Open access peer-reviewed chapter

Introductory Chapter: The Emerging Corner of the Omics Studies for Rational Drug Design

Written By

Arli Aditya Parikesit

Submitted: December 14th, 2020 Reviewed: December 18th, 2020 Published: June 16th, 2021

DOI: 10.5772/intechopen.95544

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1. Introduction

1.1 Structural bioinformatics contributions in -omics studies

The Acceleration of COVID-19 research in Proteomics and Transcriptomics studies occurred swiftly due to the massive amount of investment and advances in biomanufacturing [1, 2]. Moreover, the repurposing drug research has elicited remdesivir as the FDA-approved drug for COVID-19, despite mixed result from the WHO solidarity trial [3]. More drug leads are also currently undergoing clinical trial as well. This rapid development in the rational drug design is strongly associated with the field of structural bioinformatics. As for now (Early December 2020), there are more than 600 deposited SARS-CoV-2 Protein structures in the PDB (per December 2020, in the http://www.rcsb.org). They are SARS-CoV-2 Proteins with various conformations, and bindings with various ligands [4]. Hence, those proteins structures and their annotated functions are currently subject of the extensive COVID-19 drug development along with hefty investment from the pharmaceutical companies. However, in the other side of the story, non-protein biomolecular structures are currently still off the radar. There are only handful of initiatives for the COVID-19 transcriptomics-based drugs [5, 6, 7]. However, drug-based transcriptomics initiatives in canceromics are more mature. Breast cancer transcriptomics-based leads are currently under development [8, 9, 10]. That particular progress could be elicited due to the application of structural bioinformatics method for prediction the 2D and 3D model of the nucleic acids [11]. However, massive clinical applications still in favors for the proteomics-based drugs due to the stability of the bioassay experiments. In the area of natural products computation, the only realistic approach is still the proteomics-based one. For instance, propolis, as a resinous material from bees, was a subject of intensive molecular simulation research for its respective compounds constituency as diabetes drug candidates [12]. Hence, the same material also employed for the possibility as COVID-19 leads compound [13]. Moreover, various secondary metabolite sources were also investigated as drug candidates with the molecular simulation research, such as ayurvedic plants of India, and various sources of Chinese herbal medicine [14, 15, 16, 17, 18]. In this regard, bioinformaticians should proceed with mindful and prudent manner on the deployment of the molecular docking method as there are validation issues that should be resolved before working on the samples [19, 20]. That includes the utilization of the docking decoys, reiterations of Tc-PLIF value, cross-docking, redocking of the attached ligand, and the refinement of the RMSD parameter [21, 22, 23, 24, 25]. However, the easiest validation method is the ligand redocking, provided the structure is exist in the PDB repository. The decoy deployment is the most computationally extensive of all, albeit of its high accuracy [26]. In this end, the measurement of the computational cost should be taken into consideration when applying molecular simulation is needed [27].

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2. Pharmacogenomics and personalized medicine

Although it is still in its infancy, currently there is research that investigates the tendency that certain population group will be more vulnerable to COVID-19. For instance, Population with gene pool of Neanderthals was predicted to be vulnerable considerably [28]. In Indonesia, pharmacogenomics study has elicited correlation between anti-tuberculosis drug-induced liver injury (AT-DILI) severity and NAT2 phenotypes [29]. Moreover, expression of Long non-coding RNA (lncRNA) in diabetic patient is a useful lead for the progression of the epigenetic repertoire in the human cell [30]. Then, Molecular genomic is playing important role to annotate the cardiac function analysis [31]. The 7-day regiment deployment of the malaria drug primaquine is tolerated for patient with normal level of glucose-6-phosphate dehydrogenase (G6PD). Moreover, the resistance tendency of certain population group against malaria parasite has been observed as well [32]. Cancer is one of the most extensively studied disease in the pharmacogenomics field, in the light of the intensive computational biology tools, as tendencies for prevalence were elucidated in different races [33, 34]. Thus, the progress of epigenetics research has leveraged the pharmacogenomics field with various findings of biomarkers for cancer [35]. The various progresses in the pharmacogenomics field have shown that the molecular mechanism of diseases is beginning to be uncovered accordingly, especially with the development of epigenetics marker database [36]. Bioinformatics also play important role in annotating pharmacogenomics data, especially for the development of the genomics database and the disease-outcome prediction methods [37].

