Prediction of fibrinogen adsorption for biodegradable polymers: Integration of molecular dynamics and surrogate modeling

Abstract

This work is a part of a series of publications devoted to the development of surrogate (semiempirical) models for the prediction of fibrinogen adsorption onto polymer surfaces. Since fibrinogen is one of the key proteins involved in platelet activation and the formation of thrombosis, the modeling of fibrinogen adsorption on the surface of blood contacting medical devices is of high theoretical and practical significance. We report here, for the first time, on the incorporation three-dimensional structures of polymers obtained from atomistic simulations into conventional mesoscopic-scale calculations. Low energy conformations derived from Molecular Dynamics simulations for 45 representatives of a combinatorial library of polyarylates were used in an improved modeling procedure (referred to as "3D surrogate model") instead of simplistic two-dimensional representations of polymer structures, which were used in several previous models (collectively referred to as "2D surrogate models"). In the framework of this 3D model we created 12 model sets of polymers to account for their chirality, conformational diversity and the structural influence of a solvent. For each polymer set, three-dimensional molecular descriptors were generated and then ranked with respect to the experimental fibrinogen adsorption data by means of a Monte Carlo Decision Tree. The most significant descriptors identified by Decision Tree and the experimental dataset were utilized to predict fibrinogen adsorption using an Artificial Neural Network (ANN). The best prediction achieved by the 3D surrogate model demonstrated a noticeable improvement in the predictive quality as compared to the previously used 2D model (as evidenced by the increase in the average Pearson correlation coefficient from 0.67±0.13 to 0.54±0.12). The predictive quality of the 3D surrogate model compares favorably with the best results previously reported for extended 2D model that combines an ANN with Partial Least Squares (PLS) regression and principal component (PC) analysis. The significance of the newly developed 3D model is that it allows high accuracy prediction of fibrinogen adsorption without the need for experimentally derived descriptors and it has better predictive quality than the original 2D surrogate model due to utilization of realistic polymer representations.

Computational Methods for the Development of Polymeric Biomaterials

“…Among the adhesive proteins in plasma, fibrinogen is the most abundant and therefore it has been proposed as a marker for a polymer's tendency to support the attachment and activation of blood platelets. [79] Clearly, if the laborious experimental work necessary to synthesize the polymer, fully characterize it and assay its performance first in vitro and then in vivo could be avoided by faster and less expensive in silico studies based on the chemical structure of the polymer, it will be a tremendous help for the development of the field. The results and the computational approaches used so far in the field are summarized below.…”

“…Later on, the work was continued by Gubskaya et al [79] to include 3-D descriptors calculated following relaxation of the polymers to a minimum energy configuration using MD. The main significance of this new model is the use of a more realistic representation of the polymers structures.…”

“…Validation sets for ANN built for polymer structures minimized in water (only L-isomers are included), taken from Ref. [79] .…”

Combinatorial and High‐Throughput Methods in Macromolecular Materials Research and Development

“…[116][117][118][119] A model using three-dimensional molecular descriptors for the polymer molecular structure demonstrated good predictive capability. [171] Predictive models such as this can be used to direct future experiments and identify promising compositions that may have not been synthesized in the initial experiments. Unfortunately, while this level of modeling and data analysis should be a part of most combinatorial studies, it has not yet been widely adopted.…”

Logical Analysis of Data in Structure‐Activity Investigation of Polymeric Gene Delivery

“…Kohn and co-workers [13-16] have demonstrated in several publications that quantitative structure-activity relationship (QSAR) data modeling, described as surrogate modeling, is a useful tool for prediction of both protein adsorption on, and cellular response to, the surface of polymeric materials. Moreover, surrogate modeling may serve as a guide for the rational design of candidate materials for further investigation and focused applications.…”