Skip to main content

COVID-19: Hard Road to Find Integrated Computational Drug and Repurposing Pipeline

Part of the Studies in Computational Intelligence book series (SCI,volume 923)

Abstract

Shedding of infectious coronavirus disease (COVID-19) is affecting 215 countries and territories; quickly circulates continuously in worldwide. The vital scientific communities are rigorously looking at these public health challenges, global crisis and finding new ways to deal with this pandemic disease. Currently, there is no specific effective approved drug or vaccine available in the market to treat or prevent COVID-19. Thus, there is an urgent need for more and better research to boost up the development of effective therapeutic vaccines and drugs against this virus. Numerous solidarity clinical trial studies, high-level effort and investigations are underway. The repurposing drugs such as chloroquine and its derivatives, remdesivir, favipiravir, darunavir, umifenovir, nitazoxanide and thalidomide are being used globally for clinical trial studies to test their safety and efficacy in this pandemic virus treatment, some of which are already being tested in COVID-19 patients. The computational intelligence methods including machine learning has been useful in computer-aided drug design and drug repurposing. This chapter focus on strengthening the current understanding of the selected number of repurposing antivirals, antiretroviral, antimalarial, and anti-inflammatory drugs that can fight with COVID-19 infection. Further, we look forward to an insightful piece of drug compounds that can be used either individually or in combination.

Keywords

  • COVID-19
  • Clinical trial
  • Repurposing drugs
  • Machine learning

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-15-8534-0_15
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-981-15-8534-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Hardcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. Cherian, S. S., Agrawal, M., Basu, A., Abraham, P., Gangakhedkar, R. R., & Bhargava, B. (2020). Perspectives for repurposing drugs for the coronavirus disease 2019. Indian Journal of Medical Research, 151(2), 160.

    Google Scholar 

  2. Cascella, M., Rajnik, M., Cuomo, A., Dulebohn, S. C., & Di Napoli, R. (2020). Features, evaluation and treatment coronavirus (COVID-19). In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2020 [cited 2020 Jun 21]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK554776/.

  3. Sharma, R., Agarwal, M., Gupta, M., Somendra, S., & Saxena, S. K. (2020). Clinical characteristics and differential clinical diagnosis of novel coronavirus Disease 2019 (COVID-19). Coronavirus Disease 2019 (COVID-19), pp. 55–70. April 30, 2020.

    Google Scholar 

  4. Udugama, B., Kadhiresan, P., Kozlowski, H. N., Malekjahani, A., Osborne, M., Li, V. Y. C., et al. (2020). Diagnosing COVID-19: The disease and tools for detection. ACS Nano. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144809/.

  5. Helmy, Y. A., Fawzy, M., Elaswad, A., Sobieh, A., Kenney, S. P., & Shehata, A. A. (2020) The COVID-19 Ppandemic: A comprehensive review of taxonomy, genetics, epidemiology, diagnosis, treatment, and control. Journal of Clinical Medicine, 9(4). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7230578/.

  6. Raza, K. (2020). Artificial intelligence against COVID-19: A meta-analysis of current research. In Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data, 78, 2020. Berlin: Springer (In Press).

    Google Scholar 

  7. Researchers Use AI to Detect COVID-19 [Internet]. Imaging Technology News. 2020 [cited 2020 Jun 22]. Available from: https://www.itnonline.com/content/researchers-use-ai-detect-covid-19.

  8. Li, F. (2016). Structure, function, and evolution of coronavirus spike proteins. Annual Review of Virology, 3(1), 237–261.

    CrossRef  Google Scholar 

  9. Shang, J., Wan, Y., Luo, C., Ye, G., Geng, Q., Auerbach, A., et al. (2020). Cell entry mechanisms of SARS-CoV-2. PNAS, 117(21), 11727–11734.

    CrossRef  Google Scholar 

  10. Shereen, M. A., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses. Journal of Advanced Research, 24, 91–98.

    CrossRef  Google Scholar 

  11. Ong, E, Wong, M. U., Huffman, A., & He, Y. (2020). COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. bioRxiv.

    Google Scholar 

  12. Mohanty, S, Harun, A. I., Rashid, M., Mridul, M., Mohanty, C., & Swayamsiddha, S. (2020). Application of artificial intelligence in COVID-19 drug repurposing. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(5), 1027–1031.