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3. Metabolomics and disease biomarkers

The forefront of the metabolomics research is mainly the standard instrument in the analytical biochemistry such as HPLC and GC–MS, and also some supporting organic chemistry instrumentation such as C-NMR, H-NMR, UV, and IR [28, 29, 30, 38, 39, 40]. However, the deployment of the data science approach in interpretation of the metabolomics data enables the analysis of the large data sets, and eventually the data annotation in the biological database [41, 42]. Natural products research is one of the most dynamic fronts in the metabolomics research. Herbal medicine clinical trial toward COVID-19 patients show acceptable result for patients with mild symptoms, provided that the standard therapy still apply [43, 44, 45]. Moreover, secondary metabolite could serve as disease biomarkers. Insulin-resistant individuals will have decreased serum level of glycine, and also poor biotin metabolism [46]. Currently, probiotic metabolites are undergone extensive research to determine their possible anti-SARS-CoV-2 properties [47]. Propolis studies are also focused on the metabolomics side in order to provide sufficient data to the structural bioinformatics research, especially to determine the inhibitory activities against the SARS-CoV-2 virus [48, 49, 50, 51]. Up to today, there are still limited number of the approved natural product based drugs, such as aspirin, penicillin, and taxol [52]. Bioinformatics research has been optimized to develop a more comprehensive biological conclusion in natural product chemistry, such as the annotation of naringin role in cancer, bioactive compounds of Zingeber officinale,and caffeine-aspirin interaction [53, 54, 55]. So more efforts should be elicited to improve the completeness of the natural products library, mainly to supplement the standard chemical compounds library such as Pubchem, and drug database such as drugbank. Hence, biomarkers could be elicited not only as small organic molecules, but also as larger biomolecules. For instance, the insulin level is one of the indicator of the diabetes progression, and indicator for the therapeutic options available [56]. Diabetic biomarker could be manifested as non-coding RNA as well [57, 58].

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4. Medicinal chemistry and drug design in omics study

The metabolomics study is definitely inseparable from medicinal chemistry due to their overlapping domain [59]. The development of the metabolomics library enables the construction of the rich chemical-structures library, as well as their respective functional groups from the natural products [57, 58, 59, 60, 61, 62]. Custom-made library will supplement the existing libraries, such as the pubchem, drugbank, and KEGG database [63, 64, 65]. In this regard, the availability of the functional-groups library will support the fragment-based drug design, where specialized algorithm was devised to inhibit every corner of the protein’s cavity [66, 67]. Hence, the main challenge of this approach will be in the medicinal chemistry perspective, when extrapolating the in silico research into the in vitro one become necessary. Synthesizing custom-made compounds from fragment library will not be straightforward due to the special reaction condition needed, the existence of the new structural backbone, and the availability of the chemical regents [68]. In this end, the current standing of the medicinal chemistry field is still depend upon the derivatization of the current structural library of compounds, in order to develop new lead with feasible reaction conditions [69]. Metabolomics plays dominant role in developing such library. One of the application of this particular approach is the development of ceftaroline, which is a fifth-generation broad-spectrum cephalosporin antibiotic [70].

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5. Outlook

Parallelization in the Drug Biomanufacturing with incremental optimization will facilitate the pipeline of therapeutic agent development [71, 72]. Such biomanufacturing efforts will be enhanced with the implementation of Artificial Intelligence (AI) for massive drug screening that could be beneficial for multi-components drug lead such as herbal medication, and machine-learning based implementation for such pipelines has been devised accordingly in COVID-19 leads development [73, 74, 75, 76]. The extensive utilization of the common data science methods in bioinformatics, such as machine learning, will eventually provide insight that the management of life sciences data requires more than just becoming application users [77]. Massive automation efforts in the field of life sciences will eventually push forward with the inevitable integration with formal sciences, namely with both computer science and data science [78]. In this end, Bioinformatics will play important role to manage the experimental data from the life sciences lab [79]. For instance, utilization of the SUPERFAMILY and Gene Ontology database for annotating the protein domain expression of the Plasmodium sp.parasite could be a doable venue [80, 81]. Thus, the promises that already delivered by the omics studies will eventually shed light to the current state of the COVID-19 pandemic (per December 2020), and could elicit various therapeutic options for many more infectious diseases. However, non-communicable disease such as various types of cancer and diabetes will remain a challenging task to resolve as they invoke deep understanding of the human immunological system. It involves the utilization of the immunoinformatics tools that proceed beyond this chapter [82, 83]. Hence, it should be reminded that the basic sciences behind the omics studies could not be overlooked. Medicinal chemistry, biochemistry, molecular biology, biomedicine and biotechnology will remain important, as well as emerging sciences such as bioinformatics and data sciences.

