ABSTRACT
The objectives of this work were: (1) to select suitable compositions of tyrosine-derived polycarbonates for controlled delivery of voclosporin, a potent drug candidate to treat ocular diseases, (2) to establish a structure-function relationship between key molecular characteristics of biodegradable polymer matrices and drug release kinetics, and (3) to identify factors contributing in the rate of drug release. For the first time, the experimental study of polymeric drug release was accompanied by a hierarchical sequence of three computational methods. First, suitable polymer compositions used in subsequent neural network modeling were determined by means of response surface methodology (RSM). Second, accurate artificial neural network (ANN) models were built to predict drug release profiles for fifteen polymers located outside the initial design space. Finally, thermodynamic properties and hydrogen-bonding patterns of model drug-polymer complexes were studied using molecular dynamics (MD) technique to elucidate a role of specific interactions in drug release mechanism. This research presents further development of methodological approaches to meet challenges in the design of polymeric drug delivery systems.
ABSTRACT
To date semi-empirical or surrogate modeling has demonstrated great success in the prediction of the biologically relevant properties of polymeric materials. For the first time, a correlation between the chemical structures of poly(ß-amino esters) and their efficiency in transfecting DNA was established using the novel technique of logical analysis of data (LAD). Linear combination and explicit representation models were introduced and compared in the framework of the present study. The most successful regression model yielded satisfactory agreement between the predicted and experimentally measured values of transfection efficiency (Pearson correlation coefficient, 0.77; mean absolute error, 3.83). It was shown that detailed analysis of the rules provided by the LAD algorithm offered practical utility to a polymer chemist in the design of new biomaterials.
ABSTRACT
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. 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.