    Google Scholar 

  13. Shaffer, L. (2020). 15 drugs being tested to treat COVID-19 and how they would work. Nature Medicine. https://www.nature.com/articles/d41591-020-00019-9.

  14. Wang, Y., Zhang, D., Du, G., Du, R., Zhao, J., Jin, Y., et al. (2020). Remdesivir in adults with severe COVID-19: A randomised, double-blind, placebo-controlled, multicentre trial. The Lancet, 395(10236), 1569–1578.

    CrossRef  Google Scholar 

  15. Guy, R. K., DiPaola, R. S., Romanelli, F., & Dutch, R. E. (2020). Rapid repurposing of drugs for COVID-19. Science. Available from: https://science.sciencemag.org/content/early/2020/05/07/science.abb9332.

  16. Cao, B., Wang, Y., Wen, D., Liu, W., Wang, J., Fan, G., et al. (2020). A trial of lopinavir-ritonavir in adults hospitalized with severe COVID-19. New England Journal of Medicine, 382(19), 1787–1799.

    CrossRef  Google Scholar 

  17. Lian, N., Xie, H., Lin S., Huang, J., Zhao, J., & Lin, Q. (2020). Umifenovir treatment is not associated with improved outcomes in patients with coronavirus disease 2019: A retrospective study. Clinical Microbiology and Infection. Available from: https://www.clinicalmicrobiologyandinfection.com/article/S1198-743X(20)30234-2/abstract.

  18. Kelleni, M. T. (2020). Nitazoxanide/azithromycin combination for COVID-19: A suggested new protocol for early management. Pharmacological Research, 1(157), 104874.

    CrossRef  Google Scholar 

  19. Hashem, A. M., Alghamdi, B. S., Algaissi, A. A., Alshehri, F. S., Bukhari, A., Alfaleh, M. A., et al. (2020). Therapeutic use of chloroquine and hydroxychloroquine in COVID-19 and other viral infections: A narrative review. Travel Medicine and Infectious Disease. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7202851/.

  20. Devaux, C. A., Rolain, J.-M., Colson, P., & Raoult, D. (2020). New insights on the antiviral effects of chloroquine against coronavirus: what to expect for COVID-19? International Journal of Antimicrobial Agents, 55(5), 105938.

    CrossRef  Google Scholar 

  21. Gautret, P., Lagier, J.-C., Parola, P., Hoang, V. T., Meddeb, L., Mailhe, M., et al. (2020). Hydroxychloroquine and azithromycin as a treatment of COVID-19: Results of an open-label non-randomized clinical trial. International Journal of Antimicrobial Agents, 20, 105949.

    CrossRef  Google Scholar 

  22. Heidary, F., & Gharebaghi, R. (2020). Ivermectin: A systematic review from antiviral effects to COVID-19 complementary regimen. The Journal of Antibiotics, 12, 1–10.

    Google Scholar 

  23. Kumar, S., Zhi, K., Mukherji, A., & Gerth, K. (2020). Repurposing antiviral protease inhibitors using extracellular vesicles for potential therapy of COVID-19. Viruses, 12(5).

    Google Scholar 

  24. Wu, R., Wang, L., Kuo, H.-C. D., Shannar, A., Peter, R., Chou, P. J., et al. (2020). An update on current therapeutic drugs treating COVID-19. Current Pharmacology Reports, 1–15.

    Google Scholar 

  25. Gbinigie K, Frie K. Should azithromycin be used to treat COVID-19? A rapid review. BJGP Open. Available from: https://bjgpopen.org/content/early/2020/05/12/bjgpopen20X101094.

  26. Emery, P., Rondon, J., Parrino, J., Lin, Y., Pena-Rossi, C., van Hoogstraten, H, et al. (2019). Safety and tolerability of subcutaneous sarilumab and intravenous tocilizumab in patients with rheumatoid arthritis. Rheumatology (Oxford), 58(5), 849–58.

    Google Scholar 

  27. Khan, F., Fabbri, L., Stewart, I., Robinson, K., Smyth, A. R. & Jenkins, G. (2020). A systematic review of Anakinra, Tocilizumab, Sarilumab and Siltuximab for coronavirus-related infections. medRxiv.

