Table of Contents

INTRODUCTION

RATIONALE

OBJECTIVES

TIME FRAME

LITERATURE REVIEW

METHODOLOGY

REQUIREMENTS

REFERENCES

INTRODUCTION

Psoriasis is an immune cell mediated skin disease. Both genetic and environmental factors contribute to the onset and severity of the disease. It occurs when the immune cells starts to move from dermis to the epidermis which causes the hyper proliferation of keratinocytes. The immune cells are usually dendritic and T cells stimulate the secretion of cytokines [tumor necrosis factor (TNF) α] causing inflammation whereas interleukin 23 proliferate keratinocytes.

Classic genome wide linkage analysis has identified nine locations (loci) on different chromosomes associated with psoriasis. They are called psoriasis susceptibility 1 through 9 (PSORS1 through PSORS9). Within those loci are genes. Many of those genes are on pathways that lead to inflammation. Certain variations (mutations) of those genes are commonly found in psoriasis. The major determinant is PSORS1, which probably accounts for 35–50% of its heritability. It controls genes that affect the immune system or encode proteins that are found in the skin in greater amounts in psoriasis. PSORS1 is located on chromosome 6 in the MHC, which controls important immune functions. [1]

This disease is caused by improper functioning of a lot of factors which include signal transducer and activator of transcription 3 (STAT3), adhesion family of selectins, Wnt5a, Endothelin-1, enzyme – alpha secretase, S100 proteins, p53, Serum Response Factor, HSP70 and Bcl-x. Recently, a study has been done and it shows that Phosphodiesterase 4 can be targeted for curing this disease. [2]

Phosphodiesterases (PDEs) is a cyclic nucleotide degrading enzyme with 11 known gene families i.e, PDE 1-11. These enzymes control the levels of cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). These are secondary messengers which involved in controlling different tissues in the body. PDE4 controls the cAMP pathway in inflammatory cells. Due to this reasons this enzyme has captured the interest of scientists for many years now as this pathway is affected in a lot of diseases including asthma, chronic obstructive pulmonary disease (COPD), inflammatory bowel disease, atopic dermatitis (AD), psoriasis and rheumatoid arthritis (RA). [3] The drugs currently being used are: Clobex, Dovonex, Elocon, Humira, Kenalog, Neoral, Remicade, Soriatane, Stelara, Taclonex and Tazorac.

Pharmacophore approaches have become one of the major tools in drug discovery after the past century’s development. It shows the different interactions in a molecule including hydrogen bonding, hydrophobic interactions, ionic bonding etc. A pharmacophore model is an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response.

Pharmacophore for drugs of brain tumor, breast cancer, ovarian cancer, chronic myeloid leukemia and many other diseases have been designed using different computational tools and softwares.

Using proven pharmacophore methods, researchers can achieve surprising results from limited data. Pharmacophore modeling determines the spatial arrangement of chemical features that confer drug activity toward a target receptor. Search can be performed in large databases once a model is proposed. This will lead to a significant enhancement in the number of active analogs. This has paved a way to the discovery of new bioactive compounds. It is efficient in generating hits without using a receptor structure. It is helpful in rationalizing quantitative structure-activity relationships (QSAR).

RATIONALE

The prevalence of this disease is 2-3% in the Western countries of which, 1.5% is from United Kingdom. 2.1% people are affected by this disease in America specially the northeast of which 35% are at moderate to severe stage. 1/3rd of the patients have the disease onset due to genetic factors. Methotrexate is the most commonly used oral agent used for treating Psorisis, but it can damage the bone marrow and liver Similarly, Cyclosporin is highly effective at treating the disease, it is damaging to the kidneys Drug designing is a complicated process and requires 10-12 years with high expenditure of money. Pharmacophore is a common skeleton possessed by different drug to treat a disease. Pharmacophore designing aims to further refine the drug efficacy by improving its specificity and reducing its side effects. So for better drugs with fewer side effects pharmacophores should be designed for saving time and money.

OBJECTIVES

• Identification of phosphodiesterase 4 inhibitors.
• Generation of pharmacophore model.
• Measurement of distances between pharmacophore features using Visual Molecular dynamics.
• Validate the pharmacophore by virtual screening

TIME FRAME

Extensive literature survey and problem identification is to be completed in the first month. Submission of research proposal and preparation of an extensive list of inhibitors of the target must be completed by the end of second month. A generalized pharmacophore that contains features that will be helpful in targeting the enzyme is to be prepared in third month. Completion of thesis write up will be done in the fourth month.

