Make a submission.

JCST

Journal of Current Science and Technology

ISSN 2630-0656 (Online)

In-silico trials alternative in-vivo animal studies: potentiality, predictive modelling, and realism

  • Yash Chavan, Department of Pharmaceutics, GES’s Sir Dr. M. S. Gosavi College of Pharmaceutical Education and Research, Nashik, India
  • Sahebrao Boraste, Department of Pharmaceutics, GES’s Sir Dr. M. S. Gosavi College of Pharmaceutical Education and Research, Nashik, India
  • Smita Shelke, Department of Pharmaceutical Chemistry, GES’s Sir Dr. M. S. Gosavi College of Pharmaceutical Education and Research, Nashik, India
  • Prashant Pingale, Department of Pharmaceutics, GES’s Sir Dr. M. S. Gosavi College of Pharmaceutical Education and Research, Nashik, India, Corresponding author; E-mail: prashant.pingale@gmail.com
  • Sunil Amrutkar, Department of Pharmaceutical Chemistry, GES’s Sir Dr. M. S. Gosavi College of Pharmaceutical Education and Research, Nashik, India

Abstract

The use of very powerful models can persuade the translation from labs and animal research as well as the human trials into something simpler, less tedious, and more precise, just as digitalization has tenfold transformed industries like financial services, insurance, entertainment, and tourism.  In these times of pandemic, we have realized the value of new drug innovation.  However, at the same moment, we all happened to know how the amount of time required for a particular medication to be developed.  Our time is valuable, and we cannot afford to wait a few years for the establishment of a new medication that might fail.  R&D spending is expected to cost approximately 400 to 500 crores.  Up to 50% of the time and cost of medication and medical device production could be avoided using In-silico processes.”  The 3Rs, or refinement, reduction, and replacement reasoning represent the road to applying these strategies in a manner that guarantees appropriate outcomes that are as close to the real-world outcome as possible.  Model validation is a crucial step in achieving this degree of consistency and offering the best solution to In-vivo animal experiments.  This review article seeks to offer knowledge that can help clinical trials progress quicker and for less use of animals.

Keywords: animal studies; clinical trials; in-silico trials; PBPK; QSAR; virtual modulation

PDF (648.18 KB)

DOI: 10.14456/jcst.2021.44

References

Alderisio, F., Lombardi, M., Fiore, G., & di Bernardo, M. (2017). A novel computer-based set-up to study movement coordination in human ensembles. Frontiers in psychology, 8, Article 967. DOI: https://doi.org/10.3389/fpsyg.2017.00967  

Amin, S. A., Ghosh, K., Gayen, S., & Jha, T. (2020). Chemical-informatics approach to COVID-19 drug discovery: Monte Carlo based QSAR, virtual screening and molecular docking study of some in-house molecules as papain-like protease (PLpro) inhibitors. Journal of Biomolecular Structure and Dynamics, 1-10. DOI: 10.1080/07391102.2020.1780946

Andrade, C. H., Pasqualoto, K. F., Ferreira, E. I., & Hopfinger, A. J. (2010). 4D-QSAR: perspectives in drug design. Molecules, Molecules, 15(5), 3281-3294. DOI: https://doi.org/10.3390/molecules15053281

Archibald, K., Tsaioun, K., Kenna, J. G., & Pound, P. (2018). Better science for safer medicines: the human imperative. Journal of the Royal Society of Medicine, 111(12), 433-438, DOI: https://doi.org/10.1177%2F0141076818812783

Awasthi, M., Singh, S., Pandey, V. P., & Dwivedi, U. N. (2016). Alzheimer's disease: An overview of amyloid beta dependent pathogenesis and its therapeutic implications along with in silico approaches emphasizing the role of natural products. Journal of the neurological sciences, 361, 256-271. DOI; https://doi.org/10.1016/j.jns.2016.01.008

Aziz, N., Kim, M. Y., & Cho, J. Y. (2018). Anti-inflammatory effects of luteolin: A review of in vitro, in vivo, and in silico studies. Journal of Ethnopharmacology, 225, 342-358. DOI: https://doi.org/10.1016/j.jep.2018.05.019

