Potent synergy and sustained bactericidal activity of polymyxins combined with Gram-positive only class of antibiotics versus four Gram-negative bacteria.

BACKGROUND: Gram-negative bacteria (GNB) are becoming increasingly resistant to a wide variety of antibiotics. There are currently limited treatments for GNB, and the combination of antibiotics with complementary mechanisms has been reported to be a feasible strategy for treating GNB infection. The inability to cross the GNB outer membrane (OM) is an important reason that a broad spectrum of Gram-positive only class of antibiotics (GPOAs) is lacking. Polymyxins may help GPOAs to permeate by disrupting OM of GNB. OBJECTIVE: To identify what kind of GPOAs can be aided to broaden their anti-GNB spectrum by polymyxins, we systematically investigated the synergy of eight GPOAs in combination with colistin (COL) and polymyxin B (PMB) against GNB in vitro. METHODS: The synergistic effect of COL or PMB and GPOAs combinations against GNB reference strains and clinical isolates were determined by checkerboard tests. The killing kinetics of the combinations were assessed using time-kill assays. RESULTS: In the checkerboard tests, polymyxins-GPOAs combinations exert synergistic effects characterized by species and strain specificity. The synergistic interactions on P. aeruginosa strains are significantly lower than those on strains of A. baumannii, K. pneumoniae and E. coli. Among all the combinations, COL has shown the best synergistic effect in combination with dalbavancin (DAL) or oritavancin (ORI) versus almost all of the strains tested, with FICIs from 0.16 to 0.50 and 0.13 to < 0.28, respectively. In addition, the time-kill assays demonstrated that COL/DAL and COL/ORI had sustained bactericidal activity. CONCLUSIONS: Our results indicated that polymyxins could help GPOAs to permeate the OM of specific GNB, thus showed synergistic effects and bactericidal effects in the in vitro assays. In vivo combination studies should be further conducted to validate the results of this study.

Filling a nick in NIK: Extending the half-life of a NIK inhibitor through structure-based drug design.

Inhibition of NF-κB inducing kinase (NIK) has been pursued as a promising therapeutic target for autoimmune disorders due to its highly regulated role in key steps of the NF-κB signaling pathway. Previously reported NIK inhibitors from our group were shown to be potent, selective, and efficacious, but had higher human dose projections than desirable for immunology indications. Herein we report the clearance-driven optimization of a NIK inhibitor guided by metabolite identification studies and structure-based drug design. This led to the identification of an azabicyclo[3.1.0]hexanone motif that attenuated in vitro and in vivo clearance while maintaining NIK potency and increasing selectivity over other kinases, resulting in a greater than ten-fold reduction in predicted human dose.

Embedding TiO2microspheres into the active layer as light scatterers in a perovskite photodetector to boost light harvesting.

Here, TiO2microspheres with particle sizes of 200-400 nm are embedded in p-i-n perovskite photodetectors, which are used as light scatterers. This approach was implemented to change the light transfer path in the perovskite layer, which gives the device higher photon-capture ability in a specific incident wavelength range. Compared with a pristine device, the photocurrent and responsivity of the device based on such a structure are obviously enhanced in the ranges of 560-610 nm and 730-790 nm. The photocurrent under 590 nm incident light wavelength illumination (light intensityP= 31.42µW·cm-2) increases from 1.45µA to 1.71µA, with an increase of 17.93%, and the responsivity reaches 0.305 A·W-1. In addition, the introduction of TiO2has no additional negative impact on the carrier extraction and the dark current. Also, the response time of the device did not deteriorate. Finally, the role of TiO2as light scatterers is further verified by embedding microspheres into mixed-halide perovskite devices.

Feedback pinning control of collective behaviors aroused by epidemic spread on complex networks.