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Acknowledgments

The authors would like to thank the Institute for Research and Community Services (LPPM) of Indonesia International Institute for Life Sciences (I3L) for their heartfelt support. Thanks also go to the Indonesian Society for Bioinformatics and Biodiversity (ISBB) or Masyarakat Bioinformatika dan Biodiversitas Indonesia(MABBI) members and leaders for the thorough forum group discussion on the topic of COVID-19 pandemic, artificial intelligence for drug design, and molecular docking validation.

References

  1. 1. J.H. Martin, N.A. Bowden, Drug repurposing in the era of COVID-19: a call for leadership and government investment, Med. J. Aust. 212 (2020) 450-452.e1.https://doi.org/10.5694/mja2.50603
  2. 2. D.J. Payne, L.F. Miller, D. Findlay, J. Anderson, L. Marks, Time for a change: addressing R&D and commercialization challenges for antibacterials, Philos. Trans. R. Soc. B Biol. Sci. 370 (2015) 20140086.https://doi.org/10.1098/rstb.2014.0086
  3. 3. M. Gadebusch Bondio, M. Marloth, The “Historic Study” SOLIDARITY—Research’s Answer to the Sars-CoV-2 Pandemic, NTM Int. J. Hist. Ethics Nat. Sci. Technol. Med. (2020).https://doi.org/10.1007/s00048-020-00257-5
  4. 4. F.K. Yoshimoto, The Proteins of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS CoV-2 or n-COV19), the Cause of COVID-19, Protein J. 39 (2020) 198-216.https://doi.org/10.1007/s10930-020-09901-4
  5. 5. A.A. Parikesit, R. Nurdiansyah, The Predicted Structure for the Anti-Sense siRNA of the RNA Polymerase Enzyme (RdRp) gene of the SARS-CoV-2, Ber. Biol. 19 (2020) 97-108.https://doi.org/10.14203/beritabiologi.v19i1.3849
  6. 6. H. Uludağ, K. Parent, H.M. Aliabadi, A. Haddadi, Prospects for RNAi Therapy of COVID-19, Front. Bioeng. Biotechnol. (2020).https://doi.org/10.3389/fbioe.2020.00916
  7. 7. K. Lundstrom, Viral vectors applied for RNAi-based antiviral therapy, Viruses. (2020).https://doi.org/10.3390/v12090924
  8. 8. J. Ivan, R. Nurdiansyah, A.A. Parikesit, Computational modeling of AGO-mediated molecular inhibition of ARF6 by miR-145, Indones. J. Biotechnol. 25 (2020).https://doi.org/10.22146/ijbiotech.55631
  9. 9. P.J. Kaboli, A. Rahmat, P. Ismail, K.-H. Ling, MicroRNA-based therapy and breast cancer: A comprehensive review of novel therapeutic strategies from diagnosis to treatment., Pharmacol. Res. 97 (2015) 104-121.https://doi.org/10.1016/j.phrs.2015.04.015
  10. 10. M. Fan, R. Krutilina, J. Sun, A. Sethuraman, C.H. Yang, Z.-H. Wu, J. Yue, L.M. Pfeffer, Comprehensive analysis of microRNA (miRNA) targets in breast cancer cells., J. Biol. Chem. 288 (2013) 27480-27493.https://doi.org/10.1074/jbc.M113.491803
  11. 11. R. Wijaya, A.A. Parikesit, R. Nurdiansyah, 3D And 2D RNA Structure Prediction Of The BRCA2 Gene And Its Silencing RNA In The Breast Cancer, Walisongo J. Chem. 3 (2020) 10.https://doi.org/10.21580/wjc.v3i1.6019
  12. 12. M. Sahlan, M.N.H. Al Faris, R. Aditama, K. Lischer, A.C. Khayrani, D.K. Pratami, Molecular Docking of South Sulawesi Propolis against Fructose 1,6-Bisphosphatase as a Type 2 Diabetes Mellitus Drug, Int. J. Technol. 11 (2020) 910.https://doi.org/10.14716/ijtech.v11i5.4332
  13. 13. M. Sahlan, R. Irdiani, D. Flamandita, R. Aditama, S. Alfarraj, M.J. Ansari, A.C. Khayrani, D.K. Pratami, K. Lischer, Molecular interaction analysis of Sulawesi propolis compounds with SARS-CoV-2 main protease as preliminary study for COVID-19 drug discovery., J. King Saud Univ. Sci. 33 (2021) 101234.https://doi.org/10.1016/j.jksus.2020.101234