Badano, A., Graff, C. G., Badal, A., Sharma, D., Zeng, R., Samuelson, F. W., ... & Myers, K. J. (2018). Evaluation of digital breast tomosynthesis as replacement of full-field digital mammography using an in silico imaging trial. JAMA network open, 1(7), e185474-e185474. DOI: 10.1001/jamanetworkopen.2018.5474

Byeon, J. J., Park, M. H., Shin, S. H., Park, Y., Choi, J. M., Kim, N., ... & Shin, Y. G. (2020). In Vitro, In Silico, and In Vivo assessments of pharmacokinetic properties of ZM241385. Molecules, 25(5), 1106. DOI: https://doi.org/10.3390/molecules25051106

Cant, R., Cooper, S., Sussex, R., & Bogossian, F. (2019). What's in a name? Clarifying the nomenclature of virtual simulation. Clinical Simulation in Nursing, 27(5), 26-30. DOI: 10.1016/j.ecns.2018.11.003

Carvalho, C., Varela, S. A., Bastos, L. F., Orfão, I., Beja, V., Sapage, M., ... & Vicente, L. (2019). The relevance of in silico, in vitro and non-human primate based approaches to clinical research on major depressive disorder. Alternatives to Laboratory Animals, 47(3-4), 128-139. DOI: https://doi.org/10.1177%2F0261192919885578

Cassidy, T., & Craig, M. (2019). Determinants of combination GM-CSF immunotherapy and oncolytic virotherapy success identified through in silico treatment personalization. PLoS computational biology, 15(11), e1007495. DOI: https://doi.org/10.1371/journal.pcbi.1007495

Chase, J. G., Preiser, J. C., Dickson, J. L., Pironet, A., Chiew, Y. S., Pretty, C. G., ... & Desaive, T. (2018). Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them. Biomedical engineering online, Biomed. Eng. Online, 17(1), 1-29. DOI: https://doi.org/10.1186/s12938-018-0455-y

Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., ... & Tropsha, A. (2014). QSAR modeling: where have you been? Where are you going to?. Journal of medicinal chemistry, 57(12), 4977-5010. DOI: https://doi.org/10.1021/jm4004285

Colin, P. J., Jonckheere, S., & Struys, M. M. (2018). Target-controlled continuous infusion for antibiotic dosing: proof-of-principle in an in-silico vancomycin trial in intensive care unit patients. Clinical pharmacokinetics57(11), 1435-1447. DOI: https://doi.org/10.1007/s40262-018-0643-8

Cozza, G. (2017). The development of CK2 inhibitors: From traditional pharmacology to in silico rational drug design. Pharmaceuticals, 10(1), 26. DOI: https://doi.org/10.3390/ph10010026

Dassau, E., Palerm, C. C., Zisser, H., Buckingham, B. A., Jovanovič, L., & Doyle III, F. J. (2009). In silico evaluation platform for artificial pancreatic β-cell development—a dynamic simulator for closed-loop control with hardware-in-the-loop. Diabetes technology & therapeutics, 11(3), 187-194. DOI: https://doi.org/10.1089/dia.2008.0055

Denkert, C., Budczies, J., Regan, M. M., Loibl, S., Dell’Orto, P., von Minckwitz, G., ... & Viale, G. (2019). Clinical and analytical validation of Ki-67 in 9069 patients from IBCSG VIII+ IX, BIG1-98 and GeparTrio trial: systematic modulation of interobserver variance in a comprehensive in silico ring trial. Breast cancer research and treatment, 176(3), 557-568. DOI: https://doi.org/10.1007/s10549-018-05112-9

Desprez, B., Dent, M., Keller, D., Klaric, M., Ouédraogo, G., Cubberley, R., ... & Mahony, C. (2018). A strategy for systemic toxicity assessment based on non-animal approaches: the Cosmetics Europe Long Range Science Strategy programme. Toxicology in Vitro, 50, 137-146, DOI: https://doi.org/10.1016/j.tiv.2018.02.017

