Small changes in the polymer structure influence the adsorption behavior of fibrinogen on polymer surfaces: Validation of a new rapid screening technique

Weber, Norbert; Bolikal, Durgadas; Bourke, Sharon L.; Kohn, Joachim

doi:10.1002/jbm.a.20086

Cited by 65 publications

(64 citation statements)

References 23 publications

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“…First, MD simulations were carried out for a library of 45 structurally related polymers of polyarylates library for which experimental fibrinogen adsorption data were previously obtained [21]. Next, 3D descriptors were calculated for molecular structures obtained from (a) preliminary (local) energy minimization, (b) MD simulations in vacuum, and (c) MD simulations in implicit water.…”

Section: Discussionmentioning

confidence: 99%

“…All results are reported in detail in [21]. The polymers were prepared using a solvent casting procedure that can be briefly described as follows.…”

Section: Experimental Methodsmentioning

confidence: 99%

“…After dissolving polymers in 5%(w/v) methylene chloride, the polymer solutions were filtered through 0.45 μm PTFE filters (Whatman Inc., Clifton, NJ). Then, the polymer solutions were placed on the plates with individual polypropylene microtiter wells and dried in a temperature-controlled oven at 50°C for 3h to evaporate methylene chloride and to produce thin and smooth polymer films inside the wells [21]. The immunofluorescence assay protocol includes two incubations steps followed by fluorescence measurements performed using a Spectra Max Gemini fluorescence reader (Molecular Devices, Sunnyvale, CA).…”

Section: Experimental Methodsmentioning

confidence: 99%

“…Subsequently, the rinsing procedure was repeated 3 times and a background fluorescence measurement was taken. Fluorescently labeled antibodies were prepared [21] and allowed to bind to fibrinogen adsorbed onto polymer surfaces for 1.5 h at 37°C. Then the final fluorescence measurements were carried out with human FA to non-coated propylene used as an internal control.…”

Section: Experimental Methodsmentioning

confidence: 99%

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

Gubskaya

Kholodovych

Knight

et al. 2007

Polymer

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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.

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Section: Discussionmentioning

confidence: 99%

“…All results are reported in detail in [21]. The polymers were prepared using a solvent casting procedure that can be briefly described as follows.…”

Section: Experimental Methodsmentioning

confidence: 99%

Section: Experimental Methodsmentioning

confidence: 99%

Section: Experimental Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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

Gubskaya

Kholodovych

Knight

et al. 2007

Polymer

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“…Several laboratories are now developing accelerated screening assays for protein adsorption and cell growth [39], transfection efficacy [40], and the effect of substratum chemistry on stem cell differentiation [41]. In addition, there are significantly more rapid material test platforms available that do come close to the "high throughput" concept as it is understood in the pharmaceutical industry.…”

Section: Rapid Screening and High-throughput Evaluationmentioning

confidence: 99%

A new approach to the rationale discovery of polymeric biomaterials

Kohn

Welsh²,

Knight

2007

Biomaterials

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This paper attempts to illustrate both the need for new approaches to biomaterials discovery as well as the significant promise inherent in the use of combinatorial and computational design strategies. The key observation of this Leading Opinion Paper is that the biomaterials community has been slow to embrace advanced biomaterials discovery tools such as combinatorial methods, high throughput experimentation, and computational modeling in spite of the significant promise shown by these discovery tools in materials science, medicinal chemistry and the pharmaceutical industry. It seems that the complexity of living cells and their interactions with biomaterials has been a conceptual as well as a practical barrier to the use of advanced discovery tools in biomaterials science. However, with the continued increase in computer power, the goal of predicting the biological response of cells in contact with biomaterials surfaces is within reach. Once combinatorial synthesis, high throughput experimentation, and computational modeling are integrated into the biomaterials discovery process, a significant acceleration is possible in the pace of development of improved medical implants, tissue regeneration scaffolds, and gene/drug delivery systems.

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