Tytuł artykułu
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
In silico evolutionary optimization of cancer treatment based on multiple nano-particle (NP) assisted drug delivery systems was investigated in this study. The use of multiple types of NPs is expected to increase the robustness of the treatment, due to imposing higher complexity on the solution tackling a problem of high complexity, namely the physiology of a tumor. Thus, the utilization of metameric representations in the evolutionary optimization method was examined, along with suitable crossover and mutation operators. An opensource physics-based simulator was utilized, namely PhysiCell, after appropriate modifications, to test the fitness of possible treatments with multiple types of NPs. The possible treatments could be comprised of up to ten types of NPs, simultaneously injected in an area close to the cancerous tumour. Initial results seem to suffer from bloat, namely the best solutions discovered are converging towards the maximum amount of different types of NPs, however, without providing a significant return in fitness when compared with solutions of fewer types of NPs. As the large diversity of NPs will most probably prove to be quite toxic in lab experiments, we opted for methods to reduce the bloat, thus, resolve to therapies with fewer types of NPs. Namely, the bloat control methods studied here were removing types of NPs from the optimization genome as part of the mutation operator and applying parsimony pressure in the replacement operator. By utilizing these techniques, the treatments discovered are composed of fewer types of NPs, while their fitness is not significantly smaller.
Wydawca
Czasopismo
Rocznik
Tom
Strony
352--361
Opis fizyczny
Bibliogr. 54 poz., rys., wykr.
Twórcy
- Unconventional Computing Laboratory, University of the West of England, Bristol BS16 1QY, UK
autor
- Department of Computer Science and Creative Technologies, University of the West of England, Bristol BS16 1QY, UK
autor
- Unconventional Computing Laboratory, University of the West of England, Bristol BS16 1QY, UK
autor
- Laboratory for Meteorology, Physics and Biophysics, Faculty of Agriculture, Trg Dositeja Obradovica 8, University of Novi Sad, 21000 Novi Sad, Serbia
Bibliografia
- [1] Swain S, Kumar Sahu P, Beg S, Manohar Babu S. Nanoparticles for cancer targeting: current and future directions. Curr Drug Deliv 2016;13(8):1290–302.
- [2] Zhang Y, Li M, Gao X, Chen Y, Liu T. Nanotechnology in cancer diagnosis: progress, challenges and opportunities. J Hematol Oncol 2019;12(1):137.
- [3] Sztandera K, Gorzkiewicz M, Klajnert-Maculewicz B. Gold nanoparticles in cancer treatment. Mol Pharmaceutics 2018;16(1):1–23.
- [4] Rodrigues CF, Jacinto TA, Moreira AF, Costa EC, Miguel SP, Correia IJ. Functionalization of AuMSS nanorods towards more effective cancer therapies. Nano Res 2019;12(4):719–32.
- [5] Pairoj S, Damrongsak P, Damrongsak B, Jinawath N, Kaewkhaw R, Leelawattananon T, Ruttanasirawit C, Locharoenrat K. Antiradical properties of chemo drug, carboplatin, in cooperation with zno nanoparticles under uv irradiation in putative model of cancer cells. Biocybernetics Biomed Eng 2019;39(3):893–901.
- [6] Borkowska M, Siek M, Kolygina DV, Sobolev YI, Lach S, Kumar S, Cho Y-K, Kandere-Grzybowska K, Grzybowski BA. Targeted crystallization of mixed-charge nanoparticles in lysosomes induces selective death of cancer cells. Nat Nanotechnol 2020;15(4):331–41.
- [7] Zhao X, Yang K, Zhao R, Ji T, Wang X, Yang X, Zhang Y, Cheng K, Liu S, Hao J, et al. Inducing enhanced immunogenic cell death with nanocarrier-based drug delivery systems for pancreatic cancer therapy. Biomaterials 2016;102:187–97.
- [8] Minion LE, Chase DM, Farley JH, Willmott LJ, Monk BJ. Safety and efficacy of salvage nano-particle albumin bound paclitaxel in recurrent cervical cancer: a feasibility study. Gynecol Oncol Res Practice 2016;3(1):1–4.
- [9] Puja P, Vinita NM, Devan U, Velangani AJ, Srinivasan P, Yuvakkumar R, Arul Prakash P, Kumar P, Fluorescence microscopy-based analysis of apoptosis induced by platinum nanoparticles against breast cancer cells, Appl Organometallic Chem 34 (9) (2020) e5740. .
- [10] Tabassum DP, Polyak K. Tumorigenesis: it takes a village. Nat Rev Cancer 2015;15(8):473–83.
