Accepted Accepted Accepted Accepted
F7 0.684 2.191 0.417 12 Accepted Accepted Accepted Accepted
F8 0.685 3.075 0.462 28 Accepted Rejected Accepted Accepted
F9 0.684 2.407 0.417 12 Accepted Accepted Accepted Accepted
F10 0.68 2.038 0.364 12 Accepted Accepted Accepted Accepted
F11 0.68 2.127 0.364 12 Accepted Rejected Accepted Accepted
F12 0.68 2.123 0.364 12 Accepted Rejected Accepted Accepted
F13 0.684 2.438 0.417 12 Accepted Rejected Accepted Accepted
F14 0.528 2.305 0.364 13 Accepted Rejected Accepted Accepted
F15 0.659 2.4 0.364 13 Accepted Rejected Accepted Accepted
Nitro Group Bioisosteres
F16 0.788 2.001 0.364 11 Accepted Accepted Accepted Accepted
F17 0.848 2.179 0.333 11 Accepted Accepted Accepted Accepted
F18 0.498 2.587 0.364 11 Accepted Accepted Accepted Accepted
F19 0.878 2.066 0.417 11 Accepted Accepted Accepted Accepted
F20 0.878 2.029 0.462 11 Accepted Rejected Accepted Accepted
F21 0.888 2.581 0.462 24 Accepted Accepted Accepted Accepted
F22 0.829 2.231 0.333 11 Accepted Accepted Accepted Accepted
F23 0.799 2.042 0.333 12 Accepted Accepted Accepted Accepted
F24 0.875 2.094 0.5 12 Accepted Rejected Accepted Accepted
F25 0.844 2.511 0.5 14 Accepted Rejected Rejected Accepted
F26 0.823 2.219 0.357 11 Accepted Rejected Rejected Accepted
F27 0.906 1.851 0.417 11 Accepted Rejected Accepted Accepted
F28 0.883 2.064 0.333 11 Accepted Rejected Accepted Accepted
F29 0.686 2.031 0.385 12 Accepted Accepted Accepted Accepted
F30 0.918 2.01 0.533 35 Accepted Rejected Rejected Accepted
F31 0.902 2.021 0.562 12 Accepted Rejected Rejected Accepted
F32 0.891 1.958 0.333 36 Accepted Accepted Accepted Accepted
Trifluoromethyl Group Bioisosteres
F33 0.616 2.039 0.364 9 Accepted Accepted Accepted Accepted
F34 0.492 2.133 0.273 9 Accepted Accepted Accepted Accepted
F35 0.637 1.958 0.333 10 Accepted Accepted Accepted Accepted
F36 0.667 2.091 0.5 31 Accepted Rejected Accepted Accepted
F37 0.646 2.234 0.462 12 Accepted Accepted Accepted Accepted
F38 0.518 2.394 0.364 12 Accepted Rejected Accepted Accepted
F39 0.634 2.587 0.417 20 Accepted Accepted Accepted Accepted
F40 0.678 2.77 0.5 44 Accepted Accepted Accepted Accepted
F41 0.522 2.405 0.364 10 Accepted Rejected Accepted Accepted
F42 0.639 3.626 0.65 62 Accepted Rejected Rejected Accepted
F43 0.494 2.212 0.308 9 Accepted Rejected Accepted Accepted
F44 0.644 2.033 0.417 9 Accepted Accepted Accepted Accepted
F45 0.67 2.103 0.533 32 Accepted Rejected Rejected Accepted
F46 0.678 1.991 0.5 31 Accepted Accepted Accepted Accepted
F47 0.683 2 0.533 32 Accepted Accepted Accepted Accepted
F48 0.654 2.133 0.5 30 Accepted Rejected Accepted Accepted
F49 0.683 2.281 0.533 32 Accepted Accepted Accepted Accepted
F50 0.665 2.103 0.533 32 Accepted Accepted Accepted Accepted
Flutamide 0.68 2.07 0.364 12 Accepted Rejected Accepted Accepted

QED; a measure of drug-likeness based on the concept of desirability, Synth; synthetic accessibility score, Fsp3; The number of sp3 hybridized carbons/total carbon count, MCE-18; medicinal chemistry evolution in 2018, GT; golden triangle.

Screening of Medicinal Properties: The medicinal property of analogues are shown in Table 2. QED indicates drug-like properties. The QED value of all analogues has shown > 0.67 with some exceptions such as F2, F5, F14, F15, F18, F33-39, F41-F44, F48 and F50, whereas flutamide has 0.68 which indicates analogues may have drug like properties. All analogues will be easy to synthesise as per synthetic accessibility prediction criteria (< 6). Newer analogues of flutamide were found to be acceptable to Lipinski and the Golden Triangle (GT), indicating good bioavailability. Analogues F3, F17, and F39 have been found to meet Lipinski, Pfizer, GSK, and GT rules, whereas the Pfizer rule for flutamide is rejected.