Dickson, J. L., Stewart, K. W., Pretty, C. G., Flechet, M., Desaive, T., Penning, S., ... & Chase, J. G. (2017). Generalisability of a virtual trials method for glycaemic control in intensive care. IEEE transactions on biomedical engineering, IEEE., 65(7), 1543-1553. DOI: https://doi.org/10.1109/TBME.2017.2686432

Doke, S. K., & Dhawale, S. C. (2015). Alternatives to animal testing: A review. Saudi Pharmaceutical Journal, 23(3), 223-229. DOI: https://doi.org/10.1016/j.jsps.2013.11.002

Eekers, D. B., Roelofs, E., Jelen, U., Kirk, M., Granzier, M., Ammazzalorso, F., ... & Lambin, P. (2016). Benefit of particle therapy in re-irradiation of head and neck patients. Results of a multicentric in silico ROCOCO trial. Radiotherapy and Oncology, 121(3), 387-394. DOI: https://doi.org/10.1016/j.radonc.2016.08.020

Elfiky, A. A. (2020). SARS-CoV-2 RNA dependent RNA polymerase (RdRp) targeting: An in silico perspective. Journal of Biomolecular Structure and Dynamics, 39(9)1-9. DOI: https://doi.org/10.1080/07391102.2020.1761882

Erdemir, A. (2016). Open knee: open source modeling & simulation to enable scientific discovery and clinical care in knee biomechanics.The journal of knee surgery, 29(2), 107-116. DOI: 10.1055/s-0035-1564600

Fontana, F., Figueiredo, P., Martins, J. P., & Santos, H. A. (2020). Requirements for Animal Experiments: Problems and Challenges. Small, 2004182. DOI: https://doi.org/10.1002/smll.202004182

Galbusera, F., Niemeyer, F., Seyfried, M., Bassani, T., Casaroli, G., Kienle, A., & Wilke, H. J. (2018). Exploring the potential of generative adversarial networks for synthesizing radiological images of the spine to be used in in silico trials. Frontiers in bioengineering and biotechnology, 6, 53. DOI: https://doi.org/10.3389/fbioe.2018.00053

Ge, Z., Hu, Y., Heng, B. C., Yang, Z., Ouyang, H., Lee, E. H., & Cao, T. (2006). Osteoarthritis and therapy. Arthritis care & research, 55(3), 493-500. DOI: 10.1002/art.21994

Gericke, C., & Strittmatter, S. (2019). Science instead of animal experiments. ALTEX-Alternatives to animal experimentation, 140-141. DOI: https://doi.org/10.14573/altex.1811291

Geris, L. (2020). In silico tools predict effects of drugs on bone remodelling. Nature Reviews Rheumatology, 16(9), 475-476. DOI: https://doi.org/10.1038/s41584-020-0457-6

Geris, L., Lambrechts, T., Carlier, A., & Papantoniou, I. (2018). The future is digital: in silico tissue engineering. Current Opinion in Biomedical Engineering, 6, 92-98. DOI: https://doi.org/10.1016/j.cobme.2018.04.001

Gomeni, R., Bani, M., D’Angeli, C., Corsi, M., & Bye, A. (2001). Computer-assisted drug development (CADD): an emerging technology for designing first-time-in-man and proof-of-concept studies from preclinical experiments. European journal of pharmaceutical sciences, 13(3), 261-270. DOI: https://doi.org/10.1016/S0928-0987(01)00111-7

Gumaste, A., Coronas-Samano, G., Hengenius, J., Axman, R., Connor, E. G., Baker, K. L., ... & Verhagen, J. V. (2020). A Comparison between Mouse, In Silico, and Robot Odor Plume Navigation Reveals Advantages of Mouse Odor Tracking. ENeuro, 7(1), ENEURO.0212-19.2019. DOI: https://dx.doi.org/10.1523%2FENEURO.0212-19.2019

Haidar, A., Wilinska, M. E., Graveston, J. A., & Hovorka, R. (2013). Stochastic virtual population of subjects with type 1 diabetes for the assessment of closed-loop glucose controllers. IEEE Transactions on Biomedical Engineering, 60(12), 3524-3533. DOI: https://doi.org/10.1109/TBME.2013.2272736

