Login Register Cart 0
Generic placeholder image

Current Drug Metabolism

Editor-in-Chief

ISSN (Print): 1389-2002
ISSN (Online): 1875-5453

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Application of Advanced Technologies in Natural Product Research: A Review with Special Emphasis on ADMET Profiling

Author(s): Pobitra Borah, Sangeeta Hazarika, Satyendra Deka*, Katharigatta N. Venugopala, Anroop B. Nair, Mahesh Attimarad, Nagaraja Sreeharsha and Raghu P. Mailavaram

Volume 21, Issue 10, 2020

Page: [751 - 767] Pages: 17

DOI: 10.2174/1389200221666200714144911

Price: $65

Abstract

The successful conversion of natural products (NPs) into lead compounds and novel pharmacophores has emboldened the researchers to harness the drug discovery process with a lot more enthusiasm. However, forfeit of bioactive NPs resulting from an overabundance of metabolites and their wide dynamic range have created the bottleneck in NP researches. Similarly, the existence of multidimensional challenges, including the evaluation of pharmacokinetics, pharmacodynamics, and safety parameters, has been a concerning issue. Advancement of technology has brought the evolution of traditional natural product researches into the computer-based assessment exhibiting pretentious remarks about their efficiency in drug discovery. The early attention to the quality of the NPs may reduce the attrition rate of drug candidates by parallel assessment of ADMET profiling. This article reviews the status, challenges, opportunities, and integration of advanced technologies in natural product research. Indeed, emphasis will be laid on the current and futuristic direction towards the application of newer technologies in early-stage ADMET profiling of bioactive moieties from the natural sources. It can be expected that combinatorial approaches in ADMET profiling will fortify the natural product-based drug discovery in the near future.

Keywords: Natural products, drug discovery, ADMET profiling, databases, virtual screening, in silico methods, traditional medicines.