LITERATURE REVIEW

Chandrasekaran Meganathan, Sugunadevi Sakkiah, Yuno Lee, Jayavelu Venkat Narayanan and Keun Woo Lee created a structure based pharmacophore model of crystal structure (PDB ID: 3 MJ2) using LigandScout with three hydrogen-bond acceptors (HBA), one hydrogen-bond donor (HBD), one ring aromatic (RA), and one hydrophobic (HY) for inhibitors for interleukin-2-inducible T-cell kinase. ITK is considered to play an important role in the treatment of Th2-mediated inflammatory diseases as well as HIV. Two compounds (CD_01889 and compound 11513) were identified as useful potential leads in the design of novel ITK inhibitors. [4]

Cyclooxygenase (COX) enzymes catalyses the first march in the biogenesis of prostaglandins and are the pharmacological targets of non-steroidal anti-inflammatory drugs (NSAIDs). Saranyah K., Sukesh.K., and Dinesh Kumar K made a pharmacophore of known inhibitor S-58 screened with more than 100,000 ligands from Asinex Phase Database using Schrodinger. The best five ligands showed good orientation with the known inhibitor S-58 and further taken for the drug likeness analysis. [5]

Thangapandian Sundarapandian, John Shalini, Sakkiah Sugunadevi, Lee Keun Woo worked on the pharmacophore made of one HBA, two HBD and one HYP feature for the inhibitors of Zinc-dependent histone deacetylase 8. The inhibition of this enzyme has been reported to be a novel strategy in cancer treatment. [6]

Hui Yu., Hongwei Jin., Lidan Sun, Liangren Zhang, Gang Sun, Zhanli Wang, Yongchun Yu designed a pharmacophore model consisting of 4 features: one hydrogen bond acceptor, one hydrogen bond donor, and two hydrophobic features for for Toll-like receptors 7 (TLR7) agonists. TLR7 is a member of the Toll-like receptor (TLR) family which plays a fundamental role in pathogen recognition and activation of innate immunity. [7]

Mur ligase family are considered as promising emerging targets for novel antibacterial drug design. Andrej Perdih, Andreja Kovac, Gerhard Wolber, Didier Blanot, Stanislav Gobec and Tom Solmajer worked benzene 1,3-dicarboxylic acid inhibitors of bacterial MurD and MurE ligases by making a pharmacophore using LigandScout. A novel rhodanine-containing glutamic acid based MurE inhibitor 7 was discovered. Moreover, the virtual screening disclosed a novel class of promising dual inhibitors of MurD and MurE, possessing a benzene 1,3-dicarboxylate moiety acting as a rigid replacement of the glutamic acid function. [8]

Human African trypanosomiasis, a fatal disease epidemic in sub-Saharan Africa and Yaxue Zhao, Qing Wang, Qingqing Meng, Dazhong Ding, Huaiyu Yang, Guangwei Gao, Dawei Li, Weiliang Zhu and Huchen Zhou showed in their research first discovery of inhibitors targeting the synthetic domain of T. brucei Leucyl-tRNA synthetase (TbLeuRS). Virtual scrrening using combination of pharmacophore- and docking-based approaches against a model of TbLeuRS synthetic active site was carried out and a number of 2-pyrrolinones were discovered as TbLeuRS inhibitors. [9]

P. Rani1 and V. Kumar designed a phamacophore of the inhibitors of human immunodeficiency virus-1 reverse transcriptase (HIV-1 RT) for the cure of AIDS. There are two types of HIV-1 reverse transcriptase (HIV-1 RT) inhibitors. One type of HIV-1 RT inhibitors is commonly referred to as a nucleoside inhibitor. This inhibitor inserts as nucleoside analog into DNA and acts as chain-terminating agent, thus terminating viral synthesis. The other type is called the non-nucleoside reverse transcriptase inhibitor (NNRTI). NNRTIs bind to a non-nucleoside binding pocket (NNBP) to inhibit the activity of reverse transcriptase. Pyridinone and its derivatives are a class of NNRTIs that have demonstrated good activity in HIV-1 RT inhibition. Pharmacophore model consists of one hydrogen bond acceptor (A), one hydrophobic (H), one hydrogen bond donor (D) and one aromatic ring (R) features. This model can be useful to rationally design new pyridinone molecules as HIV-1 RT inhibitors and also to identify new promising molecules HIV-1RT inhibitors. [10]