Hamza, H., Salim, N., & Saeed, F. (2016). Quantitative structure activity relationships in computer aided molecular design. Jurnal Teknologi, 78(9-3). DOI: https://doi.org/10.11113/jt.v78.9723

Hanrahan, A. J., Sylvester, B. E., Chang, M. T., Elzein, A., Gao, J., Han, W., ... & Solit, D. B. (2020). Leveraging systematic functional analysis to benchmark an in silico framework distinguishes driver from passenger MEK mutants in cancer. Cancer Research, 80(19), 4233-4243. DOI: 10.1158/0008-5472

Hawłas, H. J., & Lewenstain, K. (2011). In Silico Simulator as a Tool for Designing of Insulin Pump Control Algorithm. In: Jabloński R., Březina T. (eds) Mechatronics. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-23244-2_76

Hayashi, Y., Marumo, Y., Takahashi, T., Nakano, Y., Kosugi, A., Kumada, S., ... & Onuki, Y. (2019). In silico predictions of tablet density using a quantitative structure-property relationship model. International journal of pharmaceutics, 558, 351-356. DOI: https://doi.org/10.1016/j.ijpharm.2018.12.087

Hoffmann, A. L., Troost, E. G., Huizenga, H., Kaanders, J. H., & Bussink, J. (2012). Individualized dose prescription for hypofractionation in advanced non-small-cell lung cancer radiotherapy: an in silico trial. International Journal of Radiation Oncology* Biology* Physics, 83(5), 1596-1602. DOI: https://doi.org/10.1016/j.ijrobp.2011.10.032

Hundal, J., Carreno, B. M., Petti, A. A., Linette, G. P., Griffith, O. L., Mardis, E. R., & Griffith, M. (2016). pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome medicine, 8(1), 1-11. DOI: https://doi.org/10.1186/s13073-016-0264-5

Isaza-Restrepo, A., Gómez, M. T., Cifuentes, G., & Argüello, A. (2018). The virtual patient as a learning tool: a mixed quantitative qualitative study. BMC Medical Education. 18(1), 297. DOI: 10.1186/s12909-018-1395-8

Jafarpour, S., Ayat, H., & Ahadi, A. M. (2015). Design and antigenic epitopes prediction of a new trial recombinant multiepitopic rotaviral vaccine: in silico analyses. Viral immunology, 28(6), 325-330. DOI: https://doi.org/10.1089/vim.2014.0152

Jamei, M. (2016). Recent advances in development and application of physiologically-based pharmacokinetic (PBPK) models: a transition from academic curiosity to regulatory acceptance. Current pharmacology reports, 2(3), 161-169. DOI: 10.1007/s40495-016-0059-9

Jeena, P. M., Bishai, W. R., Pasipanodya, J. G., & Gumbo, T. (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, 55(2), 539-545. DOI: 10.1128/AAC.00763-10

Jiang, Z., Abbas, H., Jang, K. J., Beccani, M., Liang, J., Dixit, S., & Mangharam, R. (2016, August). In-silico pre-clinical trials for implantable cardioverter defibrillators. In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 169-172 IEEE. DOI: https://doi.org/10.1109/EMBC.2016.7590667

Kato, T., Nakagawa, H., Mikkaichi, T., Miyano, T., Matsumoto, Y., & Ando, S. (2020). Establishment of a clinically relevant specification for dissolution testing using physiologically based pharmacokinetic (PBPK) modeling approaches. European Journal of Pharmaceutics and Biopharmaceutics, 151, 45-52. DOI: https://doi.org/10.1016/j.ejpb.2020.03.012

Kent, C. (2021). InSilicoTrials: accelerating the uptake of simulation in clinical research. Medical Device Network, 11thfebruary2021. URL: https://www.medicaldevice-network.com/features/insilicotrials/

Kiran, G., Karthik, L., Devi, M. S., Sathiyarajeswaran, P., Kanakavalli, K., Kumar, K. M., & Kumar, D. R. (2020). In silico computational screening of Kabasura Kudineer-official Siddha formulation and JACOM against SARS-CoV-2 spike protein. Journal of Ayurveda and integrative medicine, 2020 May 25. [Epub ahead of print]. DOI: https://doi.org/10.1016/j.jaim.2020.05.009

