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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Review Article

In Silico Trial Approach for Biomedical Products: A Regulatory Perspective

Author(s): Jobin Jose*, Shifali S., Bijo Mathew and Della Grace Thomas Parambi

Volume 25, Issue 12, 2022

Published on: 10 March, 2022

Page: [1991 - 2000] Pages: 10

DOI: 10.2174/1386207325666220105150147

Price: $65

Abstract

The modern pharmaceutical industry is transitioning from traditional methods to advanced technologies like artificial intelligence. In the current scenario, continuous efforts are being made to incorporate computational modeling and simulation in drug discovery, development, design, and optimization. With the advancement in technology and modernization, many pharmaceutical companies are approaching in silico trials to develop safe and efficacious medicinal products. To obtain marketing authorization for a medicinal product from the concerned National Regulatory Authority, manufacturers must provide evidence for the safety, efficacy, and quality of medical products in the form of in vitro or in vivo methods. However, more recently, this evidence was provided to regulatory agencies in the form of modeling and simulation, i.e., in silico evidence. Such evidence (computational or experimental) will only be accepted by the regulatory authorities if it considered as qualified by them, and this will require the assessment of the overall credibility of the method. One must consider the scrutiny provided by the regulatory authority to develop or use the new in silico evidence. The United States Food and Drug Administration and European Medicines Agency are the two regulatory agencies in the world that accept and encourage the use of modeling and simulation within the regulatory process. More efforts must be made by other regulatory agencies worldwide to incorporate such new evidence, i.e., modeling and simulation (in silico) within the regulatory process. This review article focuses on the approaches of in silico trials, the verification, validation, and uncertainty quantification involved in the regulatory evaluation of biomedical products that utilize predictive models.

Keywords: In silico, regulatory agency, verification, validation, biomedical assessment, modeling, simulation.

