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Abdelmoneim, A. H., Khalil, S. I., Awadelkareem Osman Fadl, H., Rewane, A., & Elbager, S. G. (2021). Exploring the Power and Promise of In Silico Clinical Trials with Application in COVID-19 Infection. Sudan Journal of Medical Sciences (SJMS), 16(3), 355–370. https://doi.org/10.18502/sjms.v16i3.9697
https://doi.org/10.18502/sjms.v16i3.9697
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authors
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Abdelrahman H. Abdelmoneim
Abdelrahman H. Abdelmoneim
Clinical Immunology Resident, Sudan Medical Specialization Board, Khartoum, Sudan
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Safinaz I. Khalil
Safinaz I. Khalil
Department of Pharmacology, Faculty of Medicine, University of Medical Sciences and Technology, Khartoum, Sudan
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Hiba Awadelkareem Osman Fadl
Hiba Awadelkareem Osman Fadl
Department of Hematology, Faculty of Medical Laboratory Sciences, Al-Neelain University, Khartoum, Sudan
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Ayesan Rewane
Ayesan Rewane
Department of Allergy and Immunology, Rush University Medical Center, Chicago, Illinois, USA
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Sahar G. Elbager
Sahar G. Elbager
Department of Hematology, Faculty of Medical Laboratory Sciences, University of Medical Sciences and Technology, Khartoum, Sudan
Published Date
- Sep 30, 2021
Exploring the Power and Promise of In Silico Clinical Trials with Application in COVID-19 Infection
Abstract
Background: COVID-19 pandemic has dramatically engulfed the world causing catastrophic damage to human society. Several therapeutic and vaccines have been suggested for the disease in the past months, with over 150 clinical trials currently running or under process. Nevertheless, these trials are extremely expensive and require a long time, which presents the need for alternative cost-effective methods to tackle this urgent requirement for validated therapeutics and vaccines. Bearing this in mind, here we assess the use of in silico clinical trials as a significant development in the field of clinical research, which holds the possibility to reduce the time and cost needed for clinical trials on COVID-19 and other diseases.
Methods: Using the PubMed database, we analyzed six relevant scientific articles regarding the possible application of in silico clinical trials in testing the therapeutic and investigational methods of managing different diseases.
Results: Successful use of in silico trials was observed in many of the reviewed evidence.
Conclusion: In silico clinical trials can be used in refining clinical trials for COVID-19 infection.
Keywords: in silico, clinical trials, COVID-19, SARS-CoV-2, vaccine How
References
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[31] Jeena, P. M., Bishai, W. R., Pasipanodya, J. G., et al. (2011). In silico children and the glass mouse model: clinical trial simulations to identify and individualize optimal isoniazid doses in children with tuberculosis. Antimicrobial Agents and Chemotherapy, vol. 55, no. 2, pp. 539–545.
[32] Wang, H., Milberg, O., Bartelink, I. H., et al. (2019). In silico simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model. Royal Society Open Science, vol. 6, no. 5, 190366.
[33] Hilsenbeck, S. G. and Osborne, C. K. (2006). Is there a role for adjuvant tamoxifen in progesterone receptor-positive breast cancer? An in silico clinical trial. Clinical Cancer Research, vol. 12, pp. 1049s–1055s.
[34] Roelofs, E., Engelsman, M., Rasch, C., et al. (2012). Results of a multicentric in silico clinical trial (ROCOCO): comparing radiotherapy with photons and protons for nonsmall cell lung cancer. Journal of Thoracic Oncology, vol. 7, pp. 165–176.
[35] Lim, Y., Song, T. J., Hwang, W., et al. (2019). Synergistic effects of sanghuang⁻danshen bioactives on arterial stiffness in a randomized clinical trial of healthy smokers: an integrative approach to in silico network analysis. Nutrients, vol. 11, no. 1, p. 108.
[36] Rodrigues, L., de Lima, I., Ricardo Albano dos Santos, L., et al. (2018). Towards a standardized protocol for conducting randomized clinical trial for software. Procedia Computer Science, vol. 138, pp. 125–130.
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[38] Renz, A., Widerspick, L., and Dräger, A. (2020). FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2. Bioinformatics, vol. 36, no. 2, pp. i813–i821.
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[2] Janoško, I., Polonec, T., Kuchar, P., et al. (2017). Computer simulation of car aerodynamic properties. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, vol. 65, no. 5, pp. 1505–1514.
