In Silico Studies as Support for Natural Products Research

Abstract

It can be argued that in silico studies do not receive enough attention despite being a key part of addressing the limitations of our laboratory facilities, the high cost of chemicals, and the equipment required for wet laboratory activities. Natural product studies are demanding higher costs of chemicals, reagents, and varied laboratory facilities. This becomes a serious limitation in getting data from natural product studies. In silico studies use chemical structures as inputs as well as software and online web servers to generate data to support, predict, and validate wet laboratory activities. Interaction studies use computational tools to calculate binding energies and other associated properties. Predictions are based on the structure-activity relationships derived from previously conducted preclinical and clinical studies. As a main component of in silico studies, the physicochemical and pharmacokinetic properties of small molecules can be determined using online web servers such as SwissADME and absorption, distribution, metabolism, excretion, and toxicity web servers. An interaction study uses molecular docking software such as AutoDock, AutoDock Vina, GOLD, and online servers such as SwissDock. Furthermore, the stabilities of complexes considered in interaction studies can be confirmed using molecular dynamics simulation software such as VMD. Prediction of activity spectra for substances (PASS) is widely used to predict biological activities for molecules based on multilevel neighborhoods of atom descriptors. In silico studies have played an important role in medicinal chemistry, pharmacology, and related research for screening, interaction studies, prediction, and other related purposes. Results of in silico predictions will not be far from wet lab activities as in most cases these studies consider previously attempted clinical and preclinical biological activities. Some examples are presented here to encourage the use of in silico studies.

Keywords

binding energy / in silico / molecular docking / natural products / PASS

References

[1] Ekins, S., Mestres, J., & Testa, B. (2007). In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. British Journal of Pharmacology, 152(1), 21-37. https://doi.org/10.1038/sj.bjp.0707306

[2] Costa, R. P., Lucena, L. F., Silva, L. M. A., Zocolo, G. J., Herrera—Acevedo, C., Scotti, L., ..., & Scotti, M. T. (2021). The SistematX web portal of natural products: An update. Journal of Chemical Information and Modeling, 61(6), 2516-2522. https://doi.org/10.1021/acs.jcim.1c00083

[3] Moradi, M., Golmohammadi, R., Najafi, A., Moghaddam, M. M., Fasihi—Ramandi, M., & Mirnejad, R. (2022). A contemporary review on the important role of in silico approaches for managing different aspects of COVID—19 crisis. Informatics in Medicine Unlocked, 28, 100862. https://doi.org/10.1016/j.imu.2022.100862

[4] Essa, M. M., Akbar, M., & Guillemin, G. (2016). The benefits of natural products for neurodegenerative diseases. Switzerland: Springer.

[5] Cordell, G. A., & Colvard, M. D. (2012). Natural products and traditional medicine: Turning on a paradigm. Journal of Natural Products, 75(3), 514-525. https://doi.org/10.1021/np200803m

[6] Sampangi—Ramaiah, M. H., Vishwakarma, R., & Shaanker, R. U. (2020). Molecular docking analysis of selected natural products from plants for inhibition of SARS—CoV—2 main protease. Current Science, 118(7), 1087-1092.

[7] Patra, J., Singh, D., Jain, S., & Mahindroo, N. (2021). Application of docking for lead optimization. In M. S. Coumar (Ed.), Molecular docking for computer—aided drug design (pp.271-294). Academic Press. https://doi.org/10.1016/B978-0-12-822312-3.00012-6

[8] Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2012). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 64, 4-17. https://doi.org/10.1016/j.addr.2012.09.019

[9] Valasani, K. R., Vangavaragu, J. R., Day, V. W., & Yan, S. S. (2014). Structure based design, synthesis, pharmacophore modeling, virtual screening, and molecular docking studies for identification of novel cyclophilin D inhibitors. Journal of Chemical Information and Modeling, 54(3), 902-912. https://doi.org/10.1021/ci5000196

[10] Loftsson, T. (2015). hysicochemical properties and pharmacokinetics. In T. Loftsson (Ed.), Essential pharmacokinetics: A primer for pharmaceutical scientists (pp. 85-104). Academic Press. https://doi.org/10.1016/B978-0-12-801411-0.00003-2