Graphical Abstract
[1]
Pappalardo, F.; Russo, G.; Tshinanu, F.M.; Viceconti, M. In silico clinical trials: concepts and early adoptions. Brief. Bioinform., 2019, 20(5), 1699-1708.
[http://dx.doi.org/10.1093/bib/bby043] [PMID: 29868882]
[2]
Zhuang, X.; Lu, C. PBPK modeling and simulation in drug research and development. Acta Pharm. Sin. B, 2016, 6(5), 430-440.
[http://dx.doi.org/10.1016/j.apsb.2016.04.004] [PMID: 27909650]
[3]
Mah, J.T.; Low, E.S.; Lee, E. In silico SNP analysis and bioinformatics tools: A review of the state of the art to aid drug discovery. Drug Discov. Today, 2011, 16(17-18), 800-809.
[http://dx.doi.org/10.1016/j.drudis.2011.07.005] [PMID: 21803170]
[4]
Badano, A.; Badal, A.; Glick, S.; Graff, C.G.; Samuelson, F.; Sharma, D. In silico imaging clinical trials for regulatory evaluation: initial considerations for VICTRE, a demonstration study. Proc. SPIE 10132, Med. Imag. Phys. Med. Imag., 2017., 1013220.
[5]
Faris, O.; Shuren, J. An FDA viewpoint on unique considerations for medical-device clinical trials. N. Engl. J. Med., 2017, 376(14), 1350-1357.
[http://dx.doi.org/10.1056/NEJMra1512592] [PMID: 28379790]
[6]
Viceconti, M.; Clapworthy, G.; Van Sint Jan, S. The virtual physiological human - a European initiative for in silico human modelling. J. Physiol. Sci., 2008, 58(7), 441-446.
[http://dx.doi.org/10.2170/physiolsci.RP009908] [PMID: 18928640]
[7]
Viceconti, M.; Hunter, P. The virtual physiological human: ten years after. Annu. Rev. Biomed. Eng., 2016, 18(1), 103-123.
[http://dx.doi.org/10.1146/annurev-bioeng-110915-114742] [PMID: 27420570]
[8]
Jiang, Z.; Abbas, H.; Jang, K.J.; Beccani, M.; Liang, J.; Dixit, S.; Mangharam, R. In silico pre-clinical trials for implantable cardioverter defibrillators. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 2016, 2016, 169-172.
[http://dx.doi.org/10.1109/EMBC.2016.7590667] [PMID: 28268306]
[9]
Viceconti, M.; Cobelli, C.; Haddad, T.; Himes, A.; Kovatchev, B.; Palmer, M. In silico assessment of biomedical products: the conundrum of rare but not so rare events in two case studies. Proc. Inst. Mech. Eng. H, 2017, 231(5), 455-466.
[http://dx.doi.org/10.1177/0954411917702931] [PMID: 28427321]
[10]
Rodriguez, B.; Carusi, A.; Abi-Gerges, N.; Ariga, R.; Britton, O.; Bub, G.; Bueno-Orovio, A.; Burton, R.A.; Carapella, V.; Cardone-Noott, L.; Daniels, M.J.; Davies, M.R.; Dutta, S.; Ghetti, A.; Grau, V.; Harmer, S.; Kopljar, I.; Lambiase, P.; Lu, H.R.; Lyon, A.; Minchole, A.; Muszkiewicz, A.; Oster, J.; Paci, M.; Passini, E.; Severi, S.; Taggart, P.; Tinker, A.; Valentin, J.P.; Varro, A.; Wallman, M.; Zhou, X. Hu-man-based approaches to pharmacology and cardiology: an interdisciplinary and intersectorial workshop. Europace, 2016, 18(9), 1287-1298.
[http://dx.doi.org/10.1093/europace/euv320] [PMID: 26622055]
[11]
Hunter, P.J.; Smith, N.P. The cardiac physiome project. J. Physiol., 2016, 594(23), 6815-6816.
[http://dx.doi.org/10.1113/JP273415] [PMID: 27905135]
[12]
Jean-Quartier, C.; Jeanquartier, F.; Jurisica, I.; Holzinger, A. In silico cancer research towards 3R. BMC Cancer, 2018, 18(1), 408.
[http://dx.doi.org/10.1186/s12885-018-4302-0] [PMID: 29649981]
[13]
Viceconti, M.; Dall’Ara, E. From bed to bench: How in silico medicine can help ageing research. Mech. Ageing Dev., 2019, 177, 103-108.
[http://dx.doi.org/10.1016/j.mad.2018.07.001] [PMID: 30005915]
[14]
Zand, R.; Abedi, V.; Hontecillas, R.; Lu, P.; Noorbakhsh-Sabet, N.; Verma, M.; Leber, A. Development of synthetic patient populations and in silico clinical trials. In: Accelerated Path to Cures; Bassaganya-Riera, J., Ed.; Springer: Cham, 2018; pp. 57-77.
[http://dx.doi.org/10.1007/978-3-319-73238-1_5]
[15]
Passini, E.; Britton, O.J.; Lu, H.R.; Rohrbacher, J.; Hermans, A.N.; Gallacher, D.J.; Greig, R.J.H.; Bueno-Orovio, A.; Rodriguez, B. Human in silico drug trials demonstrate higher accuracy than animal models in predicting clinical pro-arrhythmic cardiotoxicity. Front. Physiol., 2017, 8, 668.