[3] Geringer, J., Imbert, L., and Kim, K. (Eds.). (2014). Computational modeling of hip implants. In Computational modelling of biomechanics and biotribology in the musculoskeletal system (pp. 389–416). Sawston, United Kingdom: Woodhead Publishing.
[4] Wong, K. K. L., Wang, D., Ko, J. K. L., et al. (2017). Computational medical imaging and hemodynamics framework for functional analysis and assessment of cardiovascular structures. BioMedical Engineering OnLine, vol. 16, article 35.
[5] Food and Drug Administration. (2016). Reporting of computational modeling studies in medical device submissions (Docket No. FDA-2013-D-1530). Retrieved from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/ reporting-computational-modeling-studies-medical-device-submissions
[6] DiMasi, J., Grabowski, H., and Hansen, R. (2015). The cost of drug development. The New England Journal of Medicine, vol. 372, p. 1972.
[7] Wong, C., Siah, K., and Lo, A. (2018). Estimation of clinical trial success rates and related parameters. Biostatistics, vol. 20, no. 2, pp. 273–286.
[8] Mehta, D., Jackson, R., Paul, G., et al. (2017). Why do trials for Alzheimer’s disease drugs keep failing? A discontinued drug perspective for 2010-2015. Expert Opinion on Investigational Drugs, vol. 26, no. 6, pp. 735–739.
[9] Viceconti, M., Cobelli, C., Haddad, T., et al. (2017). In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, vol. 231, pp. 455–466.
[10] Viceconti, M., Henney, A., and Morley-Fletcher, E. (2016). In silico clinical trials: how computer simulation will transform the biomedical industry. International Journal of Clinical Trials, vol. 3, no. 2.
[11] Nyman, J., Uppuganti, S., Makowski, A., et al. (2015). Predicting mouse vertebra strength with micro-computed tomography-derived finite element analysis. BoneKEy Reports, vol. 4, p. 664.
[12] Kong, S. H., Ahn, D., Kim, B. R., et al. (2020). A novel fracture prediction model using machine learning in a community-based cohort. JBMR Plus, vol. 4, no. 3, e10337.
[13] Umscheid, C. A., Margolis, D. J., and Grossman, C. E. (2011). Key concepts of clinical trials: a narrative review. Postgraduate Medicine, vol. 123, pp. 194–204.
[14] Ferriter, M. and Huband, N. (2005). Does the non-randomized controlled study have a place in the systematic review? A pilot study. Criminal Behaviour and Mental Health: CBMH, vol. 15, pp. 111–120.
[15] Davis, J. S., Ferreira, D., Denholm, J. T., et al. (2020). Clinical trials for the prevention and treatment of coronavirus disease 2019 (COVID-19): the current state of play. The Medical Journal of Australia, preprint.
[16] Ciliberto, G. and Cardone, L. (2020). Boosting the arsenal against COVID-19 through computational drug repurposing. Drug Discovery Today, vol. 25, no. 6, pp. 946–948.
[17] Ma, C., Wang, L., Tao, X., et al. (2014). Searching for an ideal vaccine candidate among different MERS coronavirus receptor-binding fragments–the importance of immunofocusing in subunit vaccine design. Vaccine, vol. 32, pp. 6170–6176.
[18] Six, A., Bellier, B., Thomas-Vaslin, V., et al. (2012). Systems biology in vaccine design. Microbial Biotechnology, vol. 5, pp. 295–304.
[19] WHO. (2021). Draft landscape of COVID-19 candidate vaccines. Retrieved from: https: //www.who.int/publications/m/item/draft-landscape-of-covid-19-candidate-vaccines
[20] Hessler, G. and Baringhaus, K. H. (2018). Artificial intelligence in drug design. Molecules, vol. 23, no. 10, p. 2520.
[21] Yu, W. H., Zhao, P., Draghi, M., et al. (2018). Exploiting glycan topography for computational design of Env glycoprotein antigenicity. PLOS Computational Biology, vol. 14, e1006093.
[22] Goodswen, S. J., Kennedy, P. J., and Ellis, J. T. (2014). Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology. Bioinformatics, vol. 30, pp. 2381–2383.
[23] Abdelmageed, M. I., Abdelmoneim, A. H., Mustafa, M. I., et al. (2020). Design of a multiepitope-based peptide vaccine against the E protein of human COVID-19: an immunoinformatics approach. BioMed Research International, vol. 2020, 2683286.
