• 1.

    Etheridge, M. L. et al. The big picture on nanomedicine: the state of investigational and approved nanomedicine products Nanomedicine9, 1–14 (2013).

  • 2.

    Su, Y. L. & Hu, S. H. Functional nanoparticles for tumor penetration of therapeutics. Pharmaceutics10, 1–21 (2018).

    CAS  Google Scholar 

  • 3.

    Tran, S., DeGiovanni, P.-J., Piel, B. & Rai, P. Cancer nanomedicine: a review of recent success in drug delivery. Clin. Transl. Med.6, 44 (2017).

  • 4.

    Hare, J. I. et al. Challenges and strategies in anti-cancer nanomedicine development: an industry perspective. Adv. Drug Deliv. Rev.108, 25–38 (2017).

    CAS  Google Scholar 

  • 5.

    Strambeanu, N., Demetrovici, L., Dragos, D. & Lungu, M. in Nanoparticles’ Promises and Risks: Characterization, Manipulation, and Potential Hazards to Humanity and the Environment (eds Lungu, M., Neculae, A., Bunoiu, M. & Biris, C.) (Springer, 2015).

  • 6.

    Roberts, W. G. & Palade, G. E. G. Increased microvascular permeability and endothelial fenestration induced by vascular endothelial growth factor. J. Cell Sci.108, 2369–2379 (1995).

    CAS  Google Scholar 

  • 7.

    Tong, R., Hemmati, H. D., Langer, R. & Kohane, D. S. Photoswitchable nanoparticles for triggered tissue penetration and drug delivery. J. Am. Chem. Soc.134, 8848–8855 (2012).

    CAS  Google Scholar 

  • 8.

    Kong, S. D. et al. Magnetically vectored nanocapsules for tumor penetration and remotely switchable on-demand drug release. Nano Lett.10, 5088–5092 (2010).

    CAS  Google Scholar 

  • 9.

    Wang, B. et al. Simultaneously overcome tumor vascular endothelium and extracellular matrix barriers via a non-destructive size-controlled nanomedicine. J. Control. Release268, 225–236 (2017).

    CAS  Google Scholar 

  • 10.

    Bazak, R. et al. Cancer active targeting by nanoparticles: a comprehensive review of literature. J. Cancer Res. Clin. Oncol.141, 769–784 (2015).

    CAS  Google Scholar 

  • 11.

    Hauert, S. & Bhatia, S. N. Mechanisms of cooperation in cancer nanomedicine: towards systems nanotechnology. Trends Biotechnol.32, 448–455 (2014).

    CAS  Google Scholar 

  • 12.

    Li, Y. et al. Cell and nanoparticle transport in tumour microvasculature: the role of size, shape and surface functionality of nanoparticles. Interface Focus6, 1–15 (2016).

    Google Scholar 

  • 13.

    Park, J.-H. et al. Cooperative nanomaterial system to sensitize, target, and treat tumors. Proc. Natl Acad. Sci.107, 981–986 (2010).

    CAS  Google Scholar 

  • 14.

    Von Maltzahn, G. et al. Nanoparticles that communicate in vivo to amplify tumour targeting. Nat. Mater.10, 545–552 (2011).

    Google Scholar 

  • 15.

    Fu, Y. et al. A feasible strategy for self-assembly of gold nanoparticles: via dithiol-PEG for photothermal therapy of cancers. RSC Adv.8, 6120–6124 (2018).

    Google Scholar 

  • 16.

    Xiao, Z. et al. DNA self-assembly of targeted near-infrared-responsive gold nanoparticles for cancer thermo-chemotherapy. Angew. Chem.—Int. Edn.51, 11853–11857 (2012).

    CAS  Google Scholar 

  • 17.

    Bae, Y. H. & Park, K. Targeted drug delivery to tumors: myths, reality and possibility. J. Control. Release153, 198–205 (2011).

    CAS  Google Scholar 

  • 18.

    Lookman, T., Balachandran, P. V., Xue, D. & Yuan, R. Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design. npj Comput. Mater.5, 1–17 (2019).

    Google Scholar 

  • 19.

    Deisboeck, T. S., Zhang, L., Yoon, J. & Costa, J. In silico cancer modeling: is it ready for prime time? Nat. Clin. Practice Oncol.6, 34–42 (2009).