Asma Abro, Saima Kulsoom and Naveeda Riaz created a pharmacophore against ovarian cancer of microtubule-stabilizing anti-mitotic agents (MSAAs). The pharmacophore contains one HBD, two HBAs and one hydrophobic feature. A large number of classes of MSAAs is taken into account and hence a unique pharmacophore than all of the previous studies was generated. [11]

A study done by Amadeu Gavalda and Richard S Roberts shows that the inhibitors of Phosphodiesterase 4 are Roflumilast, Apremilast, Revamilast, Oglemilast, Tetomilast, MEM1414, Ronomilast, RPL554 and AN2728. [12]

METHODOLOGY

REQUIREMENTS

This research will be conducted in the Biotechnology Lab at Kinnaird College for Women, Lahore.

1. Computer system

• 2 GHz processer and 2 GB RAM
• OS Windows 8

2. High speed internet
3. Phosphodieterase 4 inhibitors
4. Computational softwares and databases

REFERENCES

1. Hintz D.R, Their R, Steinwachs S, Kronenberg S, Fritsche E, Sachs B et al. Allelic Variants of Drug Metabolizing Enzymes as Risk Factors in Psoriasis. The journal of investigative dermatology 2003 May;120(5):765-70.

2. Varadwaj P.K, Sharma A, Kumar R. An Overview of Psoriasis with Respect to its Protein Targets. Egyptian Dermatology Online Journal 6, June 2010 (1): 1.

3. Kumar N, Goldminz N.A, Kim N, Gottlieb A.B. Phosphodiesterase 4-targeted treatments for autoimmune diseases. BMC Medicine 2013, 11:96.

4. Meganathan C, Sakkiah S, Lee Y, Narayanan J.V, Lee K.W. Discovery of potent inhibitors for interleukin-2-inducible T-cell kinase: structure-based virtual screening and molecular dynamics simulation approaches. J Mol Model (2013) 19:715–726.

5. Saranyah K., Sukesh.K., and Dinesh Kumar K, Lilly M. Saleena. Structure Based Pharmacophore Modeling and Virtual Screening for Identification of Novel Inhibitor for Cyclooxygenase-2. International Conference on Bioscience, Biochemistry and Bioinformatics IPCBEE vol.5 (2011) IACSIT Press, Singapore.

6. Sundarapandian T, Shalini J, Sugunadevi S, Woo L.K. Docking-enabled pharmacophore model for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery. Journal of Molecular Graphics and Modelling 29 (2010) 382–395.

7. Yu H, Jin H, Sun L, Zhang L, Sun G, Wang Z, et al. Toll-Like Receptor 7 Agonists: Chemical Feature Based Pharmacophore Identification and Molecular Docking Studies. Plos One March 2013Volume 8 Issue 3; e56514.

8. Perdih A, Kovac A, Wolber G, Blanot D, Gobec S, Solmajer T. Discovery of novel benzene 1,3-dicarboxylic acid inhibitors of bacterial MurD and MurE ligases by structure-based virtual screening approach. Bioorganic & Medicinal Chemistry Letters 19 (2009);2668–2673.

9. Zhao Y, Wanga Q, Meng Q, Ding D, Yang H, Gao G, et al. Identification of Trypanosoma brucei leucyl-tRNA synthetase inhibitors by pharmacophore- and docking-based virtual screening and synthesis. Bioorganic & Medicinal Chemistry 20 (2012);1240–1250.

10. Rani P and Kumar V. Development of pharmacophore models for predicting HIV-1 reverse transcriptase inhibitory activity of pyridinone derivatives Pharmaceutical Chemistry Journal, Vol. 45, No. 1, April, 2011 (Russian Original Vol. 45, No. 1, January, 2011).

11. Abro A, Kulsoom S and Riaz N. Pharmacophore model generation for microtubule-stabilizing anti-mitotic agents (MSAAs) against ovarian cancer. Med Chem Res (2013) 22:4322–4330.

13. Gavalda A and Roberts R.S. Phosphodiesterase-4 inhibitors: a review of current developments (2010 — 2012). Expert Opin. Ther. Patents (2013) 23(8):997-1016.
14. Kiani Y.S, Kalsoom S and Riaz N. In silico ligand-based pharmacophore model generation for the identification of novel Pneumocystis carinii DHFR inhibitors. Med Chem Res (2013) 22:949–963.