In Pursuit of an Allosteric Human Tropomyosin Kinase A (hTrkA) Inhibitor for Chronic Pain.

Human ß-nerve growth factor (ß-NGF) and its associated receptor, human tropomyosin receptor kinase A (hTrkA), have been demonstrated to be key factors in the perception of pain. However, efficacious small molecule therapies targeting the intracellularly located hTrkA kinase have not been explored thoroughly for pain management. Herein, we report the pharmacological properties of a selective hTrkA allosteric inhibitor, 1. 1 was shown to be active against the full length hTrkA, showing preferential binding for the inactive kinase, and was confirmed through the X-ray of hTrkA···1 bound complex. 1 was also found to inhibit ß-NGF induced neurite outgrowth in rat PC12 cells. Daily oral administration of 1 improved the joint compression threshold of rats injected intra-articularly with monoiodoacetate over a 14-day period. The efficacy of 1 in a relevant chronic pain model of osteoarthritis coupled with in vitro confirmation of target mediation makes allosteric hTrkA inhibitors potential candidates for modulating pain.

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. NEW METHOD: We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. RESULTS: The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. COMPARISON WITH EXISTING METHODS: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. CONCLUSIONS: We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra.

A multi-site feasibility study for personalized medicine in canines with osteosarcoma.

BACKGROUND: A successful therapeutic strategy, specifically tailored to the molecular constitution of an individual and their disease, is an ambitious objective of modern medicine. In this report, we highlight a feasibility study in canine osteosarcoma focused on refining the infrastructure and processes required for prospective clinical trials using a series of gene expression-based Personalized Medicine (PMed) algorithms to predict suitable therapies within 5 days of sample receipt. METHODS: Tumor tissue samples were collected immediately following limb amputation and shipped overnight from veterinary practices. Upon receipt (day 1), RNA was extracted from snap-frozen tissue, with an adjacent H&E section for pathological diagnosis. Samples passing RNA and pathology QC were shipped to a CLIA-certified laboratory for genomic profiling. After mapping of canine probe sets to human genes and normalization against a (normal) reference set, gene level Z-scores were submitted to the PMed algorithms. The resulting PMed report was immediately forwarded to the veterinarians. Upon receipt and review of the PMed report, feedback from the practicing veterinarians was captured. RESULTS: 20 subjects were enrolled over a 5 month period. Tissue from 13 subjects passed both histological and RNA QC and were submitted for genomic analysis and subsequent PMed analysis and report generation. 11 of the 13 samples for which PMed reports were produced were communicated to the veterinarian within the target 5 business days. Of the 7 samples that failed QC, 4 were due to poor RNA quality, whereas 2 were failed following pathological review. Comments from the practicing veterinarians were generally positive and constructive, highlighting a number of areas for improvement, including enhanced education regarding PMed report interpretation, drug availability, affordable pricing and suitable canine dosing. CONCLUSIONS: This feasibility trial demonstrated that with the appropriate infrastructure and processes it is possible to perform an in-depth molecular analysis of a patient's tumor in support of real time therapeutic decision making within 5 days of sample receipt. A number of areas for improvement have been identified that should reduce the level of sample attrition and support clinical decision making.