Knaak, J. B., Dary, C. C., Power, F., Thompson, C. B., & Blancato, J. N. (2004). Physicochemical and biological data for the development of predictive organophosphorus pesticide QSARs and PBPK/PD models for human risk assessment. Critical Reviews in Toxicology, 34(2), 143-207. DOI: https://doi.org/10.1080/10408440490432250

Kononowicz, A. A., Woodham, L. A., Edelbring, S., Stathakarou, N., Davies, D., Saxena, N., ... & Zary, N. (2019). Virtual patient simulations in health professions education: systematic review and meta-analysis by the digital health education collaboration. Journal of medical Internet research, 21(7), e14676. DOI: 10.2196/14676

Kormazeva, E. S., & Soloviev, V. Y. (2017). PBPK-Model Biodistribution of Gold and Silver Nanoparticles in the Body of Laboratory Animals and Humans at Different Ways of Income. In Nano Hybrids and Composites, 13, 301-305. DOI: https://doi.org/10.4028/www.scientific.net/NHC.13.301

Koshak, A. E., & Koshak, E. A. (2020). Nigella sativa l. as a potential phytotherapy for covid-19: A mini-review of in-silico studies. Current Therapeutic Research, 100602. DOI: https://doi.org/10.1016/j.curtheres.2020.100602

Kostewicz, E. S., Aarons, L., Bergstrand, M., Bolger, M. B., Galetin, A., Hatley, O., ... & Dressman, J. (2014). PBPK models for the prediction of in vivo performance of oral dosage forms. European Journal of Pharmaceutical Sciences, 57, 300-321. DOI: https://doi.org/10.1016/j.ejps.2013.09.008

Lang, A., Volkamer, A., Behm, L., Roblitz, S., Ehrig, R., Schneider, M., ... & Buttgereit, F. (2018). In silico methods-Computational alternatives to animal testing. ALTEX: Alternatives to Animal Experimentation, 35(1), 124-126. DOI: https://doi.org/10.14573/altex.1712031

Li, N. Y., Verdolini, K., Clermont, G., Mi, Q., Rubinstein, E. N., Hebda, P. A., & Vodovotz, Y. (2008). A patient-specific in silico model of inflammation and healing tested in acute vocal fold injury. PloS one3(7), e2789. DOI: https://doi.org/10.1371/journal.pone.0002789

Liu, M., & Lv, Y. (2018). Reconstructing bone with natural bone graft: a review of in vivo studies in bone defect animal model. Nanomaterials, 8(12), 999. DOI: https://doi.org/10.3390/nano8120999

Luraghi, G., Rodriguez Matas, J. F., & Migliavacca, F. (2021). In silico approaches for transcatheter aortic valve replacement inspection. Expert Review of Cardiovascular Therapy, 1-10. DOI: https://doi.org/10.1080/14779072.2021.1850265

Madden, J. C., Enoch, S. J., Paini, A., & Cronin, M. T. (2020). A review of in silico tools as alternatives to animal testing: principles, resources and applications. Alternatives to Laboratory Animals, 48(4), 146-172, DOI: https://doi.org/10.1177%2F0261192920965977

Magni, L., Raimondo, D. M., Bossi, L., Dalla Man, C., De Nicolao, G., Kovatchev, B., & Cobelli, C. (2007). Model predictive control of type 1 diabetes: an in silico trial, Journal of Diabetes Science and Technology, 1(6), 804-812. DOI: https://doi.org/10.1177%2F193229680700100603

Mallet, D. G. (2012). In silico experimental modeling of cancer treatment. International Scholarly Research Notices, 2012, Article ID 828701. DOI: 10.5402/2012/828701

Mancini, E., Quax, R., De Luca, A., Fidler, S., Stohr, W., & Sloot, P. M. (2018). A study on the dynamics of temporary HIV treatment to assess the controversial outcomes of clinical trials: An in-silico approach. PloS one13(7), e0200892. DOI: https://doi.org/10.1371/journal.pone.0200892

Martí-Bonmatí, L., Alberich-Bayarri, Á., Ladenstein, R., Blanquer, I., Segrelles, J. D., Cerdá-Alberich, L., ... & Neri, E. (2020). PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers. European Radiology Experimental, 4, 1-11. DOI: https://doi.org/10.1186/s41747-020-00150-9

