Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-19T20:10:37.339Z Has data issue: false hasContentIssue false
  • English
  • Français

Using Non-linear Regression to Predict Bioresponse in a Combinatorial Library of Biodegradable Polymers

Published online by Cambridge University Press:  01 February 2011

Jack R. Smith ,
Doyle Knight ,
Joachim Kohn ,
Khaled Rasheed ,
Norbert Weber  and
Sascha Abramson
Jack R. Smith
Affiliation:
Department of Chemistry and Chemical Biology and the New Jersey Center for Biomaterials Rutgers, The State University of New Jersey, New Brunswick, NJ 09803
Doyle Knight
Affiliation:
Department of Mechanical and Aerospace Engineering and Center for Computational Design Rutgers, The State University of New Jersey, New Brunswick, NJ 09803
Joachim Kohn
Affiliation:
Department of Chemistry and Chemical Biology and the New Jersey Center for Biomaterials Rutgers, The State University of New Jersey, New Brunswick, NJ 09803
Khaled Rasheed
Affiliation:
Department of Computer Science, The University of Georgia, Athens, GA 30602
Norbert Weber
Affiliation:
Department of Chemistry and Chemical Biology and the New Jersey Center for Biomaterials Rutgers, The State University of New Jersey, New Brunswick, NJ 09803
Sascha Abramson
Affiliation:
Department of Chemistry and Chemical Biology and the New Jersey Center for Biomaterials Rutgers, The State University of New Jersey, New Brunswick, NJ 09803
Get access

Abstract

We have developed an empirical method to model bioresponse to the surfaces of biodegradable polymers in a combinatorial library using Artificial Neural Networks (ANN) in conjunction with molecular modeling and machine learning methodology. We validated the procedure by modeling human fibrinogen adsorption to 22 structurally distinct polymers. Subsequently, the method was used to model the more complicated phenomena of rat lung fibroblast and normal human fetal foreskin fibroblast proliferation in the presence of 24 and 44 different polymers, respectively. In each case, the root mean square (rms) percent error of the prediction was substantially less than the experimental variation, showing that the models can distinguish high and low performing polymers based on structure/property information. Using this method to screen candidate materials in terms of specific bioresponse prior to extensive experimental testing will greatly facilitate materials development for biomedical applications.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 804: Symposium JJ – Combinatorial and Artificial Intelligence Methods in Materials Science II , 2003 , JJ5.7
Copyright
Copyright © Materials Research Society 2004

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1 Wiechert, W., J. Biotechnol., 94(1), 37 (2002).Google Scholar
2 Hann, M.M., Leach, A.R. and Harper, G., J. Chem. Inf. Comput. Sci., 41(3), 856 (2001).Google Scholar
3 Brocchini, S., James, K., Tangpasuthadol, V., Kohn, J., J. Amer. Chem. Soc., 119, 4553 (1997)Google Scholar
4 Brocchini, S., James, K., Tangpasuthadol, V., Kohn, J., J. Biomed. Mater. Res., 42, 66 (1998).Google Scholar
5 Ertel, S.I., Kohn, J., J. Biomed. Mater. Res., 28, 919 (1994).Google Scholar
6 Weber, N., Bolikal, D., Bourke, S. L., Kohn, J., J. Biomed. Mater. Res. 2003 (in press).Google Scholar
7 MOE (The Molecular Operating Environment) Version 2003.02, software available from Chemical Computing Group Inc., 1010 Sherbrooke Street West, Suite 910, Montreal, Canada H3A 2R7. http://www.chemcomp.com.Google Scholar
8 http://www.disat.unimib.it/chm/Dragon.htm Google Scholar
9 C5.0, RuleQuest Research Pty Ltd, 30 Athena Avenue, St Ives NSW 2075, Australia. http://www.rulequest.com/see5-info.html Google Scholar
10 Mitchell, T.M., “Machine Learning”, McGraw-Hill, New York 1997.Google Scholar
11 Hertz, J., Palmer, R.G., Krogh, A., “Introduction to the Theory of Neural Computation”, Vol. 1. Addison-Wesley Publishing Co., 1991.Google Scholar
12 Rasheed, K., Hirsh, H., Gelsey, A., Artificial Intelligence in Engineering, 11 (3), 295 (1997).Google Scholar
13 Labute, P., “MOE LogP(Octanol/Water) Model”, (unpublished).Google Scholar
14 Wildman, S.A., Crippen, G.M., J. Chem. Inf. Comput. Sci,. 39(5), 868 (1999).Google Scholar
15 Burden, F.R., Quant. Struct.-Act. Rel., 16, 309 (1997).Google Scholar