Mining for osteogenic surface topographies: In silico design to in vivo osseo-integration

Biomaterials

Volumn 137, Issue , 2017, Pages 49-60

  Hulshof, Frits F B ^a,b   Papenburg, Bernke ^c   Vasilevich, Aliaksei ^b   Hulsman, Marc ^d   Zhao, Yiping ^c   Levers, Marloes ^c   Fekete, Natalie ^c   de Boer, Meint ^f   Yuan, Huipin ^g   Singh, Shantanu ^e   Beijer, Nick ^b   Bray, Mark Anthony ^e   Logan, David J ^e   Reinders, Marcel ^d   Carpenter, Anne E ^e   van Blitterswijk, Clemens ^b   Stamatialis, Dimitrios ^a   de Boer, Jan ^b  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b MAASTRICHT UNIVERSITY   (Netherlands)

^c Materiomics bv   (Netherlands)

^d DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

^e BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^f UNIVERSITY OF TWENTE   (Netherlands)

^g XPand Biotechnology BV   (Netherlands)

Author keywords

Bone implants; Computational modeling; Differentiation; High throughput screening; Micro fabrication; Surface topography

Indexed keywords

BONE; CELL CULTURE; DIFFERENTIATION (CALCULUS); STEM CELLS; TOPOGRAPHY;

BONE IMPLANT; BONE MARROW-DERIVED MESENCHYMAL STEM CELLS; COMPUTATIONAL MODEL; HIGH THROUGHPUT SCREENING; OSTEOGENIC DIFFERENTIATION; OSTEOGENIC POTENTIAL; PHYSICOCHEMICAL PARAMETERS; SUBSTRATE PARAMETERS;

SURFACE TOPOGRAPHY;

ALKALINE PHOSPHATASE BONE ISOENZYME; BIOMATERIAL; NANOMATERIAL; TITANIUM;

ANIMAL TISSUE; ARTICLE; BONE DEVELOPMENT; BONE MARROW DERIVED MESENCHYMAL STEM CELL; CELL DIFFERENTIATION; CELL SHAPE; COMPUTER MODEL; CONTROLLED STUDY; FEMALE; FEMUR; HUMAN; HUMAN CELL; IN VITRO STUDY; IN VIVO STUDY; NONHUMAN; OSSEOINTEGRATION; PRIORITY JOURNAL; PROTEIN EXPRESSION; RABBIT MODEL; SURFACE PROPERTY; ANIMAL; BONE PROSTHESIS; BONE REGENERATION; CELL ADHESION; CELL CULTURE; CELL PROLIFERATION; CHEMISTRY; COMPUTER SIMULATION; LEPORIDAE; MECHANICS; MESENCHYMAL STROMA CELL; PHYSIOLOGY; PROCEDURES; TISSUE ENGINEERING; TISSUE SCAFFOLD;

ANIMALS; BONE REGENERATION; BONE SUBSTITUTES; CELL ADHESION; CELL PROLIFERATION; CELLS, CULTURED; COMPUTER SIMULATION; FEMALE; HUMANS; MECHANICAL PHENOMENA; MESENCHYMAL STROMAL CELLS; NANOSTRUCTURES; OSTEOGENESIS; RABBITS; SURFACE PROPERTIES; TISSUE ENGINEERING; TISSUE SCAFFOLDS; TITANIUM;

EID: 85019837582     PISSN: 01429612     EISSN: 18785905     Source Type: Journal    
DOI: 10.1016/j.biomaterials.2017.05.020     Document Type: Article

References (41)

