Augmenting NK cell-based immunotherapy by targeting mitochondrial apoptosis.

Interest in harnessing natural killer (NK) cells for cancer immunotherapy is rapidly growing. However, efficacy of NK cell-based immunotherapy remains limited in most trials. Strategies to augment the killing efficacy of NK cells are thus much needed. In the current study, we found that mitochondrial apoptosis (mtApoptosis) pathway is essential for efficient NK killing, especially at physiologically relevant effector-to-target ratios. Furthermore, NK cells can prime cancer cells for mtApoptosis and mitochondrial priming status affects cancer-cell susceptibility to NK-mediated killing. Interestingly, pre-activating NK cells confers on them resistance to BH3 mimetics. Combining BH3 mimetics with NK cells synergistically kills cancer cells in vitro and suppresses tumor growth in vivo. The ideal BH3 mimetic to use in such an approach can be predicted by BH3 profiling. We herein report a rational and precision strategy to augment NK-based immunotherapy, which may be adaptable to T cell-based immunotherapies as well.

Human Antibodies that Slow Erythrocyte Invasion Potentiate Malaria-Neutralizing Antibodies.

The Plasmodium falciparum reticulocyte-binding protein homolog 5 (PfRH5) is the leading target for next-generation vaccines against the disease-causing blood-stage of malaria. However, little is known about how human antibodies confer functional immunity against this antigen. We isolated a panel of human monoclonal antibodies (mAbs) against PfRH5 from peripheral blood B cells from vaccinees in the first clinical trial of a PfRH5-based vaccine. We identified a subset of mAbs with neutralizing activity that bind to three distinct sites and another subset of mAbs that are non-functional, or even antagonistic to neutralizing antibodies. We also identify the epitope of a novel group of non-neutralizing antibodies that significantly reduce the speed of red blood cell invasion by the merozoite, thereby potentiating the effect of all neutralizing PfRH5 antibodies as well as synergizing with antibodies targeting other malaria invasion proteins. Our results provide a roadmap for structure-guided vaccine development to maximize antibody efficacy against blood-stage malaria.

Combination Cancer Therapy Can Confer Benefit via Patient-to-Patient Variability without Drug Additivity or Synergy.

Combination cancer therapies aim to improve the probability and magnitude of therapeutic responses and reduce the likelihood of acquired resistance in an individual patient. However, drugs are tested in clinical trials on genetically diverse patient populations. We show here that patient-to-patient variability and independent drug action are sufficient to explain the superiority of many FDA-approved drug combinations in the absence of drug synergy or additivity. This is also true for combinations tested in patient-derived tumor xenografts. In a combination exhibiting independent drug action, each patient benefits solely from the drug to which his or her tumor is most sensitive, with no added benefit from other drugs. Even when drug combinations exhibit additivity or synergy in pre-clinical models, patient-to-patient variability and low cross-resistance make independent action the dominant mechanism in clinical populations. This insight represents a different way to interpret trial data and a different way to design combination therapies.

Analysis of a Therapeutic Antibody Cocktail Reveals Determinants for Cooperative and Broad Ebolavirus Neutralization.

Structural principles underlying the composition of protective antiviral monoclonal antibody (mAb) cocktails are poorly defined. Here, we exploited antibody cooperativity to develop a therapeutic mAb cocktail against Ebola virus. We systematically analyzed the antibody repertoire in human survivors and identified a pair of potently neutralizing mAbs that cooperatively bound to the ebolavirus glycoprotein (GP). High-resolution structures revealed that in a two-antibody cocktail, molecular mimicry was a major feature of mAb-GP interactions. Broadly neutralizing mAb rEBOV-520 targeted a conserved epitope on the GP base region. mAb rEBOV-548 bound to a glycan cap epitope, possessed neutralizing and Fc-mediated effector function activities, and potentiated neutralization by rEBOV-520. Remodeling of the glycan cap structures by the cocktail enabled enhanced GP binding and virus neutralization. The cocktail demonstrated resistance to virus escape and protected non-human primates (NHPs) against Ebola virus disease. These data illuminate structural principles of antibody cooperativity with implications for development of antiviral immunotherapeutics.

Proof-of-concept studies with a computationally designed Mpro inhibitor as a synergistic combination regimen alternative to Paxlovid.

