Synthesis, Characterisation, Anti-Cancer Activity and Docking Studies of

4-(4-(dimethylamino) phenyl)-1,3,6-triphenylpiperidin-2-one

 

S Sumathi1, Charles Christopher Kanakam2, B. Thanuja3*

1Research and Development Centre, Bharathiar University, Coimbatore, Tamilnadu, India.

1Sri Sairam Institute of Technology, Chennai, Tamilnadu, India.

2Department of Chemistry, Valliammai Engineering College, Kattankulathur, Tamilnadu, India.

3Sri Sairam Engineering College, Chennai, Tamilnadu, India.

*Corresponding Author E-mail: thanuja.che@sairam.edu.in

 

ABSTRACT:

The newly synthesised ligand 4-(4-(dimethylamino)phenyl)-1,3,6-triphenylpiperidin-2-one [DMP] with phenyl united piperidine moieties within the main cyclic chain was synthesized through the Michael addition reaction and qualitative analysis was characterised by FT-IR, NMR (13C, 1H). The present work deals with the computational analysis of a synthetic compound as a ligand with anticancer activity. The molecule is analysed for its druggable property and biological significance using several softwares. The molecule was docked with the receptor protein bearing the PDB ID 1UNG. The results of the anti-cancer of synthesised ligand DMP are correlated with the docking calculations performed on protein Cyclin-dependent kinase 5 using ligand fit protocol available through Acclerys Discovery studio 2.1

 

KEYWORDS: Michael addition, Cyclin-dependent kinase, Docking studies, Anti-cancer.

 

 


1. INTRODUCTION:

The piperidine ring is an omnipresent structural feature of many alkaloid natural products and drug applicants. A recent 10-year period there were thousands of piperidine compounds cited in clinical and preclinical studies.1 Piperidones are somewhat less prominent, but often they help a role as advanced intermediates prior to their conversion to piperidines. Reviews updating progress in the stereo selective syntheses of substituted piperidines have appeared recently.2 The large volume of published literature over the past three years precludes a comprehensive review. Procedures were selected on the basis of variety and stereo chemistry, with the intent of the reader with a diversity of choices for the synthesis of these useful heterocycles.

 

In the current research investigation, we synthesised 4-(4-(dimethylamino) phenyl)-1,3,6-triphenylpiperidin-2-one [DMP]. We chose N,2-diphenylacetamide as a starting precursor since we have prepared this in bulk.3

 

N,2-diphenylacetamide was dissolved in ethanol with an equivalent amount of chalcones. A catalytic amount of sodium hydride was added to initiate the reaction and then the mixture was refluxed.

 

Our interest was to study the synthesis of piperidone compounds by one pot Michael addition process. The main aim of this project is to develop efficient synthetic methodology which requires lesser reaction time and reduces the number of steps involved in the synthesis. The reaction was completed in 30 minutes as evident from TLC (petroleum ether: ethyl acetate (85: 15, v/v) which has showed the appearance of a new spot.

 

Scheme 1. Synthesis of 4-(4-(dimethylamino) phenyl)-1,3,6-triphenylpiperidin-26-one

 

Then the reaction mixture was poured into an ice-cold water; the yellow precipitate was filtered, dried and purified through column chromatography; the yield was 80% and m.p is 124°C. [Scheme 1]

 

2. MATERIALS AND METHODS:

2.1. Retrieval of the Structure of the Protein:

The structure of the drug target protein Cyclin-dependent kinase 5 and its X-ray crystallographic structure with 2.3Ǻ was retrieved from protein data bank. 4 Protein identification number 1UNG, commonly known as PDB ID.

 

2.2. Protein Preparation:

The raw protein from the protein databank with the PDB ID 1UNG human protein Cyclin-dependent kinase 5 is further prepared for docking studies.5-7 Initially, all other chemical moieties, present in protein are removed. All water molecules were removed and on the final stage hydrogen atoms were added to the target protein molecule. Energy minimization was performed to remove the bad steric clashes with 1000 steps at RMS gradient of 0.1 and 0.03 respectively. Priorly a suitable force field CHARMm, available with Accelrys life science software 8-12 was applied.