- [11] Groten J, Venkatraman A, Mertelsmann R. Chapter 12–modeling and simulating carcinogenesis. In: Deigner H-P, Kohl M, editors. Precision Medicine. Academic Press; 2018. p. 277–95. https://doi.org/10.1016/B978-0-12-805364-5.00012-3.
- [12] Makale MT, Kesari S, Wrasidlo W. The autonomic nervous system and cancer, Biocybernetics and Biomedical. Engineering 2017;37(3):443–52.
- [13] Gener P, Montero S, Xandri-Monje H, Díaz-Riascos ZV, Rafael D, Andrade F, Martínez-Trucharte F, González P, Seras-Franzoso J, Manzano A, et al. ZileutonTM loaded in polymer micelles effectively reduce breast cancer circulating tumor cells and intratumoral cancer stem cells, Nanomedicine: Nanotechnology. Biology Med 2020;24 102106.
- [14] Persidis A. Cancer multidrug resistance. Nature Biotechnol 1999;17(1):94–5.
- [15] Eyler CE, Rich JN. Survival of the fittest: cancer stem cells in therapeutic resistance and angiogenesis, Journal of clinical oncology: official journal of the American Society of. Clinical Oncol 2008;26(17):2839.
- [16] Easwaran H, Tsai H-C, Baylin SB. Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol Cell 2014;54(5):716–27.
- [17] Li Y, Liu X, Pan W, Li N, Tang B. Photothermal therapy-induced immunogenic cell death based on natural melanin nanoparticles against breast cancer. Chem Commun 2020;56 (9):1389–92.
- [18] Yoon HY, Selvan ST, Yang Y, Kim MJ, Yi DK, Kwon IC, Kim K. Engineering nanoparticle strategies for effective cancer immunotherapy. Biomaterials 2018;178:597–607.
- [19] Yang C, Bromma K, Di Ciano-Oliveira C, Zafarana G, van Prooijen M, Chithrani DB. Gold nanoparticle mediated combined cancer therapy. Cancer Nanotechnol 2018;9(1):1–14.
- [20] Ma L, Kohli M, Smith A. Nanoparticles for combination drug therapy. ACS Nano 2013;7(11):9518–25.
- [21] Zhang RX, Wong HL, Xue HY, Eoh JY, Wu XY. Nanomedicine of synergistic drug combinations for cancer therapy–strategies and perspectives. J Controlled Release 2016;240:489–503.
- [22] Shrestha B, Tang L, Romero G. Nanoparticles-mediated combination therapies for cancer treatment. Adv Ther 2019;2(11):1900076.
- [23] Parhi P, Mohanty C, Sahoo SK. Nanotechnology-based combinational drug delivery: an emerging approach for cancer therapy. Drug Discovery Today 2012;17(17–18):1044–52.
- [24] Jaeger S, Igea A, Arroyo R, Alcalde V, Canovas B, Orozco M, Nebreda AR, Aloy P. Quantification of pathway cross-talk reveals novel synergistic drug combinations for breast cancer. Cancer Res 2017;77(2):459–69.
- [25] Nevala WK, Butterfield JT, Sutor SL, Knauer DJ, Markovic SN. Antibody-targeted paclitaxel loaded nanoparticles for the treatment of cd20+ b-cell lymphoma. Sci Rep 2017;7:45682.
- [26] Sykes EA, Dai Q, Sarsons CD, Chen J, Rocheleau JV, Hwang DM, Zheng G, Cramb DT, Rinker KD, Chan WC. Tailoring nanoparticle designs to target cancer based on tumor pathophysiology. Proc Nat Acad Sci 2016;113(9):E1142–51.
- [27] Dogra P, Butner JD, Chuang Y-L, Caserta S, Goel S, Brinker CJ, Cristini V, Wang Z. Mathematical modeling in cancer nanomedicine: a review. Biomed. Microdevices 2019;21(2):40.
- [28] Stillman NR, Kovacevic M, Balaz I, Hauert S, In silico modelling of cancer nanomedicine, across scales and transport barriers, NPJ Comput Mater 6 (92) (2020). doi:10.1038/s41524-020-00366-8.
- [29] Johnston ST, Faria M, Crampin EJ. Isolating the sources of heterogeneity in nano-engineered particle–cell interactions. JR Soc Interface 2020;17(166):20200221.
- [30] Ghaffarizadeh A, Heiland R, Friedman SH, Mumenthaler SM, Macklin P. Physicell: An open source physics-based cell simulator for 3-d multicellular systems. PLoS Comput Biol. 2018;14(2) e1005991.