    Google Scholar 

  28. King, A., Vail, A., O’Leary, C., Hannan, C., Brough, D., Patel H, et al. (2020). Anakinra in COVID-19: important considerations for clinical trials. The Lancet Rheumatology. Available from: https://www.thelancet.com/journals/lanrhe/article/PIIS2665-9913(20)30160-0/abstract.

  29. Qazi, S., & Raza, K. (2020). Smart biosensors for an efficient point of care (PoC) health management. In J. Chaki, N. Dey, & De D (Eds.), Smart biosensors in medical care (pp. 65–85). London: Academic Press. Available from: http://www.sciencedirect.com/science/article/pii/B9780128207819000048 (Advances in ubiquitous sensing applications for healthcare).

  30. Raza, K., & Qazi, S. (2019). Nanopore sequencing technology and Internet of living things: A big hope for U-healthcare. In N. Dey, J. Chaki, & R. Kumar (Eds.), Sensors for health monitoring (Vol. 5, pp. 95–116). London: Academic Press. Available from: http://www.sciencedirect.com/science/article/pii/B9780128193617000051 (Advances in ubiquitous sensing applications for healthcare).

  31. Ke, Y.-Y., Peng, T.-T., Yeh, T.-K., Huang, W.-Z., Chang, S.-E., Wu, S.-H., et al. (2020). Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomedical Journal. Available from: http://www.sciencedirect.com/science/article/pii/S2319417020300494.

  32. Réda, C., Kaufmann, E., & Delahaye-Duriez, A. (2020). Machine learning applications in drug development. Computational and Structural Biotechnology Journal, 1(18), 241–252.

    CrossRef  Google Scholar 

  33. Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016). A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing, 2016(1), 67.

    CrossRef  Google Scholar 

  34. Li, J., Zheng, S., Chen, B., Butte, A. J., Swamidass, S. J., & Lu, Z. (2016). A survey of current trends in computational drug repositioning. Briefings in Bioinformatics, 17(1), 2–12.

    CrossRef  Google Scholar 

  35. Ekins, S., Puhl, A. C., Zorn, K. M., Lane, T. R., Russo, D. P., Klein, J. J., et al. (2019). Exploiting machine learning for end-to-end drug discovery and development. Nature Materials, 18(5), 435–441.

    CrossRef  Google Scholar 

  36. Yella, J. K., Yaddanapudi, S., Wang, Y., & Jegga, A. G. (2018). Changing trends in computational drug repositioning. Pharmaceuticals (Basel), 11(2). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6027196/.

  37. Kumar, A., Gupta, P. K., & Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome, 14(4), 569–573.

    CrossRef  Google Scholar 

  38. Lavecchia, A. (2015). Machine-learning approaches in drug discovery: Methods and applications. Drug Discovery Today, 20(3), 318–331. https://doi.org/10.1016/j.drudis.2014.10.012.

    CrossRef  Google Scholar 

  39. Prykhodko, O., Johansson, S. V., Kotsias, P.-C., Arús-Pous, J., Bjerrum, E. J., Engkvist, O., & Chen, H. (2019). A de novo molecular generation method using latent vector based generative adversarial network. Journal of Cheminformatics, 11(1). https://doi.org/10.1186/s13321-019-0397-9.

  40. Ozsoy, M. G., Özyer, T., Polat, F., & Alhajj, R. (2018). Realizing drug repositioning by adapting a recommendation system to handle the process. BMC Bioinformatics, 19(1). https://doi.org/10.1186/s12859-018-2142-1.

  41. Mandlik, V., Bejugam, P. R., & Singh, S. (2016). Application of artificial neural networks in modern drug discovery. Artificial Neural Network for Drug Design, Delivery and Disposition 123–139. https://doi.org/10.1016/b978-0-12-801559-9.00006-5.

Download references

Acknowledgements

AS acknowledges funding from the Indian Council of Medical Research, New Delhi (Grant No. 45/17/2019-PHA/BMS) for financial assistance. SQ is supported by DST-Inspire Fellowship, Department of Science & Technology, Government of India.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Saurabh Verma .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Sahu, A., Qazi, S., Raza, K., Verma, S. (2021). COVID-19: Hard Road to Find Integrated Computational Drug and Repurposing Pipeline. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-15-8534-0_15

Download citation