Martinez, M. N., Gehring, R., Mochel, J. P., Pade, D., & Pelligand, L. (2018). Population variability in animal health: Influence on dose‐exposure‐response relationships: Part II: Modelling and simulation. Journal of veterinary pharmacology and therapeutics, 41(4), E68-E76. DOI: https://doi.org/10.1111/jvp.12666

Micheletto, F., Dalla Man, C., Kolterman, O., Chiquette, E., Herrmann, K., Schirra, J., ... & Cobelli, C. (2013). In silico design of optimal ratio for co-administration of pramlintide and insulin in type 1 diabetes. Diabetes technology & therapeutics, 15(10), 802-809. DOI: https://doi.org/10.1089/dia.2013.0054

Moche, M., Busse, H., Futterer, J. J., Hinestrosa, C. A., Seider, D., Brandmaier, P., ... & Reinhardt, M. (2020). Clinical evaluation of in silico planning and real-time simulation of hepatic radiofrequency ablation (ClinicIMPPACT Trial). European radiology, 30(2), 934-942. DOI: https://doi.org/10.1007/s00330-019-06411-5

Morrison, T. M., Pathmanathan, P., Adwan, M., & Margerrison, E. (2018). Advancing regulatory science with computational modeling for medical devices at the FDA's office of science and engineering laboratories. Frontiers in medicine, 5, 241. DOI: https://doi.org/10.3389/fmed.2018.00241

Muñoz-Fontela, C., Dowling, W. E., Funnell, S. G., Gsell, P. S., Riveros-Balta, A. X., Albrecht, R. A., ... & Barouch, D. H. (2020). Animal models for COVID-19. Nature, 586(7830), 509-515. DOI: https://doi.org/10.1038/s41586-020-2787-6

Naseri, V., Chavoshzadeh, Z., Mizani, A., Daneshfard, B., Ghaffari, F., Abbas‐Mohammadi, M., ... & Naseri, M. (2020). Effect of topical marshmallow (Althaea officinalis) on atopic dermatitis in children: A pilot double‐blind active‐controlled clinical trial of an in‐silico‐analyzed phytomedicine. Phytotherapy Research, 35(3):1389-1398. DOI: 10.1002/ptr.6899

No authors listed. (2021). Pharma business international, InSilicoTrials leverages simulation in a new way to cut time and cost of drug development. Pharma Business International, 11th February 2021. URL: https://www.pbiforum.net/mag/featured/insilicotrials-leverages-simulation-in-a-new-way-to-cut-time-and-cost-of-drug-development/

Paci, M., Passini, E., Klimas, A., Severi, S., Hyttinen, J., Rodriguez, B., & Entcheva, E. (2020). All-optical electrophysiology refines populations of in silico human iPSC-CMs for drug evaluation. Biophysical journal, 118(10), 2596-2611. DOI: https://doi.org/10.1016/j.bpj.2020.03.018

Padmos, R. M., Józsa, T. I., El-Bouri, W. K., Payne, S. J., & Hoekstra, A. G. (2019, September). Connecting arterial blood flow to tissue perfusion for in silico trials of acute ischaemic stroke. In CompBioMed Conf., London, UK, 25–27 September 2019.

Pappalardo, F., Pennisi, M., Reche, P. A., & Russo, G. (2019). Toward computational modelling on immune system function. BMC Bioinformatics, 21, Article number: 546 (2020). DOI: https://doi.org/10.1186/s12859-019-3239-x

Pappalardo, F., Russo, G., Tshinanu, F. M., & Viceconti, M. (2019). In silico clinical trials: concepts and early adoptions. Briefings in bioinformatics, 20(5), 1699-1708. DOI: https://doi.org/10.1093/bib/bby043

Park, M. H., Shin, S. H., Byeon, J. J., Lee, G. H., Yu, B. Y., & Shin, Y. G. (2017). Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin. The Korean journal of physiology & pharmacology, 21(1), 107-115. DOI: https://doi.org/10.4196/kjpp.2017.21.1.107

