Other Medical Sciences Commons^™

A Machine Learning Approach For Predicting Clinical Trial Patient Enrollment In Drug Development Portfolio Demand Planning, Ahmed Shoieb

Masters Theses

One of the biggest challenges the clinical research industry currently faces is the accurate forecasting of patient enrollment (namely if and when a clinical trial will achieve full enrollment), as the stochastic behavior of enrollment can significantly contribute to delays in the development of new drugs, increases in duration and costs of clinical trials, and the over- or under- estimation of clinical supply. This study proposes a Machine Learning model using a Fully Convolutional Network (FCN) that is trained on a dataset of 100,000 patient enrollment data points including patient age, patient gender, patient disease, investigational product, study phase, blinded …

Some Reactions Of Phenothiazine And Certain Benzophenothiazines, John Charles Gilmer

Doctoral Dissertations

Summary

The consideration of the medical importance of phenothiazine derivatives and the known cancer chemotherapeutic value of the nitrogen mustards made the synthesis of some nitrogen mustard derivatives of the phenothiazines of particular interest. In the course of this research the N-{2-[bis-(2-chloroethyl)amino]ethyl} derivatives of phenothiazine, 7H-benzo[c]phenothiazine, 7H-dibenzo[c, h] phenothiazine, and 14H-dibenzo[a, h] phenothiazine were synthesized for anticancer evaluation. Also submitted to the Cancer Chemotherapy National Service Center for testing were the N-(2-chloroethyl) monofunctional nitrogen mustard derivatives of the first three phenothiazines mentioned above.