[11] Loftsson, T.. (2015). Introduction. In T. Loftsson (Ed.), Essential pharmacokinetics: A primer for pharmaceutical scientists (pp. 1-8). Academic Press. https://doi.org/10.1016/B978-0-12-801411-0.00001-9

[12] Attique, S. A., Hassan, M., Usman, M., Atif, R. M., Mahboob, S., Al—Ghanim, K. A., ..., & Nawaz, M. Z. (2019). A molecular docking approach to evaluate the pharmacological properties of natural and synthetic treatment candidates for use against hypertension. International Journal of Environmental Research and Public Health, 16(6), 923. https://doi.org/10.3390/ijerph16060923

[13] Abdullahi, M., & Adeniji, S. E. (2020). In—silico molecular docking and ADME/pharmacokinetic prediction studies of some novel carboxamide derivatives as anti—tubercular agents. Chemistry Africa, 3(4), 989-1000. https://doi.org/10.1007/s42250-020-00162-3

[14] Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: A free web tool to evaluate pharmacokinetics, drug—likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1), 42717. https://doi.org/10.1038/srep42717

[15] Zoete, V., Daina, A., Bovigny, C., & Michielin, O. (2016). SwissSimilarity: A web tool for low to ultra high throughput ligand—based virtual screening. Journal of Chemical Information and Modeling, 56(8), 1399-1404. https://doi.org/10.1021/acs.jcim.6b00174

[16] Singh, A., & Vellapandian, C. (2024). In silico and pharmacokinetic assessment of echinocystic acid effectiveness in Alzheimer’s disease like pathology. Future Science OA, 10(1), FSO904. https://doi.org/10.2144/fsoa-2023-0150

[17] Lagunin, A., Stepanchikova, A., Filimonov, D., & Poroikov, V. (2000). PASS: Prediction of activity spectra for biologically active substances. Bioinformatics, 16(8), 747-748. https://doi.org/10.1093/bioinformatics/16.8.747

[18] Abdou, W. M., Kamel, A. A., Khidre, R. E., Geronikaki, A., & Ekonomopoulou, M. T. (2012). Synthesis of 5—and 6—N—heterocyclic methylenebisphosphonate derivatives and evaluation of their cytogenetic activity in normal human lymphocyte cultures. Chemical Biology & Drug Design, 79(5), 719-730. https://doi.org/10.1111/j.1747-0285.2012.01327.x

[19] Filimonov, D. A., Lagunin, A. A., Gloriozova, T. A., Rudik, A. V., Druzhilovskii, D. S., Pogodin, P. V., & Poroikov, V. V. (2014). Prediction of the biological activity spectra of organic compounds using the PASS online web resource. Chemistry of Heterocyclic Compounds, 50, 444-457. https://doi.org/10.1007/s10593-14-1496-1

[20] Ahmad, I., Azminah, A., Mulia, K., Yanuar, A., & Munim, A. (2019). Angiotensin—converting enzyme inhibitory activity of polyphenolic compounds from Peperomia pellucida (L) Kunth: An in silico molecular docking study. Journal of Applied Pharmaceutical Science, 9(8), 25-31. http://dx.doi.org/10.7324/JAPS.2019.90804.1

[21] Laskowski, R. A., & Thornton, J. M. (2022). PDBsum extras: SARS—Cov—2 and AlphaFold models. Protein Science, 31(1), 283-289. https://doi.org/10.1002/pro.4238

[22] Rabbi, M. F., Akter, S. A., Hasan, M. J., & Amin, A. (2021). In silico characterization of a hypothetical protein from Shigella dysenteriae ATCC 12039 reveals a pathogenesis—related protein of the type—VI secretion system. Bioinformatics and Biology Insights, 15, 11779322211011140. https://doi.org/10.1177/11779322211011140

[23] Benkert, P., Biasini, M., & Schwede, T. (2011). Toward the estimation of the absolute quality of individual protein structure models. Bioinformatics, 27(3), 343-350. https://doi.org/10.1093/bioinformatics/btq662