  14. 14. A.J. Gandhi, J.D. Rupareliya, V.J. Shukla, S.B. Donga, R. Acharya, An ayurvedic perspective along with in silico study of the drugs for the management of SARS-CoV-2, J. Ayurveda Integr. Med. (2020).https://doi.org/10.1016/j.jaim.2020.07.002
  15. 15. M. Amaravani, N.K. Prasad, V. Ramakrishna, COX-2 structural analysis and docking studies with gallic acid structural analogues, Springerplus. (2012).https://doi.org/10.1186/2193-1801-1-58
  16. 16. R. V. Chikhale, S.K. Sinha, R.B. Patil, S.K. Prasad, A. Shakya, N. Gurav, R. Prasad, S.R. Dhaswadikar, M. Wanjari, S.S. Gurav, In-silico investigation of phytochemicals from Asparagus racemosus as plausible antiviral agent in COVID-19, J. Biomol. Struct. Dyn. (2020).https://doi.org/10.1080/07391102.2020.1784289
  17. 17. D. Zhang, K. Wu, X. Zhang, S. Deng, B. Peng, In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus, J. Integr. Med. 18 (2020) 152-158.https://doi.org/10.1016/j.joim.2020.02.005
  18. 18. A.A. Parikesit, R. Nurdiansyah, D. Agustriawan, Telaah Sistematis Terhadap Basis Data Bahan Alam untuk Pengembangan Produk Suplemen Herbal, Pros. SEMNASTAN. 0 (2018) 62-68.https://jurnal.umj.ac.id/index.php/semnastan/article/view/2259/1874(accessed January 28, 2018)
  19. 19. K.E. Hevener, W. Zhao, D.M. Ball, K. Babaoglu, J. Qi, S.W. White, R.E. Lee, Validation of molecular docking programs for virtual screening against dihydropteroate synthase, J. Chem. Inf. Model. 49 (2009) 444-460.https://doi.org/10.1021/ci800293n
  20. 20. D.T. Moustakas, P.T. Lang, S. Pegg, E. Pettersen, I.D. Kuntz, N. Brooijmans, R.C. Rizzo, Development and validation of a modular, extensible docking program: DOCK 5, J. Comput. Aided. Mol. Des. (2006).https://doi.org/10.1007/s10822-006-9060-4
  21. 21. A. Setiawati, F.D.O. Riswanto, S.H. Yuliani, E.P. Istyastono, Retrospective Validation of a Structure-Based Virtual Screening Protocol to Identify Ligands for Estrogen Receptor Alpha and Its Application to Identify the Alpha-Mangostin Binding Pose, Indones. J. Chem. 14 (2014) 103-108.https://doi.org/10.22146/ijc.21245
  22. 22. E.P. Istyastono, F.D.O. Riswanto, S.H. Yuliani, Computer-Aided Drug Repurposing: A Cyclooxygenase-2 Inhibitor Celecoxib as a Ligand for Estrogen Receptor Alpha, Indones. J. Chem. 15 (2015) 274-280.https://doi.org/10.22146/ijc.21196
  23. 23. A. Radwan, G.M. Mahrous, Docking studies and molecular dynamics simulations of the binding characteristics of waldiomycin and its methyl ester analog to Staphylococcus aureus histidine kinase, PLoS One. 15 (2020) e0234215.https://doi.org/10.1371/journal.pone.0234215
  24. 24. J. Shamsara, CrossDocker: a tool for performing cross-docking using Autodock Vina, Springerplus. 5 (2016) 344.https://doi.org/10.1186/s40064-016-1972-4
  25. 25. Z. Ibrahim, B.A. Tejo, M.A.M. Latif, R.A. Karjiban, A.B. Salleh, M.B.A. Rahman, In-silico Identification of Potential Protein Arginine Deiminase IV (PAD4) Inhibitors, Malaysian J. Anal. Sci. 20 (2016) 1269-1277.https://doi.org/10.17576/mjas-2016-2006-05
  26. 26. M.M. Mysinger, M. Carchia, J.J. Irwin, B.K. Shoichet, Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking, J. Med. Chem. 55 (2012) 6582-6594.https://doi.org/10.1021/jm300687e
  27. 27. T. Liu, D. Lu, H. Zhang, M. Zheng, H. Yang, Y. Xu, C. Luo, W. Zhu, K. Yu, H. Jiang, Applying high-performance computing in drug discovery and molecular simulation, Natl. Sci. Rev. 3 (2016) 49-63.https://doi.org/10.1093/nsr/nww003