Passini, E., Trovato, C., Morissette, P., Sannajust, F., Bueno‐Orovio, A., & Rodriguez, B. (2019). Drug‐induced shortening of the electromechanical window is an effective biomarker for in silico prediction of clinical risk of arrhythmias. British journal of pharmacology, 176(19), 3819-3833. DOI: https://doi.org/10.1111/bph.14786

Penha, E. S. D., Lacerda‐Santos, R., de Medeiros, L. A. D. M., Araújo Rosendo, R., Dos Santos, A., Fook, M. V. L., ... & Montagna, E. (2020). Effect of chitosan and Dysphania ambrosioides on the bone regeneration process: A randomized controlled trial in an animal model. Microscopy Research and Technique, 83(10), 1208-1216. DOI: https://doi.org/10.1002/jemt.23512

Pound, P., & Ritskes-Hoitinga, M. (2018). Is it possible to overcome issues of external validity in preclinical animal research? Why most animal models are bound to fail. Journal of translational medicine, 16(1), 304. DOI: https://doi.org/10.1186/s12967-018-1678-1

Prajapat, P., Agarwal, S., & Talesara, G. L. (2017). Significance of computer aided drug design and 3D QSAR in modern drug discovery. Journal of Medicinal and Organic Chemistry, 1(1), 1-2.

Pritzker, K. P. (1994). Animal models for osteoarthritis: processes, problems and prospects. Annals of the rheumatic diseases, 53(6), 406-420. DOI: https://dx.doi.org/10.1136%2Fard.53.6.406

Reiner, Ž., Hatamipour, M., Banach, M., Pirro, M., Al-Rasadi, K., Jamialahmadi, T., ... & Sahebkar, A. (2020). Statins and the COVID-19 main protease: in silico evidence on direct interaction. Archives of Medical Science, 16(3), 490-496. DOI: https://dx.doi.org/10.5114%2Faoms.2020.94655

Roelofs, E., Engelsman, M., Rasch, C., Persoon, L., Qamhiyeh, S., De Ruysscher, D., ... & ROCOCO Consortium. (2012). Results of a multicentric in silico clinical trial (ROCOCO): comparing radiotherapy with photons and protons for non-small cell lung cancer. Journal of Thoracic Oncology, 7(1), 165-176. DOI: https://doi.org/10.1097/JTO.0b013e31823529fc

Rostami-Hodjegan, A., & Tucker, G. (2004). ‘In silico’simulations to assess the ‘in vivo’consequences of ‘in vitro’metabolic drug–drug interactions. Drug Discovery Today: Technologies, 1(4), 441-448. DOI: 10.1016/j.ddtec.2004.10.002

Russo, G., Sgroi, G., Palumbo, G. A. P., Pennisi, M., Juarez, M. A., Cardona, P. J., ... & Pappalardo, F. (2020). Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB. BMC bioinformatics, 21(17), 1-13. DOI: https://doi.org/10.1186/s12859-020-03762-5

Samant, T. S., Dhuria, S., Lu, Y., Laisney, M., Yang, S., Grandeury, A., ... & Elmeliegy, M. (2018). Ribociclib bioavailability is not affected by gastric pH changes or food intake: in silico and clinical evaluations. Clinical Pharmacology & Therapeutics, 104(2), 374-383. DOI: https://doi.org/10.1002/cpt.940

Sarrami-Foroushani, A., Lassila, T., Macraild, M., Asquith, J., Roes, K. C., Byrne, J. V., & Frangi, A. F. (2021). In-silico trial of intracranial flow diverters confirms and expands insights from conventional clinical trials. Nature Communications, 12, 3861. DOI: https://doi.org/10.21203/rs.3.rs-147836/v2

Schmidt, B. J., Casey, F. P., Paterson, T., & Chan, J. R. (2013). Alternate virtual populations elucidate the type I interferon signature predictive of the response to rituximab in rheumatoid arthritis. BMC bioinformatics, 14(1), 1-16. DOI: https://doi.org/10.1186/1471-2105-14-221