[24] Forli, S., Huey, R., Pique, M. E., Sanner, M. F., Goodsell, D. S., & Olson, A. J. (2016). Computational protein—ligand docking and virtual drug screening with the AutoDock suite. Nature Protocols, 11(5), 905-919. https://doi.org/10.1038/nprot.2016.051

[25] O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., & Hutchison, G. R. (2011). Open Babel: An open chemical toolbox. Journal of Cheminformatics, 3, 33. https://doi.org/10.1186/1758-2946-3-33

[26] Raman, E. P., Paul, T. J., Hayes, R. L., & Brooks III, C. L. (2020). Automated, accurate, and scalable relative protein—ligand binding free—energy calculations using lambda dynamics. Journal of Chemical Theory and Computation, 16(12), 7895-7914. https://doi.org/10.1021/acs.jctc.0c00830

[27] Laskowski, R. A., Jabłońska, J., Pravda, L., Vařeková, R. S., & Thornton, J. M. (2018). PDBsum: Structural summaries of PDB entries. Protein Science, 27(1), 129-134. https://doi.org/10.1002/pro.3289

[28] Singh, T., Biswas, D., & Jayaram, B. (2011). AADS—An automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. Journal of Chemical Information and Modeling, 51(10), 2515-2527. https://doi.org/10.1021/ci200193z

[29] Batool, M., Ahmad, B., & Choi, S. (2019). A structure—based drug discovery paradigm. International Journal of Molecular Sciences, 20(11), 2783. https://doi.org/10.3390/ijms20112783

[30] Meng, X. Y., Zhang, H. X., Mezei, M., & Cui, M. (2011). Molecular docking: A powerful approach for structure—based drug discovery. Current Computer—Aided Drug Design, 7(2), 146-157. https://doi.org/10.2174/157340911795677602

[31] Huang, S. Y., & Zou, X. (2010). Advances and challenges in protein—ligand docking. International Journal of Molecular Sciences, 11(8), 3016-3034. https://doi.org/10.3390/ijms11083016

[32] Ferreira, L. G., Dos Santos, R. N., Oliva, G., & Andricopulo, A. D. (2015). Molecular docking and structure—based drug design strategies. Molecules, 20(7), 13384-13421. https://doi.org/10.3390/molecules200713384

[33] Agarwal, S., & Mehrotra, R. (2016). An overview of molecular genetics. JSM Chemistry, 4(2), 1024.

[34] Agrafiotis, D. K., Gibbs, A. C., Zhu, F., Izrailev, S., & Martin, E. (2007). Conformational sampling of bioactive molecules: A comparative study. Journal of Chemical Information and Modeling, 47(3), 1067-1086. https://doi.org/10.1021/ci6005454

[35] Krovat, E. M., Steindl, T., & Langer, T. (2005). Recent advances in docking and scoring. Current Computer—Aided Drug Design, 1(1), 93-102. https://doi.org/10.2174/1573409052952314

[36] Agarwal, S., Chadha, D., & Mehrotra, R. (2015). Molecular modeling and spectroscopic studies of semustine binding with DNA and its comparison with lomustine—DNA adduct formation. Journal of Biomolecular Structure and Dynamics, 33(8), 1653-1668. https://doi.org/10.1080/07391102.2014.968874

[37] Foloppe, N., & Hubbard, R. (2006). Towards predictive ligand design with free—energy based computational methods? Current Medicinal Chemistry, 13(29), 3583-3608. https://doi.org/10.2174/092986706779026165

[38] Li, J., Fu, A., & Zhang, L. (2019). An overview of scoring functions used for protein—ligand interactions in molecular docking. Interdisciplinary Sciences: Computational Life Sciences, 11, 320-328. https://doi.org/10.1007/s12539-019-00327-w

[39] Iman, M., Saadabadi, A., & Davood, A. (2015). Molecular docking analysis and molecular dynamics simulation study of ameltolide analogous as a sodium channel blocker. Turkish Journal of Chemistry, 39(2), 306-316. https://doi.org/10.3906/kim-1402-37

[40] Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455-461. https://doi.org/10.1002/jcc.21334

