Quantitative Structure-Activity Relationship (QSAR) / Quantitative Structure-Property Relationship (QSPR) modeling of aminoglycoside antibiotics-derived polymers for transgene delivery and expression is described. Fifty-six polymers were synthesized using combinatorial polymerization of aminoglycosides with diglycidyl ether cross-linkers. The transfection efficacy and related polymer/polyplex properties were experimentally screened and measured. The role of physicochemical properties and structures in determining efficacy of transgene expression and polymer/polyplex properties were investigated using QSAR/QSPR models based on multiple machine learning algorithms and 'building block' polymer structures. The QSAR/QSPR models were found to exhibit high predictive capabilities. Investigation of descriptors utilized in  the models suggested important physicochemical properties of both aminoglycoside and cross-linkers that contribute to the polymer/polyplex properties and their transfection efficacies of the aminoglycoside-based polycations.  A �two-step modeling� process was introduced based on two major assumptions: 1) descriptors calculated based on the structure simplification of the polymers have a direct influence on polymers physicochemical properties, such as hydrophobicity, DNA binding, molecular weight, etc.; and 2) those physicochemical properties further affect the transgene expression of the corresponding polymers. This process improved the prediction accuracy of the final model, and also helped to understand the gene delivery mechanism for aminoglycoside-based polycation vectors. The Support Vector Regression-based Online Learning Equipment (SOLE) platform was established to support this project. SOLE has a friendly user interface and includes routine elements of performing QSAR/QSPR studies that can be applied in various research areas. The SOLE web tool is now available to the public on RECCR website at http://reccr.chem.rpi.edu/SOLE/index.html.

Discovery of antibiotics-derived polymer gene delivery vectors using QSAR/QSPR modeling

248th National Meeting (2014)

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