    CAS  Google Scholar 

  • 20.

    Michor, F. & Beal, K. Improving cancer treatment via mathematical modeling: surmounting the challenges is worth the effort. Cell163, 1059–1063 (2015).

    CAS  Google Scholar 

  • 21.

    Dogra, P. et al. Mathematical modeling in cancer nanomedicine: a review. Biomed. Microdevices21, 40 (2019).

    Google Scholar 

  • 22.

    Talmadge, J. E. & Fidler, I. J. The biology of cancer metastasis: historical perspective. Cancer Res.70, 5649–5669 (2010).

    CAS  Google Scholar 

  • 23.

    Siegel, R., Miller, K. D. & Ahmedin, J. Cancer Statistics, 2017. CA: Cancer J. Clinicians67, 7–30 (2017).

    Google Scholar 

  • 24.

    Chauviere, A. H. et al. Mathematical oncology: how are the mathematical and physical sciences contributing to the war on breast cancer? Curr. Breast Cancer Rep.2, 121–129 (2010).

  • 25.

    Deisboeck, Z. & Yoon, C. In silico modelling—is it ready for prime time. Program6, 34–42 (2011).

    Google Scholar 

  • 26.

    Rejniak, K. A. & Anderson, A. R. Hybrid models of tumor growth. Wiley Interdisciplinary Rev.: Syst. Biol. Med.3, 115–125 (2011).

    CAS  Google Scholar 

  • 27.

    An, G. & Mi, Q. Agent based models in translational systems biology. Syst. Biol. Med.1, 159–171 (2009).

    CAS  Google Scholar 

  • 28.

    Deisboeck, T. S. & Stamatakos, G. S. Multiscale Cancer Modeling (CRC Press, 2010).

  • 29.

    Norton, K.-A., Gong, C., Jamalian, S. & Popel, A. Multiscale agent-based and hybrid modeling of the tumor immune microenvironment. Processes7, 1–23 (2019).

    Google Scholar 

  • 30.

    Metzcar, J., Wang, Y., Heiland, R. & Macklin, P. A review of cell-based computational modeling in cancer biology. JCO Clin. Cancer Inform.3, 1–13 (2019).

    Google Scholar 

  • 31.

    Zhu, X., Zhou, X., Lewis, M. T., Xia, L. & Wong, S. Cancer stem cell, niche and EGFR decide tumor development and treatment response: a bio-computational simulation study. J. Theor. Biol.269, 138–49 (2011).

    CAS  Google Scholar 

  • 32.

    Sefidgar, M. et al. Numerical modeling of drug delivery in a dynamic solid tumor microvasculature. Microvascular Res.99, 43–56 (2015).

    CAS  Google Scholar 

  • 33.

    Grogan, J. A. et al. Microvessel chaste: an open library for spatial modeling of vascularized tissues. Biophys. J.112, 1767–1772 (2017).

    CAS  Google Scholar 

  • 34.

    Shah, P. N. et al. Extravasation of Brownian spheroidal nanoparticles through vascular pores. Biophys. J.115, 1103–1115 (2018).

    CAS  Google Scholar 

  • 35.

    Owen, M. R. et al. Mathematical modeling predicts synergistic antitumor effects of combining a macrophage-based, hypoxia-targeted gene therapy with chemotherapy. Cancer Res.71, 2826–2837 (2011).

    CAS  Google Scholar 

  • 36.

    Chou, C. Y., Huang, C. K., Lu, K. W., Horng, T. L. & Lin, W. L. Investigation of the spatiotemporal responses of nanoparticles in tumor tissues with a small-scale mathematical model. PLoS ONE8, e59135 (2013).

  • 37.

    Nehoff, H., Parayath, N. N., Domanovitch, L., Taurin, S. & Greish, K. Nanomedicine for drug targeting: strategies beyond the enhanced permeability and retention effect. Int. J. Nanomed.9, 2539–55 (2014).

    Google Scholar 

  • 38.

    Chrastina, A., Massey, K. A. & Schnitzer, J. E. Overcoming in vivo barriers to targeted nanodelivery. Wiley Interdisciplinary Rev.: Nanomed. Nanobiotechnol.3, 421–37 (2011).

    CAS  Google Scholar 

  • 39.

