INTRODUCTION:

The drug is commonly a small organic molecule that activates or inhibits the function of a biomolecule such as a protein, which results in a therapeutic benefit to the patient^1-2. In the most basic sense, drug design involves the design of small molecules that are complementary in shape and charge to the biomolecular target with which they interact and bind it. Drug design, in general, relies on computer modeling techniques regarded as computer-aided drug design^3-6. Lastly, drug design that depends on the knowledge of the three-dimensional structure of the biomolecular target is known as structure-based drug design.

Computer modeling techniques for the prediction of binding affinity are successful to a greater extent. Still, properties like bioavailability, metabolic half-life, lack of side effects, etc., must be optimized before a ligand can become a safe and efficient drug. These other features were found to be difficult to optimize using current drug design techniques^7-10.

A drug target is a molecule which involves in a metabolic pathway unique to the pathology of a microbial pathogen^11-12. There are many approaches that attempt to attenuate the functioning of the pathway in the diseased state by causing a crucial molecule to stop functioning. Drugs can be designed which binds to the active region and inhibit the key molecule. There is another approach to enhance the normal pathway by promoting specific molecules that may have been affected in the diseased state. The design of Drug molecules is such a way that they must not affect off-target or anti-target molecules¹³. Many times there can be interactions between off-target molecules that leads to undesirable effects. Sequence homology, a method frequently used to identify such risks^14-16. There are two types of drug design; the first is ligand-based drug design, the second, structure-based drug design.

Ligand-based drug design (or indirect drug design) depends on molecules that bind to the biological target of interest¹⁷. The molecules are used to derive a pharmacophore model that defines the minimum necessary structural characteristics a molecule must possess to bind the target. Alternatively, QSAR studies derive a correlation between the calculated properties of molecules and their experimentally determined biological activities of new analogs. The structure-based drug design^18-20 on the other hand, plays a pivotal role in the design of novel drugs for several diseases. Recent advances in the large-scale determination of protein structures are improving the drug discovery process by starting with the protein structure and using it to design and identify new ligands. Literature survey reveals^21-24 that rutin and many other bio-flavonoids exhibit anti-oxidant activity by inhibiting the normal functioning of lambda-class glutathione transferases(GSTLs), for particular xenobiotic inducible GSTLs, TaGSTL1 has found that tightly bind rutin, taxifolin, etc. The 3D- structure of the target enzyme TaGSTL1 is yet to be determined; therefore, pharmacophore patterns and 2D- and 3D- descriptors of rutin were derived and analyzed to determine the structural features that govern the anti-oxidant activity.

MATERIALS AND METHODS:

Molecular structures were drawn using Chem Sketch 12 freeware and then energy optimization by MMFF94 force field using TINKER. PyMol 1.7 and Avogadro 1 were used to calculate the molecular surface potential, to determine the intra molecular H-bonds. All the software's used with default settings, operating in Linux Ubuntu 12.04^25-26. The default settings of different parameters were shown in the table below.

Table 1: default settings of different parameters

S..No	Parameters	Values
01	Maximum number of iterations	500
02	Minimum RMS gradient	0.100
03	Steric energy limit	250 kJ/Mol
04	Total charge	0
05	Spin multiplicity	1
06	Spin Pairing	UHF
07	Sate	Lowest

RESULTS AND DISCUSSION:

The final structures using the MMFF94 force field optimized structure of ASR (Rutin) is as shown in figure -1. From the figure, it is clear that the glycosidic moiety is almost perpendicular to flavones and catechol moieties; the same is true for catechol moiety. This could be a possible reason for high stability of rutin²⁷. Another possible explanation is the presence of diverse intra-molecular H- bonds.

(a) Front view of Rutin

(b) Side view (90^o) of Rutin

Fig .1:MMFF94 force field optimized structure of ASR (Rutin)

The molecular surface face potential for Rutin is depicted in figure -2 which indicates that majority Rutin is either H-bonding or Mild polar in nature. It is the flavone that imparts hydrophobic nature to it.