  28. 28. H. Zeberg, S. Pääbo, The major genetic risk factor for severe COVID-19 is inherited from Neanderthals, Nature. 587 (2020) 610-612.https://doi.org/10.1038/s41586-020-2818-3
  29. 29. R. Yuliwulandari, K. Prayuni, R.W. Susilowati, S. Subagyo, S. Soedarsono, A.S. M Sofro, K. Tokunaga, J.G. Shin, NAT2 slow acetylator is associated with anti-tuberculosis drug-induced liver injury severity in Indonesian population, Pharmacogenomics. 20 (2019) 1303-1310.https://doi.org/10.2217/pgs-2019-0131
  30. 30. M. Fachrul, D.H. Utomo, A.A. Parikesit, lncRNA-based study of epigenetic regulations in diabetic peripheral neuropathy., Silico Pharmacol. 6 (2018) 7.https://doi.org/10.1007/s40203-018-0042-8
  31. 31. A. Pasipoularides, Genomic translational research: Paving the way to individualized cardiac functional analyses and personalized cardiology, Int. J. Cardiol. 230 (2017) 384-401.https://doi.org/10.1016/j.ijcard.2016.12.097
  32. 32. S.H. Keenihan, R. Gramzinksi, S. Ratiwayanto, H. Hadiputranto, W. Riberu, S. Soebianto, F. Rusjdy, D. Syafruddin, A. Kartikasari, M. Djojosubroto, I. Setianingsih, A. Harahap, Krisin, D. Fryauff, T. Richie, Y. Charoenvit, H.A. Marwoto, S. Kumar, S. Hoffman, S. Marzuki, K. Baird,Plasmodium falciparum: Mechanisms of innate and acquired protection againstPlasmodium falciparumin Javanese transmigrant adults and children newly resident in malaria-endemic Northwest Papua, in: Adv. Exp. Med. Biol., Kluwer Academic/Plenum Publishers, 2003: pp. 83-102.https://doi.org/10.1007/978-1-4615-0059-9_7
  33. 33. S. Bernard, D. Agustriawan, Identification of microRNA targeting cancer gene of colorectal carcinoma in Caucasian population, in: 2019 Int. Conf. Inf. Commun. Technol. ICOIACT 2019, Institute of Electrical and Electronics Engineers Inc., 2019: pp. 423-427.https://doi.org/10.1109/ICOIACT46704.2019.8938488
  34. 34. M.Z. Arifin N, D. Agustriawan, A.A. Parikesit, R. Nurdiansyah, K.N. Ramanto, Identification of microRNAs targeting NAT1 and NAT2 gene transcripts in prostate cancer patients observed in different races, IOP Conf. Ser. Mater. Sci. Eng. 546 (2019) 062017.https://doi.org/10.1088/1757-899X/546/6/062017
  35. 35. D. Agustriawan, C.H. Huang, J.J.C. Sheu, S.C. Lee, J.J.P. Tsai, N. Kurubanjerdjit, K.L. Ng, DNA methylation-regulated microRNA pathways in ovarian serous cystadenocarcinoma: A meta-analysis, Comput. Biol. Chem. (2016).https://doi.org/10.1016/j.compbiolchem.2016.09.016
  36. 36. D. Agustriawan, E.B. Wijaya, C.-H. Huang, E. Lim, I.-C. Hsueh, K.-R. Tzeng, K.-L. Ng, MethmiRbase : a Database of DNA Methylation and miRNA Expression in Human Cancer, Lect. Notes Eng. Comput. Sci. I (2016) 16-19
  37. 37. C.F. Thorn, T.E. Klein, R.B. Altman, Pharmacogenomics and bioinformatics: PharmGKB, Pharmacogenomics. 11 (2010) 501-505.https://doi.org/10.2217/pgs.10.15
  38. 38. M.D. Luque de Castro, F. Priego-Capote, The analytical process to search for metabolomics biomarkers, J. Pharm. Biomed. Anal. 147 (2018) 341-349.https://doi.org/10.1016/j.jpba.2017.06.073
  39. 39. Q. Li, C. Zhao, Y. Zhang, H. Du, T. Xu, X. Xu, J. Zhang, T. Kuang, X. Lai, G. Fan, Y. Zhang, 1H NMR-Based Metabolomics Coupled With Molecular Docking Reveal the Anti-Diabetic Effects and Potential Active Components of Berberis vernae on Type 2 Diabetic Rats, Front. Pharmacol. 11 (2020) 932.https://doi.org/10.3389/fphar.2020.00932