As the SARS-CoV-2 virus continues to spread and mutate, it remains important to focus not only on preventing spread through vaccination but also on treating infection with direct-acting antivirals (DAA). The approval of Paxlovid, a SARS-CoV-2 main protease (Mpro) DAA, has been significant for treatment of patients. A limitation of this DAA, however, is that the antiviral component, nirmatrelvir, is rapidly metabolized and requires inclusion of a CYP450 3A4 metabolic inhibitor, ritonavir, to boost levels of the active drug. Serious drug-drug interactions can occur with Paxlovid for patients who are also taking other medications metabolized by CYP4503A4, particularly transplant or otherwise immunocompromised patients who are most at risk for SARS-CoV-2 infection and the development of severe symptoms. Developing an alternative antiviral with improved pharmacological properties is critical for treatment of these patients. By using a computational and structure-guided approach, we were able to optimize a 100 to 250 µM screening hit to a potent nanomolar inhibitor and lead compound, Mpro61. In this study, we further evaluate Mpro61 as a lead compound, starting with examination of its mode of binding to SARS-CoV-2 Mpro. In vitro pharmacological profiling established a lack of off-target effects, particularly CYP450 3A4 inhibition, as well as potential for synergy with the currently approved alternate antiviral, molnupiravir. Development and subsequent testing of a capsule formulation for oral dosing of Mpro61 in B6-K18-hACE2 mice demonstrated favorable pharmacological properties, efficacy, and synergy with molnupiravir, and complete recovery from subsequent challenge by SARS-CoV-2, establishing Mpro61 as a promising potential preclinical candidate.

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.

Open-ended movements structure sensorimotor information in early human development.

Human behaviors, with whole-body coordination, involve large-scale sensorimotor interaction. Spontaneous bodily movements in the early developmental stage potentially lead toward acquisition of such coordinated behavior. These movements presumably contribute to the structuration of sensorimotor interaction, providing specific regularities in bidirectional information among muscle activities and proprioception. Whether and how spontaneous movements, despite being task-free, structure and organize sensorimotor interactions in the entire body during early development remain unknown. Herein, to address these issues, we gained insights into the structuration process of the sensorimotor interaction in neonates and 3-mo-old infants. By combining detailed motion capture and musculoskeletal simulation, sensorimotor information flows among muscle activities and proprioception throughout the body were obtained. Subsequently, we extracted spatial modules and temporal state in sensorimotor information flows. Our approach demonstrated that early spontaneous movements elicited body-dependent sensorimotor modules, revealing age-related changes in them, depending on the combination or direction. The sensorimotor interactions also displayed temporal non-random fluctuations analogous to those seen in spontaneous activities in the cerebral cortex and spinal cord. Furthermore, we found recurring state sequence patterns across multiple participants, characterized by a substantial increase in infants compared to the patterns in neonates. Therefore, early spontaneous movements induce the spatiotemporal structuration in sensorimotor interactions and subsequent developmental changes. These results implicated that early open-ended movements, emerging from a certain neural substrate, regulate the sensorimotor interactions through embodiment and contribute to subsequent coordinated behaviors. Our findings also provide a conceptual linkage between early spontaneous movements and spontaneous neuronal activity in terms of spatiotemporal characteristics.

Synergy of crystallinity modulation and intercalation engineering in carbon nitride for boosted H2O2 photosynthesis.

Photosynthesis of hydrogen peroxide (H2O2) by selective oxygen reduction is a green and cost-effective alternative to the energy-intensive anthraquinone process. Although inexpensive polymeric graphitic carbon nitride (g-C3N4) exhibits the ability to produce H2O2, its disordered and amorphous structure leads to a high recombination rate of photogenerated carriers and hinders charge transfer between layers. Herein, we predict that stacked polymeric g-C3N4 with ion intercalation (K+ and I-) can improve carrier separation and transfer by multiscale computational simulations. The electronic structures of g-C3N4 were tailored and modified by intercalating K+ and I- into the layer-by-layer structures. Guided by the computational predictions, we achieved efficient solar-driven H2O2 production by employing this facile and ion-intercalated crystalline g-C3N4. An H2O2 production rate of 13.1 mM g-1 h-1 and an apparent quantum yield of 23.6% at 400 nm were obtained. The synergistic effects of crystallinity regulation and dual interstitial doping engineering triggered the formation of new light absorption centers, the establishment of rapid charge diffusion channels, and the enhancement of two-electron oxygen reduction characteristics. This work sheds light on the dual tuning of crystallinity and electronic structure and broadens the design principles of organic-conjugated polymer photocatalysts for environmental remediation and energy conservation.