« Previous Next »
Graphical Abstract

[1]
Bernardini, S.; Tiezzi, A.; Laghezza Masci, V.; Ovidi, E. Natural products for human health: an historical overview of the drug discovery approaches. Nat. Prod. Res., 2018, 32(16), 1926-1950.
[http://dx.doi.org/10.1080/14786419.2017.1356838] [PMID: 28748726]
[2]
Yuan, H.; Ma, Q.; Ye, L.; Piao, G. The traditional medicine and modern medicine from natural products. Molecules, 2016, 21(5), 559.
[http://dx.doi.org/10.3390/molecules21050559] [PMID: 27136524]
[3]
Chen, Y.; de Bruyn Kops, C.; Kirchmair, J. Data resources for the computer-guided discovery of bioactive natural products. J. Chem. Inf. Model., 2017, 57(9), 2099-2111.
[http://dx.doi.org/10.1021/acs.jcim.7b00341] [PMID: 28853576]
[4]
Twilley, D.; Lall, N. The role of natural products from plants in the development of anticancer agents. In:Natural Products and Drug Discovery; Elsevier: USA, 2018, pp. 139-178.
[http://dx.doi.org/10.1016/B978-0-08-102081-4.00007-1]
[5]
Li, J.; Zhou, B. Biological actions of artemisinin: insights from medicinal chemistry studies. Molecules, 2010, 15(3), 1378-1397.
[http://dx.doi.org/10.3390/molecules15031378] [PMID: 20335987]
[6]
Li, Y. Qinghaosu (artemisinin): chemistry and pharmacology. Acta Pharmacol. Sin., 2012, 33(9), 1141-1146.
[http://dx.doi.org/10.1038/aps.2012.104] [PMID: 22922345]
[7]
Tewari, D.; Rawat, P.; Singh, P.K. Adverse drug reactions of anticancer drugs derived from natural sources. Food Chem. Toxicol., 2019, 123, 522-535.
[http://dx.doi.org/10.1016/j.fct.2018.11.041] [PMID: 30471312]
[8]
Shi, Q.; Li, L.; Huo, C.; Zhang, M.; Wang, Y. Study on natural medicinal chemistry and new drug development. Zhongcaoyao Chin. Tradit. Herb. Drugs, 2010, 41(10), 1583-1589.
[9]
Zhao, P.; Niu, J.; David, Y.W.; Wang, J.; Sun, Y.; Li, Y. Effect and mechanism of traditional Chinese medicine and their active constituents in postmenopausal osteoporosis. Zhongguo Zhongyao Zazhi, 2012, 37(12), 1693-1699.
[PMID: 22997807]
[10]
Zhong, Y.; Zhang, X.; Cai, X.; Wang, K.; Chen, Y.; Deng, Y. Puerarin attenuated early diabetic kidney injury through down-regulation of matrix metalloproteinase 9 in streptozotocin-induced diabetic rats. PLoS One, 2014, 9(1),e85690.
[http://dx.doi.org/10.1371/journal.pone.0085690] [PMID: 24454919]
[11]
Wani, M.C.; Horwitz, S.B. Nature as a remarkable chemist: a personal story of the discovery and development of Taxol. Anticancer Drugs, 2014, 25(5), 482-487.
[http://dx.doi.org/10.1097/CAD.0000000000000063] [PMID: 24413390]
[12]
Faseleh Jahromi, M.; Liang, J.B.; Ho, Y.W.; Mohamad, R.; Goh, Y.M.; Shokryazdan, P.; Chin, J. Lovastatin in Aspergillus terreus: fermented rice straw extracts interferes with methane production and gene expression in Methanobrevibacter smithii. BioMed Res. Int., 2013, 2013,604721.
[13]
VanderMolen, K.M.; McCulloch, W.; Pearce, C.J.; Oberlies, N.H. Romidepsin (Istodax, NSC 630176, FR901228, FK228, depsipeptide): a natural product recently approved for cutaneous T-cell lymphoma. J. Antibiot. (Tokyo), 2011, 64(8), 525-531.
[http://dx.doi.org/10.1038/ja.2011.35] [PMID: 21587264]
[14]
Li, G.; Lou, H-X. Strategies to diversify natural products for drug discovery. Med. Res. Rev., 2018, 38(4), 1255-1294.
[http://dx.doi.org/10.1002/med.21474] [PMID: 29064108]
[15]
Rollinger, J.M.; Stuppner, H.; Langer, T. Virtual screening for the discovery of bioactive natural products. In: Natural compounds as drugs; Springer: Switzerland AG, 2008, Vol. I, pp. 211-249.
[http://dx.doi.org/10.1007/978-3-7643-8117-2_6]
[16]
Medina-Franco, J.L. Discovery and development of lead compounds from natural sources using computational approaches. In:Evidence-based validation of herbal medicine; Elsevier: USA, 2015, pp. 455-475.
[http://dx.doi.org/10.1016/B978-0-12-800874-4.00021-0]
[17]
Amin, S.; Khan, H. Revival of natural products: utilization of modern technologies. Curr. Bioact. Compd., 2016, 12, 103-106.
[http://dx.doi.org/10.2174/1573407212666160314195845]
[18]
Scotti, M.T.; Scotti, L. Editorial: Theoretical studies of the metabolism in drug discovery. Curr. Drug Metab., 2017, 18(6), 498-499.
[http://dx.doi.org/10.2174/138920021806170721113611] [PMID: 28877659]
[19]
Borah, P.; Deka, S.; Mailavaram, R.P.; Deb, P.K. P1 receptor agonists/antagonists in clinical trials - potential drug candidates of the future. Curr. Pharm. Des., 2019, 25(26), 2792-2807.
[http://dx.doi.org/10.2174/1381612825666190716111245] [PMID: 31333097]
[20]
Deb, P.K.; Deka, S.; Borah, P.; Abed, S.N.; Klotz, K-N. Medicinal chemistry and therapeutic potential of agonists, antagonists and allosteric modulators of A1 adenosine receptor: current status and perspectives. Curr. Pharm. Des., 2019, 25(25), 2697-2715.
[http://dx.doi.org/10.2174/1381612825666190716100509] [PMID: 31333094]
[21]
Keseru, G.M.; Makara, G.M. Hit discovery and hit-to-lead approaches. Drug Discov. Today, 2006, 11(15-16), 741-748.
[http://dx.doi.org/10.1016/j.drudis.2006.06.016] [PMID: 16846802]
[22]
Croft, A.K.; Groenewald, W.; Tierney, M.S. Medicinal chemistry and ligand profiling for evaluation of promising marine bioactive molecules. Marine Bioactive Compounds; Springer: Switzerland AG; , 2012, pp. 173-206.
[http://dx.doi.org/10.1007/978-1-4614-1247-2_7]
[23]
Iwu, M.M. Introduction: therapeutic agents from ethnomedicine. Advances in Phytomedicine; Elsevier: USA, 2002, Vol. 1, pp. 1-22.
[24]
Mukherjee, P.K. Evaluation of Indian traditional medicine. Drug Inf. J., 2001, 35(2), 623-632.
[http://dx.doi.org/10.1177/009286150103500235]
[25]
Ngo, L.T.; Okogun, J.I.; Folk, W.R. 21st century natural product research and drug development and traditional medicines. Nat. Prod. Rep., 2013, 30(4), 584-592.
[http://dx.doi.org/10.1039/c3np20120a] [PMID: 23450245]
[26]
Katz, L.; Baltz, R.H. Natural product discovery: past, present, and future. J. Ind. Microbiol. Biotechnol., 2016, 43(2-3), 155-176.
[http://dx.doi.org/10.1007/s10295-015-1723-5] [PMID: 26739136]
[27]
Fabricant, D.S.; Farnsworth, N.R. The value of plants used in traditional medicine for drug discovery. Environ. Health Perspect., 2001, 109(Suppl. 1), 69-75.
[PMID: 11250806]
[28]
Alves, R.R.; Rosa, I.M. Biodiversity, traditional medicine and public health: where do they meet? J. Ethnobiol. Ethnomed., 2007, 3(1), 14.
[http://dx.doi.org/10.1186/1746-4269-3-14] [PMID: 17376227]
[29]
Borah, P.; Saha, D.; Tamang, V.; Chakraborty, A. Evaluation of in-vitro antiurolithiatic potential of Oroxylum Indicum. Univers. J. Pharm. Sci. Res., 2017, 3(2), 1-6.
[30]
Parasuraman, S. Herbal drug discovery: challenges and perspectives. Curr. Pharmacogenomics Pers. Med. Former. Curr. Pharmacogenomics., 2018, 16(1), 63-68.
[http://dx.doi.org/10.2174/1875692116666180419153313]
[31]
Organization, W.H. WHO Traditional Medicine Strategy: 2014-2023; World Health Organization, 2013.
[32]
Karunamoorthi, K.; Jegajeevanram, K.; Xavier, J.; Vijayalakshmi, J.; Melita, L. Tamil traditional medicinal system-siddha: an indigenous health practice in the international perspectives. Tang Humanit. Med., 2012, 2(2), 12-1.
[http://dx.doi.org/10.5667/tang.2012.0006]
[33]
Zhang, Q-W.; Lin, L-G.; Ye, W-C. Techniques for extraction and isolation of natural products: a comprehensive review. Chin. Med., 2018, 13(1), 20.
[http://dx.doi.org/10.1186/s13020-018-0177-x] [PMID: 29692864]
[34]
Beutler, J.A. Natural products as a foundation for drug discovery. Curr. Protocols Pharmacol., 2009, 46(1), 1-21.
[http://dx.doi.org/10.1002/0471141755.ph0911s46] [PMID: 20161632]