(a) Front view of molecular surface potential

(b) Side view (90^o)

Fig-2: Molecular surface potential

(Blue-H-bonding, Red- Mild Polar and White- hydrophobic region)

Generally, the approach in the connectivity method is different from the traditional biological method based on the assumed mechanism and Physico-chemical properties as regression variables. It seeks to extract the structural information from a data set and relate it directly to the set of activities. From this angle, it is seen that the physical significance of molecular connectivity is coterminous for molecular structure. So the zero and first-order connectivity indices were calculated for the rutin. TPSA gives the transport properties of drug moieties in the intestines and BBB. Rutin with a TPSA of 269.43, which suggests that it likely acts as hydrophilic in nature.

Lipophilicity is an imperative parameter used by medicinal chemists in drug discovery on a routine basis. It plays a pivotal role in governing kinetic and dynamic aspects of drug action. It can be defined as the ratio of the concentrations of a neutral compound in organic and aqueous phases of a two-compartment system under equilibrium conditions^28-30. which mostly calculated as log P. Moriguchi developed one of the earliest yet successful methods, MLOGP, which uses the model variables include: the sum of lipophilic and hydrophilic atoms, N/O atom proximity effect, number of unsaturated bonds, number of polar aromatic substitutes, presence of ring-type structures, number of nitrogen groups, presence of intramolecular hydrogen bonds, amphoteric properties³¹. The MLogp of Rutin was -3.148 which is negative. The calculated physio-chemical properties of Rutin have been tabulated in table-2

Table -2: In silico calculated physico-chemical properties of Rutin

S. No	Physico-Chemical Properties	Corresponding value
1	Molecular Weight (MW)	610.57
2	X⁰(Zero order connectivity index)	31.325
3	X¹ (First order connectivity index)	20.277
4	Number of H-Donating groups	10
5	Number of H-accepting groups	16
6	Number of H-Bonds	4
7	Polar surface area (due to N and O) PSA(NO)	269.43
8	Total polar surface area TPSA (Tot)	269.43
9	MLOGP (MoriguchiLipophilicity)	-3.148

In the absence of information about the 3D- structure of the target enzyme, pharmacophoric pattern and QSAR are considered voluble techniques to identify the structural features that steer the activity. In the present work, pharmacophore modeling was performed to achieve these goals. The pharmacophoric pattern of Rutin has been depicted in figures 3(a) and (b). From the pharmacophoric pattern, it is evident that the flavonoid moiety (shown by the green ball) controls the hydrophobic nature of Rutin³². It is approximately at a distance of 12.76 and 6.13A⁰ from sugar and catechol moieties, respectively. The angle between the corner of the sugar moiety, flavonoid part, and catechol moiety is 66.5⁰

Fig 3(a):2D- representation of Pharmacophore pattern of Rutin

a) With distances

((b) With angles

Fig 3(b) 3D- representation of pharmacophoric pattern for Rutin (Green colored in Angstrom unit)

Another way to understand the pharmacophoric pattern is to calculate the charge on each atom of the molecule. The charges were calculated using the MMFF94 method, depicted in figure 4 (for the sake of simplicity, Hydrogen has been eliminated). Charge on each atom using MMFF94 method (90⁰view)

(a) Front view

(b) 90^o view

Fig 4: Charge on each atom using MMFF94 method

CONCLUSION:

By using free software like Chemsketch 12 and other free wares the molecules were designed and insilico studies were conducted. The study shows that rutin has strong anti-oxidant binding activity by inhibiting the normal functioning of lambda-class glutathione transferases (GSTLs), of particular xenobiotic inducible GSTLs, TaGSTL1. The 3D- structure of the target enzyme TaGSTL1 is not determined as of now; therefore, pharmacophore patterns and 2D- and 3D- descriptors of rutin were derived and analyzed. This is done in order to detect the key structural features that govern the anti-oxidant activity. From the MMFF94 force field optimized structure of ASR (Rutin) , it is clear that the glycosidic moiety is almost perpendicular to flavones and this is the most probable reason for the high stability of rutin. It has a TPSA of 269.43, which suggests that it likely acts as hydrophilic in nature. Taking this study as the base 3D- structure of the target enzyme TaGSTL1 can be determined and further in silico studies can be conducted.