This paper investigates feedback pinning control of synchronization behaviors aroused by epidemic spread on complex networks. Based on the quenched mean field theory, epidemic control synchronization models with the inhibition of contact behavior are constructed, combined with the epidemic transmission system and the adaptive dynamical network carrying active controllers. By the properties of convex functions and the Gerschgorin theorem, the epidemic threshold of the model is obtained, and the global stability of disease-free equilibrium is analyzed. For individual's infected situation, when an epidemic disease spreads, two types of feedback control strategies depending on the diseases' information are designed: the first one only adds controllers to infected individuals, and the other adds controllers to both infected and susceptible ones. By using the Lyapunov stability theory, under designed controllers, some criteria that guarantee the epidemic controlled synchronization system achieving behavior synchronization are also derived. Several numerical simulations are performed to show the effectiveness of our theoretical results. As far as we know, this is the first work to address the controlled behavioral synchronization induced by epidemic spread under the pinning feedback mechanism. It is hopeful that we may have deeper insights into the essence between the disease's spread and collective behavior under active control in complex dynamical networks.

Phase synchronization on spatially embedded duplex networks with total cost constraint.

Synchronization on multiplex networks has attracted increasing attention in the past few years. We investigate collective behaviors of Kuramoto oscillators on single layer and duplex spacial networks with total cost restriction, which was introduced by Li et al. [Phys. Rev. Lett. 104, 018701 (2010)] and termed as the Li network afterwards. We first explore how the topology of the network influences synchronizability of Kuramoto oscillators on single layer Li networks and find that the closer the Li network is to a regular lattice, the more difficult for it to evolve into synchronization. Then, we investigate synchronizability of duplex Li networks and find that the existence of inter-layer interaction can greatly enhance inter-layer and global synchronizability. When the inter-layer coupling strength is larger than a certain critical value, inter-layer synchronization will always occur. Furthermore, on single layer Li networks, nodes with larger degrees reach global synchronization more easily than those with smaller degrees, while on duplex Li networks, due to inter-layer interaction, this phenomenon becomes much less obvious. The results are important for us to gain insight into collective behaviors of many real-world complex systems which inherently possess multiplex architecture.

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge.

Small molecule distribution coefficients between immiscible nonaqueuous and aqueous phases-such as cyclohexane and water-measure the degree to which small molecules prefer one phase over another at a given pH. As distribution coefficients capture both thermodynamic effects (the free energy of transfer between phases) and chemical effects (protonation state and tautomer effects in aqueous solution), they provide an exacting test of the thermodynamic and chemical accuracy of physical models without the long correlation times inherent to the prediction of more complex properties of relevance to drug discovery, such as protein-ligand binding affinities. For the SAMPL5 challenge, we carried out a blind prediction exercise in which participants were tasked with the prediction of distribution coefficients to assess its potential as a new route for the evaluation and systematic improvement of predictive physical models. These measurements are typically performed for octanol-water, but we opted to utilize cyclohexane for the nonpolar phase. Cyclohexane was suggested to avoid issues with the high water content and persistent heterogeneous structure of water-saturated octanol phases, since it has greatly reduced water content and a homogeneous liquid structure. Using a modified shake-flask LC-MS/MS protocol, we collected cyclohexane/water distribution coefficients for a set of 53 druglike compounds at pH 7.4. These measurements were used as the basis for the SAMPL5 Distribution Coefficient Challenge, where 18 research groups predicted these measurements before the experimental values reported here were released. In this work, we describe the experimental protocol we utilized for measurement of cyclohexane-water distribution coefficients, report the measured data, propose a new bootstrap-based data analysis procedure to incorporate multiple sources of experimental error, and provide insights to help guide future iterations of this valuable exercise in predictive modeling.

An integrated suite of modeling tools that empower scientists in structure- and property-based drug design.

Structure- and property-based drug design is an integral part of modern drug discovery, enabling the design of compounds aimed at improving potency and selectivity. However, building molecules using desktop modeling tools can easily lead to poor designs that appear to form many favorable interactions with the protein's active site. Although a proposed molecule looks good on screen and appears to fit into the protein site X-ray crystal structure or pharmacophore model, doing so might require a high-energy small molecule conformation, which would likely be inactive. To help scientists make better design decisions, we have built integrated, easy-to-use, interactive software tools to perform docking experiments, de novo design, shape and pharmacophore based database searches, small molecule conformational analysis and molecular property calculations. Using a combination of these tools helps scientists in assessing the likelihood that a designed molecule will be active and have desirable drug metabolism and pharmacokinetic properties. Small molecule discovery success requires project teams to rapidly design and synthesize potent molecules with good ADME properties. Empowering scientists to evaluate ideas quickly and make better design decisions with easy-to-access and easy-to-understand software on their desktop is now a key part of our discovery process.