  40. 40. D.C. Tan, N.K. Kassim, I.S. Ismail, M. Hamid, M.S. Ahamad Bustamam, Identification of antidiabetic metabolites from paederia foetida l. Twigs by gas chromatography-mass spectrometry-based metabolomics and molecular docking study, Biomed Res. Int. 2019 (2019).https://doi.org/10.1155/2019/7603125
  41. 41. F.C.P. Navarro, H. Mohsen, C. Yan, S. Li, M. Gu, W. Meyerson, M. Gerstein, Genomics and data science: An application within an umbrella, Genome Biol. 20 (2019) 109.https://doi.org/10.1186/s13059-019-1724-1
  42. 42. J. Godzien, A. Gil de la Fuente, A. Otero, C. Barbas, Metabolite Annotation and Identification, in: Compr. Anal. Chem., Elsevier B.V., 2018: pp. 415-445.https://doi.org/10.1016/bs.coac.2018.07.004
  43. 43. L. Ang, E. Song, H.W. Lee, M.S. Lee, Herbal Medicine for the Treatment of Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-Analysis of Randomized Controlled Trials, J. Clin. Med. 9 (2020) 1583.https://doi.org/10.3390/jcm9051583
  44. 44. X. Luo, X. Ni, J. Lin, Y. Zhang, L. Wu, D. Huang, Y. Liu, J. Guo, W. Wen, Y. Cai, Y. Chen, L. Lin, The add-on effect of Chinese herbal medicine on COVID-19: A systematic review and meta-analysis, Phytomedicine. (2020) 153282.https://doi.org/10.1016/j.phymed.2020.153282
  45. 45. W. Pang, Z. Liu, N. Li, Y. Li, F. Yang, B. Pang, X. Jin, W. Zheng, J. Zhang, Chinese medical drugs for coronavirus disease 2019: a systematic review and meta-analysis, Integr. Med. Res. 9 (2020) 100477.https://doi.org/10.1016/j.imr.2020.100477
  46. 46. S. Vaishya, R.D. Sarwade, V. Seshadri, MicroRNA, proteins, and metabolites as novel biomarkers for prediabetes, diabetes, and related complications, Front. Endocrinol. (Lausanne). 9 (2018).https://doi.org/10.3389/fendo.2018.00180
  47. 47. F. Anwar, H.N. Altayb, F.A. Al-Abbasi, A.L. Al-Malki, M.A. Kamal, V. Kumar, Antiviral effects of probiotic metabolites on COVID-19., J. Biomol. Struct. Dyn. (2020) 1-10.https://doi.org/10.1080/07391102.2020.1775123
  48. 48. H. Refaat, F.M. Mady, H.A. Sarhan, H.S. Rateb, E. Alaaeldin, Optimization and evaluation of propolis liposomes as a promising therapeutic approach for COVID-19, Int. J. Pharm. (2020).https://doi.org/10.1016/j.ijpharm.2020.120028
  49. 49. C.A. Scorza, V.C. Gonçalves, F.A. Scorza, A.C. Fiorini, A.C.G. de Almeida, M.C.M. Fonseca, J. Finsterer, Propolis and coronavirus disease 2019 (COVID-19): Lessons from nature, Complement. Ther. Clin. Pract. 41 (2020) 101227.https://doi.org/10.1016/j.ctcp.2020.101227
  50. 50. A.A. Berretta, M.A.D. Silveira, J.M. Cóndor Capcha, D. De Jong, Propolis and its potential against SARS-CoV-2 infection mechanisms and COVID-19 disease: Running title: Propolis against SARS-CoV-2 infection and COVID-19, Biomed. Pharmacother. 131 (2020) 110622.https://doi.org/10.1016/j.biopha.2020.110622
  51. 51. H.I. Güler, G. Tatar, O. Yildiz, A.O. Belduz, …, An investigation of ethanolic propolis extracts: Their potential inhibitor properties against ACE-II receptors for COVID-19 treatment by Molecular Docking Study, Sci. …. (2020)
  52. 52. A. Amit Koparde, R. Chandrashekar Doijad, C. Shripal Magdum, Natural Products in Drug Discovery, in: Pharmacogn. - Med. Plants, IntechOpen, 2019.https://doi.org/10.5772/intechopen.82860
  53. 53. A. Fadholly, A.N.M. Ansori, T.H. Sucipto, An overview of naringin: Potential anticancer compound of citrus fruits, Res. J. Pharm. Technol. 13 (2020) 5613-5619.https://doi.org/10.5958/0974-360X.2020.00979.8
  54. 54. W.E. Putra, V.D. Kharisma, H. Susanto, Potential ofZingiber officinalebioactive compounds as inhibitory agent against the IKK-B, in: AIP Conf. Proc., American Institute of Physics Inc., 2020: p. 040048.https://doi.org/10.1063/5.0002478