CONFLICT OF INTEREST:

Authors declare none.

REFERENCES:

1. Kadam S. S. QSAR, Molecular Docking and Toxicology Profile of Synthesized Derivatives of 1, 3, 4-Thidiazole-2-Amine. Curr. Pharm. Res. 2019; 9(2): 2743-2751.

2. Yuichi Matsuzaki Yuya Yoshimoto Sadayuki Arimori So Kiguchi Toshiyuki Harada Fukumatsu Iwahashi Discovery of metyl tetraprole: Identification of tetrazolinone pharmacophore to overcome QoI resistance Bioorganic and Medicinal Chemistry.2020; DOI: 10.1016/j.bmc.2019.115211

3. V. S. Kawade S. S. Kumbhar P. B. Choudhari M. S. Bhatia 3D QSAR and Pharmacophore Modelling of some Pyrimidine Analogs as CDK4 Inhibitors. Asian J. Research Chem 2015; 8(4): 231-235. DOI: 10.5958/0974-4150.2015.00040.1

4. Hanine Hadni Menana Elhallaoui Molecular docking and QSAR studies for modeling the antimalarial activity of hybrids 4-anilinoquinoline-triazines derivatives with the wild-type and mutant receptor pf-DHFR Heliyon. 2019; 5(8): https://doi.org/10.1016/j.heliyon.2019.e02357

5. Garima Verma Mohemmed Faraz Khan Wasim Akhtar Mohammad Mumtaz Alam Mymoona Akhter Ozair Alam Syed Misbahul Hasan Mohammad Shaquiquzzaman Pharmacophore modeling, 3D-QSAR, docking and ADME prediction of quinazoline based EGFR inhibitors. Arabian Journal of Chemistry. 2019; 12(8): 4815–4839 : https://doi.org/10.1016/j.arabjc.2016.09.019

6. Devi Violina Bhupen Kalita Simultaneous UV Spectrophotometric Estimation and Validation of Rutin and resveratrol in combined dosage form by Iso absorptive point method. Asian J. Pharm. Ana. 2018; 8(3): 147-152: DOI: 10.5958/2231-5675.2018.00027.3

7. Yoon. Y.K, Ali. M.A Wei. A.C Choon. T.S, Khaw. K.Y, Murugaiyah. V Osman. H Masand. V.H. Synthesis, characterization and molecular docking analysis of novel benzimidazole derivatives as cholinesterase inhibitors, Bio org Chem, 2013;49:33-9. doi: 10.1016/j.bioorg.2013.06.008.

8. Yeong Keng Yoona Mohamed Ashraf Ali Ang Chee Wei Tan Soo Choon Kooi-Yeong Khaw Vinkneswaran Murugaiyah, Hasnah Osman Masand,V.H. Synthesis, characterization and molecular docking analysis of novel benzimidazole derivatives as cholinesterase inhibitors, Bio Organic Chemistry. 2013;49: doi: 10.1016/j.bioorg.2013.06.008.

9. Rastija .V Nikolic. S Masand. V.H. Quantitative relationships between structure andlipophilicity of naturally accuring polyphenols, Acta Chim. Slov. 2013; 60(04): 781-789.