 

2.3. Preparation of the Ligand:

The ligand molecule is drawn using Chem Sketch. The figures 1shows the structure of the DMP.13

 

3. RESULTS AND DISCUSSION:

3.1. Pharmacophore Analysis of the molecules:

The DMP were subjected to pharmacophore analysis using Discovery Studio 2.0. DMP (Figure 1) shows four hydrophobic groups and one hydrogen bond acceptor. Since the hydrophobic groups are more in number, it can be expected to participate more in hydrophobic interactions than hydrogen bonding interactions. Hence the molecule can be expected to have more hydrophobic interactions.

 

 

Fig. 1. Interaction Study of the Compounds with the Protein

3.2. Interaction of DMP with the target protein:

 

Figure 2 shows the interaction of the DMP with the protein analyzed using the Discovery Studio 2.0

 

Figure 2. DMP interacting with the target protein.

 

Figure 3. 2D Interactions between DMP and 1UNG.

 

The DMP shows two hydrogen bonding interactions between OE1 of Glutamic acid at position 273 to H62 DMP and O of Glutamic acid at position 226 to H64 of the DMP. There are also several hydrophobic interactions which stabilize the complex. These interactions are of both alkyl and Pi-Alkyl types. The amino acids ALA244, LEU219, LEU248, PRO222, LYS268, CYS269 are involved in these interactions.14-18 The observed dock score is 127.706. [Fig.3]. The molecule, 4-(4-(dimethylamino) phenyl)-1,3,6-triphenylpiperidin-2-one was analysed for Lipinski's rule of 5 and was found to hold good to serve as a ligand. The logP value of the synthetic ligand was found to be 7.31.

 

 

 


Table 1. Molecular properties of the DMP

Name of the molecule /Ligand

LogP

TPSA

(Å)2

Molecular Weight g/mol

Molecular Volume

Lipinski's Rule

Bio

availability

Compound Mol-inspiration

7.31

23.55

446.59

432.39

1

-

Compound 1Swiss ADME

-

23.55

446.58

-

1 violation

MLOGP>4.15

0.55

 

 


3.4. Calculation of the Molecular Properties of the DMP:

The molecular properties of the synthsised compound was calculated using the tool Molinspiration and Swiss ADME.19-21 The properties are tabulated for the DMP compound in the table 1.

 

CONCLUSION:

The ligand has been synthesized by Michael addition. The biological importance of the synthesized compound has been analyzed by using computational approach. It has been found that the ligand could be a potential lead compound in the inhibition of the Cyclin-dependent kinase 5 protein. It is extremely important to validate the efficacy and safety of these drugs in clinical trials.

 

ACKNOWLEDGEMENTS:

The authors are thankful to their respective managements for continuous support and encouragement in pursuing this research.

 

REFERENCES:

1.      Wang Y. Yuan YQ. Guo SR. Silica Sulfuric Acid Promotes Aza-Michael Addition Reactions under Solvent-Free Condition as a Heterogeneous and Reusable Catalyst. Molecules 2009; 14:  4779-4789.

2.      Pitchai P. Sathiyaseelan M. Nepolraj A. Gengan RM. An elegant synthesis of indoloquinoline alkaloid cryptotackieine via Vilsmeier-Haack approach. Indian J. Chem.  2015; 54: 1290-1292.    

3.      Ram Janam Singh. Dharmendra Kumar Singh. Syntheses, Characterization and Biological Screening of Some Novel 1, 2, 4-Triazoles. Asian J. Research Chem. 2009; 2(4): 536-538.

4.      Malmström J. Viklund J. Slivo C. Costa A. Maudet M et al. Synthesis and structure-activity relationship of 4-(1,3-benzothiazol-2-yl)-thiophene-2-sulfonamides as cyclin-dependent kinase 5 (cdk5)/p25 inhibitors. Bioorg. Med. Chem. Lett.2012; 18: 5919-23.

