Optimal Regimens and Clinical Breakpoint of Avilamycin Against Clostridium perfringens in Swine Based on PK-PD Study.

Clostridium perfringens causes significant morbidity and mortality in swine worldwide. Avilamycin showed no cross resistance and good activity for treatment of C. perfringens. The aim of this study was to formulate optimal regimens of avilamycin treatment for C. perfringens infection based on the clinical breakpoint (CBP). The wild-type cutoff value (COWT) was defined as 0.25 µg/ml, which was developed based on the minimum inhibitory concentration (MIC) distributions of 120 C. perfringens isolates and calculated using ECOFFinder. Pharmacokinetics-pharmacodynamics (PK-PD) of avilamycin in ileal content were analyzed based on the high-performance liquid chromatography method and WinNonlin software to set up the target of PK/PD index (AUC0-24h/MIC)ex based on sigmoid Emax modeling. The PK parameters of AUC0-24h, Cmax, and Tmax in the intestinal tract were 428.62 ± 14.23 h µg/mL, 146.30 ± 13.41 µg/ml,, and 4 h, respectively. The target of (AUC0-24h/MIC)ex for bactericidal activity in intestinal content was 36.15 h. The PK-PD cutoff value (COPD) was defined as 8 µg/ml and calculated by Monte Carlo simulation. The dose regimen designed from the PK-PD study was 5.2 mg/kg mixed feeding and administrated for the treatment of C. perfringens infection. Five respective strains with different MICs were selected as the infection pathogens, and the clinical cutoff value was defined as 0.125 µg/ml based on the relationship between MIC and the possibility of cure (POC) following nonlinear regression analysis, CART, and "Window" approach. The CBP was set to be 0.25 µg/ml and selected by the integrated decision tree recommended by the Clinical Laboratory of Standard Institute. The formulation of the optimal regimens and CBP is good for clinical treatment and to control drug resistance.

Exploration of Clinical Breakpoint of Danofloxacin for Glaesserella parasuis in Plasma and in PELF.

Background: In order to establish the clinical breakpoint (CBP) of danofloxacin against G. parasuis, three cutoff values, including epidemiological cutoff value (ECV), pharmacokinetic-pharmacodynamic (PK-PD) cutoff value (COPD) and clinical cutoff value (COCL), were obtained in the present study. Methods: The ECV was calculated using ECOFFinder base on the MIC distribution of danfloxacin against 347 G. parasuis collected from disease pigs. The COPD was established based on in vivo and ex vivo PK-PD modeling of danofloxacin both in plasma and pulmonary epithelial lining fluid (PELF) using Hill formula and Monte Carlo analysis. The COCL was established based on the relationship between the possibility of cure (POC) and MIC in the clinical trials using the "WindoW" approach, nonlinear regression and CART analysis. Results: The MIC50 and MIC90 of danofloxacin against 347 G. parasuis were 2 µg/mL and 8 µg/mL, respectively. The ECV value was set to 8 µg/mL using ECOFFinder. Concentration-time curves of danofloxacin were fitted with a two-compartment PK model. The PK parameters of the maximum concentration (Cmax) and area under concentration-time curves (AUC) in PELF were 3.67 ± 0.25 µg/mL and 24.28 ± 2.70 h·µg/mL, higher than those in plasma (0.67 ± 0.01 µg/mL and 4.47 ± 0.51 h·µg/mL). The peak time (Tmax) in plasma was 0.23 ± 0.07 h, shorter than that in PELF (1.61 ± 0.15 h). The COPD in plasma and PELF were 0.125 µg/mL and 0.5 µg/mL, respectively. The COCL calculated by WindoW approach, nonlinear regression and CART analysis were 0.125-4 µg/mL, 0.428 µg/mL and 0.56 µg/mL, respectively. The 0.5 µg/mL was selected as eligible COCL. The ECV is much higher than the COPD and COCL, and the clinical breakpoint based on data in plasma was largely different from that of PELF. Conclusions: Our study firstly established three cutoff values of danofloxacin against G. parasuis. It suggested that non-wild-type danofloxacin-resistant G. parasuis may lead to ineffective treatment by danofloxacin.