[35]
Wangchuk, P.; Loukas, A. Techniques and technologies for the biodiscovery of novel small molecule drug lead compounds from natural products. In:Natural Products and Drug Discovery; Elsevier: USA, 2018, pp. 435-465.
[http://dx.doi.org/10.1016/B978-0-08-102081-4.00016-2]
[36]
Cordell, G.A.; Colvard, M.D. Natural products and traditional medicine: turning on a paradigm. J. Nat. Prod., 2012, 75(3), 514-525.
[http://dx.doi.org/10.1021/np200803m] [PMID: 22233451]
[37]
Wang, S.; Dong, G.; Sheng, C. Structural simplification of natural products. Chem. Rev., 2019, 119(6), 4180-4220.
[http://dx.doi.org/10.1021/acs.chemrev.8b00504] [PMID: 30730700]
[38]
Balunas, M.J.; Su, B.; Landini, S.; Brueggemeier, R.W.; Kinghorn, A.D. Interference by naturally occurring fatty acids in a noncellular enzyme-based aromatase bioassay. J. Nat. Prod., 2006, 69(4), 700-703.
[http://dx.doi.org/10.1021/np050513p] [PMID: 16643058]
[39]
Schuster, D. 3D pharmacophores as tools for activity profiling. Drug Discov. Today. Technol., 2010, 7(4), e203-e270.
[http://dx.doi.org/10.1016/j.ddtec.2010.11.006] [PMID: 24103796]
[40]
Newman, D.J. From Natural Products to Drugs. Phys. Sci. Rev., 2018, 4(4),,20180111.
[41]
Beutler, J.A. Natural products as a foundation for drug discovery. Curr. Protocols Pharmacol., 2019, 86(1),e67.
[http://dx.doi.org/10.1002/cpph.67] [PMID: 31539923]
[42]
Sharma, A.; Sharma, S.; Gupta, M.; Fatima, S.; Saini, R.; Agarwal, S.M. Pharmacokinetic profiling of anticancer phytocompounds using computational approach. Phytochem. Anal., 2018, 29(6), 559-568.
[http://dx.doi.org/10.1002/pca.2767] [PMID: 29667756]
[43]
Erkens, R.H.J. What every chemist should know about plant names. Nat. Prod. Rep., 2011, 28(1), 11-14.
[http://dx.doi.org/10.1039/C0NP00042F] [PMID: 21057691]
[44]
Kingston, D.G. Modern natural products drug discovery and its relevance to biodiversity conservation. J. Nat. Prod., 2011, 74(3), 496-511.
[http://dx.doi.org/10.1021/np100550t] [PMID: 21138324]
[45]
Organization, W.H.; Zdrowia, Ś.O. WHO Guidelines on Good Agricultural and Collection Practices [GACP] for Medicinal PlantsWorld Health Organization 2003.
[46]
Atanasov, A.G.; Waltenberger, B.; Pferschy-Wenzig, E-M.; Linder, T.; Wawrosch, C.; Uhrin, P.; Temml, V.; Wang, L.; Schwaiger, S.; Heiss, E.H.; Rollinger, J.M.; Schuster, D.; Breuss, J.M.; Bochkov, V.; Mihovilovic, M.D.; Kopp, B.; Bauer, R.; Dirsch, V.M.; Stuppner, H. Discovery and resupply of pharmacologically active plant-derived natural products: a review. Biotechnol. Adv., 2015, 33(8), 1582-1614.
[http://dx.doi.org/10.1016/j.biotechadv.2015.08.001] [PMID: 26281720]
[47]
Do, Q-T.; Bernard, P. Reverse pharmacognosy: a new concept for accelerating natural drug discovery. Adv. Phytomedicine, 2006, 2, 1-20.
[http://dx.doi.org/10.1016/S1572-557X(05)02001-5]
[48]
Davis, G.D.J.; Vasanthi, A.H.R. Seaweed metabolite database (SWMD): A database of natural compounds from marine algae. Bioinformation, 2011, 5(8), 361-364.
[http://dx.doi.org/10.6026/97320630005361] [PMID: 21423723]
[49]
Lei, J.; Zhou, J. A marine natural product database. J. Chem. Inf. Comput. Sci., 2002, 42(3), 742-748.
[http://dx.doi.org/10.1021/ci010111x] [PMID: 12086536]
[50]
Thomford, N.E.; Senthebane, D.A.; Rowe, A.; Munro, D.; Seele, P.; Maroyi, A.; Dzobo, K. Natural products for drug discovery in the 21st century: innovations for novel drug discovery. Int. J. Mol. Sci., 2018, 19(6), 1578.
[http://dx.doi.org/10.3390/ijms19061578] [PMID: 29799486]
[51]
Makley, L.N.; Gestwicki, J.E. Expanding the number of ‘druggable’ targets: non-enzymes and protein-protein interactions. Chem. Biol. Drug Des., 2013, 81(1), 22-32.
[http://dx.doi.org/10.1111/cbdd.12066] [PMID: 23253128]
[52]
Reymond, J-L.; Van Deursen, R.; Blum, L.C.; Ruddigkeit, L. Chemical space as a source for new drugs. MedChemComm, 2010, 1(1), 30-38.
[http://dx.doi.org/10.1039/c0md00020e]
[53]
Martins, A.; Vieira, H.; Gaspar, H.; Santos, S. Marketed marine natural products in the pharmaceutical and cosmeceutical industries: tips for success. Mar. Drugs, 2014, 12(2), 1066-1101.
[http://dx.doi.org/10.3390/md12021066] [PMID: 24549205]
[54]
Halabalaki, M.; Vougogiannopoulou, K.; Mikros, E.; Skaltsounis, A.L. Recent advances and new strategies in the NMR-based identification of natural products. Curr. Opin. Biotechnol., 2014, 25, 1-7.
[http://dx.doi.org/10.1016/j.copbio.2013.08.005] [PMID: 24484874]
[55]
Boldi, A.M. Libraries from natural product-like scaffolds. Curr. Opin. Chem. Biol., 2004, 8(3), 281-286.
[http://dx.doi.org/10.1016/j.cbpa.2004.04.010] [PMID: 15183326]
[56]
Sepe, V.; Bifulco, G.; Renga, B.; D’Amore, C.; Fiorucci, S.; Zampella, A. Discovery of sulfated sterols from marine invertebrates as a new class of marine natural antagonists of farnesoid-X-receptor. J. Med. Chem., 2011, 54(5), 1314-1320.
[http://dx.doi.org/10.1021/jm101336m] [PMID: 21309576]
[57]
Mehta, P.; Srivastava, S.; Sharma, M.; Malik, R. Discovery of novel chemotypes for competitive AMPA receptor antagonists as potential antiepileptic agents through structure-based virtual screening of natural products library. Struct. Chem., 2019, 30(4), 1159-1172.
[http://dx.doi.org/10.1007/s11224-018-1269-z]
[58]
Chen, H.; Kogej, T.; Engkvist, O. Cheminformatics in Drug Discovery, an Industrial Perspective. Mol. Inform., 2018, 37(9-10),e1800041.
[http://dx.doi.org/10.1002/minf.201800041] [PMID: 29774657]
[59]
Kirchweger, B.; Rollinger, J.M. Virtual Screening for the Discovery of Active Principles from Natural Products. In: Natural products as source of molecules with therapeutic potential; Springer: Switzerland AG, 2018, pp. 333-364.
[http://dx.doi.org/10.1007/978-3-030-00545-0_9]
[60]
Narayana, B.L.; Pran Kishore, D.; Balakumar, C.; Rao, K.V.; Kaur, R.; Rao, A.R.; Murthy, J.N.; Ravikumar, M. Molecular modeling evaluation of non-steroidal aromatase inhibitors. Chem. Biol. Drug Des., 2012, 79(5), 674-682.
[http://dx.doi.org/10.1111/j.1747-0285.2011.01277.x] [PMID: 22129073]
[61]
Foster, I.; Zhao, Y.; Raicu, I.; Lu, S. Cloud Computing and Grid Computing 360-Degree Compared. In 2008 grid computing environments workshop; IEEE, 2008, pp. 1-10.
[62]
Bali, A.; Ohri, R.; Deb, P.K. Synthesis, evaluation and docking studies on 3-alkoxy-4-methanesulfonamido acetophenone derivatives as non ulcerogenic anti-inflammatory agents. Eur. J. Med. Chem., 2012, 49, 397-405.
[http://dx.doi.org/10.1016/j.ejmech.2012.01.018] [PMID: 22318166]
[63]
Pereira, F.; Aires-de-Sousa, J. Computational methodologies in the exploration of marine natural product leads. Mar. Drugs, 2018, 16(7), 236.
[http://dx.doi.org/10.3390/md16070236] [PMID: 30011882]
[64]
Deb, P.K.; Al-Attraqchi, O.; Al-Qattan, M.N.; Prasad, M.R.; Tekade, R.K. Applications of Computers in Pharmaceutical Product Formulation. In:Dosage Form Design Parameters; Elsevier: USA, 2018, pp. 665-703.
[http://dx.doi.org/10.1016/B978-0-12-814421-3.00019-1]
[65]
Romano, J.D.; Tatonetti, N.P. Informatics and computational methods in natural product drug discovery: a review and perspectives. Front. Genet., 2019, 10, 368.
[http://dx.doi.org/10.3389/fgene.2019.00368] [PMID: 31114606]
[66]
Fatima, S.; Gupta, P.; Sharma, S.; Sharma, A.; Agarwal, S.M. ADMET profiling of geographically diverse phytochemical using chemoinformatic tools. Future Med. Chem., 2020, 12(1), 69-87.
[http://dx.doi.org/10.4155/fmc-2019-0206] [PMID: 31793338]
[67]
Medina-Franco, J.L. New Approaches for the Discovery of Pharmacologically-Active Natural Compounds; Multidisciplinary Digital Publishing Institute: Switzerland, 2019.