    Barua, S. & Mitragotri, S. Challenges associated with penetration of nanoparticles across cell and tissue barriers: a review of current status and future prospects. Nano Today9, 223–243 (2014).

    CAS  Google Scholar 

  • 40.

    Curtis, L. T., England, C. G., Wu, M., Lowengrub, J. & Frieboes, H. B. An interdisciplinary computational/experimental approach to evaluate drug-loaded gold nanoparticle tumor cytotoxicity. Nanomedicine11, 197–216 (2016).

    CAS  Google Scholar 

  • 41.

    Shah, A. B., Rejniak, K. A. & Gevertz, J. L. Limiting the development of anti-cancer drug resistance in a spatial model of micrometastases. Math. Biosci. Eng.13, 1185–1206 (2016).

    Google Scholar 

  • 42.

    Al-Obaidi, H. & Florence, A. T. Nanoparticle delivery and particle diffusion in confined and complex environments. J. Drug Deliv. Sci. Technol.30, 266–277 (2015).

    CAS  Google Scholar 

  • 43.

    Wang, Z. et al. Theory and experimental validation of a spatio-temporal model of chemotherapy transport to enhance tumor cell kill. PLoS Comput. Biol.12, e1004969 (2016).

  • 44.

    Hamis, S., Nithiarasu, P. & Powathil, G. G. What does not kill a tumour may make it stronger: In silico insights into chemotherapeutic drug resistance. J. Theor. Biol.454, 253–267 (2018).

    CAS  Google Scholar 

  • 45.

    ming Ding, H. & qiang Ma, Y. Computer simulation of the role of protein corona in cellular delivery of nanoparticles. Biomaterials35, 8703–8710 (2014).

    Google Scholar 

  • 46.

    Zhang, S., Gao, H. & Bao, G. Physical principles of nanoparticle cellular endocytosis. ACS Nano9, 8655–8671 (2015).

    CAS  Google Scholar 

  • 47.

    Martinez-Veracoechea, F. J. & Frenkel, D. Designing super selectivity in multivalent nano-particle binding. Proc. Natl Acad. Sci.108, 10963–10968 (2011).

    CAS  Google Scholar 

  • 48.

    Pascal, J. et al. Mechanistic modeling identifies drug-uptake history as predictor of tumor drug resistance and nano-carrier-mediated response. ACS Nano7, 11174–11182 (2013).

    CAS  Google Scholar 

  • 49.

    Angioletti-Uberti, S. Theory, simulations and the design of functionalized nanoparticles for biomedical applications: a soft matter perspective. npj Comput. Mater.3, 1–48 (2017).

    CAS  Google Scholar 

  • 50.

    Karolak, A., Markov, D. A., McCawley, L. J. & Rejniak, K. A. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J. Roy. Soc. Interface15, 20170703 (2018).

  • 51.

    Afonin, K. A. et al. In silico design and enzymatic synthesis of functional RNA nanoparticles. Acc. Chem. Res.47, 1731–1741 (2014).

    CAS  Google Scholar 

  • 52.

    Finley, S. D., Angelikopoulos, P., Koumoutsakos, P. & Popel, A. S. Pharmacokinetics of Anti-VEGF Agent aflibercept in cancer predicted by data-driven, molecular-detailed model. CPT: Pharmacometrics Syst. Pharmacol.4, 641–649 (2015).

    CAS  Google Scholar 

  • 53.

    Wang, Z., Bordas, V., Sagotsky, J. & Deisboeck, T. S. Identifying therapeutic targets in a combined EGFR-TGFβ R signalling cascade using a multiscale agent-based cancer model. Math. Med. Biol.29, 95–108 (2012).

    Google Scholar 

  • 54.

    Blanco, E., Shen, H. & Ferrari, M. Principles of nanoparticle design for overcoming biological barriers to drug delivery. Nature Biotechnol.33, 9 (2015).

    Google Scholar 

  • 55.

    Longmire, M., Choyke, P. L. & Kobayashi, H. Clearance properties of nano-sized particles and molecules as imaging agents: considerations and caveats. Nanomedicine3, 703–717 (2008).

    CAS  Google Scholar 

  • 56.

    Gustafson, H. H., Holt-Casper, D., Grainger, D. W. & Ghandehari, H. Nanoparticle uptake: the phagocyte problem. Nano Today10, 487–510 (2015).