Partial entropy decomposition reveals higher-order information structures in human brain activity.

The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we explore a method for capturing higher-order dependencies in multivariate data: the partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of nonnegative atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. PED gives insight into the mathematics of functional connectivity and its limitation. When applied to resting-state fMRI data, we find robust evidence of higher-order synergies that are largely invisible to standard functional connectivity analyses. Our approach can also be localized in time, allowing a frame-by-frame analysis of how the distributions of redundancies and synergies change over the course of a recording. We find that different ensembles of regions can transiently change from being redundancy-dominated to synergy-dominated and that the temporal pattern is structured in time. These results provide strong evidence that there exists a large space of unexplored structures in human brain data that have been largely missed by a focus on bivariate network connectivity models. This synergistic structure is dynamic in time and likely will illuminate interesting links between brain and behavior. Beyond brain-specific application, the PED provides a very general approach for understanding higher-order structures in a variety of complex systems.

Inter-virus relationships in mixed infections and virus-drought relationships in plants: a quantitative review.

Inter-virus relationships in mixed infections and virus-drought relationships are important in agriculture and natural vegetation. In this quantitative review, we sampled published factorial experiments to probe for relationships against the null hypothesis of additivity. Our sample captured antagonistic, additive and synergistic inter-virus relationships in double infections. Virus-drought relationships in our sample were additive or antagonistic, reinforcing the notion that viruses have neutral or positive effects on droughted plants, or that drought enhances plant tolerance to viruses. Both inter-virus and virus-drought relationships vary with virus species, host plant to the level of cultivar or accession, timing of infection, plant age and trait and growing conditions. The trait-dependence of these relationships has implications for resource allocation in plants. Owing to lagging theories, more experimental research in these fields is bound to return phenomenological outcomes. Theoretical work can advance in two complementary directions. First, the effective theory models the behaviour of the system without specifying all the underlying causes that lead to system state change. Second, mechanistic theory based on a nuanced view of the plant phenotype that explicitly considers downward causation; the influence of the plant phenotype on inter-virus relations and vice versa; the impact of timing, intensity and duration of drought interacting with viruses to modulate the plant phenotype; both the soil (moisture) and atmospheric (vapour pressure deficit) aspects of drought. Theories should scale in time, from short term to full growing season, and in levels of organisation up to the relevant traits: crop yield in agriculture and fitness in nature.

A broadly neutralizing human monoclonal antibody generated from transgenic mice immunized with HCMV particles limits virus infection and proliferation.

Human cytomegalovirus (HCMV) is a ß-herpesvirus that poses severe disease risk for immunocompromised patients who experience primary infection or reactivation. Development and optimization of safe and effective anti-HCMV therapeutics is of urgent necessity for the prevention and treatment of HCMV-associated diseases in diverse populations. The use of neutralizing monoclonal antibodies (mAbs) to limit HCMV infection poses a promising therapeutic strategy, as anti-HCMV mAbs largely inhibit infection by targeting virion glycoprotein complexes. In contrast, the small-molecule compounds currently approved for patients (e.g., ganciclovir, letermovir, and maribavir) target later stages of the HCMV life cycle. Here, we present a broadly neutralizing human mAb, designated 1C10, elicited from a VelocImmune mouse immunized with infectious HCMV particles. Clone 1C10 neutralizes infection after virion binding to cells by targeting gH/gL envelope complexes and potently reduces infection of diverse HCMV strains in fibroblast, trophoblast, and epithelial cells. Antibody competition assays found that 1C10 recognizes a region of gH associated with broad neutralization and binds to soluble pentamer in the low nanomolar range. Importantly, 1C10 treatment significantly reduced virus proliferation in both fibroblast and epithelial cells. Further, the combination treatment of mAb 1C10 with ganciclovir reduced HCMV infection and proliferation in a synergistic manner. This work characterizes a neutralizing human mAb for potential use as a HCMV treatment, as well as a possible therapeutic strategy utilizing combination-based treatments targeting disparate steps of the viral life cycle. Collectively, the findings support an antibody-based therapy to effectively treat patients at risk for HCMV-associated diseases. IMPORTANCE: Human cytomegalovirus is a herpesvirus that infects a large proportion of the population and can cause significant disease in diverse patient populations whose immune systems are suppressed or compromised. The development and optimization of safe anti-HCMV therapeutics, especially those that have viral targets and inhibition mechanisms different from current HCMV treatments, are of urgent necessity to better public health. Human monoclonal antibodies (mAbs) that prevent HCMV entry of cells were identified by immunizing transgenic mice and screened for broad and effective neutralization capability. Here, we describe one such mAb, which was found to target gH/gL envelope complexes and effectively limit HCMV infection and dissemination. Further, administration of the antibody in combination with the antiviral drug ganciclovir inhibited HCMV in a synergistic manner, highlighting this approach and the use of anti-HCMV mAbs more broadly, as a potential therapeutic strategy for the treatment of diverse patient populations.

CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy.

Combination therapy is a promising strategy for confronting the complexity of cancer. However, experimental exploration of the vast space of potential drug combinations is costly and unfeasible. Therefore, computational methods for predicting drug synergy are much needed for narrowing down this space, especially when examining new cellular contexts. Here, we thus introduce CCSynergy, a flexible, context aware and integrative deep-learning framework that we have established to unleash the potential of the Chemical Checker extended drug bioactivity profiles for the purpose of drug synergy prediction. We have shown that CCSynergy enables predictions of superior accuracy, remarkable robustness and improved context generalizability as compared to the state-of-the-art methods in the field. Having established the potential of CCSynergy for generating experimentally validated predictions, we next exhaustively explored the untested drug combination space. This resulted in a compendium of potentially synergistic drug combinations on hundreds of cancer cell lines, which can guide future experimental screens.

KGANSynergy: knowledge graph attention network for drug synergy prediction.

Combination therapy is widely used to treat complex diseases, particularly in patients who respond poorly to monotherapy. For example, compared with the use of a single drug, drug combinations can reduce drug resistance and improve the efficacy of cancer treatment. Thus, it is vital for researchers and society to help develop effective combination therapies through clinical trials. However, high-throughput synergistic drug combination screening remains challenging and expensive in the large combinational space, where an array of compounds are used. To solve this problem, various computational approaches have been proposed to effectively identify drug combinations by utilizing drug-related biomedical information. In this study, considering the implications of various types of neighbor information of drug entities, we propose a novel end-to-end Knowledge Graph Attention Network to predict drug synergy (KGANSynergy), which utilizes neighbor information of known drugs/cell lines effectively. KGANSynergy uses knowledge graph (KG) hierarchical propagation to find multi-source neighbor nodes for drugs and cell lines. The knowledge graph attention network is designed to distinguish the importance of neighbors in a KG through a multi-attention mechanism and then aggregate the entity's neighbor node information to enrich the entity. Finally, the learned drug and cell line embeddings can be utilized to predict the synergy of drug combinations. Experiments demonstrated that our method outperformed several other competing methods, indicating that our method is effective in identifying drug combinations.

Improving therapeutic synergy score predictions with adverse effects using multi-task heterogeneous network learning.

Drug combinations could trigger pharmacological therapeutic effects (TEs) and adverse effects (AEs). Many computational methods have been developed to predict TEs, e.g. the therapeutic synergy scores of anti-cancer drug combinations, or AEs from drug-drug interactions. However, most of the methods treated the AEs and TEs predictions as two separate tasks, ignoring the potential mechanistic commonalities shared between them. Based on previous clinical observations, we hypothesized that by learning the shared mechanistic commonalities between AEs and TEs, we could learn the underlying MoAs (mechanisms of actions) and ultimately improve the accuracy of TE predictions. To test our hypothesis, we formulated the TE prediction problem as a multi-task heterogeneous network learning problem that performed TE and AE learning tasks simultaneously. To solve this problem, we proposed Muthene (multi-task heterogeneous network embedding) and evaluated it on our collected drug-drug interaction dataset with both TEs and AEs indications. Our experimental results showed that, by including the AE prediction as an auxiliary task, Muthene generated more accurate TE predictions than standard single-task learning methods, which supports our hypothesis. Using a drug pair Vincristine-Dasatinib as a case study, we demonstrated that our method not only provides a novel way of TE predictions but also helps us gain a deeper understanding of the MoAs of drug combinations.

