Prospects of Utilizing Computational Techniques for the Treatment of Human Diseases

Author(s): Anuraj Nayarisseri.

Journal Name: Current Topics in Medicinal Chemistry

Volume 19 , Issue 13 , 2019

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[1]
Sinha, K.; Majhi, M.; Thakur, G.; Patidar, K.; Sweta, J.; Nayarisseri, A.; Singh, S.K. Computer aided drug designing for the identification of high affinity small molecule targeting CD20 for the clinical treatment of Chronic Lymphocytic Leukemia (CLL). Curr. Top. Med. Chem., 2018, 18(29), 2527-2542.
[2]
Patidar, K.; Deshmukh, A.; Bandaru, S.; Lakkaraju, C.; Girdhar, A.; Vr, G.; Singh, S.K. Virtual screening approaches in identification of bioactive compounds akin to Delphinidin as potential HER2 inhibitors for the treatment of breast cancer. Asian Pac. J. Cancer Prev., 2016, 17(4), 2291-2295.
[3]
Cheng, F.; Li, W.; Zhou, Y.; Shen, J.; Wu, Z.; Liu, G.; Tang, Y. admetSAR: A comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model., 2012, 52(11), 3099-3105.
[4]
Niu, N.; Wang, L. In vitro human cell line models to predict clinical response to anticancer drugs. Pharmacogenomics, 2015, 16(3), 273-285.
[5]
Chiodi, I.; Belgiovine, C.; Donà, F.; Scovassi, A.I.; Mondello, C. Drug treatment of cancer cell lines: A way to select for cancer stem cells? Cancers, 2011, 3(1), 1111-1128.
[6]
Nikolin, B.; Imamović, B.; Medanhodžić-Vuk, S.; Sober, M. High performance liquid chromatography in pharmaceutical analyses. Bosn. J. Basic Med. Sci., 2004, 4(2), 5-9.
[7]
Heller, M.; Kessler, H. NMR spectroscopy in drug design. Pure Appl. Chem., 2001, 73(9), 1429-1436.
[8]
Carnero, A. High throughput screening in drug discovery. Clin. Transl. Oncol., 2006, 8(7), 482-490.
[9]
Khandelwal, R.; Chauhan, A.P.S.; Hussain, T.; Hood, E.A.; Nayarisseri, A. Structure-based virtual screening for the identification of high affinity small molecule towards STAT3 for the clinical treatment of Osteosarcoma. Curr. Top. Med. Chem., 2018, 18(29), 2511-2526.
[10]
Sharda, S.; Sarmandal, P.; Cherukommu, S.; Dindhoria, K.; Yadav, M.; Bandaru, S.; Nayarisseri, A. A Virtual screening approach for the identification of high affinity small molecules targeting BCR-ABL1 inhibitors for the treatment of chronic myeloid leukemia. Curr. Top. Med. Chem., 2017, 17(26), 2989-2996.
[11]
Bandaru, S.; Alvala, M.; Akka, J.; Sagurthi, S.R.; Nayarisseri, A.; Kumar Singh, S.; Prasad, M.H. Identification of small molecule as a high affinity β2 agonist promiscuously targeting wild and mutated (Thr164Ile) β 2 adrenergic receptor in the treatment of bronchial asthma. Curr. Pharm. Des., 2016, 22(34), 5221-5233.
[12]
Sinha, C.; Nischal, A.; Bandaru, S.; Kasera, P.; Rajput, A.; Nayarisseri, A.; Khattri, S. An in silico approach for identification of novel inhibitors as a potential therapeutics targeting HIV-1 viral infectivity factor. Curr. Top. Med. Chem., 2015, 15(1), 65-72.
[13]
Sinha, C.; Nischal, A.; Pant, K.K.; Bandaru, S.; Nayarisseri, A.; Khattri, S. Molecular docking analysis of RN18 and VEC5 in A3G-Vif inhibition. Bioinformation, 2014, 10(10), 611.
[14]
Bandaru, S.; Marri, V.K.; Kasera, P.; Kovuri, P.; Girdhar, A.; Mittal, D.R.; Ikram, S. GV, R. Nayarisseri, A. Structure based virtual screening of ligands to identify cysteinyl leukotriene receptor 1 antagonist. Bioinformation, 2014, 10(10), 652.
[15]
Dunna, N.R.; Bandaru, S.; Raj Akare, U.; Rajadhyax, S.; Ravi Gutlapalli, V.; Yadav, M.; Nayarisseri, A. Multiclass comparative virtual screening to identify novel Hsp90 inhibitors: a therapeutic breast cancer drug target. Curr. Top. Med. Chem., 2015, 15(1), 57-64.