    CAS  Google Scholar 

  • 57.

    Fedosov, D. A., Noguchi, H. & Gompper, G. Multiscale modeling of blood flow: from single cells to blood rheology. Biomech. Model. Mechanobiol.13, 239–258 (2014).

    Google Scholar 

  • 58.

    Lopez, H. & Lobaskin, V. Coarse-grained model of adsorption of blood plasma proteins onto nanoparticles. J. Chem. Phys.143, 12B620_1 (2015).

    Google Scholar 

  • 59.

    Shao, Q. & Hall, C. K. Protein adsorption on nanoparticles: model development using computer simulation. J. Phys.: Condens. Matter28, 414019 (2016).

    Google Scholar 

  • 60.

    Maleki, R. et al. ph-sensitive loading/releasing of doxorubicin using single-walled carbon nanotube and multi-walled carbon nanotube: a molecular dynamics study. Comput. Methods Programs Biomed.186, 105210 (2020).

    Google Scholar 

  • 61.

    Yoo, J.-W., Chambers, E. & Mitragotri, S. Factors that control the circulation time of nanoparticles in blood: challenges, solutions and future prospects. Curr. Pharm. Des.16, 2298–2307 (2010).

  • 62.

    Müller, K., Fedosov, D. A. & Gompper, G. Margination of micro- and nano-particles in blood flow and its effect on drug delivery. Sci. Rep.4, 1–8 (2014).

    Google Scholar 

  • 63.

    Lin, Z., Monteiro-Riviere, N. A. & Riviere, J. E. A physiologically based pharmacokinetic model for polyethylene glycol-coated gold nanoparticles of different sizes in adult mice. Nanotoxicology10, 162–172 (2016).

    CAS  Google Scholar 

  • 64.

    Yuan, D., He, H., Wu, Y., Fan, J. & Cao, Y. Physiologically based pharmacokinetic modeling of nanoparticles. J. Pharm. Sci.108, 58–72 (2019).

    CAS  Google Scholar 

  • 65.

    Ding, H.-m & Ma, Y.-q Computational approaches to cell–nanomaterial interactions: keeping balance between therapeutic efficiency and cytotoxicity. Nanoscale Horizons3, 6–27 (2017).

    Google Scholar 

  • 66.

    Koumoutsakos, P., Pivkin, I. & Milde, F. The fluid mechanics of cancer and its therapy. Annu. Rev. Fluid Mech.45, 325–355 (2013).

    Google Scholar 

  • 67.

    Nakamura, Y., Mochida, A., Choyke, P. L. & Kobayashi, H. Nanodrug delivery: is the enhanced permeability and retention effect sufficient for curing cancer? Bioconjugate Chem.27, 2225–2238 (2016).

    CAS  Google Scholar 

  • 68.

    Björnmalm, M., Thurecht, K. J., Michael, M., Scott, A. M. & Caruso, F. Bridging bio-nano science and cancer nanomedicine. ACS Nano11, 9594–9613 (2017).

    Google Scholar 

  • 69.

    Nichols, J. W. & Bae, Y. H. Odyssey of a cancer nanoparticle: from injection site to site of action. Nano Today7, 606–618 (2012).

    CAS  Google Scholar 

  • 70.

    Fullstone, G., Wood, J., Holcombe, M. & Battaglia, G. Modelling the transport of nanoparticles under blood flow using an agent-based approach. Sci. Rep.5, 1–13 (2015).

    Google Scholar 

  • 71.

    Ferretti, S., Allegrini, P. R., Becquet, M. M. & McSheehy, P. M. Tumor interstitial fluid pressure as an early-response marker for anticancer therapeutics. Neoplasia11, 874–881 (2015).

    Google Scholar 

  • 72.

    Lameijer, M. A., Tang, J., Nahrendorf, M., Beelen, R. H. & Mulder, W. J. Monocytes and macrophages as nanomedicinal targets for improved diagnosis and treatment of disease. Expert Rev. Mol. Diagnostics13, 576–580 (2013).

    Google Scholar 

  • 73.

    Wu, M. et al. The effect of interstitial pressure on therapeutic agent transport: coupling with the tumor blood and lymphatic vascular systems. J.Theor. Biol.335, 194–207 (2014).

