Developing a Suitable Model for Water Uptake for Biodegradable Polymers Using Small Training Sets.

Prediction of the dynamic properties of water uptake across polymer libraries can accelerate polymer selection for a specific application. We first built semiempirical models using Artificial Neural Networks and all water uptake data, as individual input. These models give very good correlations (R (2) > 0.

Polymer-drug interactions in tyrosine-derived triblock copolymer nanospheres: a computational modeling approach.

A combination of molecular dynamics (MD) simulations and docking calculations was employed to model and predict polymer-drug interactions in self-assembled nanoparticles consisting of ABA-type triblock copolymers, where A-blocks are poly(ethylene glycol) units and B-blocks are low molecular weight tyrosine-derived polyarylates. This new computational approach was tested on three representative model compounds: nutraceutical curcumin, anticancer drug paclitaxel and prehormone vitamin D3. Based on this methodology, the calculated binding energies of polymer-drug complexes can be correlated with maximum drug loading determined experimentally.

Prediction of Fibrinogen Adsorption for Biodegradable Polymers: Integration of Molecular Dynamics and Surrogate Modeling.

This work is a part of a series of publications devoted to the development of surrogate (semi-empirical) 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.

A new approach to the rationale discovery of polymeric biomaterials.

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.

Using surrogate modeling in the prediction of fibrinogen adsorption onto polymer surfaces.

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.