Screening of Pharmacokinetic (ADME) Properties: Pharmacokinetic properties such as absorption (caco-2, MDCK, HIA), distribution (BBB, PPB, VDss), metabolism (CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4), excretion (CL and T1/2) have been calculated using the ADMET lab2.0 online tool and results are tabulated in Table 3 and 4. Intestinal absorption of analogues was found to be good (absorption score based on the caco-2 score and HIA score). The caco-2 score of analogues was found to be greater than -5.15 which indicates the proper in-vivo drug permeability of analogues.

HIA scores were also found in the range between 0 to 0.3 which indicates oral bioavailability of molecules. The MDCK score of analogues was found to be excellent, indicating high passive permeability. These analogues have moderate to poor BBB permeability, ranging from 0.353 to 0.919. Most of the newer analogues have a predicted plasma protein binding (PPB) score under 90%, which indicates that the high plasma protein binding causes a decrease in the free plasma fraction, which decreases distribution volume and lengthens the half-life of elimination. The volume of distribution (VDs) of all analogues has a good predicted score that is between 0.04-20. From the predicted scores of absorptions and distribution, analogues F3, F6, F17, F19, F37 and F39 and others also have good to moderate permeability effects.

TABLE 3: ABSORPTION AND DISTRIBUTION PROFILE OF THE ANALOGUES

Entry no. Caco-2 MDCK HIA BBB PPB (%) VDss Fu (%)
Aryl Group Bioisosteres
F1 -4.414 Ex 0.007 0.499 96.99 1.033 3.06
F2 -4.437 Ex 0.004 0.574 98.06 1.004 2.48
F3 -4.448 Ex 0.007 0.485 96.93 1.074 3.22
F4 -4.348 Ex 0.005 0.353 97.17 1.089 3.90
F5 -4.582 Ex 0.005 0.525 77.83 0.833 23.89
F6 -4.458 Ex 0.004 0.723 93.18 0.699 7.30
F7 -4.444 Ex 0.004 0.515 95.16 0.773 6.14
F8 -4.362 Ex 0.005 0.494 95.52 0.984 8.00
F9 -4.383 Ex 0.004 0.794 93.84 0.666 5.75
F10 -4.333 Ex 0.004 0.474 94.61 0.87 5.52
F11 -4.344 Ex 0.004 0.533 95.92 0.835 4.49
F12 -4.435 Ex 0.004 0.316 97.15 1.062 4.37
F13 -4.479 Ex 0.004 0.896 94.02 0.835 7.44
F14 -4.336 Ex 0.005 0.261 96.80 1.042 3.67
F15 -4.332 Ex 0.023 0.582 97.88 0.982 2.39
Nitro Group Bioisosteres
F16 -4.526 Ex 0.004 0.687 85.00 1.196 15.61
F17 -4.507 Ex 0.004 0.966 90.11 1.527 10.32
F18 -5.062 Ex 0.007 0.518 97.60 5.137 3.32
F19 -4.787 Ex 0.004 0.481 62.00 2.318 35.81
F20 -4.58 Ex 0.005 0.446 95.62 3.014 4.20
F21 -4.408 Ex 0.004 0.86 87.90 0.89 14.97
F22 -4.435 Ex 0.004 0.896 88.02 1.574 10.29
F23 -5.088 Ex 0.004 0.93 88.87 1.129 11.70
F24 -4.561 Ex 0.006 0.419 96.68 3.462 3.52
F25 -4.391 Ex 0.007 0.791 93.80 3.936 6.30
F26 -4.372 Ex 0.003 0.826 97.11 1.34 2.27
F27 -4.495 Ex 0.004 0.746 94.55 3.195 5.36
F28 -4.464 Ex 0.005 0.966 92.18 0.911 6.07
F29 -4.61 Ex 0.008 0.99 89.13 0.969 24.02
F30 -4.566 Ex 0.003 0.775 95.74 2.16 2.77
F31 -4.576 Ex 0.003 0.747 96.38 2.233 2.18
F32 -4.566 Ex 0.004 0.425 91.98 1.165 10.09
Trifluoromethyl Group Bioisosteres
F33 -4.406 Ex 0.005 0.88 73.78 0.843 28.74
F34 -4.399 Ex 0.006 0.919 84.50 1.057 14.68
F35 -4.909 Ex 0.005 0.875 80.52 0.936 23.62
F36 -4.472 Ex 0.003 0.739 96.58 0.806 2.94
F37 -4.326 Ex 0.01 0.731 80.12 0.909 23.43
F38 -5.52 Ex 0.004 0.872 98.18 1.28 1.59
F39 -4.404 Ex 0.005 0.781 76.36 0.83 26.13
F40 -4.485 Ex 0.003 0.642 92.80 0.851 7.98
F41 -4.79 Ex 0.004 0.811 96.53 1.263 3.62
F42 -4.683 Ex 0.004 0.69 96.28 1.015 1.15
F43 -4.249 Ex 0.004 0.557 96.00 0.986 2.68
F44 -4.333 Ex 0.004 0.881 74.80 0.927 23.54
F45 -4.489 Ex 0.003 0.699 97.30 0.842 2.09
F46 -4.42 Ex 0.004 0.824 94.79 0.924 4.86
F47 -4.441 Ex 0.004 0.806 95.96 0.915 3.98
F48 -4.466 Ex 0.003 0.735 95.56 0.664 2.70
F49 -4.471 Ex 0.002 0.598 93.96 0.756 6.25
F50 -4.375 Ex 0.003 0.779 96.90 0.728 2.63
Flutamide -4.346 Ex 0.004 0.58 95.62 0.832 4.59