[http://dx.doi.org/10.3389/fphys.2017.00668] [PMID: 28955244]
[16]
Modelling and Simulation as a transformative tool for medical devices: the transatlantic regulatory landscape 2020. Available from: https://avicenna-alliance.com/files/user_upload/Conference_ 2018/materials/AVI-005-18_Avicenna_Whitepaper2_Digital_31-0 8-18.pdf
[17]
Silverman, M.; Burgen, A.S. Application of analogue computer to measurement of intestinal absorption rates with tracers. J. Appl. Physiol., 1961, 16, 911-913.
[http://dx.doi.org/10.1152/jappl.1961.16.5.911] [PMID: 13912940]
[18]
Janes, R.G.; Osburn, J.O. The analysis of glucose measurements by computer simulation. J. Physiol., 1965, 181(1), 59-67.
[http://dx.doi.org/10.1113/jphysiol.1965.sp007745] [PMID: 5866287]
[19]
Nichol, C.A. Pharmacokinetics: selectivity of action related to physicochemical properties and kinetic patterns of anticancer drugs. Cancer, 1977, 40(1)(Suppl.), 519-528.
[http://dx.doi.org/10.1002/1097-0142(197707)40:1+<519:AID-CNCR2820400718>3.0.CO;2-4] [PMID: 328135]
[20]
Bassingthwaighte, J.B. Design and strategy for the Cardionome Project. Adv. Exp. Med. Biol., 1997, 430, 325-339.
[http://dx.doi.org/10.1007/978-1-4615-5959-7_28] [PMID: 9330741]
[21]
Popel, A.S.; Greene, A.S.; Ellis, C.G.; Ley, K.F.; Skalak, T.C.; Tonellato, P.J. The microcirculation physiome project. Ann. Biomed. Eng., 1998, 26(6), 911-913.
[http://dx.doi.org/10.1114/1.112] [PMID: 9846930]
[22]
Hunter, P.J.; Nielsen, P.M.; Bullivant, D. The IUPS physiome project. International Union of Physiological Sciences. Novartis Found. Symp., 2002, 247, 207-217.
[http://dx.doi.org/10.1002/0470857897.ch17] [PMID: 12539957]
[23]
FDA. Proposed Regulatory Framework for Modifications to Artificial Intelligence/ Machine Learning(AI/ML)-Based Software as a Medical Device(SaMD) - Discussion Paper and Request for Feedback 2019. Available from: https://www.fda.gov/files/medical %20devices/published/US-FDA-Artificial-Intelligence-and-Mach-ine-Learning-Discussion-Paper.pdf
[24]
The American Society of Mechanical Engineers - ASME. 2020. Available form: https://www.asme.org/codes-standards/
[25]
Computational Modeling Studies in Medical Device Submissions. U.S. Food and Drug Administration; , 2020. Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/reporting-computational-modeling-studies-medical-device-submissions
[26]
Sager, P.T.; Gintant, G.; Turner, J.R.; Pettit, S.; Stockbridge, N. Rechanneling the cardiac proarrhythmia safety paradigm: a meeting report from the Cardiac Safety Research Consortium. Am. Heart J., 2014, 167(3), 292-300.
[http://dx.doi.org/10.1016/j.ahj.2013.11.004] [PMID: 24576511]
[27]
Grandi, E.; Morotti, S.; Pueyo, E.; Rodriguez, B. Editorial: Safety pharmacology - risk assessment QT interval prolongation and beyond. Front. Physiol., 2018, 9, 678.
[http://dx.doi.org/10.3389/fphys.2018.00678] [PMID: 29937733]
[28]
Passini, E.; Zhou, X.; Trovato, C.; Britton, O.J.; Bueno-Orovio, A.; Rodriguez, B. The virtual assay software for human in silico drug trials to augment drug cardiac testing. J. Comput. Sci., 2020., 101202.
[http://dx.doi.org/10.1016/j.jocs.2020.101202]
[29]
Margara, F.; Wang, Z.J.; Levrero-Florencio, F.; Santiago, A.; Vázquez, M.; Bueno-Orovio, A.; Rodrigueza, B. In silico human electro-mechanical ventricular modelling and simulation for drug-induced pro-arrhythmia and inotropic risk assessment. Prog. Biophys. Mol. Biol., 2020, 159, 58-74.
[http://dx.doi.org/10.1016/j.pbiomolbio.2020.06.007] [PMID: 32710902]
[30]
Viceconti, M.; Henney, A.; Morley-Fletcher, E. In silico clinical trials: how computer simulation will transform the biomedical industry. Int. J. Clin. Trials, 2016, 3(2), 37-46.
[http://dx.doi.org/10.18203/2349-3259.ijct20161408]
[31]
In silico Clinical Trials: How computer simulation will transform Biomedical industry (Avicenna Road-map). 2020. Available from: https://avicenna-isct.org/wp-content/uploads/2016/01/AvicennaRo-admapPDF-27-01-16.pdf
