Generic placeholder image

Current Pharmaceutical Biotechnology

Editor-in-Chief

ISSN (Print): 1389-2010
ISSN (Online): 1873-4316

General Review Article

Xenobots: Applications in Drug Discovery

Author(s): Nilay Solanki*, Sagar Mahant, Swayamprakash Patel, Mehul Patel, Umang Shah, Alkesh Patel, Hardik Koria and Ashish Patel

Volume 23, Issue 14, 2022

Published on: 29 August, 2022

Page: [1691 - 1703] Pages: 13

DOI: 10.2174/1389201023666220430154520

Price: $65

Open Access Journals Promotions 2
Abstract

This review work discusses the applications of xenobots in drug discovery. These are the world's first tiny robots that are living. Robots are built of metals and other things that benefit humans to solve various issues; however, in this case, small xenobots were built utilizing Xenopus laevis, frog embryonic stem cells in the blastocyte stage. Xenobots were created by combining bioscience, artificial intelligence, and computer science. Artificial intelligence constructs several forms of design in an in vitro, In-silico model, after which software analyzes the structure; the most substantial and most noticeable forms are filtered out. Later in vivo development create the design of the Petri plate using the MMR solution and makes the same form as the in silico approach. Ultimately evaluation done based on the behavior, movement, function, and features of xenobots. Xenobots are employed in medical research, pharmaceutical research to evaluate novel dosage forms, also useful for biotechnological and environmental research. Xenobots can be utilized to cure neurodegenerative disorders such as Alzheimer's, Parkinson's disease, and cancer-related issues because of their selfrepairing properties, which allow them to repair normal damaged cells, and convey drugs to their specific target, and reduce cytotoxicity in mostly malignancy circumstances. In the future, new approaches will be employed to treat chronic illnesses and their complications.

Keywords: Xenopus laevis, xenobots, in silico, in vivo, bioengineering, medical research, pharmaceutical research.

