Parallel Synthesis and Quantitative Structure–Activity Relationship (QSAR) Modeling of Aminoglycoside-Derived Lipopolymers for Transgene Expression

Abstract

We describe the parallel synthesis of lipopolymers generated by conjugating alkanoyl chlorides to polymers derived from aminoglycoside antibiotic monomers as novel vehicles for transgene delivery and expression in mammalian cells. Parallel screening of lipopolymers led to the identification of six leads that demonstrated higher transgene expression efficacies in several cancer cells, when compared to the parental polymers as well as 25 kDa poly(ethylene imine), a current standard for polymer-mediated transgene expression. Quantitiative structure− activity relationship (QSAR)-based cheminformatics modeling was employed in order to investigate the role of lipopolymer physicochemical properties (molecular descriptors) on transgene expression efficacy. The predictive ability of the QSAR model, investgated using lipopolymers not employed for training the model, demonstrated excellent agreement with experimentally observed transgene expression. Our findings indicate that lipid substitution on aminoglycoside-derived polymers results in high levels of transgene expression compared to unsubstituted polymers. Taken together, these materials show significant promise in nonviral transgene delivery with several applications in biotechnology and medicine.

Folate receptor‐targeted aminoglycoside‐derived polymers for transgene expression in cancer cells

“…In addition, folate‐targeted therapeutics have been investigated in clinical trials, which indicates the translational promise of this strategy . We have previously demonstrated that aminoglycoside‐derived polymers and lipopolymers demonstrated effective levels of transgene expression . Although aminoglycoside antibiotics can be nephrotoxic in their monomeric form, it is not clear if polymers derived from aminoglycosides demonstrate this activity.…”

“…66 We have previously demonstrated that aminoglycoside-derived polymers and lipopolymers demonstrated effective levels of transgene expression. [36][37][38] Although aminoglycoside antibiotics can be nephrotoxic in their monomeric form, it is not clear if polymers derived from aminoglycosides demonstrate this activity. Given the high efficacy of these polymers, we hypothesized that conjugating ligands that can bind receptors on cancer cells can lead to targeted delivery vehicles.…”

“…Given their chemical diversity, we employed aminoglycosides as starting materials for the generation of bioseparation ligands, DNA‐binding ligands, and microbeads . Recently, we employed parallel synthesis and chemical informatics modeling for the generation and rapid identification of aminoglycoside‐derived polymers and lipopolymers for delivering transgenes to cancer cells . These studies have led to the identification of polymers and lipopolymers that demonstrate higher efficacies for transgene expression than commonly used reagents (e.g., branched pEI).…”

Modeling of Glass Transition Temperatures for Polymeric Coating Materials: Application of QSPR Mixture‐based Approach

“…QSPR approach is based on assumption that there is a mathematical relationship between a target activity (or property) of a chemical and its experimental or theoretical parameters (i. e. “descriptors”) . There is a limited number of research articles related to QSPR modeling of polymers . In these studies, researchers represent structures of polymers as minimal repeating units (monomers).…”

On the Virtues of Automated Quantitative Structure–Activity Relationship: The New Kid on the Block

“…Over half a century later and thousands of papers disclosing applications in numerous areas including medicinal chemistry, [3][4][5] environmental, [6][7][8][9] food, [10][11][12] and material [13][14][15][16] sciences, as well as regulatory [17][18][19] across academy, industry, and government, QSAR has proved an invaluable tool for experts and non-experts alike in identifying patterns in datasets and assisting in the process of making quantitative predictions [20][21]. In drug discovery, QSAR represents a ligand-based method, which generates models by building relationships between structural information or properties for a set of compounds and related experimental data of interest (e.g., biological activity) as the target property [22][23].…”