[68]
Koulouridi, E.; Valli, M.; Ntie-Kang, F.; da Silva Bolzani, V. A primer on natural product-based virtual screening. Phys. Sci. Rev., 2018, 4(6)
[http://dx.doi.org/10.1515/psr-2018-0105]]
[69]
Harvey, A.L.; Edrada-Ebel, R.; Quinn, R.J. The re-emergence of natural products for drug discovery in the genomics era. Nat. Rev. Drug Discov., 2015, 14(2), 111-129.
[http://dx.doi.org/10.1038/nrd4510] [PMID: 25614221]
[70]
Mohanraj, K.; Karthikeyan, B.S.; Vivek-Ananth, R.P.; Chand, R.B.; Aparna, S.R.; Mangalapandi, P.; Samal, A. IMPPAT: A Curated Database of Indian medicinal plants, phytochemistry and therapeutics. Sci. Rep., 2018, 8, 4329.
[71]
Moura Barbosa, A.J.; Del Rio, A. Freely accessible databases of commercial compounds for high- throughput virtual screenings. Curr. Top. Med. Chem., 2012, 12(8), 866-877.
[http://dx.doi.org/10.2174/156802612800166710] [PMID: 22352914]
[72]
Barbosa, A.J.M.; Roque, A.C.A. Free marine natural products databases for biotechnology and bioengineering. Biotechnol. J., 2019, 14(11),e1800607.
[http://dx.doi.org/10.1002/biot.201800607] [PMID: 31297982]
[73]
McFedries, A.; Schwaid, A.; Saghatelian, A. Methods for the elucidation of protein-small molecule interactions. Chem. Biol., 2013, 20(5), 667-673.
[http://dx.doi.org/10.1016/j.chembiol.2013.04.008] [PMID: 23706633]
[74]
Martinez Molina, D.; Jafari, R.; Ignatushchenko, M.; Seki, T.; Larsson, E.A.; Dan, C.; Sreekumar, L.; Cao, Y.; Nordlund, P. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay. Science, 2013, 341(6141), 84-87.
[http://dx.doi.org/10.1126/science.1233606] [PMID: 23828940]
[75]
Schirle, M.; Jenkins, J.L. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov. Today, 2016, 21(1), 82-89.
[http://dx.doi.org/10.1016/j.drudis.2015.08.001] [PMID: 26272035]
[76]
Tang, H.; Duggan, S.; Richardson, P.L.; Marin, V.; Warder, S.E.; McLoughlin, S.M. Target identification of compounds from a cell viability phenotypic screen using a bead/lysate-based affinity capture platform. J. Biomol. Screen., 2016, 21(2), 201-211.
[http://dx.doi.org/10.1177/1087057115622431] [PMID: 26676096]
[77]
Luo, Y.; Cobb, R.E.; Zhao, H. Recent advances in natural product discovery. Curr. Opin. Biotechnol., 2014, 30, 230-237.
[http://dx.doi.org/10.1016/j.copbio.2014.09.002] [PMID: 25260043]
[78]
Yuliana, N.D.; Khatib, A.; Choi, Y.H.; Verpoorte, R. Metabolomics for bioactivity assessment of natural products. Phytother. Res., 2011, 25(2), 157-169.
[PMID: 20658470]
[79]
Koparde, A.A.; Doijad, R.C.; Magdum, C.S. Natural products in drug discovery. Pharmacognosy-Medicinal Plants; IntechOpen: UK, 2019.
[http://dx.doi.org/10.5772/intechopen.82860]
[80]
Awale, M.; Visini, R.; Probst, D.; Arús-Pous, J.; Reymond, J-L. Chemical space: big data challenge for molecular diversity. Chimia (Aarau), 2017, 71(10), 661-666.
[http://dx.doi.org/10.2533/chimia.2017.661] [PMID: 29070411]
[81]
Singh, G.; Schulthess, D.; Hughes, N.; Vannieuwenhuyse, B.; Kalra, D. Real world big data for clinical research and drug development. Drug Discov. Today, 2018, 23(3), 652-660.
[http://dx.doi.org/10.1016/j.drudis.2017.12.002] [PMID: 29294362]
[82]
Yang, J.Y.; Sanchez, L.M.; Rath, C.M.; Liu, X.; Boudreau, P.D.; Bruns, N.; Glukhov, E.; Wodtke, A.; de Felicio, R.; Fenner, A.; Wong, W.R.; Linington, R.G.; Zhang, L.; Debonsi, H.M.; Gerwick, W.H.; Dorrestein, P.C. Molecular networking as a dereplication strategy. J. Nat. Prod., 2013, 76(9), 1686-1699.
[http://dx.doi.org/10.1021/np400413s] [PMID: 24025162]
[83]
Kibble, M.; Saarinen, N.; Tang, J.; Wennerberg, K.; Mäkelä, S.; Aittokallio, T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat. Prod. Rep., 2015, 32(8), 1249-1266.
[http://dx.doi.org/10.1039/C5NP00005J] [PMID: 26030402]
[84]
Zuo, H.; Zhang, Q.; Su, S.; Chen, Q.; Yang, F.; Hu, Y. A network pharmacology-based approach to analyse potential targets of traditional herbal formulas: An example of Yu Ping Feng decoction. Sci. Rep., 2018, 8(1), 11418.
[http://dx.doi.org/10.1038/s41598-018-29764-1] [PMID: 30061691]
[85]
Liang, J.W.; Wang, M.Y.; Olounfeh, K.M.; Zhao, N.; Wang, S.; Meng, F.H. Network pharmacology-based identifcation of potential targets of the flower of Trollius chinensis Bunge acting on anti-inflammatory effectss. Sci. Rep., 2019, 9(1), 8109.
[http://dx.doi.org/10.1038/s41598-019-44538-z] [PMID: 31147584]
[86]
Rollinger, J.M.; Langer, T.; Stuppner, H. Integrated in silico tools for exploiting the natural products’ bioactivity. Planta Med., 2006, 72(8), 671-678.
[http://dx.doi.org/10.1055/s-2006-941506] [PMID: 16783689]
[87]
Fang, J.; Liu, C.; Wang, Q.; Lin, P.; Cheng, F. In silico polypharmacology of natural products. Brief. Bioinform., 2018, 19(6), 1153-1171.
[PMID: 28460068]
[88]
Norinder, U.; Bergström, C.A. Prediction of ADMET properties. ChemMedChem Chem. Enabling Drug Discov., 2006, 1(9), 920-937.
[89]
Dhingra, M.S.; Deb, P.K.; Chadha, R.; Singh, T.; Karan, M. Synthesis, Evaluation, and molecular docking studies of Cycloalkyl/Aryl-3, 4, 5-Trimethylgallates as Potent non-ulcerogenic and gastroprotective anti-inflammatory agents. Med. Chem. Res., 2014, 23(1), 87-106.
[http://dx.doi.org/10.1007/s00044-013-0620-6]
[90]
de Groot, M.J. Designing better drugs: predicting cytochrome P450 metabolism. Drug Discov. Today, 2006, 11(13-14), 601-606.
[http://dx.doi.org/10.1016/j.drudis.2006.05.001] [PMID: 16793528]
[91]
Li, A.P. Screening for human ADME/Tox drug properties in drug discovery. Drug Discov. Today, 2001, 6(7), 357-366.
[http://dx.doi.org/10.1016/S1359-6446(01)01712-3] [PMID: 11267922]
[92]
Yang, L-P.; Zhou, Z-W.; Chen, X-W.; Li, C.G.; Sneed, K.B.; Liang, J.; Zhou, S-F. Computational and in vitro studies on the inhibitory effects of herbal compounds on human cytochrome P450 1A2. Xenobiotica, 2012, 42(3), 238-255.
[http://dx.doi.org/10.3109/00498254.2011.610833] [PMID: 21970686]
[93]
Kortagere, S.; Ekins, S. Troubleshooting computational methods in drug discovery. J. Pharmacol. Toxicol. Methods, 2010, 61(2), 67-75.
[http://dx.doi.org/10.1016/j.vascn.2010.02.005] [PMID: 20176118]
[94]
Kerns, E.H. High throughput physicochemical profiling for drug discovery. J. Pharm. Sci., 2001, 90(11), 1838-1858.
[http://dx.doi.org/10.1002/jps.1134] [PMID: 11745742]
[95]
Hodgson, J. ADMET--turning chemicals into drugs. Nat. Biotechnol., 2001, 19(8), 722-726.
[http://dx.doi.org/10.1038/90761] [PMID: 11479558]
[96]
Khakar, P.S. Two-dimensional (2D) in silico models for absorption, distribution, metabolism, excretion and toxicity (ADME/T) in drug discovery. Curr. Top. Med. Chem., 2010, 10(1), 116-126.
[http://dx.doi.org/10.2174/156802610790232224] [PMID: 19929825]
[97]
Kassel, D.B. Applications of high-throughput ADME in drug discovery. Curr. Opin. Chem. Biol., 2004, 8(3), 339-345.
[http://dx.doi.org/10.1016/j.cbpa.2004.04.015] [PMID: 15183334]
[98]
Davis, A.M.; Riley, R.J. Predictive ADMET studies, the challenges and the opportunities. Curr. Opin. Chem. Biol., 2004, 8(4), 378-386.
[http://dx.doi.org/10.1016/j.cbpa.2004.06.005] [PMID: 15288247]
[99]
Venugopala, K.N.; Ramachandra, P.; Tratrat, C.; Gleiser, R.M.; Bhandary, S.; Chopra, D.; Morsy, M.A.; Aldhubiab, B.E.; Attimarad, M.; Nair, A.B.; Sreeharsha, N.; Venugopala, R.; Deb, P.K.; Chandrashekharappa, S.; Khalil, H.E.; Alwassil, O.I.; Abed, S.N.; Bataineh, Y.A.; Palenge, R.; Haroun, M.; Pottathil, S.; Girish, M.B.; Akrawi, S.H.; Mohanlall, V. Larvicidal activities of 2-Aryl-2,3-Dihydroquinazolin -4-ones against Malaria Vector Anopheles arabiensis, In silico ADMET prediction and molecular target investigation. Molecules, 2020, 25(6), 1316.