[32]
Viceconti, M.; Pappalardo, F.; Rodriguez, B.; Horner, M.; Bischoff, J.; Tshinanu, F.M. In silico trials: verification, validation, and uncer-tainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods, 2020.
[http://dx.doi.org/10.1016/j.ymeth.2020.01.011] [PMID: 31991193]
[33]
Morrison, T.M.; Hariharan, P.; Funkhouser, C.M.; Afshari, P.; Goodin, M.; Horner, M. Assessing computational model credibility using a risk-based framework: application to hemolysis in centrifugal blood pumps. ASAIO J., 2019, 65(4), 349-360.
[http://dx.doi.org/10.1097/MAT.0000000000000996] [PMID: 30973403]
[34]
Viceconti, M.; Juárez, M.A.; Curreli, C.; Pennisi, M.; Russo, G.; Pappalardo, F. Credibility of in silico trial technologies-a theoretical fram-ing. IEEE J. Biomed. Health Inform., 2020, 24(1), 4-13.
[http://dx.doi.org/10.1109/JBHI.2019.2949888] [PMID: 31670687]
[35]
Musuamba, F.T.; Bursi, R.; Manolis, E.; Karlsson, K.; Kulesza, A.; Courcelles, E.; Boissel, J.P.; Lesage, R.; Crozatier, C.; Voisin, E.M.; Rousseau, C.F.; Marchal, T.; Alessandrello, R.; Geris, L. Verifying and validating quantitative systems pharmacology and in silico models in drug development: current needs, gaps, and challenges. CPT Pharmacometrics Syst. Pharmacol., 2020, 9(4), 195-197.
[http://dx.doi.org/10.1002/psp4.12504] [PMID: 32237207]
[36]
Oberkampf, W.L.; Roy, C.J. Verification and validation in scientific computing; Cambridge University Press, 2010.
[http://dx.doi.org/10.1017/CBO9780511760396]
[37]
Roache, P.J. Verification and validation in computational science and engineering; Albuquerque, NM: Hermosa, 1998.
[38]
Stracuzzi, D.J. Uncertainty Quantification for Machine Learning and Statistical Models; Sandia National Lab; SNL-NM: Albuquerque, NM, 2017.
[39]
Bousquet, O.; von Luxburg, U.; Rätsch, G. Advanced Lectures on Machine Learning: ML Summer Schools 2003, Canberra, Australia, February 2-14, 2003, Tübingen, Germany, August 4-16, 2003, Revised Lectures, 2004 edition; Springer: Berlin New York, 2004.
[40]
Viceconti, M. A tentative taxonomy for predictive models in relation to their falsifiability. Philos. Trans.- Royal Soc., Math. Phys. Eng. Sci., 2011, 369(1954), 4149-4161.
[http://dx.doi.org/10.1098/rsta.2011.0227] [PMID: 21969670]
[41]
Pathmanathan, P.; Gray, R.A.; Romero, V.J.; Morrison, T.M. Applicability analysis of validation evidence for biomedical computational models. ASME J. Venif. Valid. Uncert., 2017, 2(2), 021005.
[http://dx.doi.org/10.1115/1.4037671]
[42]
Haddad, T.; Himes, A.; Thompson, L.; Irony, T.; Nair, R. Incorporation of stochastic engineering models as prior information in Bayesian medical device trials. J. Biopharm. Stat., 2017, 27(6), 1089-1103.
[http://dx.doi.org/10.1080/10543406.2017.1300907] [PMID: 28281931]
[43]
Sargent, R.G. Verification, validation and accreditation of simulation models, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165)., 2000, 1, 50-59.
[44]
Pennisi, M.; Russo, G.; Motta, S.; Pappalardo, F. Agent based modeling of the effects of potential treatments over the blood-brain barrier in multiple sclerosis. J. Immunol. Methods, 2015, 427, 6-12.
[http://dx.doi.org/10.1016/j.jim.2015.08.014] [PMID: 26343337]
[45]
Gong, C.; Milberg, O.; Wang, B.; Vicini, P.; Narwal, R.; Roskos, L.; Popel, A.S. A computational multiscale agent-based model for simulat-ing spatio-temporal tumour immune response to PD1 and PDL1 inhibition. J. R. Soc. Interface, 2017, 14(134), 20170320.
[http://dx.doi.org/10.1098/rsif.2017.0320] [PMID: 28931635]
[46]
Norton, K.A.; Wallace, T.; Pandey, N.B.; Popel, A.S. An agent-based model of triple-negative breast cancer: the interplay between chemo-kine receptor CCR5 expression, cancer stem cells, and hypoxia. BMC Syst. Biol., 2017, 11(1), 68.
[http://dx.doi.org/10.1186/s12918-017-0445-x] [PMID: 28693495]
[47]
Piñero, J.; Furlong, L.I.; Sanz, F. In silico models in drug development: where we are. Curr. Opin. Pharmacol., 2018, 42, 111-121.
