Fall Classification by Machine Learning Using Mobile Phones

Fall prevention is a critical component of health care; falls are a common source of injury in the elderly and are associated with significant levels of mortality and morbidity. Automatically detecting falls can allow rapid response to potential emergencies; in addition, knowing the cause or manner of a fall can be beneficial for prevention studies or a more tailored emergency response. The purpose of this study is to demonstrate techniques to not only reliably detect a fall but also to automatically classify the type. We asked 15 subjects to simulate four different types of falls–left and right lateral, forward trips, and backward slips–while wearing mobile phones and previously validated, dedicated accelerometers. Nine subjects also wore the devices for ten days, to provide data for comparison with the simulated falls. We applied five machine learning classifiers to a large time-series feature set to detect falls. Support vector machines and regularized logistic regression were able to identify a fall with 98% accuracy and classify the type of fall with 99% accuracy. This work demonstrates how current machine learning approaches can simplify data collection for prevention in fall-related research as well as improve rapid response to potential injuries due to falls

A Unique Approach to Design Potent and Selective Cyclic Adenosine Monophosphate Response Element Binding Protein, Binding Protein (CBP) Inhibitors

The epigenetic regulator CBP/P300 presents a novel therapeutic target for oncology. Previously, we disclosed the development of potent and selective CBP bromodomain inhibitors by first identifying pharmacophores that bind the KAc region and then building into the LPF shelf. Herein, we report the “hybridization” of a variety of KAc-binding fragments with a tetrahydroquinoline scaffold that makes optimal interactions with the LPF shelf, imparting enhanced potency and selectivity to the hybridized ligand. To demonstrate the utility of our hybridization approach, two analogues containing unique Asn binders and the optimized tetrahydroquinoline moiety were rapidly optimized to yield single-digit nanomolar inhibitors of CBP with exquisite selectivity over BRD4(1) and the broader bromodomain family

GNE-781, A Highly Advanced Potent and Selective Bromodomain Inhibitor of Cyclic Adenosine Monophosphate Response Element Binding Protein, Binding Protein (CBP)

Inhibition of the bromodomain of the transcriptional regulator CBP/P300 is an especially interesting new therapeutic approach in oncology. We recently disclosed in vivo chemical tool <b>1</b> (GNE-272) for the bromodomain of CBP that was moderately potent and selective over BRD4(1). In pursuit of a more potent and selective CBP inhibitor, we used structure-based design. Constraining the aniline of <b>1</b> into a tetrahydroquinoline motif maintained potency and increased selectivity 2-fold. Structure–activity relationship studies coupled with further structure-based design targeting the LPF shelf, BC loop, and KAc regions allowed us to significantly increase potency and selectivity, resulting in the identification of non-CNS penetrant <b>19</b> (GNE-781, TR-FRET IC₅₀ = 0.94 nM, BRET IC₅₀ = 6.2 nM; BRD4(1) IC₅₀ = 5100 nΜ) that maintained good in vivo PK properties in multiple species. Compound <b>19</b> displays antitumor activity in an AML tumor model and was also shown to decrease Foxp3 transcript levels in a dose dependent manner