[http://dx.doi.org/10.3390/molecules25061316] [PMID: 32183140]
[100]
Rubio, D.M.; Schoenbaum, E.E.; Lee, L.S.; Schteingart, D.E.; Marantz, P.R.; Anderson, K.E.; Platt, L.D.; Baez, A.; Esposito, K. Defining translational research: implications for training. Acad. Med., 2010, 85(3), 470-475.
[http://dx.doi.org/10.1097/ACM.0b013e3181ccd618] [PMID: 20182120]
[101]
Jing, Y.; Bian, Y.; Hu, Z.; Wang, L.; Xie, X-Q.S. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J., 2018, 20(3), 58.
[http://dx.doi.org/10.1208/s12248-018-0210-0] [PMID: 29603063]
[102]
Xiao, Z.; Morris-Natschke, S.L.; Lee, K-H. Strategies for the optimization of natural leads to anticancer drugs or drug candidates. Med. Res. Rev., 2016, 36(1), 32-91.
[http://dx.doi.org/10.1002/med.21377] [PMID: 26359649]
[103]
He, S-M.; Chan, E.; Zhou, S-F. ADME properties of herbal medicines in humans: evidence, challenges and strategies. Curr. Pharm. Des., 2011, 17(4), 357-407.
[http://dx.doi.org/10.2174/138161211795164194] [PMID: 21385154]
[104]
Nicolaou, K.C. Advancing the drug discovery and development process. Angew. Chem. Int. Ed. Engl., 2014, 53(35), 9128-9140.
[http://dx.doi.org/10.1002/anie.201404761] [PMID: 25045053]
[105]
Lipinski, C.A. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discov. Today. Technol., 2004, 1(4), 337-341.
[http://dx.doi.org/10.1016/j.ddtec.2004.11.007] [PMID: 24981612]
[106]
Bohnert, T.; Prakash, C. ADME profiling in drug discovery and development: an overview. In:Encyclopedia of drug metabolism and interactions; Wiley: USA, 2011, pp. 1-42.
[107]
Kumar, R.; Singh, A.K.; Gupta, A.; Pandey, A.K. In-silico approaches to study therapeutic efficacy of nutraceuticals. In:Phytochemistry: An in-silico and in-vitro Update; Springer: USA, 2019, pp. 479-490.
[http://dx.doi.org/10.1007/978-981-13-6920-9_25]
[108]
Rani, A.S.; Neelima, G.; Mukhopadhyay, R.; Jyothi, K.S.N.; Sulakshana, G. In silico characterization of plant secondary metabolites. In:In silico Approach for Sustainable Agriculture; Springer: USA, 2018, pp. 251-273.
[http://dx.doi.org/10.1007/978-981-13-0347-0_15]
[109]
Lipinski, C.A. Rule of five in 2015 and beyond: target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions. Adv. Drug Deliv. Rev., 2016, 101, 34-41.
[http://dx.doi.org/10.1016/j.addr.2016.04.029] [PMID: 27154268]
[110]
Ganesan, A. The impact of natural products upon modern drug discovery. Curr. Opin. Chem. Biol., 2008, 12(3), 306-317.
[http://dx.doi.org/10.1016/j.cbpa.2008.03.016] [PMID: 18423384]
[111]
Quinn, R.J.; Carroll, A.R.; Pham, N.B.; Baron, P.; Palframan, M.E.; Suraweera, L.; Pierens, G.K.; Muresan, S. Developing a drug-like natural product library. J. Nat. Prod., 2008, 71(3), 464-468.
[http://dx.doi.org/10.1021/np070526y] [PMID: 18257534]
[112]
Zhang, M-Q.; Wilkinson, B. Drug discovery beyond the ‘rule-of-five’. Curr. Opin. Biotechnol., 2007, 18(6), 478-488.
[http://dx.doi.org/10.1016/j.copbio.2007.10.005] [PMID: 18035532]
[113]
Masimirembwa, C.; Thelingwani, R. Application of in silico, in vitro and in vivo ADMET/PK Platforms in Drug Discovery. In:Drug Discovery in Africa; Springer: USA, 2012, pp. 151-191.
[http://dx.doi.org/10.1007/978-3-642-28175-4_7]
[114]
Awortwe, C.; Fasinu, P.S.; Rosenkranz, B. Application of Caco-2 cell line in herb-drug interaction studies: current approaches and challenges. J. Pharm. Pharm. Sci., 2014, 17(1), 1-19.
[http://dx.doi.org/10.18433/J30K63] [PMID: 24735758]
[115]
Effinger, A.; O’Driscoll, C.M.; McAllister, M.; Fotaki, N. In vitro and In silico ADME Prediction. In:ADME Processes in Pharmaceutical Sciences; Springer: USA, 2018, pp. 301-330.
[http://dx.doi.org/10.1007/978-3-319-99593-9_13]
[116]
Li, A.P. Preclinical in vitro screening assays for drug-like properties. Drug Discov. Today. Technol., 2005, 2(2), 179-185.
[http://dx.doi.org/10.1016/j.ddtec.2005.05.024] [PMID: 24981846]
[117]
Hidalgo, I.J. Assessing the absorption of new pharmaceuticals. Curr. Top. Med. Chem., 2001, 1(5), 385-401.
[http://dx.doi.org/10.2174/1568026013395010] [PMID: 11899104]
[118]
Li, Y.; Shin, Y.G.; Yu, C.; Kosmeder, J.W.; Hirschelman, W.H.; Pezzuto, J.M.; van Breemen, R.B. Increasing the throughput and productivity of Caco-2 cell permeability assays using liquid chromatography-mass spectrometry: application to resveratrol absorption and metabolism. Comb. Chem. High Throughput Screen., 2003, 6(8), 757-767.
[http://dx.doi.org/10.2174/138620703771826865] [PMID: 14683481]
[119]
Manda, V.K.; Avula, B.; Ali, Z.; Wong, Y-H.; Smillie, T.J.; Khan, I.A.; Khan, S.I. Characterization of in vitro ADME properties of diosgenin and dioscin from Dioscorea villosa. Planta Med., 2013, 79(15), 1421-1428.
[http://dx.doi.org/10.1055/s-0033-1350699] [PMID: 23970424]
[120]
Scorzoni, L.; Sangalli-Leite, F.; de Lacorte Singulani, J.; de Paula, E. Silva, A.C.; Costa-Orlandi, C.B.; Fusco-Almeida, A.M.; Mendes-Giannini, M.J. Searching new antifungals: the use of in vitro and in vivo methods for evaluation of natural compounds. J. Microbiol. Methods, 2016, 123, 68-78.
[http://dx.doi.org/10.1016/j.mimet.2016.02.005] [PMID: 26853122]
[121]
Samiulla, D.S.; Vaidyanathan, V.V.; Arun, P.C.; Balan, G.; Blaze, M.; Bondre, S.; Chandrasekhar, G.; Gadakh, A.; Kumar, R.; Kharvi, G.; Kim, H.O.; Kumar, S.; Malikayil, J.A.; Moger, M.; Mone, M.K.; Nagarjuna, P.; Ogbu, C.; Pendhalkar, D.; Rao, A.V.; Rao, G.V.; Sarma, V.K.; Shaik, S.; Sharma, G.V.; Singh, S.; Sreedhar, C.; Sonawane, R.; Timmanna, U.; Hardy, L.W. Rational selection of structurally diverse natural product scaffolds with favorable ADME properties for drug discovery. Mol. Divers., 2005, 9(1-3), 131-139.
[http://dx.doi.org/10.1007/s11030-005-1297-7] [PMID: 15789560]
[122]
Żołek, T.; Maciejewska, D. Theoretical evaluation of ADMET properties for coumarin derivatives as compounds with therapeutic potential. Eur. J. Pharm. Sci., 2017, 109, 486-502.
[http://dx.doi.org/10.1016/j.ejps.2017.08.036] [PMID: 28867490]
[123]
Diukendjieva, A.; Sharif, M.A.; Alov, P.; Pencheva, T.; Tsakovska, I.; Pajeva, I. ADME/Tox properties and biochemical interactions of silybin congeners: in silico study. Nat. Prod. Commun., 2017, 12
[http://dx.doi.org/10.1177/1934578X1701200208]
[124]
Bocci, G.; Carosati, E.; Vayer, P.; Arrault, A.; Lozano, S.; Cruciani, G. ADME-space: a new tool for medicinal chemists to explore ADME properties. Sci. Rep., 2017, 7(1), 6359.
[http://dx.doi.org/10.1038/s41598-017-06692-0] [PMID: 28743970]
[125]
Mazzari, A.L.; Prieto, J.M. Herbal medicines in Brazil: pharmacokinetic profile and potential herb-drug interactions. Front. Pharmacol., 2014, 5, 162.
[http://dx.doi.org/10.3389/fphar.2014.00162] [PMID: 25071580]
[126]
Li, A.P.; Lu, C.; Brent, J.A.; Pham, C.; Fackett, A.; Ruegg, C.E.; Silber, P.M. Cryopreserved human hepatocytes: characterization of drug-metabolizing enzyme activities and applications in higher throughput screening assays for hepatotoxicity, metabolic stability, and drug-drug interaction potential. Chem. Biol. Interact., 1999, 121(1), 17-35.
[http://dx.doi.org/10.1016/S0009-2797(99)00088-5] [PMID: 10418968]
[127]
Rodrigues, A.D.; Wong, S.L. Application of human liver microsomes in metabolism-based drug-drug interactions. Vitro-in vivo correlations and the Abbott Laboratories experience. Adv. Pharmacol., 1997, 43, 65-101.
[128]