Graphical Abstract
[1]
Kriegman, S.; Blackiston, D.; Levin, M.; Bongard, J. A scalable pipeline for designing reconfigurable organisms. Proc. Natl. Acad. Sci. USA, 2020, 117(4), 1853-1859.
[http://dx.doi.org/10.1073/pnas.1910837117] [PMID: 31932426]
[2]
Blackiston, D; Lederer, E; Kriegman, S; Garnier, S; Bongard, J; Levin, M. A cellular platform for the development of synthetic living machines. Sci. Robot., 2021, 6(52), eabf1571.
[http://dx.doi.org/10.1126/scirobotics.abf1571]
[3]
Judson, K. The edge of the organic: Philosophical issues of synthetic morphology; EasyChair, 2021.
[4]
Not-bot-not-beast-scientists-create-first-ever-livingprogrammable-organism-129980. 2021.
[5]
Version, S; Version, D; View, P; View, P. The living robot ! Partfrog, part-machine. 2020, 1-3.
[6]
Coghlan, S.; Leins, K. “Living robots”: Ethical questions about xenobots. Am. J. Bioeth., 2020, 20(5), W1-W3.
[http://dx.doi.org/10.1080/15265161.2020.1746102] [PMID: 32364479]
[7]
Levin, M.; Bongard, J.; Lunshof, J.E. Applications and ethics of computer-designed organisms. Nat. Rev. Mol. Cell Biol., 2020, 21(11), 655-656.
[http://dx.doi.org/10.1038/s41580-020-00284-z] [PMID: 32782340]
[8]
Tholl, N; Chandler, D. Molecular Love (and other facts of life) the nitty gritty science of sex and reproduction Egg jelly kicks sperm into gear in frogs. 2020, 3-5.
[9]
Schmidt, M.; Lipson, H. Age-fitness pareto optimization.In Genetic programming theory and practice VIII; Springer: New York, NY, 2011, pp. 129-146.
[10]
Deblandre, G.A.; Wettstein, D.A.; Koyano-Nakagawa, N.; Kintner, C. A two-step mechanism generates the spacing pattern of the ciliated cells in the skin of Xenopus embryos. Development, 1999, 126(21), 4715-4728.
[http://dx.doi.org/10.1242/dev.126.21.4715] [PMID: 10518489]
[11]
Werfel, J.; Petersen, K.; Nagpal, R. Designing collective behavior in a termite-inspired robot construction team. Science, 2014, 343(6172), 754-758.
[http://dx.doi.org/10.1126/science.1245842] [PMID: 24531967]
[12]
Amisha, P.M.; Malik, P.; Pathania, M.; Rathaur, V.K. Overview of artificial intelligence in medicine. J. Family Med. Prim. Care, 2019, 8(7), 2328-2331.
[http://dx.doi.org/10.4103/jfmpc.jfmpc_440_19] [PMID: 31463251]
[13]
Kovács, G.L. Artificial intelligence techniques to design robotic systems. IFAC Proceedings, 1998, 31(20), 635-644.
[http://dx.doi.org/10.1016/S1474-6670(17)41868-4]
[14]
Shende, P.; Devlekar, N.P. A review on the role of artificial intelligence in stem cell therapy: An initiative for modern medicines. Curr. Pharm. Biotechnol., 2021, 22(9), 1156-1163.
[http://dx.doi.org/10.2174/1389201021666201007122524] [PMID: 33030129]
[15]
Gross, A; Product, D. Artificial intelligence in Indian agriculture., 2020, 11-14.
[16]
Čevora, G. The relationship between biological and artificial intelligence. arXiv, 2019, 1-18.
[17]
Frohnwieser, A.; Murray, J.C.; Pike, T.W.; Wilkinson, A. Using robots to understand animal cognition. J. Exp. Anal. Behav., 2016, 105(1), 14-22.
[http://dx.doi.org/10.1002/jeab.193] [PMID: 26781049]
[18]
Mitri, S.; Wischmann, S.; Floreano, D.; Keller, L. Using robots to understand social behaviour. Biol. Rev. Camb. Philos. Soc., 2013, 88(1), 31-39.
[http://dx.doi.org/10.1111/j.1469-185X.2012.00236.x] [PMID: 22816672]
[19]
Watrers, J.; Computing, E. Scientist use AI to assemble frog cells into a’ programmable organism’. FUTURE TECH360 2020, 1-6.
[20]
Urban, J.; Císař, P.; Pautsina, A.; Soukup, J.; Bárta, A. Artificial intelligence in biology; Tech. Comput: Prague, 2013, p. 326.
[21]