[41] Pettersen, E. F., Goddard, T. D., Huang, C. C., Meng, E. C., Couch, G. S., Croll, T. I., ..., & Ferrin, T. E. (2021). UCSF ChimeraX: Structure visualization for researchers, educators, and developers. Protein Science, 30(1), 70-82. https://doi.org/10.1002/pro.3943

[42] Wu, C., Liu, S., Zhang, S., & Yang, Z. (2020). Molcontroller: A VMD graphical user interface featuring molecule manipulation. Journal of Chemical Information and Modeling, 60(10), 5126-5131. https://doi.org/10.1021/acs.jcim.0c00754

[43] Lill, M. A., & Danielson, M. L. (2011). Computer—aided drug design platform using PyMOL. Journal of Computer—Aided Molecular Design, 25, 13-19. https://doi.org/10.1007/s10822-010-9395-8

[44] Laskowski, R. A., & Swindells, M. B. (2011). LigPlot+: Multiple ligand—protein interaction diagrams for drug discovery. Journal of Chemical Information and Modeling, 51(10), 2778-2786. https://doi.org/10.1021/ci200227u

[45] Dassault Systèmes . (2021). Free download: BIOVIA discovery studio visualizer. Retrieved from: https://discover.3ds.com/discovery-studio-visualizer-download

[46] Temml, V., & Schuster, D. (2021). Molecular docking for natural product investigations: Pitfalls and ways to overcome them. In M. S. Coumar (Ed.), Molecular docking for computer—aided drug design (pp. 391-405). Academic Press. https://doi.org/10.1016/B978-0-12-822312-3.00027-8

[47] Saghiri, K., Daoud, I., Melkemi, N., & Mesli, F. (2023). QSAR study, molecular docking/dynamics simulations and ADME prediction of 2—phenyl—1H—indole derivatives as potential breast cancer inhibitors. Biointerface Research in Applied Chemistry, 13(2), 154. https://doi.org/10.33263/BRIAC132.154

[48] Bitencourt—Ferreira, G., & de Azevedo, W. F. (2019). Molecular docking simulations with ArgusLab. In W. F. de Azevedo Jr. (Ed.), Docking screens for drug discovery (pp. 203-220). Springer. https://doi.org/10.1007/978-1-4939-9752-7_13

[49] Santos, L. H., Ferreira, R. S., & Caffarena, E. R. (2019). Integrating molecular docking and molecular dynamics simulations. In W. F. de Azevedo Jr. (Ed.), Docking screens for drug discovery (pp. 13-34). Springer. https://doi.org/10.1007/978-1-4939-9752-7_2

[50] Pinzi, L., & Rastelli, G. (2019). Molecular docking: Shifting paradigms in drug discovery. International Journal of Molecular Sciences, 20(18), 4331. https://doi.org/10.3390/ijms20184331

[51] Kharkar, P. S., Warrier, S., & Gaud, R. S. (2014). Reverse docking: A powerful tool for drug repositioning and drug rescue. Future Medicinal Chemistry, 6(3), 333-342. https://doi.org/10.4155/fmc.13.207

[52] Fan, J., Fu, A., & Zhang, L. (2019). Progress in molecular docking. Quantitative Biology, 7(2), 83-89. https://doi.org/10.1007/s40484-019-0172-y

[53] Prieto—Martínez, F. D., Arciniega, M., & Medina—Franco, J. L. (2018). Molecular docking: Current advances and challenges. TIP Revista Especializada en Ciencias Químico—Biológicas, 21, 65-87. https://doi.org/10.22201/fesz.23958723e.2018.0.143

[54] Torres, P. H., Sodero, A. C., Jofily, P., & Silva—Jr, F. P. (2019). Key topics in molecular docking for drug design. International Journal of Molecular Sciences, 20(18), 4574. https://doi.org/10.3390/ijms20184574

[55] Tessema, F. B., Gonfa, Y. H., Asfaw, T. B., Tadesse, M. G., Bachheti, A. J., Singab, A. N., & Bachheti, R. K. (2024). Dehydrocostus lactone from the root of Ajuga integrifolia (Buch.—Ham. Ex D. Don): Quantitative determination and in—silico study for anti—breast cancer activity. Plant Science Today, 11(1), 34-44. https://doi.org/10.14719/pst.2344

