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Using Surrogate Modeling in the Prediction of Fibrinogen Adsorption onto Polymer Surfaces

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    Using Surrogate Modeling in the Prediction of Fibrinogen Adsorption onto Polymer Surfaces
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    Department of Chemistry and Chemical Biology and the New Jersey Center for Biomaterial, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08854, Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey, New Brunswick, New Jersey 08903, Department of Computer Science, The University of Georgia, Athens, Georgia 30602, and Department of Pharmacology, University of Medicine & Dentistry of New Jersey (UMDNJ), Robert Wood Johnson Medical School and the Informatics Institute of UMDNJ, Piscataway, New Jersey 08854
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    Cite this: J. Chem. Inf. Comput. Sci. 2004, 44, 3, 1088–1097
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    https://doi.org/10.1021/ci0499774
    Published May 7, 2004
    Copyright © 2004 American Chemical Society
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    Abstract

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    We present a Surrogate (semiempirical) Model for prediction of protein adsorption onto the surfaces of biodegradable polymers that have been designed for tissue engineering applications. The protein used in these studies, fibrinogen, is known to play a key role in blood clotting. Therefore, fibrinogen adsorption dictates the performance of implants exposed to blood. The Surrogate Model combines molecular modeling, machine learning and an Artificial Neural Network. This novel approach includes an accounting for experimental error using a Monte Carlo analysis. Briefly, measurements of human fibrinogen adsorption were obtained for 45 polymers. A total of 106 molecular descriptors were generated for each polymer. Of these, 102 descriptors were computed using the Molecular Operating Environment (MOE) software based upon the polymer chemical structures, two represented different monomer types, and two were measured experimentally. The Surrogate Model was developed in two stages. In the first stage, the three descriptors with the highest correlation to adsorption were determined by calculating the information gain of each descriptor. Here a Monte Carlo approach enabled a direct assessment of the effect of the experimental uncertainty on the results. The three highest-ranking descriptors, defined as those with the highest information gain for the sample set, were then selected as the input variables for the second stage, an Artificial Neural Network (ANN) to predict fibrinogen adsorption. The ANN was trained using one-half of the experimental data set (the training set) selected at random. The effect of experimental error on predictive capability was again explored using a Monte Carlo analysis. The accuracy of the ANN was assessed by comparison of the predicted values for fibrinogen adsorption with the experimental data for the remaining polymers (the validation set). The mean value of the Pearson correlation coefficient for the validation data sets was 0.54 ± 0.12. The average root-mean-square (relative) error in prediction for the validation data sets is 38%. This is an order of magnitude less than the range of experimental values (i.e., 366%) and compares favorably with the average percent relative standard deviation of the experimental measurements (i.e., 17.9%). The effects of each of the user-defined parameters in the ANN were explored. None were observed to have a significant effect on the results. Thus, the Surrogate Model can be used to accurately and unambiguously identify polymers whose fibrinogen absorption is at the limits of the range (i.e., low or high) which is an essential requirement for assessing polymers for regenerative tissue applications.

    Copyright © 2004 American Chemical Society

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     Corresponding author phone:  (732)445-4351; fax:  (732)445-5006, e-mail:  [email protected]. Corresponding author address:  Department of Chemistry and Chemical Biology, 610 Taylor Rd, Piscataway, NJ 08854-8087.

    †

     Department of Chemistry and Chemical Biology and the New Jersey Center for Biomaterial, Rutgers, The State University of New Jersey.

    ‡

     Department of Mechanical and Aerospace Engineering, Rutgers, The State University of New Jersey.

    §

     The University of Georgia.

    ‖

     Robert Wood Johnson Medical School and the Informatics Institute of UMDNJ.

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    Cite this: J. Chem. Inf. Comput. Sci. 2004, 44, 3, 1088–1097
    Click to copy citationCitation copied!
    https://doi.org/10.1021/ci0499774
    Published May 7, 2004
    Copyright © 2004 American Chemical Society
    Request reuse permissions

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