1. [1] Drees, P., Eckardt, A., Gay, R.E., Gay, S., Huber, L.C., Mechanisms of disease: molecular insights into aseptic loosening of orthopedic implants. Nat. Clin. Pract. Rheumatol., 3, Mar, 2007, 165.
2. [2] Gao, S.S., Zhang, Y.R., Zhu, Z.L., Yu, H.Y., Micromotions and combined damages at the dental implant/bone interface. Int. J. Oral Sci., 4, Dec, 2012, 182.
3. [3] Engler, A.J., Sen, S., Sweeney, H.L., Discher, D.E., Matrix elasticity directs stem cell lineage specification. Cell, 126, Aug 25, 2006, 677.
4. [4] Mei, Y., et al. Combinatorial development of biomaterials for clonal growth of human pluripotent stem cells. Nat. Mater., 9, Sep, 2010, 768.
5. [5] Dalby, M.J., et al. The control of human mesenchymal cell differentiation using nanoscale symmetry and disorder. Nat. Mater., 6, Dec, 2007, 997.
6. [6] Murphy, W.L., McDevitt, T.C., Engler, A.J., Materials as stem cell regulators. Nat. Mater., 13, Jun, 2014, 547.
7. [7] Lovmand, J., et al. The use of combinatorial topographical libraries for the screening of enhanced osteogenic expression and mineralization. Biomaterials, 30, Apr, 2009, 2015.
8. [8] Kholodovych, V., et al. Prediction of biological response for large combinatorial libraries of biodegradable polymers: polymethacrylates as a test case. Polymer, 2008, 2435–2439.
9. [9] Kholodovych, V., et al. Accurate predictions of cellular response using QSPR: a feasibility test of rational design of polymeric biomaterials. Polymer, 7367, 2004.
10. [10] Kohn, J., Welsh, W.J., Knight, D., A new approach to the rationale discovery of polymeric biomaterials. Biomaterials, 28, Oct, 2007, 4171.
11. [11] Smith, J.R., Kholodovych, V., Knight, D., Welsh, W.J., Kohn, J., QSAR models for the analysis of bioresponse data from combinatorial libraries of biomaterials. QSAR Comb. Sci., 24, 2005, 99.
12. [12] Hook, A.L., et al. Combinatorial discovery of polymers resistant to bacterial attachment. Nat. Biotechnol., 30, Sep, 2012, 868.
13. [13] Hulsman, M., et al. Analysis of high-throughput screening reveals the effect of surface topographies on cellular morphology. Acta biomater., 15, Mar, 2015, 29.
14. [14] A. D. R. E. o. K. D. i. t. U. S. N. I. o. H, United States Renal Data System. 2014, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.
15. [15] Hessle, L., et al. Tissue-nonspecific alkaline phosphatase and plasma cell membrane glycoprotein-1 are central antagonistic regulators of bone mineralization. Proc. Natl. Acad. Sci. U. S. A., 99, Jul 09, 2002, 9445.
16. [16] Carpenter, A.E., et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol., 7, 2006, R100.
17. [17] Ronold, H.J., Ellingsen, J.E., The use of a coin shaped implant for direct in situ measurement of attachment strength for osseointegrating biomaterial surfaces. Biomaterials, 23, May, 2002, 2201.
18. [18] Monjo, M., Lamolle, S.F., Lyngstadaas, S.P., Ronold, H.J., Ellingsen, J.E., In vivo expression of osteogenic markers and bone mineral density at the surface of fluoride-modified titanium implants. Biomaterials, 29, Oct, 2008, 3771.
19. [19] Sennerby, L., Persson, L.G., Berglundh, T., Wennerberg, A., Lindhe, J., Implant stability during initiation and resolution of experimental periimplantitis: an experimental study in the dog. Clin. Implant Dent. Relat. Res., 7, 2005, 136.
20. [20] Roccuzzo, M., De Angelis, N., Bonino, L., Aglietta, M., Ten-year results of a three-arm prospective cohort study on implants in periodontally compromised patients. Part 1: implant loss and radiographic bone loss. Clin. Oral Implants Res., 21, May, 2010, 490.
21. [21] Rape, A.D., Zibinsky, M., Murthy, N., Kumar, S., A synthetic hydrogel for the high-throughput study of cell-ECM interactions. Nat. Commun., 6, Sep 09, 2015, 8129.
22. [22] Celiz, A.D., Harrington, H.C., Hook, A.L., High throughput assessment and chemometric analysis of the interaction of epithelial and fibroblast cells with a polymer library. Appl. Surf. Sci., 313, 9/15/, 2014, 926.
23. [23] Li, X., et al. Micro-scaffold array chip for upgrading cell-based high-throughput drug testing to 3D using benchtop equipment. Lab Chip, 14, Feb 07, 2014, 471.
24. [24] Gobaa, S., et al. Artificial niche microarrays for probing single stem cell fate in high throughput. Nat. methods, 8, Nov, 2011, 949.
25. [25] Unadkat, H.V., et al. An algorithm-based topographical biomaterials library to instruct cell fate. Proc. Natl. Acad. Sci. U. S. A., 108, Oct 4, 2011, 16565.
26. [26] Yuan, H., et al. Osteoinductive ceramics as a synthetic alternative to autologous bone grafting. Proc. Natl. Acad. Sci. U. S. A., 107, Aug 3, 2010, 13614.
27. [27] Barradas, A.M., Yuan, H., van Blitterswijk, C.A., Habibovic, P., Osteoinductive biomaterials: current knowledge of properties, experimental models and biological mechanisms. Eur. cells Mater., 21, May 15, 2011, 407.
28. [28] Davies, J.E., Understanding peri-implant endosseous healing. J. Dent. Educ., 67, Aug, 2003, 932.
29. [29] Silverwood, R.K., et al. Analysis of osteoclastogenesis/osteoblastogenesis on nanotopographical titania surfaces. Adv. Healthc. Mater., 5, Apr 20, 2016, 947.
30. [30] Groen, N., et al. Exploring the material-induced transcriptional landscape of osteoblasts on bone graft materials. Adv. Healthc. Mater., 4, Aug 05, 2015, 1691.
31. [31] Olivares-Navarrete, R., et al. Mediation of osteogenic differentiation of human mesenchymal stem cells on titanium surfaces by a Wnt-integrin feedback loop. Biomaterials, 32, Sep, 2011, 6399.
32. [32] Lohmann, C.H., et al. Surface roughness modulates the response of MG63 osteoblast-like cells to 1,25-(OH)(2)D(3) through regulation of phospholipase A(2) activity and activation of protein kinase A. J. Biomed. Mater. Res., 47, Nov, 1999, 139.
33. [33] Aragona, M., et al. A mechanical checkpoint controls multicellular growth through YAP/TAZ regulation by actin-processing factors. Cell, 154, 2013, 1047.
34. [34] Li, Y., et al. Biophysical regulation of histone acetylation in mesenchymal stem cells. Biophys. J., 100, Apr 20, 2011, 1902.
35. [35] de Boer, J., et al. Inhibition of histone acetylation as a tool in bone tissue engineering. Tissue Eng., 12, Oct, 2006, 2927.
36. [36] Therneau, T., rpart: Recursive Partitioning and Regression Trees. 2014.
37. [37] R. Core-Team, A Language and Environment for Statistical Computing. 2014, R Foundation for Statistical Computing http://www.R-project.org.
38. [38] Hornik, K., Hothorn, T., Zeileis, A., Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph Stat., 2006, 651–674.
39. [39] W. A. L. A. M, Classification and regression by randomForest. RNews, December, 2002, 18–22.
40. [40] Kuhn, M., Caret: Classification and Regression Training R Package Version 6.0-37. 2014 http://CRAN.R-project.org/package=caret.
41. [41] R.S.J, Abramoff, M.P., Image processing with ImageJ. Biophot. Int. 11:issue 7 (2004), 36–42.