Caco-2; the human colon adenocarcinoma cell lines, MDCK; Madin−Darby canine kidney cells, HIA; human intestinal absorption, PPB; plasma protein binding, BBB; blood–brain barrier, VD; volume distribution, Fu; the fraction unbound in plasms, Ex; Excellent.

Cytochrome P450 (CYT P450) is involved in the digestion of drugs, lipids, steroidal components, and carcinogens. Analogues may be substrate or inhibitors. If they are substrates for the enzyme CYT P450 result in the metabolism takes place with molecules, on the other hand, if they inhibit the enzyme, it will be inactive in metabolism. Analogue F3 was an inhibitor while analogues F17 and F34 were substrates for all five isozymes. Approximately 50% of analogues have an excellent clearance score, indicating a low risk of toxicity. Among them analogue F3 and F29 have excellent clearance scores (≥ 5) and analogue F17 has a moderate clearance score (≤ 5). T1/2 of analogues F3 and F39 were found under the range (0 to 0.3) which indicates excellent clearance from the body.

TABLE 4: METABOLISM AND EXCRETION PROFILE OF THE ANALOGUES

Entry no. CYP1A2 CYP2C19 CYP2C9 CYP2D6 CYP3A4 CL T1/2
Aryl Group Bioisosteres
F1 - - - + - 4.349 0.3
F2 - - + - + 4.579 0.495
F3 - - - - - 6.774 0.283
F4 + + - + + 3.408 0.419
F5 + + - + + 6.446 0.4
F6 - - + + - 5.882 0.297
F7 - + - + - 5.217 0.454
F8 + + + + + 4.821 0.345
F9 - - + + - 6.586 0.642
F10 + + + + + 3.795 0.308
F11 + + + + + 4.608 0.224
F12 + + - - - 4.096 0.259
F13 - - - + + 6.624 0.246
F14 + - + + + 3.016 0.232
F15 + - + + + 1.226 0.207
Nitro Group Bioisosteres
F16 + + - + + 7.88 0.192
F17 - + + + + 2.634 0.252
F18 + + + + + 0.783 0.321
F19 - + + + + 8.645 0.547
F20 + + + + + 7.332 0.295
F21 + + + + + 4.611 0.37
F22 + + + + + 4.077 0.366
F23 + + + + + 5.808 0.216
F24 + + - + + 5.312 0.17
F25 + + - + + 2.284 0.198
F26 + - + + - 7.543 0.169
F27 + + + + + 9.662 0.322
F28 + + - + + 8.524 0.297
F29 - + + - - 2.998 0.411
F30 + + - + + 5.648 0.095
F31 + + - + - 5.31 0.076
F32 + + - + + 1.355 0.691
Trifluoromethyl Group Bioisosteres
F33 + + + + + 6.474 0.882
F34 - + + + + 2.213 0.58
F35 - - + + + 8.056 0.696
F36 + - + + + 1.385 0.234
F37 + + + + + 4.194 0.689
F38 + + + + + 3.464 0.316
F39 + + + + + 4.76 0.762
F40 + + + + + 4.775 0.429
F41 + + + + + 3.047 0.485
F42 + - + + - 1.74 0.07
F43 + - + + - 5.556 0.604
F44 + + + + + 5.252 0.615
F45 + - + + - 1.436 0.2
F46 + - - + - 4.765 0.364
F47 + - - - - 4.396 0.299
F48 + - + + - 3.098 0.272
F49 + + + - + 5.391 0.341
F50 + - + - - 3.617 0.24
Flutamide + - + + + 4.681 0.237

(-); indicates inhibitor, (+); indicates substrate of human cytochrome P450 (five isozymes-1A2, 3A4, 2C9, 2C19 and 2D6), CL; the clearance of a drug, T1/2; the half-life of a drug.