[56] Tessema, F. B., Gonfa, Y. H., Asfaw, T. B., Tadesse, T. G., Tadesse, M. G., Bachheti, A., ..., & Bachheti, R. K. (2023). Flavonoids and phenolic acids from aerial part of Ajuga integrifolia (Buch.—Ham. Ex D. Don): Anti—shigellosis activity and in silico molecular docking studies. Molecules, 28(3), 1111. https://doi.org/10.3390/molecules28031111

[57] Rahman, M. M., Islam, M. R., Akash, S., Mim, S. A., Rahaman, M. S., Emran, T. B., ..., & Wilairatana, P. (2022). In silico investigation and potential therapeutic approaches of natural products for COVID—19: Computer—aided drug design perspective. Frontiers in Cellular and Infection Microbiology, 12, 929430. https://doi.org/10.3389/fcimb.2022.929430

[58] Cai, C., Wu, Q., Hong, H., He, L., Liu, Z., Gu, Y., ..., & Fang, J. (2021). In silico identification of natural products from Traditional Chinese Medicine for cancer immunotherapy. Scientific Reports, 11(1), 3332. https://doi.org/10.1038/s41598-021-82857-2

[59] Fang, J., Liu, C., Wang, Q., Lin, P., & Cheng, F. (2018). In silico polypharmacology of natural products. Briefings in Bioinformatics, 19(6), 1153-1171. https://doi.org/10.1093/bib/bbx045

[60] Joshi, T., Sharma, P., Joshi, T., & Chandra, S. (2020). In silico screening of anti—inflammatory compounds from Lichen by targeting cyclooxygenase—2. Journal of Biomolecular Structure and Dynamics, 38(12), 3544-3562. https://doi.org/10.1080/07391102.2019.1664328

[61] Tessema, F. B., Belachew, A. M., Gonfa, Y. H., Asfaw, T. B., Admassie, Z. G., Bachheti, A., ..., & Tadesse, M. G. (2024). Efficacy of fumigant compounds from essential oil of feverfew (Chrysanthemum parthenium L.) against maize weevil (Sitophilus zeamais Mots.): Fumigant toxicity test and in—silico study. Bulletin of the Chemical Society of Ethiopia, 38(2), 457-472.

[62] Alshahrani, M. Y., Alshahrani, K. M., Tasleem, M., Akeel, A., Almeleebia, T. M., Ahmad, I., ..., & Saeed, M. (2021). Computational screening of natural compounds for identification of potential anti—cancer agents targeting MCM7 protein. Molecules, 26(19), 5878. https://doi.org/10.3390/molecules26195878

[63] Tessema, F. B., Gonfa, Y. H., Asfaw, T. B., Tadesse, M. G., & Bachheti, R. K. (2024). In silico molecular docking approach to identify potential antihypertensive compounds from Ajuga integrifolia Buch.—Ham. Ex D. Don (Armagusa). Advances and Applications in Bioinformatics and Chemistry, 17, 47-59. https://doi.org/10.2147/AABC.S392878

[64] Rahaman, A., Almalki, A. A., Rafeeq, M. M., Akhtar, O., Anjum, F., Mashraqi, M. M., ..., & Jamal, Q. M. S. (2021). Identification of potent natural resource small molecule inhibitor to control Vibrio cholera by targeting its outer membrane protein U: An in silico approach. Molecules, 26(21), 6517. https://doi.org/10.3390/molecules26216517

[65] Tessema, F. B., Gonfa, Y. H., Asfaw, T. B., Tadesse, M. G., Tadesse, T. G., Bachheti, A., ..., & Bachheti, R. K. (2023). Targeted HPTLC profile, quantification of flavonoids and phenolic acids, and antimicrobial activity of Dodonaea angustifolia (Lf) leaves and flowers. Molecules, 28(6), 2870. https://doi.org/10.3390/molecules28062870

[66] Petrini, L., Pennati, G., & Fotiadis, D. I. (2022). Editorial: Verification and validation of in silico models for biomedical implantable devices. Frontiers in Medical Technology, 4, 856067. https://doi.org/10.3389/fmedt.2022.856067