Hypothalamic-Pituitary-Adrenal Axis Pediatric Safety Studies Submitted to the FDA.

BACKGROUND: Corticosteroid use has been associated with hypothalamic-pituitary-adrenal (HPA) axis suppression which can predispose the pediatric patient to multiple immune- and growth-related adverse effects. The objectives of this review were to identify the pediatric drug development programs involving corticosteroids and the associated pediatric HPA axis suppression studies submitted to the US Food and Drug Administration (FDA), capture FDA guidance topic related recommendations, and determine the consistency of HPA axis data in prescription corticosteroid labeling. METHODS: A review of FDA submissions from January 2002 to July 2018 involving corticosteroid products and HPA axis testing in pediatric patients was conducted. The adrenal function testing methods, number of pediatric HPA axis dedicated studies, duration of these studies, and the labeling outcomes were assessed. RESULTS: Of the 437 total drug products that were submitted to FDA, only 36 products were corticosteroids or a corticosteroid combination product yielding a total of 83 pediatric clinical studies. Twenty-four of the 36 products included 37 HPA axis suppression dedicated studies which employed different measurement methods. The pediatric HPA axis suppression trial data collected did not necessitate any new actionable recommendations in the FDA labeling. CONCLUSION: Future pediatric drug development program goals would be to determine whether HPA axis suppression studies should be conducted, establish optimal testing methods if HPA axis testing is performed, continue to update guidances for industry, and actionable labeling recommendations. However, regulatory policy related to conducting pediatric HPA axis studies requires additional scientific research and discussion by the pediatric drug development community.

SuperRF: Enhanced 3D RF Representation Using Stationary Low-Cost mmWave Radar.

This paper introduces SuperRF- which takes radio frequency (RF) signals from an off-the-shelf, low-cost, 77GHz mmWave radar and produces an enhanced 3D RF representation of a scene. SuperRF is useful in scenarios where camera and other types of sensors do not work, or not allowed due to privacy concerns, or their performance is impacted due to bad lighting conditions and occlusions, or an alternate RF sensing system like synthetic aperture radar (SAR) is too large, inconvenient, and costly. Applications of SuperRF includes navigation and planning of autonomous and semi-autonomous systems, human-robot interactions and social robotics, and elderly and/or patient monitoring in-home healthcare scenarios. We use low-cost, off-the-shelf parts to capture RF signals and to train SuperRF. The novelty of SuperRF lies in its use of deep learning algorithm, followed by a compressed sensing-based iterative algorithm that further enhances the output, to generate a fine-grained 3D representation of an RF scene from its sparse RF representation - which a mmWave radar of the same class cannot achieve without instrumenting the system with large sized multiple antennas or physically moving the antenna over a longer period in time. We demonstrate the feasibility and effectiveness through an in-depth evaluation.

EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching.

Human sensing, motion trajectory estimation, and identification are central to a wide range of applications in many domains such as retail stores, surveillance, public safety, public address, smart homes and cities, and access control. Existing solutions either require facial recognition or installation and maintenance of multiple units, or they lack long-term re-identification capability. In this paper, we propose a novel system - called EyeFi- that combines WiFi and camera on a standalone device to overcome these limitations. EyeFi integrates a WiFi chipset to an overhead camera and fuses motion trajectories obtained from both vision and RF modalities to identify individuals. In order to do that, EyeFi uses a student-teacher model to train a neural network to estimate the Angle of Arrival (AoA) of WiFi packets from the CSI values. Based on extensive evaluation using real-world data, we observe that EyeFi improves WiFi CSI based AoA estimation accuracy by more than 30% and offers 3,800 times computational speed over the state-of-the-art solution. In a real-world environment, EyeFi's accuracy of person identification averages 75% when the number of people varies from 2 to 10.