Screening of Toxicity Profile: Toxicity parameters of newer analogues such as Drug Induced Liver Injury (DILI), mutagenicity (Ames), androgen receptor-a nuclear hormone receptor (NR-AR) were calculated using ADMET lab 2.0 online tool and their results are shown in Table 5. The DILI scores for analogues F6, F17, F19, F25, F34, F35, F37 and F42 are moderate (0.3 to 0.7). However, analogues F7, F37 and F41 are safe, while flutamide has high liver toxicity (0.858). Studies have shown that analogues such as F17, F34, F38, F41 do not cause human hepatotoxicity (H-HT) as compared to Flutamide (0.578). The mutagenicity of analogues such as F1, F2, F3, F5, F7, F8, F17, F19, F20, F21, F23, F24, F25, F26 and F42 was predicted to be safer than flutamide (0.498), indicating that the analogues could not cause mutagenesis. The rat oral acute toxicity (ROA) method is used to determine acute toxicity in rats and mice, which is an important safety profile for drug candidates. Based on the outcome of ROA, analogues are as safe as flutamide, except for F19. The carcinogenicity of chemicals is a serious issue because of their powerful effects on wellness and because they can damage the genome or disrupt cellular metabolism. According to the results of carcinogenicity scores, analogues such as F17, F19, F20, F21, F22, F23, F24, F25, F33, F34, F35, F38, F39, F41 and F42 were found to have a safe predictive value. NR-AR plays a vital role in AR-dependent PC, as well as other androgen-related diseases. In more than 75% of cases, analogues bind to the NR-AR, inhibiting the activity of the Androgen receptor. In general, analogues F1-F4, F6, F7, F9, F23, F36, F38 and F39 were found to have more than 0.8 scores.

TABLE 5: TOXICITY PROFILE OF THE ANALOGUES

Entry no. H-HT DILI Ames ROA Carc. NR-AR NR-AR-LBD
Aryl Group Bioisosteres
F1 0.724 0.572 0.196 0.159 0.74 0.847 0.095
F2 0.552 0.671 0.177 0.271 0.737 0.807 0.178
F3 0.708 0.58 0.205 0.199 0.741 0.85 0.103
F4 0.736 0.654 0.488 0.238 0.844 0.849 0.017
F5 0.919 0.622 0.008 0.041 0.882 0.089 0.002
F6 0.485 0.43 0.458 0.115 0.725 0.845 0.009
F7 0.485 0.185 0.29 0.108 0.588 0.826 0.011
F8 0.338 0.675 0.013 0.06 0.751 0.011 0.003
F9 0.379 0.609 0.457 0.128 0.52 0.816 0.007
F10 0.662 0.87 0.624 0.123 0.793 0.861 0.032
F11 0.626 0.874 0.616 0.063 0.788 0.862 0.027
F12 0.62 0.729 0.498 0.058 0.753 0.844 0.048
F13 0.527 0.714 0.216 0.069 0.682 0.803 0.007
F14 0.749 0.889 0.5 0.158 0.731 0.874 0.075
F15 0.405 0.934 0.305 0.118 0.683 0.878 0.12
Nitro Group Bioisosteres
F16 0.596 0.696 0.524 0.234 0.357 0.749 0.006
F17 0.258 0.347 0.204 0.084 0.183 0.732 0.006
F18 0.454 0.691 0.993 0.079 0.965 0.001 0.013
F19 0.481 0.384 0.039 0.868 0.231 0.795 0.004
F20 0.82 0.456 0.129 0.243 0.123 0.619 0.003
F21 0.654 0.533 0.116 0.156 0.117 0.762 0.003
F22 0.416 0.688 0.391 0.127 0.111 0.742 0.006
F23 0.352 0.95 0.284 0.105 0.147 0.847 0.003
F24 0.65 0.488 0.04 0.153 0.153 0.191 0.002
F25 0.33 0.399 0.012 0.01 0.111 0.61 0.002
F26 0.426 0.522 0.063 0.113 0.553 0.572 0.004
F27 0.263 0.839 0.023 0.139 0.285 0.786 0.004
F28 0.913 0.896 0.105 0.17 0.391 0.836 0.037
F29 0.049 0.733 0.042 0.138 0.304 0.789 0.007
F30 0.848 0.78 0.121 0.535 0.329 0.745 0.005
F31 0.84 0.815 0.114 0.512 0.293 0.722 0.005
F32 0.218 0.973 0.017 0.339 0.082 0.852 0.004
Trifluoromethyl Group Bioisosteres
F33 0.575 0.419 0.832 0.046 0.262 0.635 0.021
F34 0.156 0.39 0.964 0.024 0.109 0.36 0.205
F35 0.58 0.4 0.918 0.054 0.079 0.035 0.005
F36 0.48 0.839 0.884 0.329 0.534 0.828 0.008
F37 0.598 0.395 0.629 0.037 0.378 0.709 0.005
F38 0.109 0.5 0.978 0.018 0.147 0.815 0.016
F39 0.589 0.225 0.698 0.045 0.136 0.8 0.013
F40 0.585 0.535 0.944 0.17 0.428 0.756 0.016
F41 0.066 0.247 0.96 0.029 0.112 0.659 0.019
F42 0.338 0.374 0.143 0.317 0.55 0.382 0.006
F43 0.316 0.455 0.93 0.049 0.214 0.719 0.017
F44 0.659 0.778 0.913 0.045 0.268 0.744 0.035
F45 0.441 0.853 0.885 0.392 0.599 0.838 0.006
F46 0.461 0.921 0.97 0.168 0.369 0.825 0.017
F47 0.41 0.925 0.969 0.166 0.389 0.842 0.013
F48 0.495 0.827 0.931 0.078 0.489 0.812 0.005
F49 0.489 0.694 0.912 0.669 0.531 0.733 0.008
F50 0.506 0.846 0.937 0.123 0.665 0.868 0.017
Flutamide 0.578 0.858 0.498 0.067 0.786 0.862 0.018

H-HT; the human hepatotoxicity, DILI; drug-induced liver injury, Ames; Test for mutagenicity, ROA; rat oral acute toxicity, NR-AR; androgen receptor - a nuclear hormone receptor, NR-AR-LBD; molecule bind with LBD of androgen receptor, Carc.; carcinogenicity.