[16]
Bandaru, S.; Ponnala, D.; Lakkaraju, C.; Kumar, C.; Bhukya, U.S.; Nayarisseri, A. Identification of high affinity non-peptidic small molecule inhibitors of MDM2-p53 interactions through structure-based virtual screening strategies. Asian Pac. J. Cancer Prev., 2014, 16, 3759-3765.
[17]
Akare, U.R.; Bandaru, S.; Shaheen, U.; Singh, P.K.; Tiwari, G.; Singare, P.; Nayarisseri, A.; Banerjee, T. Molecular docking approaches in identification of high affinity inhibitors of human SMO receptor. Bioinformation, 2014, 10(12), 737-742.
[18]
Bandaru, S.; Hema Prasad, M.; Jyothy, A.; Nayarisseri, A.; Yadav, M. Binding Modes and Pharmacophoric Features of Muscarinic Antagonism and β2 Agonism (MABA) Conjugates. Curr. Top. Med. Chem., 2013, 13(14), 1650-1655.
[19]
Nayarisseri, A.; Moghni, S.M.; Yadav, M.; Kharate, J.; Sharma, P.; Chandok, K.H.; Shah, K.P. In silico investigations on HSP90 and its inhibition for the therapeutic prevention of breast cancer. J. Pharma Res., 2013, 7(2), 150-156.
[20]
Shaheen, U.; Akka, J.; Hinore, J.S.; Girdhar, A.; Bandaru, S.; Sumithnath, T.G.; Nayarisseri, A.; Munshi, A. Computer aided identification of sodium channel blockers in the clinical treatment of epilepsy using molecular docking tools. Bioinformation, 2015, 11(3), 131-137.
[21]
Vuree, S.; Dunna, N.R.; Khan, I.A.; Alharbi, K.K.; Vishnupriya, S.; Soni, D.; Shah, P.; Chandok, H.; Yadav, M.; Nayarisseri, A. Pharmacogenomics of drug resistance in Breast Cancer Resistance Protein (BCRP) and its mutated variants. J. Pharm. Res., 2013, 6(7), 791-798.
[22]
Gudala, S.; Khan, U.; Kanungo, N.; Bandaru, S.; Hussain, T.; Parihar, M.L.; Mundluru, H.P. Identification and pharmacological analysis of high efficacy small molecule inhibitors of EGF-EGFR interactions in clinical treatment of non-small cell lung carcinoma: A computational approach. Asian Pac. J. Cancer Prev., 2015, 16, 8191-8196.
[23]
Babitha, P.P.; Sahila, M.M.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Molecular docking and pharmacological investigations of rivastigmine-fluoxetine and coumarin–tacrine hybrids against acetyl choline esterase. Bioinformation, 2015, 11(8), 378-386.
[24]
Natchimuthu, V.; Bandaru, S.; Nayarisseri, A.; Ravi, S. Design, synthesis and computational evaluation of a novel intermediate salt of N-cyclohexyl-N-(cyclohexylcarbamoyl)-4-(trifluoromethyl) benzamide as potential potassium channel blocker in epileptic paroxysmal seizures. Curr. Top. Med. Chem., 2016, 64, 64-73.
[25]
Sahila, M.M.; Babitha, P.P.; Bandaru, S.; Nayarisseri, A.; Doss, V.A. Molecular docking based screening of GABA (A) receptor inhibitors from plant derivatives. Bioinformation, 2015, 11(6), 280-289.
[26]
Bandaru, S.; Tarigopula, P.; Akka, J.; Marri, V.K.; Kattamuri, R.K.; Nayarisseri, A.; Mangalarapu, M.; Vinukonda, S.; Mundluru, H.P.; Sagurthi, S.R. Association of Beta 2 adrenergic receptor (Thr164Ile) polymorphism with Salbutamol refractoriness in severe asthmatics from Indian population. Gene, 2016, 592(1), 15-22.
[27]
Khandekar, N.; Singh, S.; Shukla, R.; Tirumalaraju, S.; Bandaru, S.; Banerjee, T.; Nayarisseri, A. Structural basis for the in vitro known acyl-depsipeptide 2 (ADEP2) inhibition to Clp 2 protease from Mycobacterium tuberculosis. Bioinformation, 2016, 12(3), 92-97.
[28]
Nasr, A.B.; Ponnala, D.; Sagurthi, S.R.; Kattamuri, R.K.; Marri, V.K.; Gudala, S.; Nayarisseri, A. Molecular Docking studies of FKBP12-mTOR inhibitors using binding predictions. Bioinformation, 2015, 11(6), 307-315.
[29]
