Methacrylate polymers are commonly used in many biomedical applications and theoretically several thousand polymers are possible with different side chain groups attached to the methacrylate backbone. These side chains confer a broad range of physical, chemical, mechanical and biological characteristics to the members of this polymer library. A rapid computational screening method to identify a specific member from this polymer library for a given application would be of great value to biomaterial researchers and industry. The Kohn Lab has developed quantitative structure-performance relationship (QSPR) models as such a screening tool and has successfully validated this tool using methacrylate library as an example. To make models valid across broadly structurally diverse members of this library, 33 side chains were chosen. This allows studying several thousand polymers. Firstly, 33 homopolymers can be synthesized using each monomer alone. Combinations of all 33 homopolymers in 50:50, 25:75, and 75:25 manner lead to more than three thousand copolymers. Terpolymers with composition 33:33:33 will result in more than thirty thousand polymers. Thus, the total number of polymers in the library becomes nearly 40,000 with a wide range of physical, mechanical, chemical and biological properties .
The Kohn Lab has, since its inception, championed the Combinatorial Computational Method (CCM) that unites combinatorial synthesis, rapid screening, and computational modeling in a biomaterial discovery tool. In this integrated approach a virtual library is formulated with a number of related monomer repeat units. The library includes all possible homo-, co-, and terpolymers. It is time consuming to run biological screening techniques for an entire library. Therein lies the advantage of using computational tools. Comprehensive and detailed computational tools, (e.g. ab initio quantum calculations, atomistic or molecular mechanics) cannot be implemented for a large library of polymers. Rather, the semi-empirical, quantitative structure property relationship (QSPR) method, which is widely applied in the pharmaceutical industry, can be used with reasonable accuracy. A representative number of polymers are synthesized from the library, and physical and biological assays are carried out. Using these properties QSPR technique is employed to develop a predictive model for the property of interest, and based on the comparison of predicted and experimental values, the validity of the model is assessed.
Such predictive models were developed and validated for glass transition temperature, fibrinogen adsorption, cell attachment and cell proliferation of the methacrylate library. Figure 1 shows the 33 side chains that were used to synthesize over 150 homo-, co- and terpolymers in the Kohn Lab parallel synthesizer. These polymers were then characterized by measuring glass transition temperature, fibrinogen adsorption, cell attachment and cell proliferation using standard methods. These data were used to build computational models that were validated by predicting the data for a validation set of polymers. This study and its conclusions are being disseminated in a peer reviewed publication, and this site will be updated with the results from this study in the near future.
.jpg)
|
Figure 1. Monomers of methacrylate polymers |
Our Science