Lehmann, J.M.; McKee, D.D.; Watson, M.A.; Willson, T.M.; Moore, J.T.; Kliewer, S.A. The human orphan nuclear receptor PXR is activated by compounds that regulate CYP3A4 gene expression and cause drug interactions. J. Clin. Invest., 1998, 102(5), 1016-1023.
[http://dx.doi.org/10.1172/JCI3703] [PMID: 9727070]
[129]
Moore, J.T.; Kliewer, S.A. Use of the nuclear receptor PXR to predict drug interactions. Toxicology, 2000, 153(1-3), 1-10.
[http://dx.doi.org/10.1016/S0300-483X(00)00300-0] [PMID: 11090943]
[130]
Hu, Z.; Yang, X.; Ho, P.C.L.; Chan, S.Y.; Heng, P.W.S.; Chan, E.; Duan, W.; Koh, H.L.; Zhou, S. Herb-drug interactions: a literature review. Drugs, 2005, 65(9), 1239-1282.
[http://dx.doi.org/10.2165/00003495-200565090-00005] [PMID: 15916450]
[131]
Annaert, P.P.; Brouwer, K.L.R. Assessment of drug interactions in hepatobiliary transport using rhodamine 123 in sandwich-cultured rat hepatocytes. Drug Metab. Dispos., 2005, 33(3), 388-394.
[http://dx.doi.org/10.1124/dmd.104.001669] [PMID: 15608134]
[132]
Moeller, T.A.; Shukla, S.J.; Xia, M. Assessment of compound hepatotoxicity using human plateable cryopreserved hepatocytes in a 1536-well-plate format. Assay Drug Dev. Technol., 2012, 10(1), 78-87.
[http://dx.doi.org/10.1089/adt.2010.0365] [PMID: 22053711]
[133]
Waterfield, C.J.; Westmoreland, C.; Asker, D.S.; Murdock, J.C.; George, E.; Timbrell, J.A. Ethionine toxicity in vitro: the correlation of data from rat hepatocyte suspensions and monolayers with in vivo observations. Arch. Toxicol., 1998, 72(9), 588-596.
[http://dx.doi.org/10.1007/s002040050547] [PMID: 9806431]
[134]
Fotakis, G.; Timbrell, J.A. In vitro cytotoxicity assays: comparison of LDH, neutral red, MTT and protein assay in hepatoma cell lines following exposure to cadmium chloride. Toxicol. Lett., 2006, 160(2), 171-177.
[http://dx.doi.org/10.1016/j.toxlet.2005.07.001] [PMID: 16111842]
[135]
van Meerloo, J.; Kaspers, G.J.L.; Cloos, J. Cell Sensitivity assays: the MTT assay. In:Cancer cell culture: methods and protocolsmethods in molecular biology; Cree, I.A., Ed.; Humana Press: Totowa, NJ, 2011, pp. 237-245.
[http://dx.doi.org/10.1007/978-1-61779-080-5_20.]
[136]
Niu, Q.; Zhao, C.; Jing, Z. An evaluation of the colorimetric assays based on enzymatic reactions used in the measurement of human natural cytotoxicity. J. Immunol. Methods, 2001, 251(1-2), 11-19.
[http://dx.doi.org/10.1016/S0022-1759(01)00314-3] [PMID: 11292477]
[137]
Korzeniewski, C.; Callewaert, D.M. An enzyme-release assay for natural cytotoxicity. J. Immunol. Methods, 1983, 64(3), 313-320.
[http://dx.doi.org/10.1016/0022-1759(83)90438-6] [PMID: 6199426]
[138]
Senft, A.P.; Dalton, T.P.; Shertzer, H.G. Determining glutathione and glutathione disulfide using the fluorescence probe o-phthalaldehyde. Anal. Biochem., 2000, 280(1), 80-86.
[http://dx.doi.org/10.1006/abio.2000.4498] [PMID: 10805524]
[139]
Neuschwander-Tetri, B.A.; Roll, F.J. Glutathione measurement by high-performance liquid chromatography separation and fluorometric detection of the glutathione-orthophthalaldehyde adduct. Anal. Biochem., 1989, 179(2), 236-241.
[http://dx.doi.org/10.1016/0003-2697(89)90121-8] [PMID: 2774172]
[140]
Li, A.P. In vitro evaluation of human xenobiotic toxicity: scientific concepts and the novel integrated discrete multiple cell co-culture (IdMOC) technology. ALTEX, 2008, 25(1), 43-49.
[http://dx.doi.org/10.14573/altex.2008.1.43] [PMID: 18360727]
[141]
Gayathri, L.; Karthikeyan, B.S.; Rajalakshmi, M.; Dhanasekaran, D.; Li, A.P.; Akbarsha, M.A. Metabolism-dependent cytotoxicity of citrinin and ochratoxin A alone and in combination as assessed adopting integrated discrete multiple organ co-culture (IdMOC). Toxicol. In vitro, 2018, 46, 166-177.
[http://dx.doi.org/10.1016/j.tiv.2017.09.022] [PMID: 28951292]
[142]
Li, A.P.; Bode, C.; Sakai, Y. A novel in vitro system, the integrated discrete multiple organ cell culture (IdMOC) system, for the evaluation of human drug toxicity: comparative cytotoxicity of tamoxifen towards normal human cells from five major organs and MCF-7 adenocarcinoma breast cancer cells. Chem. Biol. Interact., 2004, 150(1), 129-136.
[http://dx.doi.org/10.1016/j.cbi.2004.09.010] [PMID: 15522266]
[143]
Gacem, M.A.; Telli, A.; Gacem, H.; Ould-El-Hadj-Khelil, A. Phytochemical screening, antifungal and antioxidant activities of three medicinal plants from algerian steppe and sahara (preliminary screening studies). SN Appl. Sci., 2019, 1(12), 1721.
[http://dx.doi.org/10.1007/s42452-019-1797-1]
[144]
Puerto Galvis, C.E.; Kouznetsov, V.V. Synthesis of zanthoxylamide protoalkaloids and their in silico ADME-Tox screening and in vivo toxicity assessment in zebrafish embryos. Eur. J. Pharm. Sci., 2019, 127, 291-299.
[http://dx.doi.org/10.1016/j.ejps.2018.10.028] [PMID: 30395926]
[145]
Lammer, E.; Carr, G.J.; Wendler, K.; Rawlings, J.M.; Belanger, S.E.; Braunbeck, T. Is the fish embryo toxicity test (FET) with the zebrafish (Danio rerio) a potential alternative for the fish acute toxicity test? Comp. Biochem. Physiol. C Toxicol. Pharmacol., 2009, 149(2), 196-209.
[http://dx.doi.org/10.1016/j.cbpc.2008.11.006] [PMID: 19095081]
[146]
Henn, K.; Braunbeck, T. Dechorionation as a tool to improve the fish embryo toxicity test (FET) with the zebrafish (Danio rerio). Comp. Biochem. Physiol. C Toxicol. Pharmacol., 2011, 153(1), 91-98.
[http://dx.doi.org/10.1016/j.cbpc.2010.09.003] [PMID: 20869464]
[147]
Bai, H.; Kong, W-W.; Shao, C-L.; Li, Y.; Liu, Y-Z.; Liu, M.; Guan, F-F.; Wang, C-Y. Zebrafish embryo toxicity microscale model for ichthyotoxicity evaluation of marine natural products. Mar. Biotechnol. (NY), 2016, 18(2), 264-270.
[http://dx.doi.org/10.1007/s10126-016-9688-6] [PMID: 26838966]
[148]
Sobanska, M.; Scholz, S.; Nyman, A-M.; Cesnaitis, R.; Gutierrez Alonso, S.; Klüver, N.; Kühne, R.; Tyle, H.; de Knecht, J.; Dang, Z.; Lundbergh, I.; Carlon, C.; De Coen, W. Applicability of the fish embryo acute toxicity (FET) test (OECD 236) in the regulatory context of Registration, Evaluation, Authorisation, and Restriction of Chemicals (REACH). Environ. Toxicol. Chem., 2018, 37(3), 657-670.
[http://dx.doi.org/10.1002/etc.4055] [PMID: 29226368]
[149]
Lagorce, D.; Douguet, D.; Miteva, M.A.; Villoutreix, B.O. Computational analysis of calculated physicochemical and ADMET properties of protein-protein interaction inhibitors. Sci. Rep., 2017, 7, 46277.
[http://dx.doi.org/10.1038/srep46277] [PMID: 28397808]
[150]
Guantai, E.M.; Chibale, K. Natural product-based drug discovery in Africa: the need for integration into modern drug discovery paradigms. In:Drug Discovery in Africa; Springer: USA, 2012, pp. 101-126.
[http://dx.doi.org/10.1007/978-3-642-28175-4_5]
[151]
Wenlock, M.C.; Barton, P. In silico physicochemical parameter predictions. Mol. Pharm., 2013, 10(4), 1224-1235.
[http://dx.doi.org/10.1021/mp300537k] [PMID: 23305561]
[152]
Sharma, S.K.; Sharma, E.; Sharma, Y.A. Review: Recent computational approaches in medicinal chemistry: computer aided drug designing and delivery. Pharma Innov., 2017, 6(5, Part A), 5.
[153]
Chandrasekaran, B.; Abed, S.N.; Al-Attraqchi, O.; Kuche, K.; Tekade, R.K. Computer-aided prediction of pharmacokinetic (ADMET) properties. In:Dosage Form Design Parameters; Elsevier: USA, 2018, pp. 731-755.
[http://dx.doi.org/10.1016/B978-0-12-814421-3.00021-X]
[154]
Broccatelli, F.; Salphati, L.; Plise, E.; Cheong, J.; Gobbi, A.; Lee, M-L.; Aliagas, I. Predicting passive permeability of drug-like molecules from chemical structure: where are we? Mol. Pharm., 2016, 13(12), 4199-4208.