    Google Scholar 

  • 74.

    Wijeratne, P. A. & Vavourakis, V. A quantitative in silico platform for simulating cytotoxic and nanoparticle drug delivery to solid tumours. Interface Focus9, 20180063 (2019).

  • 75.

    Frieboes, H. B., Wu, M., Lowengrub, J., Decuzzi, P. & Cristini, V. A computational model for predicting nanoparticle accumulation in tumor vasculature. PLoS ONE8, 1–11 (2013).

    Google Scholar 

  • 76.

    Hauert, S., Berman, S., Nagpal, R. & Bhatia, S. N. A computational framework for identifying design guidelines to increase the penetration of targeted nanoparticles into tumors. Nano Today8, 566–576 (2013).

    CAS  Google Scholar 

  • 77.

    Chamseddine, I. M., Frieboes, H. B. & Kokkolaras, M. Design optimization of tumor vasculature-bound nanoparticles. Sci. Rep.8, 17768 (2018).

  • 78.

    Thurber, G. M. & Weissleder, R. A systems approach for tumor pharmacokinetics. PLoS ONE6, e24696 (2011).

  • 79.

    Cilliers, C., Guo, H., Liao, J., Christodolu, N. & Thurber, G. M. Multiscale modeling of antibody-drug conjugates: Connecting tissue and cellular distribution to whole animal pharmacokinetics and potential implications for efficacy. The AAPS J.18, 1117–1130 (2016).

    CAS  Google Scholar 

  • 80.

    Liu, J. et al. Design of nanocarriers based on complex biological barriers in vivo for tumor therapy. Nano Today15, 56–90 (2017).

    Google Scholar 

  • 81.

    Daum, N., Tscheka, C., Neumeyer, A. & Schneider, M. Novel approaches for drug delivery systems in nanomedicine: Effects of particle design and shape. Wiley Interdisciplinary Rev.: Nanomed. Nanobiotechnol.4, 52–65 (2012).

    CAS  Google Scholar 

  • 82.

    Anselmo, A. C. et al. Elasticity of nanoparticles influences their blood circulation, phagocytosis, endocytosis, and targeting. ACS Nano9, 3169–3177 (2015).

    CAS  Google Scholar 

  • 83.

    Bao, G. et al. USNCTAM perspectives on mechanics in medicine. J. Roy. Soc. Interface11, 20140301 (2014).

  • 84.

    Zhang, S., Li, J., Lykotrafitis, G., Bao, G. & Suresh, S. Size-dependent endocytosis of nanoparticles. Adv. Mater.21, 419–424 (2009).

    Google Scholar 

  • 85.

    Yue, T. & Zhang, X. Cooperative effect in receptor-mediated endocytosis of multiple nanoparticles. ACS Nano6, 3196–3205 (2012).

    CAS  Google Scholar 

  • 86.

    Stewart, M. P., Lorenz, A., Dahlman, J. & Sahay, G. Challenges in carrier-mediated intracellular delivery: moving beyond endosomal barriers. Wiley Interdisciplinary Rev.: Nanomed. Nanobiotechnol.8, 465–478 (2016).

    Google Scholar 

  • 87.

    Martens, T. F., Remaut, K., Demeester, J., De Smedt, S. C. & Braeckmans, K. Intracellular delivery of nanomaterials: How to catch endosomal escape in the act. Nano Today9, 344–364 (2014).

    CAS  Google Scholar 

  • 88.

    Chiu, Y.-L. et al. The characteristics, cellular uptake and intracellular trafficking of nanoparticles made of hydrophobically-modified chitosan. J. Control. Release146, 152–159 (2010).

    CAS  Google Scholar 

  • 89.

    Chithrani, B. D. & Chan, W. C. Elucidating the mechanism of cellular uptake and removal of protein-coated gold nanoparticles of different sizes and shapes. Nano Lett.7, 1542–1550 (2007).

    CAS  Google Scholar 

  • 90.

    Pitt-Francis, J. et al. Chaste: a test-driven approach to software development for biological modelling. Comput. Phys. Commun.180, 2452–2471 (2009).

    CAS  Google Scholar 

  • 91.