Screening of DL and DS: Parameters like mutagenicity, tumorigenicity, irritant, reproductive, DL, and DS have been calculated using PEO. The results of DL and DS are shown in Table 6. Around 75% of analogues showed good DL scores and all newer analogues showed higher DS than flutamide. The DL score was -6.59 for analogue F19 followed by ligand F24 with a DL score of -6.14. The maximum DS was found 0.44 for analogue F2 and F5 followed by ligands F6, F7, F9 and F10 with the DL score of 0.43.

TABLE 6: DRUG LIKENESS AND DRUG SCORE OF ANALOGUES

Entry no. Toxicity Risk DL DS
M T I R
Aryl Group Bioisosteres
F1 G G G G -10.19 0.42
F2 G G G G -11.50 0.44
F3 G G G G -10.66 0.42
F4 G G G G -11.23 0.40
F5 G G G G -10.84 0.44
F6 G G G G -12.02 0.43
F7 G G G G -21.78 0.43
F8 G G G G -11.16 0.41
F9 G G G G -12.89 0.43
F10 G G G G -12.4 0.43
F11 R O G O -14.93 0.16
F12 G G G G -22.34 0.43
F13 R O G O -11.53 0.14
F14 G G G G -12.76 0.41
F15 G G G G -14.55 0.36
Nitro Group Bioisosteres
F16 R O G O -9.74 0.17
F17 R O G O -8.65 0.16
F18 R O G O -8.45 0.15
F19 R O G O -6.59 0.17
F20 R O G O -7.01 0.16
F21 R O G O -8.69 0.17
F22 R O G O -7.32 0.16
F23 R O G O -6.8 0.16
F24 R O G O -6.14 0.15
F25 R O R O -52.49 0.08
F26 R O G O -9.24 0.14
F27 R O G O -5.93 0.17
F28 R O G O -10.78 0.16
F29 R O G O -5.94 0.16
F30 R O G O -5.59 0.15
F31 R O G O -7.1 0.14
F32 R O G O -8.92 0.17
Trifluoromethyl Group Bioisosteres
F33 R O G O -6.86 0.18
F34 R O G O -9.02 0.17
F35 R O G O -5.32 0.18
F36 R O G O -7.22 0.15
F37 R O G O -7.81 0.17
F38 R O R R -6.87 0.05
F39 R O G O -8.02 0.18
F40 R O G O -8.03 0.17
F41 R R R R -6.6 0.05
F42 R O G O -6.4 0.11
F43 R O G O -9.6 0.16
F44 R O G O -6.88 0.18
F45 R O G O -8.94 0.15
F46 R O G O -4.31 0.17
F47 R O G O -5.82 0.16
F48 R O G O -5.87 0.16
F49 R O G O -9.87 0.16
F50 R O G O -11.67 0.15
Flutamide R O G O -12.9 0.16

M; mutagenic, T; Tumorigenic, I; irritant, R; reproductive, G; no toxicity risk, O; toxicity risk, R; high toxicity risk, DL; drug likeness, DS; drug score.

Molecular Docking Study of Flutamide Analogues: The structures of all ligands (Table 1) were drawn in 2D, converted into 3D, and saved as .mol/PDB file. In order to optimise the ligands for docking, they were first optimised. All the ligands we reanalysed for their interaction with proteins through docking scores. A molecular docking score identifies ligands that interact with orientation, as seen with the androgen receptor. A 3D and 2D interaction between ligands and androgen receptors is shown in Fig. 3 and 4, respectively. The ligands showed good docking poses. Table 7 displays the log values of the ligands as well as the protein-ligand interaction scores (total score values) found during docking (the docked postures obtained through visualisation). Docking poses were identified for the ligands with the target protein. Docking poses must demonstrate how the ligand fits into the binding region of the protein.