[http://dx.doi.org/10.1021/acs.molpharmaceut.6b00836] [PMID: 27806577]
[155]
Dearden, J.C. In silico prediction of ADMET properties: how far have we come? Expert Opin. Drug Metab. Toxicol., 2007, 3(5), 635-639.
[http://dx.doi.org/10.1517/17425255.3.5.635] [PMID: 17916052]
[156]
Andrade, C.H.; Silva, D.C.; Braga, R.C. In silico prediction of drug metabolism by P450. Curr. Drug Metab., 2014, 15(5), 514-525.
[http://dx.doi.org/10.2174/1389200215666140908102530] [PMID: 25204822]
[157]
Deb, P.K.; El-Rabie, D.; Junaid, A.; Siong, L.C.; Kulasekar, A.; Kulasingam, L.; Pichika, M.R. In silico binding mode analysis (molecular docking studies) and absorption, distribution, metabolism and excretion prediction of some novel inhibitors of Aurora Kinase A in clinical trials. Asian J. Chem., 2014, 26(18), 6221.
[http://dx.doi.org/10.14233/ajchem.2014.17175]
[158]
Deb, P.K.; Junaid, A.; El-Rabie, D.; Hon, T.; Nasr, E.M.; Pichika, M.R. Molecular docking studies and comparative binding mode analysis of FDA approved HIV protease inhibitors. Asian J. Chem., 2014, 26(18), 6227-6232.
[http://dx.doi.org/10.14233/ajchem.2014.17195]
[159]
Deb, P.K.; Kaur, R.; Chandrasekaran, B.; Bala, M.; Gill, D.; Kaki, V.R.; Akkinepalli, R.R.; Mailavaram, R. Synthesis, anti-inflammatory evaluation, and docking studies of some new thiazole derivatives. Med. Chem. Res., 2014, 23(6), 2780-2792.
[http://dx.doi.org/10.1007/s00044-013-0861-4]
[160]
Kishore, D.P.; Maillabaram, R.; Rao, A.R.; Rao, P.M. Antiinflammatory evaluation and docking studies of some new thienopyrimidines. Asian J. Chem., 2013, 25(18), 10583.
[http://dx.doi.org/10.14233/ajchem.2013.16184]
[161]
Ntie-Kang, F.; Lifongo, L.L.; Mbah, J.A.; Owono Owono, L.C.; Megnassan, E.; Mbaze, L.M.; Judson, P.N.; Sippl, W.; Efange, S.M. In silico drug metabolism and pharmacokinetic profiles of natural products from medicinal plants in the Congo basin. In silico Pharmacol., 2013, 1(1), 12.
[http://dx.doi.org/10.1186/2193-9616-1-12] [PMID: 25505657]
[162]
Deb, P.K.; Sharma, A.; Piplani, P.; Akkinepally, R.R. Molecular docking and receptor-specific 3D-QSAR studies of acetylcholinesterase inhibitors. Mol. Divers., 2012, 16(4), 803-823.
[http://dx.doi.org/10.1007/s11030-012-9394-x] [PMID: 22996404]
[163]
Singh, D.B.; Gupta, M.K.; Kesharwani, R.K.; Misra, K. Comparative docking and ADMET study of some curcumin derivatives and herbal congeners targeting β-amyloid. Netw. Model. Anal. Health Inform. Bioinform., 2013, 2(1), 13-27.
[http://dx.doi.org/10.1007/s13721-012-0021-7]
[164]
Zofou, D.; Tematio, E.L.; Ntie-Kang, F.; Tene, M.; Ngemenya, M.N.; Tane, P.; Titanji, V.P. New antimalarial hits from Dacryodes edulis (Burseraceae)--part I: isolation, in vitro activity, in silico “drug-likeness” and pharmacokinetic profiles. PLoS One, 2013, 8(11),e79544.
[http://dx.doi.org/10.1371/journal.pone.0079544] [PMID: 24282507]
[165]
Onguéné, P.A.; Ntie-Kang, F.; Mbah, J.A.; Lifongo, L.L.; Ndom, J.C.; Sippl, W.; Mbaze, L.M. The potential of anti-malarial compounds derived from African medicinal plants, part III: an in silico evaluation of drug metabolism and pharmacokinetics profiling. Org. Med. Chem. Lett., 2014, 4(1), 6.
[http://dx.doi.org/10.1186/s13588-014-0006-x] [PMID: 26548985]
[166]
Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; Xu, X.; Li, Y.; Wang, Y.; Yang, L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform., 2014, 6(1), 13.
[http://dx.doi.org/10.1186/1758-2946-6-13] [PMID: 24735618]
[167]
Wang, Y.; Ma, L.; Liu, P. Feature selection and syndrome prediction for liver cirrhosis in traditional Chinese medicine. Comput. Methods Programs Biomed., 2009, 95(3), 249-257.
[http://dx.doi.org/10.1016/j.cmpb.2009.03.004] [PMID: 19380172]
[168]
Choi, H.; Cho, S.Y.; Pak, H.J.; Kim, Y.; Choi, J.Y.; Lee, Y.J.; Gong, B.H.; Kang, Y.S.; Han, T.; Choi, G.; Cho, Y.; Lee, S.; Ryoo, D.; Park, H. NPCARE: database of natural products and fractional extracts for cancer regulation. J. Cheminform., 2017, 9(1), 2.
[http://dx.doi.org/10.1186/s13321-016-0188-5] [PMID: 28184254]
[169]
Chen, Y.; Garcia de Lomana, M.; Friedrich, N-O.; Kirchmair, J. Characterization of the Chemical Space of Known and Readily Obtainable Natural Products. J. Chem. Inf. Model., 2018, 58(8), 1518-1532.
[http://dx.doi.org/10.1021/acs.jcim.8b00302] [PMID: 30010333]
[170]
Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem., 2015, 58(9), 4066-4072.
[http://dx.doi.org/10.1021/acs.jmedchem.5b00104] [PMID: 25860834]
[171]
Pires, D.E.V.; Kaminskas, L.M.; Ascher, D.B. Prediction and optimization of pharmacokinetic and toxicity properties of the ligand. In:Computational Drug Discovery and Design. Methods in Molecular Biology; Gore, M.; Jagtap, U.B., Eds.; Springer: New York, NY, 2018, pp. 271-284.
[http://dx.doi.org/10.1007/978-1-4939-7756-7_14]
[172]
Ferreira, L.L.G.; Andricopulo, A.D. ADMET modeling approaches in drug discovery. Drug Discov. Today, 2019, 24(5), 1157-1165.
[http://dx.doi.org/10.1016/j.drudis.2019.03.015] [PMID: 30890362]
[173]
Tian, S.; Djoumbou-Feunang, Y.; Greiner, R.; Wishart, D.S. CypReact: A Software Tool for in silico Reactant Prediction for Human Cytochrome P450 Enzymes. J. Chem. Inf. Model., 2018, 58(6), 1282-1291.
[http://dx.doi.org/10.1021/acs.jcim.8b00035] [PMID: 29738669]
[174]
Tyzack, J.D.; Kirchmair, J. Computational methods and tools to predict cytochrome P450 metabolism for drug discovery. Chem. Biol. Drug Des., 2019, 93(4), 377-386.
[http://dx.doi.org/10.1111/cbdd.13445] [PMID: 30471192]
[175]
Stork, C.; Chen, Y.; Šícho, M.; Kirchmair, J. Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters. J. Chem. Inf. Model., 2019, 59(3), 1030-1043.
[http://dx.doi.org/10.1021/acs.jcim.8b00677] [PMID: 30624935]
[176]
Stork, C.; Wagner, J.; Friedrich, N-O.; de Bruyn Kops, C.; Šícho, M.; Kirchmair, J. Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters. ChemMedChem, 2018, 13(6), 564-571.
[http://dx.doi.org/10.1002/cmdc.201700673] [PMID: 29285887]
[177]
Cruciani, G.; Pastor, M.; Guba, W. VolSurf: a new tool for the pharmacokinetic optimization of lead compounds. Eur. J. Pharm. Sci., 2000, 11(Suppl. 2), S29-S39.
[http://dx.doi.org/10.1016/S0928-0987(00)00162-7] [PMID: 11033425]
[178]
Cruciani, G.; Crivori, P.; Carrupt, P-A.; Testa, B. Molecular Fields in Quantitative Structure-Permeation Relationships: The VolSurf Approach. J. Mol. Struct. Theochem, 2000, 503(1), 17-30.
[http://dx.doi.org/10.1016/S0166-1280(99)00360-7]
[179]
Scotti, L.; Ferreira, E.I.; Silva, M.S.; Scotti, M.T. Chemometric studies on natural products as potential inhibitors of the NADH oxidase from Trypanosoma cruzi using the VolSurf approach. Molecules, 2010, 15(10), 7363-7377.
[http://dx.doi.org/10.3390/molecules15107363] [PMID: 20966878]
[180]
Koukoulitsa, C.; Tsantili‐Kakoulidou, A.; Mavromoustakos, T.; Chinou, I. PLS analysis for antibacterial activity of natural coumarins using VolSurf descriptors. QSAR Comb. Sci., 2009, 28(8), 785-789.
[http://dx.doi.org/10.1002/qsar.200860189]
[181]
Cruciani, G.; Carosati, E.; De Boeck, B.; Ethirajulu, K.; Mackie, C.; Howe, T.; Vianello, R. MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J. Med. Chem., 2005, 48(22), 6970-6979.
[http://dx.doi.org/10.1021/jm050529c] [PMID: 16250655]
[182]
Caron, G.; Ermondi, G.; Testa, B. Predicting the oxidative metabolism of statins: an application of the MetaSite algorithm. Pharm. Res., 2007, 24(3), 480-501.
[http://dx.doi.org/10.1007/s11095-006-9199-7] [PMID: 17253156]
[183]