Si, Y., Xu, X., Hu, Y., Si, H., & Zhai, H. (2021). Novel quantitative structure-activity relationship model to predict activities of natural products against COVID-19. Chemical Biology and Drug Design, 97(4), 978-983. DOI: https://doi.org/10.1111/cbdd.13822

Srivastava, A., Siddiqui, S., Ahmad, R., Mehrotra, S., Ahmad, B., & Srivastava, A. N. (2020). Exploring nature’s bounty: identification of Withania somnifera as a promising source of therapeutic agents against COVID-19 by virtual screening and in silico evaluation. Journal of Biomolecular Structure and Dynamics, 1-51. DOI: 10.1080/07391102.2020.1835725

Staines, R. (2021). EU-funded project encourages use of simulation in drug development. Pharmaphorum, February 10 2021. URL: https://pharmaphorum.com/news/eu-funded-project-encourages-use-of-simulation-in-drug-development/

Suhaimi, F., Chase, J. G., Le Compte, A. J., Preiser, J. C., Lin, J., & Shaw, G. M. (2010). Validation of a model-based virtual trials method for tight glycaemic control in intensive care. Biomedical Engineering Online, volume 9, Article number: 84 (2010). DOI: 10.1186/1475-925X-9-84

Toffanin, C., Visentin, R., Messori, M., Di Palma, F., Magni, L., & Cobelli, C. (2017). Toward a run-to-run adaptive artificial pancreas: In silico results. IEEE Transactions on Biomedical Engineering, 65(3), 479-488. DOI: https://doi.org/10.1109/TBME.2017.2652062

van Baardwijk, A., Bosmans, G., Bentzen, S. M., Boersma, L., Dekker, A., Wanders, R., ... & De Ruysscher, D. (2008). Radiation dose prescription for non–small-cell lung cancer according to normal tissue dose constraints: an In silico clinical trial. International Journal of Radiation Oncology* Biology* Physics, 71(4), 1103-1110. DOI: https://doi.org/10.1016/j.ijrobp.2007.11.028

Van Norman, G. A. (2020). Limitations of animal studies for predicting toxicity in clinical trials: part 2: potential alternatives to the use of animals in preclinical trials. Basic to Translational Science, 5(4), 387-397. DOI: https://doi.org/10.1016/j.jacbts.2020.03.010

Verma, J., Khedkar, V. M., & Coutinho, E. C. (2010). 3D-QSAR in drug design-a review. Current topics in medicinal chemistry, 10(1), 95-115. DOI: https://doi.org/10.2174/156802610790232260

Viceconti, M., Juárez, M. A., Curreli, C., Pennisi, M., Russo, G., & Pappalardo, F. (2019). Credibility of in silico trial technologies—A theoretical framing. IEEE journal of biomedical and health informatics, 24(1), 4-13. DOI: https://doi.org/10.1109/JBHI.2019.2949888

Vodovotz, Y., Chow, C. C., Bartels, J., Lagoa, C., Prince, J. M., Levy, R. M., ... & Clermont, G. (2006). In silico models of acute inflammation in animals. 235-244. DOI: 10.1097/01.shk.0000225413.13866.fo

Xing, D., Chen, J., Yang, J., Heng, B. C., Ge, Z., & Lin, J. (2016). Perspectives on animal models utilized for the research and development of regenerative therapies for articular cartilage. Current Molecular Biology Reports, 2(2), 90-100. DOI: 10.1007/s40610-016-0038-2

Zindler, J. D., Schiffelers, J., Lambin, P., & Hoffmann, A. L. (2018). Improved effectiveness of stereotactic radiosurgery in large brain metastases by individualized isotoxic dose prescription: an in silico study. Strahlentherapie und Onkologie, 194(6), 560-569. DOI: https://doi.org/10.1007/s00066-018-1262-x

Zinn, E., Pacouret, S., Khaychuk, V., Turunen, H. T., Carvalho, L. S., Andres-Mateos, E., ... & Vandenberghe, L. H. (2015). In silico reconstruction of the viral evolutionary lineage yields a potent gene therapy vector. Cell reports, 12(6), 1056-1068. DOI: https://doi.org/10.1016/j.celrep.2015.07.019

Approved By TCI (2020 - 2024)

Indexed in

Search