Exploring the Power and Promise of In Silico Clinical Trials with Application in COVID-19 Infection
Keywords:
in silico, clinical trials, COVID-19, SARS-CoV-2, vaccine
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.
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preliminary design. Paper presented at the 28th International Congress of the
Aeronautical Sciences. Retrieved from: https://www.researchgate.net/publication/
233919742_Aircraft_System_Simulation_for_Preliminary_Design
[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.