- [31] Ryerkerk ML, Averill RC, Deb K, Goodman ED. Solving metameric variable-length optimization problems using genetic algorithms. Genet Program Evolvable Mach 2017;18(2):247–77.
- [32] Ryerkerk ML, Averill RC, Deb K, Goodman ED. A survey of evolutionary algorithms using metameric representations. Genet Program Evolvable Mach 2019;20(4):441–78.
- [33] Preen RJ, Bull L, Adamatzky A. Towards an evolvable cancer treatment simulator. BioSystems 2019;182:1–7.
- [34] Tsompanas M-A, Bull L, Adamatzky A, Balaz I, Haploiddiploid evolution: Nature’s memetic algorithm, arXiv preprint arXiv:1911.07302 (2019).
- [35] Tsompanas M-A, Bull L, Adamatzky A, Balaz I, Utilizing differential evolution into optimizing targeted cancer treatments, arXiv preprint arXiv:2003.11623 (2020).
- [36] Tsompanas M-A, Bull L, Adamatzky A, Balaz I. Novelty search employed into the development of cancer treatment simulations. Inform Med Unlocked 2020 100347.
- [37] Ozik J, Collier N, Heiland R, An G, Macklin P. Learning-accelerated discovery of immune-tumour interactions. Mol Syst Design Eng 2019;4(4):747–60.
- [38] Tsompanas M-A, Bull L, Adamatzky A, Balaz I, Evolving nano particle cancer treatments with multiple particle types, (under review) (2020).
- [39] Tsompanas M-A, Bull L, Adamatzky A, Balaz I. In silico optimization of cancer therapies with multiple types of nanoparticles applied at different times. Comput Methods Programs Biomed 2020;105886.
- [40] Poli R, Langdon WB, McPhee NF, Koza JR. A field guide to genetic programming. Lulu Com 2008.
- [41] Wagner M, Neumann F. Parsimony pressure versus multiobjective optimization for variable length representations. In: International Conference on Parallel Problem Solving from Nature. Springer; 2012. p. 133–42.
- [42] Mora JC, Barón JMC, Santos JMR, Payán MB. An evolutive algorithm for wind farm optimal design. Neurocomputing 2007;70(16–18):2651–8.
- [43] Weicker N, Szabo G, Weicker K, Widmayer P. Evolutionary multiobjective optimization for base station transmitter placement with frequency assignment. IEEE Trans Evol Comput 2003;7(2):189–203.
- [44] Stanley KO, Miikkulainen R. Evolving neural networks through augmenting topologies. Evol Comput 2002;10 (2):99–127.
- [45] PhysiCell: An open source physics-based cell simulator, http://physicell.org/, [Online; accessed Jan-2020] (2020).
- [46] Luke S, Panait L. Fighting bloat with nonparametric parsimony pressure. In: International Conference on Parallel Problem Solving from Nature. Springer; 2002. p. 411–21.
- [47] Ryerkerk M, Averill R, Deb K, Goodman E. A novel selection mechanism for evolutionary algorithms with metameric variable-length representations. Soft Comput 2020:1–14.
- [48] Fong EJ, Strelez C, Mumenthaler SM. A perspective on expanding our understanding of cancer treatments by integrating approaches from the biological and physical sciences. SLAS DISCOVERY: Adv Sci Drug Discovery 2020;25(7):672–83.
- [49] Anderson AR, Maini PK. Mathematical oncology. Bull Math Biol 2018;80(5):945–53.
- [50] Enderling H, Alfonso JCL, Moros E, Caudell JJ, Harrison LB. Integrating mathematical modeling into the roadmap for personalized adaptive radiation therapy. Trends Cancer 2019;5(8):467–74.
- [51] Hao Y, Zhou X, Li R, Song Z, Min Y. Advances of functional nanomaterials for cancer immunotherapeutic applications. Wiley Interdisciplinary Rev: Nanomedicine Nanobiotechnol 2020;12(2) e1574.
- [52] Ozik J, Collier N, Wozniak JM, Macal C, Cockrell C, Friedman SH, Ghaffarizadeh A, Heiland R, An G, Macklin P. High-throughput cancer hypothesis testing with an integrated physicell-emews workflow. BMC Bioinform 2018;19(18):81–97.
- [53] Balaz I, Petrić T, Kovacevic M, Tsompanas M-A, Stillman N. Harnessing adaptive novelty for automated generation of cancer treatments. Biosystems 2021;199 104290.
- [54] Benzekry S. Artificial intelligence and mechanistic modeling for clinical decision making in oncology. Clinical Pharmacol Ther 2020;108(3):471–86.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-36e097e2-118f-40bf-ae15-bd95de4cb1d8