Matrix measure-based distributed impulsive consensus on nonlinear multi-agent systems with mixed time-varying delays.

This paper concentrates on researching the global and exponential leader-following consensus issue for an array of nonlinear multi-agent systems with the system time-varying delay and the distributed time-varying delay. An innovative distributed impulsive controller is proposed to force the states of all agents to track the trajectories of leader agent. By jointly introducing the matrix measure protocol, the comparison principle for impulsive systems, and the average impulsive interval, sufficient criteria for the realization of leader-following consensus are derived. In addition, considering different functions of impulsive signal, two different convergence rates are precisely estimated by utilizing the parameter variation formula, respectively. Finally, two numerical examples are given to demonstrate the effectiveness of proposed control strategy and the rightness of theoretical analysis.

Mechanism and deterioration pattern of sandstone surrounding rock voiding at bottom of heavy-haul railway tunnel.

This study combines laboratory experiments and discrete element simulation methods to analyze the mechanism and deterioration patterns of sandstone surrounding rock voiding the bottom of a heavy-haul railway tunnel. It is based on previously acquired measurement data from optical fiber grating sensors installed in the Taihangshan Mountain Tunnel of the Wari Railway. By incorporating rock particle wastage rate results, a method for calculating the peak strength and elastic modulus attenuation of surrounding rock is proposed. Research indicates that the operation of heavy-haul trains leads to an instantaneous increase in the dynamic water pressure on the bottom rock ranging 144.4-390.0%, resulting in high-speed water flow eroding the rock. After 1-2 years of operation, the bottom water and soil pressures increase by 526.5% and 390.0%, respectively. Focusing on sandstone surrounding rock with high observability, laboratory experiments were conducted to monitor the degradation stages of infiltration, particle loss, and voiding of rock under the action of dynamic water flow. The impact of water flow on the "cone-shaped" bottom rock deformation was also clarified. The extent of rock deterioration and voiding was determined using miniature water and soil pressure sensors in conjunction with discrete element numerical simulations. The measured rock particle loss was used as a criterion. Finally, a fitting approach is derived to calculate the peak strength and elastic modulus attenuation of surrounding rock, gaining insight into and providing a reference for the maintenance and disposal measures for the bottom operation of heavy-haul railway tunnels.

Discovery of DNL343: A Potent, Selective, and Brain-Penetrant eIF2B Activator Designed for the Treatment of Neurodegenerative Diseases.

Eukaryotic translation initiation factor 2B (eIF2B) is a key component of the integrated stress response (ISR), which regulates protein synthesis and stress granule formation in response to cellular insult. Modulation of the ISR has been proposed as a therapeutic strategy for treatment of neurodegenerative diseases such as vanishing white matter (VWM) disease and amyotrophic lateral sclerosis (ALS) based on its ability to improve cellular homeostasis and prevent neuronal degeneration. Herein, we report the small-molecule discovery campaign that identified potent, selective, and CNS-penetrant eIF2B activators using both structure- and ligand-based drug design. These discovery efforts culminated in the identification of DNL343, which demonstrated a desirable preclinical drug profile, including a long half-life and high oral bioavailability across preclinical species. DNL343 was progressed into clinical studies and is currently undergoing evaluation in late-stage clinical trials for ALS.

Aperiodically intermittent saturation consensus on multi-agent systems with discontinuous dynamics.

This paper mainly investigates the exponential consensus problem for the multi-agent systems (MASs) with nonlinear discontinuous dynamics and time-varying delay. A novel aperiodically intermittent distributed control strategy is proposed to force the state of each agent to the common trajectory, where the configuration of control width and rest width can be aperiodic. Meanwhile, in order to limit the control effects into certain reasonable ranges, an improved saturation algorithm is proposed, which effectively reduces the non-smoothness of the control signal. Sufficient conditions for the exponential consensus on the discontinuous MASs are obtained through the Filippov differential inclusion (FDI), the Lie derivative method (LDM) and the measurement selection theorem (MST). Finally, the validity and feasibility of the main theories is demonstrated by numerical simulations.