Ring-fused 2-pyridones effective against multidrug-resistant Gram-positive pathogens and synergistic with standard-of-care antibiotics.

The alarming rise of multidrug-resistant Gram-positive bacteria has precipitated a healthcare crisis, necessitating the development of new antimicrobial therapies. Here we describe a new class of antibiotics based on a ring-fused 2-pyridone backbone, which are active against vancomycin-resistant enterococci (VRE), a serious threat as classified by the Centers for Disease Control and Prevention, and other multidrug-resistant Gram-positive bacteria. Ring-fused 2-pyridone antibiotics have bacteriostatic activity against actively dividing exponential phase enterococcal cells and bactericidal activity against nondividing stationary phase enterococcal cells. The molecular mechanism of drug-induced killing of stationary phase cells mimics aspects of fratricide observed in enterococcal biofilms, where both are mediated by the Atn autolysin and the GelE protease. In addition, combinations of sublethal concentrations of ring-fused 2-pyridones and standard-of-care antibiotics, such as vancomycin, were found to synergize to kill clinical strains of VRE. Furthermore, a broad range of antibiotic resistant Gram-positive pathogens, including those responsible for the increasing incidence of antibiotic resistant healthcare-associated infections, are susceptible to this new class of 2-pyridone antibiotics. Given the broad antibacterial activities of ring-fused 2-pyridone compounds against Gram-positive (GmP) bacteria we term these compounds GmPcides, which hold promise in combating the rising tide of antibiotic resistant Gram-positive pathogens.

The downside of metabolic diversity: Postingestive rearrangements by specialized insects.

Deploying toxins in complex mixtures is thought to be advantageous and is observed during antagonistic interactions in nature. Toxin mixtures are widely utilized in medicine and pest control, as they are thought to slow the evolution of detoxification counterresponses in the targeted organisms. Here we show that caterpillars rearrange key constituents of two distinct plant defense pathways to postingestively disable the defensive properties of both pathways. Specifically, phenolic esters of quinic acid, chlorogenic acids (CAs), potent herbivore and ultraviolet (UV) defenses, are reesterified to decorate particular sugars of 17-hydroxygeranyllinalool diterpene glycosides (HGL-DTGs) and prevent their respective anti­herbivore defense functions. This was discovered through the employment of comparative metabolomics of the leaves of Nicotiana attenuata and the frass of this native tobacco's specialist herbivore, Manduca sexta larvae. Feeding caterpillars on leaves of transgenic plants abrogated in each of the two pathways, separately and together, revealed that one of the fully characterized frass conjugates, caffeoylated HGL-DTG, originated from ingested CA and HGL-DTGs and that both had negative effects on the defensive function of the other compound class, as revealed by rates of larval mass gain. This negative defensive synergy was further explored in 183 N. attenuata natural accessions, which revealed a strong negative covariance between the two defense pathways. Further mapping analyses in a biparental recombinant inbred line (RIL) population imputed quantitative trait loci (QTLs) for the two pathways at distinct genomic locations. The postingestive repurposing of defense metabolism constituents reveals a downside of deploying toxins in mixtures, a downside which plants in nature have evolved to counter.

Targeting of REST with rationally-designed small molecule compounds exhibits synergetic therapeutic potential in human glioblastoma cells.

BACKGROUND: Glioblastoma (GBM) is an aggressive brain cancer associated with poor prognosis, intrinsic heterogeneity, plasticity, and therapy resistance. In some GBMs, cell proliferation is fueled by a transcriptional regulator, repressor element-1 silencing transcription factor (REST). RESULTS: Using CRISPR/Cas9, we identified GBM cell lines dependent on REST activity. We developed new small molecule inhibitory compounds targeting small C-terminal domain phosphatase 1 (SCP1) to reduce REST protein level and transcriptional activity in glioblastoma cells. Top leads of the series like GR-28 exhibit potent cytotoxicity, reduce REST protein level, and suppress its transcriptional activity. Upon the loss of REST protein, GBM cells can potentially compensate by rewiring fatty acid metabolism, enabling continued proliferation. Combining REST inhibition with the blockade of this compensatory adaptation using long-chain acyl-CoA synthetase inhibitor Triacsin C demonstrated substantial synergetic potential without inducing hepatotoxicity. CONCLUSIONS: Our results highlight the efficacy and selectivity of targeting REST alone or in combination as a therapeutic strategy to combat high-REST GBM.

