Integration of Combinatorial Synthesis, Rapid Screening, and Computational Modeling in Biomaterials Development

Abstract

Summary: The advent of high‐throughput combinatorial synthesis techniques in drug discovery has stimulated efforts to apply these techniques to the discovery of biomaterials. To be of practical utility, combinatorial approaches to biomaterials design require (i) the availability of parallel synthesis techniques to generate libraries of polymers, (ii) efficient assays for the rapid characterization of biorelevant material properties, and (iii) computational methods to efficiently model different biological responses in the presence of polymers. Here we report the integration of these methodologies and illustrate the potential of this approach to accelerate the development of new biomaterials. The parallel synthesis of a library of 112 biodegradable polyarylates has been reported previously. This library was used to develop efficient screening techniques to determine biorelevant polymer properties (fibrinogen adsorption, gene expression in macrophages, growth of fetal rat lung fibroblasts (RLFs)). A Surrogate (semiempirical) Model was developed (i) to determine molecular‐scale polymer properties that correlate to various biological responses, and (ii) to predict fibrinogen adsorption and RLF growth on polymeric surfaces. For 38 out of 45 polymers, the model predicted the amount of fibrinogen adsorbed correctly within the error of the experimental measurements. The growth of rat lung fibroblasts was correctly predicted by the model for 41 out of 48 polymers. The correlation factor between the model's predicted values and the experimentally determined data was 0.54 ± 0.09 and 0.69 ± 0.12 for fibrinogen adsorption and RLF growth, respectively. The results presented here demonstrate the utility of combinatorial and computational approaches for the rational design of polymers for biomedical applications.Design of the library of polyarylates, which are copolymers of a diacid and a diphenol. Chemical diversity was created by variations in the structure of the diacid (marked as “Y”) and the pendent chain (marked as “R”).magnified imageDesign of the library of polyarylates, which are copolymers of a diacid and a diphenol. Chemical diversity was created by variations in the structure of the diacid (marked as “Y”) and the pendent chain (marked as “R”).

Poly(2‐oxazoline)s: Alive and Kicking

“…[7][8][9] High-throughput experimentation methods have also been applied by other research groups to determine structure-property relationships in, e.g., polymeric sensor materials, [10] crosslinked materials, [11] and biomaterials. [12] In our studies, we have chosen the cationic ring-opening polymerization of 2-oxazolines, which was first reported in 1966 by four independent research groups, [13][14][15][16] for the library preparation. The polymerization of 2-oxazolines is known to proceed by a living mechanism under the appropriate polymerization conditions that suppress the occurrence of chaintransfer and chain-termination reactions.…”

A new approach to the rationale discovery of polymeric biomaterials

“…It is difficult to discover such optimized polymer structures by a random walk through design space. In fact, the more complex the polymer structure the greater the need for a systematic discovery process that implements a computational modeling component to reduce the time and cost associated with exhaustive testing of hundreds of candidate polymer structures [35].…”

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

“…We have recently reported on the design of surrogate (semi-empirical) models [13][14][15] for the prediction of cellular response to and fibrinogen adsorption on the surfaces of tyrosine-derived polyarylates synthesized in Kohn's research group [16][17][18]. In these studies, the polyarylates served as a "test case" for the validation of the fibrinogen adsorption models -the methodology is quite general and can be readily adopted to other polymer libraries.…”

“…Starting from the pioneering work of Smith and coauthors [13] it has become an ideal dataset for calibrating and testing computational models for prediction of bioresponse [14,15,[19][20]. The library comprises 112 structurally related alternating co-polymers (see Fig.…”