10. Manohar D. Kengar Rohit S. Howal Dattatray B. Aundhakar Amit V. Nikam Priyajit S. Hasabe. Physico-chemical Properties of Solid Drugs: A Review. Asian J. Pharm. Tech. 2019; 9 (1):53-59; DOI: 10.5958/2231-5713.2019.00010.2

11. Pavel Polishchuk Alina Kutlushina Dayana Bashirova Olena Mokshyna and Timur Madzhidov. Virtual Screening Using Pharmacophore Models Retrieved from Molecular Dynamic Simulations Int. J. Mol. Sci. 2019; 20(23): DOI: 10.3390/ijms20235834

12. Mahajan. D.T. Masand Patil. K.N Hadda T.B,Rstija.V, Integrated GUSAR and QSAR analysis for antimalarial activity of synthetic prodiginines against multi drug resistant strain, Med. Chem. Res,. 2013;22(5): 2284-2292: DOI 10.1007/s00044-012-0223-7

13. Yashpal S Kori Bhumika Yogi Sujeet Gupta Antihaemorrhoid Activity of Isolated and Semi – Synthesized Rutin Derivative from Euphorbia hirta Linn. Research J. Pharm. and Tech 2020; 13(3):1333-1338. doi: 10.5958/0974-360X.2020.00246.2

14. Yue Dong Min Liu Jian Wang Zhuang Dinga Bin Sun Construction of antifungal dual-target (SE, CYP51) pharmacophore models and the discovery of novel antifungal inhibitors RSC Adv. 2019; 9(45): https://doi.org/10.1039/C9RA03713F

15. V. Masand D. T. Mahajan K. N. Patil K. D. Chinchkhede R. Jawarkar T. Hadda Ahmed A. Alafeefy I. G. Shibi, quantum mechanical and field similarity based analysis of xanthose derivatives as α- glucosidase inhibitors, Med. Chem Res. 2012;21: 4523-4534: DOI:10.1007/s00044-012-9995-z

16. Muhammad Arba Jasriati Jasriati. Structure-based Pharmacophore Modelling for identifying VEGFR2 Inhibitor. Research J. Pharm. and Tech. 2020; 13(7): 3129-3134: DOI: 10.5958/0974-360X.2020.00553.3

17. Abdullatif Bin Muhsinah Abdulrhman Alsayari Kamal Yoonus Thajudeen Yahya I Asiri Mahadevan Nanjaian. Simultaneous estimation of Rutin and Quercetin in Bidens pilosa, Convolvulus arvensis and Neuradap rocumbens by RP-HPLC. Research J. Pharm. and Tech. 2020; 13(7): 3305-3310: DOI: 10.5958/0974-360X.2020.00586.7

18. Chavan.. Bandgar. H.V, B.P, Adsul L.K., Dhakane. V.D.,Bhale. P.S,Thakare. V.N.,Masand., Design,synthesis,characterization and anti-inflammatory evaluation of novel pyrazole amalgamated flavones, Bioorg.Med.Chem.Lett. 2013; 23(3): 1315-1321: DOI: 10.1016/j.bmcl.2012.10.031

19. K.V.V.S. Krishna K. Gouri Sankar M. Syam Vardhan S. Rama Krishna T. Tamizhmani. Simultaneous estimation of Gallic acid and rutin in Kaishore guggulu and Vatari guggulu by HPTLC. Res. J. Pharmacognosy and Phytochem. 2018; 10(3): 221-225: DOI: 10.5958/0975-4385.2018.00036.5

20. JianGao Baowei Chen Haijian Lin Yi Liu Yao Wei Fabo Chena WenboLia. Identification and characterization of the glutathione S-Transferase (GST) family in radish reveals a likely role in anthocyanin biosynthesis and heavy metal stress tolerance. Gene.2020; 743(15): DOI: 10.1016/j.gene.2020.144484

21. Preeti Tiwari Rakesh K. Patel. Quantification of Quercetin and Rutin in Arjunarishta Prepared by Traditional and Modern Methods by Validated HPTLC Densitometry. Asian J. Research Chem. 2011; 4(6): 1019-1024.

