No abstract

New therapies are needed to eradicate androgen resistant, prostate cancer. Prostate cancer usually metastasizes to bone where the concentration of calcium is high, making Ca a promising toxin. Ionophores can deliver metal cations into cells, but are currently too toxic for human use. We synthesized a new rotaxane (CEHR2) that contains a benzyl 15-crown-5 ether as a blocking group to efficiently bind Ca. CEHR2 transfers Ca from an aqueous solution into CHCl to greater extent than alkali metal cations and Mg. It also transfers Ca to a greater extent than CEHR1, which is a rotaxane with an 18-crown-6 ether as a blocking group. CEHR2 was more toxic against the prostate cancer cell lines PC-3, 22Rv1, and C4-2 than CEHR1. This project demonstrates that crown ether rotaxanes can be designed to bind a targeted metal cation, and this selective cation association can result in enhanced toxicity.

Rotaxanes are unique mechanical devices that hold great promise as sensors. We report on two new rotaxanes that contain an acid or base sensitive trigger and readily disassemble in a wide range of environments. Disassemblage was observed under TLC and 1 H-NMR analysis. The axle is highly charged, which enhances solubility in aqueous environments, and can be readily derivatized with sensor components. The trigger was swapped in a one-pot method, which is promising for the rapid production of a series of sensors.

We used 352 published data points to develop multivariate linear regression, regression tree, and random forest models that predict the chemical composition of light oil from hydrothermal liquefaction of biomass. The mean absolute error calculated from ten-fold cross-validation indicates the random forest model had the best predictive ability, followed by regression tree and multivariate linear regression models. The random forest method is also more scalable than multivariate linear regression for data points outside the range of the dataset. The decision tree methods yield minimal information for improving understanding of the HTL process chemistry. Multivariate linear regression, on the other hand, identified previously unknown ternary interactions. For example, interactions involving lipid, lignin, and protein increase the abundance of N-containing compounds in the light oil. Further experimentation with lipid, lignin, and protein model compounds showed the formation of large amounts of undesirable long-chain amides in oil. This work shows that using multiple statistical models can further deepen the understanding of the HTL process in addition to providing tools that predict process outcomes.