    Winner, K. R. et al. Spatial modeling of drug delivery routes for treatment of disseminated ovarian cancer. Cancer Res.76, 1320–1334 (2016).

    CAS  Google Scholar 

  • 92.

    Li, J. F. & Lowengrub, J. The effects of cell compressibility, motility and contact inhibition on the growth of tumor cell clusters using the Cellular Potts Model. J. Theor. Biol.343, 79–91 (2014).

    Google Scholar 

  • 93.

    Michalski, P. J. & Loew, L. M. SpringSaLaD: a spatial, particle-based biochemical simulation platform with excluded volume. Biophys. J.110, 523–529 (2016).

    CAS  Google Scholar 

  • 94.

    Ghaffarizadeh, A., Heiland, R., Friedman, S. H., Mumenthaler, S. M. & Macklin, P. PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol.14, e1005991 (2018).

  • 95.

    Ghaffarizadeh, A., Friedman, S. H. & MacKlin, P. BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations. Bioinformatics32, 1256–1258 (2016).

    CAS  Google Scholar 

  • 96.

    Juarez, E. F., Garri, C., Ghaffarizadeh, A., Macklin, P. & Kani, K. Quantification of cancer cell migration with an integrated experimental-computational pipeline. F1000Research7, 1296 (2018).

    Google Scholar 

  • 97.

    Letort, G. et al. PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling. Bioinformatics35, 1188–1196 (2019).

    CAS  Google Scholar 

  • 98.

    Andrews, S. S., Addy, N. J., Brent, R. & Arkin, A. P. Detailed simulations of cell biology with Smoldyn 2.1. PLoS Comput. Biol.6, e1000705 (2010).

  • 99.

    Hepburn, I., Chen, W., Wils, S. & De Schutter, E. STEPS: efficient simulation of stochastic reaction-diffusion models in realistic morphologies. BMC Syst. Biol.6, 36 (2012).

  • 100.

    Chen, W. & De Schutter, E. Parallel STEPS: large scale stochastic spatial reaction-diffusion simulation with high performance computers. Frontiers Neuroinform.11, 1–15 (2017).

    CAS  Google Scholar 

  • 101.

    Cummings, P. T. & Gilmer, J. B. Open-source molecular modeling software in chemical engineering. Curr. Opin. Chem. Eng.23, 99–105 (2019).

    Google Scholar 

  • 102.

    Schmidt, J., Marques, M. R., Botti, S. & Marques, M. A. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater.5, 1–36 (2019).

    Google Scholar 

  • 103.

    Alber, M. et al. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digital Med.2, 1–11 (2019).

    Google Scholar 

  • 104.

    Bishop, C. M. Pattern Recognition and Machine Learning (springer, 2006).

  • 105.

    Alpaydin, E. Introduction to Machine Learning (MIT press, 2020).

  • 106.

    Asadi, H., Rostamizadeh, K., Salari, D. & Hamidi, M. Preparation of biodegradable nanoparticles of tri-block pla–peg–pla copolymer and determination of factors controlling the particle size using artificial neural network. J. Microencapsulation28, 406–416 (2011).

    CAS  Google Scholar 

  • 107.

    Shalaby, K. S. et al. Determination of factors controlling the particle size and entrapment efficiency of noscapine in peg/pla nanoparticles using artificial neural networks. Int. J. Nanomed.9, 4953 (2014).

    CAS  Google Scholar 

  • 108.

    Liu, R. et al. Classification nanosar development for cytotoxicity of metal oxide nanoparticles. Small7, 1118–1126 (2011).

    CAS  Google Scholar 

  • 109.

    Hataminia, F., Noroozi, Z. & Eslam, H. M. Investigation of iron oxide nanoparticle cytotoxicity in relation to kidney cells: a mathematical modeling of data mining. Toxicol. in Vitro59, 197–203 (2019).

  • 110.

    Labouta, H. I., Asgarian, N., Rinker, K. & Cramb, D. T. Meta-analysis of nanoparticle cytotoxicity via data-mining the literature. ACS Nano13, 1583–1594 (2019).

    CAS  Google Scholar 

  • 111.

    Findlay, M. R., Freitas, D. N., Mobed-Miremadi, M. & Wheeler, K. E. Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties. Environ. Sci.: Nano5, 64–71 (2018).

    CAS  Google Scholar 

  • 112.