22. Shivayogi S. Narasagoudr, Veena G. Hegde, Ravindra B. Chougale, Saraswati P. Masti, Shyamkumar Vootla, Ravindra B. Malabadi. Physico-chemical and functional properties of rutin induced chitosan/poly (vinyl alcohol) bioactive films for food packaging applications. Food Hydrocolloids. 2020; 109: https://doi.org/10.1016/j.foodhyd.2020.106096

23. Ramachandran Dittakavi Kranthi Raj Kodamala Soujanya Kaki PardhasaradhiMathi., In Vitro Antibacterial and In Silico Evaluation of Indole based Molecules as Staphylococcus Aureus Sortase A Inhibitors. Res. J. Chem. Environ. 2018; 22 (6).

24. Shrestha Sharma, Syed Arman Rabbani, Jasjeet K Narang, Faheem Hyder Pottoo, Javed Ali, Shobhit Kumar, Sanjula Baboota. Role of Rutin Nanoemulsion in Ameliorating Oxidative Stress: Pharmacokinetic and Pharmacodynamics Studies. Chemistry and Physics of Lipids. 2020;22: https://doi.org/10.1016/j.chemphyslip.2020.104890

25. Praveen Patidar, Sameer Singh, Darshan Dubey, Kamlesh Dashora. Estimation of Rutin in Tephrosia purpurea by HPTLC Method. Research J. Pharm. and Tech. 2013; 6(1): 58-60.

26. Opo, F.A.D.M., Rahman, M.M., Ahammad, F. et al Structure based pharmacophore modeling, virtual screening, molecular docking and ADMET approaches for identification of natural anti-cancer agents targeting XIAP protein. Science Report. 2021;11: https://doi.org/10.1038/s41598-021-83626-x

27. K.Vijaya Sri, Guda Santhoshini, D. Ravi Sankar, K.Niharika. Formulation and Evaluation of Rutin Loaded Nanosponges. Asian J. Res. Pharm. Sci. 2018; 8(1):21-24: DOI: 10.5958/2231-5659.2018.00005.X

28. David P. Dixon, Patrick G. Steel and Robert Edwards. Roles for gluthathionetransferases in antioxidant recycling, Plant Signaling and Behavior. 2011; 6(8): 1223-1227: DOI:10.4161/psb.6.8.16253

29. Mohan. A ,Kirubakaran. R, Parray. J.A , Sivakumar. R, Murugesh. E , Govarthanan. M., Ligand-based pharmacophore filtering, atom based 3D-QSAR, virtual screening and ADME studies for the discovery of potential ck2 inhibitors. Journal of Molecular Structure. 2020; 1205(10): DOI:10.1016/j.molstruc.2019.127670

30. Ramasamy Arivukkarasu, Aiyalu Rajasekaran, Syed haffan bin hussain, Mohammed Ajnas. Simultaneous detection of Rutin, Quercetin, Gallic acid, Caffeic acid, Ferulic acid, Coumarin, Mangiferin and Catechin in Hepatoprotective commercial herbal formulations by HPTLC technique. Res. J. Pharmacognosy and Phytochem. 2018; 10(1): 59-62: http://dx.doi.org/10.5958/0975-4385.2018.00009.2

31. David Schaller, Dora Šribar, Theresa Noonan, Lihua Deng, Trung Ngoc Nguyen, Szymon Pach, David Machalz, Marcel Bermudez, Gerhard Wolber. Next generation 3D pharmacophore modeling. WIREs Comput Mol Sci. 2020;10(4): https://doi.org/10.1002/wcms.1468.

32. Shiv Kumar Patel, Moumita Sinha, Mitashree Mitra. Glutathione-S-transferase M1 and T1 gene Polymorphism as Risk Factors of Oral Squamous Cell Carcinoma: A Preliminary Investigation. Research J. Pharm. and Tech. 2012;5(7): 918-920.

Received on 22.11.2021 Modified on 13.04.2022

Research J. Pharm. and Tech 2023; 16(4):1940-1944.

DOI: 10.52711/0974-360X.2023.00318