Pragyan, P.; Kesharwani, S.S.; Nandekar, P.P.; Rathod, V.; Sangamwar, A.T. Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations. Mol. Divers., 2014, 18(4), 865-878.
[http://dx.doi.org/10.1007/s11030-014-9534-6] [PMID: 25028215]
[184]
Sim, S.; Ramirez, J.L.; Dimopoulos, G. Dengue virus infection of the Aedes aegypti salivary gland and chemosensory apparatus induces genes that modulate infection and blood-feeding behavior. PLoS Pathog., 2012, 8(3),e1002631.
[http://dx.doi.org/10.1371/journal.ppat.1002631] [PMID: 22479185]
[185]
Yang, H.; Lou, C.; Sun, L.; Li, J.; Cai, Y.; Wang, Z.; Li, W.; Liu, G.; Tang, Y. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics, 2019, 35(6), 1067-1069.
[http://dx.doi.org/10.1093/bioinformatics/bty707] [PMID: 30165565]
[186]
Cheng, F.; Li, W.; Zhou, Y.; Shen, J.; Wu, Z.; Liu, G.; Lee, P.W.; Tang, Y. admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J. Chem. Inf. Model., 2012, 52(11), 3099-3105.
[http://dx.doi.org/10.1021/ci300367a] [PMID: 23092397]
[187]
ul Qamar, M. T.; Maryam, A.; Muneer, I.; Xing, F.; Ashfaq, U. A.; Khan, F. A.; Anwar, F.; Geesi, M. H.; Khalid, R. R.; Rauf, S. A. Computational screening of medicinal plant phytochemicals to discover potent pan-serotype inhibitors against Dengue virus. Sci. Rep., 2019, 9(1), 1-16.
[PMID: 30626917]
[188]
Rahman, N.; Basharat, Z.; Yousuf, M.; Castaldo, G.; Rastrelli, L.; Khan, H. Virtual screening of natural products against type II transmembrane serine protease (TMPRSS2), the priming agent of Coronavirus 2 (SARS-CoV-2). Molecules, 2020, 25(10), 2271.
[http://dx.doi.org/10.3390/molecules25102271] [PMID: 32408547]
[189]
Egieyeh, S.A.; Syce, J.; Malan, S.F.; Christoffels, A. Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs. Malar. J., 2016, 15(1), 50.
[http://dx.doi.org/10.1186/s12936-016-1087-y] [PMID: 26823078]
[190]
Ruiz-Torres, V.; Losada-Echeberría, M.; Herranz-López, M.; Barrajón-Catalán, E.; Galiano, V.; Micol, V.; Encinar, J.A. New Mammalian Target of Rapamycin (mTOR) Modulators Derived from Natural Product Databases and Marine Extracts by Using Molecular Docking Techniques. Mar. Drugs, 2018, 16(10), 385.
[http://dx.doi.org/10.3390/md16100385] [PMID: 30326670]
[191]
Al Sharif, M.; Alov, P.; Vitcheva, V.; Diukendjieva, A.; Mori, M.; Botta, B.; Tsakovska, I.; Pajeva, I. Natural modulators of nonalcoholic fatty liver disease: Mode of action analysis and in silico ADME-Tox prediction. Toxicol. Appl. Pharmacol., 2017, 337, 45-66.
[http://dx.doi.org/10.1016/j.taap.2017.10.013] [PMID: 29056366]
[192]
Judson, P.N.; Long, A.; Murray, E.; Patel, M. Assessing Confidence in Predictions Using Veracity and Utility - A Case Study on the Prediction of Mammalian Metabolism by Meteor Nexus. Mol. Inform., 2015, 34(5), 284-291.
[http://dx.doi.org/10.1002/minf.201400184] [PMID: 27490274]
[193]
Onguéné, P.A.; Simoben, C.V.; Fotso, G.W.; Andrae-Marobela, K.; Khalid, S.A.; Ngadjui, B.T.; Mbaze, L.M.; Ntie-Kang, F. In silico toxicity profiling of natural product compound libraries from African flora with anti-malarial and anti-HIV properties. Comput. Biol. Chem., 2018, 72, 136-149.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.12.002] [PMID: 29277258]
[194]
Vedani, A.; Dobler, M.; Smieško, M. VirtualToxLab - a platform for estimating the toxic potential of drugs, chemicals and natural products. Toxicol. Appl. Pharmacol., 2012, 261(2), 142-153.
[http://dx.doi.org/10.1016/j.taap.2012.03.018] [PMID: 22521603]
[195]
Vedani, A.; Smiesko, M.; Spreafico, M.; Peristera, O.; Dobler, M. VirtualToxLab-in silico prediction of the toxic (endocrine-disrupting) potential of drugs, chemicals and natural products. Two years and 2,000 compounds of experience: a progress report. ALTEX, 2009, 26(3), 167-176.
[http://dx.doi.org/10.14573/altex.2009.3.167] [PMID: 19907904]
[196]
Vedani, A.; Dobler, M.; Spreafico, M.; Peristera, O.; Smiesko, M. VirtualToxLab-in silico prediction of the toxic potential of drugs and environmental chemicals: evaluation status and internet access protocol. ALTEX, 2007, 24(3), 153-161.
[http://dx.doi.org/10.14573/altex.2007.3.153] [PMID: 17891320]
[197]
Smieško, M.; Vedani, A. VirtualToxLab: exploring the toxic potential of rejuvenating substances found in traditional medicines.In: In silico methods for predicting drug toxicity. methods in molecular biology; Benfenati, E., Ed.; Springer: New York, NY, 2016, pp. 121-137.
[http://dx.doi.org/10.1007/978-1-4939-3609-0_7.]
[198]
Lavanya, P.; Ramaiah, S.; Anbarasu, A. Computational analysis reveal inhibitory action of nimbin against dengue viral envelope protein. Virusdisease, 2015, 26(4), 243-254.
[http://dx.doi.org/10.1007/s13337-015-0280-x] [PMID: 26645034]
[199]
Alam, S.; Khan, F. 3D-QSAR, Docking, ADME/Tox studies on Flavone analogs reveal anticancer activity through Tankyrase inhibition. Sci. Rep., 2019, 9(1), 5414.
[http://dx.doi.org/10.1038/s41598-019-41984-7] [PMID: 30932078]
[200]
Alam, S.; Khan, F. Virtual screening, Docking, ADMET and system pharmacology studies on Garcinia caged xanthone derivatives for anticancer activity. Sci. Rep., 2018, 8(1), 5524.
[http://dx.doi.org/10.1038/s41598-018-23768-7] [PMID: 29615704]
[201]
Mohammad, T.; Khan, F.I.; Lobb, K.A.; Islam, A.; Ahmad, F.; Hassan, M.I. Identification and evaluation of bioactive natural products as potential inhibitors of human microtubule affinity-regulating kinase 4 (MARK4). J. Biomol. Struct. Dyn., 2019, 37(7), 1813-1829.
[http://dx.doi.org/10.1080/07391102.2018.1468282] [PMID: 29683402]
[202]
Vora, J.; Patel, S.; Sinha, S.; Sharma, S.; Srivastava, A.; Chhabria, M.; Shrivastava, N. Molecular docking, QSAR and ADMET based mining of natural compounds against prime targets of HIV. J. Biomol. Struct. Dyn., 2019, 37(1), 131-146.
[http://dx.doi.org/10.1080/07391102.2017.1420489] [PMID: 29268664]
[203]
ADMET Property Prediction | QSPR | Physicochemical | ADME, Available at: https://www.simulations-plus.com/software/admetpredictor/[Accessed: May 17, 2020];
[204]
Computational tools for ADMET http://crdd.osdd.net/admet.php[May 17, 2020];
[205]
Chemistry Software for Analytical and Chemical Knowledge Management Available at: https://www.acdlabs.com/[Accessed: May 17, 2020];
[206]
US EPA, O. OncoLogicTM - A Computer System to Evaluate the Carcinogenic Potential of Chemicals, Available at: https://www.epa.gov/tsca-screening-tools/oncologictm-computer-system-evaluate-carcinogenic-potential-chemicals[Accessed: May 17, 2020];
[207]
Home-ADMElab: ADMET Prediction|ADMET Predictor| QSAR|ADMET Database http://admet.scbdd.com/[Accessed: May 17, 2020];
[208]
Optibrium - StarDrop, Available at: https://www.optibrium.com/stardrop/ [Accessed: May 17, 2020];
[209]
CORINA Symphony - Managing and Profiling Molecular Datasets | MN-AM, Available at: https://www.mn-am.com/products/corinasymphony[Accessed: May 17, 2020];
[210]
Huang, Y.; Wang, H.; Wang, H.; Wen, R.; Geng, X.; Huang, T.; Shi, J.; Wang, X.; Wang, J. Structure-based virtual screening of natural products as potential stearoyl-coenzyme a desaturase 1 (SCD1) inhibitors. Comput. Biol. Chem., 2020.86107263
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107263] [PMID: 32315834]
[211]
Jia, C-Y.; Li, J-Y.; Hao, G-F.; Yang, G-F. A drug-likeness toolbox facilitates ADMET study in drug discovery. Drug Discov. Today, 2020, 25(1), 248-258.
[http://dx.doi.org/10.1016/j.drudis.2019.10.014] [PMID: 31705979]

Rights & Permissions Print Cite
Article Metrics
14
© 2024 Bentham Science Publishers | Privacy Policy