  55. 55. V. Dhea Kharisma, A. Nur, M. Ansori, A. Fadholly, T.H. Sucipto, Molecular Mechanism of Caffeine-Aspirin Interaction in Kopi Balur 1 as Anti-Inflammatory Agent: A Computational Study, 2020.https://doi.org/10.37506/IJFMT.V14I4.12274
  56. 56. World Health Organization, Global Report on Diabetes, Isbn. 978 (2016) 88.https://doi.org/ISBN978 92 4 156525 7
  57. 57. C. Guay, R. Regazzi, Circulating microRNAs as novel biomarkers for diabetes mellitus, Nat. Rev. Endocrinol. 9 (2013) 513-521.https://doi.org/10.1038/nrendo.2013.86
  58. 58. E. Guarino, C.D. Poggi, G.E. Grieco, V. Cenci, E. Ceccarelli, I. Crisci, G. Sebastiani, F. Dotta, Circulating MicroRNAs as biomarkers of gestational diabetes mellitus: Updates and perspectives, Int. J. Endocrinol. 2018 (2018).https://doi.org/10.1155/2018/6380463
  59. 59. M. Frédérich, B. Pirotte, M. Fillet, P. De Tullio, Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine, J. Med. Chem. 59 (2016) 8649-8666.https://doi.org/10.1021/acs.jmedchem.5b01335
  60. 60. L. Li, R. Li, J. Zhou, A. Zuniga, A.E. Stanislaus, Y. Wu, T. Huan, J. Zheng, Y. Shi, D.S. Wishart, G. Lin, MyCompoundID: Using an evidence-based metabolome library for metabolite identification, Anal. Chem. 85 (2013) 3401-3408.https://doi.org/10.1021/ac400099b
  61. 61. A. Yanuar, A. Mun’im, A.B.A. Lagho, R.R. Syahdi, M. Rahmat, H. Suhartanto, Medicinal Plants Database and Three Dimensional Structure of the Chemical Compounds from Medicinal Plants in Indonesia, Int. J. Comput. Sci. 8 (2011) 180-183.http://arxiv.org/abs/1111.7183(accessed March 23, 2014)
  62. 62. A.A. Parikesit, B. Ardiansah, D.M. Handayani, U.S.F. Tambunan, D. Kerami, Virtual screening of Indonesian flavonoid as neuraminidase inhibitor of influenza a subtype H5N1, IOP Conf. Ser. Mater. Sci. Eng. 107 (2016) 012053.https://doi.org/10.1088/1757-899X/107/1/012053
  63. 63. D.S. Wishart, C. Knox, A.C. Guo, S. Shrivastava, M. Hassanali, P. Stothard, Z. Chang, J. Woolsey, DrugBank: a comprehensive resource for in silico drug discovery and exploration., Nucleic Acids Res. 34 (2006) D668–D672
  64. 64. S. Kim, P.A. Thiessen, E.E. Bolton, J. Chen, G. Fu, A. Gindulyte, L. Han, J. He, S. He, B.A. Shoemaker, J. Wang, B. Yu, J. Zhang, S.H. Bryant, PubChem substance and compound databases, Nucleic Acids Res. 44 (2016) D1202–D1213.https://doi.org/10.1093/nar/gkv951
  65. 65. M. Kanehisa, M. Furumichi, M. Tanabe, Y. Sato, K. Morishima, KEGG: New perspectives on genomes, pathways, diseases and drugs, Nucleic Acids Res. 45 (2017) D353–D361.https://doi.org/10.1093/nar/gkw1092
  66. 66. T. Wang, M.-B. Wu, Z.-J. Chen, H. Chen, J.-P. Lin, L.-R. Yang, Fragment-based drug discovery and molecular docking in drug design., Curr. Pharm. Biotechnol. 16 (2015) 11-25.http://www.ncbi.nlm.nih.gov/pubmed/25420726(accessed February 24, 2015)
  67. 67. Y. Chen, D.T. Pohlhaus, In silico docking and scoring of fragments., Drug Discov. Today. Technol. 7 (2010) e147–e202.https://doi.org/10.1016/j.ddtec.2010.11.002
  68. 68. D. Seebach, Organic Synthesis—Where now?, Angew. Chemie Int. Ed. English. 29 (1990) 1320-1367.https://doi.org/10.1002/anie.199013201
  69. 69. R. Liu, X. Li, K.S. Lam, Combinatorial chemistry in drug discovery, Curr. Opin. Chem. Biol. 38 (2017) 117-126.https://doi.org/10.1016/j.cbpa.2017.03.017