Hutchison, C.A., III; Chuang, R.Y.; Noskov, V.N.; Assad-Garcia, N.; Deerinck, T.J.; Ellisman, M.H.; Gill, J.; Kannan, K.; Karas, B.J.; Ma, L.; Pelletier, J.F.; Qi, Z.Q.; Richter, R.A.; Strychalski, E.A.; Sun, L.; Suzuki, Y.; Tsvetanova, B.; Wise, K.S.; Smith, H.O.; Glass, J.I.; Mer-ryman, C.; Gibson, D.G.; Venter, J.C. Design and synthesis of a minimal bacterial genome. Science, 2016, 351(6280)
[http://dx.doi.org/10.1126/science.aad6253] [PMID: 27013737]
[22]
Sasai, Y.; Eiraku, M.; Suga, H. In vitro organogenesis in three dimensions: Self-organising stem cells. Development, 2012, 139(22), 4111-4121.
[http://dx.doi.org/10.1242/dev.079590] [PMID: 23093423]
[23]
Park, S.J.; Gazzola, M.; Park, K.S.; Park, S.; Di Santo, V.; Blevins, E.L.; Lind, J.U.; Campbell, P.H.; Dauth, S.; Capulli, A.K.; Pasqualini, F.S.; Ahn, S.; Cho, A.; Yuan, H.; Maoz, B.M.; Vijaykumar, R.; Choi, J.W.; Deisseroth, K.; Lauder, G.V.; Mahadevan, L.; Parker, K.K. Phototactic guidance of a tissue-engineered soft-robotic ray. Science, 2016, 353(6295), 158-162.
[http://dx.doi.org/10.1126/science.aaf4292] [PMID: 27387948]
[24]
Tang-Schomer, M.D.; White, J.D.; Tien, L.W.; Schmitt, L.I.; Valentin, T.M.; Graziano, D.J.; Hopkins, A.M.; Omenetto, F.G.; Haydon, P.G.; Kaplan, D.L. Bioengineered functional brain-like cortical tissue. Proc. Natl. Acad. Sci. USA, 2014, 111(38), 13811-13816.
[http://dx.doi.org/10.1073/pnas.1324214111] [PMID: 25114234]
[25]
Nawroth, J.C.; Lee, H.; Feinberg, A.W.; Ripplinger, C.M.; McCain, M.L.; Grosberg, A.; Dabiri, J.O.; Parker, K.K. A tissue-engineered jelly-fish with biomimetic propulsion. Nat. Biotechnol., 2012, 30(8), 792-797.
[http://dx.doi.org/10.1038/nbt.2269] [PMID: 22820316]
[26]
Cheney, N.; Bongard, J. SunSpiral, V.; Lipson, H. Scalable co-optimization of morphology and control in embodied machines. J. R. Soc. Interface, 2018, 15(143), 20170937.
[http://dx.doi.org/10.1098/rsif.2017.0937] [PMID: 29899155]
[27]
Lipson, H.; Pollack, J.B. Automatic design and manufacture of robotic lifeforms. Nature, 2000, 406(6799), 974-978.
[http://dx.doi.org/10.1038/35023115] [PMID: 10984047]
[28]
Bongard, J.; Zykov, V.; Lipson, H. Resilient machines through continuous self-modeling. Science, 2006, 314(5802), 1118-1121.
[http://dx.doi.org/10.1126/science.1133687] [PMID: 17110570]
[29]
Huntington, M.D.; Lauhon, L.J.; Odom, T.W. Subwavelength lattice optics by evolutionary design. Nano Lett., 2014, 14(12), 7195-7200.
[http://dx.doi.org/10.1021/nl5040573] [PMID: 25380062]
[30]
Jakobi, N. Evolutionary robotics and the radical envelope-of-noise hypothesis. Adapt. Behav., 1997, 6(2), 325-368.
[http://dx.doi.org/10.1177/105971239700600205]
[31]
Kusumoto, D.; Yuasa, S. The application of convolutional neural network to stem cell biology. Inflamm. Regen., 2019, 39(1), 14.
[http://dx.doi.org/10.1186/s41232-019-0103-3] [PMID: 31312276]
[32]
Piquereau, J.; Ventura-Clapier, R. Maturation of cardiac energy metabolism during perinatal development. Front. Physiol., 2018, 9, 959.
[http://dx.doi.org/10.3389/fphys.2018.00959] [PMID: 30072919]
[33]
Waldner, C.; Roose, M.; Ryffel, G.U. Red fluorescent Xenopus laevis: A new tool for grafting analysis. BMC Dev. Biol., 2009, 9(1), 37.
[http://dx.doi.org/10.1186/1471-213X-9-37] [PMID: 19549299]
[34]
Riddell, S.R.; Warren, E.H. Allogeneic stem cell transplantation. DeVita, Hellman, Rosenberg’s Cancer Princ. Pract. Oncol.,, 2018, 2020-2035.
[35]
Musharraf. Stem cells will now fight Parkinson’s disease – Sharing Information, Comment ADDA. Shar. Inf., 2021, 1-10.
[36]
Levin, M. The computational boundary of a “self”: Developmental bioelectricity drives multicellularity and scale-free cognition. Front. Psychol., 2019, 10, 2688.
[http://dx.doi.org/10.3389/fpsyg.2019.02688] [PMID: 31920779]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy