The FDA-approved drug bexarotene has been predominantly utilized for the treatment of cutaneous T-cell lymphoma (CTLC), but has shown promise as an off label treatment for various other cancers as well as Alzheimer's disease (AD). However, harmful side effects such as hypothyroidism have catalyzed a search for alternative rexinoids which retain similar levels of RXR agonism while reducing the undesirable effects incurred by bexarotene. This honors thesis outlines the steps taken to design and synthesize novel analogues of the selective retinoid-X-receptor (RXR) agonist 4-[1-(3,5,5,8,8-pentamethyl-5,6,7,8-tetrahydro-2-naphthyl)ethynyl]benzoic acid (bexarotene). Corresponding NMR spectra indicates the successful construction of four novel compounds which are structurally similar to known, biologically-evaluated rexinoids that have induced fewer side effects while stimulating greater levels of RXR selectivity as compared to bexarotene. Future In vitro analyses of these four analogues coupled with the recognized efficacy of their parent compounds demonstrate the chemotherapeutic potential of structurally modified bexarotene analogues
Although just 4% of these students completed the course, models were developed that could predict correctly nearly 80% of the time which students would complete the course and which would not, based on each student’s first day of work in the online course. Logistic regression was used as the primary tool to predict completion and focused on variables associated with self-regulated learning (SRL) and demographic variables available from survey information gathered as students begin edX courses (the MOOC platform employed).
The strongest SRL predictor was the amount of time students spent in the course on their first day. The number of math skills obtained the first day and the pace at which these skills were gained were also predictors, although pace was negatively correlated with completion. Prediction models using only SRL data obtained on the first day in the course correctly predicted course completion 70% of the time, whereas models based on first-day SRL and demographic data made correct predictions 79% of the time.