  70. 70. H. Zhang, Y. Xu, P. Jia, Y. Zhu, G. Zhang, J. Zhang, S. Duan, W. Kang, T. Wang, R. Jing, J. Cheng, Y. Liu, Q. Yang, Global trends of antimicrobial susceptibility to ceftaroline and ceftazidime–avibactam: a surveillance study from the ATLAS program (2012-2016), Antimicrob. Resist. Infect. Control. 9 (2020) 166.https://doi.org/10.1186/s13756-020-00829-z
  71. 71. C.L. Gargalo, I. Udugama, K. Pontius, P.C. Lopez, R.F. Nielsen, A. Hasanzadeh, S.S. Mansouri, C. Bayer, H. Junicke, K. V. Gernaey, Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes, J. Ind. Microbiol. Biotechnol. 47 (2020) 947-964.https://doi.org/10.1007/s10295-020-02308-1
  72. 72. A. Boulila, M. Ayadi, S. Marzouki, S. Bouzidi, Contribution to a biomedical component production using incremental sheet forming, Int. J. Adv. Manuf. Technol. 95 (2018) 2821-2833.https://doi.org/10.1007/s00170-017-1397-4
  73. 73. M.F.A.-H. Ginoga, R. Trisminingsih, W.A. Kusuma, Drug-Target Visualization on IJAH Analytics Using Sankey Diagram, in: 2020 Int. Conf. Comput. Sci. Its Appl. Agric., IEEE, 2020: pp. 1-6.https://doi.org/10.1109/ICOSICA49951.2020.9243285
  74. 74. A. Reinaldo, W.A. Kusuma, H. Rahmawan, Y. Herdiyeni, Implementation of Breadth-First Search Parallel to Predict Drug-Target Interaction in Plant-Disease Graph, in: 2020 Int. Conf. Comput. Sci. Its Appl. Agric., IEEE, 2020: pp. 1-5.https://doi.org/10.1109/ICOSICA49951.2020.9243216
  75. 75. D. Afdhal, K.W. Ananta, W.S. Hartono, Adverse Drug Reactions Prediction Using Multi-label Linear Discriminant Analysis and Multi-label Learning, 2020 Int. Conf. Adv. Comput. Sci. Inf. Syst. (2020) 69-76.https://doi.org/10.1109/ICACSIS51025.2020.9263166
  76. 76. F. Sulistiawan, W.A. Kusuma, N.S. Ramadhanti, A. Tedjo, Drug-Target Interaction Prediction in Coronavirus Disease 2019 Case Using Deep Semi-Supervised Learning Model, in: 2020 Int. Conf. Adv. Comput. Sci. Inf. Syst., IEEE, 2020: pp. 83-88.https://doi.org/10.1109/ICACSIS51025.2020.9263241
  77. 77. S. Dash, S.K. Shakyawar, M. Sharma, S. Kaushik, Big data in healthcare: management, analysis and future prospects, J. Big Data. 6 (2019) 1-25.https://doi.org/10.1186/s40537-019-0217-0
  78. 78. D. Besozzi, L. Manzoni, M.S. Nobile, S. Spolaor, M. Castelli, L. Vanneschi, P. Cazzaniga, S. Ruberto, L. Rundo, A. Tangherloni, Computational Intelligence for Life Sciences, Fundam. Informaticae. 171 (2019) 57-80.https://doi.org/10.3233/FI-2020-1872
  79. 79. A.A. PARIKESIT, D. ANUROGO, R.A. PUTRANTO, Pemanfaatan bioinformatika dalam bidang pertanian dan kesehatan (The utilization of bioinformatics in the field of agriculture and health), E-Journal Menara Perkeb. 85 (2017).https://doi.org/10.22302/iribb.jur.mp.v85i2.237
  80. 80. F.H. Hasanah, E. Sulistyaningsih, W.D. Sawitri, The Expression of The PfEMP1-DBL2β Recombinant Protein of Plasmodium falciparum Welch, 1897 Isolated From Indonesia, J. ILMU DASAR. 21 (2020) 67.https://doi.org/10.19184/jid.v21i1.10494
  81. 81. A.A. Parikesit, D.H. Utomo, N. Karimah, Protein Domain Annotation of Plasmodium spp. Circumsporozoite Protein (CSP) Using Hidden Markov Model-based Tools, J. Biol. Indones. 14 (2018) 185-190.https://doi.org/10.14203/jbi.v14i2.3737
  82. 82. N. Tomar, R.K. De, Immunoinformatics: an integrated scenario, Immunology. 131 (2010) 153-168.https://doi.org/10.1111/j.1365-2567.2010.03330.x
  83. 83. D.R. Flower, Immunoinformatics: Predicting Immunogenicity in Silico, Humana, 2007.http://books.google.co.id/books?id=IJtZurJ5BvoC

Written By

Arli Aditya Parikesit

Submitted: December 14th, 2020 Reviewed: December 18th, 2020 Published: June 16th, 2021