5.      Testa B. Crivori P. Reist M. Carrupt PA. The influence of lipophilicity on the pharmacokinetic behavior of drugs: Concepts and examples. Perspect Drug Discov Des. 2000; 19: 179–211.

6.      Cronin MTD. The role of hydrophobicity in toxicity prediction. Current Computer-Aided Drug Design. 2006; 2: 405–413.

7.      Animisha M. Radha Krishna N. Chinna Babu P. Ramakrishna C. Satyanarayana R. Docking Studies of Piperine-Vitamin a conjugate to sudy the Increase in Bioavailablity of Vitamin A. Research Journal of Pharmacy and Technology.2017;10:2189-2193.

8.      Yogesh Jadhav. Rajeev Varma. Vrushali Patil A S. Bobade SV. Athlekar. Abhay Chowdhary. Synthesis and Study of Some 1, 2, 4-Triazole derivatives. Research J. Pharm. and Tech.2010; 3 (4): 1144-1147.

9.      Tetko IV. Prediction of physicochemical properties. In: Ekins S, editor. Computational toxicology: Risk assessment for pharmaceutical and environmental chemicals. New Jersey.2007; John Wiley and Sons, Inc.241–275.

10.   Ertl P.  Rohde B.  Selzer P. Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties. J Med Chem. 2000; 43(20): 3714-3717.

11.   Boobis A. Gundert-Rem U. Kremers P. Macheras P. Pelkonen O. In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15. Eur J Pharm Sci. 2002; 17: 183-193.

12.   Hecht D. Fogel GB. Computational intelligence methods for ADMET prediction. Frontiers in Drug Design and Discovery. 2009; 4:  351-377.

13.   Daisy P. Singh SK. Vijayalakshmi P. Selvaraj C. Rajalakshmi M. Suveena S. A database for the predicted pharmacophoric features of medicinal compounds. Bioinformation. 2011; 6(4): 167-168.

14.   Hosea NA. Jones HM. Predicting pharmacokinetic profiles using in silico derived parameters. Mol Pharm. 2013; 10: 1207-1215.

15.   Hou T. Wang J. Zhang W. Wang W. Xu X. Recent advances in computational prediction of drug absorption and permeability in drug discovery. Curr Med Chem. 2006; 13: 2653- 2667.

16.   Moroy G. Martiny VY. Vayer P. Villoutreix BO. Miteva MA. Toward in silico structure-based ADMET prediction in drug discovery. Drug Discov Today. 2012; 17: 44-55.

17.   Urso R. Blardi P. Giorgi G. A short introduction to pharmacokinetics. European Review for Medical and Pharmacological Sciences. 2002; 6: 33-44.

18.   A. Ranganadha Reddy TC. Venkateswarulu D. John Babu N. Shyamala Devi. Homology Modeling, Simulation and Docking Studies of Tau-Protein Kinase. Research Journal of Pharmacy and Technology.2014;7:376-388

19.   Wolber G. Seidel T. Bendix F. Langer. Molecule-pharmacophore super positioning and pattern matching in computational drug design. Drug Discovery Today. 2007; 13: 23-29. 

20.   Momany FA. Rone RJ. Validation of the general-purpose QUANTA 3.2/CHARMm force field. Journal of Computational Chemistry.1990; 13: 888-900.

21.   Liu T.Altman RB. Identifying Druggable Targets by Protein Microenvironments Matching: Application to Transcription Factors. Pharmacometrics Systems Pharmacology. 2014; 3: 93.

 

 

 

 

Received on 25.02.2021           Modified on 03.09.2021

Accepted on 10.12.2021         © RJPT All right reserved

Research J. Pharm. and Tech. 2022; 15(6):2483-2485.

DOI: 10.52711/0974-360X.2022.00414