[24] Tang, Y. W., Schmitz, J. E., Persing, D. H., et al. (2020). Laboratory diagnosis of COVID-19: current issues and challenges. Journal of Clinical Microbiology, vol. 58, no. 6, e00512–20.
[25] Abraham Peele, K., Srihansa, T., Krupanidhi, S., et al. (2020). Design of multi-epitope vaccine candidate against SARS-CoV-2: a in-silico study. Journal of Biomolecular Structure & Dynamics, vol. 39, no. 10, pp. 3793–3801.
[26] Orwoll, E. S., Marshall, L. M., Nielson, C. M., et al. (2009). Finite element analysis of the proximal femur and hip fracture risk in older men. Journal of Bone and Mineral Research, vol. 24, pp. 475–483.
[27] Man, C. D., Micheletto, F., Lv, D., et al. (2014). The UVA/PADOVA type 1 diabetes simulator: new features. Journal of Diabetes Science and Technology, vol. 8, no. 1, pp. 26–34.
[28] Melhem, M., Delor, I., Pérez-Ruixo, J. J., et al. (2018). Pharmacokinetic– pharmacodynamic modelling of neutrophil response to G-CSF in healthy subjects and patients with chemotherapy-induced neutropenia. British Journal of Clinical Pharmacology, vol. 84, pp. 911–925.
[29] Thorlund, K., Golchi, S., Haggstrom, J., et al. (2019). Highly Efficient Clinical Trials Simulator (HECT): software application for planning and simulating platform adaptive trials [version 2; peer review: 2 approved, 2 approved with reservations]. Gates Open Research, vol. 3, p. 780.
[30] Visentin, R., Giegerich, C., Jäger, R., et al. (2016). Improving efficacy of inhaled technosphere insulin (Afrezza) by postmeal dosing: in-silico clinical trial with the University of Virginia/Padova type 1 diabetes simulator. Diabetes Technology & Therapeutics, vol. 18, pp. 574–585.
[31] Jeena, P. M., Bishai, W. R., Pasipanodya, J. G., et al. (2011). In silico children and the glass mouse model: clinical trial simulations to identify and individualize optimal isoniazid doses in children with tuberculosis. Antimicrobial Agents and Chemotherapy, vol. 55, no. 2, pp. 539–545.
[32] Wang, H., Milberg, O., Bartelink, I. H., et al. (2019). In silico simulation of a clinical trial with anti-CTLA-4 and anti-PD-L1 immunotherapies in metastatic breast cancer using a systems pharmacology model. Royal Society Open Science, vol. 6, no. 5, 190366.
[33] Hilsenbeck, S. G. and Osborne, C. K. (2006). Is there a role for adjuvant tamoxifen in progesterone receptor-positive breast cancer? An in silico clinical trial. Clinical Cancer Research, vol. 12, pp. 1049s–1055s.
[34] Roelofs, E., Engelsman, M., Rasch, C., et al. (2012). Results of a multicentric in silico clinical trial (ROCOCO): comparing radiotherapy with photons and protons for nonsmall cell lung cancer. Journal of Thoracic Oncology, vol. 7, pp. 165–176.
[35] Lim, Y., Song, T. J., Hwang, W., et al. (2019). Synergistic effects of sanghuang⁻danshen bioactives on arterial stiffness in a randomized clinical trial of healthy smokers: an integrative approach to in silico network analysis. Nutrients, vol. 11, no. 1, p. 108.
[36] Rodrigues, L., de Lima, I., Ricardo Albano dos Santos, L., et al. (2018). Towards a standardized protocol for conducting randomized clinical trial for software. Procedia Computer Science, vol. 138, pp. 125–130.
[37] Viceconti, M., Pappalardo, F., Rodriguez, B., et al. (2020). In silico trials: verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods, vol. 185, no. 1, pp. 120–127.
[38] Renz, A., Widerspick, L., and Dräger, A. (2020). FBA reveals guanylate kinase as a potential target for antiviral therapies against SARS-CoV-2. Bioinformatics, vol. 36, no. 2, pp. i813–i821.
[39] Russo, G., Pennisi, M., Viceconti, M., et al. (2020). In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform. BMC Bioinformatics, vol. 21, no. 17, article 527.