Aperiodically intermittent event-triggered pinning control on cluster synchronization of directed complex networks.

This paper is dedicated to investigating the exponential cluster synchronization in a class of nonlinearly coupled complex networks with non-identical nodes and an asymmetrical coupling matrix. A novel aperiodically intermittent pinning control (APIPC) protocol is presented, which takes full account of the cluster-tree topology structure of the networks and pins only the nodes in the current cluster that have directional links to neighboring clusters. Since it is difficult to precisely determine the intermittent control instants and rest instants of APIPC in advance, the event-triggered mechanism (ETM) is thus proposed. Based on the concept of the minimal control ratio and the segmentation analysis method, sufficient requirements for realizing the exponential cluster synchronization are derived. Moreover, the Zeno behavior of ETM is excluded by rigorous analysis. Eventually, the effectiveness and advantages of the established theorems and control strategies are demonstrated by two numerical simulations.

Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries.

DNA-Encoded Chemical Libraries (DELs) have emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against carbonic anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell renal cell carcinoma. We present a new ligand identification and hit-to-lead strategy driven by machine learning models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX-screening datasets obtained from three different DELs were used to train machine learning models for generating novel hits, dissimilar to elements present in the original DELs. Out of the 152 novel potential hits that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC50 < 1 µM). To generate lead compounds that are functionalized with anticancer payloads, analogues of top hits were prioritized for synthesis based on the predicted CAIX affinity and synthetic feasibility. Three lead candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed an in vitro KD of 5.7 nM and selectively targeted tumors in mice bearing human renal cell carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel hits and for the successful translation of lead candidates for in vivo targeting applications.

A sensitive LC-MS/MS assay to quantitate free payload Aur0101 from ADC PYX-201 in rat and monkey plasma.

Aim: Aur0101 is a cytotoxic and small-molecule microtubule depolymerizing agent, and is the payload conjugated to antibody-drug conjugate PYX-201. Developing and validating a sensitive bioanalytical method to quantitate Aur0101 was novel and crucial in preclinical PYX-201 studies. Materials & methods: Reference standard Aur0101 and its stable isotope labelled internal standard Aur0101-d8 were used in this LC-MS/MS method. Results: This sensitive assay was validated at a lower limit of quantitation of 15 pg/ml and successfully applied to support preclinical rat and monkey toxicology studies. Preclinical plasma toxicokinetic parameters were presented. Conclusion: A sensitive and robust LC-MS/MS assay was validated for Aur0101 in rat and monkey plasma.

Quantitation of total antibody (tAb) from antibody drug conjugate (ADC) PYX-201 in rat and monkey plasma using an enzyme-linked immunosorbent assay (ELISA) and its application in preclinical studies.

PYX-201 is an investigative ADC oncology drug composed of a monoclonal human immunoglobulin G (IgG) antibody targeting the extra domain B splice variant of fibronectin (EDB + FN) conjugated to an auristatin payload through a cleavable linker. Effective measurement of PYX-201 tAb is the key to ADC drug PYX-201 preclinical pharmacokinetics (PK) assessment. PYX-201 monoclonal antibody (mAb) was used as the reference standard, goat anti-human IgG polyclonal antibody (pAb) or rabbit anti-human Kappa light chain mAb was employed as the capture antibody, and mouse mAb or goat pAb anti-human IgG the crystallizable fragment (Fc) (horseradish peroxidase (HRP)) was utilized as the detection antibody in this ELISA. This assay was validated with a dynamic range 250 - 10,000 ng/mL and 250 - 6000 ng/mL in rat and monkey K2EDTA plasma, respectively. PYX-201 tAb bioanalytical ELISA assay was reported for the first time in any biological matrix. This is the first time for a bioanalytical method to be validated for a tAb from an ADC drug targeting EDB + FN in any biological matrix.

Discovery of a First-in-Class Small-Molecule Ligand for WDR91 Using DNA-Encoded Chemical Library Selection Followed by Machine Learning.