[67] Preet, G., Astakala, R. V., Gomez—Banderas, J., Rajakulendran, J. E., Hasan, A. H., Ebel, R., & Jaspars, M. (2023). Virtual screening of a library of naturally occurring anthraquinones for potential anti—fouling agents. Molecules, 28(3), 995. https://doi.org/10.3390/molecules28030995

[68] Awadelkareem, A. M., Al—Shammari, E., Elkhalifa, A. E. O., Adnan, M., Siddiqui, A. J., Snoussi, M., ..., & Ashraf, S. A. (2022). Phytochemical and in silico ADME/Tox analysis of Eruca sativa extract with antioxidant, antibacterial and anticancer potential against Caco—2 and HCT—116 colorectal carcinoma cell lines. Molecules, 27(4), 1409. https://doi.org/10.3390/molecules27041409

[69] Rajagopal, K., Kalusalingam, A., Bharathidasan, A. R., Sivaprakash, A., Shanmugam, K., Sundaramoorthy, M., & Byran, G. (2023). In silico drug design of anti—breast cancer agents. Molecules, 28(10), 4175. https://doi.org/10.3390/molecules28104175

[70] Liu, T. T., Chen, Y. K., Adil, M., Almehmadi, M., Alshabrmi, F. M., Allahyani, M., ..., & Peng, Q. (2023). In silico identification of natural product—based inhibitors targeting IL—1β/IL—1R protein—protein interface. Molecules, 28(13), 4885. https://doi.org/10.3390/molecules28134885

[71] Ionov, N., Druzhilovskiy, D., Filimonov, D., & Poroikov, V. (2023). Phyto4Health: Database of phytocomponents from russian pharmacopoeia plants. Journal of Chemical Information and Modeling, 63(7), 1847-1851. https://doi.org/10.1021/acs.jcim.2c01567

[72] Tajiani, F., Ahmadi, S., Lotfi, S., Kumar, P., & Almasirad, A. (2023). In—silico activity prediction and docking studies of some flavonol derivatives as anti—prostate cancer agents based on Monte Carlo optimization. BMC Chemistry, 17(1), 87. https://doi.org/10.1186/s13065-023-00999-y

[73] Aghajani, J., Farnia, P., Farnia, P., Ghanavi, J., & Velayati, A. A. (2022). Molecular dynamic simulations and molecular docking as a potential way for designed new inhibitor drug without resistance. Tanaffos, 21(1), 1-14. https://www.tanaffosjournal.ir/article_254222.html

[74] Kasabe, B., Ahire, G., Patil, P., Punekar, M., Davuluri, K. S., Kakade, M., ..., & Cherian, S. (2023). Drug repurposing approach against chikungunya virus: An in vitro and in silico study. Frontiers in Cellular and Infection Microbiology, 13, 1132538. https://doi.org/10.3389/fcimb.2023.1132538

[75] Ramírez, D., & Caballero, J. (2016). Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target? International Journal of Molecular Sciences, 17(4), 525. https://doi.org/10.3390/ijms17040525

[76] Nendza, M., Aldenberg, T., Benfenati, E., Benigni, R., Cronin, M. T. D., Escher, S., ..., & Vermeire, T. (2010). Data quality assessment for in silico methods: A survey of approaches and needs. In M. Cronin & J. Madden (Eds.), In silico toxicology (pp. 59-117). The Royal Society of Chemistry. https://doi.org/10.1039/9781849732093-00059

[77] Musuamba, F. T., Bursi, R., Manolis, E., Karlsson, K., Kulesza, A., Courcelles, E., ..., & Geris, L. (2020). Verifying and validating quantitative systems pharmacology and in silico models in drug development: Current needs, gaps, and challenges. CPT: Pharmacometrics & Systems Pharmacology, 9(4), 195-197. https://doi.org/10.1002/psp4.12504

[78] Hewitt, M., Ellison, C. M., Cronin, M. T., Pastor, M., Steger—Hartmann, T., Munoz—Muriendas, J., ..., & Madden, J. C. (2015). Ensuring confidence in predictions: A scheme to assess the scientific validity of in silico models. Advanced Drug Delivery Reviews, 86, 101-111. https://doi.org/10.1016/j.addr.2015.03.005