FIG. 3: 3D DOCKING POSES OF COMPOUND F3, F17, F39 AND FLUTAMIDE

FIG. 4: 2D DOCKING POSES OF COMPOUND F3, F17, F39 AND FLUTAMIDE

Based on the docked conformations of the androgen receptor complex, intermolecular docking simulations were conducted and energy values calculated. Most of the ligands had good binding scores with 2AM9. The interaction was measured by the binding energy of the best ligand pose measured in kcal/mol. The binding poses and their energy are listed in Table 7. The obtained docking scores are between -7.76 to -9.75Å. All the ligands docked within the binding pocket region suggest their shape complements with the androgen receptor. The 3-dimentional presentation of the docking pose of ligand molecules like F3, F17, F39, and Flutamide with androgen receptors is shown in Fig. 3. Compared to flutamide as a standard, most compounds have very good docking scores. Ligands like F3, F17, F19, and F37 showed higher docking scores and multiple docking poses. In ligand F1, multiple interactions were observed, including 845ARG, 858GLN, and 845ARG at distances of 2.99Å, 2.55Å, and 2.98Å, respectively. Ligand F6 had multiple interactions with amino acid residues such as 752ARG at different distances of 2.83Å, 2.32Å, and 2.99Å. There was no interaction between Ligand F7 and any amino acid residue. Ligand F9 shows the interaction with the 746VAL residue of amino acid at a distance of 2.74Å. Ligand F19 interacted with 746PHE and 752ARG residue of amino acid at distance 2.99Å and 2.83Å, respectively. In addition, Ligand F37 has interacted with various amino acid residues such as 752ARG, 708GLY, and 704LEU at a distance of 2.25Å, 2.52Å, and 2.89Å, respectively. Furthermore, ligands F3, F17, and F39 were found to show interaction with the same amino acid residue 746VAL at a distance of 2.99Å, 2.13Å and 2.31Å, respectively, as shown in Table 7. These compounds might be powerful androgen receptor inhibitors, based on the results. In this study, it has been observed that some compounds show common amino acid residue (746VAL) interactions with ligands which have a significant role in binding and biological activity. 746Val protein amino acid residue and flutamide interacted with carbon-hydrogen residue which is also shown in the literature 34. 746VAL might account for this anti-tumour activity. An important outcome of this study may result in the design of novel androgen receptor antagonists along with docking analysis.

TABLE 7: DOCKING SCORE OF THE ANALOGUES

Entry no. Docking score (Kcal/mol) Amino acids interaction
Aryl Group Bioisosteres
F1 -7.76024 1500N-854ARG,2.99A, 1543N-858GLN,2.55A, 1500N-854ARG,2.93A, 1543N-858GLN,2.49A, 1497N-854ARG,2.33A
F3 -7.88016 1180N-746VAL,2.99A
F4 -9.55183 1826S-784CYS,2.53A
F6 -8.74 1284N-752ARG, 2.83A, 1284N-752ARG, 2.32A, 1283N-752ARG, 2.99A
F7 - No Hydrogen Bonds
F9 -9.052 1284N-752ARG, 2.74A
Nitro Group Bioisosteres
F17 -8.75 1180N-746VAL, 2.131A
F19 -9.48 1482O-764PHE, 2.99A, 1284N-752ARG, 2.83A
F21 -9.71 1166O-745MET, 2.900A
Trifluoromethyl Group Bioisosteres
F37 -9.01 1284N-752ARG, 2.25A, 559N-708GLY, 2.52A, 495O-704LEU, 2.89A
F38 -9.75 1812N-783GLN,2.99A, 3494N-883LYS, 2.59A
F39 -8.81937 1180N-746VAL,2.31A
F40 -7.90854 1740N-779ARG,2.56A, 1740N-779ARG,2.99A
Flutamide -8.26474 1180N-746 VAL,2.25A

CONCLUSION: Flutamide is one of the antiandrogen drugs used in the treatment of PC. Hepatotoxicity is a major side effect, which is why drugs are pulled from the market. Flutamide was structurally modified by using a bioisosteric approach to get less toxic compounds than flutamide. The in-silico design of drugs is a promising method for developing antiandrogen drugs. In the design of newer analogues of flutamide, a bioisosteric approach was used. As part of the investigation, ADMET lab 2.0 and PEO were used to calculate ADMET properties, DL and DS. For the docking study, Argus Lab 4.0.1 was used to confirm the best docking score by interaction between ligand and protein. A docking study of F3, F7, F17, F19, F37, and F39 ligands demonstrated that these ligands had better binding characteristics with the androgen receptor model in comparison to the other ligands. The docking study reflects that the ligands interact with the androgen receptor, which is evident by the docking scores. Ligands F3, F17, F39 and flutamide had similar interactions (746Val amino acid residue). The common amino acid residue 746Val plays a crucial role in the activity and binding affinity of the selected compounds. The data obtained from ADMET properties prediction, DL, DS, and docking studies of ligands, compounds F3, F17, and F39 could be promising drugs in the management of PC.

ACKNOWLEDGMENTS: The authors wish to thank the Head, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG) for providing required facilities to perform the research work.

CONFLICTS OF INTEREST: The authors declare that they have no known financial conflicts of interest.