Bandaru, S.; Alvala, M.; Nayarisseri, A.; Sharda, S.; Goud, H.; Mundluru, H.P.; Singh, S.K. Molecular dynamic simulations reveal suboptimal binding of salbutamol in T164I variant of β2 adrenergic receptor. PLoS One, 2017, 12(10)e0186666
[30]
Jain, D.; Udhwani, T.; Sharma, S.; Gandhe, A.; Reddy, P.B.; Nayarisseri, A.; Singh, S.K. Design of novel JAK3 inhibitors towards rheumatoid arthritis using molecular docking analysis. Bioinformation, 2019, 15(2), 68-78.
[31]
Monteiro, A.F.M.; Viana, J.D.O.; Nayarisseri, A.; Zondegoumba, E.N.; Junior, F.J.B.M.; Scotti, M.T.; Scotti, L. Computational studies applied to flavonoids against alzheimer’s and parkinson’s diseases. Oxid. Med. Cell. Longev., 2018, 2018, 21.
[32]
Nayarisseri, A.; Hood, E.A. Advancement in microbial cheminformatics. Curr. Top. Med. Chem., 2018, 18(29), 2459-2461.
[33]
Gokhale, P.; Chauhan, A.P.S.; Arora, A.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. FLT3 inhibitor design using molecular docking based virtual screening for acute myeloid leukemia. Bioinformation, 2019, 15(2), 104-115.
[34]
Shukla, P.; Khandelwal, R.; Sharma, D.; Dhar, A.; Nayarisseri, A.; Singh, S.K. Virtual screening of IL-6 inhibitors for idiopathic arthritis. Bioinformation, 2019, 15(2), 121-130.
[35]
Udhwani, T.; Mukherjee, S.; Sharma, K.; Sweta, J.; Khandekar, N.; Nayarisseri, A.; Singh, S.K. Design of PD-L1 inhibitors for lung cancer. Bioinformation, 2019, 15(2), 139-150.
[36]
Rao, D.M.; Nayarisseri, A.; Yadav, M.; Patel, D. Comparative modeling of methylentetrahydrofolate reductase (MTHFR) enzyme and its mutational assessment: In silico approach. Int. J. Bioinform. Res, 2010, 2(1), 5-9.
[37]
Kelotra, S.; Jain, M.; Kelotra, A.; Jain, I.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. An in silico appraisal to identify high affinity anti-apoptotic synthetic tetrapeptide inhibitors targeting the mammalian caspase 3 enzyme. Asian Pac. J. Cancer Prev., 2014, 15(23), 10137-10142.
[38]
Gutlapalli, V.R.; Sykam, A.; Nayarisseri, A.; Suneetha, S.; Suneetha, L.M. Insights from the predicted epitope similarity between Mycobacterium tuberculosis virulent factors and its human homologs. Bioinformation, 2015, 11(12), 517-524.
[39]
Nayarisseri, A.; Yadav, M.; Wishard, R. Computational evaluation of new homologous down regulators of Translationally Controlled Tumor Protein (TCTP) targeted for tumor reversion. Interdiscip. Sci., 2013, 5(4), 274-279.
[40]
Praseetha, S.; Bandaru, S.; Nayarisseri, A.; Sureshkumar, S. Pharmacological analysis of vorinostat analogues as potential anti-tumor agents targeting human histone deacetylases: an epigenetic treatment stratagem for cancers. Asian Pac. J. Cancer Prev., 2016, 17(3), 1571-1576.
[41]
Majhi, M.; Ali, M.A.; Limaye, A.; Sinha, K.; Bairagi, P.; Chouksey, M.; Shukla, R.; Kanwar, N.; Hussain, T.; Nayarisseri, A.; Singh, S.K. An In silico investigation of potential egfr inhibitors for the clinical treatment of colorectal cancer. Curr. Top. Med. Chem., 2018, 18(27), 2355-2366.
[42]
Sharma, K.; Patidar, K.; Ali, M.A.; Patil, P.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Structure-based virtual screening for the identification of high affinity compounds as potent vegfr2 inhibitors for the treatment of renal cell carcinoma. Curr. Top. Med. Chem., 2018, 18(25), 2174-2185.
[43]
Shameer, K.; Nayarisseri, A.; Duran, F.X.R.; González-Díaz, H. Improving neuropharmacology using big data, machine learning and computational algorithms. Curr. Neuropharmacol., 2017, 15(8), 1058-1061.
[44]