WD40 repeat-containing protein 91 (WDR91) regulates early-to-late endosome conversion and plays vital roles in endosome fusion, recycling, and transport. WDR91 was recently identified as a potential host factor for viral infection. We employed DNA-encoded chemical library (DEL) selection against the WDR domain of WDR91, followed by machine learning to predict ligands from the synthetically accessible Enamine REAL database. Screening of predicted compounds identified a WDR91 selective compound 1, with a KD of 6 ± 2 µM by surface plasmon resonance. The co-crystal structure confirmed the binding of 1 to the WDR91 side pocket, in proximity to cysteine 487, which led to the discovery of covalent analogues 18 and 19. The covalent adduct formation for 18 and 19 was confirmed by intact mass liquid chromatography-mass spectrometry. The discovery of 1, 18, and 19, accompanying structure-activity relationship, and the co-crystal structures provide valuable insights for designing potent and selective chemical tools against WDR91 to evaluate its therapeutic potential.

DEGAS: sharing and tracking target compound ideas with external collaborators.

To minimize the risk of failure in clinical trials, drug discovery teams must propose active and selective clinical candidates with good physicochemical properties. An additional challenge is that today drug discovery is often conducted by teams at different geographical locations. To improve the collaborative decision making on which compounds to synthesize, we have implemented DEGAS, an application which enables scientists from Genentech and from collaborating external partners to instantly access the same data. DEGAS was implemented to ensure that only the best target compounds are made and that they are made without duplicate effort. Physicochemical properties and DMPK model predictions are computed for each compound to allow the team to make informed decisions when prioritizing. The synthesis progress can be easily tracked. While developing DEGAS, ease of use was a particular goal in order to minimize the difficulty of training and supporting remote users.

Application of New Sensor Technology and Few-Shot Learning in Education Based on IoT Era.

In recent years, with the rapid development of emerging Internet of Things technology and short-range wireless communication technology, smart healthcare monitoring network technology has become a research hotspot. It provides convenience for people and enhances the development of people's own healthcare awareness. This paper aims to study how to make its application in the field of smart healthcare education more applicable through the use of related technologies in the Internet of Things era and few-shot learning. For this reason, this paper proposes to optimize and improve the new sensor technology and the algorithm of few-shot learning, and to adjust some parameters as a whole. At the same time, related experiments and analysis are designed for the improved algorithm to study and understand its performance. The experimental results in this paper show that the improved algorithm improves its application effect by 36.9% and is relatively more applicable than the unimproved algorithm.

Matrix Measure-Based Event-Triggered Impulsive Quasi-Synchronization on Coupled Neural Networks.

In this article, the quasi-synchronization for a kind of coupled neural networks with time-varying delays is investigated via a novel event-triggered impulsive control approach. In view of the randomly occurring uncertainties (ROUs) in the communication channels, the global quasi-synchronization for the coupled neural networks within a given error bound is considered instead of discussing the complete synchronization. A kind of distributed event-triggered impulsive controllers is presented with considering the Bernoulli stochastic variables based on ROUs, which works at each event-triggered impulsive instant. According to the matrix measure method and the Lyapunov stability theorem, several sufficient conditions for the realization of the quasi-synchronization are successfully derived. Combining with the mathematical methodology with the formula of variation of parameters and the comparison principle for the impulsive systems with time-varying delays, the convergence rate and the synchronization error bound are precisely estimated. Meanwhile, the Zeno behaviors could be eliminated in the coupled neural network with the proposed event-triggered function. Finally, a numerical example is presented to prove the results of theoretical analysis.

Fixed-Time Synchronization of Complex Dynamical Networks: A Novel and Economical Mechanism.

Fixed-time synchronization of complex networks is investigated in this article. First, a completely novel lemma is introduced to prove the fixed-time stability of the equilibrium of a general ordinary differential system, which is less conservative and has a simpler form than those in the existing literature. Then, sufficient conditions are presented to realize synchronization of a complex network (with a target system) within a settling time via three different kinds of simple controllers. In general, controllers designed to achieve fixed-time stability consist of three terms and are discontinuous. However, in our mechanisms, the controllers only contain two terms or even one term and are continuous. Thus, our controllers are simpler and of more practical applicability. Finally, three examples are provided to illustrate the correctness and effectiveness of our results.