REFERENCES:

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F: Global Cancer Statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin 2021; 71: 209-249. doi:10.3322/caac.21660
  2. Mohler JL, Titus MA, Bai S, Kennerley BJ, Lih FB, Tomer KB and Wilson EM: Activation of the androgen receptor by intratumoral bioconversion of androstanediol to dihydrotestosterone in prostate cancer. Cancer Res 2011; 71(4): 1486-1497. doi:10.1158/0008-5472.CAN-10-1343
  3. Zhang A, Zhang J, Plymate S and Mostaghel EA: Classical and non-classical roles for pre-receptor control of DHT metabolism in prostate cancer progression. Horm Cancer 2016; 7: 104-113. doi:10.1007/s12672-016-0250-9
  4. Michaud JE, Billups KL and Partin AW: Testosterone and prostate cancer: an evidence-based review of pathogenesis and oncologic risk. Ther Adv Urol 2015; 7(6): 378–387. doi:10.1177/1756287215597633
  5. Brzoskwinia M, Pardyak L, Rak A, Kaminska A, Hejmej A, Marek S, Kotula-Balak M and Bilinska B: Flutamide alters the expression of chemerin, apelin, and vaspin and their respective receptors in the testes of adult rats. Int J Mol Sci 2020; 21: 4439. doi:10.3390/ijms21124439
  6. Paunikar VM and Barapatre SA: Relationship between endogenous testosterone and prostate carcinoma. Fam Med Prim Care Rev 2022; 11(7): 3735-3739. doi:10.4103/jfmpc.jfmpc_2349_21
  7. Hejmej A and Bilinska B: The effects of flutamide on cell-cell junctions in the testis, epididymis, and prostate. Reprod Toxicol 2018; 81: 1-16. doi:10.1016/j.reprotox.2018.06.014
  8. Kesavan G and Chen S: Highly sensitive manganese oxide/hexagonal boron nitride nanocomposite: An efficient electrocatalyst for the detection of anti-cancer drug flutamide. Microchem J 2021; 163: 105906. doi:10.1016/j.microc.2020.105906
  9. Blobaum AL, Byers FW, Bridges TM, Locuson CW, Conn PJ, Lindsley CW and Daniels JS: A screen of approved drugs identifies the androgen receptor antagonist flutamide and its pharmacologically active metabolite 2-hydroxy-flutamide as heterotropic activators of cytochrome P450 3A in vitro and in vivo. Drug Metab Dispos 2015; 43: 1718-1726. doi:10.1124/dmd.115.064006
  10. Ball AL, Kamalian L, Alfirevic A, Lyon JJ, and Chadwick AE: Identification of the additional mitochondrial liabilities of 2-hydroxyflutamide when compared with its parent compound, flutamide in HepG2 cells. Toxicol Sci 2016; 153(2): 341–351. doi:10.1093/toxsci/kfw126
  11. Zhang L, Guo J, Zhang Q, Zhou W, Li J, Yin J, Cui L, Zhang T, Zhao J, Carmichael PL, Middleton A and Peng S: Flutamide induces hepatic cell death and mitochondrial dysfunction via inhibition of Nrf2-mediated heme oxygenase-1. Oxid Med Cell Longev 2018; 2018: 1–12. doi:10.1155/2018/8017073
  12. Ding Y, Ma H, Xu Y, Yang F, Li Y, Shi F and Lu Y: Potentiation of flutamide-induced hepatotoxicity in mice by Xian-Ling-Gu-Bao through induction of CYP1A2. J Ethnopharmacol 2021; 278(6): 114299. doi:10.1016/j.jep.2021.114299
  13. Khan MF and Murphy CD: Nitroreduction of flutamide by cunninghamella elegans NADPH: Cytochrome P450 reductase. Biochem Biophys Rep 2022; 29: 101209. doi:10.1016/j.bbrep.2022.101209
  14. Venkatesh K, Muthukutty B, Chen S, Karuppiahc C, Amanulla B, Yang C and Ramaraj SK: Nanomolar level detection of non-steroidal antiandrogen drug flutamide based on ZnMn2O4 nanoparticles decorated porous reduced graphene oxide nanocomposite electrode. J Hazard Mater 2020; 405: 124096. doi: 10.1016/j.jhazmat.2020.124096
  15. Dick A and Cocklin S: Bioisosteric Replacement as a Tool in Anti-HIV Drug Design. Pharmaceuticals 2020; 13: 36. doi:10.3390/ph13030036
  16. Losev TV, Gerasimov IS, Panova MV, Lisov AA, Abdyusheva YR, Rusina PV, Zaletskaya E, Stroganov OV, Medvedev MG and Novikov FN: Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design. J Chem Inf Model 2023; 63(4): 1239–1248. doi: 10.1021/acs.jcim.2c01212
  17. Tse EG, Houston SD, Williams CM, Savage GP, Rendina LM, Hallyburton I, Anderson M, Sharma R, Walker GS, Obach RS and Todd MT: Nonclassical Phenyl Bioisosteres as Effective Replacements in a Series of Novel Open-Source Antimalarials. J Med Chem 2020; 63(20): 11585-11601. doi:10.1021/acs.jmedchem.0c00746