[http://dx.doi.org/10.1016/j.coph.2018.08.007] [PMID: 30205360]
[48]
Madabushi, R.; Benjamin, J.M.; Grewal, R.; Pacanowski, M.A.; Strauss, D.G.; Wang, Y.; Zhu, H.; Zineh, I. The US Food and Drug Admin-istration’s model‐informed drug development paired meeting pilot program: early experience and impact. Clin. Pharmacol. Ther., 2019, 106(1), 74-78.
[http://dx.doi.org/10.1002/cpt.1457] [PMID: 31081932]
[49]
Guidance Document on the Validation of (Quantitative) StructureActivity Relationship [(Q)SAR] Models | en | OECD 2020. Available from: https://www.oecd.org/env/guidance-document-on-the-validation-of-quantitative-structure-activity-relationship-q-sar-models-9789264085442-en.htm
[50]
Reporting the results of population pharmacokinetic analyses - European Medicines Agency 2020. Available form: https://www.ema.europa.eu/en/reporting-results-population-pharmacokinetic-analyses
[52]
Reflection paper on the use of extrapolation in the development of medicines for pediatrics. 2020. Available form: https://www.ema. europa.eu/en/documents/scientific-guideline/adopted-reflection-paper-use-extrapolation-development-medicines-paediatrics-revision-1_en.pdf
[53]
Reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation - European Medicines Agency 2020. Available form: https://www.ema.europa.eu/en/reporting-physiologica-lly-based-pharmacokinetic-pbpk-modelling-simulation
[55]
Kuemmel, C.; Yang, Y.; Zhang, X.; Florian, J.; Zhu, H.; Tegenge, M.; Huang, S.M.; Wang, Y.; Morrison, T.; Zineh, I. Consideration of a credibility assessment framework in model‐informed drug development: potential application to physiologically‐based pharmacokinetic modeling and simulation. CPT Pharmacometrics Syst. Pharmacol., 2020, 9(1), 21-28.
[http://dx.doi.org/10.1002/psp4.12479] [PMID: 31652029]
[56]
Hoekstra, A; Chopard, B; Coveney, P Multiscale modelling and simulation: a position paper. Philos Trans A Math Phys Eng Sci, 2021, 372(2021), 20130377.
[57]
Malagrinò, I. In silico clinical trials: A new dawn in biomedical research. HUMANA. MENTE J. Philosoph. Stud., 2016, 9(30), 87-104.
[58]
Viceconti, M.; Olsen, S.; Nolte, L.P.; Burton, K. Extracting clinically relevant data from finite element simulations. Clin. Biomech. (Bristol, Avon), 2005, 20(5), 451-454.
[http://dx.doi.org/10.1016/j.clinbiomech.2005.01.010] [PMID: 15836931]
[59]
Valerio, L.G., Jr Application of advanced in silico methods for predictive modeling and information integration. Expert Opin. Drug Metab. Toxicol., 2012, 8(4), 395-398.
[http://dx.doi.org/10.1517/17425255.2012.664636] [PMID: 22432718]
[60]
Boyer, S. The use of computer models in pharmaceutical safety evaluation. Altern. Lab. Anim., 2009, 37(5), 467-475.
[http://dx.doi.org/10.1177/026119290903700505] [PMID: 20017577]
[61]
MacLeod, R.; Gill, H.S. Generating preclinical evidence for MHRA - an in silico clinical trial examining the safety of a novel device for knee arthritis treatment. Bath Biomechanics Symposium, 2019.
[62]
Carvalho, C.; Varela, S.A.M.; Marques, T.A.; Knight, A.; Vicente, L. Are in vitro and in silico approaches used appropriately for animal-based major depressive disorder research? PLoS One, 2020, 15(6), e0233954.
[http://dx.doi.org/10.1371/journal.pone.0233954] [PMID: 32579547]
[63]
Orwoll, E.S.; Marshall, L.M.; Nielson, C.M.; Cummings, S.R.; Lapidus, J.; Cauley, J.A.; Ensrud, K.; Lane, N.; Hoffmann, P.R.; Kopper-dahl, D.L.; Keaveny, T.M. Finite element analysis of the proximal femur and hip fracture risk in older men. J. Bone Miner. Res., 2009, 24(3), 475-483.
[http://dx.doi.org/10.1359/jbmr.081201] [PMID: 19049327]
[64]
Viceconti, M.; Hunter, P.; Hose, R. Big data, big knowledge: big data for personalized healthcare. IEEE J. Biomed. Health Inform., 2015, 19(4), 1209-1215.
[http://dx.doi.org/10.1109/JBHI.2015.2406883] [PMID: 26218867]
[65]
Hood, L.; Balling, R.; Auffray, C. Revolutionizing medicine in the 21st century through systems approaches. Biotechnol. J., 2012, 7(8), 992-1001.