    Jones, D. E., Ghandehari, H. & Facelli, J. C. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles. Comput. Methods Programs Biomed.132, 93–103 (2016).

    Google Scholar 

  • 113.

    Sason, H. & Shamay, Y. Nanoinformatics in drug delivery. Israel J. Chem. https://doi.org/10.1002/ijch.201900042 (2019).

  • 114.

    Sutton, R. S. & Barto, A. G. Reinforcement Learning: An Introduction (MIT press, 2018).

  • 115.

    Jin, Y. Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evolut. Comput.1, 61–70 (2011).

    Google Scholar 

  • 116.

    Zhao, Y., Kosorok, M. R. & Zeng, D. Reinforcement learning design for cancer clinical trials. Statistics Med.28, 3294–3315 (2009).

    Google Scholar 

  • 117.

    Warmuth, M. K. et al. Active learning with support vector machines in the drug discovery process. J. Chem. Inform. Comput. Sci.43, 667–673 (2003).

    CAS  Google Scholar 

  • 118.

    Yamankurt, G. et al. Exploration of the nanomedicine-design space with high-throughput screening and machine learning. Nature Biomed. Eng.3, 318–327 (2019).

    CAS  Google Scholar 

  • 119.

    Parvinian, B. et al. Credibility evidence for computational patient models used in the development of physiological closed-loop controlled devices for critical care medicine. Frontiers Physiol.10, 220 (2019).

  • 120.

    Morrison, T. M. et al. Assessing computational model credibility using a risk-based framework: application to hemolysis in centrifugal blood pumps. ASAIO J.65, 349 (2019).

    Google Scholar 

  • 121.

    Software as a Medical Device (SaMD): Key Definitions (2013). Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-131209-samd-key-definitions-140901.pdf.

  • 122.

    Software as a Medical Device- (SaMD): Possible Framework for Risk Categorization and Corresponding Considerations (2014). Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-140918-samd-framework-risk-categorization-141013.pdf.

  • 123.

    Software as a Medical Device- (SaMD): Application of Quality Management Systems. (2014). Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-151002-samd-qms.pdf.

  • 124.

    Software as a Medical Device- (SaMD): Clinical Evaluation (2017). Available at: http://www.imdrf.org/docs/imdrf/final/technical/imdrf-tech-170921-samd-n41-clinical-evaluation_1.pdf.

  • 125.

    Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) (2019). Available at: https://www.fda.gov/media/122535/download.

  • 126.

    Weis, J. A. et al. Predicting the response of breast cancer to neoadjuvant therapy using a mechanically coupled reaction-diffusion model. Cancer Res.10, 4333–4347 (2015).

    Google Scholar 

  • 127.

    Macklin, P., Edgerton, M. E., Thompson, A. M. & Cristini, V. Patient-calibrated agent-based modelling of ductal carcinoma in situ (DCIS): From microscopic measurements to macroscopic predictions of clinical progression. J. Theor. Biol.301, 122–140 (2012).

    Google Scholar 

  • 128.

    Korsunsky, I. et al. Systems biology of cancer: a challenging expedition for clinical and quantitative biologists. Frontiers Bioeng. Biotechnol.2, 27 (2014).

  • 129.

    Faratian, D., Bown, J. L., Smith, V. A., Langdon, S. P. & Harrison, D. J. Cancer Systems Biology 245–263 (Humana Press, Totowa, NJ, 2010).

  • 130.

    Werner, H. M., Mills, G. B. & Ram, P. T. Cancer systems biology: a peek into the future of patient care? Nat. Rev. Clin. Oncol.11, 167–176 (2014).

  • 131.

    Kim, M. Y. et al. Tumor self-seeding by circulating cancer cells. Cell139, 1315–1326 (2009).

    Google Scholar 

  • 132.

    Koutsoukas, A. et al. From in silico target prediction to multi-target drug design: current databases, methods and applications. J. Proteomics74, 254–2574 (2011).

    Google Scholar 

  • 133.

    Macklin, P. et al. Progress Towards Computational 3-D Multicellular Systems Biology 225–246 (Springer International Publishing, 2016).

  • 134.

    Rockne, R. C. et al. The 2019 mathematical oncology roadmap. Physical Biology16, 041005 (2019).

  • Source