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial thematic issue: Chemoinformatics models for pharmaceutical design, part 2. Curr. Pharm. Des., 2016, 22(34), 5177-5178.
[45]
Basak, S.C.; Nayarisseri, A.; González-Díaz, H.; Bonchev, D. Editorial thematic issue: Chemoinformatics models for pharmaceutical design, Part 1. Curr. Pharm. Des., 2016, 22(33), 5041-5042.
[46]
Kelotra, A.; Gokhale, S.M.; Kelotra, S.; Mukadam, V.; Nagwanshi, K.; Bandaru, S.; Nayarisseri, A.; Bidwai, A. Alkyloxy carbonyl modified hexapeptides as a high affinity compounds for Wnt5A protein in the treatment of psoriasis. Bioinformation, 2014, 10(12), 743-749.
[47]
Chandrakar, B.; Jain, A.; Roy, S.; Gutlapalli, V.R.; Saraf, S.; Suppahia, A.; Verma, A.; Tiwari, A.; Yadav, M.; Nayarisseri, A. Molecular modeling of Acetyl-CoA carboxylase (ACC) from Jatropha curcas and virtual screening for identification of inhibitors. J. Pharm. Res., 2013, 6(9), 913-918.
[48]
Nayarisseri, A.; Singh, S.K. Functional inhibition of VEGF and EGFR suppressors in cancer treatment. Curr. Top. Med. Chem., 2019, 19(3), 178-179.
[49]
Monteiro, A.F.M.; Viana, J.D.O.; Nayarisseri, A.; Zondegoumba, E.N.; Junior, F.J.B.M.; Scotti, M.T.; Scotti, L. Computational studies applied to flavonoids against alzheimer’s and parkinson’s diseases. Oxid. Med. Cell. Longev., 2018, 20187912765
[50]
Marques, K.M.R.; do Desterro, M.R.; de Arruda, S.M.; de Araújo Neto, L.N.; de Lima, M.d.C.A.; de Almeida, S.M.V.; da Silva, E.C.D.; de Aquino, T.M.; da Silva-Júnior, E.F.; de Araújo-Júnior, J.X.; Silva, M.d.M.; Dantas, M.D.d.A.; Santos, J.C.C.; Figueiredo, I.M.; Bazin, M-A.; Marchand, P.; da Silva, T.G.; Mendonça, Junior, F.J.B. 5-Nitro-thiophene-thiosemicarbazone derivatives present antitumor activity mediated by apoptosis and dna intercalation. Curr. Top. Med. Chem., 2019, 19(13), 1074-1091.
[51]
Azimi, F.; Ghasemi, J.B.; Saghaei, L.; Hassanzadeh, F.; Mahdavi, M.; Sadeghi-aliabadi, H.; Scotti, M.T.; Scotti, L. Identification of essential 2D and 3D chemical features for discovery of the novel tubulin polymerization inhibitors. Curr. Top. Med. Chem., 2019, 19(13), 1092-1120.
[52]
Tugcu, G.; Sipahi, H.; Aydın, A. Application of a validated QSTR model for repurposing cox-2 inhibitor coumarin derivatives as potential antitumor agents. Curr. Top. Med. Chem., 2019, 19(13), 1121-1128.
[53]
Sharda, S.; Khandelwal, R.; Adhikary, R.; Sharma, D.; Majhi, M.; Hussain, T.; Nayarisseri, A.; Singh, S.K. A computer-aided drug designing for pharmacological inhibition of mutant alk for the treatment of non-small cell lung cancer. Curr. Top. Med. Chem., 2019, 19(13), 1129-1144.
[54]
Kartsev, V.; Geronikaki, A.; Petrou, A.; Lichitsky, B.; Sirakanyan, S.; Kostic, M.; Smiljkovic, M.; Soković, M. Griseofulvin derivatives: Synthesis, molecular docking and biological evaluation. Curr. Top. Med. Chem., 2019, 19(13), 1145-1161.
[55]
Kanakaveti, V.; Sakthivel, R.; Rayala, S.K.; Gromiha, M.M. Forging new scaffolds from old: combining scaffold hopping and hierarchical virtual screening for identifying novel bcl-2 inhibitors. Curr. Top. Med. Chem., 2019, 19(13), 1162-1172.
[56]
Ali, M.A.; Vuree, S.; Goud, H.; Hussain, T.; Nayarisseri, A.; Singh, S.K. Identification of high-affinity small molecules targeting gamma secretase for the treatment of alzheimer’s disease. Curr. Top. Med. Chem., 2019, 19(13), 1173-1187.


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Article Details

VOLUME: 19
ISSUE: 13
Year: 2019
Page: [1071 - 1074]
Pages: 4
DOI: 10.2174/156802661913190827102426

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