Microbial diversity and ecological complexity emerging from environmental variation and horizontal gene transfer in a simple mathematical model.

BACKGROUND: Microbiomes are generally characterized by high diversity of coexisting microbial species and strains, and microbiome composition typically remains stable across a broad range of conditions. However, under fixed conditions, microbial ecology conforms with the exclusion principle under which two populations competing for the same resource within the same niche cannot coexist because the less fit population inevitably goes extinct. Therefore, the long-term persistence of microbiome diversity calls for an explanation. RESULTS: To explore the conditions for stabilization of microbial diversity, we developed a simple mathematical model consisting of two competing populations that could exchange a single gene allele via horizontal gene transfer (HGT). We found that, although in a fixed environment, with unbiased HGT, the system obeyed the exclusion principle, in an oscillating environment, within large regions of the phase space bounded by the rates of reproduction and HGT, the two populations coexist. Moreover, depending on the parameter combination, all three major types of symbiosis were obtained, namely, pure competition, host-parasite relationship, and mutualism. In each of these regimes, certain parameter combinations provided for synergy, that is, a greater total abundance of both populations compared to the abundance of the winning population in the fixed environment. CONCLUSIONS: The results of this modeling study show that basic phenomena that are universal in microbial communities, namely, environmental variation and HGT, provide for stabilization and persistence of microbial diversity, and emergence of ecological complexity.

The Engineered Lysin, CF-370, is Active Against Antibiotic-Resistant Gram-Negative Pathogens In vitro and Synergizes with Meropenem in Experimental Pseudomonas aeruginosa Pneumonia.

BACKGROUND: Lysins (cell wall hydrolases) targeting Gram-negative organisms require engineering to permeabilize the outer membrane and access subjacent peptidoglycan to facilitate killing. In the current study, the potential clinical utility for engineered lysin, CF-370, was examined in vitro and in vivo against Gram-negative pathogens important in human infections. METHODS: MICs and bactericidal activity were determined using standard methods. An in vivo proof-of-concept efficacy study was conducted using a rabbit acute pneumonia model caused by Pseudomonas aeruginosa. RESULTS: CF-370 exhibited potent antimicrobial activity, with MIC50/90 values (in µg/mL) for: P. aeruginosa, 1/2; Acinetobacter baumannii, 1/1; Escherichia coli, 0.25/1; Klebsiella pneumoniae, 2/4; Enterobacter cloacae 1/4; and Stenotrophomonas maltophilia 2/8. CF-370 furthermore demonstrated: i) bactericidal activity; (ii) activity in serum; iii) a low propensity for resistance; iv) anti-biofilm activity; and v) synergy with antibiotics. In the pneumonia model, CF-370 alone decreased bacterial densities in lungs, kidneys and spleen vs. vehicle control, and demonstrated significantly increased efficacy when combined with meropenem (vs either agent alone). CONCLUSIONS: CF-370 is the first engineered lysin described with potent broad spectrum in vitro activity against multiple clinically-relevant Gram-negative pathogens, as well as potent in vivo efficacy in an animal model of severe invasive multi-system infection.

Revisiting Aurora Kinase B: A promising therapeutic target for cancer therapy.

Cancer continues to be a major health concern globally, although the advent of targeted therapy has revolutionized treatment options. Aurora Kinase B is a serine-threonine kinase that has been explored as an oncology therapeutic target for more than two decades. Aurora Kinase B inhibitors show promising biological results in in-vitro and in-vivo experiments. However, there are no inhibitors approved yet for clinical use, primarily because of the side effects associated with Aurora B inhibitors. Several studies demonstrate that Aurora B inhibitors show excellent synergy with various chemotherapeutic agents, radiation therapy, and targeted therapies. This makes it an excellent choice as an adjuvant therapy to first-line therapies, which greatly improves the therapeutic window and side effect profile. Recent studies indicate the role of Aurora B in some deadly cancers with limited therapeutic options, like triple-negative breast cancer and glioblastoma. Herein, we review the latest developments in Aurora Kinase B targeted research, with emphasis on its potential as an adjuvant therapy and its role in some of the most difficult-to-treat cancers.