Application of the novel framework has identified the dyslipidemia medication gemfibrozil as a candidate drug to reduce Alzheimer’s disease risk.
A combination of pharmacoepidemiologic studies and Mendelian randomization genetic analysis revealed preferential protection in Black but not white individuals. The findings support ongoing studies of telmisartan for Alzheimer’s, particularly in racially diverse cohorts.
A multifront Cleveland Clinic research project aims to use $14 million in NIH funding to identify mechanisms and targets underlying AF that may lead to personalized therapies.
Treatment of preclinical models of glioblastoma with a BACE1 inhibitor reprogrammed tumor-promoting macrophages into tumor-suppressing macrophages, destroying tumor cells in the process.
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A large insurance database analysis has found a 69% relative risk reduction for Alzheimer’s development over six years, prompting mechanistic and phase 2 randomized studies.
Traditional methods of drug discovery cannot keep up with the rapid emergence and mutation of human coronavirus epidemics. Feixiong Cheng, PhD, has applied a network-based prediction model to identify targets for drug repurposing for the treatment of COVID-19.
One driver of increased pharmaceutical spending is the high failure rate of expensive and time-consuming randomized control trials. deepDTnet, a network-based deep learning methodology for novel target identification and in silico drug repurposing, may aid in the development of novel, effective treatment strategies for complex diseases.