  18. Kumari S, Carmona AV, Tiwari AK and Trippier PC: Amide bond bioisosteres: strategies, synthesis, and successes. J Med Chem 2020; 63(21): 12290-12358. doi:10.1021/acs.jmedchem.0c00530
  19. Basuri TS and Meman AS: Role of Bioinformatics, Chemoinformatics and Proteomic In Biomarker Identification And Drug Target Validation In Drug Discovery Processes. Int J Pharm Sci Res 2011; 2(10): 2521-2533.
  20. Bhati S, Kaushik V and Singh J: In-silico identification of piperazine linked thiohydantoin derivatives as in-silico identification of piperazine linked thiohydantoin derivatives as novel androgen antagonist in prostate cancer treatment. Int J Pept Res Ther 2018; 25(3): 845-860. doi:10.1007/s10989-018-9734-5
  21. Shan J and Ji C: MolOpt: A Web Server for Drug Design using Bioisosteric Transformation. Curr Comput.-Aided Drug Des 2020; 16(4): 460-466. doi:10.2174/1573409915666190704093400
  22. Xiong G, Wu Z, Yi J, Fu L, Yang Z, Hsieh C, Yin M, Zeng X, Wu C, Lu A, Chen X, Hou T and Cao D: ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res 2021; 49: W5-W14. doi:10.1093/nar/gkab255
  23. Dong J, Wang NN, Yao ZJ, Zhang L, Cheng Y, Ouyang D, Lu A and Cao D: ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database. J Cheminformatics 2018; 10(29): 1-11. doi:10.1186/s13321-018-0283-x
  24. Wang N, Deng Y, Zhu M, Wang J, Wen M, Yao Z, Dong J, Lu A and Cao D: Article ADME properties evaluation in drug discovery: prediction of caco-2 cell permeability using a combination of NSGA-II and boosting. J Chem Inf Model 2016; 56(4): 763-773.
  25. Lei T, Li Y, Song Y, Li D, Sun H and Hou T: ADMET evaluation in drug discovery:15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling. J Cheminformatics 2016; 8(6): 1-19. doi:10.1186/s13321-016-0117-7
  26. Srivastava R: Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities. ACS Omega 2021; 6 (38): 24891-24901. doi: 10.1021/acsomega.1c03736
  27. Zerroug A, Belaidi S, Benbrahim I, Sinha L and Chtita S: Virtual screening in drug-likeness and structure/activity relationship of pyridazine derivatives as Anti-Alzheimer drugs. J King Saud Univ Sci 2019; 31(4): 595–601.
  28. Morris GM and Lim-Wilby M: Molecular docking. Methods mol biol 2008; 443: 365-382. doi:10.1007/978-1-59745-177-2_19
  29. Kitchen DB, Decornez H, Furr JR and Bajorath J: Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 2004; 3: 935-949. doi:10.1038/nrd1549
  30. Thompson MA: Molecular docking using ArgusLab, an efficient shape-based search algorithm and the A Score scoring function. ACS meeting, Philadelphia 2004. Available at: http://www.arguslab.com/arguslab.com/ArgusLab.html.
  31. Tanguenyongwatana P and Jongkon N: Molecular docking study of tyrosinase inhibitors using ArgusLab 4.0.1: A comparative study. Thai J Pharm Sci 2016; 40(1): 1-53.
  32. Wang R, Lai L and Wang S: Further development and validation of empirical scoring functions for structure-based binding affinity prediction. J Comput.-Aided Mol Des 2002; 16(1): 11-26. doi:10.1023/a:1016357811882
  33. Karami TK, Hailu S, Feng S, Graham R and Gukasyan HJ: Eyes on Lipinski's Rule of Five: A New “Rule of Thumb” for Physicochemical Design Space of Ophthalmic Drugs. J Ocul Pharmacol Ther 2022; 38(1): 43-55.
  34. Suhandi C, Fadhilah E, Silvia N, Atusholihah A, Prayoga RR, Megantara S and Muchtaridi M: Molecular Docking Study of Mangosteen (Garcinia mangostana L.) Xanthone-Derived Isolates as Anti Androgen. Indones J Cancer Chemoprevention 2021; 12(1): 11-20.

    How to cite this article:

    Gupta AK, Mishra A and Jain SK: In-silico design of flutamide analogues as androgen receptor antagonist and molecular docking studies. Int J Pharm Sci & Res 2023; 14(12): 5785-01. doi: 10.13040/IJPSR.0975-8232.14(12).5785-01.

    All © 2023 are reserved by International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

Article Information

26

5785-5801

3196 KB

248

0

English

IJPSR

Ajay Kumar Gupta, Achal Mishra and Sanmati Kumar Jain *

Drug Discovery and Research Laboratory, Department of Pharmacy, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur, Chhattisgarh, India.

sanmatijain72@yahoo.co.in

20 April 2023

04 July 2023

28 July 2023

10.13040/IJPSR.0975-8232.14(12).5785-01

01 December 2023

Download