[http://dx.doi.org/10.1002/biot.201100306] [PMID: 22815171]
[66]
Ji, Z.; Yan, K.; Li, W.; Hu, H.; Zhu, X. Mathematical and computational modeling in complex biological systems. BioMed Res. Int., 2017, 2017, 5958321.
[http://dx.doi.org/10.1155/2017/5958321] [PMID: 28386558]
[67]
Morrison, T.M.; Pathmanathan, P.; Adwan, M.; Margerrison, E. Advancing regulatory science with computational modeling for medical devices at the FDA’s office of science and engineering laboratories. Front. Med. (Lausanne), 2018, 5, 241.
[http://dx.doi.org/10.3389/fmed.2018.00241] [PMID: 30356350]
[68]
DiMasi, J.A.; Grabowski, H.G.; Hansen, R.W. Innovation in the pharmaceutical industry: new estimates of R&D costs. J. Health Econ., 2016, 47, 20-33.
[http://dx.doi.org/10.1016/j.jhealeco.2016.01.012] [PMID: 26928437]
[69]
Brodland, G.W. How computational models can help unlock biological systems. In: Seminars in cell & developmental biology; Academic Press, 2015; 47, pp. 62-73.
[http://dx.doi.org/10.1016/j.semcdb.2015.07.001]
[70]
An, G.; Bartels, J.; Vodovotz, Y. In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev. Res., 2011, 72(2), 187-200.
[http://dx.doi.org/10.1002/ddr.20415] [PMID: 21552346]
[71]
Hausheer, F.H.; Kochat, H.; Parker, A.R.; Ding, D.; Yao, S.; Hamilton, S.E.; Petluru, P.N.; Leverett, B.D.; Bain, S.H.; Saxe, J.D. New ap-proaches to drug discovery and development: a mechanism-based approach to pharmaceutical research and its application to BNP7787, a novel chemoprotective agent. Cancer Chemother. Pharmacol., 2003, 52(Suppl. 1), S3-S15.
[http://dx.doi.org/10.1007/s00280-003-0653-5] [PMID: 12819940]
[72]
Michelson, S.; Sehgal, A.; Friedrich, C. In silico prediction of clinical efficacy. Curr. Opin. Biotechnol., 2006, 17(6), 666-670.
[http://dx.doi.org/10.1016/j.copbio.2006.09.004] [PMID: 17046236]
[73]
Zloh, M.; Kirton, S.B. The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions. Future Med. Chem., 2018, 10(4), 423-432.
[http://dx.doi.org/10.4155/fmc-2017-0151] [PMID: 29380627]
[77]
Madabushi, R.; Benjamin, J.M.; Grewal, R.; Pacanowski, M.A.; Strauss, D.G.; Wang, Y.; Zhu, H.; Zineh, I. The US food and drug admin-istration’s model-informed drug development paired meeting pilot program: early experience and impact. Clin. Pharmacol. Ther., 2019, 106(1), 74-78.
[http://dx.doi.org/10.1002/cpt.1457] [PMID: 31081932]
[78]
Bonate, P. Be a Model Communicator: And Sell Your Models to Anyone , 2014; p. 244.
[79]
European Medicines Agency (EMA). Guideline on the reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation Available from: https://www.ema.europa.eu/en/docu ments/scientific-guideline/guideline-reporting-physiologically-based-pharmacokinetic-pbpk-modelling-simulation_en.pdf
[81]
Guideline on the use of pharmacokinetics and pharmacodynamics in the development of antimicrobial medicinal products 2016. Available from: https://www.ema.europa.eu/en/documents/scien-tific-guideline/guideline-use-pharmacokinetics-pharmacodynamic-s-development-antimicrobial-medicinal-products_en.pdf
[82]
Passini, E.; Britton, O.J.; Bueno-Orovio, A.; Rodriguez, B. Human in silico trials on drug-induced changes in electrophysiology and calci-um dynamics using the virtual assay software. J. Pharmacol. Toxicol. Methods, 2019, 99, 106595.
[http://dx.doi.org/10.1016/j.vascn.2019.05.104]
[83]
Qasim, M.; Farinella, G.; Zhang, J.; Li, X.; Yang, L.; Eastell, R.; Viceconti, M. Patient-Specific Finite Element Minimum Physiological Strength as Predictor of the Risk of Hip Fracture: The effect of methodological determinants. Osteoporos. Int., 2016, 1-8.
[http://dx.doi.org/10.1007/s00198-0163597-4]
[84]
Li, X.; Viceconti, M.; Cohen, M.C.; Reilly, G.C.; Carré, M.J.; Offiah, A.C. Developing CT based computational models of pediatric femurs. J. Biomech., 2015, 48(10), 2034-2040.
[http://dx.doi.org/10.1016/j.jbiomech.2015.03.027] [PMID: 25895643]

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